D936etukansi.fm Page 1 Thursday, May 31, 2007 2:13 PM
D 936
OULU 2007
UNIVERSITY OF OULU P.O. Box 7500 FI-90014 UNIVERSITY OF OULU FINLAND
U N I V E R S I TAT I S
S E R I E S
SCIENTIAE RERUM NATURALIUM
Professor Mikko Siponen
HUMANIORA
TECHNICA
Professor Harri Mantila
Professor Juha Kostamovaara
MEDICA
Professor Olli Vuolteenaho
SCIENTIAE RERUM SOCIALIUM
Senior Assistant Timo Latomaa
ACTA
U N I V E R S I T AT I S O U L U E N S I S
Firoozeh Mousavinasab
E D I T O R S
Firoozeh Mousavinasab
A
B
C
D
E
F
G
O U L U E N S I S
ACTA
A C TA
D 936
EFFECTS OF LIFESTYLE
AND GENETIC FACTORS
ON THE LEVELS OF SERUM
ADIPONECTIN, A NOVEL
MARKER OF THE METABOLIC
SYNDROME, IN FINNISH
SERVICEMEN
SCRIPTA ACADEMICA
Communications Officer Elna Stjerna
OECONOMICA
Senior Lecturer Seppo Eriksson
EDITOR IN CHIEF
Professor Olli Vuolteenaho
EDITORIAL SECRETARY
Publications Editor Kirsti Nurkkala
ISBN 978-951-42-8503-5 (Paperback)
ISBN 978-951-42-8504-2 (PDF)
ISSN 0355-3221 (Print)
ISSN 1796-2234 (Online)
FACULTY OF MEDICINE,
DEPARTMENT OF PUBLIC HEALTH SCIENCE AND GENERAL PRACTICE,
UNIVERSITY OF OULU;
DEPARTMENT OF MEDICINE,
UNIVERSITY OF KUOPIO;
UNIT OF GENERAL PRACTICE,
OULU UNIVERSITY HOSPITAL;
OULU CITY HEALTH CENTRE AND OULU DEACONESS INSTITUTE
D
MEDICA
ACTA UNIVERSITATIS OULUENSIS
D Medica 936
FIROOZEH MOUSAVINASAB
EFFECTS OF LIFESTYLE AND
GENETIC FACTORS ON
THE LEVELS OF SERUM
ADIPONECTIN, A NOVEL MARKER
OF THE METABOLIC SYNDROME,
IN FINNISH SERVICEMEN
Academic dissertation to be presented, with the assent of
the Faculty of Medicine of the University of Oulu, for
public defence in the Auditorium of Kastelli Research
Centre (Aapistie 1), on June 26th, 2007, at 12 noon
O U L U N Y L I O P I S TO, O U L U 2 0 0 7
Copyright © 2007
Acta Univ. Oul. D 936, 2007
Supervised by
Professor Sirkka Keinänen-Kiukaanniemi
Professor Markku Laakso
Reviewed by
Professor Jaakko Kaprio
Docent Jorma Lahtela
ISBN 978-951-42-8503-5 (Paperback)
ISBN 978-951-42-8504-2 (PDF)
http://herkules.oulu.fi/isbn9789514285042/
ISSN 0355-3221 (Printed)
ISSN 1796-2234 (Online)
http://herkules.oulu.fi/issn03553221/
Cover design
Raimo Ahonen
OULU UNIVERSITY PRESS
OULU 2007
Mousavinasab, Firoozeh, Effects of lifestyle and genetic factors on the levels of serum
adiponectin, a novel marker of the metabolic syndrome, in Finnish servicemen
Faculty of Medicine, Department of Public Health Science and General Practice, University of
Oulu, P.O.Box 5000, FI-90014 University of Oulu, Finland; Department of Medicine, University of
Kuopio, Yliopistonranta 1 A, FI-70210 Kuopio, Finland; Unit of General Practice, Oulu University
Hospital, P.O. Box 22, FI-90029 OYS, Finland; Oulu City Health Centre, P.O. Box 8, FI-90015
Oulun kaupunki, Finland; Oulu Deaconess Institute, Uusikatu 50, FI-90100 Oulu, Finland
Acta Univ. Oul. D 936, 2007
Oulu, Finland
Abstract
Metabolic syndrome (MetS) is a combination of disorders that increase one's risk for type 2 diabetes
(DM2) and cardiovascular disease (CVD). Both lifestyle and genetic factors have been established to
be involved in the aetiology of MetS. Improving our knowledge about the pathophysiology of MetS
could provide more effective therapeutic approaches and reduce the risk of developing DM2 and
CVD. Lower levels of adiponectin, an adipose-derived protein, has been shown to be associated with
the components of MetS. Common variants in a number of candidate genes related to MetS have been
shown to be associated with changes in the serum adiponectin level.
This study was designed to evaluate the putative effects of military lifestyle, as well as common
polymorphisms of the peroxisome proliferator activated receptor gamma 2 (PPARγ2), insulin
receptor substrate-1 (IRS-1) and adiponectin (APM1) genes on serum adiponectin level in a cohort of
Finnish servicemen.
Results of this study have showed that serum adiponectin significantly decreased during the sixmonth follow up in military service compared to baseline levels. This decrease was even shown in
subjects that experienced a 5-10 % weight loss after six-months. Subjects with the Ala12Ala genotype
of PPARγ2 had significantly higher levels of serum adiponectin compared with subjects with the
Pro12Ala and Pro12Pro genotypes. Subjects having the X12Ala genotype of PPARγ2 with > 10%
weight reduction showed a significant increase in serum adiponectin compared to other groups during
the follow up. Those having the Ala12Ala genotype of PPARγ2 + Gly972Gly genotype of IRS-1
combination had significantly higher adiponectin compared with subjects with the
Pro12Pro + Gly972Gly and Pro12Ala + Gly972Gly genotype combinations. Adiponectin levels were
significantly higher in men with the T276T genotype compared with subjects with the G276T or
G276G genotypes of SNP+276 of the APM1 gene.
In conclusion, this study shows a possible impact of a military lifestyle as well as, candidate gene
variations, and their interactions upon the regulation of serum adiponectin levels as a marker of MetS.
This study could serve as a pilot for the further extensive studies with longer follow up periods as well
as more accurate information on specific lifestyle factors.
Keywords: metabolic syndrome, adiponectin, genes, military lifestyle, plasma lipids
"I prefer a short life with width to a narrow one with
length".
Stated Pur Sina (Avicenna), Iranian famous physician
(980–1037AD), in response to his colleagues and friends,
who advised him to slow down and take life in
moderation.
Dedicated to: Iman & Negin
6
Acknowledgment
This study was mostly carried out at the Department of Public Health Science and
General Practice of the University of Oulu during the years 2002–2007. Here I
would like to offer my sincere thanks to all of those who have contributed to this
project.
First and foremost I thank God, the Lord of the world, who holds everything
in his hands and without whose help we are not able to do anything.
I wish to offer my special thanks to my main supervisor, Professor Sirkka
Keinänen-Kiukaanniemi MD, PhD, Head of the Department of Public Health
Science and General Practice of the University of Oulu. Without her support,
help, encouragement and valuable advice during these years, the completion of
this work would not have been possible. Besides of being an excellent supervisor
she was a good friend who listened to me whenever there was a need. I am really
glad that I had the chance to get know Sirkka Keinänen-Kiukaanniemi in my life.
I am also indebted to my second supervisor, Professor Markku Laakso, MD,
PhD, from the Department of Medicine at the University of Kuopio, whose
valuable professional advice toward the completion of this thesis is truly
appreciated. In addition, I thank him for conducting the genotyping of the
candidate genes in the laboratory of Kuopio University Hospital. Working with
Professor Markku Laakso was an honour and a pleasure to me.
I also offer my thanks to Mr. Jari Jokelainen M.Sc, from the Department of
Public Health Science and General Practice, University of Oulu, for his guidance
and helpful advice as well as comments on the area of statistics. His experience
played a very important role in the completion of this project.
Thanks are also due to the Mr. Martti Lampela from the Department of Public
Health Science and General Practice, University of Oulu, who checked the format
of the final version of my thesis and helped in many other ways.
I would also like to thank Mr. Markku Koiranen from the Department of
Public Health Science and General Practice, for giving some information about
the data at the beginning of my study.
I am grateful to Docent Pentti Koskela and the laboratory staff of the National
Public Health Institute for their kind helps with the measurements of serum
adiponectin. I also thank Docent Pentti Koskela for reading the articles and giving
suggestions for their improvement.
7
A sincere thank to Professor Jaakko Kaprio MD, PhD, from the Department
of Public Health, University of Helsinki and Docent Jorma Lahtela MD, PhD,
from the Department of Medicine, University of Tampere, who have reviewed
draft of the thesis. Their professional input, comments and constructive criticisms
on this thesis are greatly appreciated.
I would like to thank Professor Markku Timonen MD, PhD, from the
Department of Public Health Science and General Practice, University of Oulu,
for reading the final version of my thesis and offering suggestions for
improvements.
I am also deeply grateful to my colleagues of the Diabetes Group for their
kindness and help, especially Dr. Tuula Tähtinen, whose previously selected
cohort has been used in the present study. Dr. Mauno Vanhala and Dr. Jorma
Oikarinen my co-authors, are also gratefully acknowledged for their help in
collecting the data at the beginning of study and kind co-operation.
The entire staff of the Department of Public Health Science and General
Practice, University of Oulu, are gratefully acknowledged for having created a
pleasant and collaborative atmosphere. I am especially thankful to Mrs. Ritva
Mannila and Mrs. Aino Räinä for their kind help whenever I was in need of some.
I would also like to thank the study nurses Pirjo Härkönen and Eero
Saastamonen from Oulu Deaconess Institute for their great impact on the
collection of primary data and the blood samples as well as for their kind help
during the whole study period.
I am grateful to Professor Victor Benno Meyer-Rochow from JACOBS
University, Bremen, Germany and Department of Physiology, University of Oulu,
for the English language revision of this thesis.
