Dementing disorders are caused
by progressive neurodegeneration,
leading to loss of episodic memory
and other cognitive functions and
disabilities impairing independent
daily living. The prevalence
of dementia is increasing and
pathological processes in the brain
occur over 10-20 years before the
onset of clinical symptoms. The
aim of this study was to investigate
different biomarkers in cerebrospinal
fluid and plasma to find possible
tools for early and differential
diagnosis of Alzheimer’s disease and
Frontotemporal lobar degeneration.
Publications of the University of Eastern Finland
Dissertations in Health Sciences
isbn 978-952-61-1941-0
issn 1798-5706
dissertations | 310 | Anna Junttila | Cerebrospinal Fluid and Plasma Biomarkers in the Differential Diagnosis of...
Anna Junttila
Cerebrospinal Fluid and
Plasma Biomarkers in
the Differential Diagnosis
of Neurodegenerative
Diseases
Anna Junttila
Cerebrospinal Fluid and
Plasma Biomarkers in
the Differential Diagnosis
of Neurodegenerative
Diseases
Publications of the University of Eastern Finland
Dissertations in Health Sciences No 310
ANNA JUNTTILA
Cerebrospinal Fluid and Plasma Biomarkers
in the Differential Diagnosis of
Neurodegenerative Diseases
To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for
public examination in Canthia (Ca102), Kuopio, on Friday, December 11th 2015, at 12 noon
Publications of the University of Eastern Finland
Dissertations in Health Sciences
Number 310
Department of Neurology, Institute of Clinical Medicine, School of Medicine, Faculty of Health
Sciences, University of Eastern Finland
Kuopio
2015
Grano Oy
Jyväskylä, 2015
Series Editors:
Professor Veli-Matti Kosma, M.D., Ph.D.
Institute of Clinical Medicine, Pathology
Faculty of Health Sciences
Professor Hannele Turunen, Ph.D.
Department of Nursing Science
Faculty of Health Sciences
Professor Olli Gröhn, Ph.D.
A.I. Virtanen Institute for Molecular Sciences
Faculty of Health Sciences
Professor Kai Kaarniranta, M.D., Ph.D.
Institute of Clinical Medicine, Ophthalmology
Faculty of Health Sciences
Lecturer Veli-Pekka Ranta, Ph.D. (pharmacy)
School of Pharmacy
Faculty of Health Sciences
Distributor:
University of Eastern Finland
Kuopio Campus Library
P.O.Box 1627
FI-70211 Kuopio, Finland
http://www.uef.fi/kirjasto
ISBN (print): 978-952-61-1941-0
ISBN (pdf): 978-952-61-1942-7
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
III
Author’s address:
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Supervisors:
Professor Hilkka Soininen, M.D., Ph.D.
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Sanna-Kaisa Herukka, M.Sc, M.D., Ph.D.
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Professor Anne M. Remes, M.D., Ph.D.
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Reviewers:
Professor Irina Elovaara, M.D., Ph.D.
Department of Neurology, School of Medicine
University of Tampere
TAMPERE
FINLAND
Docent Anders Paetau, M.D., Ph.D.
Department of Pathology, School of Medicine
University of Helsinki
HELSINKI
FINLAND
Opponent:
Professor Matti Viitanen, M.D., Ph.D.
Department of Geriatry, School of Medicine
University of Turku
TURKU
FINLAND
IV
V
With great honor,
To My Mother
VI
VII
Junttila, Anna
Cerebrospinal Fluid and Plasma Biomarkes in the Differential Diagnosis of Neurodegenerative Diseases
University of Eastern Finland, Faculty of Health Sciences
Publications of the University of Eastern Finland. Dissertations in Health Sciences 310. 2015. 87 p.
ISBN (print): 978-952-61-1941-0
ISBN (pdf): 978-952-61-1942-7
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
ABSTRACT
Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) are the two most
common dementing diseases. Both diseases are associated with disease specific
neuropathological features, but also unspecific and mixed neuropathological changes are
also common. The neuropathological processes begin over two decades before the onset of
any clinical symptoms of dementia. Early diagnosis of dementing diseases is challenging
since the clinical diagnosis is based on symptoms, neuropsychological findings and brain
imaging. The specific diagnostic cerebrospinal fluid (CSF) biomarkers; amyloid β-42 (Aβ 142), t-Tau and phosphorylated-Tau are available for clinical use only in AD. There is an
unmet need for new neuropathological and disease specific biomarkers for clinical use to
improve the accurate diagnosis for each neurodegenerative disease at an early stage. The
definite diagnosis is based to the post-mortem neuropathological examinations.
The aim was to investigate the levels of plasma granulin (GRN), CSF AD biomarkers (Aβ
1-42, t-Tau and phosphorylated-Tau) as well as CSF TDP-43 (transactive response (TAR)
DNA-binding protein (TARDBP) which has a molecular weight of 43 kDa (TDP-43)) levels
in patients with different neurodegenerative diseases with or without a known genetic
background. It was found that AD patients (n = 258) had higher plasma granulin levels than
controls (n = 114) and the presence of the A allele of rs5848 in GRN led to a dose-dependent
reduction in plasma granulin levels in AD patients. CSF AD biomarker levels were
analyzed from patients with FTLD (n = 30) and amyotrophic lateral sclerosis (ALS) (n = 10)
with the C9ORF72 expansion. Decreased levels of CSF Aβ 1-42 were detected in 25 % of
cases and one or two biomarkers were abnormal in 30 % of cases. There were no statistical
differences in the CSF biomarker levels between FTLD and ALS patients. CSF TDP-43 and
AD biomarker levels were measured in FTLD (n = 69) and ALS (n = 21) patients. One third
of the patients were the C9ORF72 expansion carriers. The CSF TDP-43 levels were
significantly higher in patients with ALS than in the FTLD patients and this finding was
independent of the C9ORF72 expansion carrier status. There was no difference in the CSF
TDP-43 or AD biomarker levels between C9ORF72 expansion carriers and non-carriers in
these two diagnostic groups.
The studied biomarkers were not specific for different neurodegenerative diseases or
assumed neuropathological changes with the known genetic background. The C9ORF72
expansion is typically associated with TDP-43 neuropathology, while AD neuropathology
in patients with the C9ORF72 expansion is rare. Nevertheless, the levels of CSF Aβ 1-42
may be abnormal in patients with the C9ORF72 expansion while there was no difference in
the CSF TDP-43 levels between the C9ORF72 expansion carriers and the non-carriers.
Increased levels of CSF TDP-43 are associated with ALS and CSF TDP-43 may be a marker
of rapid progressions of diseases associated with TDP-43 pathology, but the CSF TDP-43
level is not useful in the differential diagnosis of the C9ORF72 expansion associated disease.
A low granulin level in plasma was usually associated with the A allele in rs5848 in GRN
and this supports the concept that this variation results in a functional change in GRN.
VIII
National Library of Medicine Classification: QY 220, WL 141, WL 358.5, WT 155
Medical Subject Headings: Neurodegenerative Diseases/diagnosis; Alzheimer Disease/diagnosis;
Frontotemporal Lobar Degeneration/diagnosis; Dementia/diagnosis; Amyotrophic Lateral Sclerosis/diagnosis;
Early Diagnosis; Biological Markers; Cerebrospinal Fluid; Intercellular Signaling Peptides and Proteins/blood;
Amyloid beta-Peptides; Tau Proteins; DNA-Binding Proteins
IX
Junttila, Anna
Aivoselkäydinnesteen ja plasman merkkiaineet aivoja rappeuttavien sairauksien erotusdiagnostiikassa
Itä-Suomen yliopisto, terveystieteiden tiedekunta
Publications of the University of Eastern Finland. Dissertations in Health Sciences 310. 2015. 87 s.
ISBN (print): 978-952-61-1941-0
ISBN (pdf): 978-952-61-1942-7
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
TIIVISTELMÄ
Alzheimerin tauti (AT) ja otsa-ohimolohkorappeumat ovat kaksi yleisintä
neurodegeneratiivista dementiaan johtavaa sairautta. Kumpaankin muistisairauteen liittyy
taudeille ominaiset neuropatologiset muutokset, vaikkakin sekatyyppisiä neuropatologisia
muutoksia nähdään usein. Neuropatologisten muutosten ilmaantuminen alkaa jo
vuosikymmeniä ennen kliinisiä muistisairauden oireita ja varhainen kliininen diagnostiikka
on haastavaa ja siihen voidaan päästä vasta, kun potilaalla on selkeitä kliinisiä oireita.
Muistisairauksien kliininen diagnostiikka perustuu oirekuvan lisäksi neuropsykologisiin ja
kuvantamistutkimusten
löydöksiin,
joskin
varma
diagnoosi
saadaan
vasta
neuropatologisen tutkimuksen perusteella. Kliinisessä käytössä on kolme selkäydinnesteen
merkkiainetta (Aβ 1-42, tau- ja fosforyloitunut tau-proteiini), jotka parantavat AT:n
diagnostiikkaa. Jotta muisti- ja neurodegeneratiivisten sairauksien varhainen ja
neuropatologian tunnistava diagnostiikka paranisi, tarvitaan uusia verestä tai
selkäydinnesteestä määritettäviä merkkiaineita.
Tutkimuksen tarkoituksena oli tutkia plasman granuliinipitoisuuksia (GRN),
aivoselkäydinnesteen AT merkkiaineita- ja TDP-43 (TAR-deoksiribonukleiinihappoon
sitoutuva 43 kDa:n kokoinen proteiini) pitoisuuksia eri neurodegeneratiivisten sairauksien
kohorteissa, joista osassa tapauksissa sairauden geneettinen tausta oli tunnistettu. AT
potilailla (n = 258) todettiin kontrolleja (n = 114) korkeampia granuliinipitoisuuksia, mutta
GRN:n rs 5848:n A alleeli aiheutti annosvasteisesti plasman granuliinipitoisuuden
alenemisen. Aivoselkäydinnesteen AT merkkiaineiden pitoisuudet määritettiin 30 otsaohimolohkorappeumaa ja 10 amyotroofista lateraaliskleroosia (ALS) sairastavalta
potilaalta, joilla kaikilla oli todettu C9ORF72-toistojakso mutaatio. Aivoselkäydinnesteen
Aβ 1-42 pitoisuus oli alentunut 25 %:lla potilaista ja yhden tai kahden merkkiaineen
pitoisuus oli poikkeava 30 %:lla potilaista. Aivoselkäydinnesteen TDP-43 pitoisuus
määritettiin 69 otsa-ohimolohkorappeumaa ja 21 ALS potilaalta. Kolmasosalla potilaista oli
todettu C9ORF72-toistojakso mutaatio. Aivoselkäydinnesteen TDP-43 pitoisuudet olivat
korkeampia ALS potilailla verrattuna otsa-ohimolohkorappeumaa sairastaviin, mutta
aivoselkäydinnesteen TDP-43 ja AT merkkiaineiden pitoisuuksissa ei ollut eroa C9ORF72toistojakso mutaation kantajien ja ei-kantajien välillä.
Tutkitut biologiset merkkiaineet eivät näytä olevan spesifisiä eri tautiryhmien tai
geneettisen taustan perusteella oletettavan neuropatologian suhteen. Tunnettua on, että
C9ORF72-toistojakso mutaatioon liittyy TDP-34 neuropatologia ja AT:n neuropatologisia
muutoksia tähän mutaatioon liittyen nähdään harvoin. Tästä huolimatta tutkimuksemme
osoittaa, että aivoselkäydinnesteen AT merkkiaineet voivat olla poikkeavia C9ORF72
mutaatioon liittyvässä otsa-ohimolohkorappeumassa, kun taas aivoselkäydinnesteen TDP43 pitoisuuden perusteella ei pystytä erottelemaan C9ORF72-toistojakso mutaation kantajia
ei-kantajista. Aivoselkäydinnesteen korkeammat TDP-43 pitoisuudet todettiin ALS
potilailla, joten tämä voi olla merkki TDP-43 patologiaan liittyvän taudin nopeasta
progressiosta. AT potilailla GRN:n rs 5848:n A alleeli aiheuttaa annosvasteisesti plasman
granuliinipitoisuuden alenemisen ja tämä tukee ajatusta siitä, että variaatio aiheutuu
GRN:n toiminnallisesta muutoksesta.
X
Luokitus: QY 220, WL 141, WL 358.5, WT 155
Yleinen Suomalainen asiasanasto: Alzheimerin tauti; otsa-ohimolohkorappeumat; dementia; amyotrofinen
lateraaliskleroosi; diagnoosi; markkerit; aivo-selkäydinneste; veri; veriplasma; peptidit; proteiinit
XI
Acknowledgements
This doctoral thesis was carried out in the Department of Neurology, School of Medicine,
Faculty of Health Sciences, University of Eastern Finland and Kuopio University Hospital
during 2011-2015.
I owe my deepest gratitude to Professor Hilkka Soininen, my supervisor and the Head of
the Department of Neurology and the Faculty of Health Sciences in University of Eastern
Finland. Thank you for the opportunity to be part of the Clinical Alzheimer Research
Group and to work with great facilities. Thank you for your advices not only in the field of
research but also in personal life as well. I admire your dedication to work and science.
I am deeply grateful to my supervisor M.Sc, M.D., Ph.D. Sanna-Kaisa Herukka for warm
guidance in helping me navigate through the world of neuroscience. You have always been
available to me whenever needed and never tired of answering my questions about this
thesis or even in practical aspects. Even though you have many projects running at the
same time you have made time and found a place for my things as well. Your kindness and
support has been truly unique.
I wish to express my warmest gratitude to my supervisor Professor Anne M. Remes.
Thank you for your enthusiastic attitude and many valuable comments on this thesis. I
admire the way that you bring inspiratione to every project that you tackle. You are greatly
liked by the medical students which is no surprise. I hope that some day I can inspire
young researchers in the way you have inspired me.
I wish to thank the official reviewers of this thesis, Professor Irina Elovaara and Docent
Anders Paetau, for their constructive criticism and valuable comments for this thesis.
I would like to thank my co-authors Professor Pentti J. Tienari, Professor Mikko
Hiltunen, Associate Professor Annakaisa Haapasalo, Docent Seppo Helisalmi, Docent Päivi
Hartikainen, Ph.D. Jayashree Viswanathan, Ph.D. Teemu Natunen, Ph.D. J.P. Pursiheimo,
Professor Irina Alafuzoff, Professor Miia Kivipelto, Ph.D. Maria Pikkarainen, M.D. Virpi
Moilanen, Ph.D. Marita Siloaho, M.Sc. Anna Kiviharju, B.M. Mari Kuvaja, Technicians Tarja
Kauppinen and Lilja Jansson for their collaboration and instructive comments on my work.
Thanks also to M.D., Ph.D. Valtteri Julkunen and M.D., Ph.D. Toni Seppälä for your support
and advice during this process.
I want to thank the priceless team behind this thesis in the laboratory and the office.
Päivi and Tarja, your kindness and encouragement have meant very much for me during
these years. From you, I learned so much about laboratory work and other practical aspects
of scientific work. Esa, thank you for the endless assistance with my computer problems.
Tuija and Sari, thank you for the help with practical problems and making it easier to
survive with all the paper work.
I want to thank Käyppärit, Cursus Circus, KuoLO, Finnish Medical Students’
Association and Junior Doctors’ Association in Finland and the people with whom I have
studied and worked. Sonja, thank you for standing right beside me and supporting me
during these busy and stressful years, you mean a lot for me. Thank you Ellu, Katja, Mari,
Juli, Jenna, Timo, Anssi and Tytti for your friendship and being part of my life. Special
thanks to dear Saara for being my friend and soulmate for over 20 years. Thank you also to
the families Ahlberg, Lindgren-Poutanen, Keränen-Raatikainen, Govenius-Nieminen and
Haimakainen, for being part of our life and the endless discussions of this and that during
the years.
I express my warmest thanks to my fabulous girls, Maru, Laura and Minna. I cannot
describe how happy I am to have every one of you in my life. You are more than friends for
me, you know all about me and still you stay there for me. I appreciate your support and
friendship during these years we have known each other. Hopefully, we have many years
ahead also, I love you all.
XII
I owe my deepest gratitude to my dear mom Sirpa. Thank you for guiding me past the
pitfalls of life and giving me the opportunity to study and make my dreams come true. You
have taught me the meaning of hard work and perseverance. I love you. Thank you to my
dear siblings Taru, Jere, Lassi and Laura, I am very proud of you. Thank you Saku for being
part of our family.
