Risk factors for
Amyotrophic lateral sclerosis
Lifestyle, environment and genetics
Meinie Seelen
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©2015 Meinie Seelen
All rights reserved. No part of this publication may be reproduced or transmitted in any
form or by any means, electronical or mechanical, including photocopy, recording, or any
information storage or retrieval system, without permission in writing from the author.
The copyright of the articles that have been published has been transferred to the respective
journals.
ISBN: 978-90-393-6350-8
Cover: Milou Bicker Design
Design: wenz iD, Wendy Schoneveld
Printed by: Proefschrift All in One
The studies described in this thesis were performed at the Brain Center Rudolf Magnus,
Department of Neurology, University Medical Center Utrecht, The Netherlands.
Funding of the studies described in this thesis was provided by the Netherlands ALS
Foundation, Prinses Beatrix Fonds (PB 0703), VSB fonds, H Kersten and M Kersten
(Kersten Foundation), J R van Dijk, the Adessium Foundation, the European Community's
Health Seventh Framework Programme (FP7/2007-2013), ZonMW under the frame of
E-Rare-2, the ERA-Net for Research on Rare Diseases, EU Joint Programme –
Neurodegenerative Disease Research (JPND) project, the Netherlands Organisation for
Health Research and Development (Vici scheme to LHvdB).
The publication of this thesis was financially supported by ChipSoft BV.
2014226 Meinie Seelen_binnenwerk.indd 2
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RISK FACTORS FOR AMYOTROPHIC LATERAL SCLEROSIS
LIFESTYLE, ENVIRONMENT AND GENETICS
Risicofactoren voor amyotrofische laterale sclerose
Leefstijl, omgeving en genen
(met een samenvatting in het Nederlands)
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Universiteit Utrecht
op gezag van de rector magnificus, prof. dr. G.J. van der Zwaan,
ingevolge het besluit van het college voor promoties in het openbaar te verdedigen
op dinsdag 16 juni 2015 des middags te 2.30 uur
door
Meinie Seelen
geboren op 12 januari 1986 te Arnhem
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Promotoren: Prof. dr. L.H. van den Berg
Prof. dr. J.H. Veldink
Copromotoren: Dr. M.A. van Es
Dr. ir. R.C.H. Vermeulen
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CONTENTS
CHAPTER 1
Introduction
9
PART I - LIFESTYLE
CHAPTER 2
Lifetime physical activity and the risk of ALS
17
CHAPTER 3
Prior medical conditions and the risk of ALS
31
CHAPTER 4
Presymptomatic BMI, dietary fat and alcohol consumption as
independent risk factors for ALS
49
PART II - ENVIRONMENT
CHAPTER 5
Occupational exposure to diesel motor exhaust increases the risk
of ALS
67
CHAPTER 6
Long-term exposure to air pollution is associated with an
increased risk of ALS
85
CHAPTER 7
Residential exposure to extremely low frequency electromagnetic
fields and the risk of ALS
101
PART III - GENETICS
CHAPTER 8
No mutations in hnRNPA1/A2B1 in Dutch patients with ALS,
FTD and IBM
107
CHAPTER 9
Large scale genetic screening in sporadic ALS identifies modifiers
in C9orf72 repeat carriers
115
CHAPTER 10
General discussion
137
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ADDENDUM
Nederlandse samenvatting (Summary in Dutch)
Dankwoord (Acknowledgements)
List of publications
About the author
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150
155
158
160
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CHAPTER 1
Introduction
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CHAPTER 1
Incidence (/100,000 person-years)
Amyotrophic lateral sclerosis (ALS) is said to be one of the most devastating
neurodegenerative disorders in adults. It is characterized by rapidly progressive motor
neuron loss in the brain and spinal cord. Initial presentation of patients with ALS is usually
muscle weakness of limbs or difficulties with speech. Eventually these symptoms progress
to paralysis, swallowing difficulties and generally respiratory failure. Fifty percent of
patients die within three years from symptom onset.1-3 Cognitive impairment is
increasingly recognized as a feature and can be seen in up to 40% of ALS patients.4 To
date, there is no treatment that can significantly slow or stop disease progression. Only
one drug (Riluzole) is currently available, which offers just a modest prolongation of
survival (by approximately 3 months) in patients with ALS.5
Each year about 400-500 people in the Netherlands are diagnosed with ALS.1 The lifetime
risk of ALS is estimated at 1:400, with a median incidence of ALS of 2.8/100.000 person
years. ALS can occur at any adult age, with a median age at onset of 63 years. Men are
slightly more frequently affected than women, with a male-female ratio of ~1.5. Gender
specific incidence rates show an evident decrease of the incidence after the age of 75 years,
in contrast to other neurodegenerative disorders (Figure 1.1). This indicates that ALS is
not a disease of ageing, such as Alzheimer’s and Parkinson’s disease, but that age is an
important factor in its pathogenesis. The incidence rate, age and gender distribution in
the Netherlands are similar to those described for other European countries.6, 7
Multiple pathophysiological mechanisms cumulate to cause motor neuron degeneration
in ALS, such as glutamate excitotoxicity, oxidative stress, dysregulated RNA metabolism,
neuroinflammation, mitochondrial dysfunction, protein aggregation and impaired axonal
transport.8 However, most research has been performed in animal models or in specific
genetic variants of familial ALS. The relative contribution of the different pathways for
14
Men
12
Women
Total
10
8
6
4
2
0
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84
>85
Age group (years)
Figure 1.1 Age and gender specific incidence rates of ALS in the Netherlands. Obtained from Huisman
et al.1
10
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Introduction
subtypes of ALS may vary and it is still unknown whether those mechanisms are applicable
to all ALS patients.
The majority of ALS patients appears to be sporadic, whereas only 5-10% of patients have
a familial form of ALS, in which the index patient has a first- or second-degree family
member with ALS.9 Familial ALS cases have enabled the identification of several genetic
triggers, of which mutations in superoxidase dismutase 1 (SOD1), fused in sarcoma (FUS),
transactive response DNA binding protein 43 (TARDBP) and chromosome 9 open reading
frame 72 (C9orf72) are the most frequent.10
While such advances have contributed to our current understanding of the causes of most
familial ALS cases, they only explain a small fraction of the far larger group of sporadic
ALS cases. In sporadic ALS the etiology seems to be more complex, with multiple risk
factors interacting to cause ALS: not only genetic factors, but also lifestyle and
environmental factors. A twin study showed that the heritability component of sporadic
ALS accounts for approximately 61%, with the remaining part explained by lifestyle and
environmental factors.11 With this knowledge, the etiology of a large proportion of
sporadic ALS cases remains still unexplained.
Aim
Within this thesis, we aim to achieve a high level of evidence for genetic and epidemiological
studies to identify risk factors that predispose to sporadic ALS. Elucidating risk factors
provides new insight into pathogenic mechanisms of ALS and eventually may identify
targets for novel therapeutics.
Prospective ALS study the Netherlands
In 2006 the Prospective ALS study the Netherlands (PAN), a large nationwide population-based
case-control study, was initiated by the ALS center the Netherlands.1 Within this study
we continuously recruit all incident ALS patients in the Netherlands and subsequently
include population based controls (matched on gender and age to the patients). Hereby,
we avoid the risk of referral and selection bias. Moreover, previous studies showed that
prevalent patients differ significantly from incident patients in age at onset, site of onset
and survival,1 emphasizing the importance of inclusion of incident ALS patients.
Patients with ALS had to meet the criteria for possible, probable (laboratory supported)
or definite ALS according to the revised El Escorial criteria.12 These diagnostic criteria
were developed and revised to enhance participation of patients in therapeutic trials and
clinical and genetic research. All participants are registered in our national database, after
which (a) medical records of patients are retrieved to obtain clinical characteristics, (b) all
participants are asked to complete a detailed questionnaire on lifestyle and environmental
factors, and (c) blood samples are collected for isolation of DNA.
11
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CHAPTER 1
OUTLINE OF THE THESIS
Part I – Lifestyle
Many different lifestyle factors have been proposed in the pathogenesis of ALS, though
smoking is the only exposure that has been consistently identified as risk factor.13 The
evidence for other lifestyle factors (e.g. alcohol consumption, physical activity, prior
trauma, dietary intake) is less consistent, in part due to low case numbers and poor study
design.
One perception of patients diagnosed with ALS is that they are “fitter” before clinical
onset of disease compared to age and sex matched controls, because of an observed
decreased incidence of cardiovascular diseases amongst these patients.15, 16. An explanation
might be that physical activity is a risk factor for developing ALS, fuelled by anecdotal
observations of famous athletes diagnosed with ALS, such as the 1930s American baseball
player Lou Gehrig (to whom the disease was named after as an eponym in the United
States).14 Other previously suggested associations between prior medical conditions and
ALS risk are psychiatric disorders,17, 18 autoimmune diseases19, 20 and cancer21, 22. Which
may indicate that there are subgroups of ALS with different pathophysiological
mechanisms underlying the disease. Last, since dietary habits may have the potential to
modify pathophysiological mechanisms in ALS, the role of diet as a risk factor for the
development of ALS has been of particular interest.
In the first part of this thesis we analyzed whether lifestyle factors, such as increased
physical activity, co-occurring medical conditions and an altered dietary intake, are risk
factors for sporadic ALS (chapters 2, 3 and 4).
Part II – Environment
Environmental risk factors can be ubiquitous and therefore important to a large proportion
of the population. People can either be exposed to environmental risk factors through
their occupations or in their residential area. For example, the first observations of an
increased risk of ALS among agricultural workers date back more than 35 years and since
then pesticides have frequently been suggested as the underlying occupational exposure
responsible for the increased ALS risk.23, 24 Other environmental exposures of interest are
for example metals,25-27 solvents,25, 28 air pollution,29, 30 and electric shocks or electromagnetic
fields,31, 32 all of which have been inconsistently associated with ALS or neurodegenerative
disorders in general. In these previous studies, an objective and quantitative method to
assess environmental exposures is often lacking.
In the second part of this thesis, we assessed the association of environmental factors, i.e.
multiple occupational exposures, residential exposure to air pollution and residential
exposure to electromagnetic fields, with the risk of developing ALS (chapters 5, 6 and 7).
The exposure assessment was carried out by applying objective and valid tools, i.e. job
exposure matrices (JEM) for occupational exposures33, 34 and land use regression (LUR)
models for residential exposures.35, 36
12
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Introduction
Part III - Genetics
New causative genetic variants for ALS are discovered at an increasing pace, with the
most frequently observed variants include the SOD1, FUS, TARDBP and C9orf72 genes.10
Despite the progress of gene discoveries in ALS, these genes explain only a minority of
ALS cases. This can be partly explained by a large genetic heterogeneity in ALS, which is
seen by locus heterogeneity, incomplete penetrance, multiple ALS mutations seen in one
family (oligogenics) and genetic overlap with other diseases (pleiotropy).
The genetic architecture of a complex trait or disease, such as ALS, is based on the allele
frequency of disease variants, their effect sizes and the number of variants contributing
to the risk. Few reports have shown that oligogenic inheritance within families does occur,
with co-occurring variants in rare genes with large disease effect and more common
susceptibility genes with intermediate disease effect.37 Furthermore, it has been argued
that the distinction between familial and sporadic ALS is rather arbitrary,38 indicating that
this oligogenic model may be applicable to all ALS cases.
In addition, many ALS genes, but especially C9orf72, have marked genetic pleiotropy,
meaning that it also causes other phenotypic traits in carriers of the mutation. C9orf72
was initially discovered in ALS and frontotemporal dementia, but the gene has now been
implicated in many different neurodegenerative and psychiatric diseases including
Alzheimer’s disease, Parkinsonism, Huntington’s disease phenocopies, schizophrenia
and bipolar disorder.39-42 Other ALS genes with a complex phenotyope are VCP, hnRNPA1
and hnRNPA2B1, which were discovered in multisystem proteinopathy (a disorder
presenting as ALS, frontotemporal dementia, Paget’s disease of the bone or inclusion
body myositis).43, 44
In the third part of this thesis, we analyzed the contribution of variants in hnRNPA1 and
hnRNPA2B1 in Dutch patients with ALS, frontotemporal dementia and inclusion body
myositis (chapter 8) and we analyzed the oligogenicity of apparently sporadic ALS
including known ALS genes (chapter 9).
13
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CHAPTER 1
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Schreiber H, et al. Cognitive function in bulbar- and spinal-onset amyotrophic lateral sclerosis.
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Turner MR, et al. Autoimmune disease preceding amyotrophic lateral sclerosis: an
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Hemminki K, et al. Familial risks for amyotrophic lateral sclerosis and autoimmune diseases.
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Freedman DM, et al. The association between cancer and amyotrophic lateral sclerosis. Cancer
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Fois AF, et al. Cancer in patients with motor neuron disease, multiple sclerosis and Parkinson's
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Gunnarsson LG, et al. An epidemic-like cluster of motor neuron disease in a Swedish county
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26. Weisskopf MG, et al. Prospective study of occupation and amyotrophic lateral sclerosis
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28. Gunnarsson LG, et al. A case-control study of motor neurone disease: its relation to heritability,
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markers of traffic-derived and environmental manganese air pollution in two Canadian cities.
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32. Frei P, et al. Residential distance to high-voltage power lines and risk of neurodegenerative
diseases: a Danish population-based case-control study. Am J Epidemiol. 2013; 177: 970-8.
33. Sutedja NA, et al. Exposure to chemicals and metals and risk of amyotrophic lateral sclerosis:
a systematic review. Amyotroph Lateral Scler. 2009; 10: 302-9.
34. Huss A, et al. Electric shocks at work in Europe: development of a job exposure matrix. Occup
Environ Med. 2013; 70: 261-7.
35. Eeftens M, et al. Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance,
PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project. Environ Sci
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36. Beelen R, et al. Development of NO2 and NOx land use regression models for estimating air
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38. Al-Chalabi A and Lewis CM. Modelling the effects of penetrance and family size on rates of
sporadic and familial disease. Hum Hered. 2011; 71: 281-8.
39. DeJesus-Hernandez M, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region
of C9orf72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011; 72: 245-56.
40. Beck J, et al. Large C9orf72 hexanucleotide repeat expansions are seen in multiple
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41. Meisler MH, et al. C9orf72 expansion in a family with bipolar disorder. Bipolar Disord. 2013;
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42. Lesage S, et al. C9orf72 repeat expansions are a rare genetic cause of parkinsonism. Brain.
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43. Kim HJ, et al. Mutations in prion-like domains in hnRNPA2B1 and hnRNPA1 cause
multisystem proteinopathy and ALS. Nature. 2013; 495: 467-73.
44. Watts GD, et al. Inclusion body myopathy associated with Paget disease of bone and
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PART I - LIFESTYLE
CHAPTER 2
Lifetime physical activity and the risk of
amyotrophic lateral sclerosis
Published in: Journal of Neurology, Neurosurgery and Psychiatry (2012)
Meinie Seelen1*, Mark HB Huisman1*, Sonja W de Jong1, Kirsten RIS Dorresteijn1,
Perry TC van Doormaal1, Anneke J van der Kooi2, Marianne de Visser2,
H Jurgen Schelhaas3, Leonard H van den Berg1# Jan H Veldink1#
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, The Netherlands
2
Department of Neurology, Amsterdam Medical Center,
University of Amsterdam, The Netherlands
3
Department of Neurology, Donders Institute for Brain, Cognition and Behaviour,
Center for Neuroscience, Radboud University Nijmegen Medical Center,
The Netherlands
1
* These authors contributed equally to the manuscript
# These authors contributed equally to the manuscript
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CHAPTER 2
ABSTRACT
Background
It has been hypothesized that physical activity is a risk factor for developing amyotrophic
lateral sclerosis (ALS), fuelled by observations that professional soccer players and Gulf
War veterans are at increased risk. In a population-based study, we determined the relation
between physical activity and risk of sporadic ALS, using an objective approach for
assessing physical activity.
Methods
636 sporadic ALS patients and 2,166 controls, both population-based, completed a semistructured questionnaire on lifetime history of occupations, sports and hobbies. To
objectively compare energy cost of a lifetime history of occupational and leisure time
physical activities and to reduce recall bias, metabolic equivalent scores were assigned to
each activity based on the Compendium of Physical Activities.
Results
ALS patients had significantly higher levels of leisure time physical activity compared
with controls (OR 1.08, 95% CI 1.02 to 1.14, p=0.008). No significant difference was found
between patients and controls in the level of vigorous physical activities, including
marathons and triathlons, or in occupational activity. Cumulative measures of physical
activity in quartiles did not show a dose-response relationship.
Conclusion
An increased risk of ALS with higher levels of leisure time physical activity was found in
the present study. The lack of association with occupational physical activity and the
absence of a dose-response relationship strengthen the hypothesis that not increased
physical activity per se, but rather a genetic profile or lifestyle promoting physical fitness
increase ALS susceptibility.
18
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Physical activity and ALS risk
INTRODUCTION
Sporadic amyotrophic lateral sclerosis (ALS) is believed to be a complex disease, with
multiple genetic and environmental factors causing motor neuron degeneration.1 Ever
since Lou Gehrig, the legendary 1930s baseball player known as 'The Iron Horse’, died
from ALS, it has been hypothesized that physical activity is a risk factor for developing
this disease. Although assuming an association based on an individual well-known patient
is fraught with risk, the hypothesis has been fuelled by recent observations that professional
soccer and football players, and Gulf War, veterans are at increased risk of sporadic ALS.26
Several theories have been proposed that may explain the possible association of physical
activity with ALS susceptibility.7-9
Although some studies have suggested a relation between physical activity and the risk of
ALS, the results may have been biased due to methodological shortcomings, inherent in
studying a relatively low-incidence disease.3, 10-13 A population-based case-control study
can alleviate some of these limitations and, therefore, provide a high level of evidence in
ALS exogenous risk factor studies.
We performed a large population-based case control study in The Netherlands to
determine the relation between physical activity and the risk of sporadic ALS, adjusted
for known risk factors, using an objective quantitative approach for assessing physical
activity, and taking into account the lifetime history of occupational and leisure time
activities of each patient and control. To minimise recall bias, we measured the energy
cost of the lifetime history of occupational and leisure time physical activities in an
objective manner by assigning metabolic equivalent (MET) scores to each activity based
on the Compendium of Physical Activities.14
METHODS
Study Population
The Prospective ALS study the Netherlands (PAN), is a population-based case-control
study performed in the Netherlands during the period 1 January 2006 to 31 December
2010. Complete case ascertainment was ensured by continuous recruitment through
multiple sources: neurologists, rehabilitation physicians, the Dutch Neuromuscular Patient
Association and our ALS website.
All patients diagnosed with possible, probable (laboratory supported) or definite ALS
according to the revised El Escorial criteria were included.15 Medical records were
scrutinised for eligibility of the patients, excluding patients with an ALS mimic syndrome
or with a first, second or third degree family member with ALS. As exogenous factors probably - had only a minor role in the development of ALS in patients with the highly
penetrant C9orf72 repeat expansion, these patients, 43 in total, were excluded from our
analysis.16-18
19
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CHAPTER 2
To ascertain population-based controls, the general practitioner of the participating
patient was asked to select individuals from his register in alphabetical order starting at
the surname of the patient. The Dutch health care system ensures that every inhabitant
is registered with a general practitioner, which makes this roster representative of the
population. Controls were matched to the patients for gender and age (±5 years). This
study, however, did not use individual matching, meaning that some general practitioners
delivered several controls, while others delivered none. As can be seen in Table 2.1, our
case and control groups were well matched for age and gender. Blood relatives or spouses
of patients were not eligible to be controls, to prevent overmatching.
Ethics approval was provided by the institutional review board of the University Medical
Centre Utrecht. All participants gave written informed consent.
Data collection
A structured questionnaire was used to collect demographic and clinical characteristics
of participants and to obtain data regarding lifetime physical activities. Participants were
asked to recollect all their jobs and to describe the various activities they had to perform
during these jobs. They were also asked to list all their leisure time activities, consisting
of sports and hobbies. For each activity, the participant was asked to state the number of
years and how many hours per week the activity was performed. Specific questions were
asked about vigorous physical activities (eg, marathon, triathlon, etc.). This questionnaire
was part of a larger questionnaire containing questions on several other exogenous factors.
Participants were, therefore, blinded to the hypothesis being tested. In the patient group,
only data referring to the period before disease onset were analysed. Survival status of
patients was recorded up to 8 August 2011 and obtained through the municipal personal
records database or from the general practitioner. If the questionnaire was not completed
in full or if data were found to be inconsistent, participants were approached by telephone
to complete or correct the data. To ensure blinding, all questionnaires were coded prior
to processing and analysis.
Classification of physical activities
To objectively quantify the cumulative lifetime physical activity level of participants, all
reported activities were scored and coded based on the Compendium of Physical
Activities.14 The Compendium provides a coding scheme that links specific activities
performed in various settings with their respective MET. The definition of a MET is the
ratio of work metabolic rate to a standard resting metabolic rate. A MET score of 1.0 (i.e.
the standard or resting metabolic rate while sitting quietly) is defined as 1 kcal × kg-1 body
weight × h-1. MET levels for specific activities, as reported in the Compendium, were
established by reviewing published and unpublished studies that measured the energy
cost of human physical activities. The compendium describes 605 specific activities.
Assignment of MET scores to the activities enabled us to calculate cumulative scores of
all reported physical activities:
20
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Physical activity and ALS risk
where k represents an activity from the lifetime job or leisure time history. Because of the
magnitude of the cumulative score, it was divided by 1000. Activities that had a MET
score of ≤ 1.5 (eg, listening to music, reading, playing chess, needlework) were not included
in the analysis. Subsequently, military service (not occupation) or periods spent as a
homemaker were excluded because of difficulties quantifying these activities. Military
service was mandatory for male study participants, during a 15 to 24 month period around
the age of 18 years and will therefore have minimal influence on total cumulative physical
activity. In our study, 34% of patients compared with 35% of controls joined the military
service (χ2 test: p=0.73), and 12% of both patients and controls reported periods spent as
a homemaker (χ2 test: p=0.77).
Statistical methods
Univariate and multivariate logistic regression were used to determine the association of
physical activity and the risk of ALS. Standard unconditional logistic regression was used
as the study did not include individual case-control pairs but was frequency-matched.
The risk of ALS with cumulative scores of physical activity was analysed separately for
leisure time activity, occupational activity and total activity (the combined leisure time
and occupational activity) as a continuous variable. Furthermore, to determine a doseresponse relationship, physical activity was categorised into quartiles based on the data
of controls. The first quartile with the lowest intensity in physical activities was defined
as the reference category. Multivariate logistic regression was used to determine the
association between the four levels of physical activity and ALS. A separate multivariate
logistic regression analysis was performed to determine the effect of vigorous physical
activity (ever/never) on the risk of ALS. OR and p values were derived from these analyses.
In the multivariate model, the ORs were adjusted for gender, age (at onset for patients and
at the date the questionnaire was completed for controls), level of education (divided into
seven categories ranging from no education to university), premorbid body mass index,
current alcohol consumption and current smoking. In patients, current alcohol
consumption and current smoking were determined at the time of disease onset, so before
diagnosis and before the questionnaire was completed.
To determine a difference in the maximum intensity of the activities performed, the
maximum MET scores were calculated (excluding the duration in years or the hours per
week) and analysed using the Mann Whitney U test.
A Cox regression analysis was performed to determine whether survival of patients was
associated with physical activity. Survival was defined as the time from symptom onset
to death or to the censoring date of 8 August 2011. The HR derived from these analyses
were adjusted for gender, age at onset, site of onset and current smoking. Physical activity
was entered into the model as a continuous variable. The same method was used to
21
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determine the effect of physical activity on the age at onset of ALS patients, adjusting for
gender and site of onset. To adjust appropriately for age, an interaction term of diagnosis
and physical activity was introduced into the Cox regression analysis using age at time of
completing the questionnaire for controls.
In the above mentioned models, we performed a complete case analysis, using only those
cases without any missing values. A Bonferroni correction for multiple testing was applied
adjusting for three tests (leisure time, occupational and total activity), a p value of
0.05/3=0.017 was considered significant.
RESULTS
In the population-based study, 636 (84%) of the 760 patients who gave informed consent
to participate in the study between 1 January 2006 and 31 December 2010, returned the
questionnaire. Of the 2332 population-based controls who gave informed consent, 93%
returned their questionnaires (2166 controls). Table 2.1 shows the characteristics of 636
patients and 2166 controls. The patient characteristics of the responders and the nonresponders were similar. Of the 2802 participants, 2281 (81.4%) had completed the
questionnaires on physical activities without any missing values in duration in years or
hours per week. The distributions for gender, age at onset and site of onset in ALS patients
were similar to those previously reported in population-based studies.19
A greater amount of leisure time physical activity was associated with an increased risk
of ALS in the present study (adjusted OR 1.08, p=0.008) (Table 2.2).
2.0
Mean difference: p = 0.004
Mean activity score
1.5
1.0
0.5
0.0
ALS patients
Controls
Figure 2.1 Mean leisure time activity of ALS
patients and controls. Patients mean = 1.51, 95%
CI 1.30 to 1.72, controls mean = 1.25, 95% CI
1.18 to 1.32. ALS, amyotrophic lateral sclerosis.
22
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Physical activity and ALS risk
Table 2.1 Baseline demographic and clinical characteristics of participants
ALS patients
Controls
Characteristic
(n = 636)
(n = 2166)
p Value
Male (n (%))
395 (62.1)
1259 (58.1)
0.17
Age (years) (median (range))*
63 (23 to 87)
62 (20 to 91)
0.91
Site of onset (n (%))
Bulbar
204 (32.3)
Spinal
427 (67.7)
El Escorial classification (n (%))
Definite
112 (17.8)
Probable
280 (44.6)
Probable lab supported
111 (17.7)
Possible
119 (18.9)
Education (n (%))
No education
2 (0.3)
3 (0.1)
Primary school
54 (8.5)
131 (6.1)
Junior vocational education
127 (20.0)
356 (16.5)
Lower general secondary education
149 (23.4)
474 (21.9)
Intermediate vocational education
106 (16.7)
410 (18.9)
Higher general secondary education
45 (7.1)
186 (8.6)
0.02
College/University
153 (24.1)
604 (27.9)
BMI (kg/m2) (median (range))
24.1 (12 to 48)
25.6 (16 to 53)
<0.001
Current smoking (n (%))
133 (20.9)
288 (13.3)
<0.001
Current alcohol consumption (n (%))
475 (74.7)
1846 (85.3)
<0.001
ALS, amyotrophic lateral sclerosis; BMI, body mass index.
* Age at onset in patients, and age on which questionnaire was completed in controls.
This is also illustrated in Figure 2.1, showing the mean cumulative scores for leisure time
activity (patient mean=1.51, 95% CI 1.30 to 1.72; control mean=1.25, 95% CI 1.18 to 1.32;
p=0.004). Occupational and total physical activity were not associated with the risk of
ALS (Table 2.2); no dose-response relationship was seen with physical activity (Figure 2.2)
and none of the vigorous physical activities showed a significant association with ALS
(Table 2.3).
Maximum MET scores did not differ significantly between ALS patients and controls,
implying that there was no difference in the maximum intensity of activities (all p values
>0.35, not shown).
Survival analyses showed that none of the cumulative measures of physical activity was
associated with survival (all p values >0.10). Of 636 patients, 63% died before the censoring
date of 8 August 2011. The cumulative measures of leisure time, occupational and total
activity did, however, show a significant relation with age at onset (all HR 0.94 to 0.95,
23
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p values ≤0.009). In order to show whether this effect was specific for patients or valid for
age at the time of the questionnaire for controls, two additional analyses were performed:
(1) an interaction term of diagnosis and physical activity was introduced into the model
(all p values >0.45), and (2) a multivariate Cox regression was performed in controls using
questionnaire completion as the event (p≤0.002). Both indicated that the relationship
between physical activity and age at onset was an age related effect and thus not disease
related. Kaplan-Meier curves of total activity of both survival and age at onset are shown
in the online Supplementary Figure S2.1.
1.6
Leisure time activity
Occupational activity
Total activity
1.4
OR
1.2
1.00
1.0
0.8
1.01
1.12
0.72
1.00
0.95 0.99
1.00
0.97
0.95
0.82
0.81
0.6
0.4
Q1 Q2 Q3 Q4
Q1 Q2 Q3 Q4
Q1 Q2 Q3 Q4
Figure 2.2 Odds ratios (OR) with 95% confidence intervals for the relationship between quartiles of
leisure time, occupational and total activity and the risk of ALS.
ORs were adjusted for gender, age at onset, body mass index, current smoking, current alcohol
consumption and level of education. The physical activity score was categorized into quartiles (Q)
based on the data of controls. Q1, 1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile.
Table 2.2 Odds ratios for the relationship between ALS and the cumulative scores of physical
activity
Crude OR
Variable
Adjusted OR*
(95% CI)
p Value†
(95% CI)
p Value†
Leisure time activity
1.08 (1.02 to 1.13)
0.005
1.08 (1.02 to 1.14)
0.008
Occupational activity
1.02 (0.99 to 1.06)
0.19
1.00 (0.96 to 1.04)
0.90
Total activity
1.03 (0.99 to 1.06)
0.12
1.02 (0.98 to 1.06)
0.30
ALS, amyotrophic lateral sclerosis.
* Adjusted for gender, age, body mass index, current smoking, current alcohol consumption and
level of education. † Bonferroni adjusted p values of < 0.017 (0.05/3) were considered significant.
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Table 2.3 Vigorous physical activities among ALS patients and controls
Variable
ALS patients
Controls
Adjusted OR*
(n = 635)
(n = 2167)
(95% CI)
p Value†
Vigorous physical activity (n (%))
103 (16)
296 (14)
1.24 (0.96 to 1.61)
0.10
Marathon
12 (1.9)
32 (1.5)
1.15 (0.58 to 2.29)
0.69
Triathlon
3 (0.5)
6 (0.3)
1.21 (0.29 to 4.98)
0.80
Ice skating tours >200km
7 (1.1)
18 (0.8)
1.35 (0.54 to 3.37)
0.52
ALS, amyotrophic lateral sclerosis.
* Adjusted for gender, age, body mass index, current smoking, current alcohol consumption and
level of education. † Bonferroni adjusted p values of < 0.017 (0.05/3) were considered significant.
DISCUSSION
Evidence for an increased risk of ALS with higher levels of leisure time physical activity
is provided by the present population-based case-control study. Occupational physical
activity and performing vigorous physical activities, however, do not appear to modify
ALS susceptibility in this study. The discrepancy between leisure time and occupational
physical activity strengthens the hypothesis that physical activity itself is not causative
per se but that being athletic is a phenotypic expression of a genetic profile, mediated by
exogenous factors, that increases the risk of ALS.20-23 Our observation that none of the
physical activity measures was related to age at onset or survival further supports this
hypothesis.
Two systematic reviews on the association between ALS and physical activity concluded
that there is a consistent pattern of well-designed studies showing no link between physical
activity and sporadic ALS.11, 12 The best evidence available at that time was provided by a
single population based case-control study that showed no association.24 After publication
of these reviews, however, a small but well-designed European population-based pilot
case-control study identified an increased risk of ALS with higher levels of physical
activity.13 In concordance with these conflicting results, a third and the most recent,
systematic review concluded that current evidence for physical activity as a risk factor in
motor neuron disease is not of sufficient caliber to allow undisputed conclusions.8
The conflicting results found in studies on the association between physical activity and
ALS, may partly be due to differences in methodological design. These differences concern:
(1) the blinding of interviewers to disease status of respondents or the hypotheses being
tested; (2) referral bias, which was common with cases often ascertained at specialist
clinics; (3) adjustment for confounders, which was not carried out in all analyses; and (4)
the method of assessing physical activity, which in most studies was susceptible to recall
bias.8, 11, 12 Recall bias is due to differential recall of past exposures between patients and
controls. As ALS patients actively search for an explanation of their disease or may have
an assumption about the underlying cause, case-control studies in ALS using questionnaires
25
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are prone to this bias. Our study was designed to minimize the risk of recall and referral
bias. First, recall bias was reduced by using the Compendium of Physical Activities14 to
quantify objectively physical activity based on type of occupation or type of leisure time
activities, instead of directly asking participants how physically active they have been in
their life or during the listed activities. As the questionnaire on leisure time and
occupational activities was part of a more comprehensive questionnaire, participants were
blinded to the study hypothesis, which further reduced the risk of recall bias. Interviewers,
who called participants to complete returned questionnaires, were also unaware of the
hypothesis being tested.
Referral bias may occur when patients are ascertained from tertiary care centres. It has
been demonstrated that ALS patients attending these referral centres do not represent a
random sample of all ALS patients.25, 26 A difference in physical activity levels of these
patients compared with non-referred patients will lead to biased results. The populationbased design using multiple sources to ensure complete case ascertainment minimized
the risk of referral bias in the present study, which was strengthened by the observation
that the demographics of the patients in our study resembled those of patients in other
population-based studies.19, 27, 28
We acknowledge certain limitations of the present study: 18.6% of the participants had
at least one missing value for the duration of, or the hours per week spent on, one of the
listed activities, even after being called by an interviewer to complete the returned
questionnaire. This is probably the result of the level of detail of the questionnaire
concerning past events. The fact that this information was so elaborate, however, enabled
us to precisely quantify lifetime energy expenditure during leisure time and occupational
activities. Also, it is noteworthy that ALS patients had significantly less higher education
(p<0.02), which is congruent with a previous observation that there is a preponderance
among ALS patients of blue-collar jobs for which a higher level academic education is
often not required. Nevertheless, our controls may have been better educated as people
with a higher education tend to participate in scientific surveys more readily.29 The effects,
however, of this observation will have been minimal as we adjusted all analyses for
education. Further, we acknowledge that the quantification of the lifetime energy
expenditure is still an estimate of real energy expenditure. A study, however, in which
these data are prospectively being collected will probably not be feasible in a low incidence
disease such as ALS. Finally, although our study was designed to maximize blinding of
the participants to the hypothesis of the study, we cannot exclude the fact that a proportion
of the patients may have been aware of the theory of physical activity as a possible risk
factor, which may have been a source of residual recall bias.
Our finding that an increased leisure time physical activity is related to an increased risk
of ALS but occupation activity is not, raises doubts regarding the role of physical activity
in causing ALS. Because of existing cellular and genetic evidence supporting the biological
plausibility of the association, some have suggested that physical activity is indeed
causative.8, 30, 31 Several genes associated with the response to exercise (i.e. ciliary
neurotrophic factor, leukaemia inhibitory factor and vascular endothelial growth factor
26
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Physical activity and ALS risk
2) have been identified as possible modifiers of ALS susceptibility.32-34 Also, oxidative stress
and glutamate excitotoxicity are considered candidate mechanisms to link ALS and
physical activity.7, 8, 35 The biologically plausible link between physical activity and ALS
has been carefully reviewed.8
Biological plausibility alone, however, does not prove causation. Useful time-tested criteria
for determining whether an association is causal are designed by Bradford Hill.36, 37 The
Bradford-Hill criteria include strength, consistency, specificity, temporality, dose-response
relation, plausibility, coherence, experiment and analogy. The associations found in the
present study do not meet most of these criteria. First, strength. If an association is weak,
it is more plausible that underlying actual causative factors that go hand-in-hand with the
studied factor are in fact responsible for the observed association. In our study, if physical
activity were causative, an increase in physical activity of 10 000 MET, which can be
provided by 50 years of 50 hours cycling per week for example, would be associated with
an increase of odds of developing ALS of only 2.2. Further, if we had applied a more
stringent threshold that also corrects for the analyses on vigorous physical activities
(threshold p=0.05/7=0.007), the association (p=0.008) would not even have been
significant, further emphasising the weakness of the association. Second, consistency. A
real causative factor is more likely to be repeatedly observed in different studies, using
different methodologies and performed in different places, circumstances and times.