I acknowledge the financial support I received during these years from the
University of Oulu, Oulu University Hospital and Finnish Diabetes Association
(Diabetestutkimussäätiö).
I offer my sincerest thanks to my dear husband Saeid and our lovely children
Iman and Negin who have always reminded me what is really important in the
life. Thank you for being my wonderful co-workers in our common project: life. I
specially thank Saeid for being supportive during these years.
I am also grateful to my dear brother Fazlollah who, besides of being a
wonderful brother, has been a supportive friend of mine since our childhood.
I also want to thank my dear maternal aunt, Fakhri who has always spiritually
supported and cared for me during my life.
8
Finally but immensely, special thanks to my loving parents Reza and Aghdas
for their devotion and support over the years and across the distance. They pushed
me forward during this study with questions like: “Hasn’t your project been done
yet?!” Writing is not sufficient to show my deep appreciation and feeling toward
them.
Oulu, May 2007
Firoozeh Mousavinasab
9
10
Abbreviations
A
AACE/ACE
Acrp30
Adipo R
Ala
AMPK
ANCOVA
ANOVA
APM1
Arg
ATP III
ATP
BMI
C
CHD
CNS
C-peptide
CRP
CVD
DBP
DM2
DNA
EGIR
FFA
G
GBP28
Gly
HAPMAP
HDL
HMW
HOMA
HPA
HRP
IDF
Adenine
American Association of Clinical Endocrinologists / American
College of Endocrinology
Adipocyte complement-related protein of 30 kDa
Adiponectin receptor
Alanine
5’ adenosine monophosphate-activated protein kinase
Analysis of covariance
Analysis of variance
Adipose most abundant gene transcript 1
Arginine
Adult Treatment Panel III
Adenosine triphosphate
Body mass index
Cytosine
Coronary heart disease
Central nervous system
Connecting peptide
C reactive protein
Cardiovascular disease
Diastolic blood pressure
Type 2 diabetes mellitus
Deoxyribonucleic acid
European Group for the Study of Insulin Resistance
Free fatty acids
Guanine
Gelatin binding protein of 28 kDa
Glycine
Haplotype map
High density lipoprotein
High molecular weight
Homeostasis model assessment
Hypothalamic-pituitary-adrenocortical
Horseradish peroxidase
International Diabetes Federation
11
IFG
IGT
IL
IRS
IVGTT
LD
LDL
LMW
LP (a)
MetS
NCEP
NEFA
NHLBI/AHA
ODL
OGTT
PAI
PCOS
PCR
PI 3-kinase
PPAR
PPRE
Pro
PUFA
QUICKI
RNA
RXR
SAS
SBP
SFA
SGA
SNP
SPSS
T
TG
TGF
TNF
12
Impaired fasting glucose
Impaired glucose tolerance
Interleukin
Insulin receptor substrate
Intravenous glucose tolerance test
Linkage disequilibrium
Low density lipoprotein
Low molecular weight
Lipoprotein (a)
Metabolic syndrome
National Cholesterol Education Program
Nonesterified fatty acids
The National Heart, Lung, and Blood Institute/American Heart
Association
Oulu Deaconess Institute
Oral glucose tolerance test
Plasminogen activator inhibitor
Polycystic ovarian syndrome
Polymerase chain reaction
Phosphatidylinositol 3-kinase
Peroxisome-proliferator-activated receptor
PPAR response elements
Proline
Polyunsaturated fatty acid
Quantitative insulin sensitivity check index
Ribonucleic acid
Retinoid X receptor
Statistical Analysis System
Systolic blood pressure
Saturated fatty acids
Small for gestational age
Single nucleotide polymorphism
Statistical Package for the Social Sciences
Thymine
Triglycerides
Transforming growth factor
Tumour necrosis factor
TZD
U.S
VEGF
WAT
WHO
WHR
Thiazolidinedione
United state
Vascular endothelial growth factor
White adipose tissue
World Health Organization
Waist to hip ratio
13
14
List of original articles
I
Mousavinasab F, Tähtinen T, Jokelainen J, Koskela P, Vanhala M, Oikarinen J
& Keinänen-Kiukaanniemi S (2005) Lack of increase of serum adiponectin
concentrations with a moderate weight loss during six months on a highcaloric diet in military service among a young male Finnish population.
Endocrine 26 (1): 65–9.
II
Mousavinasab F, Tähtinen T, Jokelainen J, Koskela P, Vanhala M, Oikarinen
J, Keinänen-Kiukaanniemi S & Laakso M (2005) Effect of the Pro12Ala
polymorphism of the PPARg2 gene on serum adiponectin changes. Endocrine
27 (3): 307–9.
III Mousavinasab F, Tähtinen T, Jokelainen J, Koskela P, Vanhala M, Oikarinen
J, Keinänen-Kiukaanniemi S & Laakso M (2005) Common polymorphisms in
the PPARgamma2 and IRS-1 genes and their interaction influence serum
adiponectin concentration in young Finnish men. Mol Genet Metab 84 (4):
344–8.
IV Mousavinasab F, Tähtinen T, Jokelainen J, Koskela P, Vanhala M, Oikarinen
J, Keinänen-Kiukaanniemi S & Laakso M (2006) Common polymorphisms
(single-nucleotide polymorphisms SNP+45 and SNP+276) of the adiponectin
gene regulate serum adiponectin concentrations and blood pressure in young
Finnish men. Mol Genet Metab 87 (2): 147–51.
15
16
Contents
Abstract
Acknowledgment
7
Abbreviations
11
List of original articles
15
Contents
17
1 Introduction
19
2 Review of the literature
21
2.1 Metabolic syndrome................................................................................ 21
2.2 Prevalence of the metabolic syndrome ................................................... 25
2.3 Underlying factors of the metabolic syndrome ....................................... 26
2.3.1 Obesity ......................................................................................... 27
2.3.2 Insulin resistance .......................................................................... 34
2.3.3 Other underlying factors of the metabolic syndrome ................... 37
2.3.4 Finnish military service lifestyle .................................................. 40
2.3.5 Genetic Susceptibility to the metabolic syndrome ....................... 41
2.3.6 Peroxisome-Proliferator-Activated Receptor (PPAR)
genes............................................................................................. 42
2.3.7 Adiponectin gene.......................................................................... 49
2.3.8 Insulin receptor substrate (IRS) molecules and IRS-1 gene ......... 53
2.4 Genetic Epidemiology............................................................................. 54
2.4.1 Genetic epidemiology of the metabolic syndrome ....................... 57
2.5 Summary of the literature review............................................................ 58
3 Purpose of the present study
61
4 Subjects and Methods
63
4.1 Study Cohort ........................................................................................... 63
4.2 Measurements ......................................................................................... 65
4.2.1 Anthropometric measurements, medical history and
lifestyle assessments..................................................................... 65
4.2.2 Laboratory measurements ............................................................ 66
4.2.3 Estimation of insulin resistance.................................................... 66
4.2.4 Genotyping ................................................................................... 67
4.3 Statistical Analyses ................................................................................. 68
4.4 Ethical considerations ............................................................................. 69
5 Results
71
17
5.1 Baseline characteristics of study subjects and changes of values
during six months in military service (I) ................................................. 71
5.1.1 Loss analysis and sample selection effects ................................... 71
5.2 Influence of weight changes over the six-month follow-up on
serum adiponectin level (I)...................................................................... 74
5.3 Influence of common variations of PPARγ2 gene on baseline
serum adiponectin levels and interaction of these variations with
weight changes on serum adiponectin level (II & III)............................. 76
5.4 Interactive effect of the common variations of the PPARγ2 and
IRS-1 genes on serum adiponectin level at baseline (III) ........................ 78
5.5 Association of common variations of the adiponectin gene on
serum adiponectin level at baseline (IV)................................................. 79
6 Discussion
81
6.1 Evaluation of the study cohort and methods ........................................... 81
6.2 Major results of the study........................................................................ 82
6.2.1 Military service lifestyle and changes of serum
adiponectin level (I)...................................................................... 82
6.2.2 Influence of weight change on regulation of serum
adiponectin level (I)...................................................................... 84
6.2.3 Influence of PPARγ2 and IRS-1 common variations and
their interaction on regulation of serum adiponectin level
(II & III)........................................................................................ 85
6.2.4 Influence of the adiponectin gene common variations on
regulation of serum adiponectin level (IV)................................... 87
6.3 Summary and Conclusions...................................................................... 88
References
91
Original articles
115
18
1
Introduction
Over the past two decades an increase in the number of individuals with
metabolic syndrome (MetS) worldwide was observed (Eckle et al. 2005). This
increase was shown to be associated with elevated risks of type 2 diabetes (DM2)
(Zimmet et al. 2001) and cardiovascular diseases (CVD) (Grundy 2004).
Individuals with MetS are twice as likely to die from, and have a threefold higher
risk of developing heart attack or stroke compared with people without the
syndrome. They have also a five-fold greater risk of developing DM2 (Isomaa et
al. 2001, Stern et al. 2004). The importance of screening for MetS is that it helps
identify people at high risk of both DM2 and CVD (Alberti et al. 2005). Presence
of MetS according to the definition of the National Cholesterol Education
Program (NCEP) increased the risk for total and cardiovascular mortality by 40–
60% in a sample of 2322 middle-aged men followed for maximum 32.7 years. It
may indicate clinical value in diagnosing MetS in primary care (Sundström et al.
2006).
So far several definitions for this syndrome have been offered. Recently the
International Diabetes Federation (IDF) presented a new definition for the
syndrome and a waist circumference was considered a compulsory component of
the syndrome (Alberti 2005). About 47 million US residents have MetS
(including diabetes), corresponding to 22% of men and 24% of women aged ≥ 20
years (Ford et al. 2002). In Finland, using World Health Organization (WHO)
criteria for the definition of MetS, overall prevalence of MetS was ~30% in a
population-based cohort (FINRISK) in subjects aged 45–64 years and ~75% with
impaired glucose tolerance (IGT) according to the Finnish Diabetes Prevention
Study (Ilanne-Parikka et al. 2004).