Finally, thank you Mikko. I appreciate your encouragement and patience that you have
shown during these busy years. You have always believed in me, sometimes more than I
did myself. You have been there for me and listened while I have spent countless hours
talking about my thesis, without understanding a word. Thank you for sharing your life
with me, I am so proud to be your wife. I love you more than words can say.
This work was funded by the grants from Kuopio University Hospital, the Finnish Brain
Foundation, Orion Farmos Foundation, Instrumentarium Foundation, Emil Aaltonen
Foundation, Finnish Medical Foundation and Finnish Cultural Foundation North-Savo
Foundation.
Jyväskylä, October 2015
Anna Junttila
XIII
List of the original publications
This dissertation is based on the following original publications:
I
Kämäläinen A*, Viswanathan J*, Natunen T, Helisalmi S, Kauppinen T,
Pikkarainen M, Pursiheimo JP, Alafuzoff I, Kivipelto M, Haapasalo AK, Soininen
H, Herukka SK and Hiltunen M: GRN variant rs5848 reduces plasma and brain
levels of granulin in AD patients. Journal of Alzheimer’s Disease, 33: 23-27, 2013.
*Authors had equal contribution to this study.
II
Kämäläinen A, Herukka SK, Hartikainen P, Helisalmi S, Moilanen V, Knuuttila
A, Jansson L, Tienari PJ and Remes AM: Cerebrospinal fluid biomarkers for
Alzheimer’s disease in patients with frontotemporal lobar degeneration and
amyotrophic lateral sclerosis with the C9ORF72 repeat expansion. Dementia and
geriatric cognitive disorders. 39: 287-293, 2015.
III
Junttila A*, Kuvaja M, Hartikainen P, Siloaho M, Helisalmi S, Moilanen V,
Kiviharju A, Jansson L, Tienari PJ, Remes AM** and Herukka SK**: Cerebrospinal
fluid TDP-43 in FTLD and ALS patients with and without the C9ORF72
hexanucleotide expansion. Submitted. * Née Kämäläinen **Authors had equal
contribution to this study.
The publications were adapted with the permission of the copyright owners.
XIV
XV
Contents
1 INTRODUCTION ........................................................................................................................... 1
2 REVIEW OF THE LITERATURE ................................................................................................. 3
2.1 Mild cognitive impairment ...................................................................................................... 3
2.2 Alzheimer’s disease ................................................................................................................... 4
2.2.1 Epidemiology ...................................................................................................................... 4
2.2.2 Clinical aspects and diagnostics ....................................................................................... 5
2.2.3 Genetics ................................................................................................................................ 7
2.2.4 Neuropathology and molecular pathology .................................................................... 7
2.3 Frontotemporal lobar degeneration ........................................................................................ 9
2.3.1 Epidemiology ...................................................................................................................... 9
2.3.2 Clinical aspects and diagnostics ....................................................................................... 9
2.3.3 Genetics .............................................................................................................................. 12
2.3.4 Neuropathology and molecular pathology .................................................................. 14
2.4 Amyotrophic lateral sclerosis ................................................................................................ 15
2.4.1 Epidemiology .................................................................................................................... 15
2.4.2 Clinical aspects and diagnostics ..................................................................................... 15
2.4.3 Genetics .............................................................................................................................. 16
2.4.4 Neuropathology and molecular pathology .................................................................. 17
2.5 Other dementing disorders .................................................................................................... 18
2.5.1 Vascular dementia ............................................................................................................ 18
2.5.2 Dementia with Lewy bodies ........................................................................................... 18
2.5.3 Creutzfeldt-Jakob disease ................................................................................................ 19
2.6 Biomarkers in neurodegenerative diseases ......................................................................... 20
2.6.1 Biomarker research in general ........................................................................................ 20
2.6.2 Challenges in biomarker research .................................................................................. 21
2.6.3 Currently used in clinics .................................................................................................. 21
2.6.4 Biomarker research in future .......................................................................................... 22
3 AIMS OF THE STUDY ................................................................................................................ 25
4 GENERAL EXPERIMENTAL PROCEDURES ........................................................................ 27
XVI
4.1 Patients ...................................................................................................................................... 27
4.2 Samples ..................................................................................................................................... 27
4.3 ELISA ........................................................................................................................................ 27
4.4 RNA, DNA and protein analyses from brain tissue .......................................................... 28
4.5 Genotyping ............................................................................................................................... 28
4.6 Statistical analyses ................................................................................................................... 29
4.7 Ethical aspects .......................................................................................................................... 29
5 GRN VARIANT rs5848 REDUCES PLASMA AND BRAIN LEVELS OF GRANULIN IN
ALZHEIMER’S DISEASE PATIENTS......................................................................................... 31
5.1 Introduction ............................................................................................................................. 32
5.2 Materials and methods ........................................................................................................... 32
5.2.1 Samples .............................................................................................................................. 32
5.2.2 Plasma measurement of granulin .................................................................................. 32
5.2.3 RNA, DNA and protein analyses from brain tissue .................................................... 32
5.2.4 Statistical analyses ............................................................................................................ 33
5.3 Results ....................................................................................................................................... 33
5.4 Discussion ................................................................................................................................. 37
6 CSF ALZHEIMER’S DISEASE BIOMARKERS IN FTLD AND ALS PATIENTS WITH
THE C9ORF72 REPEAT EXPANSION ........................................................................................ 39
6.1 Introduction ............................................................................................................................. 39
6.2 Materials and methods ........................................................................................................... 39
6.2.1 Subjects and samples ....................................................................................................... 39
6.2.2 Measurements ................................................................................................................... 41
6.2.3 Statistical analyses ............................................................................................................ 41
6.3 Results ....................................................................................................................................... 41
6.4 Discussion ................................................................................................................................. 44
7 CEREBROSPINAL FLUID TDP-43 IN FTLD AND ALS PATIENTS WITH AND
WITHOUT THE C9ORF72 HEXANUCLEOTIDE EXPANSION............................................ 47
7.1 Introduction ............................................................................................................................. 48
7.2 Materials and methods ........................................................................................................... 48
7.2.1 Subjects and samples ....................................................................................................... 48
7.2.2 Measurements ................................................................................................................... 49
7.2.3 Statistical analyses ............................................................................................................ 49
XVII
7.3 Results ....................................................................................................................................... 50
7.4 Discussion ................................................................................................................................. 53
8 DISCUSSION ................................................................................................................................ 55
8.1 CSF AD biomarkers in FTLD and AD .................................................................................. 55
8.2 CSF TDP-43 in FTLD and ALS .............................................................................................. 56
8.3 Granulin levels and rs5848 in AD patients .......................................................................... 57
8.4 Measurements of CSF or plasma by ELISA ......................................................................... 58
8.5 Strength and limitation of the study ..................................................................................... 59
8.6 Future insights ......................................................................................................................... 59
9 CONCLUSIONS............................................................................................................................ 61
10 REFERENCES .............................................................................................................................. 63
XVIII
XIX
Abbreviations
Aβ-42
Amyloid β-42
AD
Alzheimer’s disease
ADL
Activities of daily living
MND
Motor neuron disease
ALS
Amyotrophic lateral sclerosis
MRI
Magnetic resonance imaging
APP
Amyloid precursor protein
MTL
Medial temporal lobe
APOE
Apolipoprotein E
PD
Parkinson’s disease
bvFTD
Behavioural variant
PDD
Parkinson’s disease dementia
frontotemporal dementia
PET
Positron emission
MMSE
Mini-Mental State
Examination
CJD
Creutzfeldt-Jakob disease
CSF
Cerebrospinal fluid
PGRN
Progranulin
C9ORF72
Chromosome 9 open reading
PNFA
Progressive nonfluent aphasia
frame 72
PSEN1
Presenilin 1
DLB
Dementia with lewy bodies
PSEN2
Presenilin 2
DSM-V
Diagnostic and Statistical
sALS
Sporadic ALS
Manual of Mental Disorders
sCJD
Sporadic CJD
n:o 5
SD
Semantic dementia
EEG
Electroencephalography
SPECT
Single photon emission
EOAD
Early-onset AD
fALS
Familial ALS
FTLD
Frontotemporal lobar
binding protein with Mr
degeneration
43kDa
tomography
computed tomography
TDP-43
Transactive response DNA
gCJD
Genetic CJD
GRN
Granulin
iCJD
Iatrogenic CJD
VaD
Vascular dementia
IHC
Immunohistochemistry
VCI
Vascular cognitive
LOAD
Late-onset AD
MAPT
Microtubule-associated
protein tau
MCI
Mild cognitive impairment
TSE
Transmissible spongiform
encephalopathy
impairment
vCJD
Variant CJD
XX
1 Introduction
Dementia is a clinical syndrome most often caused by progressive neurodegeneration,
leading to loss of episodic memory and other cognitive functions and disabilities impairing
independent daily living. Dementing disorders represent an enormous social burden and
economical challenge as the world’s population ages.
The prevalence of dementia is increasing rapidly all around the world. The prevalence of
dementia for the population over 60-year-old ranges from 5 % to 7 % and the prevalence is
doubling every five years. The number of people with dementia doubles every 20 years and
it has been estimated that by 2050 there will be 115 million people suffering from dementia
(Prince et al. 2013). The prevalence of dementia in over 65-years-old is higher in women
than in men and the number of affected people doubles for every five year increase in age
(van der Flier & Scheltens 2005). The emotional and financial burden caused by dementia is
increasing while the population gets older and the proportion of the demented people gets
ever larger (van der Flier & Scheltens 2005).
So far our knowledge of etiology of dementia is limited. The most important risk factor
for dementia is age but also lifestyle (e.g. use of alcohol, head traumas) and environmental
factors have been recognized as risk factors for dementia (van der Flier & Scheltens 2005).
After intensive research, genetic risk factors have also been identified.
The genetic research has revealed many genetic risk factors for the dementing disorders
as well as identifying mutations in genes causing neurodegenerative diseases which has
helped in the clarification of the molecular pathology and pathological pathways
underlying these diseases, especially in the familial forms of neurodegenerative diseases.
Furthermore, the insights into the pathological basis of disease with a known genetic
background have also contributed to understanding the pathogenesis of the sporadic forms
of dementing disorders. One common feature of the neurodegenerative diseases is the
accumulation of fibrillary aggregates of proteins resulting from protein misfolding (Lausted
et al. 2014).
The most common type of dementia is Alzheimer’s disease which has a prevalence of 4.4
% in individuals aged over 65 years (Lobo et al. 2000). Frontotemporal lobar degeneration is
the cause of dementia approximately in 5-10 % of all cases and in 10-20 % of cases under 65
years which means that frontotemporal lobar degeneration is the second most common
dementia in under 65-years-olds (Ratnavalli et al. 2002). Vascular dementia and dementia
with Lewy bodies are also significant causes of dementia. Motor neuron diseases, e.g.
amyotrophic lateral sclerosis can be accompanied by concomitant frontal cognitive
disturbances and frontotemporal lobar degeneration; it has been estimated that frontal
cognitive impairment is seen in approximately 50 % of amyotrophic lateral sclerosis cases
(Ringholz et al. 2005).
At the present moment, neurodegenerative diseases are chronic and incurable illnesses.
Pathological processes in the brain occur over 10-20 years before the onset of clinical
symptoms. The suffering due to these diseases can last from months to decades. Intensive
research has been conducted attempting to clarify the basis of the neurodegenerative
diseases since this is best way to achieve earlier diagnosis which is looked on as the key to
developing disease modifying drugs and treatments (Van Deerlin 2012). Biomarkers are
needed in order to obtain an earlier and more accurate diagnosis. In particular, disease
specific biomarkers would make it possible to monitor disease progression and response to
therapies as well as being a tool to assist in the differential diagnosis of neurodegenerative
disorders.
At present, there are no cerebrospinal fluid (CSF) or plasma biomarkers for recognizing
any of the neurodegenerative diseases other than Alzheimer’s disease in the early stages of
2
the disease even though much research has been conducted in this field. Often the disease
diagnosis is clinical and based on clinical course, neuropsychological tests and brain
imaging, and ultimately the definite diagnosis has to wait until after the post-mortem
neuropathological examinations. It is known that neurodegenerative diseases are partially
clinically overlapping and share to some extent the same pathological and genetic
background, e.g. tau pathology in both Alzheimer’s disease and frontotemporal lobar
degeneration.
There is a need to identify specific biomarkers to help in the early diagnosis of
dementing disorders, to assist in the differential diagnosis and to monitor the effects of
drugs and other treatments for the neurodegenerative diseases. The ideal biomarker should
be sensitive, specific, non-invasive, cost-effective and easy to perform in the clinics (Lausted
et al. 2014).
3
2 Review of the Literature
2.1 MILD COGNITIVE IMPAIRMENT
The improvements in health care have increased the life expectancy which means that there
are now more and more individuals over 65 years of age. Many elderly people complain of
memory problems and a deterioration in cognitive function. Memory, language skills,
attention and judgment are all a part of cognition. There is a transitional zone between
cognition changes in normal aging and those encountered in different types of dementia;
this has been named in different ways over the years e.g. dementia prodrome, age-related
cognitive decline and isolated memory impairment. Nowadays, the term mild cognitive
impairment (MCI) is widely used to describe the situation where the individual has a
cognitive impairment but is not demented (Petersen et al. 2004, Winblad et al. 2004).
Usually, the impairment can be detected in memory or in some of the other cognitive
domains, but the activities of daily living (ADL) are unaffected (Petersen et al. 2001).
The prevalence of memory complaints varies from 22 % to 56 % depending on average
age of the population being studied, the types of questions asked as well as the sex and
educational level (DeCarli 2003). Thus, the prevalence of MCI varies depending on the MCI
criteria, study design and population. The Mayo criteria were the original criteria for MCI
and they included memory complaints, normal activities of daily living, objective memory
impairment for age, normal general condition for age and no dementia. In the Mayo
studies, individuals progressed to dementia at a rate of 12 % per year (Petersen et al. 2004,
Petersen et al. 1999). The criteria were revised in 2004 and the clinical phenotypes were
divided into amnestic and non-amnestic MCI and furthermore into the subtypes of single
and multiple domain classifications (Petersen et al. 2004, Winblad et al. 2004).
Figure 1. The classification of the MCI subtypes (modified from Winblad et al. 2004).
4
The MCI subtypes have different prognoses in that some of the patients with MCI
progress to AD or to some other dementing disorder whereas some of the patients are
stable or may even recover (Winblad et al. 2004). Subjects with single and multidomain
amnestic MCI are more likely to progress to AD and individuals with non-amnestic
multidomain MCI are more likely to progress to the other dementias than AD. Nonetheless,
the presence of MCI seems to be an independent risk factor for developing a memory
disorder (Busse et al. 2006). There are multiple etiologies behind MCI e.g.
neurodegenerative diseases, metabolic disturbances, psychiatric diseases or traumas
(Winblad et al. 2004). The MCI diagnosis is based on neuropsychological tests,
neuroimaging and biological markers (Eshkoor et al. 2015). There is no specific or curative
treatment for MCI but nonpharmacological treatments (e.g. exercise, social activities and
healthy diet), control of vascular risk factors (hypertension, hyperlipidemia, diabetes
mellitus) and antioxidants have been recommended (Eshkoor et al. 2015).
Etiology
Degenerative
Clinical
classification
Amnestic
MCI
Nonamnestic
MCI
Single
domain
Alzheimer’s
disease
Multiple
domain
Alzheimer’s
disease
Single
domain
Frontotemporal
dementia
Multiple
domain
Dementia with
Lewy bodies
Vascular
Vascular
dementia
Vascular
dementia
Figure 2. The classification of the MCI subtypes according to clinical phenotype and
hypothesized etiology (modified from Petersen et al. 2009).
2.2 ALZHEIMER’S DISEASE
2.2.1 Epidemiology
Alzheimer’s disease (AD) was first described by Alois Alzheimer in 1907 when he
examined a 51-year-old female patient, Auguste D. who had suffered a progressive
memory loss, hallucinations and behavioural problems. Post-mortem histological analyses
revealed the presence of both neurofibrillary tangles and senile plaques, much later the
disease was given the name of Alzheimer’s disease (Allsop 2000).