Previous studies, as already emphasized, have shown large inconsistencies, and even within
the present study there is an inconsistency between occupational and leisure time physical
activity.11-13, 24 Finally, the absence of a dose-response relation also does not support the
notion that causation is the most likely interpretation of the association between leisure
time physical activity and ALS. Recent findings of a beneficial vascular risk profile in both
patients and their relatives,6 a reduced frequency of coronary heart disease premorbidly
in ALS,22, 38 and an increased risk of ALS with physical fitness, but not muscle strength,21
further indicate that a common factor underlies both physical/cardiovascular fitness and
risk of ALS.39 A genetic profile, therefore, modified by exogenous factors, that both
promotes physical fitness and increases ALS susceptibility might be a more credible
explanation for the associations between physical activity and ALS.20, 22
In conclusion, the present population-based case-control study strengthens this hypothesis.
Identifying genetic, developmental and environmental factors that contribute to physical
fitness may provide a worthwhile lead in unravelling the pathophysiological mechanisms
in ALS.
27
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A) 100
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low level
38. Sutedja NA, et al. Beneficial vascular risk profile is associated
with amyotrophic lateral
high level
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80
39. Wicks P. Hypothesis: higher prenatal testosterone predisposes ALS patients to improved
athletic performance and manual professions. Amyotroph Lateral Scler. 2012; 13: 251-3.
60
40
20
0
SUPPLEMENTAL MATERIAL
20
40
60
80
Age at onset (years)
100
B)
low level
high level
100
80
80
60
60
Alive (%)
Affected (%)
A)
40
20
low level
high level
40
20
0
0
20
40
60
Age at onset (years)
80
0
5
10
15
Survival (years)
B) 100 S2.1 Kaplan-Meier curves comparing high (dashed line) versus low (solid line) level total
Figure
activity in relation to (A) age at onset low
andlevel
(B) survival. Log Rank test for age at onset, p = 0.51, and
survival, p = 0.77.
high level
Alive (%)
80
60
29
40
20
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CHAPTER 3
Prior medical conditions and
the risk of amyotrophic lateral sclerosis
Journal of Neurology (2014) Meinie Seelen1, Perry TC van Doormaal1, Anne E Visser1, Mark HB Huisman1,
Margot HJ Roozekrans1, Sonja W de Jong1, Anneke J van der Kooi2, Marianne de Visser2,
Nicol C Voermans3, Jan H Veldink*1, Leonard H van den Berg*1
Department of Neurology, Brain Center Rudolf Magnus
University Medical Center Utrecht, The Netherlands
2
Department of Neurology, Academic Medical Center
University of Amsterdam, The Netherlands
3
Department of Neurology, Donders Institute for Brain, Cognition and BehaviorCenter
for Neuroscience, Radboud University Nijmegen Medical Center, The Netherlands
1
* These authors contributed equally to this work
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ABSTRACT
Sporadic amyotrophic lateral sclerosis (ALS) is believed to be a complex disease in which
multiple exogenous and genetic factors interact to cause motor neuron degeneration.
Elucidating the association between medical conditions prior to the first symptoms of
ALS could lend support to the theory that specific subpopulations are at risk of developing
ALS and provide new insight into shared pathogenic mechanisms. We performed a
population-based case-controls study in The Netherlands, including 722 sporadic ALS
patients and 2268 age and gender matched controls. Data on medical conditions and use
of medication were obtained through a structured questionnaire. Multivariate analyses
showed that hypercholesterolemia (OR 0.76, 95% CI 0.63-0.92, P = 0.006), the use of statins
(OR 0.45, 95% CI 0.35-0.59, P = 1.86 x 10-9) or immunosuppressive drugs (OR 0.26, 95%
CI 0.08-0.86, P = 0.03) were associated with a decreased risk of ALS. Head trauma was
associated with an increased ALS susceptibility (OR 1.95, 95% CI 1.11-3.43, P = 0.02). No
association was found with autoimmune diseases, cancer, psychiatric disorders or
cardiovascular diseases, or survival. The lower frequency of hypercholesterolemia and
less use of statins in ALS patients indicate a favorable lipid profile prior to symptom onset
in at least a subpopulation of ALS. Prior head trauma is a risk factor for ALS and the
significantly lower use of immunosuppressive drugs in ALS patients could suggest a
protective effect. The identification of specific subpopulations at risk for ALS may provide
clues towards possible pathogenic mechanisms.
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease in which
the vast majority of cases appear to be sporadic (approximately 90-95%). Sporadic ALS is
believed to be a complex disease in which multiple exogenous and genetic factors interact
to cause motor neuron degeneration.1 Elucidating the association between medical
conditions and medication use prior to the first symptoms of ALS could lend support to
the theory that specific subpopulations are at risk of developing ALS and provide new
insight into shared pathogenic mechanisms.
Associations with increased or decreased risk of developing ALS have been reported for
cardiovascular diseases,2-4 psychiatric disorders,5, 6 cancer,7, 8 autoimmune diseases9, 10 and
(head) trauma11, 12 preceding ALS onset, though sometimes with conflicting results. To
obtain a high level of evidence for the association of ALS and a wide range of prior medical
conditions and use of medication, we performed a large population-based, case-control
study in the Netherlands including incident ALS cases and gender- and age-matched
controls.
32
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Prior medical conditions and ALS risk
METHODS
Study Population
Between January 1st, 2006 and March 31st, 2011, we performed a population-based casecontrol study entitled “Prospective ALS study the Netherlands” (PAN). The Netherlands
provides ideal circumstances for a population-based study. It is a densely populated
country with healthcare which is accessible to all inhabitants and a well-developed
infrastructure.13 Complete acquisition of all patients with ALS was ensured by continuous
recruitment through multiple sources: neurologists, rehabilitation physicians, the Dutch
Neuromuscular Patient Association and our ALS website. All patients newly diagnosed
as possible, probable (laboratory supported) or definite ALS according to the revised El
Escorial Criteria were included.14 Excluded were patients who had a first, second or third
degree family member with ALS, defined as familial ALS.
In order to obtain population-based controls, the general practitioner of the participating
patient was asked to select individuals from his register in alphabetical order starting at
the surname of the patient. In The Netherlands, the healthcare system ensures that every
inhabitant is registered at a general practitioner, which makes this list representative of
the population. The controls were frequency-matched to the patients for gender and age
(plus or minus five years). Spouses or blood-relatives of the patients were not eligible to
be controls to prevent overmatching. We determined the C9orf72 repeat in all ALS patients
and in one-third of controls, by performing a repeat-primed PCR reaction as described
previously.15, 16
Data Collection
A structured questionnaire was used to collect clinical characteristics of the participants
and to obtain data regarding medical history and premorbid use of medication. In these
questionnaires, participants were asked to recollect their entire medical history (including
the year of the medical event), and their past use of medication (including the years they
used the medication). In the patient group, only the data referring to the period before
symptom onset were analyzed. All questionnaires were checked thoroughly for missing
data or inconsistencies, and participants were approached by telephone to complete or
correct the data. To ensure blinding, all questionnaires were coded prior to processing
and analysis. The survival status of patients was obtained by checking the municipal
population register and/or contacting the general practitioner on a 3-monthly basis for
both patients and controls.
Classification of Data
Medical history was categorized into eight main groups based on expert opinion:
cardiovascular diseases, neurodegenerative diseases, psychiatric disorders, cancer,
infectious diseases, autoimmune diseases, trauma and surgery. The use of medication was
scored and coded using the Anatomical Therapeutic Chemical (ATC) classification system
of the World Health Organization Collaborating Centre for Drug Statistics Methodology.17
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One of the major purposes of the ATC coding system is to enable the presentation and
comparison of medication consumption statistics internationally. The ATC classification
system divides medication into 14 main groups according to the organ or system on which
it acts. Further classification into 4 levels of subgroups is based on the pharmacological,
therapeutic and chemical properties of the medication. The assignment of the ATC codes
enabled us to compare premorbid use of medication at different levels based on an
internationally accepted classification system.
Statistical Analysis
Differences in baseline characteristics were evaluated using the χ2 test for categorical
variables and the Mann-Whitney U test for continuous variables. A multivariate logistic
regression model was used to determine the association between medical history,
premorbid use of medication and the risk of ALS. The odds ratios (OR) and 95% confidence
intervals (CI) derived from these analyses were adjusted for gender, age (at onset for
patients and at the date the questionnaire was completed for controls), education (three
levels: elementary school, middle/high school and college/university), current smoking
and current alcohol consumption (current meaning at onset for patients and at the time
the questionnaire was completed for controls).
Since C9orf72 repeat expansions were discovered to be an important factor in ALS
causation and patients with a C9orf72 repeat expansion may represent a subgroup with
different lifestyle and environmental factors, we performed a separate logistic regression
analysis excluding apparently sporadic ALS patients with a C9orf72 repeat expansion.
Subsequently, we also performed a logistic regression analysis in the subgroup of patients
with a C9orf72 repeat expansion.
To determine whether survival of patients was associated with medical history and
premorbid use of medication, a Cox proportional hazard model was used. The hazard
ratios (HR) derived from these analyses were adjusted for gender, age at onset and site of
onset. The same method was used to evaluate the effect of medical history and premorbid
use of medication on the age at onset of ALS patients, adjusted for gender and site of onset.
RESULTS
Clinical Characteristics
In this population-based study, 722 (83%) of 867 sporadic ALS patients and 2268 (95%)
of 2454 controls, returned the questionnaire. Table 3.1 presents the characteristics of
patients and controls. The patient characteristics of the responders and the non-responders
were similar. The distribution of age at onset, gender and site of onset in ALS patients
were found to be similar to those previously reported in Europe.18
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Table 3.1 Demographic and clinical characteristics of participants
ALS patients
(n = 722)
Controls
(n = 2268)
P Value
Characteristic
Male, n (%)
Age, y, median (IQR)a
Bulbar site of onset, n (%)
435 (60.2)
1316 (58.0)
0.29
62.8 (56.7-69.6)
63.1 (57.3-69.9)
0.29
237 (32.8)
El Escorial classification, n (%)
Definite
117 (17.2)
Probable
295 (43.4)
Probable lab supported
129 (19.0)
Possible
128 (18.9)
Education, n (%)
No education
Elementary school
3 (0.4)
5 (0.2)
65 (9.0)
142 (6.3)
Middle school/High school
478 (66.2)
1481 (65.4)
College/University
176 (24.4)
637 (28.1)
0.02
Current smoking, n (%)
144 (19.9)
294 (13.0)
<0.001
Current alcohol consumption, n (%)
527 (73.0)
1925 (84.9)
<0.001
24.1 (22.1-26.5)
25.6 (23.6-27.8)
<0.001
43 (5.9)
0 (0.0)
<0.001
BMI, kg/m2, median (IQR)
C9orf72 repeat expansion, n (%)b
Abbreviations: ALS = amyotrophic lateral sclerosis; BMI = body mass index. a Age at onset in
patients, and age at which questionnaire was completed in controls. b C9orf72 repeats were
analyzed in all 722 ALS patients and in 762 controls (33.6%).
Medical Conditions Prior to ALS Onset
575 patients (80%) compared to 1850 controls (82%) reported one or multiple medical
conditions prior to ALS onset (P = 0.25). Table 3.2 shows the association of medical
conditions with the risk of developing ALS in multivariate analyses. Hypercholesterolemia
was associated with a decreased risk (OR 0.76, 95% CI 0.63-0.92, P = 0.006, Figure 3.1).
Because all other cardiovascular diseases, except stroke, showed ORs < 1.0, we also
analyzed the association between ever having had any of these cardiovascular diseases
and the risk of ALS which showed no association (OR 0.85, 95% CI 0.72-1.02, P = 0.08).
When we took current smoking and current alcohol consumption out of the multivariate
model, the ORs remained similar (data not shown). However, when we added body mass
index (BMI) to the model, the association with hypercholesterolemia was no longer
significant (OR 0.84, 95% CI 0.69-1.02, P = 0.08), indicating that BMI is either a confounder
or a factor in the same cascade with hypercholesterolemia.
Trauma was associated with an increased risk of developing ALS (OR 1.36, 95% CI 1.031.79, P = 0.03), which was mainly caused by the contribution of head trauma (i.e. skull
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fractures, concussion or intracranial hemorrhage; OR 1.95, 95% CI 1.11-3.43, P = 0.02).
Taking current smoking and current alcohol consumption out of the multivariate analyses,
or adding BMI, did not essentially alter the ORs (data not shown).
None of the other categories was associated with ALS susceptibility. Other diseases that
have previously been linked to ALS risk,5-10 such as Parkinson’s disease (Table 3.2),
psychotic illness (Table 3.2), various autoimmune diseases and several cancer types (Table
3.3), were analyzed but no associations were found.
Medication Use Prior to ALS Onset
393 patients (54%) and 1297 controls (57%) used one or multiple medications at onset of
disease for patients or at time of completing the questionnaire for controls (P = 0.19).
Table 3.4 shows the ATC-coded main groups. Medication of the ‘cardiovascular system’
was associated with a decreased ALS susceptibility (OR 0.79, 95% CI 0.65-0.96, P = 0.02).
Analyzing the ATC-coded subgroups showed that this association was due to the use of
lipid-modifying agents (statins) (OR 0.45, 95% CI 0.35-0.59, P = 1.86 x 10-9, Table S3.1,
Figure 3.1). Removing current smoking and current alcohol consumption from the
multivariate analyses did not change the association with ALS risk; adding BMI led to a
non-significant association with medication of the ‘cardiovascular system’ (OR 0.95, 95%
CI 0.78-1.16, P = 0.61), and to a modest increase in the OR for statin use (OR 0.51, 95%
CI 0.39-0.61, P = 4.84 x 10-7).
The main group ‘antineoplastic and immunomodulating agents’ was associated with a
decreased risk of ALS (OR 0.35, 95% CI 0.14-0.91, P = 0.03). Analyzing the subgroups
showed that an equal frequency of patients and controls had undergone endocrine therapy
(0.3% in both groups, Table S3.1), either for breast cancer or for prostate cancer. The
association was mainly caused by medication from the subgroups ‘antineoplastic agents’
and ‘immunosuppressants’, all of which appeared to be immunosuppressive in nature
(Table S3.2). When grouped together, a significant association of immunosuppressive
*
ALS patients
Controls
Prevalence (%)
**
Hypercholesterolemia
Statin use
Figure 3.1 Prevalence of hypercholesterolemia and statin use in ALS patients and controls
* P = 0.006, calculated by logistic regression analysis for the association between hypercholesterolemia
and the risk of developing ALS. ** P = 1.86 x 10-9, calculated by logistic regression analysis for the
association between statin use and the risk of developing ALS.
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drugs and a decreased risk of ALS was found (OR 0.26, 95% CI 0.08-0.86, P = 0.03).
Removing current smoking and current alcohol consumption from the multivariate
analyses, or adding BMI did not essentially alter the ORs (data not shown).
C9orf72 Repeat Expansion
Of the 722 apparently sporadic ALS patients, 43 had a C9orf72 repeat expansion. Patients
with a C9orf72 repeat expansion were significantly younger (58.5 vs. 63.3 years, P = 0.001),
were more often female (58% vs. 39%, P = 0.01) and had more often a bulbar site of onset
(42% vs. 32%, P = 0.21). Of the one-third (n = 732) of the controls who where analyzed,
none had a C9orf72 repeat expansion. Excluding patients with a C9orf72 repeat expansion
did not significantly change any of the results. To determine whether patients with a
C9orf72 repeat expansion may represent a subgroup with different lifestyle and
environmental factors, we subsequently analyzed the subgroup of patients with a C9orf72
repeat expansion. No significant associations were found (Table S3.3).
Table 3.2 Association of ALS with premorbid medical conditions
P Value
ALS patients
n (%)
Controls
n (%)
OR (95% CI)a
352 (48.8)
1170 (51.6)
0.85 (0.72-1.02)
0.08
Medical Condition
Cardiovascular diseases
Diabetes
49 (6.8)
184 (8.1)
0.72 (0.51-1.01)
0.06
Hypercholesterolemia
188 (26.0)
702 (31.0)
0.76 (0.63-0.92)
0.006
Hypertension
233 (32.3)
784 (34.6)
0.90 (0.75-1.08)
0.26
Stroke
10 (1.4)
30 (1.3)
0.99 (0.48-2.07)
0.99
Myocardial infarction
31 (4.3)
108 (4.8)
0.90 (0.59-1.38)
0.64
Peripheral arterial disease
6 (0.8)
30 (1.3)
0.51 (0.21-1.25)
0.14
Neurodegenerative diseases
6 (0.8)
8 (0.4)
2.19 (0.74-6.50)
0.16
Parkinson's disease
3 (0.4)
5 (0.2)
1.51 (0.35-6.54)
0.58
Psychiatric disorders
16 (2.2)
44 (1.9)
1.09 (0.6-1.96)
0.78
1 (0.1)
5 (0.2)
0.67 (0.08-5.81)
0.71
Cancer
Psychotic illness
55 (7.6)
205 (9.0)
0.86 (0.62-1.18)
0.35
Infectious diseases
75 (10.4)
243 (10.7)
0.99 (0.75-1.32)
0.99
Autoimmune diseases
15 (2.1)
57 (2.5)
0.79 (0.44-1.42)
0.43
Trauma
81 (11.2)
208 (9.2)
1.36 (1.03-1.79)
0.03
20 (2.8)
35 (1.5)
1.95 (1.11-3.43)
0.02
278 (38.5)
917 (40.4)
0.94 (0.79-1.12)
0.47
Head trauma
Surgery
Abbreviations: ALS = amyotrophic lateral sclerosis; OR= odds ratio; CI = confidence interval.
a
ORs are adjusted for gender, age, education, current smoking and current alcohol use.
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Table 3.3 Prevalence of various autoimmune diseases and cancer types in ALS patients and controls
Prevalence (%)
ALS patients
(n = 722)
Prevalence (%)
controls
(n = 2268)
P Value
15 (2.1)
57 (2.5)
0.51
Addison's disease
0 (0.0)
0 (0.0)
-
Ankylosing spondylitis
0 (0.0)
0 (0.0)
-
Autoimmune heamolytic anemia
0 (0.0)
2 (0.09)
0.43
Autoimmune disease
Celiac disease
0 (0.0)
2 (0.09)
0.43
Crohn's disease
1 (0.14)
1 (0.04)
0.39
Dermatomyositis
0 (0.0)
0 (0.0)
-
Graves' hyperthyroidism
1 (0.14)
5 (0.22)
0.67
Hashimoto's thyroid disease
0 (0.0)
2 (0.09)
0.43
Multiple sclerosis
1 (0.14)
0 (0.0)
0.08
Myasthenia gravis
0 (0.0)
4 (0.17)
0.26
Polymyalgia rheumatica
1 (0.14)
5 (0.22)
0.67
Psoriasis
3 (0.42)
9 (0.40)
0.95
Rheumatoid arthritis
5 (0.69)
18 (0.79)
0.79
Scleroderma
1 (0.14)
1 (0.04)
0.39
Sjogren's syndrome
0 (0.0)
2 (0.09)
0.43
Systemic lupus erythematosus
0 (0.0)
1 (0.04)
0.57
Ulcerative colitis
2 (0.28)
5 (0.22)
0.78
Vitiligo
0 (0.0)
0 (0.0)
-
55 (7.6)
205 (9.0)
0.24
0.64
Cancer
Breast cancer
10 (1.4)
37 (1.6)
Colorectal cancer
4 (0.6)
22 (1.0)
0.29
Non-Hodgkin’s Lymphoma
1 (0.1)
5 (0.2)
0.67
Kidney cancer
0 (0.0)
9 (0.4)
-
Leukemia (all)
1 (0.1)
5 (0.2)
0.67
Lung cancer
2 (0.3)
4 (0.2)
0.60
Ovarian cancer
0 (0.0)
3 (0.1)
-
Prostate cancer
9 (1.2)
32 (1.4)
0.74
Skin cancer
17 (2.4)
57 (2.5)
0.81
0.36
Urinary bladder cancer
1 (0.1)
8 (0.4)
Uterine corpus and cervical cancer
5 (0.7)
11 (0.5)
0.51
Other types of cancera
5 (0.7)
12 (0.5)
0.61
Other types of cancer were cancer from the pancreas, liver, spleen, thyroid, brain, vocal cord,
salivary gland, and testicles.
a
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Table 3.4 Association of ALS with premorbid use of medication, based on the ATC classification
system
ALS patients
n (%)
Controls
n (%)
OR (95% CI)a
P Value
(A)
Alimentary tract and
metabolism
130 (18.0)
408 (18.0)
0.92 (0.73-1.16)
0.50
(B)
Blood and blood-forming
organs
118 (16.3)
381 (16.8)
0.93 (0.73-1.19)
0.56
(C)
Cardiovascular system
251 (34.8)
887 (39.1)
0.79 (0.65-0.96)
0.02
(D) Dermatologicals
1 (0.1)
12 (0.5)
0.26 (0.03-2.05)
0.20
(G) Genito-urinary system and sex
hormones
40 (5.5)
134 (5.9)
0.96 (0.67-1.40)
0.85
(H) Systemic hormonal
preparations
23 (3.2)
104 (4.6)
0.69 (0.43-1.12)
0.14
(J)
Anti-infectives for systemic use
2 (0.3)
15 (0.7)
0.51 (0.12-2.28)
0.38
(L)
Antineoplastic and
immunomodulating agents
6 (0.8)
40 (1.8)
0.35 (0.14-0.91)
0.03
49 (6.8)
152 (6.7)
0.99 (0.70-1.40)
0.94
106 (14.7)
240 (10.6)
1.29 (1.00-1.69)
0.05
0 (0)
5 (0.2)
-
(M) Musculo-skeletal system
(N) Nervous system
(P)
Antiparasitic products,
insectides and repellents
-
(R)
Respiratory system
63 (8.7)
153 (6.7)
1.23 (0.89-1.69)
0.21
(S)
Sensory organs
6 (0.8)
26 (1.1)
0.82 (0.33-2.02)
0.66
Abbreviations: ALS = amyotrophic lateral sclerosis; ATC = anatomical therapeutic chemical; OR
= odds ratio; CI = confidence interval. a ORs are adjusted for gender, age, education, current
smoking and current alcohol use.
Survival and Age at Onset Analyses
None of the categories of medical conditions or premorbid use of medication was
associated with survival. Specifically, patients with hypercholesterolemia or patients using
statins did not have a significant difference in survival compared to patients without
hypercholesterolemia or not using statins (HR 1.14, 95% CI 0.92-1.41, P = 0.22; and HR
1.19, 95% CI 0.89-1.60, P = 0.23 respectively).
Cox regression analysis of age at onset of ALS patients showed that diabetes, myocardial
infarction and premorbid use of medication of three ATC main groups (alimentary tract
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and metabolism; blood and blood forming organs; and cardiovascular system) were
significantly related to age at onset. However, because of a potentially confounding factor
– age (older individuals are more likely to have comorbidities), the analysis had to be
adjusted appropriately for this effect, in two different ways. First of all, an interaction
term of case-control status with medical history or premorbid use of medication was
introduced into the model. The interaction terms for diabetes, myocardial infarction and
for the three medication groups were non-significant. Secondly, the multivariate Cox
regression analysis was performed in controls using the filling out of the questionnaire
as the event. This showed a similar association for these variables with ‘age at event’ in
controls compared to the association with ‘age at onset’ in patients. Both indicate that the
relation between medical history, premorbid use of medication and age at onset, is an
age-related effect and not an ALS-specific effect.
DISCUSSION
In this large population-based, case-control study, we found that hypercholesterolemia
and statin use prior to symptom onset are associated with a decreased risk of ALS, which
indicates a premorbid favorable lipid profile in at least a subset of ALS patients. Secondly,
the use of immunosuppressive drugs is significantly lower in ALS patients, which could
suggest a protective effect on ALS susceptibility. Thirdly, antecedent head trauma may be
a risk factor for ALS. Lastly, we did not find evidence for an effect of prior medical
conditions or medications on survival, symptom onset or C9orf72 genotype.
Hypercholesterolemia is less frequently reported among ALS patients compared to
controls, which is a proxy for a favorable lipid profile. This is in line with our previous
findings in a smaller, independent, hospital-based study that ALS patients have lower
blood lipid levels and use less statins compared to controls.2 In contrast, in a French ALS
cohort, the frequency of hyperlipidemia was higher in patients compared to controls,19
while in an Italian study no altered lipid levels were found in ALS patients.20 These
discrepancies are probably due to differences in the control population, or they might
reflect differences between populations and lifestyles (i.e. differences in dietary habits).
The observation of a favorable lipid profile is consistent with the significantly lower
premorbid BMI found in ALS patients compared to controls (24.1 versus 25.6 kg/m2, P <
0.001, shown in Table 3.1) as was also reported in previous studies.2, 21 Adding BMI to the
multivariate model resulted in a non-significant association between hypercholesterolemia
and ALS, which suggests that BMI is either a confounder or that hypercholesterolemia
and BMI are in the same cascade. In contrast with lower lipid levels and a lower BMI,
there is evidence for a high fat, high caloric dietary intake in ALS patients before onset of
symptoms.22 This discrepancy in energy homeostasis can be explained by an increased
metabolic rate, which may be present in ALS patients prior to symptom onset as has also
been demonstrated in SOD1 mouse models of ALS.23, 24
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In line with a lower frequency of premorbid hypercholesterolemia, we found a lower use
of statins in patients, which is in contrast with previous studies from the WHO Foundation
Collaborating Centre for International Drug Monitoring and the Food and Drug
Administration. Both registered disproportionally high prevalence of ALS cases among
subjects using statins.25, 26 This signal could not, however, be validated by retrospective
pooling of 41 clinical trials on statin use or within a population-based, case-control study
in Denmark.26, 27 Our study even showed evidence for a possible protective effect of statins
in ALS. A neuroprotective and anti-inflammatory effect of statins has been reported in
other neurodegenerative diseases (i.e. Alzheimer’s and Parkinson’s disease),28, 29 in multiple
sclerosis,30 as well as in the wobbler mouse model of motor neuron degeneration.31
We did not find a longer survival in patients with hypercholesterolemia or a shorter
survival in patients on statins as was reported in case-control studies in ALS.2, 19, 32, 33 Most
of those studies, however, found an association with survival in a univariate model without
adjusting for covariates. Two studies showed that the association disappeared after
adjustment for age at onset, site of onset, BMI or forced vital capacity.2, 32, 34 This was also
true in our study: in a univariate model, there was a significant association between statin
use and a shorter survival (HR 1.46, 95% CI 1.09-1.94, P = 0.01). The difference with our
null finding in the multivariate model can be explained, since patients using statins were
significantly older than patients not using statins (median 67 vs. 61 years, P = 0.002). In
addition, in the multivariate model both age at onset and site of onset were significantly
associated with survival (in contrast with statin use), implicating that these factors
influence disease progression.
We found a significant association for trauma, specifically head trauma, and ALS risk.
Head trauma consisted of severe traumatic brain injuries (i.e. skull fractures, concussion
or intracranial hemorrhage); no microtrauma were included in our study. Previously, head
trauma was considered to be a risk factor for ALS, initially in a study on Italian soccer
players,35 followed by other reports in veterans and population studies.12, 36, 37 Although
more research is required to confirm the association,38 our study further supports head
trauma as a risk factor for ALS. Even when analyzing head trauma occurring at least five
years prior to symptom onset, to exclude possible injuries due to incipient ALS, an
association was found with ALS (OR 1.86, 95% CI 1.01-3.42, P < 0.05).
The use of immunosuppressive agents is significantly lower in ALS patients, which may
suggest that immunosuppressive agents have a protective effect on ALS susceptibility in
controls, but the relatively low frequency of their use makes it difficult to determine this
with certainty. A lower use of immunosuppressive agents is at least consistent with the
lack of association with autoimmune diseases in our population. In contrast to our study,
two hospital-based, record linkage studies found an increased frequency of autoimmune
diseases amongst ALS patients and their relatives.9, 10 Furthermore, cardiovascular diseases,
psychiatric disorders and cancer, all of which have previously been linked to ALS,2-8 did
not show an association with the risk of developing ALS in our study. These differences
may be due to a population-specific effect, with environmental and genetic diversity
between populations influencing phenotype and predisposition.
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A limitation of the study is that by not applying a multiple test adjustment method, we
might have rejected a true null hypothesis. Due to the exploratory nature of this study,
the use of multiple test adjustment can be considered too conservative.39 Furthermore,
we acknowledge that in questionnaires, differential recall of exposures between patients
and controls may be a limitation.40 Patients may be more eager to find an explanation for
their disease, which is why they tend to over-report events. In addition, cognitive and
executive impairment, which occurs in ALS, might have influenced recall as well. This
risk of recall bias was reduced by calling participants to complete the questionnaires and
by blinding both the participants, and the interviewers for the hypotheses being tested.
For validation of our questionnaire, we checked the medical records of 122 participants,
which showed a ≥95% concordance between the questionnaires and the medical records.
In conclusion, the diagnosis of ALS may be associated with a favorable lipid profile prior
to symptom onset in at least a subpopulation of ALS. Furthermore, trauma, and specifically
head trauma, is shown to be a risk factor for ALS. These findings provide new insight into
possible pathogenic mechanisms in ALS.
Ethical standard
Ethical approval was obtained from the institutional review board of the University
Medical Center Utrecht. All participants gave written informed consent for inclusion in
the study.
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of C9orf72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011; 72: 245-56.
van Rheenen W, et al. Hexanucleotide repeat expansions in C9orf72 in the spectrum of motor
neuron diseases. Neurology. 2012; 79: 878-82.
World Health Organization. Guidelines for ATC classification and DDD assignment. 2012.
Logroscino G, et al. Incidence of amyotrophic lateral sclerosis in Europe. J Neurol Neurosurg
Psychiatry. 2010; 81: 385-90.
Dupuis L, et al. Dyslipidemia is a protective factor in amyotrophic lateral sclerosis. Neurology.
2008; 70: 1004-9.
Chio A, et al. Lower serum lipid levels are related to respiratory impairment in patients with
ALS. Neurology. 2009; 73: 1681-5.
O'Reilly EJ, et al. Premorbid body mass index and risk of amyotrophic lateral sclerosis.
Amyotroph Lateral Scler Frontotemporal Degener. 2013; 14: 205-11.
Nelson LM, et al. Population-based case-control study of amyotrophic lateral sclerosis in
western Washington State. II. Diet. Am J Epidemiol. 2000; 151: 164-73.
Bouteloup C, et al. Hypermetabolism in ALS patients: an early and persistent phenomenon. J
Neurol. 2009; 256: 1236-42.
Dupuis L, et al. Evidence for defective energy homeostasis in amyotrophic lateral sclerosis:
benefit of a high-energy diet in a transgenic mouse model. Proc Natl Acad Sci U S A. 2004;
101: 11159-64.
Edwards IR, et al. Statins, neuromuscular degenerative disease and an amyotrophic lateral
sclerosis-like syndrome: an analysis of individual case safety reports from vigibase. Drug Saf.
2007; 30: 515-25.
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26. Colman E, et al. An evaluation of a data mining signal for amyotrophic lateral sclerosis and
statins detected in FDA's spontaneous adverse event reporting system. Pharmacoepidemiol
Drug Saf. 2008; 17: 1068-76.
27. Sorensen HT, et al. Statin use and risk of amyotrophic lateral sclerosis and other motor neuron
disorders. Circ Cardiovasc Qual Outcomes. 2010; 3: 413-7.
28. Arvanitakis Z, et al. Statins, incident Alzheimer disease, change in cognitive function, and
neuropathology. Neurology. 2008; 70: 1795-802.
29. Wahner AD, et al. Statin use and the risk of Parkinson disease. Neurology. 2008; 70: 1418-22.
30. Ciurleo R, et al. Role of statins in the treatment of multiple sclerosis. Pharmacol Res. 2014; 87:
133-43.
31. Iwamoto K, et al. Atorvastatin treatment attenuates motor neuron degeneration in wobbler
mice. Amyotroph Lateral Scler. 2009; 10: 405-9.
32. Drory VE, et al. Influence of statins treatment on survival in patients with amyotrophic lateral
sclerosis. J Neurol Sci. 2008; 273: 81-3.
33. Dorst J, et al. Patients with elevated triglyceride and cholesterol serum levels have a prolonged
survival in amyotrophic lateral sclerosis. J Neurol. 2011; 258: 613-7.
34. Zinman L, et al. Are statin medications safe in patients with ALS? Amyotroph Lateral Scler.
2008; 9: 223-8.
35. Chio A, et al. Severely increased risk of amyotrophic lateral sclerosis among Italian professional
football players. Brain. 2005; 128: 472-6.
36. Schmidt S, et al. Association of ALS with head injury, cigarette smoking and APOE genotypes.
J Neurol Sci. 2010; 291: 22-9.
37. Chen H, et al. Head injury and amyotrophic lateral sclerosis. Am J Epidemiol. 2007; 166: 8106.
38. Armon C and Nelson LM. Is head trauma a risk factor for amyotrophic lateral sclerosis? An
evidence based review. Amyotroph Lateral Scler. 2012; 13: 351-6.
39. Bender R and Lange S. Adjusting for multiple testing--when and how? J Clin Epidemiol. 2001;
54: 343-9.
40. Coughlin SS. Recall bias in epidemiologic studies. J Clin Epidemiol. 1990; 43: 87-91.
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Prior medical conditions and ALS risk
SUPPLEMENTAL MATERIAL
Table S3.1 Association of ALS with medication use of the ATC coded subgroups of the
“cardiovascular system”, and “antineoplastic and immunomodulating agents”
ATC
code
ALS patients
n (%)
Controls
n (%)
OR (95% CI)a
P Value
Cardiac therapy
C01
21 (2.9)
70 (3.1)
0.95 (0.57-1.59)
.85
Antihypertensives
C02
3 (0.4)
19 (0.8)
0.33 (0.08-1.45)
.14
Diuretics
C03
69 (9.6)
210 (9.3)
1.01 (0.74-1.36)
.97
Peripheral vasodilators
C04
0 (0.0)
1 (0.0)
-
-
Subgroups
Cardiovascular systemb
Vasoprotectives
C05
1 (0.1)
0 (0.0)
-
-
Beta blocking agents
C07
108 (15.0)
365 (16.1)
0.89 (0.70-1.14)
.36
Calcium channel blockers
C08
45 (6.2)
161 (7.1)
0.88 (0.62-1.25)
.47
Renin-angiotensin system
C09
128 (17.7)
415 (18.3)
0.96 (0.76-1.21)
.72
Lipid modifying agents
C10
90 (12.5)
487 (21.5)
0.45 (0.35-0.59)
<.001
Antineoplastic and immunomodulating agents
Antineoplastic agents
L01
0 (0.0)
6 (0.3)
-
-
Endocrine therapy
L02
2 (0.3)
7 (0.3)
0.80 (0.16-4.06)
.79
Immunostimulants
L03
0 (0.0)
0 (0.0)
-
-
Immunosuppressants
L04
4 (0.6)
27 (1.2)
0.31 (0.09-1.04)
.06
Abbreviations: ATC = anatomical therapeutic chemical; ALS = amyotrophic lateral sclerosis; OR
= odds ratio; CI = confidence interval. a ORs are adjusted for gender, age, education, current
smoking and current alcohol consumption. b ATC subgroup C06 does no longer exist.