MetS is caused by a combination of lifestyle and genetic factors. Clustering
of MetS in families indicated that there may be a genetic component for this
syndrome. It has been suggested that so-called thrifty genes, which have ensured
energy storage during periods of starvation, could contribute to the development
of diseases like obesity, diabetes and hypertension (Neel 1962). Common variants
in a number of candidate genes such as peroxisome proliferator activated receptor
gamma 2 (PPARγ2) gene have been associated with insulin resistance in many
studies .
Recent studies have shown that adipose tissue is not just a passive energy
storage depot but is also known to express and secrete a variety of bioactive
peptides, known as adipokines, which act at both local (autocrine/paracrine) and
19
systemic (endocrine) levels. In addition to the efferent signals, adipose tissue
expresses numerous receptors that allow it to respond to afferent signals from
traditional hormone systems as well as the central nervous system (CNS) (Wiecek
et al. 2002, Kershaw & Flier 2004).
Adiponectin is one of the adipocyte-derived molecules (Scherer et al. 1995),
which has plural bio-functions such as anti-diabetic, anti-atherosclerosis, and antiinflammatory ones. The circulating adiponectin concentrations have been shown
to decrease with an accumulation of visceral adipose tissue (Arita et al. 1999).
Different environmental factors related to an increase in visceral fat and
hypoadiponectinemia are therefore involved in the development of the MetS
(Arita et al. 1999).
The present study aimed to investigate the effect of a military lifestyle, some
candidate genes´ polymorphisms and their interactions on the level of serum
adiponectin in a group of Finnish servicemen during a six-month period of
military service.
20
2
Review of the literature
2.1
Metabolic syndrome
The MetS was first identified by Kylin in 1923, who noted that hypertension,
hyperglycaemia and gout tend to cluster together (Kylin 1923). Later in 1947,
Vague drew attention to upper body adiposity (android or male-type obesity) as
an obesity phenotype that was commonly related to metabolic abnormalities
associated with DM2 and CVD (Vague 1947). The modern definition of MetS
including obesity, hypertension, diabetes and hyperlipidemia was first suggested
in the 1960s. The metabolic syndrome term was first used by German researchers
like Haller and colleagues (Vitarius 2005). In 1988 Reaven described a cluster of
several cardiovascular risk factors including insulin resistance, hyperglycaemia,
hypertension, low level of high-density-lipoprotein (HDL) cholesterol and raised
triglycerides (TG). He suggested that insulin resistance and compensatory
hyperinsulinaemia underlie the cluster of these metabolic disorders and called it
Syndrome X (Reaven 1988). In early 1990 Ferrannini suggested that the
underlying cause of the syndrome was insulin resistance, therefore favouring the
term insulin resistance syndrome (Ferrannini et al. 1991). Thereafter this
syndrome was given several names including insulin resistance syndrome,
cardiovascular MetS, deadly quartet (Kaplan 1989, DeFronzo & Ferrannini 1991)
and dysmetabolic syndrome (Groop & Melander 2001).
In 1998 the WHO proposed a definition for the syndrome (Table1) and chose
to name it the “Metabolic Syndrome” rather than insulin resistance syndrome
(Alberti & Zimmet 1998). The WHO definition of MetS was based on an oral
glucose tolerance test. Obesity in the WHO definition was assessed by either
body mass index (BMI) or waist-to-hip ratio (WHR). Furthermore
microalbominuria was introduced as one of the four components of the syndrome.
For subjects with normal glucose tolerance, insulin resistance measurement was
needed to assess the occurrence of MetS (Alberti & Zimmet 1998). A problem
with the WHO definition of MetS was that with using the suggested cut-off point
for WHR, about 70% of normoglycaemic men became obese (Isomaa et al.
2001). Another major problem with the WHO definition of the MetS was the
definition of insulin resistance for subjects with normal glucose tolerance. The
glycaemic clamp technique is the golden standard for the measurement of insulin
resistance (Ferrannini & Mari 1998), but it is time-consuming and expensive to
use in epidemiologic studies. The homeostasis model assessment (HOMA),
21
fasting insulin, frequently sampled intravenous glucose tolerance test (Wallace &
Matthews 2000), or quantitative insulin sensitivity check index (QUICKI) (Arie
et al. 2000) have been used as surrogate markers for the measurement of insulin
sensitivity. Selection of the method and cut-off values for the method can result in
different estimates of the prevalence of insulin resistance. Furthermore, since the
prevalence of microalbuminuria is very low among subjects with normal or
impaired glucose tolerance, this component does not have much influence on the
prevalence of MetS in non-diabetic subjects (Isomaa et al. 2001).
In 1999 the European Group for the Study of Insulin Resistance (EGIR)
(Balkau & Charles 1999) and subsequently the National Cholesterol Education
Program’s Adult Treatment Panel (NCEP ATP III) (Expert Panel on Detection,
Evaluation, and Treatment of High Blood Cholesterol in Adults 2001) announced
new definitions for the syndrome (Table1). According to the EGIR group, the
insulin resistance syndrome term was preferred and the definition was restricted
to non-diabetic subjects. Waist circumferences were used as a measure of obesity
and EGIR did not accept microalbuminuria as a component of the MetS. The ATP
III definition of MetS was based on fasting values of TG, HDL cholesterol and
glucose, and the measurement of waist circumference and blood pressure
(Table1). Thereafter in 2003 the AACE/ACE (American Association of Clinical
Endocrinologists/American College of Endocrinology) defined the syndrome and
preferred the term insulin resistance syndrome. According to this definition
insulin resistance defined as having at least one of the nine CVD risk factors
(Table 1) in combination with at least two of four abnormalities listed in Table 1
(Bloomgarden 2004).
Because the risk of DM2 is present at much lower levels of adiposity in Asian
rather than European populations (Tan et al. 2004), a need for ethnic-specific cutoff points, at least for obesity, was suggested. Recently the International Diabetes
Federation (IDF) has offered a new definition for MetS. Central obesity, assessed
by waist circumference was agreed to be a compulsory component of the
definition (Table1) and ethnic specific values for it are separately defined (Alberti
2005) (Table 2).
22
23
Waist circumference
≥ 94 cm in men, ≥ 80 cm in cm (> 40 in) in men and
women
Dyslipidaemia:
TG > 177 mg/dL
(≥ 2.0 mmol/L) and/or
BMI > 30 kg/m2and/or
waist: hip ratio > 0.9 in
men > 0.85 in women
Raised plasma
triglycerides:
≥ 150 mg/dL (≥ 1.7
Low HDL-cholesterol:
< 40 mg/dL (< 1.0 mmol/L)
or treatment for
dyslipidemia
Low HDL cholesterol:
< 35 mg/dL (< 0.9 mmol/L)
in men or < 39 mg/dL
(< 1.0 mmol/L) in women
mmol/L) and/or
Abdominal obesity:
Central obesity:
Central obesity:
or specific treatment for
this abnormality
(< 1·29 mmol/L) in women
in men and < 50 mg/dl
< 40 mg/dl (< 1·03 mmol/L)
Reduced HDL-cholesterol:
abnormality
women
< 50 mg/dl in women
< 40 mg/dl in men and
HDL cholesterol:
mg/dL (< 1.29 mmol/L) in
mmol/L) in men and < 50
< 40 mg/dL (< 1.03
Low HDL cholesterol :
treatment for this
mmol/L) or specific
> 150 mg/dl (≥ 1·7 mmol/L) ≥ 150 mg/dL (≥ 1·7
mmol/L) or treatment
Triglycerides:
Raised TG level:
following:
Plus any two of the
TG ≥ 150 mg/dL (≥ 1.7
following:
values (Table 2). If BMI is
Hypertriglyceridaemia:
women
> 88 cm (> 35 in) in
Waist circumference > 102
following:
following:
factors
Plus two or more of the
obesity can be assumed
Plus two or more of the
Additional
Any three or more of the
diabetic population)
Plus two of the following:
> 30 kg/m² then central
values among the non-
glucose tolerance
With ethnic-specific cut-off
the CVD (footnote)
25% of fasting insulin
glucose or impaired
Central obesity:
Metabolic syndrome
specified risk factors for
At least one of the
syndrome
Insulin resistance
IDF (2005)
by DM2 or impaired fasting as hyperinsulinaemia—top
Not specified
Metabolic syndrome
AACE/ACE (2003)
Insulin resistance identified Insulin resistance (defined
syndrome
Insulin resistance
NCEP ATP III (2001)
factor
Metabolic syndrome
EGIR (1999)
Required
used
preferred or
Term
WHO (1998)
Table 1. Definition of the metabolic syndrome (Modified from Bloomgarden 2004 & Alberti 2005).
24
mm Hg or treatment in a
(≥ 6.1/7.8 mmol/L), but non glucose
diabetics
albumin:creatinine ratio
≥ 30 mg/g
or treatment for elevated
≥ 100 mg/dL (≥ 5·6
glucose:
excluded)
syndrome
define presence of
but is not necessary to
strongly recommended,
glucose tolerance test is
If above 5·6 mmol/L oral
diagnosed DM2.