AD is the most common type of dementia with a prevalence of 4.4 % in people over 65
years of age (Lobo et al. 2000). In 2006, approximately 27 million people were living with
AD worldwide and it has been estimated that by 2050 that number will quadruple
(Brookmeyer et. al. 2007). It has been estimated that most of the demented people live in the
developing countries and the number of demented people is increasing (Ferri et al. 2005).
The recent study based on inhabitants of the United States in 2010, stated that 32 % of
people over 85 years had AD and the majority (82 %) of all the AD cases were over 75 years
old (Hebert et al. 2013). AD is more common in females than males; approximately twothirds of all cases are in females (Hebert et al. 2013).
5
The incidence of AD in individuals aged 65 years to 69 years is 0.6 % but if one examines
those over 85 years then the incidence has risen to 8.4 %, stressing the importance of age as
a risk factor for AD (Hebert et al. 1995).
2.2.2 Clinical aspects and diagnostics
AD is a dementing disorder where the progressive memory loss disrupts daily life, patients
become unable to take care of daily tasks and are disoriented with respect to both time and
place. There are clear declines in the patient’s ability to solve problems or make plans.
Changes in personality appear while the disease progresses and depression and apathy are
often seen. As the disease progresses, the patients need more and more intensive care
(Alzheimer’s & dementia 2014).
AD is divided into early-onset and late-onset forms. Early-onset AD (EOAD) begins
before the age of 65 years and it is more usually familial. Late-onset AD (LOAD) begins
typically after the age of 65 years and it represents the sporadic form of the disease.
Nonetheless, there are no differences in either the neuropathological changes or in the
symptoms between EOAD and LOAD. Different genes are associated for both of the forms
and those will be described in section 2.1.3.
The diagnosis of AD is based on clinical criteria, neuroimaging, neurocognitive tests,
blood and CSF tests and the exclusion of other diseases. The American Psychiatric
Association released the fifth edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5) in 2013. These criteria divide dementia into the mild and major
cognitive disorders. In 1984, McKhann et al. published a set of criteria for AD under the
auspices of the National Institute of Neurological Disorders and Stroke – Alzheimer’s
disease and Related Disorders (NINCDS-ADRDA) (McKhann et al. 1984). As new
techniques became available, the diagnostic methods for recognizing AD became more
specific and there was a need to update the criteria. In 2007, Dubois et al. published the new
revised NINCDS-ADRDA criteria for AD (Dubois et. al. 2007). Furthermore, in 2010 Dubois
et al. again revised the NINCDS-ADRDA criteria for AD (Dubois et al. 2010). Both the
NINCDS-ADRDA criteria and DSM-5 criteria are used in clinics.
The most typical symptom of AD is the impairment of episodic memory and in the
NINCDS-ADRDA criteria, the progressive memory dysfunction has to have lasted for at
least six months (Dubois et al. 2007). In the clinic, there are tests that assess a patient’s
memory skills; one test used all around the world is the Mini Mental State of Examination
(MMSE). The cognitive deficits can be evaluated by the Consortium to Establish a Registry
for Alzheimer’s Disease (CERAD) test battery. The Alzheimer’s Disease Co-operative Study
– Activities of Daily Living (ADCS-ADL) examines the patient’s functional abilities. Since it
is important to differentiate between depression and memory disorder, the Geriatric
Depression Scale (GDS) is widely used. Furthermore, neuropsychological tests are usually
tests with multiple domains e.g. evaluating the patient’s executive functions, memory,
visuospatial skills and verbal abilities (Loewenstein et al. 2001). The best sensitivity and
specificity are achieved by using many components of these neuropsychological tests
(Chapman et al. 2010).
Both structural and functional brain imaging are used in the clinical diagnostics of
dementing diseases. In AD, global brain atrophy is the typical structural brain imaging (CT
or MRI) appearance; in the early stages of the disease, the atrophy is mostly located to
medial temporal lobe (MTL) focused on the entorhinal cortex (EC) and hippocampus (HC).
The presence of symmetrical medial temporal atrophy can differentiate AD from ageing
with a sensitivity and specificity of around 80–85 % (Duara et al. 2008). The typical AD
pattern seen with functional imaging (FDG-PET (fluorodeoxyglucose positron emission
tomography) or HMPAO-SPECT (hexamethylpropylene amine oxime- single photon
emission computed tomography) is bilateral hypometabolism and hypoperfusion in the
temporal and parietal cortices; the sensitivity and specificity in diagnosing AD versus other
neurodegenerative diseases has been reported as 80-90 % (Hoffman et al. 2000). Amyloid
6
PET imaging is a sensitive and specific means of detecting brain amyloid in individuals in
vivo, and its values have been shown to correlate closely with autopsy conducted measures
of the fibrillar amyloid load. Thus this technique has considerable potential value in ruling
in/out AD pathology as the cause of the cognitive decline in a patient with a cognitive
impairment (Clark et al. 2011). However, amyloid PET is mainly used in clinical drug trials
not in routine clinical diagnostics.
Cerebrospinal fluid (CSF) biomarkers for AD are used in the clinic to diagnose AD and
also to differentiate AD from the other neurodegenerative diseases. In 1995, Motter et al.
described the decreased level of amyloid-β-42 (Aβ-42) in CSF and the increased level of Tau
in CSF (Motter et al. 1995). The reduced levels of CSF Aβ-42 were attributable to the
formation of Aβ-42 in the senile plaques in the brain. After years of study, it became
evident that the amyloid plaques in the brain and the decreased level of CSF Aβ-42 levels
could be considered as being diagnostic for AD. At the same time, the increased levels of tTau and phospho-Tau in CSF were recognized as biomarkers of AD (Tapiola et al. 2009,
Schoonenboom et al. 2008).
Table 1. Revised NINCDS-ADRDA criteria for AD (adapted from Dubois et al. 2007).
Probable AD: A plus one or more supportive features B, C, D, or E
Core diagnostic criteria
A. Presence of an early and significant episodic memory impairment that includes the
following features:
1. Gradual and progressive change in memory function reported by patients or informants
over more than 6 months
2. Objective evidence of significantly impaired episodic memory on testing: this generally
consists of recall deficit that does not improve significantly or does not normalise with cueing
or recognition testing and after effective encoding of information has been previously
controlled
3. The episodic memory impairment can be isolated or associated with other cognitive
changes at the onset of AD or as AD advances
Supportive features
B. Presence of medial temporal lobe atrophy
• Volume loss of hippocampi, entorhinal cortex, amygdala evidenced on MRI with qualitative
ratings using visual scoring (referenced to well characterised population with age norms) or
quantitative volumetry of regions of interest (referenced to well characterised population with
age norms)
C. Abnormal cerebrospinal fluid biomarker
• Low amyloid β1–42 concentrations, increased total-Tau concentrations, or increased
phospho-Tau concentrations, or combinations of the three
• Other well validated markers to be discovered in the future
D. Specific pattern on functional neuroimaging with PET
• Reduced glucose metabolism in bilateral temporal parietal regions
• Other well validated ligands, including those that foreseeably will emerge such as Pittsburg
compound B or FDDNP
E. Proven AD autosomal dominant mutation within the immediate family
Exclusion criteria
Exclusion of other diseases by disease history and clinical features
Criteria for definite AD
AD is considered definite if the following are present:
• Both clinical and histopathological (brain biopsy or autopsy) evidence of the disease, as
required by the NIA-Reagan criteria for the post-mortem diagnosis of AD; criteria must both
be present
• Both clinical and genetic evidence (mutation on chromosome 1, 14, or 21) of AD; criteria
must both be present
7
The routine blood tests are part of the examinations done in all memory patients to
exclude secondary causes of memory problems e.g. reduced levels of vitamin B12, anemia
and problems with electrolytes. There does not seem to be any specific biomarker for AD
which can be detected in blood, even though much research effort has been expanded.
2.2.3 Genetics
The genetic studies of AD have revealed the presence of autosomal dominant mutations in
different genes and polymorphisms that elevate the risk of AD.
There are autosomal dominant mutations in three main genes in EOAD: amyloid
precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2). The mutations in
APP, PSEN1 and PSEN2 explain approximately 71 % of familial cases of AD (Ertekin-Taner
2007) while those mutations explain only approximately 1 % of all AD cases (Mendez 2012).
Mutations in APP located on chromosome 21 were first described in 1991 (Goate et al.
1991) (http://www.molgen.ua.ac.be/Admutations/). APP is a glycoprotein but its normal
functions are not clear. APP is processed by the non-amyloidogenic pathway which
involves α-secretase cleavage and in the amyloidogenic pathway where it is cleaved by
both β- and γ-secretase into different lengths (Alberici et al. 2014). Mutations in APP cause
increasing amounts of amyloid-β-42 (Aβ-42) or decreasing amounts of amyloid-β-40 (Aβ40) or both (Walker et al. 2005, Schellenberg & Montine 2012).
Mutations in PSEN1 are located on chromosome 14q24.2 and in PSEN2 on chromosome
1q42.13 (http://www.molgen.ua.ac.be/Admutations/). PSEN1 and PSEN2 are involved to
the amyloidogenic pathway via the γ-secretase complex. The presenilins are enzymes that
catalyze the cleavage of APP and the mutated γ-secretase complex produces more Aβ-42
than normally. Mutations increase the accumulation of Aβ-42 in the brain and also increase
the ratio of Aβ-42 to Aβ-40 (Brunkan & Goate 2005, Jarrett et al. 1993). In summary, the
mutations in APP and the presenilins lead to a higher Aβ-42/Aβ-40 ratio and increased
levels of senile amyloid plaques in the brain (the neuropathological details will be
introduced in section 2.2.4).
The main risk gene for LOAD is apolipoprotein E (APOE) allele epsilon 4 (ε4) and this
occurs in a dose dependent manner; this link was described in 1993 (Corder et al. 1993).
APOE is a lipid protein which has many roles in synaptic function, Aβ-trafficking and
intracellular signaling (Mahley et al. 2009). There are three alleles of APOE: ε2, ε3 and ε4. In
the general population, the most common allele is ε3 found in approximately 78 % of
individuals while ε2 has 7 % and ε4 has 14 % of individuals (Strittmatter & Roses 1995).
APOE ε2 decreases the risk of AD and increases the age of disease onset whereas APOE ε4
increases the risk of AD and decreases its onset in a dose dependent manner. The APOE ε3
is considered to be the neutral allele i.e. it does not change the risk of AD or the affect the
onset age (Corder et al 1993, Strittmatter & Roses 1995). The highest risk is in individuals
with the genotype ε4/ε4 while the very rare genotype ε2/ε2 has the lowest risk according to
values emerging from a large population study (Farrer et al. 1997). APOE genotype ε4
increases the risk of AD and cognitive decline through Aβ-dependent and Aβ-independent
pathways. Aβ production, aggregation and clearance are differentially regulated
depending on APOE isoform. APOE ε4 also promotes proinflammatory responses that
could aggravate AD pathogenesis (Liu et al. 2013).
2.2.4 Neuropathology and molecular pathology
As far as is known there are two major pathologies behind AD, disturbances in amyloid
and tau processing.
Amyloid pathology
Amyloid plaques consisting of different lengths of amyloid β (Aβ) peptides were first
described in 1984 (Glenner & Wong 1984). Aβ-40 and Aβ-42 are the two most common
forms of amyloid plaques. There are two types of plaques, diffuse plaques which are
8
encountered also in normal aging and neuritic (senile) plaques which contain amyloid
fibrils of Aβ surrounded by degenerating neurons, reactive astrocytes and inflammatory
cells.
As earlier described, the mutations in APP, PSEN1 and PSEN2 cause pathological
amyloid processing in the brain. APP is found in e.g. endoplasmic retinaculum, endosomes
and Golgi apparatus. In the non-amyloidogenic pathway, α-secretase cleaves APP into a
soluble APP and p3 and inhibits the production of Aβ (Gandy et al. 2003). In the
amyloidogenic pathway, APP is first cleaved by β-secretase into secretorial APP.
Subsequently, γ-secretase cleaves the APP from specific points producing Aβ-40, Aβ-42 and
some other molecules which do not play a role in AD pathology. PSEN1 and PSEN2 are
part of the γ-secretase multiprotein complex and therefore are needed for γ-secretase
activation and the production of Aβ-40 and Aβ-42 (Wolfe 2006, Vassar et al. 1999).
The parts of the MTL in the brain are hierarchically involveld with Aβ deposits (Thal et
al. 2002). Involvement of the brain regions with Aβ deposits can be divided into five
phases: Phase 1 with Aβ deposits in the frontal, parietal, temporal or occipital neocortex;
Phase 2 with Aβ deposits in the entorhinal and insular cortex; Phase 3 with Aβ deposits in
the diencephalic nuclei and striatum; Phase 4 with Aβ deposits in brainstem nuclei
(substantia nigra, red nucleus, central gray, superior and inferior collicle, inferior olivary
nucleus, and intermediate reticular zone); and phase 5 with Aβ deposits in cerebellum
(pontine nuclei, locus coeruleus, parabrachial nuclei, reticulo-tegmental nucleus, dorsal
tegmental nucleus, and oral and central raphe nuclei) (Alafusoff et al. 2009, Thal et al. 2002).
Tau pathology
Tau is an intracellular microtubule associated protein in the central nervous system. Tau
can exist in either its phosphorylated or dephosphorylated form. With normal forms and at
physiological concentrations, tau stabilizes axons via its presence in microtubules,
participates in axonal transport and it has also a role in neuronal survival (Lee et al. 2001,
Buée et al. 2000). Tau exists in six isoforms which vary in length from 352 to 441 amino
acids and each of those isoforms has different physiological functions (Harrington 2012,
Buée et al. 2000).
The pathological tau consists of intraneuronal neurofibrillary tangles (NFT) which have
become aggregated into straight or paired helical filaments (PHF). The neurodegenerative
disorders which display the pathological tau are called the tauopathies (Lee et al. 2001).
Abnormal phosphorylation of tau leads to its dysfunction and its inability to stabilize axons
and furthermore this form has been proved to be neurotoxic (Ballatore et al. 2007).
Figure 3. Development of neurofibrillary tangles (NFT) in the brain. NFTs are shown in
transentorhinal region in stages I and II, in limbic regions in stages III and IV and in neocortical
regions in stages V and VI. Modified from Neurobiology of aging (Braak & Braak 1997) with the
permission from Elsevier.
NFTs are first seen in entorhinal cortex and when MCI is diagnosed the NFTs are seen
also in hippocampus. With disease progression, NFTs appear in temporal, parietal and
9
frontal cortices (Harrington 2012). NFTs in the brain occur in sequential manner and the
development can be divided into six stages: Stage I-II NFTs are shown in transentorhinal
region and there are no symptoms of AD, Stage III-IV NFTs are shown in limbic stages in
the brain and clinical symptoms of AD occur and Stage V-VI NFTs are shown also in
neocortical stages of the brain and patient has AD (Braak & Braak 1997). Neuropathological
changes in AD brain should be ranked by ABC score which is based on Aβ plaque score
(modified version of Thal et al. 2002 phases of Aβ deposits), Braak NFT stage and CERAD
neuritic plaque score (Montine et al. 2012). In stages V-VI (neocortical NFTs) the agreement
of findings by observer is 91 % whereas in mild stages I or II of NFTs the agreement is only
50 % so it is recommended that the investigations of lesions should be made by at least two
observers to make correct classification (Alafusoff et al. 2008).
2.3 FRONTOTEMPORAL LOBAR DEGENERATION
2.3.1 Epidemiology
Frontotemporal lobar degeneration (FTLD) was first described by Arnold Pick in 1892
when he examined a patient with progressive behavioural symptoms and aphasia. At postmortem, the patient was observed to have left frontotemporal atrophy. In 1911, specimens
from patients with behavioural symptoms were recognized to have silver-staining
intraneuronal inclusions (Pick bodies) and swollen neurons (Pick cells) when examined
under the microscope by Alois Alzheimer.
FTLD is a clinically and pathologically heterogeneous group of syndromes characterized
by behavioural changes and language problems. These have been associated with the
degeneration of the frontal and temporal lobes of the brain. It has been estimated that FTLD
is the cause of dementia in approximately 5-10 % of all cases and in 10-20 % of cases in
subjects less than 65 years which means that FTLD is the second most common dementia in
the population under 65-years of age (Ratnavalli et al. 2002). The prevalence of FTLD has
varied in different population based studies from 2.7 to 15.1 per 100 000 in people aged
under 65 years (Rosso et al. 2003, Ratnavalli et al. 2002). The incidence of FTLD is
approximately 3.3 – 3.5 per 100 000 person years (Mercy et al. 2008, Knopman et al. 2004).