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Table S3.2 Prevalences of medication use of the ATC coded subgroups of “antineoplastic and
immunomodulating agents” in ALS patients and controls
ATC code
ALS patients
n (%)
Controls
n (%)
6 (0.3)
Subgroups
Antineoplastic agents
L01
0 (0.0)
Chlorambucil
L01AA02
0 (0.0)
1 (0.0)
Methotrexate
L01BA01
0 (0.0)
1 (0.0)
Imatinib
L01XE01
0 (0.0)
1 (0.0)
Hydroxycarbamide
L01XX05
0 (0.0)
2 (0.1)
L01
0 (0.0)
1 (0.0)
Chemotherapy (not specified)
L02
2 (0.3)
7 (0.3)
Goserelin
L02AE03
0 (0.0)
1 (0.0)
Tamoxifen
L02BA01
1 (0.1)
2 (0.1)
Bicalutamide
L02BB03
0 (0.0)
4 (0.2)
Letrozole
L02BG04
1 (0.1)
1 (0.0)
Immunostimulants
L03
0 (0.0)
0 (0.0)
Immunosuppressantsb
L04
4 (0.6)
27 (1.2)
Endocrine therapya
Leflunomide
L04AA13
0 (0.0)
1 (0.0)
Etanercept
L04AB01
0 (0.0)
3 (0.1)
Ciclosporin
L04AD01
1 (0.1)
2 (0.1)
Tacrolimus
L04AD02
0 (0.0)
2 (0.1)
Azathioprine
L04AX01
2 (0.3)
4 (0.2)
Thalidomide
L04AX02
0 (0.0)
1 (0.0)
Methotrexate
L04AX03
1 (0.1)
17 (0.7)
Abbreviations: ATC = anatomical therapeutic chemical; ALS = amyotrophic lateral sclerosis. a Total
number of controls using endocrine therapy (n = 7) is less than the combined individual numbers,
because 1 control used >1 endocrine therapy. b Total number of controls using immunosuppressant
(n = 27) is less than the combined individual numbers, because three controls used >1
immunosuppressant.
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Prior medical conditions and ALS risk
Table S3.3 Association of ALS with premorbid medical conditions, in sporadic ALS patients with
a C9orf72 repeat expansion (n = 43) compared to 762 controls with no C9orf72 repeat expansion
Cardiovascular diseases
Diabetes
ALS patients
n (%)
Controls
n (%)
OR (95% CI)a
P Value
18 (41.9)
394 (51.7)
0.81 (0.43-1.56)
.54
0 (0.0)
62 (8.1)
-
-
Hypercholesterolemia
9 (20.9)
235 (30.8)
0.70 (0.33-1.52)
.37
Hypertension
13 (30.2)
265 (34.8)
0.94 (0.47-1.88)
.87
Stroke
0 (0.0)
10 (1.3)
-
-
Myocardial infarction
0 (0.0)
40 (5.2)
-
-
Peripheral arterial disease
0 (0.0)
7 (0.9)
-
-
Neurodegenerative diseases
0 (0.0)
2 (0.3)
-
-
Parkinson's disease
0 (0.0)
0 (0.0)
-
-
Psychiatric disorders
1 (2.3)
14 (1.8)
0.96 (0.12-7.66)
.97
0 (0.0)
4 (0.5)
-
-
Cancer
Psychotic illness
1 (2.3)
68 (8.9)
0.28 (0.04-2.10)
.22
Infectious diseases
4 (9.3)
80 (10.5)
0.89 (0.31-2.59)
.83
Autoimmune diseases
1 (2.3)
22 (2.9)
0.57 (0.07-4.58)
.60
Trauma
2 (4.7)
79 (10.4)
0.47 (0.11-1.98)
.30
Head trauma
Surgery
0 (0.0)
11 (1.4)
-
-
15 (34.9)
304 (39.9)
0.81 (0.42-1.55)
.52
Abbreviations: ALS = amyotrophic lateral sclerosis; OR= odds ratio; CI = confidence interval.
a
ORs are adjusted for gender, age, education, current smoking and current alcohol use.
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CHAPTER 4
Pre-symptomatic BMI, dietary fat
and alcohol consumption as independent risk factors
for amyotrophic lateral sclerosis
In preparation Mark H B Huisman1, Meinie Seelen1, Perry T C van Doormaal1, Sonja W de Jong1,
Jeanne H M de Vries2, Anneke J van der Kooi3, Marianne de Visser3,
H Jurgen Schelhaas4, Leonard H van den Berg1*, Jan H Veldink1*
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, The Netherlands.
2
Division of Human Nutrition, Wageningen University, the Netherlands.
3
Department of Neurology, Amsterdam Medical Center,
University of Amsterdam, The Netherlands.
4
Department of Neurology, Donders Institute for Brain, Cognition and Behaviour,
Center for Neuroscience, Radboud University Nijmegen Medical Center,
The Netherlands.
1
*Joint last authors
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ABSTRACT
Background
Dietary intake may influence pathophysiological mechanisms in sporadic amyotrophic
lateral sclerosis (ALS). We, therefore, aimed to systematically determine the relation
between premorbid dietary intake and the risk of sporadic ALS, in order to provide better
insight into which mechanisms are possibly involved in ALS pathophysiology.
Methods
In a population-based case-control study in The Netherlands, including 674 patients and
2,093 controls, we studied the premorbid intake of nutrients in relation to the risk of ALS,
using a food frequency questionnaire adjusted for confounding factors and corrected for
multiple comparisons, while minimizing recall bias.
Findings
Pre-symptomatic total daily energy intake in patients was significantly higher compared
with controls (p < 0·01), while pre-symptomatic BMI was significantly lower in patients
(p = 0·02). Higher premorbid intakes of total fat, saturated fat, trans fatty acids, and
cholesterol were associated with an increased risk of ALS, while higher intake of alcohol
was associated with a decreased risk. These associations were independent of total energy
intake, age, gender, BMI, education, smoking and lifetime physical activity.
Interpretation
The combination of a positive association of a low premorbid BMI and a high fat intake,
together with prior evidence from ALS SOD1 mouse models and earlier reports on
premorbid BMI, supports a role for altered energy metabolism prior to clinical onset of
ALS.
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Premorbid BMI, dietary intake and ALS risk
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder,
characterized by the progressive loss of upper and lower motor neurones, with a median
survival from onset of three years, no cure and an aetiology that is still poorly understood.1,
2
Oxidative stress, mitochondrial dysfunction, excitotoxicity, disrupted axonal transport
and inflammation are mechanisms that are potentially involved in ALS pathophysiology.
Some of these mechanisms may be influenced by dietary nutrients. The intake of dietary
antioxidants, for example, may reduce oxidative stress.3, 4
Previous studies did not identify a consistent nutrient that modifies susceptibility to
ALS.3-13 Most associations have not been replicated, and contradictory results exist for
the association with total fat intake.3, 7 Decreased risk of ALS with higher levels of vitamin
E intake, a potent cellular antioxidant, is one of the associations that has been reported
more than once.4, 6, 11 Furthermore, a recent study replicated the observation that a higher
intake of polyunsaturated fatty acids (PUFA) is associated with a decreased risk of ALS.4,
14
These studies suggest that nutrients, that can have direct neuroprotective properties or
influence pathways known to be involved in ALS pathogenesis, may be associated with
ALS.
Since diet is highly modifiable, in our large population-based case-control study, we set
out to test the relation between premorbid intake of many nutrients and the risk of ALS
and the progression of the disease, adjusted for confounding factors and corrected for
multiple comparisons.
METHODS
Study design and participants
The Prospective ALS study The Netherlands (PAN) is a population-based case-control
study performed in The Netherlands from January 1st, 2006, until September 30st, 2011.
During the study period, all patients newly diagnosed with possible, probable (laboratorysupported) or definite ALS, according to the revised El Escorial criteria, were included.15
Multiple sources were used to ensure complete case ascertainment: neurologists,
rehabilitation physicians, the Dutch Neuromuscular Patient Association and our ALS
website. Medical records were scrutinized for eligibility of the patients, excluding patients
with an ALS-mimic syndrome or with a first, second or third degree family member with
ALS, defined as familial ALS. Patients with a C9orf72 repeat expansion were excluded
from our analysis. An expanded repeat in C9orf72 was assessed by performing a repeatprimed PCR reaction as described previously.16
Population-based controls were selected from the register of the general practitioner (GP)
taking care of the participating patient with ALS. In The Netherlands, the health care
system ensures that every inhabitant is registered at a general practitioner which makes
this record representative of the population. The GP was asked to select individuals from
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his register in alphabetical order, starting at the surname of the patient. The controls were
matched to the patients for gender and age (plus or minus five years). Blood-relatives or
spouses of the patients were not eligible to be controls to prevent overmatching.
Ethical approval was provided by the institutional review board of the University Medical
Centre Utrecht. All participants gave written informed consent.
Procedures
Patients and controls were asked to fill in a 199-item food frequency questionnaire (FFQ)
that covered the food consumption over the previous month. However, if dietary habits
had changed since onset of symptoms, patients were asked to recall their dietary habits
over the one-month period prior to the onset of muscle weakness or bulbar signs, to avoid
a possible influence of disease on their intake. Food items for the original questionnaire
were chosen on the basis of data from the Dutch National Food Consumption Survey of
1992,17 and updated based on a 1998 survey.18 The selected food items for this FFQ covered
about 95% of the intake of total energy, total fat, fatty acids and cholesterol of the Dutch
population and was validated for this purpose.19 Considering the hypotheses of the present
study, the questionnaire was extended with questions on the intake of foods which
contributed > 0·5% to the population intake of protein, carbohydrates, dietary fibres,
alcohol, calcium, vitamin B2, vitamin C, vitamin E, lycopene, flavonoids, glutamate and
phyto-oestrogens. For several food items, additional questions were included on
preparation method or portion sizes. Consumed amounts were calculated using standard
household measures.20 For nutrient calculations, the 2006 Dutch Food Composition Table
was used for energy, macronutrients and vitamin C;21 national reports by TNO Nutrition
and Food Research for calcium, vitamin B2 and vitamin E; publications for flavonoids;22,
23
the US Department of Agriculture table for phyto-oestrogens (isoflavones);24 publications
for glutamate and monosodium glutamate;25-29 and the US Department of Agriculture
table for lycopene.24
If necessary, patients and control subjects were contacted by telephone to clarify
inconsistencies or missing data in the questionnaire. FFQs of 5 controls were not included
in the analyses because of implausible low or high reported energy intakes. For this,
theoretical physical activity levels (PALs) were calculated, dividing reported energy intake
by the Basal Metabolic Rate, using Schofield’s formulae, and compared to the lower and
upper cut-off limits for these PALs.30 All questionnaires remained anonymous during the
analyses, and all data were entered in a blinded fashion.
A second, self-administered general questionnaire was filled out by the participants to
obtain data on age, gender, level of education, smoking habits, anthropometrical
characteristics and a lifetime history of occupations, sports and hobbies.31
Data on survival of patients, up to February 1, 2012, was monitored using the municipal
population register.
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Premorbid BMI, dietary intake and ALS risk
Statistical analysis
Baseline characteristics were tested for differences using Pearson’s Chi square and the
Mann-Whitney U test. To determine odds ratios (ORs) for the association between the
intake of a specific nutrient and ALS, we performed a binary logistic regression with three
levels of adjustment: (1) adjusting for age (at onset for patients, age at which questionnaire
was completed in controls), gender and education; (2) additionally adjusted for BMI
(premorbid in patients), smoking (current or not) and lifetime physical activity; and (3)
additionally adjusted for total energy intake. The lifetime physical activity was calculated
from the lifetime history of occupations, sports and hobbies, and has been described
elsewhere.31 We also determined the relation between nutrient intake and risk of ALS
using the multivariate nutrient density model designed by Willett et al, which is another
frequently used model, to account for total energy intake:32
The meaning of the coefficient β1 for the nutrient density (i.e. energy provided by nutrient
/ total energy) is the difference in disease risk associated with a difference in 1% of energy
from the nutrient while total energy intake is kept constant. In nutrients that do not yield
energy, nutrient density was expressed as nutrient intake in milligrams per 1,000
kilocalories of energy intake. In this analysis we also adjusted for age, gender, education,
BMI, smoking and physical activity.
An additional logistic regression, with the same covariates, was performed in which
nutrient intake was categorized into quintiles, based on the nutrient intake in controls.
The lowest quintile served as the reference group and the five-level variables were also
entered into the model as continuous variables to determine whether there was a linear
trend.
Those nutrients that were significantly associated with ALS, either in the analysis with
absolute values of nutrient intake or in the analysis with quintiles of intake, were analyzed
together in a multivariate binary logistic regression to determine which of these nutrients
were independently associated with ALS. This analysis was performed with the maximal
level of adjustment.
Finally, Cox regression analysis was performed to determine the association between
survival from onset and the intake nutrients. The hazard ratios (HR) derived from these
analyses were adjusted for gender, age at onset, site of onset, premorbid BMI, energy
intake, education, current smoking, and lifetime physical activity.31 The same method was
used to determine the effect of nutrient intake on the age at onset of ALS patients.
To adjust appropriately for age, an interaction term of diagnosis and nutrient or dietary
pattern was introduced to the Cox regression analysis using age at time of completing the
questionnaire for controls.
All tests were two-sided, and a Bonferroni correction was applied to the alpha level to
adjust for multiple comparisons. The Bonferroni adjusted p-values are shown in the tables.
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Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data
interpretation, or writing of the report. All authors had full access to all the data in the
study and the corresponding author had final responsibility for the decision to submit for
publication.
RESULTS
Informed consent to participate in the study was given by 885 (90%) of a total of 986
eligible patients identified between January 1, 2006 and September 31, 2011. Of the
questionnaires sent to these 885 patients, 747 were returned (84%). 674 (87%) of these
patients had completed the questionnaires without any omissions and were included in
the analyses. A total of 2480 population-based controls were selected from the GP’s
register, and 2385 of these returned their questionnaire (response rate 96%). Of these 2385
controls, 2093 (88%) had completed the questionnaires without any missing value and
were included. Table 4.1 shows the characteristics of the 674 patients and 2093 controls
included in the analyses. Gender, mean age at onset, and frequency of bulbar onset did
not differ significantly between responders and non-responders. Cases and controls were
similar for the matching variables, gender and age.
Pre-symptomatic BMI was significantly lower in patients than controls (p=0·02) (Table
4.1). In contrast, pre-symptomatic daily energy intake as calculated from the FFQ was
significantly higher in patients compared with controls (p<0·01). Total lifetime physical
activity scores (during both leisure and occupational time) did not differ between patients
and controls (p=0·2).
Table 4.2 shows the adjusted ORs for the association between the premorbid intake of
individual nutrients and the risk of ALS. Higher intakes of total fat, saturated fat, trans
fatty acids, and cholesterol were independently associated with an increased risk of ALS,
irrespective of the level of adjustment and irrespective of using absolute intake or nutrient
density in the analysis. In the maximal adjusted model, higher intakes of vegetable protein,
polysaccharides, fibres, and flavonoids were associated with a decreased risk of ALS. The
relation with quintiles of intake of these nutrients, and the p values for trend across
quintiles are illustrated in Figure 4.1 (significant associations) and in Supplementary Figure
S4.1 (non-significant associations). Figure 4.1 shows that alcohol is significantly related
to a decreased risk of ALS (p for trend: <0·001).
From the different dietary fats, we only included the intake of saturated fat in the
multivariate analysis, since both trans fatty acids and cholesterol were highly correlated
with the intake of saturated fat (trans fatty acids: r = 0·95; cholesterol: r = 0·73). Besides
saturated fat, the intake of vegetable protein, polysaccharides, fibres, alcohol and flavonoids
were analyzed together in the multivariate model, since these nutrients were significantly
associated with risk of ALS in the maximal adjusted model or in the analysis with quintiles
of intake (Table 4.2, Figure 4.1). The multivariate analysis shows that only a higher intake
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Premorbid BMI, dietary intake and ALS risk
Figure 4.1 Odds ratios for the relationship between ALS and quintiles of nutrient intake. Adjusted
for energy intake, age (at onset in patients; at questionnaire in controls), gender, BMI, education,
current smoking, and lifetime physical activity. P values shown are for the trend across quintiles.
Table 4.1 Demographic and clinical characteristics of participants
Variable
Age at first weakness, yr, mean (SD)a
ALS patients
Controls
(n=674)
(n=2,093)
62·4 (11·0)
62·6 (10·0)
0·9
1219 (58)
0·1
0·01
Age at diagnosis, yr, mean (SD)
63·6 (11·0)
Gender: M, n (%)
418 (62)
Bulbar onset, n (%)
218 (32)
P value
El Escorial classification, n (%)
Definite
119 (18)
Probable
301 (45)
Probable lab supported
126 (19)
Possible
128 (19)
Education, n (%)
No education / primary school
60 (9)
128 (6)
Secondary school
448 (67)
1369 (66)
College / University
166 (25)
594 (28)
Current smoking, n (%)
133 (20)
277 (13)
<0·01
Lifetime physical activity, activity score, median (IQR)
3·8 (2·0-6·1)
3·6 (2·1-5·6)
0·2
Body mass index, kg/m2, mean (SD)
25·7 (4·0)
26·0 (3·7)
0·02
Energy intake, kcal/day, mean (SD)
2,258 (730)
2,119 (619)
<0·01
Age at onset in patients, and age at which the questionnaire was completed in controls.
ALS = amyotrophic lateral sclerosis; IQR = interquartile range; SD = standard deviation
a
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1·37 (1·18-1·58)
1·45 (1·31-1·61)
Cholesterol
0·96 (0·89-1·04)
0·03
0·02
1·24 (1·07-1·43)
0·96 (0·81-1·13)
1·02 (1·00-1·03)
1·00 (1·00-1·00)
0·51 (0·27-0·95)
1·04 (1·01-1·07)
0·99 (0·93-1·05)
Vitamin C
Vitamin E
Lycopene
Flavonoids
Glutamate
Phytoestrogens
0·98 (0·93-1·04)
1·04 (1·01-1·07)
0·53 (0·28-1·00)
1·00 (1·00-1·00)
1·02 (1·00-1·03)
0·98 (0·83-1·16)
1·25 (1·08-1·45)
1·03 (1·00-1·05)
0·95 (0·87-1·03)
0·85 (0·51-1·41)
1·03 (0·98-1·09)
1·10 (1·04-1·16)
1·05 (1·01-1·08)
1·46 (1·31-1·62)
1·03 (1·02-1·04)
1·02 (1·01-1·04)
1·06 (0·96-1·18)
1·06 (0·92-1·23)
1·02 (1·01-1·04)
1·18 (1·04-1·34)
1·22 (1·12-1·32)
1·26 (1·17-1·35)
1·08 (1·05-1·11)
1·39 (1·20-1·61)
1·03 (0·83-1·28)
1·19 (1·07-1·33)
Adjusted ORc
(95% CI)
0·6
0·01
0·05
0·97
0·04
0·8
0·003
0·03
0·2
0·5
0·2
0·001
0·006
<0·001
<0·001
0·006
0·3
0·4
0·008
0·01
<0·001
<0·001
<0·001
<0·001
0·8
0·001
p valueb
0·98 (0·93-1·04)
1·01 (0·97-1·04)
0·36 (0·18-0·70)
1·00 (1·00-1·00)
0·98 (0·96-1·01)
0·83 (0·69-1·00)
1·07 (0·88-1·29)
0·99 (0·97-1·02)
0·91 (0·84-0·99)
0·25 (0·13-0·49)
0·86 (0·79-0·94)
1·03 (0·96-1·11)
0·94 (0·88-1·01)
1·08 (1·05-1·12)
1·03 (1·01-1·05)
1·00 (0·98-1·02)
1·03 (0·93-1·15
1·03 (0·89-1·19)
1·00 (0·98-1·02)
0·92 (0·76-1·13)
1·24 (1·04-1·47)
1·43 (1·25-1·64)
1·14 (1·07-1·23)
1·25 (1·03-1·52)
0·43 (0·30-0·61)
0·97 (0·80-1·18)
Adjusted ORd
(95% CI)
0·5
0·6
0·002
0·4
0·2
0·05
0·5
0·7
0·03
<0·001
0·001
0·4
0·09
<0·001
0·001
0·9
0·6
0·7
0·9
0·4
0·02
<0·001
<0·001
0·02
<0·001
0·8
p valueb
0·98 (0·96-1·00)
0.2
0.004
0.3
0.4
1·00 (1·00-1·01)
0·95 (0·84-1·07)
0·95 (0·90-1·00)
0·98 (0·97-0·99)
0.05
0·97 (0·93-1·00)
0·97 (0·92-1·02)
0.9
0.08
1·03 (0·68-1·56)
0.7
0·03
0·78 (0·68-0·89)
1·00 (0·99-1·01)
0·01
<0·001
0·97 (0·95-0·99)
0.2
0.6
1·01 (0·99-1·02)
<0·001
1·08 (1·05-1·12)
0·99 (0·98-1·00)
0.8
<0·001
2·19 (1·42-3·38)
0.9
1·21 (0·13-11·2)
0·94 (0·57-1·55)
0.8
0.2
0·97 (0·92-1·01)
0.99
0.02
1·05 (1·01-1·09)
1·01 (0·05-21·0)
<0·001
1·09 (1·06-1·13)
0·92 (0·53-1·59)
0·05
<0·001
1·03 (1·01-1·05)
0·85 (0·78-0·91)
1·04 (1·00-1·09)
0·7
<0·001
0·99 (0·95-1·04)
a
p valueb
Nutrient density model
Adjusted ORd
(95% CI)
Adjusted for age (at onset in patients; at questionnaire in controls), gender, and education
b
Bold indicates Bonferroni significant values of p; Bonferroni adjusted ɑ: 0·05/25 = 0·002
c
Adjusted for age (at onset in patients; at questionnaire in controls), gender, education, BMI, current smoking, and lifetime physical activity
d
Adjusted for age (at onset in patients; at questionnaire in controls), gender, education, BMI, current smoking, lifetime physical activity, and total energy intake
ALS = amyotrophic lateral sclerosis; CI = confidence interval; OR = Odds ratio
0·7
0·9
0·05
0·6
0·004
1·03 (1·00-1·05)
Vitamin B2
0·03
0·4
0·4
0·3
<0·001
0·004
<0·001
<0·001
0·006
0·3
0·4
0·007
0·007
<0·001
<0·001
<0·001
<0·001
0·8
0·002
p valueb
Binary logistic regression with absolute nutrient intake
Calcium
Vitamins and minerals
Alcohol
1·03 (0·98-1·09)
0·81 (0·49-1·34)
Polysaccharides
Fibres
1·11 (1·05-1·17)
Mono- and disaccharides
1·05 (1·02-1·08)
1·03 (1·02-1·04)
Trans fatty acids
1·06 (0·92-1·22)
Eicosapentaenoic acid (EPA)
1·06 (0·95-1·18)
1·02 (1·01-1·04)
Alpha Lipolic Acid (ALA)
1·02 (1·01-1·04)
1·19 (1·05-1·35)
Polyunsaturated
Omega 3 fatty acids, total
1·23 (1·13-1·33)
Monounsaturated
Docosahexaenoic acid (DHA)
1·27 (1·18-1·36)
Saturated
1·09 (1·05-1·12)
Animal
Carbohydrates, total
Fat, total
1·03 (0·83-1·27)
1·18 (1·06-1·31)
Protein, total
Vegetable
Adjusted ORa
(95% CI)
Nutrient
Table 4.2 Adjusted ORs for the relationship between ALS and nutrient intake
CHAPTER 4
56
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Premorbid BMI, dietary intake and ALS risk
of saturated fat is independently associated with an increased risk of ALS (p=0·04), while
a higher intake of alcohol is independently associated with a decreased risk of ALS
(p=0·03). Also, a higher premorbid BMI is associated in this multivariate analysis with a
decreased risk of ALS (p=0·01). Total energy intake is not significantly associated with
risk of ALS in this model (p=0·09).
No significant associations between nutrient intake and survival were found with
multivariate Cox regression analysis (not shown). Several significant associations between
nutrients and age at onset were identified. An interaction term of case-control status and
the nutrient introduced into the model was, however, not significant for any of these
associations; furthermore, the same associations were found when Cox regression was
performed in controls using questionnaire completion as the event. These both findings
indicate that the associations between nutrients and age at onset are an age-related effect
and thus not disease-specific.
Table 4.3 Adjusted ORs for the relationship between ALS and nutrient intakes in a multivariate
model
Nutrient
Adjusted ORa (95% CI)
P value
Protein, vegetable
0·997 (0·991-1·004)
0·4
Fat, saturated
1·002 (1·000-1·004)
0·04
Polysaccharides
0·999 (0·998-1·000)
0·1
Fibres
0·998 (0·985-1·010)
0·7
Alcohol
0·999 (0·998-1·000)
0·03
Flavonoids
0·996 (0·987-1·004)
0·3
Total energy intake
1·000 (1·000-1·001)
0·09
Premorbid BMI
0·967 (0·944-0·992)
0·01
Adjusted for age (at onset in patients; at questionnaire in controls), gender, education, BMI, current
smoking, lifetime physical activity, and total energy intake.
ALS = amyotrophic lateral sclerosis; CI = confidence interval; OR = Odds ratio
a
DISCUSSION
In the present population-based case-control study, we found an increased risk of sporadic
ALS with higher premorbid intake of total fat, saturated fat, trans fatty acids, and
cholesterol and a low intake of alcohol. Furthermore, the pre-symptomatic daily energy
intake in patients was significantly higher compared with controls, while pre-symptomatic
BMI was significantly lower in patients. The combination of a positive association of a
high total energy intake, a low premorbid BMI, a high fat intake and a low intake of
alcohol, corrected for lifetime physical activity, supports a role for an altered energy
metabolism prior to clinical onset of ALS.
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The finding that a higher intake of fat is associated with an increased risk of developing
ALS corroborates observations in a population-based case-control study in ALS in
Western Washington State.3 Another case-control study, however, found a contradicting
result: a decreased risk of ALS with a higher intake of fat.7 Differences in study design
may explain this discrepancy. The inclusion of only clinic-based patients may have caused
referral bias in the latter study.33 Furthermore, in the Western Washington State study
and the present study, only incident cases were included, and it is well-known that patient
characteristics show large differences between an incident and prevalent cohort of ALS
patients.1 This illustrates the importance of adopting a population-based approach when
using a case-control design in ALS.
Multiple studies have shown that ALS patients, after symptom onset, have an altered
energy metabolism.34-38 In one study, the mean weight-adjusted resting energy expenditure
was 15·7% higher than in a group of controls, and in another study all 11 familial ALS
patients and 17 of the 33 sporadic ALS patients were hypermetabolic.34, 35 There is also a
growing body of evidence that the mutant SOD1 mouse model of ALS shows metabolic
alterations. Resting and total energy expenditure of G86R and G93A mice, when compared
to wild-type littermates, were shown to be markedly increased, also in pre-symptomatic
mice.39 In addition, increased lipolysis has recently been shown to occur in SOD1 mutant
mice, also in pre-symptomatic mice.40 It has been suggested that mitochondrial uncoupling
protein 3 (UCP3) plays a role in this increased energy expenditure, since higher levels of
expression of UCP3 have been found both in an animal model of ALS and in human
biopsies, while transgenic mice that overexpress UCP3 in muscles are lean and hyperphagic
due to hypermetabolism through mitochondrial uncoupling.41-43 Our finding that presymptomatic daily energy intake in patients is higher, while pre-symptomatic BMI is
lower, something which has also been shown previously in large cohort and case-control
studies,44, 45 supports an increased energy expenditure in pre-symptomatic ALS patients.
Since fat has a high caloric density, the higher premorbid intake of fat in ALS patients in
the present study may be a compensatory mechanism for this increased energy expenditure
to prevent weight and muscle loss. This may also explain the positive effect of hypercaloric
enteral nutrition on survival in ALS patients in a recent phase 2 trial.46 A previous study,
however, has shown that a high-fat diet itself increases resting energy expenditure, which
may support a hypothesis that high intake of fat in pre-symptomatic ALS patients is not
a compensation for increased energy expenditure, but may have, partly, caused the
increased energy expenditure.47 It remains uncertain whether these findings are part of a
disease-causing chain of events in ALS or whether they represent secondary phenomena.
Our observations further emphasize the importance of a comparison in a future phase 3
trial: to establish whether a high-carbohydrate, high-caloric diet is to be preferred to a
high-fat, high-caloric diet in ALS.46 Nevertheless, the present study lends support to the
hypothesis that altered energy metabolism may already be present in pre-symptomatic
ALS patients.
There are several possible explanations for the observed decreased risk of ALS associated
with a higher intake of alcohol. One previous population-based study could not identify
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Premorbid BMI, dietary intake and ALS risk
an association between alcohol consumption and ALS, but only 161 patients were
included.48 Other, relatively small, studies have shown conflicting results but suffered
from bias, since only clinic-based referral patients were included or there was no detailed
record on alcohol consumption.49, 50 A previous study has shown that a lyophilized extract
of red wine, which contains several antioxidant compounds, was able to block glutamateinduced apoptosis in cerebellar granule neurones.51 Furthermore, an in vivo experiment
carried out on mutant SOD1 mice showed that survival in mice fed with lyophilized red
wine was significantly increased compared to control, untreated animals. In our study,
however, the association between intake of alcohol and the risk of ALS was independent
of the intake of red wine (not shown), and so the association cannot be attributed only to
the possible protective effect of antioxidants in red wine.
Two previous case-control studies have shown that a high intake of polyunsaturated fatty
acids (PUFA) is associated with a decreased risk of ALS.4, 7 PUFA have neuroprotective
properties, since they exert beneficial effects on excitotoxicity, inflammation and oxidative
stress.52 Also in Parkinson disease, a decreased risk has been found with a higher intake
of PUFA.53 In our present study, we did not observe a significant association between
intake of PUFA and risk of ALS; nor did we find an association between the risk of ALS
and the intake of omega 3 fatty acids, which is a subtype of PUFA. In a recent cohort study,
only a higher intake of this subtype was associated with a decreased risk of ALS.14 There
is, however, also in our study, a trend towards a decreased risk of ALS with higher intake
of PUFA (p trend = 0·1). Despite the relatively large study sample, the power may still have
been too small to identify a significant association. In addition, the FFQs differ between
the studies, which may have contributed to inconsistent results. The FFQ used in the
present study covered all relevant sources of omega 3 fatty acids and other PUFA, including
several types of fish, oils and supplements.
The present study does not lend support to the hypothesis that dietary antioxidants have
an independent protective effect on developing ALS, which has been suggested, since
prior research showed a role for oxidative stress in the pathogenesis of ALS.3, 4, 6, 11, 13
Although in the univariate analysis, adjusted for confounders, a higher intake of flavonoids
was associated with a decreased risk of ALS (p=0·002), in the multivariate analysis
including other nutrients, this association was not significant (p=0·3). The present study,
therefore, does not confirm previous findings that intake of vitamin E and the antioxidative
carotenoids are inversely related to the risk of ALS.4, 11, 13 Our previous study which showed
a possible protective effect of a high vitamin E intake in developing ALS only included
patients ascertained in tertiary care centres.4 It has been demonstrated that ALS patients
attending these referral centres do not represent a random sample of all ALS patients.33
A difference in vitamin E intake by these patients compared with non-referred patients
may have led to biased results. The second study that showed a possible protective effect
of vitamin E was a pooled analysis of five prospective cohort studies.11 In that study,
however, there was a discrepancy between a lower ALS risk associated with higher dietary
vitamin E intake and a lack of association with overall supplemental vitamin E intake. In
our study, vitamin E intake was calculated from both dietary and supplemental intake as
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CHAPTER 4
derived from the food-frequency questionnaire. We, therefore, cannot lend further support
to a protective effect of vitamin E intake on developing ALS.
Besides the strengths of our study, which include a relatively large study sample, the use
of a validated questionnaire, a population-based setting, a control population representative
of the general population, a correction for multiple comparisons, and a correction for
many possible confounders, including physical activity, we also have to acknowledge
limitations. Since ALS patients search actively for an explanation for their disease or may
have an assumption about the underlying cause, case-control studies in ALS using
questionnaires are inevitably prone to recall bias. Blinding the participants to the study
hypotheses with an elaborate food frequency questionnaire, covering 199 food items, and
the short time between date of diagnosis and date on which the questionnaire was
completed (median 2·3 months) may have reduced this source of bias in our study. Another
limitation is that bulbar symptoms may have affected usual dietary habits and, subsequently,
how patients filled in the questionnaire, despite the fact that patients were asked to recall
their dietary habits during the period before the onset of bulbar signs. A sensitivity analysis
excluding bulbar onset patients did not essentially change results, suggesting that the
identified associated dietary pattern was not the result of disease-related dietary changes.
In conclusion, the combination of a positive association of a low premorbid BMI and a
high fat intake, together with prior evidence from ALS SOD1 mouse models and earlier
reports on premorbid BMI, supports a role for altered energy metabolism prior to clinical
onset of ALS.
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PANEL: RESEARCH IN CONTEXT
Evidence before this study
We searched PubMed up to January 10, 2015, for reports in English, and excluding animal
studies, with the terms "amyotrophic lateral sclerosis" and “consumption” or “diet” or
“intake”, yielding 152 reports. We screened these reports for content and identified 15
papers addressing the relation between intake of food items or nutrients and the risk of
ALS in a case-control or cohort design. 6 papers reported on a non-population-based
case-control study, which is prone to referral bias.4, 5, 8, 10, 12, 54 Between 77 and 377 ALS
patients were included in these studies, and except for a decreased risk of ALS with a
higher intake of fruit8, 54 and vegetables8, 10 that was found in two studies, none of the
associations was replicated in one of the other studies. Two papers reported on the same
population-based case-control study with 161 included ALS patients, and showed that a
higher fat and glutamate intake was associated with an increased risk of ALS.3, 48 7 papers
reported on the results of the Cancer Prevention Study II cohort, and 5 of these papers
pooled these data with data of 4 other cohorts, with a maximum of 1,153 documented
ALS cases.6, 9, 11, 13, 14, 55, 56 Since the results are reported in separate papers, the analyses are
not adjusted for multiple comparisons. These papers showed that a higher intake of
chicken, ω-3 polyunsaturated fatty acid, cartenoids, and vitamin E are associated with a
decreased risk of ALS.
Added value of this study
To our knowledge, this is the largest population-based case-control study to date on the
relation between premorbid dietary intake and the risk of sporadic ALS. We show that
presymptomatic intake of fat and total daily energy intake in patients are higher, while
presymptomatic BMI is significantly lower. This supports a role for altered energy
metabolism prior to clinical onset of ALS, and provide further insight into the
pathophysiological process of ALS.
Further, this is the first study to show that alcohol is associated with a decreased risk of
ALS.
Implications of all the available evidence
The finding that a higher intake of fat is associated with an increased risk of developing
ALS corroborates the observations in a prior population-based case-control study, and
warrants further research on energy metabolism prior to clinical onset of ALS.3
The observation of a decreased risk of ALS with higher alcohol intake first needs
replication by another study.
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54. Jin Y, et al. Dietary intake of fruits and beta-carotene is negatively associated with amyotrophic
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64
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SUPPLEMENTAL MATERIAL
Figure S4.1 Odds ratios for the relationship between ALS and quintiles of nutrient intake. Adjusted
for energy intake, age (at onset in patients; at questionnaire in controls), gender, BMI, education,
current smoking, and lifetime physical activity.
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PART II - ENVIRONMENT
CHAPTER 5
Occupational exposure to diesel motor exhaust
increases the risk of amyotrophic lateral sclerosis
In preparation Meinie Seelen1*, Mark H.B. Huisman1*, Loes van Boxmeer1*, Anne E. Visser1,
Perry T.C. van Doormaal1, Joost Raaphorst2, Anneke J. van der Kooi3,
Marianne de Visser3, Roel C.H. Vermeulen4, Leonard H. van den Berg1, Jan H. Veldink1
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, the Netherlands.
2
Department of Neurology, Donders Institute for Brain, Cognition and Behaviour,
Center for Neuroscience, Radboud University Nijmegen Medical Center, the Netherlands.
3
Department of Neurology, Amsterdam Medical Center, University of Amsterdam,
the Netherlands.
4
Environmental Epidemiology Division, Institute for Risk Assessment Sciences,
Utrecht University, the Netherlands.
1
* These authors contributed equally.