140–200 mg/dl (diabetes is mmol/L) or previously
postglucose challenge
Raised fasting plasma
diagnosed hypertension
treatment of previously
Diastolic ≥ 85 mm Hg or
Systolic ≥ 130 mm Hg or
Raised blood pressure:
IDF (2005)
(PCOS), 8-Nonalcoholic fatty liver disease, 9-Acanthosis nigricans.
ethnicity, 5-Family history of type 2 diabetes, hypertension, or CVD, 6-A history of gestational diabetes or glucose intolerance, 7-Polycystic ovary syndrome
1-Overweight: BMI > 25 kg/m2 and/or waist circumference > 40 in in men and > 35 in in women, 2-Sedentary lifestyle, 3-Age > 40 years, 4-Non-Caucasian
< 7.0/11.1 mmol/L
glucose ≥ 110 mg/dl
rate ≥ 20 µg/min or
Fasting glucose:
≥ 100 mg/dL (≥ 5.6mmol/L) 110–125 mg/dl or 120-min
Fasting/2 h plasma
Raised fasting glucose:
Hyperglycaemia:
Microalbuminuria:
Urinary albumin excretion
diastolic)
hypertension
treatment for hypertension
medication and/or high
Blood pressure:
AACE/ACE (2003)
Blood pressure ≥ 130/ ≥ 85 > 130/85 mmHg
Raised blood pressure:
Hg systolic or ≥ 90 mm Hg
(≥ 140/90 mmHg) or
patient with history of
Hypertension:
Raised arterial pressure:
Antihypertensive
NCEP ATP III (2001)
blood pressure (≥ 140 mm
EGIR (1999)
WHO (1998)
Table 1. (Continued).
Table 2. Ethnic-specific values for waist circumference (Modified from Alberti 2005).
Country/Ethnic group
Waist Circumferences (measure of central obesity)
Europeans
Men
≥ 94
Women
≥ 80
South Asians
Men
≥ 90
Women
≥ 80
Chinese
Men
≥ 90
Women
≥ 80
Japanese
Men
≥ 85
Women
≥ 90
Ethnic South and Central Americans
Use South Asian recommendations until
more specific data are available
Sub-Saharan Africans
Use European data until more specific data
are available
Eastern Mediterranean and Middle east (Arab)
Populations
Use European data until more specific data
are available
2.2
Prevalence of the metabolic syndrome
Prevalence of MetS depends on the definition of the syndrome, ethnicity, sex and
age. Genetic predisposition, hormonal (loss of oestrogen at the time of
menopause) and environmental factors (high fat diet, smoking, sedentary
behaviour, lack of exercise etc.) contribute to the development of the syndrome
(Isomaa et al. 2001, Ford et al. 2002, Jenson 2003). Based on the WHO definition
(Alberti & Zimmet 1998), prevalence of MetS in the Botnia Project has been low
in middle-aged women with normal glucose tolerance (6%) but about 80% in
patients with DM2 (Isomaa et al. 2001). In subjects with normal glucose
tolerance the prevalence increased with age and was higher in men than in
women. Using the NCEP ATPIII definition in a population of 8814 American men
and women, the age-adjusted prevalence of the MetS was 24% in men and 23.4%
in women. The prevalence increased from 6.7% in people aged 20–29 years to
25
about 40% among the subjects aged 60 years or more (Ford et al. 2002). In a
Finnish study (Lakka et al. 2002) on men aged 42–60 years, prevalence of MetS
was 14.2% when the WHO definition was used and 8.8% when the NCEP ATPIII
definition was applied. In 2000 about 47 million U.S residents (up to 25% of the
adult population) had features indicative of MetS (Ford et al. 2002). AfricanAmerican and Mexican-American women had higher prevalences of MetS than
men of the same ethnic groups (Ford et al. 2002). In a population-based study of
men and women in France aged 35–64 years (Marques et al. 2002), prevalence of
the insulin resistance syndrome based on the WHO definition, was higher in men
than in women (23% vs. 12%). In Iran prevalence of the syndrome was less than
10% for both men and women in the age-group of 20–29 years, rising to 38% and
67%, respectively, in the age-group of the 60–69 year olds (Azizi et al. 2003).
Similarly, in a French population, prevalence increases from < 5.6% in the agegroup of the 30–39 year olds to 17.5% in the 60–64 year olds were reported
(Marques et al. 2002). In a study (Mikkola et al. 2007) of young healthy Finnish
conscripts (18–28 year old) overall prevalence of MetS using IDF and NCEP
ATPIII criteria was 6.8 and 3.5%, respectively.
Most studies have concentrated on MetS prevalence in middle-aged and older
individuals. However, it is becoming increasingly clear that risk factors for its
development are evident much earlier in life (Sinaiko et al. 1999). Obesity in
children is increasing at an alarming rate (Rössner 2002). Moreover, many obese
children become obese adults, and this correlates with an increased incidence of
MetS in adulthood (Vanhala et al. 1998).
2.3
Underlying factors of the metabolic syndrome
The mechanisms underlying MetS are not fully known. It appears that MetS can
be created by multiple underlying risk factors and its pathogenesis is therefore
multifactorial (Zimmet et al. 1999, Isomaa 2003, Enrique Caballero 2005,
Grundy 2005, Grundy 2006). The National Heart, Lung, and Blood
Institute/American Heart Association (NHLBI/AHA) conference on the definition
of MetS (Grundy et al. 2004), identified three potential aetiologic categories: (1)
The first is visceral obesity, which was found to be responsible for the excessive
release of free fatty acids, cytokines and other pro-inflammatory products, which
are implicated in the development of insulin resistance, hypertension and
dyslipidemia; (2) Insulin resistance as the second putative cause of MetS (3) The
third aetiology is thought to include independent factors, including
26
immunological, vascular, hepatic ones, etc. Both genetic and environmental
causes were determined to play a role in each category. Several factors further
exacerbate the syndrome such as physical inactivity, advancing age, endocrine
dysfunction, and genetic aberrations, which affect different risk factors (Grundy
2006). The main features of the MetS are therefore abdominal obesity, insulin
resistance, atherogenic dyslipidaemia, hypertension, pro-inflammatory and
prothrombotic states (Grundy 2004). These features are strong risk factors for
DM2 and CVD, with additional possible complications including cholesterol
gallstones, sleep apnoea, polycystic ovary syndrome (PCOS) in women,
hypogonadism in men, fatty liver and some types of cancer in both genders
(Grundy 2004).
2.3.1 Obesity
Fat distributed in the abdominal region, which manifest itself through an increase
in waist circumference, is a risk factor for both DM2 and CVD and more closely
associated with insulin resistance than with any other adipose tissue compartment
(Abate et al. 1995, Carr et al. 2004). The strong connection between obesity
(specially abdominal obesity) and metabolic risk factors led ATP III to define
MetS essentially as a clustering of metabolic complications of obesity (Grundy et
al. 2004).
Obesity contributes to high serum cholesterol, low HDL cholesterol,
hypertriglyceridemia, and increased numbers of small, dense “low-densitylipoprotein” (LDL) particles, hypertension and hyperglycaemia (Molly &
Brunzell 2004). Non-esterified fatty acid (NEFA) released from visceral adipose
tissue drain into the portal vein and serve as gluconeogenic substrates in the liver
(Arner 1997). Also an increased flow of substrates, cytokines and hormones
secreted from the insulin-resistant adipose tissue to skeletal muscle, liver and the
endothelium play a crucial role in affecting glucose metabolism and vascular
function.
Both altered NEFA metabolism and endocrine function hypotheses imply that
visceral adipose tissue is causally involved in the pathophysiology of MetS that is
often found in patients with visceral obesity. However, another possibility is that
excess intra-abdominal fat accumulation represents a marker of the relative
inability of subcutaneous insulin-sensitive adipose tissue to act as an 'energy sink',
when an individual has to handle a calorie surplus due to excess energy intake
and/or reduced energy expenditure (Lemieux 2004). Such a relative deficit in the
27
capacity of subcutaneous fat to store excess energy would result in increased
accumulation of fat at ectopic sites such as the liver, the skeletal muscle, the heart
and even in pancreatic β-cells (Miranda et al. 2005).
Abdominal obesity and adipokines
It is generally accepted that white adipose tissue (WAT) in addition to its classical
function as an energy storage depot, is an important and very active endocrine
organ that plays a role in energy homeostasis and metabolism (Ahima et al.
2000). WAT has an extensive system for communication with other tissues and
organs via direct signals to skeletal muscles, adrenal cortex, the brain (through
leptin) and the sympathetic nervous system (Trayhurn & Wood 2004, Pittas et al.
2004). Apart from free fatty acids (FFA), the adipose tissue synthesises and
secrets a number of hormones and substances with autocrine, paracrine and
endocrine functions which are shown in Figure 1.
Appetite and
energy balance
Haemostasis
Lipid
metabolism
Immunity
Insulin
sensitivity
Adipose
Tissue
Angiogenesis
Blood pressure
Inflammation
Fig. 1. Functional role of adipokines (Modified from Trayhurn & Wood 2004)
The numbers of adipokines known are now over fifty and include: classical
cytokines (TNFα, IL-6, IL-8), growth factors (transforming growth factor-β;
TGF-β) and proteins of the alternative complement system (adipsin, acylationstimulating protein), proteins involved in vascular haemostasis (plasminogen
activator inhibitor-1 (PAI-1), tissue factor), regulation of blood pressure
(angiotensinogen), lipid metabolism (retinol-binding protein, cholesteryl ester
transfer protein), glucose homeostasis (adiponectin, resistin) and angiogenesis
(vascular endothelial growth factor; VEGF) as well as acute-phase and stress
responses (haptoglobin, metallothionein) (Figure 2).
28
Cytokines and cytokine-related
proteins
Leptin, TNFα, IL-(1, 6, 8, 10)
Visfatin
Complement
factors;
Fibrinolytic
Adipsin,
components
Adiponectin,
Adipocyte
like PAI-1 and
Complement
tissue factor
factor B and
Growth
Lipoproteins
factors
Lipoprotein lipase (LPL)
Apolipoprotein E
NEFAs
Fig. 2. Examples of adipocyte-derived proteins (Kershaw & Flier 2004, Trayhurn &
Wood 2004, Pittas et al. 2004)
Adiponectin
Adiponectin which is the focus of the present study, was independently
discovered in 1995 and 1996 by four groups using different methods and names
including APM1 (adipose most abundant gene transcript 1), Acrp30 (adipocyte
complement-related protein of 30 kDa), adipoQ, and GBP28 (gelatin binding
protein of 28 kDa) (Scherer et al. 1995, Maeda et al. 1996, Nakano et al. 1996,
Hu et al. 1996). Adiponectin is produced exclusively by adipocytes (Scherer et al.