The FTLD onset time is typically in the sixth decade but the age of onset can vary
(Ratnavalli et al. 2002, Johnson et al. 2005).
2.3.2 Clinical aspects and diagnostics
As earlier described, FTLD is divided into three clinical syndromes; behavioural variant
frontotemporal dementia (bvFTD), progressive non-fluent aphasia (PNFA) and semantic
dementia (SD) (Neary et al. 1998). The classification depends on the dominant symptoms
during the early stages of disease (Raskovsky et al. 2011, Neary et al. 1998). BvFTD is the
most common form of FTLD representing two thirds of the cases while PNFA and SD are
less common (Johnson et al. 2005). Overlap between syndromes is possible and it is very
common in late stages of disease. With disease progression, the parts of the brain affected
include both frontal and temporal lobes in a diffuse manner. It is possible that the FTLD
patient may develop motor dysfunction and extrapyramidal symptoms and may be
diagnosed also with amyotrophic lateral sclerosis (ALS) or Parkinson’s disease (PD)
(Kertesz et al. 2005).
Structural (MRI or CT) and functional imaging (PET and SPECT) are important tools in
recognizing the different variants of FTLD. The location of the atrophy and the changes in
metabolism and perfusion are associated with the clinical syndrome and the stage of
disease (Rosen et al. 2002).
Routine blood tests from plasma are part of the examinations of all FTLD patients to
exclude secondary causes of memory problems but there is no specific biomarker in clinical
use for FTLD or its variants either in blood or in CSF.
10
Behavioural variant frontotemporal dementia
Behavioural variant FTD is the most common subtype of FTLD. In bvFTD, the most
remarkable changes are encountered in the patient’s personality and behaviour, apathy and
disinhibition are typical symptoms of this disease (Rascovsky et al. 2011). Apathy is
associated with a loss of interest in personal issues and responsibilities, unsocial behavior
and with disease progression, there is a loss of awareness of personal hygiene. Disinhibition
includes inappropriate behaviours which are also abnormal compared to the patient’s
behavior before disease. Repetitive motor behaviours, changes in eating behavior (e.g.
overeating and weight gain) and hyperorality are seen (Neary et al. 1998, Rascovsky et al.
2013, Rascovsky et al. 2011). Many symptoms of bvFTD are similar to those found in
psychiatric disorders (e.g. depression and personality disorders) and therefore a
misdiagnosis is possible, although it can be said that the psychiatric examinations are
useful in differential diagnostics.
In the early stages of disease, the cognitive disabilities are characterized by
inattentiveness, poor judgment, problems with planning and organization and while
episodic memory is relatively normal both attention and working memory may be
impaired. In neuropsychological and cognitive tests, patient exhibit deficits in executive
tasks, mental flexibility and problem solving. Visuospatial skills are usually not impaired
(Rascovsky et al. 2011).
In bvFTD, the structural imaging with MRI typically reveals frontal atrophy and
hypometabolism and hypoperfusion in the frontal lobes of the brain. Furthermore,
dorsomedial frontal atrophy is associated with apathy and abnormal motor behaviour and
orbitofrontal atrophy is linked with disinhibition (Whitwell et al. 2009, Rosen et al. 2005).
Disease progression can also involve other parts of the brain (especially the temporal lobes)
and patients show more signs of language disorders.
The diagnostic criteria for FTLD devised by Neary et al. 1998 have been widely used in
clinics and in specialized centres; it has been reported that they have a sensitivity of 85-100
% and a specificity of 97-99 % (Knopman et al. 2005, Neary et al. 1998) The criteria were
revised by Rascovsky et al. in 2011 because of problems in the early detection of bvFTD.
The new International bvFTD Criteria Consortium (FTDC) is structured in levels of
diagnostic certainty and the criteria are more sensitive at recognizing probable and possible
bvFTD in the early stages of the disease (Rascovsky et al. 2011).
Table 2. Diagnostic criteria for bvFTD (modified from Rascovsky et al. 2011).
The following symptom must be present to meet the criteria for bvFTD: Shows progressive
deterioration of behaviour and/or cognition by observation or history must be present.
Possible bvFTD
Three of the following behavioural/cognitive symptoms must be present to meet the criteria.
A. Early behavioural disinhibition
B. Early apathy or inertia
C. Early loss of sympathy or empathy
D. Early perseverative, stereotyped or compulsive/ritualistic behaviour
E. Hyperorality and dietary changes
F. Neuropsychological profile: executive/generation deficits with relative sparing of memory
and visuospatial functions
Probable bvFTD
All of the following symptoms (A–C) must be present to meet the criteria.
A. Meets criteria for possible bvFTD
B. Exhibits significant functional decline
C. Imaging results consistent with bvFTD (Frontal and/or anterior temporal atrophy on MRI or
CT OR Frontal and/or anterior temporal hypoperfusion or hypo-metabolism on PET or SPECT)
11
Primary progressive aphasia
Primary progressive aphasia is a clinical dementia syndrome of language and speech
subdivided into three forms PNFA, SD and logopenic progressive aphasia (LPA). PNFA
and SD are most common forms and related to the FTLD disease spectrum, while logopenic
aphasia is typically associated with AD neuropathology.
Progressive nonfluent aphasia
Progressive nonfluent aphasia is progressive language disorder associated with effortful
speech, impaired speech production, comprehension of grammar (agrammatism) and
motor speech deficits. Speech apraxia is characterized by difficulties in initiating speech,
slow rate of speech and missing out of phonemes or incorrect sequencing. Writing becomes
agrammatic and reading changes to being nonfluent and effortful (Gorno-Tempini et al.
2011, Gorno-Tempini et al. 2004, Neary et al. 1998).
In neuropsychological tests, there are mild deficits in working memory and executive
functions while episodic memory and visuospatial functions tend to be preserved (GornoTempini et al. 2011, Gorno-Tempini et al. 2004).
In PNFA, the atrophy of the brain is predominantly left hemispheric, mostly located
within the left perisylvian region. Hypometabolism and hypoperfusion are seen in the left
side of the inferior and middle frontal gyrus, anterior parts of the insula and premotor and
areas (Gorno-Tempini et al. 2011, Gorno-Tempini et al. 2004).
Table 3. Diagnostic criteria for PNFA (modified from Gorno-Tempini et al. 2011).
I. Clinical diagnosis of nonfluent/agrammatic variant PPA
At least one of the following core features must be present:
1. Agrammatism in language production
2. Effortful, halting speech with inconsistent speech sound errors and distortions (apraxia of
speech)
At least 2 of 3 of the following other features must be present:
1. Impaired comprehension of syntactically complex sentences
2. Spared single-word comprehension
3. Spared object knowledge
II. Imaging-supported nonfluent/agrammatic variant diagnosis
Both of the following criteria must be present:
1. Clinical diagnosis of nonfluent/agrammatic variant PPA
2. Imaging must show one or more of the following results:
a. Predominant left posterior fronto-insular atrophy on MRI or
b. Predominant left posterior fronto-insular hypoperfusion or hypometabolism on SPECT or PET
III. Nonfluent/agrammatic variant PPA with definite pathology
Clinical diagnosis (criterion 1 below) and either criterion 2 or 3 must be present:
1. Clinical diagnosis of nonfluent/agrammatic variant PPA
2. Histopathologic evidence of a specific neurodegenerative pathology
3. Presence of a known pathogenic mutation
Logopenic aphasia
Logopenic progressive aphasia is not typically included within the FTLD disease spectrum
and the pathology behind the disease is more like AD pathology than FTLD pathology.
Patients with LPA display deficits in sentence repetitions and word retrieval. In MRI
studies, the left temporoparietal area is affected (Gorno-Tempini et al. 2011, Gorno-Tempini
et al. 2004).
12
Semantic dementia
Semantic dementia is associated with fluent, anomic aphasia and behavioural changes. The
atrophy in the brain is located in the anterior temporal lobes and in the early stages of the
disease, the atrophy is typically asymmetric.
The predominant left side atrophy in the temporal lobe causes a progressive loss of
words and objects. Speech is grammatically correct while impairment of single-word
comprehension and loss of semantic knowledge of words, objects and concepts is
dominant. Loss of meaning usually follows a hierarchical pattern, leading to the loss of
knowledge extending beyond language and multimodal agnosia (Gorno-Tempini et al.
2011, Neary et al. 1998, Hodges et al. 1992). In neuropsychological tests, patients show
impairment in naming, word-to-picture matching and categories while memory, executive
functions and spatial abilities are spared (Gorno-Tempini et al. 2011, Thompson et al. 2003,
Hodges et al. 1992).
Predominant right side atrophy in the temporal lobe causes behavioural changes which
are similar to those encountered in bvFTD. In the early stages of the disease, there is a loss
of empathy, problems in social behaviour and rigidity in daily tasks (e.g. inflexible routines
and schedules) (Gorno-Tempini et al. 2011, Seeley et al. 2005, Thompson et al. 2003). After
the disease progresses to the contralateral left temporal lobe, the patient experiences
semantic problems with speech and language (Gorno-Tempini et al. 2011, Thompson et al.
2003).
Table 4. Diagnostic criteria for SD (modified from Gorno-Tempini et al. 2011).
I. Clinical diagnosis of semantic variant PPA
Both of the following core features must be present:
1. Impaired confrontation naming
2. Impaired single-word comprehension
At least 3 of the following other diagnostic features must be present:
1. Impaired object knowledge, particularly for low frequency or low-familiarity items
2. Surface dyslexia or dysgraphia
3. Spared repetition
4. Spared speech production (grammar and motor speech)
II. Imaging-supported semantic variant PPA diagnosis
Both of the following criteria must be present:
1. Clinical diagnosis of semantic variant PPA
2. Imaging must show one or more of the following results:
a. Predominant anterior temporal lobe atrophy
b. Predominant anterior temporal hypoperfusion or hypometabolism on SPECT or PET
III. Semantic variant PPA with definite pathology
Clinical diagnosis (criterion 1 below) and either criterion 2 or 3 must be present:
1. Clinical diagnosis of semantic variant PPA
2. Histopathologic evidence of a specific neurodegenerative pathology
3. Presence of a known pathogenic mutation
2.3.3 Genetics
Approximately 40 % of FTLD patients have a positive family history of this disease (Rosso
et al. 2003). BvFTD is the most common subtype with a positive family history, especially if
there is concomitant motor neuron disease (MND) (Goldman et al. 2007).
Even though FTLD represents a clinically and pathologically heterogeneous group of
syndromes, the genetic studies have revealed several genetic mutations behind FTLD. The
most common gene mutations in FTLD disease spectrum are in microtubule-associated
protein tau (MAPT), in progranulin (GRN) and in hexanucleotide repeat expansion in
chromosome 9 open reading frame 72 (C9ORF72). In contrast, mutations in genes coding
13
valosin-containing protein (VCP), charged multivesicular body protein (CHMP2B),
transactive response DNA-binding protein (TARDBP) and fused-in-sarcoma protein (FUS)
are much rarer (Pan & Chen 2013). The updated and specified gene database for FTLD can
be found in http://www.molgen.ua.ac.be/Admutations/.
The C9ORF72 expansion is the cause of the 9p21-linked FTLD and ALS (DeJesusHernandez et al. 2011, Renton et al. 2011). The C9ORF72 expansion is present in
approximately 20-30 % of all FTLD cases and in 50 % of familial cases (Majounie et al. 2012).
The expansion can be detected by a repeat-primed polymerase chain reaction (PCR) but the
method is not able to measure the exact size of the hexanucleotide (GGGGCC)n repeat
expansion (Renton et al. 2011). The function of the encoded protein is unclear but it is
assumed that it is involved in RNA metabolism (Renton et al. 2014). Renton et al. showed
that in the Finnish population, the C9ORF72 expansion was detected in 29 % of FTLD
patients and in 48 % of familial FTLD patients; the frequencies in ALS patients were similar
(Renton et al. 2011). In global terms, the C9ORF72 expansion has been detected in 12 % of
FTLD patients and in 25 % of familial FTLD patients and in ALS patients it is present in 11
% of all ALS patients and in 38 % of familial ALS patients. The C9ORF72 expansion
frequency with concomitant FTLD-ALS is found in approximately 20-40 % of cases (Boeve
et al. 2012, Hsiung et al. 2012, Mahoney et al. 2012). The mean onset age is 58 years with a
range of 30-76 years (Majounie et al. 2012). The average disease duration is estimated to be
6-7 years but there is a wide range from 1.7 to 22 years (Boeve et al. 2012, Hsiung et al. 2012,
Mahoney et al. 2012). The typical phenotype in the FTLD disease spectrum with the
C9ORF72 expansion is bvFTD. Only a few cases of PNFA with the C9ORF72 expansion
have been reported and none in the SD cases (Hsiung et al. 2012, Renton et al. 2011). The
C9ORF72 expansion has been associated with TDP-43 pathology in the brain (DeJesusHernandez et al. 2011, Renton et al. 2011).
Mutations in the MAPT gene are located on chromosome 17q21. Mutations in MAPT
gene are found in approximately 5-10 % of all FTLD cases and in 10-25 % of familial FTLD
cases (Goldman et al. 2011, Seelaar et al. 2011). The mutated protein may have a reduced
ability to bind to microtubules causing impaired microtubule and axonal stability and
subsequent disruption of axonal transport. Then, the aggregation of tau leads to the
formation of insoluble inclusions that are neurotoxic (Spillantini et al. 1998).
The PGRN gene is located on chromosome 17q21 coding for a 593-amino acid protein
progranulin (PGRN) which is cleaved into granulin (GRN) (He & Bateman 2003). Mutations
in PGRN are present in approximately 5-10 % of all FTLD cases and in 10-25 % of familial
FTLD cases (Goldman et al. 2011, Seelaar et al. 2011). Both precursor PGRN and GRN are
growth factors that are involved in inflammation, embryogenesis, wound healing and
tumorgenesis. PGRN is highly expressed in different tissues; in the brain it is mostly
expressed in cortical and hippocampal pyramidal cells and cerebellar Purkinje cells (He &
Bateman 2003). Mutations in PGRN lead to RNA decay and a major loss of total PGRN
mRNA and protein levels via haploinsufficiency mechanism (Gass et al. 2006).
Mutations in the CHMP2B, VCP, TARDBP, FUS and p62 are rare. Mutations in the gene
CHMP2B coding for charged multivesicular body protein in chromosome 3p11 causes
damage in endosomal and/or the lysosomal trafficking and degradation system (Urwin et
al. 2009). Mutations in VCP gene in chromosome 9p13 coding valosin-containing protein
lead to disruption of the normal function of VCP which is normally involved in protein
degradation in endoplasmic retinaculum (Watts et al. 2004). In TARDBP, the mutation is
located on chromosome 1p36. The presence of abnormal TDP-43 causes increasing protein
phosphorylation and aggregation and toxicity (Lagier-Tourenne et al. 2010). Fused-insarcoma (FUS) protein is also nuclear RNA binding protein, participating in several aspects
of RNA metabolism (Kwiakowski et al. 2009, Vance et al. 2009). Mutations in gene p62 have
been shown to be linked to FTLD and ALS but the mutation is rare. In normal conditions
p62 is ubiquitin-binding protein involving to the protein homeostasis but in pathological
states it is part of the TDP-43 pathological neuronal inclusions (Miller et al. 2014).
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2.3.4 Neuropathology and molecular pathology
When examined at autopsy, FTLD patients brain display atrophy of the frontal and anterior
temporal lobes while posterior parts of the brain are affected only in the most advanced
stages of disease (Broe et al. 2003, McKhann et al. 2001). The microscopical examination
reveals a loss of pyramidal neurons, microvacuolar degeneration in the frontal and
temporal cortex, cortical gliosis and white matter axonal and myelin loss (Cairns et al. 2007,
McKhann et al. 2003). In the complex of FTLD and motor neuron disease (amyotrophic
lateral sclerosis) upper and lower motor neuron loss and corticospinal degeneration is also
observed (McKhann et al. 2003).