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CHAPTER 5
SUMMARY
Background
The association of amyotrophic lateral sclerosis (ALS) with occupations has been studied
extensively, since occupations may serve as a surrogate for a variety of environmental
exposures, possibly leading to the development of ALS. However, limited data is available
concerning direct and objective investigation of occupational exposure to environmental
toxins. In this study, occupational exposures and their association with ALS risk were
assessed through the application of job exposure matrices (JEMs), a valid and objective
exposure assessment tool.
Methods
A large population-based, case-control study was conducted in two independent
populations in Europe: 662 patients and 2,152 controls in the Netherlands, and 142
patients and 255 controls in the Republic of Ireland. Lifetime occupational history was
obtained using a structured questionnaire, and coded according to the International
Standard Classification of Occupations (ISCO). Job exposure matrices, assigning no, low,
or high exposure for 17 different agents, were applied to determine cumulative levels of
exposure before onset of disease. Odds ratios (OR) of ALS risk were estimated by
multivariate logistic regression, adjusted for potential confounders. Finally, a meta-analysis
of both populations was performed.
Findings
Cumulative occupational exposure to diesel motor exhaust was associated with an
increased risk of ALS (OR 1·10, 95% CI 1·03-1·18, p=0·004) in both populations. No
association of ALS risk was found for the groups of mineral dust, organic dust, pesticides,
metals or solvents.
Interpretation
In this study we showed, using a rigorous and population-based design, that occupational
exposure to diesel motor exhaust is a risk factor for developing ALS.
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Occupational exposures and ALS risk
INTRODUCTION
Sporadic amyotrophic lateral sclerosis (ALS) is considered to be a complex disease, in
which both genetic and environmental factors determine disease susceptibility and
outcome.1 In order to identify causative environmental factors, the association of ALS
with occupations has been studied extensively,2-13 since occupations may serve as a
surrogate for a variety of exogenous exposures (i.e. pesticides, metals, solvents, gasses and
fumes). Unfortunately, these studies faced several challenges. Numbers of cases and
controls per occupation were often too low to detect associations, while on the other hand
most of the associations that were identified could not be replicated. Directly investigating
(past) exposure to selected environmental agents instead of occupations was often limited
by the exposure assessment method.14, 15 Examples are self-reported exposures that readily
lead to differential responder bias, and the use of a job history registry, which is often
inaccurate and incomplete.
A job exposure matrix (JEM) is recognized as an objective, valid and agent-specific method
for exposure assessment in case-control studies.16-18 A JEM enables linking of occupations
to profiles of environmental exposures by providing (semi-)quantitative assessments of
exogenous exposures for each occupation. The application of a JEM in ALS has only been
applied in exposure studies of electric shocks and magnetic fields.19-21 Patients and controls
are asked to fill in all the occupations they have held during life, without any clue as to
what hypotheses will be tested, which largely avoids recall bias.22
The aim of this large, population-based case-control study, performed in two independent
populations, was to determine the association between lifetime occupational exposure to
a wide range of agents and the risk of ALS using a JEM as an unbiased, objective, and
semi-quantitative exposure assessment method.
METHODS
The Netherlands: Prospective ALS study the Netherlands (PAN)
A large population-based, case-control study was conducted in the Netherlands, between
January 2006 and December 2010, entitled the “Prospective ALS study the Netherlands”
(PAN).23 All newly diagnosed patients, with possible, probable (laboratory-supported) or
definite ALS according to the revised El Escorial Criteria, were selected.24 Medical records
of all patients were scrutinized to confirm the appropriateness of the diagnosis and to
exclude ALS mimic syndromes or other clinical conditions. Every patient who had a first,
second or third degree family member with ALS was defined as having familial ALS, and
was excluded.
Complete case ascertainment was ensured by continuous recruitment through multiple
sources: 1) Neurologists, most ALS patients visit one of the tertiary referral centers of the
ALS center the Netherlands on at least one occasion; 2) Consultants in rehabilitation
medicine; 3) the Dutch Neuromuscular Patient Association; and 4) ALS website.
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CHAPTER 5
Population-based controls were selected from the register of the general practitioner (GP)
taking care of the patient with ALS. The GP was asked to select individuals, matched to
the patient for gender and age (plus or minus five years), from his register in alphabetical
order, starting at the surname of the ALS patient. Spouses or blood-relatives of the patient
were excluded to prevent overmatching.
The institutional review board of the University Medical Center Utrecht Ethics Committee
approved this study. Informed consent was obtained from all participants.
Data ascertainment
Participants were asked to fill in a structured questionnaire on their lifetime occupational
history, including military service and periods spent as a homemaker. For each occupation
the number of years and the hours per week employed in that job were recorded. If the
questionnaire was not entirely completed or if data were found to be inconsistent,
participants were approached by telephone to complete or correct the data. Information
about education, body mass index (BMI), cigarette smoking and alcohol use was also
obtained from this questionnaire. To ensure blinding, all questionnaires were coded prior
to processing and analysis. Survival status of patients was recorded through the civil
registry, the general practitioner and the motor neuron disease association. Among
patients, only data before symptom onset were analysed.
Ireland: Irish ALS register
A second independent population-based case-control study was performed in the Republic
of Ireland between May 2011 and June 2014, through the Irish ALS register. Details of
the Irish ALS Register have been published previously.25 Briefly, the Irish ALS Register
was used to identify Irish residents diagnosed with suspected, possible, probable or definite
ALS according to the El Escorial criteria.24 Most patients attended the Beaumont Hospital
motor neuron disease clinic in Dublin. A minority of patients was seen in other neurology
clinics or was contacted to participate through the Irish Motor Neurone Disease
Association (IMNDA). For this study, we excluded patients with ALS mimic syndromes
and familial ALS, which was defined as patients with a first, second or third degree family
member with ALS.
Population-based controls were selected in the same way as in the Netherlands, by the
GP of the ALS patient and were individually matched for gender, age (plus or minus five
years) and location of current residence. Spouses and blood-relatives of ALS patients were
excluded to prevent overmatching.
The Irish ALS Register complies with Irish Data protection legislation (1988 and 2003),
and has been approved by Beaumont Hospital Ethics Committee (02/28 and 05/49). Verbal
and written consent is obtained from all participants for inclusion on the Irish ALS
Register.
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Occupational exposures and ALS risk
Data ascertainment
In Ireland, all patients were visited for a personal interview using the same structured
questionnaire on lifetime occupational history as described above in the Netherlands. The
questionnaires were handled equally: they were coded prior to processing and analysis,
and only the data before onset of symptoms was used for patients. Survival status of
patients was recorded through the civil registry, the general practitioner and the motor
neuron disease association.
Classification of occupations
All occupations were coded according to the International Standard Classification of
Occupations (ISCO) adopted by the International Labor Organization (ILO), a United
Nations specialized agency.26 The ISCO provides a systematic classification structure
covering the occupations of the whole civilian working population. Both the 1968 version
as the 1988 versions of the ISCO were used. The classification structure of the ISCO-68
has four levels, providing successively finer detail, as follows: major groups (8), minor
groups (83), unit groups (284) and occupational categories (1,506). Since the ISCO-68
lacks code numbers for military services, armed forces and homemakers, we added
supplemental major categories for these occupations. The ISCO-88 consists of ten major
groups, subdivided into sub-major groups (28), minor groups (116), and unit groups (390).
Exposure assessment
Exposures were estimated by using two general population job-exposure matrices: DOMJEM, and ALOHA-JEM. The DOM-JEM40,41 is based on five-digit ISCO-68 codes
(occupational categories), and the ALOHA-JEM42 on four-digit ISCO-88 codes (unit
groups). The JEM’s were created by occupational exposure experts, and assign exposure
intensity scores of no exposure, low or high exposure levels to each ISCO code. Cumulative
exposure is calculated by summing the product of the intensity and duration (years) for
all reported job periods over the entire working career. The exposure intensity scores of
none, low and high were transformed to 0, 1 and 2 to achieve a more balanced weighting
between intensity and duration in the calculation of cumulative exposure.
17 exposures were assessed through the DOM-JEM and ALOHA-JEM in six main groups:
mineral dust (silica, asbestos), organic dust (animal contact, endotoxin), pesticides
(herbicides, insecticides), gasses and fumes (polycyclic aromatic hydrocarbon (PAH), diesel
motor exhaust (DME)), metals (chromium, nickel) and solvents (aromatic solvents,
chlorinated solvents).
Statistical analysis
Differences in baseline characteristics between patients and controls were determined
using χ2 test for categorical variables and Mann-Withney U test for continuous variables.
For each participant, and each exposure from the Job Exposure Matrices, a lifetime
exposure index was calculated by the following formula:
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CHAPTER 5
where k represents a job from the lifetime occupational history. For each exposure,
participants were classified as never exposed (0), low lifetime exposure index (1), or high
lifetime exposure index (2). Low and high exposure represent respectively a lower or
higher exposure than the median of the cumulative exposure score among the exposed
controls. Multivariate logistic regression analysis was used to estimate odds ratios (ORs)
for the association with ALS for low and high exposure compared with no exposure,
adjusting for covariates age, gender, education, BMI, smoking and alcohol use. Age was
defined as age at onset in patients and age at date on which the questionnaire was
completed in controls. Initially, logistic regression analyses were performed separately
for the Dutch and Irish population data. p values for trend (dose-response) were obtained
using a log-transformed lifetime exposure index as a continuous variable.
Secondly, fixed effects meta-analysis was performed comparing the population data by
using a logistic regression model including the log-transformed lifetime exposure index.
To determine whether especially recent exposure may act as a trigger in developing ALS,
additional multivariate logistic regression analyses of the last job were performed. Odds
ratios for ALS associated with the exposure intensity score during the last job were
determined for exposures that had a p value ≤ 0·2 in the meta-analysis of cumulative
exposure.
Finally, in a cox regression survival analysis the relation between the exposure index of
each exposure and disease duration was investigated, with age, gender, site of onset, BMI
and current smoking as covariates. The same method was used to evaluate the effect of
exposure on the age at onset of ALS patients, adjusted for gender and site of onset.
All tests were two-sided, and a Bonferroni correction was applied to the alpha level to
adjust for multiple comparisons. Since the exposures within the main groups were highly
correlated, adjustment was applied for the six main groups (Bonferroni adjusted p value:
0·05/6 = 0·008).
RESULTS
The Netherlands
In the Netherlands, of the questionnaires send to 782 patients, 662 (85%) were returned.
Gender, median age at onset, and site of onset did not differ significantly between
responders and non-responders. 2,332 population-based controls were selected from the
GP’s register, and 2,152 of these returned their questionnaire (response rate 92%). Table
5.1 shows the baseline characteristics of sporadic ALS patients and controls included in
this study. Cases and controls were similar for the matching variables age and gender.
Furthermore, ALS cases more often had a lower educational level, a lower BMI, smoked
more often and consumed less alcohol compared to controls.
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Occupational exposures and ALS risk
Table 5.1 Demographic and clinical characteristics of participants
The Netherlands
Patients
Ireland
Controls
Patients
Controls
Variable
(n=662)
(n=2152)
(n=142)
(n=255)
Male, n (%)
410 (62)
1244 (58)
86 (61)
148 (58)
Age, y, median (IQR)a
63·4 (57-70)
62·9 (57-70)
64·5 (57-71)
67·5 (59-73)
Age at diagnosis, y, median (IQR)
64·6 (58-71)
66·5 (58-73)
213 (32)
30 (21)
Definite, n (%)
116 (18)
65 (46)
Probable, n (%)
407 (61)
35 (25)
Possible, n (%)
125 (19)
27 (19)
Missing, n (%)
14 (2)
14 (10)
Bulbar onset, n (%)
El Escorial classification
Education, n (%)*
No education / Primary school
61 (9)
134 (6)
Middle school / High school
438 (67)
College / University
162 (24)
Body Mass Index, median (IQR)*†
48 (34)
68 (27)
1417 (66)
71 (50)
137 (54)
599 (28)
22 (16)
50 (19)
24·2 (22-26)
25·6 (24-28)
25·3 (23-28)
26·3 (24-30)
Current smoker, n (%)*
134 (20)
287 (13)
21 (15)
25 (10)
Current alcohol, n (%)*
492 (74)
1832 (85)
96 (68)
195 (77)
Age at onset in patients, and age on which the questionnaire was completed in controls
* Significant difference between patients and controls at a level of <0·05 for the Dutch
population
† Significant difference between patients and controls at a level of <0·05 for the Irish population
a
Table 5.2 shows the ORs for ALS risk associated with cumulative exposures divided in
no, low and high exposure groups. Cumulative exposure to DME was the only exposure
that showed a significant linear trend for ALS risk (p=0·03) in the Netherlands, which was
no longer significant after correction for multiple testing.
Ireland
In Ireland, 164 patients with ALS and 271 controls were recruited and interviewed. Age
and gender were similar for cases and controls. Comparable to the Dutch dataset, cases
more often had a lower educational level, a lower BMI, smoked more often and consumed
less alcohol compared to controls. Compared to the Dutch ALS cases, Irish ALS cases had
a slightly later age at onset, less often a bulbar site of onset and more often a definite El
Escorial classification.
In Ireland, cumulative exposure to DME showed, a linear trend for an increased risk of
ALS (p=0·02). Moreover, a significant linear trend between ALS risk and exposures was
also observed for mineral dust (p=0·03), silica (p=0·04) and chromium (p=0·03).
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2014226 Meinie Seelen_binnenwerk.indd 74
162 (25)
>17·30
76 (3·5)
29 (4·4)
26 (3·9)
≤17·00
>17·00
203 (9·4)
75 (11)
65 (9·8)
≤18·00
>18·00
354 (16)
93 (14)
128 (19)
≤12·44
>12·44
73 (3·4)
25 (3·8)
21 (3·2)
≤16·18
>16·18
228 (11)
62 (9·4)
85 (13)
≤8·55
>8·55
227 (11)
1697 (79)
514 (78)
Never
72 (3·3)
2007 (93)
615 (93)
Never
354 (16)
1444 (67)
440 (67)
Never
204 (9·5)
1745 (81)
521 (79)
Never
76 (3·5)
2000 (93)
606 (92)
Never
1·15 (0·87-1·53)
0·86 (0·62-1·17)
1·00 (Ref)
0·89 (0·53-1·49)
1·14 (0·71-1·85)
1·00 (Ref)
1·10 (0·86-1·41)
0·85 (0·65-1·11)
1·00 (Ref)
0·98 (0·71-1·36)
1·11 (0·82-1·51)
1·00 (Ref)
0·90 (0·55-1·45)
1·27 (0·80-2·01)
1·00 (Ref)
1·01 (0·78-1·29)
0·92 (0·73-1·17)
1·00 (Ref)
OR (95% CI)†
>16·00
≤16·00
Never
>15·25
≤15·25
Never
>10·15
≤10·15
Never
>5·00
≤5·00
Never
>6·25
≤6·25
Never
>15·00
≤15·00
Never
Cumulative
exposure
20 (14)
21 (15)
100 (71)
19 (14)
12 (8·5)
110 (78)
42 (30)
20 (14)
79 (56)
26 (18)
9 (6·4)
106 (75)
20 (14)
8 (5·7)
113 (80)
48 (34)
20 (14)
73 (52)
Cases, n (%)
27 (11)
28 (11)
200 (78)
19 (7·5)
19 (7·5)
217 (85)
53 (21)
53 (21)
149 (58)
30 (12)
31 (12)
194 (76)
14 (5·5)
14 (5·5)
227 (89)
50 (20)
51 (20)
154 (60)
Controls, n
(%)
Ireland
1·41 (0·70-2·84)
1·52 (0·79-2·94)
1·00 (Ref)
1·84 (0·88-3·87)
1·10 (0·48-2·49)
1·00 (Ref)
1·36 (0·78-2·39)
0·65 (0·34-1·24)
1·00 (Ref)
1·39 (0·73-2·64)
0·39 (0·16-0·93)
1·00 (Ref)
2·29 (1·03-5·09)
0·99 (0·37-2·64)
1·00 (Ref) *
2·01 (1·09-3·68)
0·81 (0·43-1·53)
1·00 (Ref) *
OR (95% CI)†
† Adjusted for age, gender, education, body mass index current smoking and alcohol use· * P value for linear trend <0·05. No p values <0·008. PAH, polycyclic aromatic
hydrocarbon; DME, diesel motor exhaust. Cut-off values for low and high cumulative exposure are based on the median value of the controls.
Endotoxin
Animal contacts
Organic dust
Asbestos
Silica
471 (22)
139 (21)
≤17·30
471 (22)
1210 (56)
360 (54)
Never
Controls, n
(%)
Mineral dust
Cases, n (%)
Cumulative
exposure
Exposure
The Netherlands
Table 5.2 ALS risk associated with cumulative exposures for the Dutch and Irish population
CHAPTER 5
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42 (6·4)
>28·00
58 (2·7)
26 (3·9)
22 (3·3)
≤31·50
>31·50
93 (4·3)
29 (4·4)
40 (6·1)
≤28·00
>28·00
656 (31)
181 (27)
218 (33)
≤18·00
>18·00
110 (5·1)
43 (6·5)
30 (4·5)
≤8·26
>8·26
233 (11)
81 (12)
91 (14)
≤16·33
>16·33
233 (11)
1686 (78)
489 (74)
Never
110 (5·1)
1932 (90)
588 (89)
Never
659 (31)
837 (39)
262 (40)
Never
90 (4·2)
1969 (92)
592 (90)
Never
57 (2·6)
2037 (95)
613 (93)
Never
1·28 (0·96-1·71)
1·17 (0·87-1·56)
1·00 (Ref) *
0·75 (0·48-1·16)
1·18 (0·81-1·73)
1·00 (Ref)
0·92 (0·73-1·18)
0·88 (0·70-1·10)
1·00 (Ref)
1·24 (0·83-1·86)
1·07 (0·68-1·67)
1·00 (Ref)
0·99 (0·59-1·67)
1·51 (0·92-2·47)
1·00 (Ref)
1·17 (0·79-1·73)
1·09 (0·70-1·68)
1·00 (Ref)
OR (95% CI)†
>17·50
≤17·50
Never
>4·13
≤4·13
Never
>26·38
≤26·38
Never
>22·00
≤22·00
Never
>10·00
≤10·00
Never
>21·00
≤21·00
Never
Cumulative
exposure
30 (21)
25 (18)
86 (61)
18 (13)
8 (5·7)
115 (82)
53 (38)
42 (30)
46 (33)
12 (8·5)
18 (13)
111 (79)
8 (5·7)
10 (7·1)
123 (87)
12 (8·5)
18 (13)
111 (79)
Cases,
n (%)
34 (13)
34 (13)
187 (73)
24 (9·4)
25 (9·8)
206 (81)
84 (33)
84 (33)
87 (34)
16 (6·3)
17 (6·7)
222 (87)
9 (3·5)
12 (4·7)
234 (92)
17 (6·7)
17 (6·7)
221 (87)
Controls, n
(%)
Ireland
2·04 (1·06-3·93)
1·86 (0·98-3·55)
1·00 (Ref) *
1·13 (0·56-2·28)
0·53 (0·22-1·28)
1·00 (Ref)
0·96 (0·52-1·78)
0·86 (0·48-1·52)
1·00 (Ref)
1·13 (0·48-2·67)
2·30 (1·09-4·85)
1·00 (Ref)
1·11 (0·38-3·25)
1·39 (0·56-3·48)
1·00 (Ref)
1·06 (0·45-2·49)
2·29 (1·09-4·83)
1·00 (Ref)
OR (95% CI)†
† Adjusted for age, gender, education, body mass index current smoking and alcohol use. * P value for linear trend <0·05. No p values <0·008.
PAH, polycyclic aromatic hydrocarbon; DME, diesel motor exhaust. Cut-off values for low and high cumulative exposure are based on the median value of the controls.
DME
PAH
Gasses and fumes
Insecticides
Herbicides
98 (4·6)
31 (4·7)
≤28·00
99 (4·6)
1955 (91)
588 (89)
Never
Controls, n
(%)
The Netherlands
Pesticides
Cases,
n (%)
Cumulative
exposure
Exposure
Table 5.2 (continued)
Occupational exposures and ALS risk
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33 (5·0)
>13·00
25 (3·8)
>13·63
75 (11)
>20·00
60 (9·1)
>26·40
204 (9·5)
1743 (81)
205 (9·5)
542 (82)
59 (8·9)
Never
≤26·40
254 (12)
1617 (76)
258 (12)
507 (77)
79 (12)
Never
≤20·00
63 (2·9)
2026 (94)
63 (2·9)
623 (94)
13 (2·0)
Never
≤13·63
72 (3·3)
2007 (93)
73 (3·4)
609 (92)
19 (2·9)
Never
≤13·00
176 (8·2)
60 (9·1)
>33·30
0·84 (0·60-1·17)
0·86 (0·62-1·19)
1·00 (Ref)
0·79 (0·58-1·07)
0·91 (0·68-1·22)
1·00 (Ref)
1·16 (0·70-1·91)
0·68 (0·36-1·26)
1·00 (Ref)
1·35 (0·86-2·12)
0·82 (0·48-1·40)
1·00 (Ref)
1·06 (0·75-1·49)
1·16 (0·83-1·60)
1·00 (Ref)
OR (95% CI)†
>9·88
≤9·88
Never
>14·38
≤14·38
Never
>8·00
≤8·00
Never
>9·5
≤9·5
Never
>7·5
≤7·5
Never
Cumulative
exposure
19 (14)
16 (11)
106 (75)
31 (22)
20 (14)
90 (64)
5 (3·5)
2 (1·4)
134 (95)
9 (6·4)
3 (2·1)
129 (92)
22 (16)
12 (8·5)
107 (76)
Cases,
n (%)
25 (9·8)
25 (9·8)
205 (80)
33 (13)
33 (13)
189 (74)
3 (1·2)
4 (1·6)
248 (97)
4 (1·6)
4 (1·6)
247 (97)
22 (8·6)
22 (8·6)
211 (83)
Controls, n
(%)
Ireland
1·37 (0·69-2·72)
1·39 (0·68-2·82)
1·00 (Ref)
1·83 (0·99-3·40)
1·19 (0·60-2·33)
1·00 (Ref)
2·07 (0·47-9·22)
1·18 (0·18-7·72)
1·00 (Ref)
3·36 (0·97-11·6)
1·92 (0·36-10·3)
1·00 (Ref) *
1·87 (0·94-3·72)
1·08 (0·47-2·45)
1·00 (Ref)
OR (95% CI)†
† Adjusted for age, gender, education, body mass index current smoking and alcohol use. * P value for linear trend <0·05. No p values <0·008.
PAH, polycyclic aromatic hydrocarbon; DME, diesel motor exhaust. Cut-off values for low and high cumulative exposure are based on the median value of the controls.
Chlorinated solvents
Aromatic solvents
Solvents
Nickel
Chromium
1800 (84)
176 (8·2)
538 (81)
63 (9·5)
Controls, n
(%)
The Netherlands
Never
Metals
Cases,
n (%)
≤33·30
Cumulative
exposure
Exposure
Table 5.2 (continued)
CHAPTER 5
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Occupational exposures and ALS risk
However, these exposures were represented by small numbers, with less than ten cases
or controls per exposure group for silica and chromium, and the effect was not consistent
with the Dutch data. Furthermore, these linear trends were no longer significant after
correction for multiple testing.
Meta-analysis
In the meta-analysis combining both populations, DME was the only exposure that was
significantly associated with ALS risk (OR 1·10, 95% CI 1·03-1·18, p=0·004), depicted in
Figure 5.1. This association remained significant after correction for multiple testing
(p<0·008). Performing random effects instead of fixed effects meta-analysis of DME,
similar results for the association with ALS risk were found (OR 1·12, 95% CI 1·01-1·24).
None of the other cumulative exposures (i.e. within the main groups of mineral dust,
organic dust, pesticides, metals, solvents) showed a significant altered risk of developing
ALS.
Additional analysis
In the analysis of exposures during the last job performed, we assessed mineral dust,
chromium, DME, pesticides and insecticides, which all had a p value ≤0·2 in the metaanalysis of cumulative exposures. In this last job analysis, none of the exposures reached
a significant level <0·05, indicating that recent exposure may not be more important than
overall exposure (Figure S5.1).
Cox regression survival analyses showed that higher exposure to DME was associated
with a shorter survival (meta-analysis: HR 1·09, 95% CI 1·02-1·16, p=0·01), however this
association was no longer significant after correction for multiple testing. None of the
other exposures modified disease duration.
Analysis of age at onset in patients with ALS showed that DME exposure was associated
with a later age at onset (62 versus 65 years, HR 0·93, 95% CI 0·88-0·98, p=0·005).
To determine whether this effect was specific for patients, or also valid for age at
questionnaire for controls, an additional analysis was performed: an interaction term of
diagnosis and DME exposure was introduced into the model, with questionnaire
completion as the event in controls (HR 0·95, p=0·11). This analysis indicates that the
association between DME exposure and age at onset is an age related effect and not per
se disease related. None of the other exposures showed an association with age at onset.
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Mineral dust
Organic dust
Mineral dust (NL)
Organic dust (NL)
Mineral dust (IRE)
Organic dust (IRE)
*
Mineral dust (Total)
Organic dust (Total)
Silica (NL)
Animal contacts (NL)
Silica (IRE)
*
Silica (Total)
Animal contacts (IRE)
Animal contacts (Total)
Asbestos (NL)
Endotoxin (NL)
Asbestos (IRE)
Endotoxin (IRE)
Asbestos (Total)
Endotoxin (Total)
1.0
1.2
1.4
0.9
1.0
Exposure
Pesticides
Pesticides (NL)
Gasses and fumes (NL)
Pesticides (IRE)
Gasses and fumes (IRE)
Pesticides (Total)
Gasses and fumes (Total)
Herbicides (NL)
PAH (NL)
Herbicides (IRE)
PAH (IRE)
Herbicides (Total)
PAH (Total)
Insecticides (NL)
DME (NL)
Insecticides (IRE)
DME (IRE)
Insecticides (Total)
DME (Total)
0.9
1.0
1.1
1.1
1.2
1.3
Gasses and fumes
1.2
1.3
1.4
*
*
**
0.8
1.0
Metals
1.2
1.4
Solvents
Metals (NL)
Aromatic solvents (NL)
Metals (IRE)
Aromatic solvents (IRE)
Metals (Total)
Chromium (NL)
Aromatic solvents (Total)
Chromium (IRE)
*
Chromium (Total)
Nickel (NL)
Chlorinated solvents (NL)
Chlorinated solvents (IRE)
Nickel (IRE)
Chlorinated solvents (Total)
Nickel (Total)
0.8
1.2
1.6
2.0
OR
0.9
1.0
1.1
1.2
1.3
Figure 5.1 ALS risk associated with cumulative job exposures in the Dutch population (NL), Irish
population (IRE) and a meta-analysis of both populations (Total). Odds ratios (OR) and 95% confidence
intervals are shown in the figures categorized by exposure main groups. * P value <0·05; ** P value
<0·008 (Bonferroni adjusted). PAH, polycyclic aromatic hydrocarbon; DME, diesel motor exhaust.
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DISCUSSION
Using a job exposure matrix in two independent populations, we showed that a higher
cumulative lifetime exposure to DME is associated with an increased risk of ALS. No
associations with ALS were found for environmental toxins of mineral dust, organic dust,
pesticides, metals and solvents.
Epidemiological studies objectively assessing DME exposure and the association with
ALS or other neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s
disease, are lacking. However, there are few occupational studies reporting on an increased
ALS risk among truck drivers,27, 28 bus drivers,6 machine workers and operators,6, 29 and
military personnel.7, 9 In all these occupations, people are exposed to certain amounts of
DME. DME is a major component of air pollution and an important source of atmospheric
particles smaller than 0·1 μm, called nanoparticles.30 To cause neurotoxicity, these
nanoparticles need to be transported to the brain. Recently, animal studies showed that
in mice exposed to DME the blood brain barrier is compromised, leading to an increase
in neuroinflammatory markers (e.g. IgG, inducible nitric oxide synthase (iNOS), interleukin
(IL)-1B) in the brain parenchym.31, 32 Moreover, there has been accumulating evidence that
the olfactory nerve provides a direct route for delivery of these nanoparticles to the brain,
bypassing the protective blood-brain barrier, where they may be involved in
neuroinflammation and neuropathology.33, 34 Significantly higher concentrations of
nanoparticles have been identified in the olfactory bulb and frontal cortex of residents in
a highly polluted urban environment, compared with residents in low pollution cities.35
In these highly exposed residents, genes that are involved in inflammation were
significantly upregulated. Moreover, two recent papers investigated the effect of prolonged
DME exposure on the rat brain and found an increased neuroinflammatory response,
with increased pro-inflammatory cytokines TNFɑ, IL-1ɑ, IL-1β.30, 36 These prior findings
and our observation of an increased risk of ALS with higher levels of DME exposure,
suggests a role for DME exposure in motor neuron degeneration.
The toxic effect of DME may be comparable to the neurotoxicity of smoking, the only
widely accepted risk factor in ALS,37 in which neuroinflammation has also been a proposed
pathological mechanism.38 Performing the primary analysis without adjustment for
smoking showed similar elevated effect estimates, indicating that smokers do not per se
have a higher DME exposure. This suggests that both smoking and DME exposure may
independently lead to neuroinflammation and subsequently to an increased risk of ALS.
Based on these findings, it would be interesting to determine whether residential exposure
to traffic related air pollution, as an independent method compared with the occupational
exposure assessment in our study, is also associated with an increased risk of ALS.
We have to acknowledge certain limitations of the present study. Due to regulations, the
levels of occupational exposures in the past may be different from present levels. The JEM,
however, does not take into account these changes in occupational exposures over time.
Since, however, most participants in the study will have been exposed in the same period,
the effect of misclassification of exposure level due to this limitation is probably small.
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Another limitation is that the job exposure matrix assigns a similar exposure to everyone
with the same job, although exposure levels may show large variances between subjects
within a job-title. A prospective cohort study with individual exposure measurements
may be the only way to avoid this source of bias. However, bearing in mind the low
incidence of ALS, the sample size required is so large that such a study may never be
performed.
The present study had some major strengths, which were the two independent populationbased settings with control groups representative of the general population in each
country, the good quality of data on lifetime occupational history and confounders, and
application of a job exposure matrix (a valid and objective method for exposure assessment)
to investigate the association of occupational exposures with ALS, avoiding the risk of
recall bias or the effect of leading questions.
Finally, the relative risk for DME exposure in the meta-analysis was 1·1, clearly indicating
that DME exposure can be one of many, environmental and genetic, steps that are needed
to develop ALS.39
PANEL RESEARCH IN CONTEXT
Systematic review
We searched PubMed for reports published in English before December 2014, with the
following terms: (”amyotrophic lateral sclerosis”, “ALS”, “motor neuron disease”, “motor
neurone disease”, or “MND”), and (“diesel motor exhaust”, “diesel exhaust”, or “DME”). We
identified one study that linked occupations associated with ALS to DME exposure.27 In
this study, few other epidemiological studies were suggested to report on occupations
associated with ALS risk in which DME exposure may be the causal link.6, 7, 9, 27-29 None of
these studies, however, used an objective and quantitative exposure assessment.
Interpretation
Our study presents evidence for an increased risk of ALS with higher cumulative
occupational exposure to DME in two independent population-based settings, adjusted
for a wide range of potential confounder variables. In contrast with previous
epidemiological studies, we used a job exposure matrix (JEM), a recognized valid, objective
and agent-specific method in case-control studies, to assess occupational exposures.16-18
Here, we provide compelling evidence that exposure to DME may be one of many risk
factors needed to develop ALS.
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Vergara XP, et al. New electric-shock job exposure matrix. Am J Ind Med. 2012; 55: 232-40.
Huss A, et al. Electric shocks at work in Europe: development of a job exposure matrix. Occup
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Parlett LE, et al. Evaluation of occupational exposure to magnetic fields and motor neuron
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Huss A, et al. Occupational exposure to magnetic fields and electric shocks and risk of ALS:
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Vergara X, et al. Case-control study of occupational exposure to electric shocks and magnetic
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Sedgwick P. What is recall bias? BMJ. 2012; 344: e3519.
Huisman MHB, et al. Population based epidemiology of amyotrophic lateral sclerosis using
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Brooks BR, et al. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral
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Pamphlett R and Rikard-Bell A. Different occupations associated with amyotrophic lateral
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28. Kurtzke JF and Beebe GW. Epidemiology of amyotrophic lateral sclerosis: 1. A case-control
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29. Schulte PA, et al. Neurodegenerative diseases: occupational occurrence and potential risk
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30. Gerlofs-Nijland ME, et al. Effect of prolonged exposure to diesel engine exhaust on
proinflammatory markers in different regions of the rat brain. Part Fibre Toxicol. 2010; 7: 12.
31. Heidari Nejad S, et al. The effect of diesel exhaust exposure on blood-brain barrier integrity
and function in a murine model. J Appl Toxicol. 2015; 35: 41-7.
32. Oppenheim HA, et al. Exposure to vehicle emissions results in altered blood brain barrier
permeability and expression of matrix metalloproteinases and tight junction proteins in mice.
Part Fibre Toxicol. 2013; 10: 62.
33. Lucchini RG, et al. Neurological impacts from inhalation of pollutants and the nose-brain
connection. Neurotoxicology. 2012; 33: 838-41.
34. Tonelli LH and Postolache TT. Airborne inflammatory factors: "from the nose to the brain".
Front Biosci (Schol Ed). 2010; 2: 135-52.
35. Calderon-Garciduenas L, et al. Neuroinflammation, hyperphosphorylated tau, diffuse amyloid
plaques, and down-regulation of the cellular prion protein in air pollution exposed children
and young adults. J Alzheimers Dis. 2012; 28: 93-107.
36. Levesque S, et al. Air pollution & the brain: Subchronic diesel exhaust exposure
causes neuroinflammation and elevates early markers of neurodegenerative disease. J
Neuroinflammation. 2011; 8: 105.
37. Armon C. Smoking may be considered an established risk factor for sporadic ALS. Neurology.
2009; 73: 1693-8.
38. Rothstein JD. Current hypotheses for the underlying biology of amyotrophic lateral sclerosis.
Ann Neurol. 2009; 65 Suppl 1: S3-9.
39. Al-Chalabi A, et al. Analysis of amyotrophic lateral sclerosis as a multistep process: a
population-based modelling study. Lancet Neurol. 2014; 13: 1108-13.
40. Peters S, et al. Development of an exposure measurement database on five lung carcinogens
(ExpoSYN) for quantitative retrospective occupational exposure assessment. Ann Occup Hyg
2012; 56: 70-79.
41. Peters S, et al. Comparison of exposure assessment methods for occupational carcinogens in a
multi-centre lung cancer case-control study. Occup Environ Med 2011; 68: 148-153.
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obstructive pulmonary disease. Thorax 2005; 60: 645-651.
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Mineral dust
Exposure
Pesticides
Insecticides
DME
Chromium
1.00
1.25
1.50
Odds Ratio
1.75
2.00
Figure S5.1 Meta-analyses of ALS risk associated with last job exposures. Meta-analyses were
performed using a fixed effect model of the Dutch and Irish population data. Odds ratios (OR) and
95% confidence intervals are shown in the figure for each exposure separately. The five exposures
shown, were analyzed as they all had a p value of ≤ 0·2 in the meta-analysis of cumulative exposures.
DME, diesel motor exhaust.
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CHAPTER 6
Long-term exposure to traffic related air pollution
is associated with an increased risk of
amyotrophic lateral sclerosis
In preparation
Meinie Seelen1*, Rosario A Toro Campos2*, Jan H Veldink1, Anne E Visser1,
Gerard Hoek2, Anneke J van der Kooi3, Marianne de Visser3, Joost Raaphorst4,
Leonard H van den Berg1†, Roel CH Vermeulen2†
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, the Netherlands.