1995). Serum adiponectin levels in the humans range from 0.5 to 30 µg/ml, which
is about 1000 fold higher than the level of most other hormones like insulin or
leptin. In fact adiponectin accounts for 0.01% of total human protein and is the
most abundant adipose tissue protein (Stefen & Stumvoll 2002). The half-life of
adiponectin in serum is between 2.5–6 hours (Pjvani et al. 2003, Hoffstedt et al.
2004). Circulating adiponectin levels display a nocturnal decline and maximum
level in the late morning (Gavrila et al. 2003). Both hepatic and renal clearance
have been suggested for adiponectin (Zoccali et al. 2002, Tietge et al. 2004).
Adiponectin structure has a homology to tumour necrosis factor (TNF)-α and
a sequence homology (43%) to the complement protein C1q. Adiponectin consists
of three domains including a globular domain near the C terminus, a signal
sequence at the N terminus and a collagen-like domain (Berg et al. 2002). The
adiponectin protein can change to globular form, containing only the globular
29
head domain (Fruebis et al. 2001). The pharmacological effect of this globular
fragment seems to be to stimulate β-oxidation in skeletal muscle, whereas full
length adiponectin decreases hepatic glucose output (Fruebis et al. 2001, Berg et
al. 2001, Yamauchi et al. 2002). Therefore the site and the mode of action of
globular and full-length adiponectin seem to be different. In circulation,
adiponectin exists in three forms; a low molecular weight (LMW) oligomer as a
hexamer (two trimers), a middle molecular weight (MMW) hexamer and a high
molecular weight (HMW) oligomer as four to six trimers (Scherer et al. 1995,
Pajvani et al. 2003, Waki et al. 2003). HMW oligomers constitute the major part
of intracellular adiponectin, whereas the predominant form of adiponectin in the
circulation is the LMW oligomer (Pajvani et al. 2003). Monomer forms of
adiponectin have not been found in the plasma (Pajvani et al. 2003). It is still a
controversial issue which of the adiponectin isoforms are biologically active
(Tsao et al. 2002).
Adiponectin has various biological functions, including sensitizing insulin by
enhancing intracellular insulin signalling, anti-atherogenic and direct vasodilatory
and anti-inflammatory effects (Hotta et al. 2000, Ouchi et al. 2003, Yamauchi et
al. 2003, Chen et al. 2003). Genetic hypoadiponectinemia can be an important
background for atherosclerotic vascular disease and insulin resistance
(Matsuzawa et al. 2004). Adiponectin level varies in different states. Figure 3
shows the most important factors and diseases that lead to an up-regulation or
down-regulation of adiponectin in adipose tissue.
Inflammatory
stimuli ▼
Lean ▲
Obese▼
Type 1 diabetes ▲
Type 2 diabetes ▼
Lipodystrophy▼
Adiponectin
Ins. sensitive▲
Ins. resistance▼
Female▲
Male ▼
PPARγ agonist
Cardiovascular
disease
▼
treatment ▲
Fig. 3. Most important factors and diseases leading to an up or down-regulation of
adiponectin in adipose tissue (Modified from Trujillo & Schererp 2005).
Women have an about 40% higher circulating level of adiponectin compared
with men (Arita et al. 1999, Cnop et al. 2003). The level of androgens may play a
30
role for this gender difference, because androgens appear to have an inhibitory
effect on adiponectin (Nishizawa et al. 2002). In addition, women display higher
serum proportions of HMW, but not MMW and LMW multimers, than men (Waki
et al. 2003, Pajvani et al. 2003). Therefore not only total adiponectin
concentration but also multimer distributions are different in two genders.
Adiponectin levels have been shown to become reduced in patients with
CVD (Ouchi et al. 1999) and diabetic patients (Hotta et al. 2000). Epidemiologic
studies have shown that low adiponectin levels predict the later development of
DM2 (Lindsay et al. 2002, Spranger et al. 2003) and myocardial infarction
(Pischon et al. 2004, Nakamura et al. 2004). Negative correlations between
adiponectin levels and insulin resistance have been shown (Matsubara et al. 2002,
Yamamoto et al. 2002, Cnop et al. 2003). Adiponectin has been reported to be a
significant independent predictor of the metabolic syndrome in overweight Latino
youth (Shaibi et al. 2007). Moreover, adiponectin is negatively correlated with
plasma triglycerides, low density lipoprotein (LDL)-cholesterol, and positively
correlated with high density lipoprotein (HDL)-cholesterol (Yamamoto et al.
2002, Cnop et al. 2003). It has been suggested that adiponectin play an important
role in lipid metabolism. However, it is unclear whether free fatty acids (FFA)
regulate adiponectin level. One study (Staiger et al. 2002) has demonstrated that
adiponectin levels increased 6 h after the start of FFA infusions. Furthermore, the
same authors showed that administration of Acipimox, which lowers the plasma
level of FFA, did not affect plasma adiponectin level.
Strong negative correlations between plasma adiponectin levels and body
mass index (BMI) have been shown in both humans and in animals (Arita et al.
1999, Weyer et al. 2001, Cnop et al. 2003). Although the lack of variation in
circulating adiponectin levels after caloric-restriction-induced weight loss has
been demonstrated (Xydakis et al. 2004, Garaulet et al. 2004), most studies have
shown increased circulation adiponectin levels in relation to both traditionally
induced weight loss (Hotta et al. 2000) and weight loss after gastric surgery
(Yang et al. 2001, Faraj et al. 2003). Exercise training without concomitant
weight loss did not increase serum adiponectin levels. It may indicate that
adiponectin plays a role for enhanced insulin sensitivity after weight loss (Hulver
et al. 2002). Both functional and genetic studies on adiponectin strongly suggest
that reduced adiponectin levels play a causal role in the development of insulin
resistance, DM2, and atherosclerosis (Yamauchi et al. 2003).
Visceral adiposity has been shown to be an independent negative predictor of
adiponectin (Yatagai et al. 2003). Although many adipokines such as TNFα, IL–6,
31
resistin, PAI-1 and angiotensinogen increase in obesity, expression and release of
adiponectin fall in obese subjects which lead to insulin resistance (Arita et al.
1999) (Figure 4).
Adiponectin ▼
Obesity
Adipokines
Insulin
Resistance
TNFα, IL-6, Resistin,
PAI-1,
Angiotensinogen▲
Fig. 4. Adipokines, cellular mediators of MetS (Modified from Lau et al. 2005).
Heterozygous peroxisome proliferator-activated receptor-γ knockout mice
were protected from high-fat diet induced obesity, adipocyte hypertrophy, and
insulin resistance (Kubota et al. 1999) and systematic gene profiling analysis of
these mice revealed that adiponectin was over-expressed in them (Yamauchi et al.
2001). Functional analyses, including generations of adiponectin transgenic or
knockout mice, have revealed that adiponectin serves as an insulin-sensitizing
adipokine. In fact, obesity-linked down-regulation of adiponectin was a
mechanism through which obesity could cause insulin resistance and diabetes.
Replenishment of a physiological dose of recombinant adiponectin to the
lipoatrophic diabetic mice, significantly ameliorated insulin resistance (Yamauchi
et al. 2001).
The recent discovery of adiponectin receptors (Yamauchi et al. 2003) has
helped in elucidating intracellular mechanisms of adiponectin action. Adiponectin
receptor 1 (AdipoR1) is predominantly expressed in skeletal muscle and
adiponectin receptor 2 (AdipoR2) is most abundant in the liver. The receptors
have also been shown to be markedly expressed in pancreatic β-cells (Kharroubi
et al. 2003), macrophages and atherosclerotic lesions (Chinetti et al. 2004) as well
as in the brain (Yamauchi et al. 2003). AdipoR1 has a high affinity for globular
adiponectin and a low affinity for full-length adiponectin. It mediates AMPK
(5’adenosine monophosphate-activated protein kinase) activation, and facilitates
increase in glucose uptake and fatty acid oxidation in skeletal muscle (Yamauchi
et al. 2003). AdipoR2 has an intermediate affinity for both globular and fulllength adiponectin. It activates the AMPK and decreases the hepatic glucose
32
output. Most recently, it has been shown that Osmotin, which is a ligand for the
yeast homolog of the adiponectin receptor (AdipoR), activated adenosine
monophosphate-activated protein kinase (AMPK) via AdipoR in myocytes. This
fact may facilitate the development of efficient adiponectin receptor agonists
(Narasimhan et al. 2005). Adiponectin receptor agonists and adiponectin
sensitizers should serve as versatile treatment strategies for obesity-linked
diseases such as diabetes and MetS.
Adiponectin sensitizes tissues to insulin by reducing serum NEFA
concentrations. This may explain the link between reduced serum adiponectin
levels and insulin resistance (Yamauchi 2001 & 2002). Recently it has been
shown that AMPK might be responsible for increasing the muscles´ sensitivity to
insulin (Fisher 2006). AMPK could increase glucose transport by increasing the
rate of fat burning in the muscle (Fisher 2006). The effect of insulin on
adiponectin is still unclear. Insulin has been shown acutely to increase
adiponectin release (Scherer et al. 1995, Kern et al. 2003) and induces
adiponectin gene expression in human visceral adipocytes in vitro (Halleux et al.
2001) but also to decrease serum adiponectin concentrations during acute
infusions in vivo (Wang et al. 2002, Lihn et al. 2003).
Adiponectin Hypothesis
Kadowaki and Yamauchi (2005) proposed the adiponectin hypothesis, suggesting
that reduced adiponectin levels are caused by interactions of genetic factors such
as SNPs in the adiponectin gene itself and environmental factors that cause
obesity (Figure 5).