FTLD is one of the proteinopathies associated with abnormal protein inclusions in nuclei
or cytoplasm in neuronal or glial cells. With immunohistochemistry (IHC), it is possible to
make a specific neuropathologic diagnosis (Cairns et al. 2007). According to findings in IHC
and consensus recommendation provided by Mackenzie, FTLD is divided into five
pathological subtypes: 1. FTLD with tau-positive inclusions (FTLD-tau), 2. FTLD with taunegative, ubiquitin- and TDP-43-positive inclusions (FTLD-TDP), 3. FTLD with ubiquitinpositive, TDP-43 negative and fused in sarcoma protein (FUS)-positive inclusions (FTLDFUS), 4. FTLD with ubiquitin-positive, TDP-43- and FUS-negative inclusions (FTLD-UPS)
and 5. FTLD with no demonstrable inclusions (FTLD-ni, also known as dementia lacking
distinctive histopathology = DLDH) (Mackenzie et al. 2010). FTLD-UPS and FTLD-ni are
very rare.
In FTLD-tau, the major pathological protein in the central nervous system is
microtubule-associated protein tau (MAPT). Tau can exist in six isoforms which are
phosphorylated or dephosphorylated forms, varying from 352 to 441 amino acids in length
and each of those forms has its own distinctive physiological functions (Harrington 2012,
Buée et al. 2000). In normal forms and concentrations, tau stabilizes axons via microtubules,
takes part in axonal transport and has a role in neuronal survival (Lee et al. 2001, Buée et al.
2000). The pathological tau consists of intraneuronal neurofibrillary tangles (NFT) which
are aggregated straight or paired helical filaments (PHF). Abnormal phosphorylation of tau
causes dysfunction and the inability to stabilize axons and furthermore it has been
demonstrated to be neurotoxic (Ballatore et al. 2007). FTLD-tau can be detected in
approximately 35 % of all FTLD cases (Josephs et al. 2011, Cairns et al. 2007).
In FTLD-TDP, the major pathological protein in the brain is ubiquitin-positive
transactive response (TAR) DNA-binding protein (TARDBP) which has a molecular weight
of 43 kDa (TDP-43). TDP-43 is a nuclear protein involved in DNA transcription and splicing
(Buratti & Baralle 2010). In situations of neuronal damage, the pathological form of TDP-43
is ubiquitinated, phosphorylated and cleaved and becomes translocated to the cytoplasm
where it forms stress granules. It has been postulated that the loss of the normal nuclear
function or the new toxic functions are the keypoints in evoking neuronal damage (AjroudDriss & Siddique 2014). The inclusions are mostly located inside neurons in the
frontotemporal cortex and into the anterior horn cells in the spinal cord in cases where
there is also motor neuron disease (Cairns et al. 2007). FTLD with ubiquitinated inclusions
are present in approximately 40-60 % of FTLD cases and TDP-43 inclusions are the most
common pathological features of the ubiquitinated inclusions (Josephs et al. 2011, Cairns et
al. 2007). FTLD-TDP can be classified into four histological subtypes (A-D) following to the
revised consensus criteria according to the propotions of the neuronal cytoplasmic
inclusions (NCIs), neuronal intranuclear inclusions (NIIs) and dystrophic neurites (DNs):
Type A with many NCIs and DNs in layer 2 of the cortex; Type B with moderate number of
NCIs and few of DNs in all the layers of the cortex; Type C many DNs and a few number of
NCIs in layer 2 of the cortex and Type D many DNs and NIIs but only few NCIs in all the
layers of the cortex (Mackenzie et al. 2011, Cairns et al. 2007).
In FTLD-FUS, the major pathological protein in the central nervous system is fused-insarcoma (FUS) protein. FUS is a ubiquitously expressed DNA/RNA binding protein which
is involved in gene expression (Lagier-Tourenne et al. 2010). FUS is a component of the
15
neuronal cytoplasmic and intranuclear inclusions associated with neurodegeneration but
the pathological mechanism behind this effect remains unclear. FTLD-FUS is the
underlying pathology in approximately 5 % of FTLD cases (Seelaar et al. 2010).
Figure 4. A summary of the clinical, genetic and neuropathological features in FTLD. The grey
background represents the unknown field in genetics of FTLD. PSP = Progressive supranuclear
palsy, CBG = Corticobasal degeneration, AGD = Argyrophilic grain disease, IBMPFD = Inclusion
body myopathy with Paget’s disease of the bone and frontotemporal dementia. Adapted from
Sieben et al. 2012. Published with permission from Springer.
2.4 AMYOTROPHIC LATERAL SCLEROSIS
2.4.1 Epidemiology
Amyotrophic lateral sclerosis (ALS) is an adult-onset and fatal neurodegenerative disorder
of motor neurons. ALS affects the lower (spinal cord and medulla) and upper (cerebral
cortex) motor neurons. A rapidly progressive paralysis is the characteristic of this disease;
ultimately ALS causes death from respiratory failure.
The disease progression is rapid, death occurs within two to three years of symptom
onset (Rowland & Shneider 2001). The incidence of ALS is between 1.5–2.0 per 100000
population per year and the prevalence is around 6 per 100 000. The disease is more
common in males (ratio about 1.6:1) (Mitchell & Borasio 2007). ALS is subdivided into the
sporadic and familial forms, about 5.1 % of cases are familial; in Finland the rate is 11.6 %
(Byrne et al. 2011).
2.4.2 Clinical aspects and diagnostics
The diagnosis of ALS is clinical and based on disease history, typical signs of weakness and
atrophy of muscles, fasciculations, progressive upper and lower motor neuron dysfunction
16
and the exclusion of other diseases. In 1994, the El Escorial criteria for diagnosis of ALS
were published (Brooks 1994) which were revised in 2000 (Brooks et al. 2000). The criteria
list four stages of diagnosis and four body regions bulbar, cervical, thoracic and
lumbosacral (Table 1). Bulbar-onset patients present with dysarthria and dysphagia
together or as independent symptoms (Mitchell & Borasio 2007). The weakness and atrophy
of muscles progresses from the distal sites to the proximal region of the limb and
hyperreflexia is also present. ALS typically begins unilaterally from an upper or lower limb
leading to problems in the use of that limb and patients become unable to do normal
chores. Cervical-onset patients display upper limb symptoms which are usually unilateral
(Mitchell & Borasio 2007). The hypothenar hand is one characteristic of the ALS where the
hand’s little muscles in the thenar region become atrophied and disappear making it
impossible to use that hand (Menon et al. 2014). In contrast, thoracic-onset symptoms are
rare. Patients with the lumbosacral-onset exhibit lower limb unilateral symptoms such as
weakness and muscle atrophy which are attributable to the degeneration of the anteriorhorn cells (Mitchell & Borasio 2007).
Table 5. El Escorial revised criteria for ALS (Brooks et al. 2000).
Type of ALS
Criteria
Definite ALS
Upper motor neuron and lower motor neuron signs in three spinal
regions or two spinal regions and bulbar area
Probable ALS
Upper motor neuron and lower motor neuron signs in two regions
with at least some upper motor neuron signs rostral to lower motor
neuron signs
Probable ALS
(laboratory supported)
Upper motor neuron signs in one or more regions and lower motor
neuron signs defined by electromyogram in at least two regions
Possible ALS
Upper motor neuron and lower motor neuron signs in the same
region or upper motor neuron signs in at least two regions or
lower motor neuron signs rostal to upper motor neuron signs
The diagnosis of ALS is based on clinical findings and electromyography (EMG), which
reveals the chronic neurologic change by varying positive sharp waves and fibrillation
potentials which reflect the denervation in limb muscles and in cranial muscles (de
Carvalho et al. 2008). Neuroimaging and laboratory tests are important since they can
exclude other neurologic or muscular disorders e.g. stroke, multiple sclerosis or myasthenia
gravis. There is no specific biomarker for ALS in blood or in CSF available for clinical use.
Cognitive impairment and dementia are also typical symptoms of ALS and the changes
in brain are often located in its frontal sections leading to frontotemporal lobar dementia
(Murphy et al. 2007). It has been estimated that cognitive impairment is present in
approximately in 50 % of ALS cases (Ringholz et al. 2005) but the level may vary, from 48.5
% (sporadic ALS) to 62 % (familial ALS) (Wheaton et al. 2007). FTLD is seen in
approximately every fifth of ALS case (Ringholz et al. 2005). FTLD was described in more
detail in section 2.2.
2.4.3 Genetics
About 5.1 % of ALS cases are familial ALS (fALS) and the rest of the cases are sporadic ALS
(sALS) (Byrne et al. 2011). The fALS risk genes are only occasionally part of the background
of sporadic ALS. Chió et al. claimed that 67 % of fALS patients carried one of the tested ALS
risk genes and furthermore only 4.7 % of sALS carried genetic mutation. Younger patients
17
have a greater risk that the disease is due to the genetic mutations than older patients (Chió
et al. 2012).
Genetic research has identified many genes behind ALS as the techniques have become
more sophisticated and advanced but nonetheless, most of these risk genes are linked with
the fALS. An updated and specified gene database for ALS is located in ALSGene
(http://www.alsgene.org/) and in ALSod (http://alsod.iop.kcl.ac.uk/). So far, over 160
different mutations have been reported.
Mutations in different genes have been identified in 5-10 % of ALS patients. The first
identified mutation causing familial ALS was a dominant missense mutation in superoxide
dismutase 1 (SOD1). SOD1 protects cells from oxidative stress by metabolizing superoxide
radicals (Niwa et al. 2007) in motor neurons and glial cells of the spinal cord (Pasinelli &
Brown 2006, Valdmanis & Rouleau 2008). The mutation in SOD1 leads to a reduction in
dismutase activity which then allows protein misfolding and an inability to metabolize the
toxic superoxide radical, ultimately causing motor neuron degeneration. The mutated
protein causes endoplasmic reticulum stress, disruption of axonal transport and
mitochondrial dysfunction. Degeneration and damage of lower motor neurons is most
frequently encountered in the anterior horns in the spinal cord (Ajroud-Driss & Siddique
2014, Brotherton et al. 2011). The SOD1 mutation is present in 2.1 % of ALS cases and in
19.6 % of ALS mutations (Chió et al. 2012).
The most common genetic cause of ALS is the C9ORF72 expansion which can be
detected in over 60 % of the genetically related cases (DeJesus-Hernandez et al. 2011,
Renton et al. 2011, Chió et al. 2012). In all, 6.7 % of ALS cases have this mutation and the
C9ORF72 expansion mutation occurs in 62.7 % of all mutated cases (Chió et al. 2012). The
C9ORF72 mutation explains the overlap between ALS and FTLD since it is present in
approximately 40 % of fALS cases and 25 % of familial FTLD cases (Majounie et al. 2012).
The C9ORF72 expansion has been described in more detail in Chapter 2.3.3.
Transactivation response (TAR) DNA-binding protein (TARDBP) codes for the TDP-43
protein which is the major component of the ubiquitin-positive neuronal inclusions in cell
bodies of pathologic upper and lower motor neurons (Sreedharan et al. 2008). TDP-43 has
many roles in RNA metabolism in the cells and the mutation leads to the pathological
inclusions containing TDP-43 inside the cells (Buratti & Baralle 2010). TDP-43 pathology is
the underlying mechanism behind the ALS and FTLD disease spectrum (Arai et al. 2006,
Neumann et al. 2006). TARDBP mutations are present in 1.5 % of all ALS cases and 13.7 %
of all mutations (Chió et al. 2012).
Mutations in fused-in-sarcoma protein (FUS), valosin-containing protein (VCP) and
optineurin (OPTN) are very rare. FUS is a nuclear RNA binding protein involved in many
aspects of RNA metabolism and this can be detected in about 4 % of fALS cases
(Kwiakowski et al. 2009, Vance et al. 2009). VCP mutations occur in 1-2 % of fALS (Johnsson
et al. 2010). Mutations in VCP cause mitochondrial dysfunction leading to a reduction of
cellular ATP production (Bartolome et al. 2013). OPTN mutations were first detected in a
Japanese population (Maruyama et al. 2010). Optineurin has many functions in cellular
processes but it is not clear how this protein causes ALS (Maruyama et al. 2010).
2.4.4 Neuropathology and molecular pathology
In many respects, the genetic foundations of ALS can even be considered as the foundation
of molecular pathology as a science. Mutations in different genes leads to abnormal
functions and aggregates of proteins and these inflict the damage on the motor neurons.
In ALS, there is a loss of motor neurons in the pyramidal cells in the motor cortex,
anterior horn cells in spinal cord and motor nuclei of brainstem. One typical microscopic
feature of these damaged cells is the appearance of intracellular ubiquitinated inclusions.
The TDP-43 protein is the major component of the ubiquitin-positive neuronal inclusions
in the cell bodies of damaged upper and lower motor neurons (Sreedharan et al. 2008). The
C9ORF72 expansion causes TDP-43 inclusions also to appear in the pyramidal, frontal and
18
temporal cortex and hippocampus (Mackenzie et al. 2014). The physiological role of TDP-43
is to act as a nuclear protein which participates in ensuring mRNA stability as well as
having an involvement in axonal transport, transcription and splicing regulation (Buratti &
Baralle 2010). In situations of neuronal damage, the pathological form of TDP-43 is
ubiquitinated, phosphorylated and cleaved and translocated to the cytoplasm where it
forms stress granules. It has been postulated that it is either the loss of the normal nuclear
function or the appearance of new toxic functions which are the keypoints of neuronal
damage (Ajroud-Driss & Siddique 2014). FUS is also a nuclear RNA binding protein which
plays a role in RNA metabolism but it is not clear whether TDP-43 and FUS cause motor
neuron damage via the same or through different pathways (Ajroud-Driss & Siddique
2014).
2.5 OTHER DEMENTING DISORDERS
2.5.1 Vascular dementia
Vascular dementia (VaD) is also known as vascular cognitive impairment (VCI). It has been
estimated that the VCI is responsible for about 20 % of memory disorders and an overlap
with other neurodegenerative disorders especially with AD is often seen (Jellinger 2013,
Gorelick et al. 2011). The main symptom in VCI is a progressive cognitive impairment
which causes memory problems in the patient.
Multiple ischemic lesions and lacuna infarcts in patients with vascular risk factors are
typical for VCI (Jellinger 2014, Tomlinson et al. 1970). The atherosclerotic plaques
accumulate on small vessels in cerebellum and cause microinfarcts and white matter
damage (Thal et al. 2012). The extensiveness of white matter damage correlates with the
severity of the cognitive problems (Maillard et al. 2012). Amyloid plaques and tauopathy is
frequently observed in VCI (Jellinger 2013). Genetic mutations are very rarely encountered
in VCI; the most common genetic cause of in VCI is the Cerebral Autosomal Dominant
Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) syndrome
which is due to a frame shift mutation in Notch-3 (Chabriat et al. 2009). Several other genes
which are usually seen in other dementing disorders have been examined but no specific
genes have identified (Iadecola 2013). Several vascular risk factors (eg. hypertension,
diabetes) and age are independent risk factors for VCI (Sahathevan et al. 2012, Gorelick et
al. 2011).
2.5.2 Dementia with Lewy bodies
The prevalence of Dementia with Lewy bodies (DLB) is controversial; it has been estimated
that the prevalence rate is about 4 % in the community, but there are also studies which
claim that the level has been underestimated (Vann Jones et al. 2014, Boot 2013). An overlap
with Parkinson’s disease (PD) is frequent in clinics, genetics and neuropathologic findings.
DLB is a group of heterogeneous subtypes. There are three clinically recognized
subtypes: mild cognitive impairment, behavioural / psychiatric phenomena and physical
symptoms. Furthermore, in DLB, the MCI can lead to a clinical dementia. The MCI in DLB
patients can be an amnestic or nonamnestic cognitive impairment, problems with language
or in visuospatial working. Parasomnias are also possible as are REM-sleep behavioral
changes (Ferman et al. 2013, Auning et al. 2011, McKeith et al. 2005). The behavioural and
psychiatric subtype of DLB does not usually exhibit any signs of memory problems. The
psychiatric signs are rather distinctive: depression, delirium and visual hallucinations
(Auning et al. 2011, Kehagia et al. 2010, McKeith et al. 2005). The physical symptoms are
usually extrapyramidal changes which are also common in patients with PD as they are
part of the parkinsonism spectrum. The signs of orthostatic hypotension and constipation
have also been reported. One difference that clinicians can use in distinguishing between
DLB and PD in clinics is the start of the symptoms; in DBL only a short time elapses from
19
first cognitive symptoms to the appearance physical symptoms whereas in PD, the
cognitive symptoms emerge several years after the first physical symptoms (Donaghy et al.