2
Environmental Epidemiology Division, Institute for Risk Assessment Sciences,
Utrecht University, the Netherlands.
3
Department of Neurology, Amsterdam Medical Center,
University of Amsterdam, the Netherlands.
4
Department of Neurology, Donders Institute for Brain, Cognition and Behaviour,
Center for Neuroscience, Radboud University Nijmegen Medical Center, the Netherlands.
1
* These authors contributed equally
† Joint last authors
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SUMMARY
Background
Long-term exposure to air pollution has been associated with several neurodevelopmental
and neurodegenerative disorders. The association with amyotrophic lateral sclerosis (ALS)
has not been studied before.
Methods
We assessed long-term exposure to multiple air pollutants and the risk of developing ALS
in a large population-based case-control study in The Netherlands (917 cases and 2,662
individually matched controls recruited between 2006 and 2013). Residential annual
mean air pollution concentrations, averaged over the period 1992 to enrolment, were
assessed by land use regression (LUR) models developed as part of the European Study
of Cohorts for Air Pollution Effects (ESCAPE). Exposure estimates included nitrogen
oxides (NO2 and NOx), particulate matter (PM) with diameters of less than 2·5 μm (PM ),
2·5
less than 10 μm (PM10), and between 10 μm and 2·5 μm (PMcoarse), and PM2·5absorbance (a
marker for black carbon or soot). We performed conditional logistic regression analysis
by quartiles of exposure and used two different multivariate models (model 1 adjusted
for age, gender, education, smoking, alcohol use, body mass index, and social-economic
status; model 2 additionally adjusted for urbanization rate).
Findings
Risk of ALS was significantly increased for individuals in the upper quartile of
PM2·5absorbance (OR 1·57, 95% CI 1·14-2·17), NO2 (OR 1·55, 95% CI 1·08-2·21), and NOx
concentrations (OR 1·38, 95% CI 1·07-1·77) when compared to the lowest exposure
quartiles. These results, except for NOx, remained significant after additionally adjusting
for urbanisation rate. Results were similar between smokers and non-smokers, and more
pronounced for subject with a bulbar onset.
Interpretation
This is the first study to report that long-term exposure to air pollution is a potential
environmental risk factor for developing ALS.
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INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease in
which motor neuron loss results in paralysis of limbs, speech and swallowing difficulties,
and eventually respiratory failure. Fifty percent of patients with ALS die within three
years from symptom onset.1 The lifetime risk of ALS is 1:300, occurring at any adult age,
with a median age at onset of 63 years.1, 2 90-95% of ALS cases appear to be sporadic, which
is thought to have a complex aetiology, most probably caused by an interaction of multiple
genetic and exogenous factors.3 Smoking is thus far the only exogenous factor that has
been consistently identified as a risk factor.4 Other risk factors remain inconclusive in
part due to poor study design, lack of replication studies and low numbers of patients.
Long-term exposure to air pollutants has been linked to increased mortality rates,5-8
specifically to cardiovascular diseases,9, 10 respiratory diseases,7, 11, 12 and to a lesser extent
to neurodevelopmental and neurodegenerative diseases including autism,13 Parkinson’s14
and Alzheimer’s disease.15 To date, there are no epidemiological studies on the risk of
developing ALS and air pollution. However, a consistent association between smoking
and the development of ALS has been observed in both case-control and cohort studies.4
As in smoking, exposure to air pollutants, in particular to fine particulate matter, have
been hypothesised to be able to cross the blood brain barrier, or impair the blood brainbarrier leading to inflammatory and oxidative stress responses in the brain.16
We investigated the association between multiple air pollutants and the risk of ALS by
using historic residential data from a large population-based case-control study on ALS
and exposure data from the European Study of Cohorts for Air Pollution Effects (ESCAPE)
project.
METHODS
Study population
ALS patients diagnosed between January 2006 and January 2013 in The Netherlands were
enrolled into the Prospective ALS study the Netherlands (PAN). The PAN is a large
population-based case-control study with an estimated capture rate of 81% of all ALS
cases in The Netherlands.1 All patients newly diagnosed as possible, probable (laboratory
supported) or definite ALS according to the revised El Escorial Criteria were included.17
Excluded were ALS mimics and patients who had a first, second or third degree family
member with ALS, defined as familial ALS. Controls were selected through the general
practitioner of the patient, which is a representative tool to select population-based
controls. For the purpose of this study controls were post-hoc matched to the cases by
gender, age (+/- 5 years), region of residence and enrolment date (+/-1 year). Spouses or
blood-relatives of patients were not eligible to be controls to prevent overmatching.
The institutional review board of the University Medical Center Utrecht provided ethical
approval. All participants gave written informed consent for inclusion in the study.
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Exposure assessment
We estimated long-term exposure to air pollutants at the residential address of the
study participants from 1992 to the date of enrollment in the study using recently
developed Land Use Regression (LUR) models within the ESCAPE project.18, 19 In brief,
air pollution was repeatedly measured at multiple locations in 2009 to derive annual
average concentration of NO2, NOx, PM2·5 (fraction of PM smaller than 2.5 μm), PM10
(fraction of PM smaller than 10 μm), PMcoarse (fraction of PM calculated as the concentration
of PM10 minus that of PM2.5), and PM2·5 absorbance (marker for soot or black carbon).
Subsequently, LUR models were developed to explain the spatial variability in air
pollutants by spatial indicators such as traffic intensity, population density and land use.
These LUR models were then used to estimate annual ambient air pollution concentration
at the participants’ addresses. To allow for variation in air pollution concentrations
overtime, as case-control recruitment varied between 2006 and 2013, we back extrapolated
modeled concentrations in 2009 to 1992. We did not back-extrapolate further since this
was the first year in which routine monitoring data was available for all pollutants in The
Netherlands. Constant concentrations were assumed for the period 2009-2013.
Subsequently, we averaged the annual average air pollutant concentrations for each
individual from 1992 to the date of enrollment in the study. Participants (22 cases, 15
controls), of whom more than 50% of the addresses between 1992 until onset or inclusion
were missing, were excluded. We also performed analyses without any historical back
extrapolation or by using the air pollution estimate in 1992 for the whole population.
These analyses did not result in marked differences as compared to the original exposure
assignment and are therefore not presented.
Statistical analysis
Average annual air pollution exposure was divided in quartiles based on the exposure
distribution among the controls. Conditional logistic regression models were used to
determine the association between exposure to air pollutants and ALS. In addition, p
values for linear trend were calculated using the median value in each quartile as a
continuous variable. We specified two a-priori models to adjust for confounding based
on known and putative risk factors of ALS. Model 1 was adjusted for age, sex, education
(three levels: elementary school, middle/high school and college/university), premorbid
body mass index (BMI), current (before disease onset for cases) smoking status, current
alcohol use and area social economic status (SES; percentage high income at the
municipality level of residency). In model 2 we added urbanization rate as a potential
confounder to allow for a stronger control on urban, peri-urban and rural differences in
lifestyle and environmental factors. Missing values of confounder variables were imputed
with the R package Hmisc using multiple reiterations (n=10) of predictive mean matching
with optional weighted probability sampling of the other variables.
We performed several sensitivity analyses. First, we explored the possible effect of recency
of exposure by only counting the last year of exposure before onset of ALS, or the last five
years before onset. Secondly, we considered a one and five year lag prior to symptom
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onset of ALS, to determine whether incipient ALS was of influence on the association.
Thirdly, we restricted the analyses to participants who did not move during the last year
or the last five years before symptom onset, to exclude reverse causation of patients
moving closer to academic treatment centers, which are located in the larger cities in The
Netherlands. Fourth, we restricted the analysis to the cases and controls with all
confounder data available, excluding an effect of imputation. Lastly, we assessed possible
effect modification of smoking status, and we performed a subgroup analyses by site of
symptom onset (patients with bulbar or spinal onset).
Role of the funding source
The sponsor of the study had no role in study design, data collection, data analysis, data
interpretation, or writing of the report. MS, RT, LHB and RV had full access to all the data
in the study. LHB and RV had final responsibility for the decision to submit for publication.
RESULTS
The presented analyses are based on 917 patients with ALS and 2,662 individually matched
controls. Clinical characteristics of the patients with ALS, such as age at onset, site of onset
and El Escorial classification, were similar to previously reported patient characteristics
in Europe (Table 6.1).20 BMI, current smoking, current alcohol use, area SES and
urbanization rate were significantly different between patients and controls (Table 6.1).
Data on at least one of the confounder variables education, BMI, smoking and alcohol use
were missing in 27·5% of the participants and subsequently imputed. Sensitivity analysis
restricted to the non-imputed population did not result in substantially different results
compared to the analysis of the imputed population.
The mean annual concentration for each pollutant is presented in Table 6.2 by case control
status. Although, differences were small, the mean concentrations were significantly higher
among the cases than controls for PM10, PMcoarse, PM2·5 absorbance, NO2, and NOx (p<0·05,
Mann-Withney U). Pearson correlations between the different exposure measures were
generally higher than 0·6, except for PM2·5.
The ORs of all air pollutants were elevated in the category with the highest exposed
individuals compared to the reference category with the lowest exposed individuals (Table
6.3). For PM2·5absorbance, NO2, and NOx, these ORs were significantly elevated in model
1 (OR 1·67 (95% CI 1·27-2·18), OR 1·74 (95% CI 1·32-2·30), and OR 1·38 (95% CI 1·071·77)). A slight decrease in the association was found between model 1 adjusted for age,
sex, education, BMI, smoking, alcohol, and area SES and the more comprehensive
adjustment model 2 (with inclusion of urbanization rate). In model 2, PM2·5absorbance
and NO2, still showed significantly elevated ORs in the highest exposure category (OR
1·57 (95% CI 1·14-2·17) and OR 1·55 (95% CI 1·08-2·21)).
Sensitivity analyses by recency of exposure, by lagging exposures, or by limiting the
analyses to people that did not move homes in the last years did not result in materially
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different results. Stratification by smoking status revealed that the ORs for never smokers
were similar to the overall ORs (Table S6.1). Patients with a bulbar site of onset showed
higher increased ALS risks with exposure to air pollutants compared to patients with a
spinal onset (Table S6.2).
Table 6.1 Demographic and clinical characteristics of participants
ALS patients
Controls
(n=917)
(n=2662)
560 (61·1)
1633 (61·3)
0·88
63·5 (57·0-70·1)
63·5 (57·5-69·7)
0·81
74 (8·1)
190 (7·1)
0·23
Middle school/High school
603 (65·8)
1702 (63·9)
College/University
240 (26·2)
770 (28·9)
Male, n (%)
Age, y, median (IQR)*
Bulbar site of onset, n (%)
p value
321 (35·0)
El Escorial classification, n (%)
Definite
166 (18·1)
Probable
377 (41·1)
Probable lab supported
215 (23·4)
Possible
144 (15·7)
Education, n (%)
Elementary school
Premorbid BMI, kg/m2, n (%)
Underweight (<18·5)
30 (3·3)
23 (0·9)
Normal weight (18·5 - <25·0)
512 (55·8)
1143 (42·9)
Overweight (25·0 - <30·0)
300 (32·7)
1239 (46·5)
75 (8·2)
257 (9·7)
Obese (≥30·0)
<0·001
Current smoking, n (%)§
154 (16·8)
346 (13·0)
0·004
Current alcohol consumption, n (%)§
699 (76·2)
2283 (85·8)
<0·001
20·0 (18·0-23·0)
20·6 (18·0-24·0)
0·03
Very high (≥2500)
121 (13·2)
243 (9·1)
<0·001
High (1500 - <2500)
244 (26·6)
683 (25·7)
Moderately high (1000 - <1500)
235 (25·6)
659 (24·8)
Low (500 - <1000)
245 (26·7)
755 (28·4)
72 (7·9)
322 (12·1)
Area SES, median (IQR)†
Urbanization rate, addresses/km2, n (%)‡
Very low (<500)
ALS = amyotrophic lateral sclerosis; BMI = body mass index; SES = social economic status. *Age
at onset in patients, and age on which questionnaire was completed in controls. §Current = before
onset of ALS. †Social economic status is based on area level, percentage of high income is depicted.
‡Urbanization rate is based on area level, divided in five categories according to the Statistics
Netherlands (www.cbs.nl).
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Table 6.2 Mean annual air pollution exposure for ALS patients and controls
ALS patients
Controls
(n=917)
(n=2662)
PM10, μg/m , mean (SD)
31·8 (1·4)
31·6 (1·2)
<0·001
PMcoarse, μg/m3, mean (SD)
10·4 (0·7)
10·3 (0·6)
<0·001
PM2·5, μg/m3, mean (SD)
21·3 (0·9)
21·2 (0·8)
0·08
PM2·5 absorbance, 10 m , mean (SD)
1·43 (0·20)
1·40 (0·18)
<0·001
NO2, μg/m3, mean (SD)
26·9 (5·6)
26·0 (5·2)
<0·001
NOx, μg/m , mean (SD)
44·7 (9·6)
43·6 (8·9)
0·002
3
-5
-1
3
p value
ALS = amyotrophic lateral sclerosis.
DISCUSSION
In this study, we observed an increased risk of ALS associated with long-term exposure
to air pollution, specifically PM2·5absorbance and the nitrogen oxides. Associations
remained unchanged over the different multivariate models, except for NOx, and in
sensitivity analyses. This study is the first to describe the effects of long-term residential
air pollution exposure and ALS risk.
Of the different air pollutants studied here, PM2·5absorbance, NO2 and NOx are primary
traffic related pollutants, and therefore have larger spatial concentration differences in
urban areas. On the contrary, inhalable, coarse and fine particles (PM10, PMcoarse, and PM2·5)
have a considerable contribution from secondary road dust, agricultural, and construction
industries. In this study, we specifically found an increased ALS risk for the more traffic
related air pollutants PM2·5absorbance, NO2 and NOx. Interestingly, diesel exhaust is an
important source of traffic related air pollution, and this exposure has previously been
associated with ALS risk in several occupational studies: elevated ALS risk was reported
for truck drivers,21, 22 bus drivers,23 and machine workers and operators.23, 24
The observation parallels also the observations with smoking, as the only established
environmental risk factor in ALS.4 Smoking increases the risk of ALS through potentially
several mechanisms, including inflammation, oxidative stress and direct neurotoxicity
caused by fine particles, heavy metals and other chemical compounds present in cigarette
smoke.25, 26 Long-term exposure to air pollution produces inflammatory damage to the
cardiopulmonary system, but it may also lead to chronic inflammation and oxidative
stress in the brain.27, 28 Previous studies demonstrated that ultrafine particles can
circumvent the blood-brain barrier by deposition on the olfactory mucosa of the nasal
region.27-29 The particles are then translocated along the olfactory nerve into the olfactory
bulb of the brain, and may travel transneuronally to more distal sites within the brain.30,
31
In an autopsy study, residents in highly polluted urban areas had significantly higher
concentrations of fine particulates in the olfactory bulb and frontal cortex compared to
residents in low polluted areas.32 Recent experimental studies showed that there is also
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CHAPTER 6
Table 6.3 The association between ALS risk and exposure to air pollution in two different
multivariate models
Model 1*
Trend
Model 2*
Trend
OR (95% CI)
p value
OR (95% CI)
p Value
Q1 (≤30·9)
Reference
0·006
Reference
0·19
Q2 (>30·9 - ≤31·6)
0·77 (0·59-1·00)
0·75 (0·57-0·98)
Q3 (>31·6 - ≤32·2)
0·83 (0·62-1·10)
0·77 (0·57-1·05)
Q4 (>32·2)
1·29 (0·97-1·72)
1·12 (0·79-1·57)
PM10 (μg/m )
3
PMcoarse (μg/m3)
Q1 (≤9·9)
Reference
Q2 (>9·9 - ≤10·2)
0·82 (0·64-1·05)
0·01
Reference
0·77 (0·60-1·00)
Q3 (>10·2 - ≤10·5)
0·95 (0·74-1·24)
0·84 (0·64-1·11)
Q4 (>10·5)
1·24 (0·95-1·61)
1·04 (0·77-1·41)
0·24
PM2·5 (μg/m3)
Q1 (≤20·7)
Reference
Q2 (>20·7 - ≤21·3)
0·98 (0·75-1·28)
0·10
Reference
0·92 (0·70-1·21)
Q3 (>21·3 - ≤21·7)
0·85 (0·62-1·16)
0·80 (0·59-1·09)
Q4 (>21·7)
1·35 (0·97-1·88)
1·24 (0·89-1·73)
0·24
PM2·5 absorbance (10 m )
-5
-1
Q1 (≤1·29)
Reference
<0·001
Reference
Q2 (>1·29 - ≤1·38)
1·14 (0·88-1·47)
1·11 (0·85-1·44)
Q3 (>1·38 - ≤1·49)
1·12 (0·86-1·47)
1·09 (0·81-1·47)
Q4 (>1·49)
1·67 (1·27-2·18)
1·57 (1·14-2·17)
0·002
NO (μg/m )
2
3
Q1 (≤22·5)
Reference
<0·001
Reference
Q2 (>22·5 - ≤25·8)
1·38 (1·09-1·76)
1·29 (1·01-1·66)
Q3 (>25·8 - ≤29·0)
1·25 (0·97-1·63)
1·15 (0·85-1·55)
Q4 (>29·0)
1·74 (1·32-2·30)
1·55 (1·08-2·21)
0·03
NOx (μg/m3)
Q1 (≤38·2)
Reference
Q2 (>38·2 - ≤42·2)
0·98 (0·78-1·24)
0·004
0·91 (0·71-1·15)
Reference
Q3 (>42·2 - ≤47·3)
1·12 (0·87-1·43)
0·99 (0·76-1·30)
Q4 (>47·3)
1·38 (1·07-1·77)
1·17 (0·87-1·57)
0·14
*Model 1 was adjusted for sex, age, educational level, body mass index, current smoking, current
alcohol consumption, and area-level socioeconomic status; model 2 was adjusted as in model 1,
but also for urbanization rate.
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another route for small particles to enter the brain: mice exposed to diesel exhaust had
a compromised blood brain barrier, leading to an increase in neuroinflammatory
markers in the brain.33, 34 Moreover, histological evidence showed that human and animal
brains exposed to high PM concentrations had increased levels of pro-inflammatory
cytokines and markers of oxidative stress (e.g. TNF-ɑ, interleukins, NF-κB, Toll-like
receptor).31, 32, 35-37
As smoking is a known risk factor for ALS we performed stratified analyses by smoking
status. Results among non-smokers and former and current smokers were essentially
similar indicating that the observed effect of air pollution is not easily explained by
confounding due to smoking nor that there is evidence of effect modification by smoking.
Interestingly, we did observe a difference in effect estimates for the subgroups of site of
symptom onset, with stronger associations for patients with a bulbar onset. This
association has not been reported for smoking and it is unknown how exposure to air
pollutants may favour a bulbar site of onset. The only, speculative, reason could be that
the bulbar region is physically closer to the olfactory region, as compared to the spinal
lower motor neurons. Since it’s hypothesized that ALS “spreads” through the CNS by a
prion-like mechanism after the initial trigger, this could explain the more frequent bulbar
site of onset.
The most important limitation in our study is the uncertainty in air pollution estimates.
However, land use regression models have previously been reported to predict the historic
spatial variation very well and the ESCAPE models used in this study have been shown
to detect known risk of air pollution well.38, 39 Nevertheless, noteworthy limitations in the
exposure assessment are that we had only data on air pollution exposure from 1992
onwards. The early years of exposure (before 1992) might have been relevant in ALS
pathogenesis as well. However, previous studies have shown that air pollution assessment
using the baseline address versus a complete residential history only leads to minimal
attenuation of effect parameters.38, 40 That lack of data before 1992 may potentially have
resulted in some non-differential misclassification resulting most likely in bias towards
the null.
This study provides new clues for pathogenic pathways in ALS, ultimately leading towards
a better understanding, and prevention strategies. As it is the first study to report on this
possible association it is important for other studies to replicate the findings. Second to
that, our novel findings of an increased risk of developing ALS are with exposure levels
that ranges well below the existing European annual mean limit values of 25 μg/m3 for
PM2·5, and 40 μg/m3 (for PM10 and NO2) (European Commission air quality standards).41
As ambient air pollution levels are modifiable, this adds to the necessity of regulatory
public health interventions on air pollution exposure levels.
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PANEL RESEARCH IN CONTEXT
Systematic review
We searched PubMed for reports published in English before November, 2014, with the
following terms: (”amyotrophic lateral sclerosis”, “ALS”, “motor neuron disease”, “motor
neurone disease”, or “MND”), and (“air pollution”, “air pollutants”, or “particulate matter”).
We identified no previous epidemiological studies assessing residential exposure to
particulate matter air pollution in ALS. We used a method previously described within
the multicenter ESCAPE project to assess the effect of air pollution on mortality, and
cardiopulmonary diseases6, 9, 11 and applied this method to amyotrophic lateral sclerosis
population data in the Netherlands.
Interpretation
Our study has a large, population-representative, sample size, with a highly standardized
exposure assessment, and contains adjustment for a wide range of potential confounder
variables. Here, we provide evidence for the association between long-term exposure to
traffic related air pollution and an increased risk of developing ALS. Air pollution may
be one of the environmental risk factors of ALS that is ubiquitous and can be modified at
a population level. Therefore, future research replicating our findings would be of great
importance, even for a less common disorder such as ALS.
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SUPPLEMENTAL MATERIAL
Table S6.1 Subgroup analysis of non-smokers for the association between ALS
and exposure to air pollution
Non-smokers*
OR (95% CI)†
p value‡
Q1
Reference
0·18
Q2
0·72 (0·54-0·97)
Q3
0·83 (0·59-1·17)
Q4
1·15 (0·78-1·69)
PM10
PMcoarse
Q1
Reference
Q2
0·78 (0·59-1·03)
Q3
0·92 (0·67-1·25)
Q4
1·19 (0·85-1·67)
0·06
PM2·5
Q1
Reference
Q2
1·01 (0·74-1·39)
Q3
0·95 (0·66-1·35)
Q4
1·34 (0·91-1·97)
0·15
PM2·5 absorbance
Q1
Reference
Q2
1·01 (0·76-1·35)
Q3
1·06 (0·76-1·48)
Q4
1·48 (1·03-2·14)
0·01
NO2
Q1
Reference
Q2
1·31 (0·99-1·74)
Q3
1·21 (0·87-1·68)
Q4
1·48 (1·00-2·20)
0·09
NOx
Q1
Reference
Q2
0·93 (0·71-1·22)
Q3
1·07 (0·79-1·44)
Q4
1·20 (0·86-1·66)
0·16
*Non-smokers: no. of cases=759, no. of controls=1956. †Main model 2 was used
for the comparison; this confounder model was adjusted for sex, age, educational
level, current smoking, current alcohol consumption, body mass index and
socioeconomic status. ‡P value was calculated for linear trend.
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Table S6.2 Subgroup analyses for site of onset for the association between ALS and exposure to
air pollution
Bulbar site of onset*
Spinal site of onset*
OR (95% CI)†
p value‡
OR (95% CI)†
p value‡
Q1
Reference
0·10
Reference
0·65
Q2
0·77 (0·48-1·23)
0·72 (0·52-1·00)
Q3
0·81 (0·47-1·41)
0·76 (0·52-1·10)
Q4
1·41 (0·76-2·61)
1·00 (0·66-1·51)
PM10
PMcoarse
Q1
Reference
Q2
0·86 (0·56-1·33)
0·14
Reference
0·56
0·72 (0·52-0·98)
Q3
1·13 (0·70-1·84)
0·73 (0·52-1·02)
Q4
1·36 (0·79-2·34)
0·94 (0·65-1·36)
PM2·5
Q1
Reference
Q2
1·05 (0·66-1·67)
0·60
Reference
0·26
0·87 (0·62-1·22)
Q3
0·87 (0·50-1·49)
0·78 (0·53-1·15)
Q4
1·19 (0·67-2·13)
1·28 (0·84-1·93)
PM2·5 absorbance
Q1
Reference
0·004
Reference
Q2
1·17 (0·73-1·87)
1·08 (0·79-1·48)
Q3
1·22 (0·72-2·07)
1·02 (0·72-1·47)
Q4
2·07 (1·17-3·68)
1·36 (0·92-2·02)
0·09
NO2
Q1
Reference
<0·001
Reference
Q2
1·19 (0·76-1·87)
1·33 (0·98-1·18)
Q3
1·77 (1·08-2·93)
0·91 (0·63-1·33)
Q4
2·85 (1·53-5·33)
1·15 (0·74-1·79)
0·90
NOx
Q1
Reference
Q2
0·98 (0·63-1·52)
0·03
Reference
0·76
0·90 (0·67-1·21)
Q3
1·51 (0·94-2·42)
0·81 (0·58-1·13)
Q4
1·61 (0·97-2·68)
1·01 (0·70-1·45)
*Subgroups: bulbar site of onset (no. of cases=324, no. of controls=940), spinal site of onset (no.
of cases=596, no. of controls=1731). †Main model 2 was used for the comparison; this confounder
model was adjusted for sex, age, educational level, current smoking, current alcohol consumption,
body mass index and socioeconomic status. ‡P value was calculated for linear trend.
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CHAPTER 7
Residential exposure to extremely low frequency
electromagnetic fields and the risk of ALS
Published in: Neurology (2014)
Meinie Seelen1, Roel C.H. Vermeulen2, Levien S. van Dillen1, Anneke J. van der Kooi3,
Anke Huss2, Marianne de Visser3, Leonard H. van den Berg1*, Jan H. Veldink1*
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, The Netherlands
2
Institute for Risk Assessment Science, Division of Epidemiology,
Utrecht University, The Netherlands
3
Department of Neurology, Amsterdam Medical Center,
University of Amsterdam, The Netherlands
1
* These authors contributed equally
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INTRODUCTION
Several studies have reported on the possible association between the risk of developing
amyotrophic lateral sclerosis (ALS) and employment in the electrical industry, which may
be related to extremely low frequency electromagnetic field (ELF-EMF) exposure and/
or the risk of experiencing an electric shock, although no direct association has been
proven.1 Three previous studies reported on ALS risk related to living near power lines,
an important source of ELF-EMF exposure for the general population.2-4 These studies
reported a null finding but had some shortcomings as they were based on registry data
and had no detailed clinical data available. We, therefore, performed a large populationbased case-control study with detailed phenotypic data, to assess the relation between
residential exposure to ELF-EMF from power lines and the risk of ALS.
Methods
We included a total of 1139 ALS patients and 2864 frequency-matched controls, derived
from a large population-based case-control study performed in the Netherlands from
January 2006 to January 2013.5 Controls were selected from the roster of the general
practitioner of the patient and were matched to the patient on gender and age. The
Netherlands has an extensive network of overhead power lines, the locations of which
are known exactly.6 These power lines were classified into high voltage (50 kV, 110 kV
and 150 kV) and very high voltage (220 kV and 380 kV) lines.
We collected data on lifetime residential history from the Municipal Personal Records
Database. Residential data of ALS patients and controls were geocoded (assigned
coordinates to the addresses) taking the time of onset of disease into account.
Table 7.1 Baseline characteristics of patients and controls
ALS patients
Controls
(n=1139)
(n=2864)
Male gender, n (%)
680 (59.7)
1772 (61.9)
Age, median (range)a
63.5 (22.4-88.5)
63.5 (20.2-91.9)
Site of onset, n (%)
Bulbar
398 (34.9)
Spinal
736 (64.6)
El Escorial Classification, n (%)
Definite
212 (18.6)
Probable
479 (42.1)
Probable lab supported
243 (21.3)
Possible
189 (16.6)
Abbreviations: ALS = amyotrophic lateral sclerosis. aAge at symptom onset for patients; age at
inclusion for controls.
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Table 7.2 Frequencies and odds ratios of ALS patients and controls in relation to the shortest
distance ever lived to a power line
ALS patients Controls
Distance (m)
n (%)
n (%)
OR
95% CI
p-value
0 (0.0)
1 (0.0)
-
-
-
Very high voltage (220-380 kV)
0-<50
50-<200
2 (0.2)
7 (0.2)
0.73
0.15-3.50
0.69
200-<600
23 (2.0)
44 (1.5)
1.31
0.79-2.18
0.30
≥600
1114 (97.8)
2812 (98.2)
1.00
Reference
-
High voltage (50-150 kV)
0-<50
6 (0.5)
14 (0.5)
1.05
0.40-2.75
0.92
50-<200
32 (2.8)
88 (3.1)
0.91
0.60-1.37
0.64
200-<600
90 (7.9)
249 (8.7)
0.89
0.69-1.14
0.36
≥600
1011 (88.8)
2513 (87.7)
1.00
Reference
-
Abbreviations: ALS = amyotrophic lateral sclerosis; m = meters; n = number; OR = odds ratio; CI
= confidence interval; kV = kilovolt. Odd ratios were computed by logistic regression adjusting
for gender and age.
Since distance to power lines is closely associated with ELF-EMF exposure, we categorized
places of residence into corridors of distance (0-<50 m, 50-<200 m, 200-<600 m and ≥600
m). We determined the shortest distance to any power line before onset of disease, and
computed the cumulative years lived within 100 m of a power line, divided into 4
categories (<5 y, 5-<10 y, 10-<15 y, ≥15 y), to detect a possible dose-response relation.
Statistical analyses were performed using logistic regression for the association with ALS,
adjusted for gender and age (age at onset for patients and age at inclusion for controls).
Subsequently, we used cox regression for the association with survival (adjusted for gender,
age at onset and site of onset) and for the association with age at onset (adjusted for gender
and site of onset), with the exposure variable dichotomized into living <200 m and ≥200
m from a power line.
RESULTS
Baseline characteristics of patients and controls are shown in Table 7.1. We found no
increased risk of ALS in persons living in close vicinity of a power line compared to
persons who had always lived at a distance of at least 600 m (Table 7.2). Cumulative
exposure in years showed no dose-response relationship (data not shown). Survival
analysis in ALS patients showed a non-significant hazard ratio (HR) of 1.27 (95% CI 0.871.86, p=0.22). Nor was there an association between distance to a power line and age at
onset (HR 1.22, 95% CI 0.89-1.67, p=0.22).
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CHAPTER 7
DISCUSSION
Subsequent to our null findings, we performed a meta-analysis combining our results
with two previously published case-control studies.3, 4 A fixed effect model showed an
overall odds ratio of 0.90 (95% CI 0.73-1.10) for subjects living <200 m compared to ≥200
m from any high voltage power line (Figure S7.1). One of these studies only assessed the
current address at time of death,4 so important historical residential data regarding ELFEMF exposure before onset of disease might be missing. A third cohort study substantiated
our negative results, reporting a HR of 0.88 (95% CI 0.47-1.64).2
We did not find an association of ALS with residential exposure to ELF-EMF. This is
consistent with the lack of such an association in a previously published meta-analysis in
electrical occupations.1
Strengths of this study are the population-based study design, inclusion of large numbers
of patients and age and gender matched controls and prevention of recall bias by the use
of the Municipal Personal Records Database for collection of residential data. A limitation
of this study may be the low number of participants living in close vicinity to power lines
(<200 m). However, taking all studies together, one can conclude that exposure to ELFEMF from power lines does not increase the risk of developing ALS.
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Exposure to electromagnetic fields and ALS risk
REFERENCES
1.
2.
3.
4.
5.
6.
Vergara X, et al. Occupational exposure to extremely low-frequency magnetic fields and
neurodegenerative disease: a meta-analysis. J Occup Environ Med. 2013; 55: 135-46.
Huss A, et al. Residence near power lines and mortality from neurodegenerative diseases:
longitudinal study of the Swiss population. Am J Epidemiol. 2009; 169: 167-75.
Frei P, et al. Residential distance to high-voltage power lines and risk of neurodegenerative
diseases: a Danish population-based case-control study. Am J Epidemiol. 2013; 177: 970-8.
Marcilio I, et al. Adult mortality from leukemia, brain cancer, amyotrophic lateral sclerosis
and magnetic fields from power lines: a case-control study in Brazil. Rev Bras Epidemiol. 2011;
14: 580-8.
Huisman MHB, et al. Population based epidemiology of amyotrophic lateral sclerosis using
capture-recapture methodology. Journal of Neurology Neurosurgery and Psychiatry. 2011; 82:
1165-70.
TenneT. Netkaart Nederland. 2011.
SUPPLEMENTAL MATERIAL
Study
Patients
Controls
OR
[95%CI]
367
2990
1139
4706
14996
2864
0.70
0.98
0.92
[0.45; 1.09]
[0.73; 1.33]
[0.64; 1.33]
0.90
[0.73; 1.10]
Marcilio, 2011 [BRA]
Frei, 2013 [DEN]
Seelen, 2014 [NL]
Fixed effect model
Heterogeneity: I−squared=0%
OR [95% CI]
0.3
0.5
1
2
Figure S7.1 The overall odds ratio of a fixed effect model of two previously published studies
combined with our study in a forest plot, showing a non-significant result for subjects living <200m
compared to ≥200m from a power line
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PART III - GENETICS
CHAPTER 8
No mutations in hnRNPA1 and hnRNPA2B1 in
Dutch patients with amyotrophic lateral sclerosis,
frontotemporal dementia and inclusion body myositis
Published in: Neurobiology of Aging (2014)
M. Seelena, A.E. Vissera, D.J. Overstea, H.J. Kimb, A. Paludb, T.H. Wongc,
J.C. van Swietenc,d, P. Scheltensd, N.C. Voermanse, F. Baasf, J.M.B.V. de Jongg,
A.J. van der Kooig, M. de Visserg, J.H. Veldinka, J. Paul Taylorb,
M.A. Van Esa*, L.H. van den Berga*
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, The Netherlands
b
Department of Developmental Neurobiology, St Jude Children’s Research Hospital, USA
c
Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
d
Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
e
Department of Neurology, Donders Institute for Brain, Cognition and Behavior,
Center for Neurosciences, Radboud University Nijmegen Medical Center, The Netherlands
f
Department of Genome Analysis, Amsterdam Medical Center,
University of Amsterdam, The Netherlands
g
Department of Neurology, Amsterdam Medical Center,
University of Amsterdam, The Netherlands
a
* These authors contributed equally
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CHAPTER 8
ABSTRACT
Inclusion body myopathy (IBM) associated with Paget’s disease of the bone (PDB), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), sometimes called
IBMPFD/ALS or multi system proteinopathy (MSP), is a rare, autosomal dominant
disorder characterized by progressive degeneration of muscle, brain, motor neurons and
bone with prominent TDP-43 pathology. Recently two novel genes for MSP were
discovered; heterogeneous nuclear ribonucleoprotein (hnRNP) A1 and A2B1. Subsequently,
a mutation in hnRNPA1 was also identified in a pedigree with autosomal dominant familial
ALS. The genetic evidence for ALS and other neurodegenerative diseases is still
insufficient. We therefore sequenced the prion like domain of these genes in 135 familial
ALS, 1084 sporadic ALS, 68 familial FTD, 74 sporadic FTD and 31 sporadic IBM patients
in a Dutch population. We did not identify any mutations in these genes in our cohorts.
Mutations in hnRNPA1 and hnRNPA2B1 prove to be a rare cause of ALS, FTD and IBM
in the Netherlands.
INTRODUCTION
Multi system proteinopathy (MSP) is a rare, autosomal dominant, degenerative disorder,
which affects multiple organ systems (brain, spinal cord, bone and muscle). Patients may
present with inclusion body myopathy (IBM), frontotemporal dementia (FTD),
amyotrophic lateral sclerosis (ALS) or Paget’s disease of the bone (PDB). Patients with
MSP can be clinically indistinguishable from patients affected by the familial or sporadic
forms of these diseases. The affected organs in MSP show prominent TDP-43 pathology,
which is shared feature with ALS and FTD.1, 2
About 50% of MSP cases has been linked to mutations in the valosin-containing protein
(VCP) gene.3, 4 Mutations in VCP have subsequently also been identified in patients with
familial and sporadic ALS. Recently, two novel genes for MSP have been identified.