Genetic factors such as
adiponectin gene
polymorphism
Environmental factors
causing obesity such as
high fat diet
Hypoadiponectinemia
Insulin resistance
Metabolic
syndrome
Atherosclerosis
Fig. 5. Adiponectin Hypothesis (Modified from Kadowaki & Yamauchi 2005)
33
Reduced adiponectin actions also result from down-regulation of adiponectin
receptors linked to obesity. These reductions of adiponectin actions may play a
crucial role in the development of insulin resistance, DM2, MetS, and
atherosclerosis (Figure 5). According to this hypothesis, a therapeutic strategy for
DM2, MetS, and CVD may include the up-regulation of serum adiponectin, upregulation of adiponectin receptors, or the development of AdipoRs agonists.
2.3.2 Insulin resistance
Since the introduction of Syndrome X (Reaven 1988, Defronzo & Ferrannini
1991), insulin resistance has been considered an important factor connecting
many features of MetS. Several factor analyses regarding MetS have found that
more than one process underlies the full expression of the metabolic syndrome,
but insulin resistance or hyperinsulinaemia seemed to be very closely linked to
many of the features of MetS (Meigs 2000). There are some mechanisms, which
might explain the relationship of the insulin resistance with the different
metabolic disorders of MetS. For example when insulin-resistant muscle is
already overloaded with lipid from high plasma levels of nonesterified fatty acids
(NEFA), some excess NEFA presumably is diverted to the liver, promoting fatty
liver and atherogenic dyslipidemia (Grundy et al. 2004). Insulin resistance in
skeletal muscle predisposes to glucose intolerance, which can be worsened by
increased hepatic gluconeogenesis in an insulin-resistant liver.
Multiple coagulation abnormalities, such as increases in PAI-1 levels,
fibrinogen levels, von Willebrand factor levels, C-reactive protein (CRP) levels
and lipoprotein (Lp) (a) levels, have been shown to occur in cases with evidence
for MetS. These abnormalities play important roles in the development of
atherosclerosis and atherothrombosis (Kohler 2002). Increased insulin levels
appear to be a potent stimulator of PAI-1 production. Elevated PAI-1 levels are
associated with an increase risk of arterial thrombosis and also associated with the
degree of severity of atherosclerotic vascular disease. Elevated PAI-1 levels
promote the risk of CVD by progression of plaque development and its rupture
and subsequent thrombus. Lp (a) which is often elevated in MetS, has structural
homology with plasminogen so that its elevation may inhibit fibrinolysis and
contribute to thrombus development (Jenson 2003). Furthermore, insulin
resistance has also been related to elevated fibrinogen and von Willberand factor
levels. Platelet function is regulated in vivo by insulin receptors and insulin has
been shown to inhibit platelet aggregation in healthy non-obese persons, while in
34
obese persons an insulin infusion failed to inhibit platelet deposition of collagens
(Westerbacka et al. 2002). These mechanisms could partly explain the linkage of
MetS to atherothrombosis.
Insulin sensitivity assessment
Due to the association between insulin resistance with diabetes, atherosclerosis
and obesity, there has been an interest in the development of techniques to
evaluate insulin sensitivity in vivo. In recent years a variety of approaches and
techniques have been used to assess the degree of sensitivity of body tissues to
insulin (Table 3). The euglycaemic clamp is the gold-standard of sensitivity
measurements, but it is costly, time consuming, and requires sophisticated
equipment, thus remaining a laboratory procedure (Bergman et al. 1987). The oral
glucose tolerance test (OGTT) and intravenous glucose tolerance test (IVGTT)
could also estimate β-cell function and insulin sensitivity in many circumstances
(Stumvoll et al. 2000). Fasting blood glucose is easy to use in study populations
and has been used to evaluate insulin sensitivity. It supplies a good evaluation of
hepatic, but not muscular sensitivity to insulin. The homeostasis model
assessment (HOMA) is a mathematical model that predicts insulin sensitivity
simply by measuring insulinaemia and fasting blood glucose and shows good
correlation with the hyperinsulinaemic-euglycaemic clamp method. HOMA has
been shown to be a valuable alternative to the most difficult techniques in
evaluating insulin resistance in humans (Bergman et al. 1987). The Quantitative
Insulin Sensitivity Check Index (QUICKI) is another simple method, also based
in the measurement of insulinaemia and fasting blood glucose that have good
correlations with the metabolic syndrome markers, being able to discriminate
satisfactorily different states of insulin resistance in patients with different
degrees of obesity and glucose tolerance (Geloneze & Tambascia 2006, GuerreroRomero & Rodríguez-Morán 2006). Estimations of insulin sensitivity and
secretion based on post-load insulin and glucose are more reliable than fasting
states, but it has been noted that the relationship between β-cell function and
insulin sensitivity from fasting and post-load are of physiological shapes and
comparable (Albareda et al. 2000).
35
Table 3. Indices of insulin sensitivity ordered by the years (Modified from GuerreroRomero & Rodríguez-Morán 2006)
Sensitivity indices (reference)
Formula
Euglycaemic clamp
Plasma insulin is acutely raised and maintained at approximately
(DeFronzo et al. 1979)
100 muU/ml by a prime-continuous infusion. Plasma glucose is
held constant at basal levels by a variable glucose infusion using
the negative feedback principle. Under these steady-state
conditions of euglycaemia, the glucose infusion rate equals
glucose uptake by all the tissues in the body and is, therefore, a
measure of tissue sensitivity to exogenous insulin.
OGTT (WHO Expert Committee.
Measurement of circulating glucose and insulin levels every 30–
Second report on Diabetes mellitus
60 min after oral administration of 75–100 g of a glucose solution
1980)
Homeostasis model analysis index
Ins0 × Glu0 /22·5 mmol L-1
(Matthews et al. 1985)
Fasting glucose to insulin ratio
Glu0/Ins0
(Legro et al. 1988)
Fasting insulin resistance index
(Ins0 × Glu0)/25
(Duncan et al. 1995)
Fasting Belfiore's index
2/ [1 + (Ins0 × Glu0)]
(Belfiore et al. 1998)
Raynaud's index
40/Ins0
(Raynaud et al. 1999)
Fasting insulin
1/Ins0
(Hermans et al. 1999)
Stumvoll's index
0·156 − {[(4·59 × I2) − (3·21 × Ins0) − (541 × Glu0)] × 10-5}
(Stumvoll et al. 2000)
QUICKI
1/ (Log10 Ins0 + Log10 Glu0)
(Katz et al. 2000)
Bonadonna minimal model
Plasma C-peptide was measured during a hyperglycaemic clamp
(Bonadonna et al. 2003)
(~10 mmol/l) to quantify model-based first-phase secretion and
glucose sensitivity of second-phase secretion (ß). Insulin
sensitivity was quantified during the hyperglycaemic clamp.
Glu0, fasting glucose; Ins0, fasting insulin; QUICKI, quantitative insulin sensitivity check index.
36
2.3.3 Other underlying factors of the metabolic syndrome
Sedentary lifestyle
Physical activity influences many components of MetS. In some prospective
studies, higher levels of physical activity have protected against the development
of diabetes and cardiovascular disease (Berlin et al. 1990, Lakka et al. 1994,
Lynch et al. 1996), conditions that are commonly associated with MetS. Lifestyle
interventions, including regular physical activity, have been shown to reduce the
incidence of diabetes by more than half in persons with impaired glucose
tolerance (IGT) (Tuomilehto et al. 2001, Knowler et al. 2002). It has been found
that men, who maintained or improved adequate physical fitness, were less likely
to die of any medical condition or of cardiovascular disease in particular than
persistently unfit men (Blair et al. 1995). In a prospective study Laaksonen et al.
(2002) could show that in a four year follow-up of 612 middle-aged men, 107
developed MetS using the WHO definition. Men with moderate and vigorous
leisure time physical activity decreased risks of developing MetS by up to nearly
two-third in the same study population (Laaksonen et al. 2002). Among twentysix overweight males, which participated in an exercise program that consisted of
aerobic exercise 4–5 days per week for 40 min per session over 10 weeks,
adiponectin levels rose by 260% after close to one week of exercise (Kriketos et
al. 2004).
Psychosocial stress
Stress activates the sympatho-adrenal system and the hypothalamic-pituitaryadrenocortical (HPA) axis. An increase in sympathetic activity could contribute to
several aspects of the MetS, such as glucose uptake in skeletal muscle, lipolysis
and the development of hypertension (Hjemdahl 2002). Subjects with MetS
exhibited increased urinary excretion levels of cortisol metabolites as well as
normetanephrine and lower heart rate variability (Brunner et al. 2002). It was
shown that both an intravenous bolus of 25 mg of hydrocortisone in healthy
volunteers as well as endogenous cortisol over production, induced decreases of
plasma adiponectin levels (Fallo et al. 2004). The results from the Whitehall II
study suggested that psychosocial factors may influence the development of
MetS. Central obesity and components of MetS were shown to be inversely
37
related to socioeconomic status and this relation did not disappear after the
adjustment for smoking, alcohol consumption and levels of physical exercise
(Brunner et al. 1997). Similar results were observed in a study from Sweden,
where poor standards of education were related to an increased risk of MetS in
middle-aged women (Wamala et al. 1999). Successful emotion regulation may
lead to a reduced risk of the metabolic syndrome (Kinnunen et al. 2005). MetS
has been suggested to represent a mechanism to explain the increased risk of
CVD in people with low socioeconomic status. In a recent study (Chandola et al.
2006), a dose response relation was found between work stressors over a period
of 14 years and the risk of developing MetS. Employees with chronic work stress
were more than twice more likely to have the syndrome than those not
experiencing any work stress (Chandola et al. 2006).
Sleep loss
Sleep loss and sleep disturbances could contribute to the development of insulin
resistance and DM2 either directly by having an effect on components of glucose
regulation or indirectly via an impact on humoral factors that regulate hunger and
appetite like leptin and adiponectin, thus leading to weight gain and obesity
(Spiegel et al. 2005). Habitual sleep duration below 7.7 hours has been related to
increased BMI and to decreased leptin and increased ghrelin, changes that have
also been observed in reaction to food restriction and weight loss and are typically
associated with increased appetite (Taheri et al. 2004). In studies of healthy young
adults submitted to recurrent partial sleep restriction, marked alterations in
glucose metabolism (including decreased glucose tolerance and insulin
sensitivity) have been demonstrated (Spiegel et al. 2005).