2014, Auning et al. 2011, McKeith et al. 2005).
The neuropathology of DLB contains many different features which are also seen in
other neurodegenerative disorders e.g. AD. The main neuropathological feature in DLB are
alpha-synuclein aggregates Lewy bodies (LBs) and Lewy neurites (LNs) (McKeith et al.
2005). Amyloid-beta and tau pathology are very frequently detected in DLB patients
(Ballard et al. 2006). Braak et al. described the staging of DLB in 2003; this staging is based
on the location of the pathological features in the brain. The most typical locations of the
LBs and LNs are olfactory bulb and dorsal motor nucleus as well as some other brainstem
structures e.g. pons and midbrain (Braak et al. 2003).
There are several genes that have been associated with DLB and some of those genes are
also expressed in other neurodegenerative diseases e.g. in AD. Mutations in different forms
of the alpha-synucleins (SNCA) are frequent and the AD’s genetic risk factor APOE is also
more common in patients with DLB (Bras et. al. 2014). A mutation in glucocerebrosidase
(GBA) is also a known risk factor DLB (Tsuang et al. 2012). Mutations in several other genes
have also been reported but their prevalence is very rare.
2.5.3 Creutzfeldt-Jakob disease
Creutzfeldt-Jakob disease (CJD) is a fatal prion disease and part of the transmissible
spongiform encephalopathies (TSEs). The incidence of CJD is 1-2 per one million of the
population per year. There are no differences in incidence between sexes (Imran &
Mahmood 2011, Holman et al. 1996). The onset varies between 55 and 75 years but younger
and older onsets have also been reported (Imran & Mahmood 2011, Brown et al. 1986). The
mean duration of the disease is approximately eight months and death occurs within 12
months in approximately 85-90 % of patients (Brown et al. 1986).
CJD is divided into four forms: sporadic (sCJD), genetic (gCJD, also known as familial
CJD (fCJD)), iatrogenic (iCJD) and variant (vCJD). The most common form is sCJD
accounting for approximately 85 % of CJD cases and the second most common is gCJD with
approximately 10-15 % of CJD cases. The other two forms of CJD are very rare (Imran &
Mahmood 2011, Belay 1999). The major symptoms of CJD are rapidly progressive dementia
and myoclonus. Other typical symptoms for CJD patients are visual problems, cerebellar
dysfunction (e.g. muscle incoordination) and speech abnormalities. As the disease
progresses, it is accompanied by pyramidal and extrapyramidal dysfunction (e.g. tremor,
stasticity and abnormal reflexes) and behavioural changes (e.g. depression). All the
symptoms progress very rapidly, ultimately it terminates in akinetic mutism (nonresponsivity to external stimuli) (Imran & Mahmood 2011, Belay 1999). The mean age at
onset is lower in gCJD (45 to 55 years depending on mutated codon) than in sCJD (60
years). The mean duration time of the illness is mostly eight months (Belay 1999).
In early stages of disease, electroencephalography (EEG) findings can be normal but after
disease progression, typical periodic sharp wave complexes appear to the extent that
diagnostical changes are present in approximately 75-85 % of cases (Wieser et al. 2006,
Belay 1999, Brown et al. 1986). Protein 14-3-3 is a neuronal protein which occurs in different
isoforms in the brain. The CSF levels of protein 14-3-3 become elevated because of the
neuronal damage (Gmitterova et al. 2009). Protein 14-3-3 sensitivity for CJD is 96 % and
specificity varies from 96 % to 99 % (Hsich et al. 1996). In other neurodegenerative
disorders, one may also find an increase the level of CSF protein 14-3-3. Lately, Real-time
quaking-induced conversion (RT-QuIC) assays of CSF are shown to be valuable to
distinguish CDJ patients from non-CJD conditions (Orrú et al. 2015). The MRI can be
normal in the early stages of disease but after disease progression, there is the appearance
of typical high signal abnormalities in caudate nucleus and putamen or at least in two
cortical regions. Abnormalities can also be observed in diffusion-weighted images (DWIs)
and fluid-attenuated inversion recovery (FLAIR) images (Young et al. 2005).
20
The definite diagnosis of CJD depends on brain biopsy or post-mortem
neuropathological examination. The neuropathological changes occurring in the brain
consist of spongiform changes in grey matter, neuronal loss and gliosis with the absence of
inflammation. The pathological form PrPSc is predominant in the affected parts of the brain.
In sCJD, approximately 5 % of cases have amyloid plaques in the brain while in vCJD, the
amyloid plaques are very frequently surrounded by vacuoles (“florid” plaques) (Johnson
2005, Belay 1999).
2.6 BIOMARKERS IN NEURODEGENERATIVE DISEASES
2.6.1 Biomarker research in general
Biomarker is a measurable indicator of biological state or condition e.g. in plasma or CSF.
Biomarkers can be used to determine diagnosis, to measure disease processes, to evaluate
risk and prognosis of diseases or to monitore therapy effects. Biomarker should be sensitive
and specific and able to detect the neuropathological features of the examined disease.
Furthermore, biomarkers should be reliable, precise, non-invasive and inexpensive
(Lausted et al. 2014).
Despite an intensive research effort, at the moment biomarkers are only available for the
clinical diagnostics of AD and CJD. Although the clinical diagnostics of dementing diseases
is fairly accurate, the definite disease-specific diagnosis is based on the identification of
pathogenic mutation, brain biopsy or post mortem neuropathological examinations. As
people get older the prevalence of AD and also other dementing disorders is increasing and
therefore there is an urgent need for new biomarkers to be used in differential diagnosis of
neurodegenerative diseases and to monitore the effects of possible drugs or treatments in
clinics. If good biomarkers were available, this could facilitate the early and accurate
diagnosis of the underlying neuropathology during the patient’s lifetime. Today,
dementing disorders are progressive incurable diseases which have effects on patients’ life
for several years.
Biomarkers can be grouped to protein-based biomarkers, small-molecule biomarkers and
RNA-based biomarkers. Mainly the biomarker studies of neurodegenerative diseases are
focused on studies of different proteins in CSF, blood, plasma and serum. Also
investigations of biomarkers in urine and saliva have been made but so far no biomarkers
have been found. Animal models can be used to create biochemical and histological
circumstance that resemble the human brain in dementing disorders. However there are
certain problems when comparing animal models to the human brain e.g. differences in
biochemistry and disease mechanisms (Lausted et al. 2014).
Analysis of CSF is in essential role of study of neurodegenerative disorders because of its
proximity to the brain. Compared to plasma CSF has lower concentrations of protein and
RNA and therefore the samples are easier to process but also contaminations during the
invasive collection procedure are possible and may have significant effects to composition
of protein or RNA (Lausted et al. 2014). Studies of biomarkers in blood, plasma and serum
are under intensive research but so far there are no biomarkers available. Blood contains
plenty of biomolecules which are originated from different cells and organs. Some of the
biomoleculas are a result of normal cell lysis but also molecules released because of
pathological processes can be measured (Lausted et al. 2014).
Depending on studied molecule different types of methods and investigations are used.
Enzyme-Linked Immunosorbent Assay (ELISA) is a widely used method for measuring the
concentrations of investicated molecule from different liquids. The method is more detailed
described in section 4.3. 2-dimensinal gel electrophoresis (2DGE) is used to separate
proteins from samples e.g. by molecular size. Proteins can be excised of complex samples
by mass spectrometry (MS) to determine protein more specify and MS is widely used in
proteomics and metabolomics of different diseases (Shevchenko et al. 2015). For RNA
21
research the most commonly used method is quantitative polymerase chain rection (qPCR).
RNA is involved in every biological process and the levels of microRNAs (miRNA)
correlate with pathological conditions and the levels of microRNAs are measurable in
peripheral blood so that makes sample collection less invasive compared to collection of
CSF samples (Lausted et al. 2014).
2.6.2 Challenges in biomarker research
There are several clinical challenges encountered in biomarker research. One important risk
is the possibility of misdiagnosis during patient selection. Studies testing the accuracy of
CSF biomarkers in AD have usually relied on a clinical diagnosis in their patient selection.
Each dementing disease is associated with its own typical symptoms. However, there are
atypical presentations of every dementing diseases and the accuracy of clinical diagnosis
during lifetime has been estimated to vary from 37 % to 82 % depending of the stage and
type of the disease (Lopez et al. 1999).
Another challenge is that co-pathologies are common in the neurodegenerative diseases.
Typical coincident pathologies are AD neuropathology with vascular changes, but many of
the hallmarks of AD are usually also seen in patients with DLB, FTLD (Wharton et al. 2011,
Kovacs et al. 2008). The reason for this mixed neuropathology is unknown. In some cases,
co-pathologies may be explained by the simultaneous presence of two pathogenic
mutations in different genes, although the combination of two mutations in the same
individual is rare (King et al. 2013). The prevalence of mixed neuropathology can be
observed in every third patient, but in some studies, co-pathologies have been estimated to
be present in up to 60 % of cases (Lleó et al. 2015, Jellinger & Attems 2007). This kind of
mixed neuropathology is the most common among the older patients (Wharton et al. 2011,
Kovacs et al. 2008, MRC-CFAS 2001) and furthermore age itself may influence CSF
biomarkers and modify their diagnostic accuracy (Mattsson et al. 2012). In addition, the
disease process and neurodegeneration itself can also be reflected in the CSF AD biomarker
values (Fagan et al. 2009, MRC-CFAS 2001).
One challenge in biomarker research of neurodegenerative diseases is the heterogeneity
of the clinical phenotypes as well as the diversity of the neuropathology e.g. FTLD can be
divided into five pathological subtypes (Mackenzie et al. 2010) but there is a poor
correlation between phenotype and neuropathology. The identification of genetic causes
may help in predicting neuropathology in families with known mutations. However, copathologies are encountered even if there is a known causative mutation behind the disease
e.g. the AD-type neuropathology has been detected in some patients with the C9ORF72
expansion as well as with PGRN mutations in FTLD patients (Bieniek et al. 2013, King et al.
2013). Genetic testing is not a routine in diagnostics and so far it is not possible to predict
disease onset, progression or prognosis of neurodegenerative diseases by genetic
background (Lausted et al. 2014).
2.6.3 Currently used in clinics
There are CSF biomarkers for AD used in the clinic to diagnose AD and to differentiate AD
from other neurodegenerative diseases. The combination of a decreased level of CSF Aβ-42
levels and increased levels of t-Tau and phospho-Tau in CSF are diagnostic biomarkers for
AD (Tapiola et al. 2009, Schoonenboom et al. 2008). Bloudek et al. 2011 showed in their
meta-analysis that the combination of CSF Aβ-42 and t-Tau gave a mean sensitivity of 89 %
and a mean specificity of 87 % when compared AD patients and healthy controls. The
combination of CSF Aβ-42 and t-Tau gave a mean sensitivity of 86 % and a mean specificity
of 67 % when comparing AD patients to other dementias (Bloudek et al. 2011).
The reason for the reduced level of CSF Aβ-42 is that there are insoluble amyloid plaques
in the brain. It is thought that only in the very terminal stages of AD is the decrease of Aβ42 partly due to decreased production since the vast proportion of neurons producing Aβ
have already died. The increased levels of t-Tau and phospho-Tau are due to the
22
intraneuronal neurofibrillary tangles which exist in aggregated straight or paired helical
filaments. The abnormal phosphorylation of tau causes a dysfunction of the protein e.g. a
disability to stabilize axons but furthermore these phosphorylated forms of Tau are actually
neurotoxic. An increase in the levels of t-Tau and phospho-Tau in CSF is typically
associated with AD, but these kinds of changes are also seen in other neurodegenerative
diseases and pathological processes associated with neuronal loss. One can have similar
neuropathological findings between different dementing diseases and patients with mixed
neuropathology, especially in older patients (Fagan et al. 2009, Kovacs et al. 2008). An
elevated level of CSF t-Tau is also seen in some other conditions with neuronal injury e.g.
ischaemic stroke (Song et al. 2013).
In CJD patients, the CSF levels of protein 14-3-3 are increased because of the neuronal
damage (Gmitterova et al. 2009). The sensitivity of protein 14-3-3 for CJD is 96 % and
specificity varies from 96 % to 99 % (Hsich et al. 1996). Some other neurodegenerative
disorders can also increase the level of CSF protein 14-3-3; in addition, the levels of CSF tTau and phospho-Tau can be elevated because of the widespread neuronal damage in the
brain. There are case reports where CSF Tau levels have increased by several thousand-fold
and thus this biomarker has displayed better specificity for CJD than protein 14-3-3
(Chohan et al. 2010, Lyytinen et al. 2010, Bahl et al. 2009).
2.6.4 Biomarker research in future
The main pathologies behind FTLD are tau and TDP-43 pathologies; furthermore TPD-43
pathology is also a common pathology causing ALS. This has lead to experiments to
determine whether TDP-43 levels in CSF or plasma could be exploited as a tool for disease
diagnosis or differential diagnostics. There are some reports of elevated levels of TDP-43 in
CSF (Noto et al. 2011, Steinacker et al. 2008). For example, Steinacker et al. claimed that
TDP-43 levels were increased in FTLD and ALS patients compared to controls although
there was no difference between the diagnostic groups (Steinacker et al. 2008). Noto et al.
measured elevated CSF TDP-43 levels in ALS patients and also postulated that the reduced
levels of CSF TDP-43 levels could be related to the accumulation of TDP-43 within insoluble
intracellular lesions (Noto et al. 2011). It has been proposed that patients with the C9ORF72
mutation display TDP-43 pathology, and in one study, increased levels of TDP-43 levels
were detected in carriers of the C9ORF72 expansion and progranulin mutation and
individuals with the mutations had higher CSF TDP-43 levels than the FTLD patients
without any known mutations (Suárez-Calvet et al 2013).
The progranulin gene codes for different granulins with their own distinctive functions;
some of them are neurotrophic factors enhancing neuronal survival and assisting in axon
guidance. Decreased levels of granulin have been postulated to cause neurodegeneration
and to increase the risk of AD and FTLD (Kelley et al. 2010, Gass et al. 2006). There are
reports that plasma progranulin levels are reduced in patients with the PGRN mutations
(Finch et al. 2009, Ghidoni et al. 2008).
The main neuropathological feature in DLB and PDD is the presence of alpha-synuclein
aggregates, LBs and LNs (McKeith et al. 2005). Alpha-synucleins are amino-acid proteins
localized in presynaptic terminals which are involved in neurotransmission. The
overexpression of alpha-synuclein leads to aggregates called LBs which are found in
cytoplasm especially in neurons in the substantia nigra and locus coeruleus (Morra &
Donovick 2013). Amyloid-beta and tau pathologies (AD pathology) are very frequently
encountered in DLB patients (Ballard et al. 2006).
Neuroinflammation is suggested to play a key role in neurodegenerative diseases.
Activated microglia works as macrophage of the brain and it is around the plaques in the
brain and secretes anti-inflammatory sytocines (e.g. IL-4, IL-10, IL-13). Furthermore,
activated microglia has both protective and neurotoxic effects to the tissue but it is not clear
how or when the balance is interrupted. In AD patients activated microglia produces
proteins that are suggested to have connection to inflammation and tissue remodeling and
23
numerous of biomarkers are under investigation. Toll-like receptors (TLRs) and coreceptor
CD14 in microglia are able to detect pathogens and tissue damage in the brain and are in
remarkable role in surrounding Aβ deposits in the AD brain (Lausted et al. 2014, Cameron
& Landreth 2010).
Ribonucleic acid (RNA) involves in structural, informational and regulator roles in
biological processes (Lausted et al. 2014). Circulating microRNAs (miRNAs) are non-coding
17-22 nucleotides long RNAs modulating gene expression and constituting about 1 % of all
human genes. Circulating miRNAs can be detected in all body fluids and the most widely
used method is quantitative PCR. In the nervous system miRNAs involve in synaptic
plasticity and neurogenesis. In neurodegenerative diseases miRNAs are dysregulated
leading to neuronal cell death but the pathogenic mechanism is unknown. Research of
circulating miRNA has been very promising and numerous miRNAs have been linked to
neurodegenerative diseases but longitudinal studies are still required. In AD patients
increased expression of miR-34 and miR-18 has been found in peripheral blood
mononuclear cells, 12-miRNA family in peripheral blood, 7-miRNA family in plasma and
60 miRNAs differentially regulated in CSF. In ALS patients 8 miRNAs have been found in
leukocytes significantly up- or downregulated and ALS-spesific inflammatory miRNA in
monocytes (Grasso et al. 2014).