Mutations in the prion-like domain (PrLD) of the RNA binding genes heterogeneous
nuclear ribonucleoproteins A1 and A2B1 (hnRNPA1 and hnRNPA2B1) were shown to be
associated with MSP pedigrees negative for VCP.5 Interestingly, several genes that have
previously been implicated in ALS and FTD (TDP-43, FUS, TAF15, ESWR1)6 are also RNA
binding genes with a PrLD (in which the majority of pathogenic mutations are found).
Considering the clinical, pathological and genetic overlap between MSP and the classical
forms of ALS, FTD and IBM, we decided to sequence the PrLD of hnRNPA1 and
hnRNPA2B1 for mutations in a large population of sporadic and familial ALS, FTD and
IBM patients from the Netherlands.
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Mutations in hnRNPA1/A2B1 in ALS, FTD and IBM patients
METHODS
DNA samples of ALS and IBM patients were collected as part of the Prospective ALS
study The Netherlands (PAN), a population-based case-control study including both
patients with ALS and ALS mimics.7 Additionally, DNA samples were collected from FTD
patients ascertained at the Erasmus Medical Center Rotterdam. Genetic analysis of the
PrLD of hnRNPA1 (residues 251-320) and hnRNPA2B1 (residues 253-333) was performed
by Sanger sequencing. Fifty-two of the 68 familial FTD patients were screened by next
generation exome sequencing (for complete description of genetic analysis, see
supplementary data).
RESULTS
A total of 135 familial ALS, 1084 sporadic ALS, 68 familial FTD, 74 sporadic FTD and 31
sporadic IBM patients from The Netherlands were included in this study (Table 8.1). No
non-synonymous mutations were identified in the coding exons of the PrLD of hnRNPA1
and hnRNPA2B1. One potentially interesting splice variant was detected in a single case
of familial FTD (hnRNPA2B1: c.695A>G) and is predicted as disease causing by
mutationtaster.
Family history of
ALS, %
Family history of
dementia, %
Survival, y,
median (range)
Age at onset, y,
median (range)
Age at inclusion,
y, median (range)
Female, %
Number of
patients
Table 8.1 Baseline characteristics
Familial ALS
135
45.2
63.9 (34-87)
59.7 (24-82)
3.2 (0.7-19)
30.6
100.0
Sporadic ALS
1084
39.9
65.2 (24-91)
63.7 (23-90)
2.6 (0.4-33)
26.9
0.0
Familial FTD
68
48.5
63.5 (38-77)
59.5 (36-73)
8.1 (2-24)
100.0
5.9
Sporadic FTD
74
59.6
62.4 (37-79)
57.8 (30-75)
10.1 (2-22)
0.0
1.0
IBM a
31
29.4
69.9 (52-86)
-
-
-
-
Patients were included at their first visit to the clinic. No information was available on age at
onset and survival of the IBM patients.
a
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CHAPTER 8
DISCUSSION
In this study we screened the largest cohort of ALS patients (n=1219) to date for mutations
in hnRNPA1 and hnRNPA2B1. To our knowledge, this the first study in which considerable
cohorts of FTD (n=144) and IBM patients (n=31) have been analyzed. We did not identify
any non-synonymous mutations in our cohorts, which suggests that mutations in these
genes are not a common cause of ALS, FTD and IBM in The Netherlands.
In the initial publication, one mutation was found in 212 familial ALS cases and one
mutation in 305 sporadic ALS cases, however segregation of hnRNP with ALS was not
shown.5 A recent French study found no mutations in 17 patients with an MSP phenotype
and 60 FTD and FTD-ALS patients.4 A subsequent Italian study did not find mutations
in 221 familial ALS patients and 622 sporadic ALS patients.8 Combined over these three
studies a total of 568 familial ALS patients and 2011 sporadic ALS patients have been
screened. The overall impression of these data is that the frequency of hnRNPA1 and
hnRNPA2B1 mutations in ALS is low (familial ALS: 1/568= 0.17% and sporadic ALS:
1/2011= 0.05%). Alternatively, the lack of replication in this study could theoretically be
explained if the initial report were to be false positive, or more likely due to considerable
differences in the frequency of these mutations across populations as has been shown
previously for mutations in other ALS genes.9 It will therefore be interesting to see the
results of mutational screening in other populations and larger cohorts of FTD and IBM
cases. For now, it seems that in clinical practice screening for mutations in hnRNPA1 and
hnRNPA2B1 should perhaps be reserved for those patients with a family history suggesting
MSP.
Disclosure statement
The authors report no conflict of interest. All participants gave written informed consent
and the Medical Ethics Review Boards of the participating institutions approved this
study.
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Mutations in hnRNPA1/A2B1 in ALS, FTD and IBM patients
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
Neumann M, et al. Ubiquitinated TDP-43 in frontotemporal lobar degeneration and
amyotrophic lateral sclerosis. Science. 2006; 314: 130-3.
Weihl CC, et al. TDP-43 accumulation in inclusion body myopathy muscle suggests a common
pathogenic mechanism with frontotemporal dementia. J Neurol Neurosurg Psychiatry. 2008; 79:
1186-9.
Watts GD, et al. Inclusion body myopathy associated with Paget disease of bone and
frontotemporal dementia is caused by mutant valosin-containing protein. Nat Genet. 2004;
36: 377-81.
Le Ber I, et al. hnRNPA2B1 and hnRNPA1 mutations are rare in patients with "multisystem
proteinopathy" and frontotemporal lobar degeneration phenotypes. Neurobiol Aging. 2014; 35:
934 e5-6.
Kim HJ, et al. Mutations in prion-like domains in hnRNPA2B1 and hnRNPA1 cause
multisystem proteinopathy and ALS. Nature. 2013; 495: 467-73.
King OD, et al. The tip of the iceberg: RNA-binding proteins with prion-like domains in
neurodegenerative disease. Brain Res. 2012; 1462: 61-80.
Huisman MHB, et al. Population based epidemiology of amyotrophic lateral sclerosis using
capture-recapture methodology. Journal of Neurology Neurosurgery and Psychiatry. 2011; 82:
1165-70.
Calini D, et al. Analysis of hnRNPA1, A2/B1, and A3 genes in patients with amyotrophic
lateral sclerosis. Neurobiol Aging. 2013; 34: 2695 e11-2.
van Es MA, et al. Large-scale SOD1 mutation screening provides evidence for genetic
heterogeneity in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2010; 81: 562-6
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CHAPTER 8
SUPPLEMENTARY DATA
Genetic analysis
Sanger sequencing
Genomic DNA was extracted from whole blood using standard procedures according to
the chemagic DNA Blood5k Kit (magnetic bead DNA isolation procedure). DNA was
whole genome amplified using a Qiagen Repli-g Mini kit according to manufacturer’s
instructions using 50 ng of input genomic DNA per reaction. Polymerase chain reaction
(PCR) was performed of the exonic sequences encoding the core PrLD and at least 50 base
pairs of flanking intronic sequences in two amplicons for each of the two hnRNPs
(Ensembl transcript ID ENSG00000135486 for hnRNPA1 and ENSG00000122566 for
hnRNPA2B1). Sequences are listed in Supplementary Table S8.1. PCR was performed
using a thermocycling program (96 ºC for 5 minutes; 35 cycles of 96 ºC for 30 seconds,
57 ºC for 45 seconds, 72 ºC for 1 minute; 72 ºC for 5 minutes and infinite hold at 8 ºC).
PCR reactions consisted of 1.0 μL amplified DNA, 1.0 μL 10xNH4 reaction buffer, 0.2
μL dinucleotide triphosphate (dNTP; 10mM each), 0.4 μL dimethyl sulfoxide (DMSO),
0.3 μL MgCl2 (50mM), 0.1 μL Biotaq (5U/μL), 6.6 μL milli-Q, 0.2 μL of each primer (10
μM) in a total volume of 10 μL.
PCR products were checked on a 1.2% agarose gel. BigDye Terminator 3.1 sequencing kit
(Applied Biosystems, Foster City, CA, USA), DNA Analyzer 3730XL, and PolyPhred
software version 6.18 (Washington University, Seattle, WA, USA, Nickerson et al., 1997)
were used for sequencing and data analysis.
Supplementary Table S8.1 Amplicons used to sequence exon 8 and 9 of hnRNPA1 and exon 9
and 10 of hnRNPA2B1 gene
Gene
Exon
hnRNPA1
hnRNPA2B1
Forward
Reverse
8
‘5 - GACCTTAGGCGCTTAGTTGATG
‘5 - GCCCAGACATAGCAGTTAAAGG
9
‘5 - CCTCTTTACCACCTCCCTTG
‘5- TGCACTGCTCAGCTACATTAGG
9
‘5 - AGCTGGAAATGGATGTGAGG
‘5 - ACCAAGGACTTAGGACAAAGC
10
‘5 - CACCTGCAACCTTTATGTGG
‘5 - GCACTGCCCACAGTACAAAC
Whole exome sequencing
Whole exome capture and sequencing were performed by Human Genomics Facility at
Rotterdam. Exomes were captured by Nimblegen seqcap EZ human exome v2, and were
sequenced with 100 base pair reads on the Illumina HiSeq2000 platform, according to the
manufacturer’s protocol. Reads were mapped to the human reference genome sequence
(assembly GRCh37/hg19) using the Burrows-Wheeler Alignment Tool.1 Base quality
recalibration, local sequence realignment and variant filtering to minimize base calling
and mapping errors were performed by Samtools2, Picard (http://picard.sourceforge.net)
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Mutations in hnRNPA1/A2B1 in ALS, FTD and IBM patients
and Genome analysis Tool Kit (GATK)3 . The identified variants per individual were called
by using GATK and annotated by ANNOVAR4. Variants with quality score < 50, quality
over depth < 5.0, Strandbias > 0.75 and depth < 5.0 were filtered out. Variants in the
hnRNPA1 and hnRNPA2B1 were examined on their frequency in dbSNP (http://www.ncbi.
nlm.nih.gov/projects/SNP/), the 1000 genome project (www.1000genomes.org/) and the
National Heart Lung Blood Institute Exome Variant Server (EVS) (https://evs.gs.
washington.edu/EVS/). The predicted functional effects of the variants were assessed by
Polyphen-2 (http://genetics.bwh.harvard.edu/pph2/), Sorting Intolerant from Tolerant
(SIFT) (http://sift.jcvi.org/www/SIFT_enst_submit.html), PROVEAN (http://provean.
jcvi.org/seq_submit.php) and Mutation Taster (www.mutationtaster.org).
REFERENCES
1.
2.
3.
4.
Li H, et al. Fast and accurate short read alignment with Burrows-Wheeler transform.
Bioinformatics. 2009; 25, 1754-1760.
Li H, et al. 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map
format and SAMtools. Bioinformatics. 2009; 25: 2078-2079.
McKenna A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing nextgeneration DNA sequencing data. Genome Res. 2010; 20: 1297-1303.
Wang K, et al. ANNOVAR: functional annotation of genetic variants from high-throughput
sequencing data. Nucleic Acids Res. 2010; 38: e164.
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CHAPTER 9
Large scale genetic screening in sporadic
ALS identifies modifiers in C9orf72 repeat carriers
In preparation
Meinie Seelen1, Perry T.C. van Doormaal1, Wouter van Rheenen1, Reinoud J.P. Bothof1,
Tim van Riessen1, William J. Brands1, Anneke J. van der Kooi2, Marianne de Visser2,
Nicol C. Voermans3, Jan H. Veldink1*, Leonard H. van den Berg1*, Michael A. van Es1*
Department of Neurology, Brain Center Rudolf Magnus,
University Medical Center Utrecht, The Netherlands
2
Department of Neurology, Academic Medical Center,
University of Amsterdam, The Netherlands
3
Department of Neurology, Donders Institute for Brain, Cognition and Behavior,
Radboud University Nijmegen Medical Center, The Netherlands
1
* These authors contributed equally
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CHAPTER 9
ABSTRACT
Sporadic ALS is considered to be a complex disease with multiple genetic risk factors
contributing to the pathogenesis. Identification of genetic risk factors that co-occur
frequently could provide relevant insight into underlying mechanisms of motor neuron
degeneration. To dissect the genetic architecture of sporadic ALS we undertook a large
sequencing study in 755 apparently sporadic ALS cases and 959 controls, analyzing ten
ALS genes: SOD1, C9orf72, TARDBP, FUS, ANG, CHMP2B, ATXN2, NIPA1, SMN1 and
UNC13A. We observed sporadic cases with multiple genetic risk variants in 4.1% compared
to 1.3% in controls. This difference was not in excess of what is to be expected by chance
(binomial test, P = 0.59). We did observe a higher frequency than expected of C9orf72
repeat carriers with co-occurring susceptibility variants (ATXN2, NIPA1, SMN1; P = 0.001),
which is mainly due to the co-occurrence of NIPA1 repeats in 15% of C9orf72 repeat
carriers (P = 0.006).
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder characterized by motor
neuron degeneration in the primary motor cortex, brainstem and spinal cord. ALS patients
develop progressive weakness and spasticity eventually resulting in respiratory failure
and death. Survival is approximately 3 years from symptom onset.1-3 To date, there is only
one drug, riluzole, that can moderately slow disease progression.4
In about 5-10% of patients the disease is familial and the mode of transmission is mostly
autosomal dominant.5 In the majority of cases there is no apparent family history and
these cases are considered sporadic. Although the distinction between familial and
sporadic seems straightforward, there is no clear definition of familial ALS and there is
poor consensus amongst experts.6 Many familial ALS pedigrees demonstrate incomplete
penetrance. Genetic modeling studies have shown that considering non-penetrance and
small family size familial cases may well present as “apparently” sporadic.7 Even further
complicating the matter is that there have been reports of ALS pedigrees with mutations
in more than one ALS gene, suggesting that oligogenic inheritance also occurs.8-13
Therefore, it has been argued that the distinction between familial and sporadic ALS is
rather arbitrary.7
In fact it has been proposed that this distinction might be artificial in all diseases. In an
influential paper, Manolio et al. propose a model in which they present genetic risk factors
on a sliding scale with on one extreme rare mutations (<1%) with very large effect (perhaps
directly pathogenic) involved in Mendelian disorders and on the other extreme common
polymorphisms (>5%) with small effect (odds ratios < 1.5) likely to be involved in complex
/ sporadic diseases.14 They argue that the majority of genetic risk factors are likely to be
variants with intermediate frequency (1-5%) and larger effect (odds ratios >1.5). This
model seems to translate well to ALS genetics.
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Genetic modifiers of C9orf72 repeat carriers
Over the last few years great progress has been made in ALS genetics. There are over 20
familial ALS genes and several risk factors for sporadic ALS have now been identified by
GWAS and candidate gene studies.15, 16 In an attempt to dissect the genetic architecture of
sporadic ALS we undertook a large sequencing study in which we analyzed 10 ALS genes:
high risk genes (rare mutations with large effect): SOD1, C9orf72, TARDBP, FUS,
susceptibility genes with intermediate effect: ANG, CHMP2B, ATXN2, NIPA1, SMN1, and
polymorphisms with small effect: a risk SNP in UNC13A. We hypothesized that we would
find: 1) a low percentage of sporadic cases with mutations in high risk genes (familial cases
presenting as sporadic) and 2) a significant number of sporadic cases with variants in
multiple susceptibility genes and/or mutations in high risk genes combined with
susceptibility genes (perhaps contributing to non-penetrance and phenotypic variability).
The identification of genetic risk factors that co-occur frequently could provide relevant
insight into the underlying mechanisms of motor neuron degeneration, which formed
the rationale for this study.
METHODS
Subjects
Sporadic ALS patients and control subjects were recruited as part of the Prospective ALS
study The Netherlands, a population-based case-control study.1 All ALS patients included
in this study fulfilled the revised El Escorial criteria for definite or probable ALS.38 ALS
mimics and subjects with a known family history of ALS were excluded. Genomic DNA
was extracted from whole blood by means of salting-out or magnetic beads procedures.
All material was obtained with the ethical approval of the institutional review board of
the University Medical Center Utrecht. All subjects provided written informed consent.
Gene selection
To date, over 100 genes have been implicated in the cause of ALS.39 The level of supporting
evidence for each gene or gene variant varies from small to overwhelming, and is in some
cases contradictory.40 Therefore most authors assign ALS genes to different categories of
certainty, although there is no consistent nomenclature. In this study, we divided genetic
variants into three categories: 1) High risk variants, 2) Susceptibility variants and 3) Risk
SNPs. Variants were grouped according to consensus in literature (directly causal /
Mendelian or less certain (risk factor / putative ALS gene))15, 16, 41 minor allele frequency
(MAF) and effect size. We considered rare variants (MAF <0.1% in controls) with large
effect (causal / Mendelian) as high risk variants. Variants with low MAF (0.1 - 5.0% in
controls) with intermediate effect (OR between 1.5 and 10.0) as susceptibility variants.
Variants with MAF > 5.0% in controls and OR < 1.5 were termed risk SNPs.
In total 10 genes were selected for analysis in this study. They were categorized as follows:
1) High risk genes: SOD1, TARDBP, FUS, C9orf72; 2) Susceptibility variants: ANG, CHMP2B,
NIPA1, SMN1, ATXN2; and 3) Risk SNP: UNC13A. These genes were selected based on
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CHAPTER 9
the following criteria: 1) the presence of variation in the gene in the Dutch population in
previous studies. For instance, UBQLN2 mutations are a well-established, but rare cause
of ALS and mutations in this gene are not found in The Netherlands.42 Therefore UBQLN2
was not analysed in this study. For this reason, genes such as OPTN, VCP, hnRNPA1 and
hnRNPA2B1 were also not included in the study. And 2) the genes needed to be implicated
in ALS by multiple or very large studies.
Genetic analyses
To complete the entire set of genes for each subject, we used previously obtained data and
subsequently performed additional genetic analyses. Detailed information of the genetic
analyses can be found in the Supplementary Material (Methods).
In short, subjects were screened for mutations in SOD1 (NM_000454, exons 1-5), FUS
(NM_004960, exons 5, 6, 14, 15), TARDBP (NM_007375, exon 6), ANG (NM_001145,
exon 2) and CHMP2B (NM_014043, exons 1-6) by means of sanger sequencing as described
previously.19, 43-46 These exons were selected since the vast majority of known pathogenic
variants lie within these regions. Additional screening of FUS, TARDBP, ANG and CHMP2B
was performed by multiplexed targeted resequencing, carried out on a MiSeq highthroughput next-generation sequencing platform (Illumina). Bar-coded paired-end
sequencing libraries were prepared using a Truseq Custom Amplicon kit (Illumina).
Sequence Analysis Viewer software (Illumina) was used to monitor the quality of the
sequencing runs. Sequencing reads were mapped to the human genome reference build
GRCh37 using Burrows-Wheeler Aligner (BWA v0.6.1). Subsequent depth of coverage,
quality filters, variant calling and variant annotation were performed using SAMtools
v0.1.19, GATK v3.2 and the 1000 Genomes project. The impact of each mutation on the
structure, and function of the protein was predicted with PolyPhen-2 (PolyPhen-2 v2.2.2;
http://genetics.bwh.harvard.edu/pph2/) and PMut (http://mmb2.pcb.ub.es:8080/PMut/).
We performed fragment-length analyses of repeats in C9orf72 (NM_018325, long repeat
= (GGGGCC) ≥30), ATXN2 (NM_002973, intermediate repeat = (CAG) ≥29) and NIPA1
(NM_144599, long repeat = (GCG) >8), as described previously.47-49 Copy number
variations in SMN1 (NM_000344, i.e. >2 copies) were determined using multiplexed
ligation-dependent probe amplification, as described previously.50 Lastly, a previously
ALS-associated SNP in UNC13A (rs12608932, recessive model CC) was determined by
use of a TaqMan allelic discrimination assay.30
Statistical analysis
Binomial tests were performed to assess whether the observed frequency of co-occurring
variants was in excess of what would be expected on the basis of chance. To calculate the
expected frequency of co-occurring variants, we used the following formula: (the observed
number of patients carrying a variant / the total number of patients) * (the observed
number of controls carrying a variant / the total number of controls). This formula was
used in order to take into account the higher frequency of just one variant in ALS patients
(= frequency of variants in patients), multiplied by the chance probability of a second
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Genetic modifiers of C9orf72 repeat carriers
variant (= frequency of variants in controls). To perform the binomial test, we then used
the following formula in R: pbinom([observed number of patients with multiple variants],
[total number of patients], [expected frequency], lower.tail = FALSE, log.p = FALSE). If
the binomial p value is smaller than 0.05 this means that the observed frequency of cooccurring variants is higher than expected based on chance.
We examined phenotypic associations for the combination of C9orf72 repeat expansions
with susceptibility variants for age at onset, site of onset and survival. Statistical analysis
was performed on SPSS software version 21 and R v3.0.2 (CRAN: http://www.r-project.
org/).
RESULTS
A total of 755 sporadic ALS patients and 959 control subjects from The Netherlands were
included in this study. Baseline characteristics are shown in Table 9.1. An overview of the
genotyping results by gene is provided in Table 9.2. Approximately 7% of sporadic cases
were found to carry variants in high risk ALS genes (SOD1, TARDBP, FUS and C9orf72).
Repeat expansions in C9orf72 were the most common (6.1%). We identified a nonsynonymous mutation in FUS in a single control. The frequency of variants in high risk
ALS genes in controls was 0.1%. This difference between patients and controls was
significant with P = 2.20 x 10-16. Variants in susceptibility genes ANG, CHMP2B, SMN1
duplications and repeat expansions in ATXN2 and NIPA1, were found in 14.9% of patients
and in 8.4% of controls (P = 2.85 x 10-5). Lastly, 120 (15.9%) patients and 104 (10.8%)
controls were homozygous for the UNC13A risk SNP (P = 0.002 (recessive model)).
Co-occurring ALS gene variants
In 31 (4.1%) patients and in 13 (1.4%) controls we identified more than one variant in
ALS-associated genes (Table 9.3). The higher frequency of ALS patients with multiple
variants is statistically significant when applying a simple Fisher exact test with P = 7.39
x 10-4. When we subsequently performed a binomial test, in order to control for cooccurrence of multiple variants by chance (and taking the higher rate of having one ALS
mutation in patients into account), this difference did not remain significant with P = 0.59.
Table 9.1 Baseline characteristics of study population
Sporadic ALS patients
Control subjects
Subjects, No.
755
959
Female gender (%)
334 (44)
455 (47)
Age (y), median (IQR)
61 (53-69)
63 (56-70)
Bulbar site of onset, n (%)
254 (33.6)
Survival (m), median (IQR)
31 (21-45)
IQR, interquartile range. Age is depicted in years (y), survival in months (m).
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Table 9.2 Variants found by large scale genetic screening in sporadic ALS patients and control
subjects
Gene
Variant
Exon
ALS patients
Controls
Previous reports
SOD1
I99V
4
1
0
ALS43
E132K
5
1
0
Novel
2/755 (0.3)
0/959 (0.0)
Total (%)
FUS
S115N
5
1
0
ALS46
S142N
5
0
1
Novel
R495X
14
1
0
ALS46, 51
2/753 (0.3)
1/943 (0.1)
Total (%)
TARDBP
N352S
6
1
0
ALS46, 52
I383V
6
1
0
ALS45, 53
Total (%)
2/753 (0.3)
0/959 (0.0)
C9orf72
Long repeat
46
0
46/755 (6.1)
0/959 (0.0)
ANG
K17I
2
4
1
ALS/PD/CON19, 55
I46V
2
0
1
ALS/PD/CON19, 55
T80S
2
1
0
ALS19
F100I
2
1
0
ALS19
Total (%)
Total (%)
CHMP2B
ALS/FTD20, 47, 54
6/707 (0.9)
2/948 (0.2)
R22Q
2
1
0
ALS46
S103C
3
1
0
Novel
S194L
6
0
1
CON46
E201Q
6
1
0
Novel
3/738 (0.4)
1/928 (0.1)
Total (%)
ATXN2
Intermediate repeat
NIPA1
Long repeat
SMN1
Duplications
UNC13A
rs12608932 (CC)
12
Total (%)
12/755 (1.6)
41
Total (%)
7/951 (0.7)
ALS49
37/956 (3.9)
33
50/755 (6.6)
120
ALS48, 56
37
41/740 (5.5)
50
Total (%)
Total (%)
7
ALS50, 57
33/959 (3.4)
104
120/754 (15.9)
ALS58
104/958 (10.8)
ALS, amyotrophic lateral sclerosis; FTD, frontotemporal dementia; PD, Parkinson’s disease; CON,
controls. SOD1 (NM_000454, exons 1-5), FUS (NM_004960, exons 5, 6, 14, 15), TARDBP
(NM_007375, exon 6), C9orf72 (NM_018325, long repeat = (GGGGCC) ≥30), ANG (NM_001145,
exon 2), CHMP2B (NM_014043, exons 1-6), ATXN2 (NM_002973, intermediate repeat = (CAG)
≥29) and NIPA1 (NM_144599, long repeat = (GCG) >8), SMN1 (NM_000344, >2 copies), UNC13A
(NM_001080421, homozygous SNP).
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Genetic modifiers of C9orf72 repeat carriers
When we looked at subgroups, no co-occurring high risk variants (SOD1, FUS, TARDBP,
C9orf72) were found in sporadic ALS patients (Table 9.3a). The frequency of high risk
variants combined with susceptibility variants was significantly higher than would be
expected on the basis of chance (P = 0.001, Table 9.3b), which is probably due to
combinations with C9orf72 repeat expansions (11 out of 12 co-occurring variants).
Interestingly, we observed a significantly lower rate of controls (P = 0.009) with variants
in multiple high risk and susceptibility genes (without UNC13A SNP, expected 7 (0.7%)
versus actually observed 1 (0.1%)).
Clinical characteristics (i.e. gender, age at onset, site of onset, survival) of patients with
multiple genetics variants are described in the Supplementary Material (Table S9.1).
C9orf72 repeat carriers
Considering the most prominent finding was the co-occurrence of C9orf72 repeat
expansions with susceptibility variants (ATXN2, NIPA1, SMN1) and the UNC13A SNP, we
performed additional analyses. The difference was mainly explained by the combination
of C9orf72 and NIPA1 repeat expansions (binomial test, P = 5.73 x 10-4, Table 9.4). No
significant difference in observed versus expected frequency was found for C9orf72 in
combination with ATXN2, SMN1 or UNC13A (P = 0.06, P = 0.08, P = 0.13).
Considering the relatively high frequency of C9orf72 and NIPA1 repeat expansions (15%
of C9orf72 cases), we questioned whether the initial association of NIPA1 with ALS might
be driven by a high frequency of coincidental C9orf72 repeat expansions and that the
association with NIPA1 could be false positive. Therefore the association analysis on the
NIPA1 data set was repeated after excluding C9orf72 positive cases and the association still
remained, suggesting that there is a significant co-occurrence of 2 independently ALSassociated repeat expansions.
There were two cases with a C9orf72 repeat expansion combined with variants in two
other genes (C9orf72, ATXN2 and UNC13A; and C9orf72, SMN1 and UNC13A). For both
cases statistical analysis suggested that these combinations were not likely to be a chance
finding with P = 6.60 x 10-4 and P = 0.01.
Phenotypic characteristics of all C9orf72 repeat carriers with concurrent possible genetic
modifiers are shown in Table S9.2 (i.e. age at onset, site of onset, survival and co-morbid
frontotemporal dementia). Unfortunately no detailed family history on cognitive
impairment was available, since most patients were included many years ago. C9orf72
repeat carriers with a NIPA1 repeat expansion had an earlier mean age at onset (52 vs 60
years, P = 0.03) and more often a spinal onset (86 vs 53%, P = 0.21) compared to C9orf72
repeat carriers without a NIPA1 repeat expansion. C9orf72 repeat carriers with a
concomitant UNC13A SNP more often had a bulbar onset (86 vs 33%, P = 0.01) and a
shorter survival (26.3 vs 33.3 months, P = 0.48).
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Variant 2
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-
NIPA1 (long repeat)
SMN1 (duplications)
ATXN2 (intermediate repeat)
C9orf72 (long repeat)
C9orf72 (long repeat)
C9orf72 (long repeat)
ATXN2 (intermediate repeat)
NIPA1 (long repeat)
SMN1 (duplications)
ATXN2 (intermediate repeat)
SMN1 (duplications)
NIPA1 (long repeat)
ANG (K17I)
C9orf72 (long repeat)
C9orf72 (long repeat)
C9orf72 (long repeat)
ATXN2 (intermediate repeat)
ATXN2 (intermediate repeat)
SMN1 (duplications)
SMN1 (duplications)
UNC13A (rs12608932)
SOD1 (I99V)
TARDBP (N352S)
D) Combination of high risk variants, susceptibility variants and risk SNP
Total
SMN1 (duplications)
SOD1 (I99V)
C) Combination of any high risk and susceptibility variants
Total
SMN1 (duplications)
SOD1 (I99V)
-
Variant 3
B) Combination of a high risk variant with a susceptibility variant
-
A) Combination of high risk variants
Variant 1
1
1
14 (1.9)
0
1
1
3
7
1
1
12 (1.6)
1
3
7
1
0 (0.0)
ALS
patients,
n (%)
Table 9.3 The observed and expected co-occurring variants in sporadic ALS patients and control subjects
12 (1.7)
4 (0.6)
0 (0.03)
a
Expected
frequency
ALS, n (%)
0.27
0.001
-
Binomial
p-valueb
0
0
1 (0.1)
1
0
0
0
0
0
0
0 (0.0)
0
0
0
0
0 (0.0)
Controls,
n (%)
CHAPTER 9
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UNC13A (rs12608932)
SMN1 (duplications)
UNC13A (rs12608932)
UNC13A (rs12608932)
SMN1 (duplications)
1
31 (4.1)
6
4
0
0
1
5
1
2
1
7
1
32 (4.2)
0.59
0
13 (1.4)
4
6
2
1
0
0
0
0
0
0
0
Four different groups of co-occurring variants are shown, adding new possible combinations of risk variants in each step in a sliding scale of certainty.
a
To calculate the expected frequency of co-occurring variants, we used the following formula: (the observed number of patients carrying one variant
/ the total number of patients) * (the observed number of controls carrying one variant / the total number of controls). This formula was used in order
to take into account the higher frequency of just one variant in ALS patients (= frequency of variants in patients) times the chance probability of a
second variant (= frequency of variants in controls). b A binomial test was performed to compare the observed frequency of co-occurring variants in
sporadic ALS patients with the calculated expected frequency.
ALS, amyotrophic lateral sclerosis. SOD1 (NM_000454, exons 1-5), C9orf72 (NM_018325, long repeat = (GGGGCC) ≥30), ANG (NM_001145, exon
2), CHMP2B (NM_014043, exons 1-6), ATXN2 (NM_002973, intermediate repeat = (CAG) ≥29) and NIPA1 (NM_144599, long repeat = (GCG) >8),
SMN1 (NM_000344, >2 copies), UNC13A (NM_001080421, homozygous SNP).
Total
UNC13A (rs12608932)
UNC13A (rs12608932)
ATXN2 (intermediate repeat)
NIPA1 (long repeat)
NIPA1 (long repeat)
ATXN2 (intermediate repeat)
C9orf72 (long repeat)
ATXN2 (intermediate repeat)
SMN1 (duplications)
C9orf72 (long repeat)
UNC13A (rs12608932)
SMN1 (duplications)
C9orf72 (long repeat)
SMN1 (duplications)
NIPA1 (long repeat)
C9orf72 (long repeat)
C9orf72 (long repeat)
UNC13A (rs12608932)
CHMP2B (E201Q)
ATXN2 (intermediate repeat)
ATXN2 (intermediate repeat)
ANG (K17I)
Genetic modifiers of C9orf72 repeat carriers
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CHAPTER 9
Table 9.4 Frequency of C9orf72 repeat expansions with co-occurring variants (ATXN2, NIPA1,
SMN1, UNC13A)
Genes
sporadic ALS
(n=755) (%)
C9orf72 carriers Expected
(n=46) (%)
frequency (%)
Binomial
P valuea
C9orf72 + ATXN2
1 (0.13)
1 (2.2)
0.4 (0.05)
0.06
C9orf72 + NIPA1
7 (0.93)
7 (15.2)
1.8 (0.24)
5.73 x 10-4
C9orf72 + SMN1
3 (0.40)
3 (6.5)
1.6 (0.21)
0.08
C9orf72 + UNC13A
7 (0.93)
7 (15.2)
5.0 (0.66)
0.13
ALS, amyotrophic lateral sclerosis. P values result from binomial test, comparing the observed
frequency in sporadic ALS versus the expected frequency (calculated for all sporadic ALS patients).
a
DISCUSSION
In this study we attempted to dissect the genetics of sporadic ALS by analyzing 10 ALS
genes in a large cohort of population based cases and controls. We did not find an overall
increased risk of co-occurring variants in sporadic ALS patients. But we do present
compelling statistical evidence for an excess of concomitant mutations in C9orf72 repeat
carriers.
Approximately 7% of our sporadic cases were found to carry a variant in a high risk ALS
gene. We did not observe sporadic cases with simultaneous mutations in more than one
high risk ALS gene. Previous studies in familial ALS have shown pedigrees with variants
in multiple high risk ALS genes suggesting that in a percentage of familial ALS there is
oligogenic inheritance.8-10, 17, 18 The fact that these double mutations do occur in familial
ALS but not in sporadic ALS may suggest that the co-occurrence of mutations in 2 high
risk genes results in a familial rather than a sporadic presentation.
Sporadic ALS is considered to be a complex disease with multiple genetic risk factors
contributing to the disease. We therefore expected to find sporadic cases with multiple
genetic risk variants. Indeed, we observed double mutations in 4.1% of patients compared
to 1.3% of controls. Despite the 3-fold higher frequency in cases, this difference was not
statistically significant when we applied a binomial test, taking the higher rate of having
one ALS mutation in patients into account. With this test we compared the frequency of
observed double mutations in cases to the expected number of double mutations in cases
(using the frequency of mutations in controls). Although correcting for chance cooccurrence is necessary, our approach may be too strict considering that we also correct
for combinations that have never been observed, such as homozygous NIPA1 expansions.
Likewise, in a recent meta-analysis on ANG mutations in ALS and Parkinson’s disease
data from over 15,000 individuals was available in which no homozygous ANG mutations
were observed.19 Hence, we may be overcorrecting for merely theoretical possibilities.
This seems to be reinforced by the fact that the observed frequency of double mutations
was significantly lower than was to be expected in controls. We therefore hypothesize
that perhaps certain combinations of mutations may be more relevant than we can
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statistically demonstrate. The most straightforward approach to test this hypothesis is to
increase sample size.
In this study we present compelling statistical evidence for an excess of concomitant
mutations in combination with C9orf72 repeat expansions. Although C9orf72 was initially
discovered in ALS-FTD, the gene has now been implicated in many different
neurodegenerative and psychiatric diseases including Alzheimer’s disease, Parkinsonism,
Huntington’s disease phenocopies, schizophrenia and bipolar disorder.20-23 This very large
phenotypic variability associated with C9orf72 repeat expansions is poorly understood.
One of the hypotheses is that additional genetic variants determine phenotype in C9orf72
carriers. Indeed there are multiple case reports of C9orf72 cases with additional mutations
in other ALS or FTD associated genes (i.e. ANG, TARDBP, FUS, SOD1, VAPB, OPTN,
UBQLN2, MAPT, GRN, DAO).8-11, 13, 17, 24-26 However, only a few studies have systematically
analyzed multiple genes in cohorts.