Diet
Consistent relationships between plasma fatty acid composition and insulin
resistance have been shown. In a prospective cohort study Laaksonen et al.
(2002) have investigated the interaction between serum fatty acid composition
and the development of impaired fasting glycaemia or DM2 in a cohort of
middle-aged normoglycaemic men. It was found that at baseline the proportions
of serum esterified and non-esterified saturated fatty acids (SFA) were increased
and polyunsaturated fatty acids (PUFA) were decreased in men, who after 4 years,
developed impaired fasting glycaemia or DM2. Higher proportions of SFAs in
38
serum were associated with higher insulin level, lower insulin sensitivity and a
higher risk of developing DM2 (Folsom et al. 1996). The Mediterranean diet
which is low in saturated and high in monounsaturated fats has a beneficial effect
on human health (Demosthenes & Evangelos 2005). Recently a study in an urban
area of Greece has shown that this dietary pattern has been associated with 20%
lower odds of having MetS, irrespective of age, sex, physical activity, lipids and
blood pressure levels (Demosthenes et al. 2004). Close adherence to the
Mediterranean diet has been shown to be associated with higher adiponectin
levels in a population of diabetic women (Mantzoros et al. 2006).
Smoking
Numerous studies have shown that smoking increases insulin resistance
(Kawakami et al. 1997, Uchimoto et al. 1999, Nakanashi et al. 2000, Miyatake et
al. 2006, Houston et al. 2006). A fifteen-year-incidence of glucose intolerance
was greatest among current smokers than in other category of tobacco exposure
according to one study (Houston et al. 2006). In the same study never-smokers
with positive passive smoke exposure also had a greater risk of developing
glucose intolerance, compared with never-smokers with no positive passive
smoke (Houston et al. 2006). Plasma adiponectin concentration was significantly
lower in men, who were current smokers than in never-smokers; this association
was even apparent in subjects without diabetes and medication (Iwashima et al.
2005).
Intrauterine growth retardation
Some researchers have focused on environmental and genetic factors in the
control of foetal growth. An increased risk of CVD, DM2 and features of MetS,
developing later in life, have been shown in individuals with reduced foetal
growth and low birth weight (Hales et al. 1991, Barker 1992, Barker et al. 1993).
Young adults born with a low birth weight, demonstrate hyperinsulinaemia and
insulin resistance compared with controls of normal birth weight (Jaquet et al.
2000). A suggested explanation for this association is the thrifty phenotype
proposing that intrauterine under-nutrition might result in developmental
adaptations in certain tissues (endocrine, pancreas, adipose tissue, and muscles),
predisposing individuals to cardiovascular and metabolic disturbances in adult life
(Hales et al. 1991). However, the foetal insulin hypothesis suggests that
39
genetically determined insulin resistance results in low foetal growth and insulin
resistance later in life (Hattersley & Tooke 1999). A reduction in adiponectin level
in children small for their gestational age (SGA), also could contribute to the
development of the insulin resistance and atherosclerosis in adult life (Cianfarani
et al. 2004).
2.3.4 Finnish military service lifestyle
In Finland all healthy men are required to serve in the army for 6–12 months
during peace time. Each year 25000–35000 male conscripts with a mean age of
19–29 years perform their military service (Lehmuskallio et al. 1995). Military
service in Finland starts in January and July and almost 85% of the Finnish men
carry out the military service. For young Finnish females the military service is
optional. After entering the military service, medical check ups and examinations
will be done on every serviceman and if any has a health problem or disease that
makes it impossible for him to follow the military routine; he will be changed to
another service category (Tähtinen 2006).
During the military service, conscripts take breakfast, lunch and dinner at the
garrison; snacks are available for sale. In the evening some of the servicemen
who have permission to leave the garrison might eat outside the garrison or go
home. During the weekend servicemen usually go home (Koskenvuo 1996). The
amount of the daily energy intake in food served to every Finnish serviceman is
3200–3600 kcal/day, which is much higher compared with that of non-conscript,
civilian men of the same age (Rehunen 1996). It was reported that the
consumption of confectionary, doughnuts, and soft drinks significantly increased
among a group of conscripts during the military service (Tähtinen et al. 2001).
A study in Finland investigated physical fitness profiles in Finnish
servicemen during the years 1975–2004 (Santtila et al. 2006). Endurance
performance was tested through a 12-min running test (Cooper Test). Results
showed that mean body mass increased from 70.8 to 75.2 kg (P < 0.01) during the
years 1993–2004 among the conscripts. The mean distance of the conscripts´
cooper test increased first by 4% from 1975 to 1979, but then decreased by 12%
(from 2760 m to 2434 m, P < 0.001) compared with the year 2004. These results
suggest that physical fitness among young men has decreased and that body mass
has increased during the last 15 years in Finland.
Depressive syndrome has been shown to be present in some Finnish
conscripts. Marttunen et al. has compared suicides in Finnish conscripts with
40
those of young male civilians. They showed that five out of the seven cases of
suicides among the conscripts had a depressive syndrome, often of short duration
and interpersonal separation was more often preceding the event of the suicide
among the conscripts than in the other suicides (Marttunen et al. 1997). Alertness
and sleep in healthy young Finnish men in military service have been examined.
Although alertness and sleep during military service were normal on average, the
findings suggested that all conscripts may not have had enough sleep (Hilakivi et
al. 1992).
2.3.5 Genetic Susceptibility to the metabolic syndrome
In 1962 the thrifty gene theory was suggested by Neel (Neel 1962). He
hypothesized that genetic selection would favour energy-storing genes in
environments with unstable food supplies and famine to increase the probability
of survival by maximizing storage of food (Neel 1962). When the energy-storing
genotype is exposed to a westernized lifestyle with an abundant food supply, it
becomes detrimental and causes obesity and diabetes. In this sense thrifty genes
could predispose an individual to MetS.
Two major approaches are being used in the search for thrifty genes, or genes
predisposing to (abdominal) obesity; (a) The “candidate gene approach”, which
identifies the genes based on their functions and (b) the “random gene search”,
which deals with genome scan followed by positional cloning assuming no
knowledge of underlying disease. Many genome scans have been carried out
recently on the MetS or its components.
It has been assumed that common variants in a number of genes and gene
variations, influencing fat and glucose metabolism, will increase susceptibility to
MetS (Groop 2000). These genes will act together with a number of
environmental factors to develop MetS (Groop 2000). Several potential candidate
genes (e.g. genes causing monogenic obesity, genes regulating lipolysis and FFA
metabolism, genes affecting insulin sensitivity, thermo-genesis and glucose
uptake in skeletal muscles, related to inflammation) have been shown to be
associated with MetS (Table 4). Those regulating lipolysis, thermo-genesis and
glucose uptake in the skeletal muscle still remain prime candidates (Groop &
Melander 2001).
With the help of high-density arrays that permit the genotyping of hundreds
of thousands of polymorphisms, this field is moving forward very fast. Recently
41
four new loci containing variants that confer DM2 risk were identified in a French
case–control cohort (Sladek et al. 2007).
Table 4. Selected candidate genes related to the metabolic syndrome (Groop 2000,
Groop & Melander 2001, Matsuzawa et al. 2004, Song et al. 2006)
Genes causing
Genes regulating
Genes affecting
Genes affecting
Genes related to
monogenic obesity
free fatty acid
insulin sensitivity
lipid metabolism
inflammation
metabolism
Leptin
Adiponectin gene
Peroxisome
Apolipoprotein E
TNF-α
Leptin receptor
β-Adrenergic
proliferator-
11 β hydroxysteroid
C-reactive protein
Melanocortin
receptors
activated receptor
dehydrogenase
receptor
Fatty acid-binding
γ (PPARγ)
type 1
Insulin receptor
Upstream
Lipases
substrates (IRSs)
transcription
Uncoupling
Glycoprotein PC-1
factor 1
proteins
Skeletal muscle
Pro-opiomelanocortin protein 2
glycogen
syntheses
Calpain-10
2.3.6 Peroxisome-Proliferator-Activated Receptor (PPAR) genes
PPARs are members of the nuclear receptor family and consist of a group of three
isoforms: PPARα, PPARβ/δ and PPARγ (Berger & Moller 2002). The first
cloning of a PPAR (PPARα) occurred in the course of the search for the molecular
target of hepatic peroxisome proliferating agents in rodents (Issemann & Green
1990). Peroxisome proliferators induce the expression of several genes involved
in the degradation of fatty acids and result in the proliferation of peroxisomes,
hepatomegaly, and hepatocarcinogenesis in the rodent liver (Nemali et al. 1988).
PPARs play a critical role in glucose and lipid metabolism (Lenhard et al. 1997),
macrophage function (Ricote et al. 1998), and adipogenesis (Tontonoz et al.
1994). They are ligand dependent transcription factors that regulate target gene
expression by binding to specific peroxisome proliferator response elements
(PPREs) in enhancer sites of regulated genes. Each receptor binds to its PPRE as
a heterodimer with a retinoid X receptor (RXR). Upon binding an agonist
(ligand), the conformation of a PPAR is altered and stabilized so that a binding
cleft is created and recruitment of transcriptional co-activators can occur. The
result is an increase in gene transcription (Berger & Moller 2002) (Figure 6).
42
Numerous kinds of fatty acids and prostaglandins have been shown to serve as
natural ligands of the PPARs, so that it has been suggested that these receptors
play a central role in sensing nutrient levels and in modulating their metabolism.
It has been demonstrated that the PPARs are the primary targets of numerous
classes of synthetic compounds used in the treatment of DM2 and dyslipidaemia
such as thiazolidinediones (TZDs), including rosiglitazone and pioglitazone and
fibrates (Berger & Moller 2002).