“Omics” is the characterization and quantification of biological molecules that are
involving into functions or dynamics of an organism. Omics can be divided into genomics /
epigenomics / transcriptomics, proteomics and metabolomics / lipidomics and advances in
technical platforms have enabled high-throughput of molecular processes. Genomics
include investigations of genome, epigenome and transcriptome by sequencing and array
platforms usually from peripheral blood or CSF. By genome wide association studies
(GWAS) it has been possible to detect risk genes and variations and several risk genes or
locis have been found (Han et al. 2014). Proteomics is a study of protein functions,
interactions, and structures by MS or using antibody based methods. Studies of proteomics
have revealed numerous of biomarker candidates e.g. neuronal cell adhesion molecule
(NCAM), chitine 3-like 1, chromogranin A and carnosinase I in CSF of AD patients.
Proteomics are used in clinical trials and it has a place in biomarker development and
clinical interventions but the targeted biomarkers have to be combinated to clinical data
also (Shevchenko et al. 2015). Metabolomics is a study of metabolites / low-molecularweight intermediates (metabolomes) which fluctuate according to physiology and
developmental and pathologic state of cells, tissues or organs. Neuronal cells differ from
non-neuronal cells in a variety of ways and metabolomics offers a window through which
to view cell specific functions and dysfunctions. The metabolomic profile can be assessed
from different fluids and it can hint at associations between the changes in the profile and
different diseases (Jové et al. 2014).
Neuronal antibodies include sytoplasmic and nuclear antigens, intracellular synaptic
proteins and cell-surface antigens. Autoantigens have role in synaptic transmission,
plasticity and prepheral nerve stability. Antibodies seem to be pathogenic by indicating Tcell-mediated immune response against neuronal antigens but also able to respond to
treatments. There have been few antigens (e.g. N-methyl-D-aspartate receptors and
metabotropic glutamate receptors) under intensive research but so far it has not revealed
biomarkers for dementing disorders (Lancaster & Dalmau 2013).
As far as we know, neurodegerative diseases are heterogenous group of diseases with
overlap in genetics, neuropathology and clinical phenotypes. Advanced technologies have
opened new ways to approach biomarkers in different body fluids. Diagnostic analyses
should be made with longitudinal studies to get biomarkers validated and to make possible
to follow disease state. Today, biomarker studies are not focused only one or two protein
but different types of molecules and in future the combining of different biomarker data
and techniques to get biomarker profiles / panels may be the key to diagnostics and to
monitor biological and pathological processes.
24
Table 6. Summary table of the neurodegenerative disorders (references are shown in the text).
Disease
Genetic
background
Main
neuropathology
Biomarkers
Possible biomarkers
AD
APP
PSEN1
PSEN2
APOE
C9ORF72
MAPT
PGRN
CHMP2B
VCP
TARDBP
FUS
p62
C9ORF72
SOD1
TARDBP
FUS
VCP
OPTN
Amyloid plaques
Neurofibrillary
tangles
CSF Aβ-42
CSF t-Tau
CSF phosphoTau
Not available
CSF, plasma or serum PGRN
CSF Aβ-40
miRNAs
FTLD
ALS
FTLD-tau
FTLD-TDP
FTLD-FUS
FTLD-UPS
FTLD-ni
TDP-43
Not available
CSF or plasma TDP-43
CSF, plasma or serum PGRN
CSF Aβ-40
miRNAs
CSF or plasma TDP-43
miRNAs
25
3 Aims of the Study
The early diagnosis of dementing diseases is challenging and there is an unmet need for
new tools for identifying accurate neuropathology in the early stage of these diseases and
clinical diagnostics. There are only a few biomarkers used in diagnostics of the AD; the
other dementing disorders do not have any biomarker. The aim of the study was to identify
new biomarkers for use in the diagnostics of the dementing disorders. More specifically,
the aims of the study were:
1. To examine the usefulness of plasma granulin as a biomarker to differentiate AD
patients from healthy controls and to investigate the effect of rs5848 variation on
granulin levels.
2. To clarify whether there are changes in CSF AD biomarker levels in well
characterized FTLD and ALS patients with the C9ORF72 expansion.
3. To evaluate the CSF TDP-43 levels as a biomarker in FTLD and ALS patients with
different genetic backgrounds.
26
27
4 General Experimental Procedures
4.1 PATIENTS
The patient cohort in study I consisted of 258 patients with probable AD according to
NINCDS-ADRDA criteria and 114 age-matched healthy control subjects. The control
subjects were participants in longitudinal follow-up studies during which their
neuropsychological and functional performances were repeatedly assessed to exclude
subjects with dementia. These subjects had been previously genotyped for rs5848 and
APOE and all the subjects with available plasma samples from that cohort were included in
these analyses. The inferior temporal cortices from a separate set of 24 neuropathologically
confirmed AD patients were used in the expression analyses. The patients in this study
were from ongoing clinical projects.
The patient cohort in study II consisted of 40 cases with the C9ORF72 repeat expansion.
All of the cases were diagnosed in two university hospitals (Kuopio and Oulu). The clinical
phenotypes were FTLD in 29 cases, ALS in 10 cases and FTLD-ALS in one case. 30 of the
C9ORF72 carriers (20 FTLD and 10 ALS patients) were also included to study III. Study III
consisted of 69 FTLD patients (29 % the C9ORF72 expansion carriers) and 21 ALS patients
(48 % the C9ORF72 expansion carriers). The FTLD diagnosis was made according to the
Neary criteria (Neary et al. 1998). The demographic characteristics of the patients in the
original publications are summarized in Table 7.
Table 7. Demographic data of patients in the original publications.
Study
Group
n (Male % / Female %)
Age
Study I
AD
Controls
258 (29 / 71)
114 (39 / 61)
73.6 ± 7.4
69.3 ± 5.7
Study II
FTLD
ALS
FTLD-ALS
29 (38 / 62)
10 (40 / 60)
1 (100 / 0)
61.6 ± 8.4
58.7 ± 6.4
58 ± 0
All the C9ORF72 carriers
Study III
FTLD
ALS
69 (42 / 58)
21 (48 / 52)
65.8 ± 9.7
61.9 ± 8.0
29 % the C9ORF72 carriers
48 % the C9ORF72 carriers
4.2 SAMPLES
The CSF and the plasma samples were collected during diagnostic procedure and stored in
polypropylene tubes at -70°C until analyzed. The CSF samples were obtained by lumbar
puncture.
4.3 ELISA
Enzyme-Linked Immunosorbent Assay (ELISA) is a method for measuring the
concentrations of the substances being examined, for example in blood or plasma. The
28
analyzed compunds are detected by monoclonal or polyclonal antibodies. The specific
antibody has been pre-coated onto a microplate. First the standards and samples are added
and incubated with a protein specific antibody. Then the unbound conjugates are washed
away and the substrate is added. The samples develop a blue color as a result of the
working solution. The reaction is stopped with sulphuric acid and the intensity of the
yellow colour developing is measured in a spectrophotometer at 450 nm. The intensity of
the colour is proportional to the concentration of the examined compound.
In study I, the granulin levels in plasma were measured in duplicate with a commercial
ELISA (AdipoGen Inc., Korea) and results were analysed blinded to diagnosis. For granulin
detection polyclonal antibody was used.
In studies I-III, the CSF Aβ 1-42, t-Tau and phospho-Tau levels were measured in our
diagnostic laboratory using a commercial ELISA (Innogenetics, Ghent, Belgium) according
to the manufacturer’s protocol. For Tau detection monoclonal antibody AT120 is used with
two biotinylated tau-spesific antibodies HT7bio and BT2bio and for p-Tau detection
monoclonal antibody HT7 is used with biotinylated AT270Bio. For Aβ-42 detection
monoclonal antibody 21F12 is used with biotinylated antibody 3D6. Samples were
measured in duplicates, and the results were analyzed blinded to diagnosis.
In study III, the CSF TDP-43 levels were measured using a commercial ELISA (Cusabio,
P.R. China) according to the manufacturer’s protocol. For TDP-43 detection the TDPspesific antibody was used. Samples were measured in triplicates, and the results were
analyzed blinded to diagnosis.
4.4 RNA, DNA AND PROTEIN ANALYSES FROM BRAIN TISSUE
In study I the RNA, DNA and proteins were extracted from frozen brain tissue samples
(Supplementary data in section 5). cDNA synthesis was performed using Dynamo qPCR kit
(Finnzymes) and cDNAs were amplified with the 7900HT quantitative PCR machine
(Applied Biosystems) using the KAPA PROBE FAST qPCR kit (Universal Probe Library,
Roche) (Supplementary data in section 5). A comparative ∆∆Ct method was used to analyze
the GAPDH-normalized granulin mRNA levels. DNA samples were genotyped for SNP
rs5848 in GRN gene using cycle sequencing (ABI PRISM® 3100 Genetic Analyzer, Applied
Biosystems). Protein lysates were analysed using Western blotting (10) with antibodies
against granulin (MAB2420; R&D Systems, Inc.) and GAPDH (ab8245; Abcam).
4.5 GENOTYPING
In studies I-III, the APOE genotyping for the three isoforms (ε2, ε3 and ε4) was done using
a PCR -based method with forward PCR primer 5’-GCA CGG CTG TCC AAG GAG CTG
CAG GC-3’ and reverse PCR primer 5’-GGC GCT CGC GGA TGG CGC TGA G-3 (Jellinger
et al. 2010, Ben-Avi et al. 2004).
In studies II and III, the C9ORF72 expansions (> 40 repeats) were detected using a repeatprimed PCR method. The repeat-primed PCR determines if the sample carries a pathogenic
expansion but it does not measure the number of the repeats in the pathogenic expansions
(Renton et al. 2011).
29
4.6 STATISTICAL ANALYSES
In study I, the statistical analyses were performed using SPSS 19. The multiple analyses
were done with ANOVA, Kruskal-Wallis, and Mann-Whitney U-test with Bonferroni
correction. The correlations were calculated with the Spearman’s correlation test.
In study II, the statistical analyses were performed using SPSS 19 and SPSS 21. The test of
normality was conducted with the Shapiro-Wilk test, and the statistical analyses were
performed using Mann-Whitney and Chi-Square. Correlations were analyzed with the
Pearson’s correlation test.
In study III, the statistical analyses were performed with SPSS 21. The tests for normality
were done using Kolmogorov-Smirnov test and Shapiro-Wilk test. The statistical analyses
were performed using a T-test, Mann-Whitney and Chi-Square. Correlations were analyzed
using with Pearson’s correlation test.
All results were given as mean ± SD unless otherwise stated. Statistical significance was
set at p < 0.05 in all the studies.
4.7 ETHICAL ASPECTS
These studies were approved by the Ethics Committees of the Kuopio (I-III) and Oulu
University Hospitals (II and III), in accordance with the principles of the Declaration of
Helsinki (II and III). All patients agreed to participate in these studies, and blood and CSF
samples were collected after obtaining written informed consent from patients and/or their
legal representatives (I-III).
55
8 Discussion
Plasma and CSF biomarkers are novel tools for early and differential diagnosis of
neurodegenerative diseases. Biomarkers may also be useful in the monitoring of drug
responses. Today there are only three known CSF biomarkers in the diagnostics of AD: Aβ42, t-Tau and phospho-Tau that are shown to reflect the AD neuropathological changes in
the brain. Changes in these biomarkers can be detected even many years before onset of
clinical symptoms. Genetic research has lead to a clarification of the molecular pathology
and to a better understanding of the pathological pathways behind the disorders. The aim
of this study was to determine the role of the known AD biomarkers in the diagnostics of
other dementing disorders and to evaluate the role of few new possible biomarkers in CSF
or plasma in neurodegenerative diseases.
8.1 CSF AD BIOMARKERS IN FTLD AND ALS
We investigated CSF AD biomarkers in 40 FTLD and ALS patients with the C9ORF72
expansion (Study II) and we also compared CSF AD biomarker levels between the
C9ORF72 expansion carriers and non-carriers in patients with FTLD and ALS (Study III).
The hypothesis was that AD biomarkers should mainly be normal in patients with the
C9ORF72 expansion, because the C9ORF72 expansion is typically associated with TDP-43
pathology.
Surprisingly, decreased CSF Aβ-42 levels were found in 25 % of cases and one or two
biomarkers were abnormal in 30 % of cases (Study II). Elevated CSF t-Tau or phospho-Tau
was found only in single cases. This finding was particularly striking as the cut-off level in
our clinical laboratory is very strict, i.e. according to our earlier studies, the false positive
rate with this test is low. An abnormal CSF t-Tau levels was found in 29 % and CSF Aβ-42
levels in 22 % of FTLD patients with the C9ORF72 non-carriers. An abnormal CSF t-Tau
levels was found in 5 % and abnormal CSF Aβ-42 levels in 20 % with the C9ORF72 carriers.
The same trend was also seen in ALS patients, 36 % of the C9ORF72 non-carriers had
abnormal CSF t-Tau levels and 10 % of the C9ORF72 carriers had abnormal CSF t-Tau
levels. In ALS patients the CSF Aβ-42 levels were abnormal in 30 % of the C9ORF72 carriers
when non-carriers did not show abnormal Aβ-42 levels at all. All three AD biomarkers
were abnormal in three FTLD patients (4 %) and in ALS patients no one had three AD
biomarkers abnormal. There was no difference in the CSF AD biomarker levels between
C9ORF72 expansion carriers and non-carriers in the different diagnostic groups (Study III).
This was also surprising, because the relatively high number of FTLD patients without the
C9ORF72 expansion may represent tau-pathology, which may have been reflected in the
elevation of CSF t-Tau and phospho-Tau levels.
There are only a few earlier studies of AD biomarkers in the patients with the C9ORF72
expansion. Wallon et al. examined a cohort of 114 AD patients and found only three (2.6 %)
the C9ORF72 expansion carriers in whom all three CSF AD biomarkers were positive. The
profile of the C9ORF72 expansion carriers was atypical AD phenotype including language
impairment and behavioural symptoms as the main symptoms. The neuropathological data
was not available, thus those patients may have been misdiagnosed as suffering from AD
instead of FTLD. (Wallon et al. 2012). In contrast to our findings, Mahouney et al. found
normal CSF AD biomarkers from five patients who were C9ORF72 expansion carriers.
Neuropathological examinations revealed TDP-43 and AD-type of pathology in one FTLDALS patient (Mahouney et al. 2012). The number of cases was relatively small in both
studies.
56
Bloudek et al. 2011 showed in their meta-analysis that as independent biomarkers
decreased CSF Aβ-42 gave a mean sensitivity of 80 % and a mean specificity of 82 %,
increased CSF t-Tau gave a mean sensitivity of 82 % and a mean specificity of 90 % and for
CSF phospho-tau a mean sensitivity of 80 % and a mean specificity of 83 % when compared
AD patients and healthy controls. Furthermore, independent CSF biomarkers decreased
CSF Aβ-42 gave a mean sensitivity of 73 % and a mean specificity of 67 %, increased CSF tTau gave a mean sensitivity of 78 % and a mean specificity of 75 % and for CSF phosphotau a mean sensitivity of 79 % and a mean specificity of 80 % when comparing AD patients
to other dementias. As earlier described in section 2.6.3 the combination of AD biomarkers
gives higher sensitivity and specificity when compared AD patients to healthy controls
than to other dementing disorders (Bloudek et al. 2011). According to this, the sensitivity
and specificity levels are lower when comparing AD to other dementing disorders. This
supports the variation in AD biomarkers with FTLD and ALS patients despite of the genetic
background. Even though the AD biomarkers are still a valuable tool in diagnostics of AD
but the value of AD biomarkers for other dementing disorders is incomplete. It is also
remarkable that one independent biomarker gives clearly lower sensitivity and spesifictiy
than combination of biomarkers.