A French study on the role of ATXN2 expansions in neurodegeneration found C9orf72
expansions in 5.5% of sporadic ALS cases with ATXN2 expansions, and ATXN2 expansions
in 1.8% of C9orf72 positive sporadic ALS cases, with even higher frequencies in familial
ALS and FTD cases.27 Another recent study by van Blitterswijk et al. also provided evidence
for ATXN2 as a disease modifier of C9orf72.28 In our data set we did not observe a significant
co-occurrence of ATXN2 in C9orf72 repeat carriers (n = 1). However, the frequency of
co-occurring ATXN2 and C9orf72 repeat expansions in our study is comparable to that of
the other studies. Therefore our findings do not contradict the previous studies.
Several studies demonstrated that UNC13A homozygous ALS cases have a shorter
survival.29, 30 A recent study demonstrated that multiple genetic factors influence
phenotypic features in C9orf72 ALS.31 UNC13A was found to negatively influence survival
in C9orf72-ALS. In this study we also observed a shorter survival for C9orf72 – UNC13A
ALS cases (26.3 vs 33.3 months).
In the current study, we observed a high frequency of NIPA1 repeat expansions in C9orf72
positive sporadic ALS cases (15.2% compared to 5.5% in all other sporadic ALS cases and
only 3.9% in controls). This difference was significant and represents the discovery of a
novel phenotypic modifier of the C9orf72 phenotype.
The NIPA1 expansion is highly interesting as it is solely made up of alanines, the majority
being encoded by a polymorphic (GCG)n repeat (most frequently (GCG)7 and (GCG)8).32
A shared feature of all polyalanine disease pathogenesis is prominent protein aggregation.33
In vitro experiments have shown that peptides containing 7-15 alanine repeats undergo
variable levels of conformational transition from a monomeric α-helix to a predominant
β-sheet. However, when the repeat size is >15, there is complete conversion from monomer
to β-sheet.34 In vivo experiments show that expanded polyalanine tracts have a pronounced
tendency to adopt β-sheet complexes that promote strong protein–protein interactions,
leading to insoluble protein assemblies. The level of protein aggregation in polyalanine
diseases correlates with the size of the polyalanine expansion tract.33 There is also
considerable evidence suggesting that misfolded polyalanine-containing proteins are
targeted for degradation by the ubiquitin-proteasome system.35
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Allelic mutations in NIPA1 cause Spastic Paraplegia 6 (SPG6), which is an upper motor
neuron syndrome.36 Copy number variations in NIPA1 are associated with ALS.37 And our
data shows that NIPA1 repeat expansions are a risk factor for ALS independent of C9orf72.
There is thus considerable evidence that variation in NIPA1 affects the motor system in
various ways. It therefore seems plausible that a NIPA1 repeat expansion in the context
of a C9orf72 repeat expansion would drive towards a motor neuron disease phenotype.
Indeed, it seems there is a relatively consistent phenotype in C9orf72 – NIPA1 ALS cases
(relatively young, spinal onset).
To our knowledge, this is the first report on ALS cases carrying 3 different ALS risk
factors. Two out of these three cases are C9orf72 positive, which again suggests that it are
the additional genetic factors that determine phenotype in C9orf72 carriers.
In summary, there is an increasing number of reports on both familial and sporadic ALS
patients with mutations in multiple ALS (risk) genes. To date, very few studies have
systematically addressed this phenomenon, but it seems that collaborative and larger
studies will be able to identify frequently co-occurring variants, which in turn could
provide novel inroads for creating disease models and perhaps therapeutic strategies.
Although the high phenotypic variability associated with C9orf72 repeat expansions is not
well understood, there is mounting evidence that additional genetic factors determine
phenotype. Several genes have been implicated to date, including UNC13A and ATXN2
repeat expansions. In this study we identified a novel modifier, NIPA1.
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31. van Blitterswijk M, et al. Genetic modifiers in carriers of repeat expansions in the C9orf72
gene. Mol Neurodegener. 2014; 9: 38.
32. Chai JH, et al. Identification of four highly conserved genes between breakpoint hotspots
BP1 and BP2 of the Prader-Willi/Angelman syndromes deletion region that have undergone
evolutionary transposition mediated by flanking duplicons. Am J Hum Genet. 2003; 73: 898-925.
33. Messaed C and Rouleau GA. Molecular mechanisms underlying polyalanine diseases. Neurobiol
Dis. 2009; 34: 397-405.
34. Shinchuk LM, et al. Poly-(L-alanine) expansions form core beta-sheets that nucleate amyloid
assembly. Proteins. 2005; 61: 579-89.
35. Abu-Baker A, et al. Involvement of the ubiquitin-proteasome pathway and molecular
chaperones in oculopharyngeal muscular dystrophy. Hum Mol Genet. 2003; 12: 2609-23.
36. Rainier S, et al. NIPA1 gene mutations cause autosomal dominant hereditary spastic paraplegia
(SPG6). Am J Hum Genet. 2003; 73: 967-71.
37. Blauw HM, et al. A large genome scan for rare CNVs in amyotrophic lateral sclerosis. Hum Mol
Genet. 2010; 19: 4091-9.
38. Brooks BR, et al. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral
sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000; 1: 293-9.
39. Lill CM, et al. Keeping up with genetic discoveries in amyotrophic lateral sclerosis: the ALSoD
and ALSGene databases. Amyotroph Lateral Scler. 2011; 12: 238-49.
40. Abel O, et al. Credibility analysis of putative disease-causing genes using bioinformatics. PLoS
One. 2013; 8: e64899.
41. Ajroud-Driss S and Siddique T. Sporadic and hereditary amyotrophic lateral sclerosis (ALS).
Biochim Biophys Acta. 2014.
42. van Doormaal PT, et al. UBQLN2 in familial amyotrophic lateral sclerosis in The Netherlands.
Neurobiol Aging. 2012; 33: 2233 e7- e8.
43. van Es MA, et al. Large-scale SOD1 mutation screening provides evidence for genetic
heterogeneity in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2010; 81: 562-6.
44. Groen EJ, et al. FUS mutations in familial amyotrophic lateral sclerosis in the Netherlands.
Arch Neurol. 2010; 67: 224-30.
45. Ticozzi N, et al. Mutational analysis of TARDBP in neurodegenerative diseases. Neurobiol
Aging. 2011; 32: 2096-9.
46. van Blitterswijk M, et al. Genetic overlap between apparently sporadic motor neuron diseases.
PLoS One. 2012; 7: e48983.
47. van Rheenen W, et al. Hexanucleotide repeat expansions in C9orf72 in the spectrum of motor
neuron diseases. Neurology. 2012; 79: 878-82.
48. Van Damme P, et al. Expanded ATXN2 CAG repeat size in ALS identifies genetic overlap
between ALS and SCA2. Neurology. 2011; 76: 2066-72.
49. Blauw HM, et al. NIPA1 polyalanine repeat expansions are associated with amyotrophic
lateral sclerosis. Hum Mol Genet. 2012; 21: 2497-502.
50. Blauw HM, et al. SMN1 gene duplications are associated with sporadic ALS. Neurology. 2012;
78: 776-80.
51. Waibel S, et al. Novel missense and truncating mutations in FUS/TLS in familial ALS.
Neurology. 2010; 75: 815-7.
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52. Kuhnlein P, et al. Two German kindreds with familial amyotrophic lateral sclerosis due to
TARDBP mutations. Arch Neurol. 2008; 65: 1185-9.
53. Rutherford NJ, et al. Novel mutations in TARDBP (TDP-43) in patients with familial
amyotrophic lateral sclerosis. PLoS Genet. 2008; 4: e1000193.
54. Renton AE, et al. A hexanucleotide repeat expansion in C9orf72 is the cause of chromosome
9p21-linked ALS-FTD. Neuron. 2011; 72: 257-68.
55. Greenway MJ, et al. ANG mutations segregate with familial and 'sporadic' amyotrophic lateral
sclerosis. Nat Genet. 2006; 38: 411-3.
56. Elden AC, et al. Ataxin-2 intermediate-length polyglutamine expansions are associated with
increased risk for ALS. Nature. 2010; 466: 1069-75.
57. Corcia P, et al. Abnormal SMN1 gene copy number is a susceptibility factor for amyotrophic
lateral sclerosis. Ann Neurol. 2002; 51: 243-6.
58. van Es MA, et al. Genome-wide association study identifies 19p13.3 (UNC13A) and 9p21.2 as
susceptibility loci for sporadic amyotrophic lateral sclerosis. Nat Genet. 2009; 41: 1083-7.
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SUPPLEMENTARY MATERIAL
Supplementary Methods – Genetic analyses
Sanger Sequencing
Venous blood samples were drawn using 10-mL EDTA tubes, and genomic DNA was
extracted from whole blood using a standard salting-out procedure or by using
magnetic beads (chemagic kit, Perkin Elmer).
All five exons of SOD1 (NM_000454) were sequenced using BigDye Terminator 3.1
technology, after initial touchdown PCR amplification, as described previously.1
The following primers were used: SOD1-1-F, CGTCGTAGTCTCCTGCAGCG, and
SOD1-1-R, GCGGGCGACCCGCTCCTAGC; SOD1-2-F,
GGGTAAAGGTAAATCAGCTG, and SOD1-2-R, ATCTAACTAGGGTGAACAAG;
SOD1-3-F, CCCAGAAGTCGTGATGCAGG, and SOD1-3-R,
CCATATGAACTCCAGAAAGC; SOD1-4-F, TGCAAATTTGTGTCTACTCAGTC, and
SOD1-4-R, CCGCGACTAACAATCAAAGTC; SOD1-5-F,
GGTAGTGATTACTTGACAGC, and SOD1-5-R, CAGGTACTTTAAAGCAACTC.
PCR products were sequenced on an ABI3730xl sequencer (Applied Biosystems). Each
mutation was confirmed on genomic DNA.
Sequencing was performed on FUS (NM_004960, exons 5, 6, 14, 15), using a
96-capillary DNA Analyzer 3730XL and a BigDye Terminator 3.1 sequencing kit
(Applied Biosystems, Foster City, California) as described previously.2 The following
primers were used in this study for exon 5, 6, 14 and 15 respectively: FUS-5-F,
CACGACGTTGTAAAACGACTGGACTCCACTAAAAGTGAAAGG, and FUS-5-R,
GGATAACAATTTCACACAGGAAAATGGGCTGCAGACAAAG; FUS-6-F,
GAGGGTTCCTGTCTTGTTTC, and FUS-6-R, CCTCACAGATCCCTAGACAAC;
FUS-14-F, CACGACGTTGTAAAACGACGAGCTGGGACCAAAGAATCC, and
FUS-14-R, GGATAACAATTTCACACAGGCCCCTGAGTTAATTTTCCTTCC;
FUS-15-F, CACGACGTTGTAAAACGACGGTAGGAGGGGCAGATAGG, and
FUS-15-R, GGATAACAATTTCACACAGGCTTGGGTGATCAGGAATT. All
mutations were confirmed in independent experiments on genomic DNA. Sequence
data were analyzed in PolyPhred.
Mutational screening of exon 6 of TARDBP (NM_007375) was performed by
touchdown PCR using the following primers TDP43-6-F,
AGTAAAACGACGGCCAGTTGAATCAGTGGTTTAATCTTCTTTG; and TDP436-R, GCAGGAAACAGCTATGACCAAAATTTGAATTCCCACCATTC as described
previously.3 These primers anneal to adjacent intronic and 3’UTR regions of exon 6 and
contain 5’ tails encoding M13 forward and reverse. PCR-products were subsequently
purified by incubation with Exonuclease I and Shrimp Alkaline Phosphatase, sequenced
with M13 primers using the BigDyeTerminator v3.1 Cycle Sequencing Kit (Applied
Biosystems, Foster City, CA, USA) and then resolved by capillary electrophoresis on an
ABI 3730XL DNA Analyzer (Applied Biosystem). Sequence analysis was performed
using the PHRED/PHRAP/Consed software suite (http://www.phrap.org/) and
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variations in the sequences were identified with the Polyphred v6.15 software.
Sequencing was performed on the single coding exon of ANG (NM_001097577, exon
2), using a 96- capillary DNA Analyzer 3730XL (Applied Biosystems, Foster City, CA)
and BigDye Terminator 3.1 chemistry as described previously.4 The following primers
were used in this study: ANG-1-F, GTTCTTGG GTCTACCACACC and ANG-1-R,
AATGGAAGGCAAGGA CAGC. The sequences were aligned using the PHRED/
PHRAP/Consed package (http://www.phrap.org/), and variants were identified using
the software application PolyPhred. When a variant was identified, this was confirmed
by independent experiments using newly prepared samples from stock DNA.
Coding regions of all six exons CHMP2B (NM_014043.3) were screened for mutations
using touchdown PCR, as described previously.5 The following primers were used:
CHMP2B-1-F, CCGCAGACGTGAGGAAAG, and CHMP2B-1-R,
CTCCAGGGACAGTAGGCAGA; CHMP2B-2-F, GCGCCCAGCCAATATAAGAT, and
CHMP2B-2-R, GCCATGTGCCTTCTTCCTAGT; CHMP2B-3-F,
CTTCATGATCGGGGACAAAG, and CHMP2B-3-R,
CAGGAGGTGCTTTTAAATCTGC; CHMP2B-4-F,
TTTGATGTGTTCCCTTTTGACTT, and CHMP2B-4-R,
TCATCATTTCTGCCTTCGTG; CHMP2B-5-F, TTCACTGAGTTTGCCTTCTGT,
and CHMP2B-5-R, CGTGCATTAGGAAACATTTGG; CHMP2B-6-F,
GGAGGTGCATGGTTTTTATTTC, and CHMP2B-6-R,
TTGGCAGCTGTAACCACCTA (for PCR), GAAATCTGCACTGTGCTTGG (for
sequencing). Sanger sequencing and data analysis were performed with BigDye
Terminator 3.1 sequencing kit (Applied Biosystems, Foster City, California), DNA
Analyzer 3730XL (Applied Biosystems) and PolyPhred. Each mutation was confirmed
on genomic DNA.
High-Throughput Next-Generation Sequencing
Additional screening for non-synonymous mutations in FUS (exon 5, 6, 14, 15), TARDBP
(exon 6), ANG (exon 2) and CHMP2B (exon 1-6) was carried out on a MiSeq highthroughput next-generation sequencing platform (Illumina). Designstudio (Illumina) was
used to create a Truseq Custom Amplicon project, which involved the use of specific DNA
oligo stretches to sequence all exons and flanking region of the abovementioned genes of
interest. Standard Truseq Custom Amplicon Library Preparation protocol was followed
to create genetic libraries containing all regions of interest of our samples. By barcoding
each sample with unique indexed primers, ligated to the target regions and amplified by
means of standard PCR, we multiplexed 95 samples of patients and control subjects per
sequencing run. We chose a paired-end read with a 2x 250 basepair read length for the
approximately 28 amplicons (amplicon length 425 bp) representing our regions of interest,
which ensured excellent coverage of over 500 resequencing reactions for each amplicon.
Sequence Analysis Viewer software (Illumina) was used to monitor the quality of every
sequencing run, and secondary analyses were performed on MiSeq Reporter software to
accomplish variant calling for every sample. Sequencing reads were mapped to the human
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genome reference build GRCh37 using Burrows-Wheeler Aligner (BWA v0.6.1).
Subsequent depth of coverage, quality filters, variant calling and variant annotation were
performed using SAMtools v0.1.19, GATK v3.2 and the 1000 Genomes project. Using
SAMtools the average per sample depth of coverage was calculated to be 4332. GATK was
used to perform local indel realignment, recalibrate base quality scores, call variants and
assign sample genotypes. Variants would “pass” quality filters based on whether they met
a series of quality control criteria. These criteria included a minimum variant quality score
of 30, and adapted GATK filters (“QD” <2.0, “FS” >500, “MQ” <40.0, “HaplotypeScore”
>300, “MQRankSum” <-12.5 and “ReadPosRankSum” <-8.0, values). Next, those variants
not characterised as silent, intronic or non-coding were added to the final dataset.
Fragment-length Analysis
A repeat primed PCR was performed to assess the GGGGCC repeat in C9orf72
(NM_018325), as described elsewhere.6 In short, a primed PCR protocol was used on
50ng genomic DNA with the following primer sequences: forward primer 5’ – 6FAMAGTCGCTAGAGGCGAAAGC – 3’, reverse primer 5’ –
TACGCATCCCAGTTTGAGACGGGGGCCGGGGCCGGGGCCGGGG – 3’, and
anchor primer 5’ – TACGCATCCCAGTTTGAGACG – 3’. Fragment analysis was
accomplished on an ABI3730xl sequencer and fragment sizes were analysed with
GeneMapper software version 3.7. Furthermore, all samples were genotyped at the
repeat sites at least two times. Alleles with 30 or more GGGGCC hexanucleotide
repeats were defined as expanded.
The CAG repeat of ATXN2 (NM_002973) was amplified using following primers:
forward primer 5’ – 6FAM-GGGCCCCTCACCATGTCG – 3’ and reverse primer 5’
– CGGGCTTGCGGACATTGG – 3’, as described previously.7 The GCG repeat in exon
1 of NIPA1 (NM_144599) was genotyped with the following primer sequences: a
fluorescently labelled forward primer 5’ – 6FAM-GCCCCTCTTCCTGCTCCT – 3’
and reverse primer with sequence 5’ – CGATGCCCTTCTTCTGTAGC – 3’, as
described previously.8 Repeat lengths were determined on a ABI3130xl sequencer.
For determination of the fragment length Peak Scanner software version 1.0 (Applied
Biosystems) was used.
Multiplexed Ligation-dependent Probe Amplification
Copy number variation in SMN1 (NM_000344) was identified by multiplexed ligationdependent probe amplification (MLPA) assays were run using standard protocols (www.
mlpa.com), as described previously.9 We used the SALSA P060 MLPA kit (MRC Holland,
the Netherlands), containing 2 probes specifically targeted to SMN1, and control probes
targeted to other chromosomal loci for normalization and assay quality control. A total
of 50 –100 ng of genomic DNA was used in each MLPA assay. Data normalization and
analysis were performed with GeneMarker software (SoftGenetics, State College, PA)
using standard parameters.
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TaqMan Allelic Discrimination Assay
TaqMan allelic discrimination assay was used to determine the single nucleotide
polymorphism (SNP) in UNC13A at position rs12608932, as previously described.10
The following primer and probe sequences were used [VIC/FAM]:
ATCCATCCACCCATCAATTTATCCA[A/C]CCATCCATTTTTCGTCTGTCCACCA.
Allelic PCR products were analysed using specific probes on the ABI Prism 7900HT
Sequence Detection System. SDS software version 2.3 (Applied Biosystems) was used to
analyze allelic variant calls for each sample.
REFERENCES
1.
van Es MA, Dahlberg C, Birve A, Veldink JH, van den Berg LH, Andersen PM. Large-scale
SOD1 mutation screening provides evidence for genetic heterogeneity in amyotrophic lateral
sclerosis. J Neurol Neurosurg Psychiatry 2010;81:562-566.
2. Groen EJ, van Es MA, van Vught PW, et al. FUS mutations in familial amyotrophic lateral
sclerosis in the Netherlands. Archives of neurology 2010;67:224-230.
3. Ticozzi N, LeClerc AL, van Blitterswijk M, et al. Mutational analysis of TARDBP in
neurodegenerative diseases. Neurobiology of aging 2011;32:2096-2099.
4. van Es MA, Schelhaas HJ, van Vught PW, et al. Angiogenin variants in Parkinson disease and
amyotrophic lateral sclerosis. Annals of neurology 2011;70:964-973.
5. van Blitterswijk M, Vlam L, van Es MA, et al. Genetic overlap between apparently sporadic
motor neuron diseases. PloS one 2012;7:e48983.
6. van Rheenen W, van Blitterswijk M, Huisman MH, et al. Hexanucleotide repeat expansions in
C9ORF72 in the spectrum of motor neuron diseases. Neurology 2012;79:878-882.
7. Van Damme P, Veldink JH, van Blitterswijk M, et al. Expanded ATXN2 CAG repeat size in
ALS identifies genetic overlap between ALS and SCA2. Neurology 2011;76:2066-2072.
8. Blauw HM, van Rheenen W, Koppers M, et al. NIPA1 polyalanine repeat expansions are
associated with amyotrophic lateral sclerosis. Human molecular genetics 2012;21:2497-2502.
9. Blauw HM, Barnes CP, van Vught PW, et al. SMN1 gene duplications are associated with
sporadic ALS. Neurology 2012;78:776-780.
10. van Es MA, Veldink JH, Saris CG, et al. Genome-wide association study identifies 19p13.3
(UNC13A) and 9p21.2 as susceptibility loci for sporadic amyotrophic lateral sclerosis. Nature
genetics 2009;41:1083-1087.
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Table S9.1 Clinical information on ALS patients with co-occurring variants
Gene 1
Gene 2
SOD1 (I99V)
SMN1
TARDBP (N352S)
UNC13A
ANG (K17I)
ATXN2
CHMP2B (E201Q)
UNC13A
C9orf72
ATXN2
C9orf72
NIPA1
C9orf72
C9orf72
Gene 3
Gender
Age at
Onset
(y)
Site of Onset
Survival (m)
M
60
Bulbar
51
M
42
Cervical
58
F
77
Bulbar
21
M
75
Spinal
4
M
62
Bulbar
22
F
50
Lumbosacral
32
NIPA1
M
52
Bulbar
22
NIPA1
F
59
Lumbosacral
6
C9orf72
NIPA1
M
57
Lumbosacral
30
C9orf72
NIPA1
M
52
Lumbosacral
14
C9orf72
NIPA1
F
62
Lumbosacral
75
C9orf72
NIPA1
M
37
Cervical
87
C9orf72
SMN1
F
63
Bulbar
30
C9orf72
SMN1
M
63
Lumbosacral
55
C9orf72
SMN1
F
66
Lumbosacral
61
C9orf72
UNC13A
M
54
Bulbar
26
C9orf72
UNC13A
F
56
Bulbar
23
C9orf72
UNC13A
M
60
Bulbar
33
C9orf72
UNC13A
M
53
Lumbosacral
14
C9orf72
UNC13A
F
56
Bulbar
36
ATXN2
SMN1
F
51
Cervical
67
NIPA1
UNC13A
M
50
Cervical
48
NIPA1
UNC13A
F
77
Lumbosacral
13
NIPA1
UNC13A
M
74
Bulbar
16
NIPA1
UNC13A
M
60
Bulbar
30
SMN1
UNC13A
M
55
Lumbosacral
34
SMN1
UNC13A
F
62
Bulbar
19
SMN1
UNC13A
M
70
Cervical
29
SMN1
UNC13A
F
71
Cervical
45
SMN1
UNC13A
F
69
Thoracic
37
SMN1
UNC13A
F
73
Thoracic
14
SMN1
UNC13A
UNC13A
M, male; F, female; y, years; m, months
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Genetic modifiers of C9orf72 repeat carriers
Table S9.2 Possible genetic modifiers of C9orf72 repeat carriers
No
Age at onset,
mean (SD)
Bulbar site of
onset, n (%)
Survival, m,
mean (SD)
Co-morbid
FTD, n (%)
NIPA1 -
39
60.3 (7.9)
18 (47)
31.0 (12.7)
4 (11)
NIPA1 +
7
52.7 (8.2)
1 (14)
38.0 (30.9)
1 (14)
0.03
0.21
0.95
0.59
P value*
ATXN2 -
45
59.3 (8.4)
18 (40)
32.4 (16.5)
5 (11)
ATXN2 +
1
62.0 (-)
1 (100)
22.0 (-)
0 (0)
0.52
0.41
0.44
0.89
P value*
SMN1 -
43
59.1 (8.5)
18 (42)
31.7 (16.4)
4 (9)
SMN1 +
3
64.0 (1.7)
1 (33)
42.5 (17.7)
1 (33)
0.1
1
0.28
0.30
P value*
UNC13A -
39
59.7 (8.9)
13 (33)
33.3 (17.4)
5 (13)
UNC13A +
7
57.7 (3.9)
6 (86)
26.3 (7.5)
0 (0)
0.58
0.01
0.48
0.42
P value*
FTD, frontotemporal dementia; m, months; patients with (NIPA1+) and without (NIPA1-) a NIPA1
long repeat; patients with (ATNX2+) and without (ATXN2-) intermediate repeat; patients with
(SMN1+) and without (SMN1-) SMN1 duplications; patients with (UNC13A+) and without (UNC13A-)
the recessive model of the UNC13A SNP (rs12608932).
*P value of age at onset amd survival is based on Mann Withney U test, P value of site of onset and
co-morbid FTD is based on Fisher exact test.
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General discussion
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In this thesis I describe the results of studies aiming to identify risk factors for amyotrophic
lateral sclerosis (ALS). We have used a population-based case-control design to perform
(1) epidemiological risk factor studies, examining lifestyle factors and environmental
exposures, and (2) genetic studies determining genetic risk variants in ALS. Within these
studies we have established novel exposures, which had not been implicated in ALS before,
including exposure to traffic related air pollution and diesel motor exhaust. We were able
to identify these exposures in two independent populations, in The Netherlands and
Ireland, as well as through different study approaches (residential and occupational
exposure assessment), presenting convincing evidence for the positive association with
ALS risk. These and other ALS associated risk factors described in this thesis (e.g. physical
activity, head trauma, high dietary fat intake, low BMI and cholesterol levels, and low
alcohol consumption) have provided us with new clues for pathophysiological mechanisms,
such as mitochondrial dysfunction, oxidative stress, neuroinflammation and glutamate
excitotoxicity. Moreover, we have shown that ALS has a complex etiology in which
multiple genetic (and most probably also lifestyle and environmental) factors co-occur to
cause ALS. We specifically identified co-occurring repeat expansions in C9orf72 and NIPA1.
Taking all studies together, we have generated new hypotheses for future functional
biological studies to elucidate pathophysiological mechanisms leading to this complex
and devastating disease.
Risk factors for amyotrophic lateral sclerosis
Physical activity
Physical activity as a risk factor for ALS is a frequently discussed topic, fuelled by anecdotal
observations of famous athletes diagnosed with ALS, such as the 1930s American baseball
player Lou Gehrig.1 Second to that, the association is biologically plausible, because of
existing cellular and genetic evidence indicating that vigorous physical activity may induce
oxidative stress and glutamate excitotoxicity.2 Also, several genes associated with the
response to exercise, i.e ciliary neurotrophic factor, leukaemia inhibitory factor and
vascular endothelial growth factor 2, have been identified as possible modifiers of ALS
susceptibility.3-5
In this thesis, we report an increased risk of ALS with higher levels of leisure time physical
activity (chapter 2). However, the lack of an association with occupational physical activity
and the absence of a dose-response relationship strengthens the hypothesis that increased
physical activity is not causally related with ALS risk, but that it may be a mutual genetic
profile or lifestyle both promoting physical activity and increasing ALS susceptibility
(“born to run” versus “run to death”). Moreover, multiple epidemiological studies were
performed on the association between physical activity and ALS, which have shown
conflicting results from harmful,6-8 null9-11 to beneficial effects,12 which emphasizes the
complex relation between physical activity and ALS.
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Discussion
Head trauma
We observed an increased risk of ALS after prior head trauma (chapter 3). This is consistent
with several small studies and anecdotal reports suggesting that athletes exposed to
repeated (minor) head trauma (e.g. in boxing, rugby, ice hockey, soccer players) were
associated with long-term risks of neurodegenerative diseases.13, 14 For example, boxing
has been known to cause chronic traumatic encephalopathy. In these patients with chronic
traumatic encephalopathy, TDP-43 positive inclusions were found in the brain, a
pathological hallmark of aggregated proteins that is frequently found in patients with ALS
as well.15, 16 Moreover, a complex interplay of pathological mechanisms have been suggested
following traumatic brain injury, such as glutamate excitotoxicity, oxidative stress,
neuroinflammation and mitochrondrial dysfunction.17-19
An important issue to consider in the association between head trauma and ALS is reverse
causality. The exact time of symptom onset of ALS is difficult to pinpoint, and incipient
ALS may lead to for example tripping and subsequently head trauma. We therefore also
analyzed the risk of ALS and head trauma occurring at least five years prior to symptom
onset. In this additional analysis we still found an increased risk of ALS, making the issue
of reverse causality less likely. Nevertheless, as was proposed in a recent letter to The
Lancet Neurology, sound, evidence-based, high-quality studies are needed to investigate
the long-term consequences of repeated, low-intensity head trauma in professional
sportspeople and the associated long-term risks of developing devastating and lifethreatening neurodegenerative disorders, such as ALS.13
Premorbid low cholesterol levels, low BMI and a high dietary fat intake
In chapter 3, we observed a decreased risk of ALS in individuals with hypercholesterolemia
or individuals using statins, indicating a relatively favorable lipid profile prior to onset in
at least a subpopulation of ALS. This favorable lipid profile is consistent with the
significantly lower premorbid body mass index (BMI) we found in ALS patients compared
to controls. Previous studies support this finding with lower ALS rates among overweight
and obese individuals and reports of ALS patients being more likely to have always been
slim.8, 20
There is, however, also evidence for a high dietary fat and high caloric intake in ALS
patients before onset of symptoms (chapter 4). This increased risk of ALS with high dietary
fat intake has been reported previously in a smaller case-control study in the United
States.21 The imbalance in intake (high fat/caloric diet) and output (low cholesterol levels/
BMI) implies a role for an altered energy metabolism prior to symptom onset of ALS. It
seems that patients with ALS “burn” more calories in a resting state as well as during
exercise, also revered to as hypermetabolic.22 Furthermore, there is a growing body of
evidence from animal studies in which mouse models of ALS show metabolic alterations
with markedly increased resting and total energy expenditure, and increased lipolysis,
premorbidly as well as after symptom onset.23, 24 It has been suggested that mitochondrial
uncoupling protein 3 (UCP3) plays a role in this increased energy expenditure, since higher
levels of expression of UCP3 have been found both in an animal model of ALS and in
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human biopsies.25-27 This implicates an important role of mitochondria in ALS
pathogenesis. However, further clinical and experimental studies are needed to concentrate
on the complex relations between (defective) energy metabolism and ALS.
The hypothesis of an increased energy metabolism provides for new therapeutic options.
It has previously been reported that mild obesity was associated with greater survival in
patients with ALS,28 and that a high caloric diet increased survival in a mouse model.23
Compensating for the increased energy expenditure in ALS by initiating a high caloric
diet may therefore prolong survival in ALS patients. Furthermore, a small randomized
phase 2 clinical trial showed that there might be a more favorable outcome in patients on
a hyper-carbohydrate hypercaloric diet compared to patients on a isocaloric diet or a
hyper-fat hypercaloric diet.29 More research, however, is needed to specify which diet may
be the most beneficial for patients with ALS.
Alcohol
We observed a decreased risk of ALS with a higher intake of alcohol (chapter 4). A finding
that has not been reported in two previous epidemiological studies on alcohol
consumption.30, 31 Still, functional biological studies did reveal a potential neuroprotective
effect of the constituents of red wine. A lyophilized extract of red wine, which contains
several antioxidant compounds, was able to block glutamate-induced apoptosis in
cerebellar granule neurones.32 Furthermore, an in vivo experiment carried out on mutant
SOD1 mice showed that survival in mice fed with lyophilized red wine was significantly
increased compared to untreated animals. In our study, however, the association between
intake of alcohol and the risk of ALS was independent of the intake of red wine, and so
the association cannot be attributed only to the possible protective effect of antioxidants
in red wine.
Smoking, diesel motor exhaust and air pollution
Thus far, the only widely accepted environmental risk factor in ALS is smoking.33 Within
the Prospective ALS study the Netherlands (PAN), we previously reported an increased
risk of ALS with smoking.34 In this thesis, we observed an increased ALS risk in individuals
exposed to diesel motor exhaust by occupation in two independent populations, in The
Netherlands and in Ireland (chapter 5). Interestingly, in chapter 6 in a residential exposure
study, we observed an increased ALS risk in individuals who are exposed to traffic related
ambient air pollution, specifically PM2.5absorbance and nitrogen oxides. These
observations parallel the observations with smoking. Smoking increases the risk of ALS
through several potential mechanisms, including neuroinflammation, oxidative stress and
direct neurotoxicity caused by fine particles, heavy metals and other chemical compounds
present in cigarette smoke.35, 36 Ultrafine particles can circumvent the blood-brain barrier
by deposition on the olfactory mucosa of the nasal region.37-39 These particles are then
translocated along the olfactory nerve into the olfactory bulb of the brain, and may travel
transneuronally to more distal sites within the brain.40, 41 Similar pathological mechanisms
of neuroinflammation and oxidative stress, are suggested for diesel motor exhaust and
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Discussion
traffic related air pollution.37, 39 Importantly, diesel motor exhaust and traffic related air
pollution are environmental risk factors for ALS which are ubiquitous and can be modified
at a population level. This adds to the necessity of regulatory public health interventions
on air pollution exposure levels. It might be interesting to study whether patient groups
with a specific gene mutation are at increased risk of developing ALS while exposed to
smoking and/or diesel motor exhaust.
Electromagnetic fields
Studying a different residential exposure, extremely low frequency electromagnetic fields
(ELF-EMF), did not show an increased ALS risk (chapter 7), which is in concordance with
previous residential exposure reports.42-44 However, occupational exposure studies primarily
conclude that exposure to ELF-EMF is a risk factor for ALS.45-47 This discrepancy may be
due to an increased risk of electric shocks or higher exposure levels of ELF-EMF in electrical
occupations. Animal studies suggested that if ELF-EMF exposure would have an effect on
developing ALS, it may cause damage to motor neurons through oxidative stress, though
convincing evidence for this underlying biological mechanism is still lacking.48
hnRNPA1/A2B1
Recently, new variants were identified in the prion-like domain of heterogeneous nuclear
ribonucleoproteins (hnRNPs) A1 and A2B1 in families with multisystem proteinopathy
(MSP).49 MSP is a clinical syndrome incorporating ALS, frontotemporal dementia,
inclusion body myositis and Paget’s disease of the bone. In chapter 8 of this thesis we did
not identify any mutations in hnRNPA1 and hnRNPA2B1 in ALS, FTD and IBM (non-MSP)
patients in the Netherlands, also meaning that we did not find evidence for genetic
pleiotropy of these genes. Few other studies assessed the frequency of mutations in
hnRNPA1 and hnRNPA2B1 in ALS patients.49-52 Combined over these studies a total of 598
familial ALS patients and 2142 sporadic ALS patients have been screened, and only two
patients carried a mutation (0.17% in familial ALS and 0.05% in sporadic ALS). The overall
impression of these data is that the frequency of hnRNPA1 and hnRNPA2B1 mutations in
ALS is low or population-specific. For now, it seems that in clinical practice screening for
mutations in hnRNPA1 and hnRNPA2B1 should perhaps be reserved for those patients
with a family history suggesting MSP.
Disease model in ALS
Oligogenic model
In ALS patients with the same genetic background, the phenotypic heterogeneity can still
be large, with for example on one end young onset and on the other end late onset or no
disease onset at all. This heterogeneity is even expanding beyond ALS, showing pleiotropy.
For example, in C9orf72 repeat carriers, this pleiotropy contains ALS and frontotemporal
dementia, possibly even expanding to Parkinson’s disease, schizophrenia and bipolar
disorder.53-56 The phenotype the C9orf72 repeat carriers express may be dependent on
other co-occurring factors.
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Previous studies in familial ALS have shown pedigrees with variants in multiple high risk
ALS genes suggesting that in a percentage of familial ALS there is oligogenic inheritance.57-61
In chapter 9, we studied the genetic architecture of sporadic ALS. Sporadic ALS is
considered to be a complex disease with multiple genetic risk factors contributing to the
disease. We observed co-occurring mutations in 4.1% of patients compared to 1.3% of
controls. Despite the almost 3-fold higher frequency in cases, this difference was not in
excess of what is to be expected by chance (binomial test, P = 0.59). We did, however,
observe a higher frequency than expected of C9orf72 repeat carriers with co-occurring
susceptibility variants ATXN2, NIPA1 and SMN1 (P = 0.001), which was mainly due to the
co-occurrence of NIPA1 repeats in 15% of C9orf72 repeat carriers. This represents the
discovery of a novel phenotypic modifier of the C9orf72 phenotype and emphasizes the
theory of an oligogenic, or even polygenic, model in ALS.