The association between Aβ and TDP-43 has been discussed. TDP-43 pathology is also
seen in AD patients but there has been debate about whether the TDP-43 inclusions are the
same in AD, FTLD and ALS. The expression of Aβ is associated with TDP-43
phosphorylation and its accumulation into the cytosol. Furthermore, clearance of Aβ
prevents the increase of TDP-43 and therefore TDP-43 aggregation is believed to be
triggered by Aβ. It is possible that this underlying link between Aβ and TDP-43 explains
the abnormal levels of CSF Aβ-42 in the C9ORF72 expansion carriers (Chang et al. 2015,
Caccamo et al. 2010). Furthermore, AD-type neuropathology was detected in nearly one
third of the cases with the C9ORF72 FTLD patients with Tau pathology more common than
amyloid pathology (Bieniek et al. 2013, King et al. 2013), but in the study II, there were only
a few cases with abnormal levels of CSF t-Tau or phospho-Tau (Study II) while in study III
the abnormal CSF t-Tau was found in 22 % and phospho-Tau in 24 % in total cohort (Study
III). In meta-analysis by van Harten et al. it was shown that CSF t-Tau levels were elevated
in FTLD, DLB and VaD patients as compared to controls but the levels were lower
compared to AD with sensitivity of 73 % to 74 % and specificity of 74 % to 90 % (van Harten
et al. 2011). In the study of memory clinic AD type of biomarker profile was seen in 47 % of
patients with DLB, 38 % in corticobasal degeneration and almost 30 % in FTLD and VaD.
Furthermore, patients with psychiatric disorder or subjective memory impairment had
mostly normal AD biomarkers and the concordance between clinical and neuropathologic
diagnosis was 85 %. (Schoonenboom et al. 2012). As summarize the AD type of biomarker
profile seems to be relatively common also in other dementing disorders.
It is also remarkable that changes in AD biomarkers in different disease groups may be
explained by mixed neuropathology, especially in older people irrespective of the genetic
background. AD and mixed type of dementia increases significantly by age which supports
the idea of mixed pathology as the main reason for dementia in very old patients (Jellinger
& Attems 2010, MRC-CFAS 2001). The cohort of 3303 individuals showed that the
propotion of patients with mixed diagnoses was 53.3 % (Kovacs et al. 2008). Interestingly,
even though mixed AD and vascular pathology has high prevalence in older people there
are people with dementia with limited pathological findings but also people with obvious
pathological findings but no clinical dementia (Wharton et al. 2011).
8.2 CSF TDP-43 IN FTLD AND ALS
We investigated the role of a new possible biomarker CSF TDP-43 in FTLD and ALS
patients (n = 90). The C9ORF72 repeat expansion was found in one third of the patients
57
whereas the remaining patients were expansion negative (Study III). There was no
difference in the CSF TDP-43 levels between the C9ORF72 expansion carriers and noncarriers in the different diagnostic groups, but the TDP-43 levels were higher with ALS
patients in total cohort. The CSF TDP-43 levels did not correlate with the CSF Aβ-42, t-Tau
or phospho-Tau levels.
The increased plasma or CSF levels of TDP-43 in patients with FTLD or ALS have been
observed also in other previous studies. Suárez-Calvet et al. reported elevated CSF and
plasma phosphorylated TDP-43 levels by applying the sandwich ELISA in patients carrying
the C9ORF72 expansion or the GRN mutation. That study included ten C9ORF72 expansion
carriers in the 88 plasma samples and two in the C9ORF72 expansion carriers of 25 CSF
samples of FTLD patients i.e. the cohort was very small (Suárez-Calvet et al. 2013). Noto et
al. detected increased levels of CSF TDP-43 in a cohort of 29 ALS patients and 50 patients of
neurodegenerative or inflammatory disease controls. CSF TDP-43 levels were measured by
ELISA and the analysis was determined as having a sensitivity of 59.3 % and specificity of
96 %. In their study, the lower levels of CSF TDP-43 indicated a shorter survival time and
they postulated that was attributable to the higher levels of insoluble TDP-43. (Noto et al.
2011). Kasai et al. detected increased levels of CSF TDP-43 by ELISA in 30 ALS patients
compared to 29 age-matched healthy controls (Kasai et al. 2009). Steinacker et al. analyzed
CSF TDP-43 levels by TDP-43 immunoblot in a cohort of 12 FTLD patients, 15 ALS patients,
9 ALS + FTLD patients, 3 ALS patients with additional signs of frontal dishibition and 13
controls. The main finding was increased levels of CSF TDP-43 in FTLD and ALS patients
compared to controls. (Steinacker et al. 2008). The genetic background was available only in
the study of Suárez-Calvet et al. and the neuropathology of patients was not available in
any of the studies. AD biomarker levels had not been investigated in any of these reports.
There is one study of opposite findings with respect to the TDP-43 levels. Feneberg et al.
studied the levels of TDP-43 in CSF, lymphocytes and serum by one and two dimensional
Western blotting and quantitative mass spectrometry. They claimed that the TDP-43 that
they detected in CSF had mainly originated from blood and thus they concluded that CSF
or blood TDP-43 levels would not be a useful diagnostic tool for neurodegenerative
diseases. Their cohort was relatively small (n = 13) and their genetic backgrounds or the
neuropathology were not available (Feneberg et al. 2014).
It is known that TDP-43 is normally present in nucleus but in situations of neuronal
damage, the pathological form of TDP-43 is ubiquitinated, phosphorylated and then
cleaved and translocated into the cytoplasm where it forms stress granules. In AD, it has
been shown that the levels of AD biomarkers can be linked to a progression of the disease
(Seppälä et al. 2011). This supports the concept that the increased CSF TDP-43 levels
associated with ALS are attributable to the more rapid rate of the neurodegenerative
process. In ALS, there is a massive damage of neurons which may explain the increased
levels of CSF TDP-43. It is not clear whether the duration of disease in ALS patients has any
effect on levels of CSF TDP-43. In conclusion, according to the studies conducted by our
group and others, the CSF levels of TDP-43 are of no value if one wishes to distinguish the
C9ORF72 expansion carriers from the non-carriers.
8.3 GRANULIN LEVELS AND RS 5848 IN AD PATIENTS
It was found that AD patients had higher plasma granulin levels than controls, but the
presence of the A allele of rs5848 in GRN led to a dose-dependent reduction in plasma
granulin levels. Men had lower levels of plasma granulin than women and men carrying
the A allele displayed also a dose-dependent reduction in plasma granulin levels (Study I).
PGRN is the precursor of granulins which are neurotrophic factors that enhance
neuronal survival and are involved in assisting axon guidance. Over 70 different mutations
in PGRN have been reported in patients with FTLD. Most of the PGRN mutations are null
58
mutations that cause loss of normal functions of granulin or haploinsufficiency (Lee et al.
2011, Cruts et al. 2006, Gass et al. 2006). Since the genetic variation of PGRN has been linked
to multiple neurodegenerative diseases, it may be a potentially valuable biomarker and
therapeutic target.
Chen et al. conducted a meta-analysis of 16 studies of PGRN polymorphism in different
neurodegenerative diseases (AD, FTLD, ALS, PD) and found that that a single-nucleotide
polymorphism (SNP) rs 5848 was associated with an increased risk of neurodegenerative
diseases. (Chen et al. 2015). FTLD and AD patients carrying the A allele in the rs 5848 of
PGRN have shown reduced granulin levels in plasma (Hsiung et al. 2011, Fenoglio et al.
2009, Rademakers et al. 2008). Decreased levels of granulin have been proposed to trigger
neurodegeneration (Viswanathan et al. 2009, Brouwers et al. 2008). This present study is
also in agreement with some others, i.e. those patients with the A allele have reduced levels
of granulin in plasma (Hsiung et al. 2011, Lee et al. 2011, Fenoglio et al. 2009, Viswanathan
et al. 2009, Brouwers et al. 2008, Rademakers et al. 2008). Nicholson et al. studied the
relationship between plasma and CSF PGRN levels finding that both plasma and CSF
PGRN levels increased with age but the correlation between plasma and CSF PGRN, age,
sex and genetic factors affect differentially to PGRN levels (Nicholson et al. 2014). Larger
sample cohorts will be needed before it will be possible to draw any firm conclusions about
the usefulness of PGRN in the diagnostics of AD or whether there is a gender-specific risk
effect of rs5848 in AD.
8.4 MEASUREMENTS OF CSF OR PLASMA BY ELISA
CSF is produced partly by the choroid plexus and partly by the interstitial fluid from the
brain parenchyma. CSF is in direct contact with the brain and therefore many changes in
brain metabolism are reflected in CSF. In AD, the changes in the biomarker levels are
affected by the neuropathological changes occurring in the brain i.e. there are decreases in
the levels of Aβ-42 as the amyloid plaques are being produced, and t-Tau and phospho-Tau
increase as the neurodegenerative process proceeds. Blood-brain-barrier causes changes in
levels of different biomarkers between CSF and plasma or serum. Furthermore, many
possible biomarkers are produced both in the central nervous system and other parts of the
body. Therefore, CSF tends to be more often used to find biomarkers for neurodegenerative
diseases. Unfortunately, CSF sample collection from lumbar punction is an invasive
procedure and even although the risk is low, there is a possibility for complications.
Therefore, a plasma bound biomarker would be preferable since obtaining a blood sample
is a routine procedure which can be done in everyday clinical practice. Many studies are
underway attempting to identify novel biomarkers in plasma or serum but they have not
revealed any potential biomarkers which could be useful in the clinics.
ELISA (Enzyme-Linked Immunosorbent Assay) is a widely used technique in biomarker
diagnostics. The CSF and plasma measurements conducted in this thesis were undertaken
with commercial ELISA kits. One limitation of ELISA is that it is an antibody based
method, and therefore the accuracy of the ELISA is dependent on the quality and affinity of
the commercial antibody. Different antibodies may recognize slightly different forms of the
compound that is to be measured even although they are supposed to have been raised
against the same compound. This may lead to different results when using different kits
even if they are meant to be analyzing the same possible biomarker. This may explain some
of the conflicting results between studies examining the same biomarkers. Many of the
putative biomarkers are present at very low concentrations in body fluids, making precise
quantitative detection extremely difficult. In addition, possible cross-reactivity with
molecules other than the study’s biomarker candidate may lead to erroneous results.
Furthermore, some antibodies carried by the patient whose sample is being measured may
affect the values reported by the test. Therefore before being adopted for clinical use, the kit
59
and the work flow from the clinic to the laboratory need to be carefully validated. In other
words, the values obtained with the ELISA technique depend largely on the quality of used
antigens and kits and the skills of the operator.
In the work described in this thesis, AD biomarker levels were measured with a
commercial ELISA kit and the method had been validated earlier in our laboratory and
used in diagnostics for hospitals (Herukka et al. 2005). Even though this kit has been used
for over ten years in numerous laboratories, the results obtained with the kit have tended to
vary in different laboratories; this was observed when the same samples were measured in
laboratories participating in an on-going quality control scheme (Mattsson et al. 2013). This
finding emphasizes the fact that when studying biomarkers in a research setting, one needs
to interpret the results with great caution before embarking on any clinical use; for example
even if differences are found between diagnostic groups, there are still many steps to take
before the novel biomarker will reach the clinics. In the case of AD CSF biomarkers, the
solution to the problem of the varying levels between different laboratories has been to
determine cut-off points for each laboratory, not to use one universal value.
The limitations of ELISA method were manifested in study III. According to a normal
validation procedure, before initiating the actual measurements, the kit was tested first by
measuring CSF samples not intended for inclusion in this study. The most significant
finding was that there was a notable drift in the measured values. The kit gave significantly
higher values at the beginning of the plate than at the end of the plate even for the same
samples. Furthermore, there was higher than acceptable variance between the individual
measurements from a single sample. Therefore, the analyses used in the publication were
done by using a half of the plate in one run and all samples were measured in triplicate
instead of the normal duplicate analysis. This emphasizes that if CSF TDP-43 levels are to
be measured by ELISA in the future, the commercial kit will need to be improved to ensure
that its results are reliable and reproducible.
8.5 STRENGTH AND LIMITATION OF THE STUDY
One major strength of the study is detailed clinical and neuropsychological data which was
available for a large number of patients as well as a comprehensive medical history, CSF
AD biomarker values and a knowledge of the genetic background. The cohorts in studies of
this thesis were rather large compared to previous studies, but nonetheless the cohorts
would need to be even larger if one wishes to do conduct a validation of biomarkers to be
used in the clinic. Though, the neuropathological data was not available from all of the
patients included to the studies. There are certain limitations associated with the use of
ELISA that have been mentioned above.
8.6 FUTURE INSIGHTS
It is evident that specific biomarkers can be very useful in several scenarios; to assist in the
early diagnosis of dementing disorders, to help in differential diagnosis and to investigate
disease modifying drugs and to monitor novel treatments for neurodegenerative diseases.
If one wishes to develop and validate biomarkers, it is necessary to conduct large cohort
studies in subjects with a known genetic background and a recognized neuropathology
behind the diseases. Further studies to clarify the neuropathology will be needed to reveal
the molecular mechanisms behind these devastating neurodegenerative diseases.
This study determined levels of known CSF AD biomarkers in different cohorts as well
as evaluating putative new biomarkers in plasma and CSF. Interesting results were
achieved but new biomarkers (plasma granulin, CSF TDP-43) for neurodegenerative
60
diseases in clinics were not found. In generally biomarker studies need larger cohorts, the
method of choice has to be sensitive and specific enough to be used as a tool in clinics.
One possible approach would be to analyze CSF TDP-43 and AD biomarker levels in a
cohort of AD, FTLD and ALS patients to determine whether there is a link between the CSF
levels of TDP-43 and Aβ. It would be interesting to determine both CSF and plasma TDP-43
levels to clarify whether there is any linkage between the levels in these different body
fluids. Another interesting trial would be to use CSF (or plasma) TDP-43 levels in a followup-study in different disease groups to determine longitudinal changes in their
concentrations. In this kind of follow-up-study, it may also be possible to determine
whether the levels of CSF (or plasma) TDP-43 predict survival time of the patient.
Biomarker studies with new methods are under an intensive research and have revealed
potential biomarkers for neurodegenerative diseases. Furthermore, in future the studies of
biomarkers may be focusing more on biomarker panels in diagnostics and monitoring
biological and pathological processes. As earlier described, the AD biomarkers are more
sensitive and specific to AD when used as combination than as independent biomarker so it
is quite obvious that also in future the diagnostics of neurodegenerative diseases will be
based on combination of biomarkers (and other tasks like neuroimaging) rather than in one
specified biomarker. E.g miRNA studies and omics have revealed plenty of potential
biomarkers and this data conducted with traditional biomarker data, genetic and
neuropathological data may be the potential way for finding new manners of an approach
to neurodegerative diseases.
61
9 Conclusions
1. The dose-dependent reduction in the granulin levels in plasma and brain due to the
presence of the A allele of rs5848 corroborate the proposal that this genetic variation
causes a functional change in GRN.
2. Changes in CSF AD biomarkers are seen in patients with the C9ORF72 expansion.
Thus, especially the finding of a decreased level of CSF Aβ-42 does not exclude the
possibility of the C9ORF72 associated FTLD in clinical diagnostics.
3. CSF TDP-43 concentrations cannot distinguish the C9ORF72 expansion carriers from
the non-carriers and therefore the assay of CSF TDP-43 is not valid in the differential
diagnostics of FTLD and ALS, but it is possible that elevated levels of CSF TDP-43
may be a marker of the rapid progression of those neurodegenerative diseases
associated with TDP-43 pathology.
62
63
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Dementing disorders are caused
by progressive neurodegeneration,
leading to loss of episodic memory
and other cognitive functions and
disabilities impairing independent
daily living. The prevalence
of dementia is increasing and
pathological processes in the brain
occur over 10-20 years before the
onset of clinical symptoms. The
aim of this study was to investigate
different biomarkers in cerebrospinal
fluid and plasma to find possible
tools for early and differential
diagnosis of Alzheimer’s disease and
Frontotemporal lobar degeneration.
Publications of the University of Eastern Finland
Dissertations in Health Sciences
isbn 978-952-61-1941-0
issn 1798-5706
dissertations | 310 | Anna Junttila | Cerebrospinal Fluid and Plasma Biomarkers in the Differential Diagnosis of...
Anna Junttila
Cerebrospinal Fluid and
Plasma Biomarkers in
the Differential Diagnosis
of Neurodegenerative
Diseases
Anna Junttila
Cerebrospinal Fluid and
Plasma Biomarkers in
the Differential Diagnosis
of Neurodegenerative
Diseases
Publications of the University of Eastern Finland
Dissertations in Health Sciences No 310