Multistep model
A recent paper published in The Lancet Neurology assessed a hypothetically multistep
model in ALS.62 This model has previously been applied to cancer epidemiology, and is
about multiple lifestyle, environmental and genetic factors sequentially needed to cause
the disease (e.g. a multistep process). In this paper, multiple ALS populations were assessed
and a six step process was suggested to be needed to develop ALS.62 Interestingly, this
multistep model was calculated based on age at onset and incidence data from several
population based ALS studies, including the PAN, and is consistent with the oligogenic
architecture described in my thesis. It would be interesting to identify the environmental
and life style factors necessary on top of two or three mutations, which in different
combinations might drive ALS causation.
Clinical syndrome
As mentioned above, ALS is considered a complex disorder with large phenotypic diversity
in between patients, e.g. in age at onset, site of onset, involvement of upper and lower
motor neurons, cognitive impairment and survival. Furthermore there is heterogeneity
in lifestyle, environmental and genetic risk factors, which may be explained by the quality
(e.g. prevalent patient cohorts) and size of the studies performed. However, an alternative
explanation for these heterogeneous results would be that ALS is not a single disease with
a unique, yet unidentified cause, but a clinical syndrome in which multiple pathogenic
pathways may lead to the phenotype of ALS.
The combination of a multistep model and the concept of ALS as a clinical syndrome may
explain why there is large phenotypic diversity in between patients and may explain the
heterogenic results in risk factor studies.
Future directions
Identifying subtypes of ALS
As ALS appears to be a complex and heterogeneous disease, it becomes increasingly
important to identify subtypes of ALS by a more comprehensive approach. Subgroup
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Discussion
analysis can be performed using latent class cluster analysis, identifying clusters of ALS
patients with an identical cause. This analysis can be based on either lifestyle/
environmental, genetic or phenotypic factors. The clusters will eventually be characterized
on the strongest of these factors. This will lead to more homogeneous clusters or subtypes
of ALS, which will improve power and provide for more solid and consistent results in
future studies using these clusters. This approach is also compatible with the hypothesis
of a multistep model in which variable combinations of factors lead to the development
of ALS.
Gene-environment interaction studies
In gene-environment interaction studies we aim to find an increased disease risk when
both the genetic variant and the lifestyle/environmental factor are present. For example,
a study in Parkinson’s disease assessed occupational exposures (such as pesticides, metals
and solvents) and their interaction with variants in genes influencing metabolism of
chemicals.63 They found possible interaction effects between GSTM1 genotype (a genetic
risk factor for Parkinson’s disease) and solvents exposure. This approach of hypothesis
driven gene-environment interactions would be interesting to apply to ALS research as
well, for example mutations in SOD1, FUS, TARDBP or C9orf72 repeat expansion and their
interaction with air pollution or head trauma. In which case, the disease risk would be
higher when both factors are present compared to when no or just one of the factors is
present.
A second way to approach this analysis is to perform a genome-exposome wide interaction
study. With the newest techniques and the availability of large amounts of lifestyle/
environmental and genetic data, this hypothesis-free method would gain in associations
otherwise not found in solely genome wide or exposome wide studies. Last, it would be
good to combine the cluster analysis of ALS with genetic and lifestyle/environmental
factors to identify the factors that lead to ALS subtypes.
Ultimately, we aim to understand the biological pathways that lead to motor neuron
degeneration by identifying as many risk factors for ALS as needed and by performing
integrated gene-environment interaction analyses, if possible within identified subtypes.
The final goal is to translate these findings to clinical uses and to provide for new treatment
strategies or even preventive measures for this devastating disease.
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ADDENDUM
Nederlandse samenvatting - Summary in Dutch
Dankwoord - Acknowledgements
About the author
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NEDERLANDSE SAMENVATTING
Titel: Risicofactoren voor amyotrofische laterale sclerose
Introductie
Amyotrofische laterale sclerosis (ALS) is een progressieve neurodegeneratieve aandoening
waarbij de motorische zenuwcellen in de hersenen, hersenstam en het ruggenmerg
langzaam afsterven. De ziekte kenmerkt zich door toenemende spierzwakte in armen en
benen, moeite met spreken en slikken en zwakte van de ademhalingsspieren. Het beloop
van de ziekte is zeer variabel. Gemiddeld overlijden patiënten 3 jaar na ontstaan van de
eerste verschijnselen, waarbij twintig procent van de patiënten langer leeft dan 5 jaar.
Jaarlijks krijgen 400-500 Nederlanders de diagnose ALS. Naast de motorische uitval
ontwikkelt een klein deel van de patiënten ook een stoornis in het denken of het gedrag,
ook wel fronto-temporale dementie (FTD) genoemd, een vorm van dementie met
gedragsveranderingen. Tot op heden is er geen curatieve behandeling voor ALS: het sinds
1995 geregistreerde medicijn Riluzole verlengt het leven gemiddeld slechts met drie
maanden.
Een klein percentage patiënten met ALS heeft een erfelijke vorm, waarbij er meerdere
personen binnen één familie gedurende hun leven ALS ontwikkelen (familiaire ALS). Bij
deze patiënten is er sprake van een belangrijke genetische factor (DNA-afwijking) als
oorzaak van de ziekte, die wordt overgeërfd binnen de familie. Bij de meeste patiënten
(in 90-95%) gaat het echter om de sporadische vorm, waarbij ALS verder niet binnen de
familie voorkomt. De oorzaak van sporadische ALS is waarschijnlijk meer complex: een
combinatie van risicofactoren die gezamenlijk de ziekte veroorzaken. Hierbij gaat het
zowel om leefstijlfactoren, als omgevingsfactoren, als genetische factoren.
In de drie verschillende delen van dit proefschrift worden deze risicofactoren uiteengezet:
in deel I leefstijlfactoren, in deel II omgevingsfactoren, en in deel III genetische factoren.
Het onderzoek beschreven in dit proefschrift is onderdeel van de Prospectieve ALS studie
Nederland (PAN studie). In deze studie worden sinds 2006 prospectief alle nieuwgediagnosticeerde ALS patiënten in Nederland geïncludeerd. Via de huisarts worden
controlepersonen benaderd die gematcht zijn aan de patiënt op basis van geslacht en
leeftijd. Van zowel de patiënten als de controlepersonen wordt informatie over
risicofactoren verzameld door gebruik te maken van uitgebreide en gestructureerde
vragenlijsten en wordt bloed afgenomen voor DNA onderzoek.
Deel I - Leefstijlfactoren
Fysieke inspanning is één van de veelbesproken factoren in het internationale ALS
onderzoek. De relatie met fysieke inspanning is eerder geopperd in een Italiaanse studie
waarin een verhoogde incidentie van ALS werd beschreven onder professionele
voetballers. Tevens zijn er diverse bekende sporters die gediagnosticeerd zijn met ALS,
zoals Lou Gehrig, een Amerikaanse honkbalspeler naar wie de ziekte in Amerika vernoemd
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Nederlandse samenvatting
is, of de recent gediagnosticeerde Nederlandse profvoetballer Fernando Ricksen.
In hoofdstuk 2 van dit proefschrift hebben we onderzocht of er een relatie is tussen de
mate van fysieke inspanning en het risico op ALS. Hieruit blijkt dat patiënten met ALS in
hun vrije tijd (sport en hobby’s) meer fysieke inspanning leveren dan controlepersonen
(gemiddelde cumulatieve MET score 1.51 vs. 1.25; MET = metabole equivalent van fysieke
activiteiten). Patiënten leveren echter niet meer fysieke inspanning in hun beroep of
ondergaan niet vaker extreme inspanningen, zoals het lopen van een marathon. Tevens
hebben we geen dosis-respons relatie gevonden, dat wil zeggen dat het risico op ALS niet
toeneemt naarmate de mate van fysieke inspanning toeneemt. Concluderend hebben we
in ons onderzoek geen oorzakelijk verband kunnen aantonen tussen fysieke inspanning
en ALS. Mogelijk is er wel een gezamenlijke basis (predispositie) die er enerzijds voor
zorgt dat iemand aanleg heeft om fysiek actief te zijn, zoals in het sporten, en anderzijds
een verhoogd risico op ALS geeft.
In hoofdstuk 3 hebben we onderzocht of er andere ziekten zijn die verband houden met
ALS. Zo hebben we aangetoond dat patiënten met ALS vaker dan controlepersonen in de
voorgeschiedenis een ernstig hoofdletsel hebben gehad, zoals een hersenschudding of
schedelbasisfractuur (2.8% vs. 1.5%). Hoofdtrauma lijkt dus een mogelijke risicofactor
voor ALS te zijn. Hierbij is het echter wel van groot belang om rekening te houden met
de mogelijkheid van omgekeerde causaliteit, wat inhoudt dat het hoofdtrauma juist het
gevolg zou kunnen van een beginnende maar nog onderkende ALS. Rekening houdend
met deze eventuele omgekeerde causaliteit hebben we de analyse herhaald, waarbij
hoofdtrauma opgelopen binnen vijf jaar voor de eerste symptomen van ALS geëxcludeerd
werden. Ook dan blijkt er nog steeds een significant verband tussen hoofdtrauma en ALS,
en blijft de hypothese, dat hoofdtrauma een risicofactor is voor ALS, staan. Bovendien
wordt er in de literatuur steeds meer aandacht besteed aan de gevolgen van herhaald (licht
tot matig) hoofdtrauma, zoals bij boksen, rugby, en ook bij voetbal gezien wordt. Er wordt
beschreven dat deze sporters een verhoogd risico hebben om op de lange termijn
geheugenstoornissen of andere centraal neurologische aandoeningen (zoals mogelijk ook
ALS) te krijgen. Bij boksers met chronische traumatische encefalopathie worden tevens
dezelfde pathologische afwijkingen in hersenen gevonden als bij ALS (TDP-43 inclusies),
wat het verband tussen hoofdtrauma en ALS ondersteunt.
In eerdere onderzoeken is vaak gesuggereerd dat patiënten met ALS een gezonder
cardiovasculair profiel hebben: zowel bij patiënten als bij hun familieleden komen
cardiovasculaire ziekten minder vaak voor. Ook dit hebben wij in hoofdstuk 3
gestructureerd onderzocht. Het blijkt dat patiënten met ALS een lager cholesterol gehalte
hebben in vergelijking met controlepersonen: ze rapporteren minder vaak
hypercholesterolemie, 26% vs. 31%, en ze gebruiken beduidend minder statines, 12% vs.
21%. Dit sluit aan bij de bevinding dat patiënten gemiddeld een lagere body mass index
(BMI) hebben, mediaan van 24 kg/m2 vs. 26 kg/m2. Een lager cholesterol gehalte en een
lager BMI dragen bij aan een gezonder cardiovasculair risicoprofiel, maar dit lijkt op een
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meer indirecte relatie met ALS te berusten. We hebben daarnaast geen verband gevonden
tussen ALS en auto-immuun aandoeningen, kanker of psychiatrische aandoeningen.
Voeding bevat allerlei bestanddelen die het risico op ziekten kunnen verhogen (zoals
suikers en vetten) of juist kunnen beschermen tegen ziekten (zoals vitaminen). We hebben
een grote studie uitgevoerd naar het verband tussen voedingspatronen en het risico op
ALS (hoofdstuk 4). In samenwerking met Wageningen University is een gevalideerde
voedsel-frequentie-vragenlijst afgenomen met meer dan 199 items. Uit de resultaten
blijkt dat de totale hoeveelheid energie (in kilocalorieën, kCal) die patiënten tot zich nemen
voordat zij ziek worden (2258 kCal), aanmerkelijk hoger is dan bij controlepersonen (2119
kCal). Daarnaast vinden we dat patiënten meer verzadigde vetzuren en cholesterol
innemen dan controle personen.
Wanneer je bovenstaande in context tot eerder genoemde resultaten plaatst, lijkt er een
verstoring in het evenwicht te zijn tussen input (verhoogde vet inname) en output (lager
cholesterol gehalte en lagere BMI). Dit wijst mogelijk op een verstoord energiemetabolisme bij patiënten met ALS, waarbij ze meer calorieën verbranden in rust en bij
beweging dan normaal, ook wel hypermetabolisme genoemd. Mogelijk wordt dit
veroorzaakt door dysfunctie van de mitochondriën, de energiefabriekjes van het lichaam.
De resultaten wijzen niet op een schadelijk effect van een hoogcalorisch dieet. Er is dus
geen bezwaar tegen het toevoegen van extra calorieën aan de voeding als
compensatiemechanisme voor het hypermetabolisme en om een daling van het
lichaamsgewicht te beperken.
Nutriënten die een mogelijke beschermende werking zouden kunnen hebben, zijn onder
andere glutamaat en antioxidanten, zoals vitamine C, vitamine E en lycopeen. We hebben
echter geen verschil kunnen vinden in de inname van deze nutriënten tussen patiënten
en controlepersonen. Wel hebben we in hoofdstuk 4 laten zien dat de inname van alcohol
geassocieerd is met een lager risico op ALS. Eerdere studies suggereren een beschermend
effect van specifiek rode wijn, welke antioxidanten bevat en mogelijk glutamaat
geïnduceerde celdood kan tegen gaan. Wij hebben echter niet kunnen aantonen dat de
relatie tussen alcohol en een verlaagd risico op ALS gedreven wordt door het drinken van
rode wijn.
Deel II - Omgevingsfactoren
In het eerste hoofdstuk (hoofdstuk 5) van het deel over omgevingsfactoren, onderzoeken
we beroepsmatige blootstelling aan potentieel schadelijke stoffen, zoals mineralen, dierlijke
en plantaardige stoffen, pesticiden, gassen, metalen en oplosmiddelen. Hieruit blijkt dat
hoge blootstelling aan diesel uitlaatgassen, zoals bij vrachtwagen- en buschauffeurs,
militair personeel, mijnwerkers en spoorwegwerkers, een verhoogd risico op ALS geeft.
Mogelijk dat inhalatie van de schadelijke uitlaatgassen zorgt voor een ontstekingsreactie
van de zenuwcellen, dan wel voor oxidatieve stress, leidend tot ALS.
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Nederlandse samenvatting
Behalve onderzoek naar blootstellingen vanuit het beroep, hebben we ook blootstelling
aan gevaarlijke stoffen onderzocht vanuit de woonomgeving. Hiervoor hebben we
historische adresgegevens verzameld van alle patiënten en controlepersonen via de
gemeentelijke basisadministratie. Deze adresgegevens hebben we vervolgens in
samenwerking met het Institute for Risk Assessment Sciences (IRAS) kunnen geocoderen
en koppelen aan blootstellingskaarten van Nederland. Op deze manier hebben we in
hoofdstuk 6 de relatie tussen blootstelling aan luchtvervuiling vanuit de woonomgeving
en ALS op een gestructureerde manier kunnen onderzoeken. De gegevens over de
luchtvervuiling concentraties zijn verzameld als onderdeel van een Europese studie, de
European Study of Cohorts for Air Pollution Effects (ESCAPE). Uit ons Nederlandse
onderzoek blijkt dat patiënten voorafgaand aan het ontstaan van de eerste symptomen
gemiddeld een hogere blootstelling hadden aan fijnstof (specifiek fijnstof dat terug te
vinden is in roetfilters; PM2.5absorbance) en stikstofoxiden (zoals NO2), in vergelijking
met controlepersonen. Zelfs waarden van luchtvervuiling onder de Europese richtlijn
vormen een risico voor ALS. Deze bevindingen onderbouwen tevens de relatie tussen
beroepsmatige blootstelling aan diesel uitlaatgassen en een verhoogd risico op ALS zoals
beschreven in hoofdstuk 5.
In hoofdstuk 7 hebben we de geografische gegevens gekoppeld aan een zeer gedetailleerde
kaart van Nederland met daarop de precieze locaties van alle hoogspanningslijnen. De
afstand tot een hoogspanningslijn staat gelijk aan de mate van blootstelling aan
elektromagnetische straling. Voor elk woonadres van patiënten met ALS en
controlepersonen hebben we met deze afstandsberekening de mate van blootstelling aan
elektromagnetische straling vastgesteld. Uit de analyse blijkt dat er geen verschil is in
afstand van de woning tot de hoogspanningslijnen tussen patiënten met ALS en
controlepersonen. Blootstelling aan elektromagnetische straling vanuit de woonomgeving
is dus geen grote risicofactor voor ALS.
Deel III - Genetische factoren
In het laatste deel van mijn proefschrift heb ik genetische risicofactoren voor ALS
onderzocht. Hoofdstuk 8 beschrijft twee genen, hnRNPA1 en hnRNPA2B1, waarin DNA
mutaties gevonden zijn in Amerikaanse families met multisysteem proteïnopathie (MSP).
Dit is een zeldzame aandoening waarbij er degeneratie optreedt in verschillende
orgaansystemen van het lichaam (hersenen, motorische zenuwen, spieren of botten) en
er sprake is van karakteriserende TDP-43 pathologie. MSP is een overkoepelende naam
voor symptomen die passen bij zowel inclusion body myositis (IBM: spierziekte), ziekte
van Paget (botziekte), fronto-temporale dementie (FTD), als ook ALS. We hebben
onderzocht of we in een Nederlandse populatie patiënten met enkel ALS (n=1219), FTD
(n=142) of IBM (n=32) ook mutaties in deze genen konden vinden. Dit blijkt echter niet
het geval, en ook vergelijkbare studies in Frankrijk en Italië rapporteren geen mutaties in
deze genen. Hieruit hebben we geconcludeerd dat als mutaties in hnRNPA1 en hnRNPA2B1
al een risicofactor voor ALS vormen, dat deze dan in ieder geval zeldzaam zijn.
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In hoofdstuk 9 hebben we onderzocht of er vaker dan verwacht meerdere mutaties in
verschillende bekende ALS genen in één persoon samen voorkomen. Uit eerder onderzoek
is gebleken dat er bij patiënten met familiaire ALS vaker dan verwacht op basis van kans
sprake is van meerdere mutaties. In de huidige studie hebben we dit ook onderzocht voor
patiënten met sporadische ALS, waarbij de genetische predispositie minder duidelijk is.
In deze studie hebben we in 4.1% van de patiënten met ALS en in 1.3% van de
controlepersonen meerdere genetische mutaties gevonden. Dit is echter niet significant
verschillend als je corrigeert voor de hogere toevalskans van ALS patiënten op één mutatie.
Wel hebben we een significant verhoogde frequentie gevonden van patiënten die naast
een C9orf72 repeat expansie nog een tweede variatie hebben in ATXN2, NIPA1 of SMN1.
Specifiek de combinatie van C9orf72 en NIPA1 repeat expansies komen samen vaker voor
dan je zou verwachten op basis van kans. Patiënten met een C9orf72 repeat expansie laten
een grote diversiteit zien in klinische presentatie (zoals in leeftijd op het moment van de
eerste symptomen, de lokalisatie van de eerste symptomen, of het al dan niet hebben van
cognitieve klachten). Het is goed mogelijk dat een tweede genetische variatie (zoals een
NIPA1 repeat expansie) bepaalt welk fenotype iemand ontwikkelt.
Discussie
In hoofdstuk 10 vat ik de beschreven studies samen en plaats ik deze in een breder kader.
Hierin bespreek ik onder andere dat ALS een complexe aandoening is waarbij een
combinatie van leefstijlfactoren, omgevingsfactoren en genetische factoren een rol speelt
in de pathogenese van ALS. Een recente Europese epidemiologische studie toont aan dat
er een combinatie van zes verschillende stappen (factoren) nodig is om ALS te ontwikkelen.
De complexiteit van de aandoening is ook terug te zien in de grote klinische diversiteit
van ALS, zoals het verschil in leeftijd op het moment van de eerste symptomen (op elke
volwassen leeftijd), de lokalisatie van de eerste symptomen (bijvoorbeeld zwakte van de
armen of benen, spraak- of slikstoornissen), het al dan niet hebben van cognitieve klachten
(in het kader van fronto-temporale dementie) en de ziekteduur. In plaats van het concept
ALS als één ziekte zou je kunnen hypothetiseren dat deze diversiteit gezien moet worden
in het kader van ALS als syndroom met verschillende subtypen. Verder onderzoek naar
subtypen van ALS en combinaties van risicofactoren is nodig om meer inzicht te krijgen
in deze dodelijke ziekte.
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Dankwoord
DANKWOORD
(Acknowledgements)
Zonder hoogte- en dieptepunten geen proefschrift. Gelukkig stond ik er tijdens mijn
promotieonderzoek niet alleen voor. Ik wil hier graag een aantal mensen bedanken.
Allereerst alle ALS patiënten en familieleden: dank dat jullie allen jullie kostbare tijd
hebben vrijgemaakt voor het onderzoek. Mijn waardering voor jullie inzet en medewerking
in een voor jullie zo roerige tijd is groot. Natuurlijk wil ik daarbij ook alle controle
personen bedanken die belangeloos aan het onderzoek naar ALS hebben meegewerkt.
Professor van den Berg, beste Leonard. Ik voel me nog steeds vereerd dat ik in jouw groep
mijn promotieonderzoek heb mogen uitvoeren. Een groep die bloeit en groeit onder jouw
duidelijke visie met als ultieme doel het ontwikkelen van nieuwe medicijnen voor ALS.
Met jouw humor en enthousiasme weet jij mij en iedereen in de onderzoeksgroep te
stimuleren tot onderzoek op wereldniveau.
Professor Veldink, beste Jan. Ik heb veel ontzag voor jouw diversiteit in kennis van
zowel statistiek, epidemiologie als genetica. Jij kwam altijd met nieuwe ideeën en
ingenieuze statistische oplossingen op de momenten dat ik het niet meer zag. En dat met
een nooit aflatende en bewonderingswaardige gedrevenheid. Dank voor alle fijne
samenwerking.
Dr. Vermeulen, beste Roel. Jij hebt met jouw kritische blik en frisse wind een waardevolle
bijdrage geleverd om de studies naar omgevingsfactoren in relatie met ALS naar een hoger
niveau te brengen. Jouw ervaring op dit gebied vanuit het IRAS en de samenwerking voor
het koppelen van de data was onmisbaar voor de kwaliteit van het onderzoek.
Dr. van Es, beste Michael. Je weet mij (en vele anderen) altijd mee te nemen in jouw
oneindig enthousiasme en ideeën over hoe we verder moeten komen in het ontrafelen
van de genetische achtergrond van ALS en aanverwante neurologische aandoeningen.
Een overredingskracht waar ik jaloers op ben.
Leden van de beoordelingscommissie, geachte prof. dr. F.L.J. Visseren, prof. dr. L.J.
Kappelle, prof. dr. M.J.B. Taphoorn, prof. dr. ir. D.J.J. Heederik, prof. dr. P.I.W. de Bakker.
Hartelijk dank dat ik u mijn proefschrift ter beoordeling mocht voorleggen.
Zonder de goede opzet van de PAN studie zou dit proefschrift en heel veel ander
onderzoek in de ALS onderzoeksgroep niet mogelijk zijn geweest. Sonja, als eerste van
de PAN onderzoekers, en daarna ook Mark, Perry en nu natuurlijk opgevolgd door Anne:
bedankt voor alle inspanningen en zeer waardevolle brainstormmomenten.
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CHAPTER 10
Gezien het succes van de PAN studie en de almaar groeiende dataset en onderzoeksprojecten
is ook het ondersteunende PAN team in de tijd sterk uitgebreid. Petra en Hermieneke,
toen ik begon met mijn promotieonderzoek waren jullie mijn steun en toeverlaat bij alle
hobbels die we in de dataverzameling tegenkwamen, mijn dank daarvoor is groot. Maar
ook de rest van het PAN team verdient het hier genoemd te worden. Met jullie hulp zal
er zeker nog veel meer mooi onderzoek worden gerealiseerd.
Perry, Wouter en Kristel, dank voor jullie hulp en voornamelijk kennis ten aanzien van
de genetica voor de laatste twee hoofdstukken van dit proefschrift. Altijd goed om te
kunnen sparren. Peter, William, Raymond en Jelena, bedankt voor jullie ondersteuning
bij het labwerk.
Alle andere labmatties: Oliver, Anna, Max, Ewout, Lotte, Renske, Frank, Henk-Jan, Gijs,
Annelot, Marloes, Camiel, Dianne, Marc, Sandra. Ik heb veel geleerd van de soms verhitte
(en veelal op luide toon gevoerde) discussies en natuurlijk ook veel lol gehad samen. Ik
kan scooter inmiddels bijna waarderen, ik heb geen vliegangst meer en ik mis de koffietijd
(en chocolade) op het lab nu al.
Nienke, zonder jou zou de MND poli lang niet zo goed lopen. Inmiddels regel jij de zaken
daar niet meer alleen, maar nu samen met Kim. Keep up the good work, voor al die
patiënten met ALS.
Annemarie, ook jij bent onmisbaar in alles wat je regelt en organiseert. Dank daarvoor.
Mijn paranimfen, Renée en Anne, ik ben zo blij dat jullie naast mij zullen staan op 16 juni.
Renée, onze paden kruisen elkaar inmiddels zo vaak dat we ook echt niet meer om elkaar
heen kunnen en gelukkig maar! Wat mij betreft hoeft dat nooit meer te veranderen. Anne,
je bent begonnen als student bij mij en ik had me geen betere opvolging in het PAN
onderzoek kunnen wensen. Als “PAN-dames” hebben we echt wel laten zien dat hard
werken en lol hebben heel goed samen gaat.
Mijn collega’s van de afdeling neurologie van het MCH Westeinde. Ook al hebben jullie
weinig direct met mijn promotieonderzoek te maken gehad, toch heb ik de laatste loodjes
volbracht terwijl ik ben begonnen aan de opleiding tot neuroloog bij jullie. Ik voel me
zeer op mijn plaats in de kliniek in Den Haag en ik vind het een voorrecht om deel van
de groep uit te mogen maken.
Gerbrand, Renée, Joep, Bart en David, mede studenten vanaf jaar één. Niet iedereen zie
ik nog even vaak, maar het voelt altijd goed om elkaar weer te zien en te spreken. Laten
we dat zo af en toe vooral zo houden. Voor Gerbrand en Renée geldt het ‘af en toe’ gelukkig
niet en ik hoop dat dat nog heel lang zo blijft en dat we nog eindeloos veel samen kunnen
delen.
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Dankwoord
Kirsten, Keetie, Rozemarijn, Marije, Nienke, Elleke, Lotte, Jenneke, Svetlana, Judith,
Marjolijn, Renée, Perijne en alle anderen van het nog steeds groeiende en bloeiende
medisch vrouwen dispuut Agnodice. Ik vind het heel bijzonder dat ik er vanaf het begin
bij heb mogen zijn en dat we samen al zoveel lief en leed hebben kunnen delen tijdens de
studie. De eerste huwelijken en baby (nog geen meervoud) hebben we inmiddels ook al
gehad. Hopelijk volgen er nog vele mooie momenten.
Suzanne, Froukje (en alle andere teamgenootjes), we staan nu al weer heel wat jaren samen
op het korfbalveld, al dan niet in hetzelfde team, maar altijd weten we elkaar weer te
vinden. Zowel om het hoofd leeg te maken tijdens het sporten, danwel om achteraf onder
een drankje alles te kunnen bespreken, en dat is echt heel fijn.
Fieke, dankzij onze moeders kennen we elkaar al sinds mijn geboorte. Inmiddels hebben
wij al die jaren al een zeer waardevolle vriendschap. Ik ben heel blij dat we die ondanks
onze veranderende levens nog steeds hebben.
De gehele schoonfamilie van Bergeijk-Lommerse, dank voor jullie betrokkenheid en
interesse, iedere keer weer.
Pap, mam, Tessa en Daan, jullie onverwoestbare steun en geloof in mijn kunnen is mij
zeer waardevol. Menige frustratie heb ik bij jullie kunnen uiten en weer loslaten.
Een warmer thuis kan ik mij niet wensen. Gelukkig Daan, geef jij voor de afwisseling ook
altijd de nodige nuchtere kritiek op het hele geneeskundige wereldje.
Tot slot, lieve Leendert, jij bent werkelijk mijn alles voor altijd.
Meinie.
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CHAPTER 10
LIST OF PUBLICATIONS
In this thesis
Seelen M*, Huisman MHB*, de Jong SW, Dorresteijn KR, van Doormaal PT, van der Kooi
AJ, de Visser M, Schelhaas HJ, van den Berg LH, Veldink JH. (2013) Lifetime time physical
activity and the risk of amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry,
Sep;84(9):976-81.
Seelen M, Visser AE, Overste DJ, Kim HJ, Palud A, Wong TH, van Swieten JC, Scheltens
P, Voermans NC, Baas F, de Jong JM, van der Kooi AJ, de Visser M, Veldink JH, Taylor JP,
van Es MA*, van den Berg LH*. (2014) No mutations in hnRNPA1 and hnRNPA2B1 in
Dutch patients with amyotrophic lateral sclerosis, frontotemporal dementia and inclusion
body myositis. Neurobiology of aging, Aug;35(8):1956.
Seelen M, Vermeulen RCH, van Dillen LS, van der Kooi AJ, Huss A, de Visser M, van den
Berg LH*, Veldink JH*. (2014) Residential exposure to extremely low frequency
electromagnetic fields and the risk of ALS. Neurology, Nov;83(19):1767-9.
Seelen M, van Doormaal PTC, Visser AE, Huisman MHB, Roozekrans MHJ, de Jong SW,
van der Kooi AJ, de Visser M, Voermans NC, Veldink JH*, van den Berg LH*. (2014) Prior
medical conditions and the risk of amyotrophic lateral sclerosis. J of Neurology,
Oct;261(10):1949-56.
Other publications
Seelen M*, Cats EA*, Vlam L*, van Vught PW, van den Berg LH, van der Pol WL. (2011)
Multifocal motor neuropathy is not associated with genetic variation in PTPN22, BANK1,
Blk, FCGR2B, CD1A/E, and TAG-1 genes. J Pheriph Nerv Syst, Sep;16(3);179.
van Rheenen W, van Blitterswijk M, Huisman MH, Vlam L, van Doormaal PT, Seelen M,
Medic J, Dooijes D, de Visser M, van der Kooi AJ, Raaphorst J, Schelhaas HJ, van der Pol
WL, Veldink JH*, van den Berg LH*. (2012) Hexanucleotide repeat expansions in C9ORF72
in the spectrum of motor neuron diseases. Neurology, Aug;79(9):878 .
van Rheenen W, Diekstra FP, van Doormaal PT, Seelen M, Kenna K, McLaughlin R,
Shatunov A, Czell D, van Es MA, van Vught PW, van Damme P, Smith BN, Waibel S,
Schelhaas HJ, van der Kooi AJ, de Visser M, Weber M, Robberecht W, Hardiman O, Shaw
PJ, Shaw CE, Morrison KE, Al-Chalabi A, Andersen PM, Ludolph AC, Veldink JH*, van
den Berg LH*. (2013) H63D polymorphism in HFE is not associated with amyotrophic
lateral sclerosis. Neurobiology of aging, May;34(5):1517.
Beeldman E, Jaeger B, Raaphorst J, Seelen M, Veldink J, van den Berg L, de Visser M,
Schmand B. (2014) The verbal fluency index: normative data for cognitive testing in ALS.
Amyotroph Lateral Scler Frontotemporal Degener, Sep;15(5-6):388-91.
158
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Addendum
Dopper EGP, Seelen M, de Jong FJ, Veldink JH, van den Berg LH, van Swieten JC. (2013)
Repeat-expansie in het C9orf72 gen: een link tussen frontotemporale dementie en
amyotrofische laterale sclerose. Ned Tijdschr Geneeskd, 157:A6271.
Al-Chalabi A, Calvo A, Chio A, Colville S, Ellis CM, Hardiman O, Heverin M, Howard
RS, Huisman MHB, Keren N, Leigh PN, Mazzini L, Mora G, Orrell RW, Rooney J, Scott
KM, Scotton WJ, Seelen M, Shaw CE, Sidle KS, Swingler R, Tsuda M, Veldink JH, Visser
AE, van den Berg LH, Pearce N. (2014) A Multistep model of amyotrophic lateral sclerosis.
Lancet Neurology, Nov;13(11):1108-13.
Gallo V, Brayne C, Forsgren L, Barker RA, Petersson J, Hansson O, Lindqvist D, Ruffmann
C, Ishihara L, Luben R, Arriola L, Bergareche A, Gavrila D, Erro ME, Vanacore N,
Sacerdote C, Bueno-de-Mesquita HB, Vermeulen R, Seelen M, Sieri S, Masala G, Ramat
S, Kyrozis A, Thricopolou A, Panico S, Mattiello A, Kaaks R, Teucher B, Katzke V, Kloss
M, Curry L, Calboli F, Riboli E, Vineis P, Middleton L. (2014) Parkinson’s disease case
ascertainment in the EPIC cohort: the NueroEPIC4PD study. Neurodegener Dis, accepted.
Submitted/in preparation
Huisman MHB, Seelen M, van Doormaal PTC , de Jong SW, de Vries JHM, van der Kooi
AJ, de Visser M, Schelhaas HJ, van den Berg LH*, Veldink JH*. Presymptomatic BMI, and
fat and alcohol consumption as independent risk factors for amyotrophic lateral sclerosis.
Seelen M*, Toro Campos RA*, Veldink JH, Visser AE, Hoek G, van der Kooi AJ, de Visser
M, Raaphorst J, van den Berg LH#, Vermeulen RCH#. Long-term exposure to traffic related
air pollution is associated with an increased risk of amyotrophic lateral sclerosis.
Seelen M, van Doormaal PTC, van Rheenen W, Bothof RJP, van Riessen T, Brands WJ, van
der Kooi AJ, de Visser M, Voermans NC, Veldink JH, van den Berg LH#, van Es MA#. Large
scale genetic screening in sporadic ALS identifies modifiers in C9orf72 repeat carriers.
Seelen, M*, Huisman MHB*, van Boxmeer L*, Visser AE, van Doormaal PTC, Raaphorst
J, van der Kooi AJ, de Visser M, Vermeulen RCH, van den Berg LH, Veldink JH.
Occupational exposure to diesel motor exhaust increases the risk of ALS.
Walhout R*, Schmidt R*, Westeneg HJ, Verstraete E, Seelen M, van Rheenen W, de Reus
MA, Hendrikse J, Veldink JH, van den Heuvel MP#, van den Berg LH#. Effects of the
C9orf72 repeat expansion: morphological changes in the brain of asymptomatic carriers
and patients with amyotrophic lateral sclerosis.
*# These authors crontibuted equally to the manuscript
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ABOUT THE AUTHOR
Curriculum vitae
Meinie Seelen werd geboren in Arnhem op 12 januari 1986. Na het voltooien van het
VWO aan het Arentheemcollege te Arnhem, studeerde zij geneeskunde aan de Universiteit
Utrecht. Tijdens haar studie deed zij wetenschappelijk onderzoek in het Laboratorium
voor Experimentele Neurologie in het UMC Utrecht naar multifocale motorische
neuropathie. Hierna heeft zij gewerkt aan een extra-curriculair onderzoeksproject naar
dopa-responsieve dystonie in het Skane University Hospital, in Lund (Zweden). Na haar
artsexamen in 2010 heeft zij ervaring op gedaan als arts op de afdeling neurologie in de
Tergooiziekenhuizen, te Blaricum. In 2011 begon zij aan haar promotieonderzoek onder
leiding van prof. dr. L.H. van den Berg en prof. dr. J.H. Veldink, dat resulteerde in dit
proefschrift. Per 2015 is zij begonnen aan de opleiding tot neuroloog in het MC
Haaglanden.
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