Differentiation
Proliferation
Immune/Inflammation
response
Lipid metabolism
Ligand
Cofactors
PPAR
RXR
PPRE
Target
gene
Fig. 6. Function of PPARs (Modified from Kuenzli & Saurat 2003)
The PPARγ gene is located on chromosome 3q25. Two PPARγ isoforms are
expressed in the mouse (Zhu et al. 1995) and humans (Elbrecht et al. 1996), so
called γ1 and γ2, which differ in 30 additional amino acids at the N terminus.
PPARγ2 is expressed primarily in adipose tissue (Fajas et al. 1997) and has the
ability to promote preadipocyte differentiation, to mediate lipogenesis in
adipocytes, and to enhance insulin sensitivity (Spiegelman 1998, Berger & Moller
2002, Francis et al. 2003). Lehmann and colleagues have shown that TZDs, antidiabetic agents are potent and selective activators of PPARγ (Lehmann et al.
1995) whose insulin-sensitizing actions are mediated largely by effects inside the
adipose tissue. Thus far, TZDs treatment has been shown to diminish the lipid
content of liver but not skeletal muscle (Mayerson et al. 2002).
Adipose tissue is the primary target for the systemic insulin-sensitizing
actions of PPARγ ligands (Figure 7), which alter the expression of genes involved
in lipid uptake, metabolism, and insulin action in adipocytes (Rangwala & Lazar
2004). Ligand interactions with the receptor mediate specific changes in adipose
gene expressions such as fatty-acid transporter 1 contributing to reduced
production of FFAs, enhancing adipocyte insulin signalling, lipid uptake and
anabolic lipid metabolism, and attenuating lipolysis and FFA release processes,
which in turn, are predicted to have insulin-sensitizing effects in muscle and liver.
43
Consequently, lipid levels in the adipose tissue rise while circulating FFAs
decrease (Bays et al. 2004).
uptake ▼FFAs
ŸFA ▲FA
uptake
źFFAs
▼Lipolysis
ź Lipolysis
▲Insulin
sensitizing
ŸInsulin
sensitizing
▲Insulin
▲Adipose factors (adiponectin)
factors (adiponectin)
action
Ÿorź
tissue
in muscle
gene
ŸInsulin
action
PPARγ
▼Expression/action of
Or
PPARȖ
expression
and liver
źExpression/action
in muscle
and
insulin resistance
ligandligand in adipose
▼ Gene
of insulin resistance
liver
(resistin)
expression factors factors
tissue
(resistin)
▲Small insulin
ŸSmallsensitive
insulin adipocytes
sensitive
adipocytes
▼Visceral
adipocity
źVisceral adipocity
decrease (Bays et al. 2004).
Fig. 7. Potential mechanisms of insulin sensitization by PPARȖ ligands (Modified from
Moller 2001)
Changes in the expression of other genes may contribute to locally increased
insulin action in adipose tissues and/or reduced visceral adiposity (Moller 2001).
Some data suggest that PPARȖ agonists might improve insulin secretion in DM2
subjects (Bays et al. 2004). Data from patients with DM2 and animal models also
demonstrated that PPARȖ agonists could function as ‘adipose remodelling factors’
that redistribute lipids from insulin-resistant, lipolytic visceral-fat depots into
subcutaneous fat (Kawai et al. 1999, Myazaki et al. 2002, Mayerson et al. 2002),
fat that contains small, newly-differentiated, insulin-responsive adipocytes
(Boden et al. 2003).
In addition to altering fat deposition, PPARȖ agonists modulate the endocrine
activity of adipose tissue by regulating the synthesis of secreted adipocyte
proteins (‘adipokines’) that affect insulin signalling in hepatic and peripheral
tissues (Rajala & Scherer 2003). For example, adiponectin, which potentiates
insulin sensitivity in the liver (Berg et al. 2001) and skeletal muscle (Yamauchi et
al. 2001), is up-regulated in response to PPARȖ activation by TZDs (Matsuda et
al. 2001, Maeda et al. 2001, Combs et al. 2002, Bajaj et al. 2004). Because
PPARȖ is predominantly expressed in adipose tissue and adipose tissue appears to
be essential for TZD-induced improvement in insulin sensitivity, this upregulation of adiponectin by TZDs suggests that adiponectin plays an important
role in mediating the anti-diabetic effect of TZDs. Other anti-diabetic drugs
44
improve insulin sensitivity without an increase in adiponectin level (Combs et al.
2002, Phillips et al. 2003).
A number of genetic variants in the PPARγ gene have been identified. A
highly prevalent Pro12Ala polymorphism is the result of a CCA-to-GCA
missense mutation in codon 12 of exon B of the PPARγ gene. This exon encodes
the NH2-terminal residue that defines the adipocyte-specific PPARγ2 isoform.
The Pro12Ala polymorphism in PPARγ2 was first identified in 1997 (Yen et al.
1997). The Ala12 allele frequency varies across ethnic populations, being low in
Asians and Africans (1–3%) and as high as 20% in Caucasians (Meirhaeghe &
Amouyel 2004). To elucidate the association of Pro12Ala polymorphism of
PPARγ2 gene with the components of the metabolic syndrome many studies have
been carried out of which the main ones are summarized in Table 5. The Ala 12
allele was initially shown to be associated with increasing degrees of obesity
(Beamer et al. 1998). In several additional studies, the Ala12 allele seemed
associated with lower BMI, improved insulin sensitivity, and reduced incidence
of DM2 (Deeb et al. 1998, Altshuler et al. 2000). In contrast, other groups failed
to detect an association of Ala12 with altered metabolic parameters (Mori et al.
1998, Ringel et al. 1999). The significance of associations between this
polymorphism and DM2, insulin resistance, and obesity, remains an issue of
debate (Mancini et al. 1999, Ringel et al. 1999, Clement et al. 2000, Hara et al.
2000, Meirhaeghe et al. 2000, Altshuler et al. 2000). The association of the
Pro12Ala with serum adiponectin level was investigated in several studies
(Yamamoto et al. 2002, Thamer et al. 2003, Takata et al. 2004, Orio et al. 2004).
Two studies found no influence on adiponectin concentration (Thamer et al. 2003,
Orio et al. 2004) while two other studies including Japanese populations showed
significantly lower levels of serum adiponectin in subjects with the Pro12Ala
allele of PPARγ2 (Yamamoto et al. 2002, Takata et al. 2004). Some studies have
investigated the association between the Pro12Ala allele of PPARγ2 gene and the
blood pressure. One study failed to show any association between the blood
pressure and the Pro12Ala allele (Gouni Berthold et al. 2005). However, a recent
study (Stefanski et al. 2006) has found that the Pro12Ala variant is associated
with an increased mean 24-h diastolic blood pressure in obese diabetic patients.
45
46
364
402
53.4
Mixed
Lean
Diabetics
diabetics
Lower
No difference
diabetics
and obese non
in overweight
Asians
Improved in
towards
Tendency
Kawasaki et al.
(2002)
Non
Diabetics
541
Mixed
+
55–69
difference
No
Improved
415
Random
Healthy
Asians
Mixed
Mixed
Improved
Hara et al. (2000)
35–64
20–31
tolerant
women
Caucasians 464
Caucasians
Danish
Controls
Glucose
(2002)
Gonzalez et al.
EK et al. (2001)
Caucasians
Males
Better
in women
70
616
EK et al. (2001)
Swedish
against
diabetics
Diabetics
Protection
Non
Mixed
DM2
53–70
Higher
No difference
level
Adiponectin
Douglas et al. (2001) Caucasians 935
lower
Higher
pressure profiles
sensitivity
lipid
Blood
Insulin
diabetics
Mixed
Non
44–46
Non
BMI
(1998)
Caucasians 333
Mixed
Comment
Deeb et al.
43–64
sex
diabetics
Caucasians 686
Beamer et al.
Age
(1998)
Population No
References
level and development of diabetes in different studies.
Table 5. Association of X/Ala genotype of the PPR gamma 2 gene with BMI, Insulin sensitivity, Blood pressure, Lipids, adiponectin
47
Caucasians 522
Asians
Lindi et al. (2002)
Mori et al. (1998)
Europeans 120
Orio et al. (2004)
and obese)
(overweight
PCOS
120
Females
patients
CAD
diabetics
Non
Controls
19–27
Mixed
Mixed
+
1170
White
Masud et al. (2003)
241
Indians
Muller et al. (2003)
diabetics
24–29
difference
Higher
difference
No
weight
Diabetics
No
BMI
Normal
Overweight
Comment
312
Mixed
Males
Mixed
sex
Non
45.2
51
40–68
Age
+
Mancini et al. (1999) Caucasians 131
215
Population No
References
Table 5. (Continued).
Improved
difference
No
pressure profiles
sensitivity
lipid
Blood
Insulin
No difference
level
Adiponectin
interventions
lifestyle
not with
subjects but
in IGT
Development
DM2
48
Rosmond et al.
Zietz et al. (2002)
(2002)
Yamamoto et al.
595
Caucasians 560
Asians
Indians
30–65
Mixed
Mixed
Mixed
Mixed
subjects
Diabetic
Lean
normal
Normal
Higher in
difference
No
Higher
group (<50)
younger
Higher in
BMI
IGT
Diabetics
Lean
18–69
21–29
Asians
Tai et al. (2004)
4038
Asians
Takata et al. (2004)
247
diabetics
(2003)
Mixed
Non
35–37
Thamer et al.
Caucasians 648
Lean
Mixed
(2001)
39–66
Diabetic
Obese
Caucasians 663
Mixed
Males
Healthy
Comment
Swarbrick et al.
56–72
58
Females
sex
Obese
Europeans 216
Caucasians 284
37–73
Age
(2006)
Stefanski et al.
(2003)
253
Asian
Rhee et al.
(2006)
Population No
References
Table 5. (Continued).
pressure
sensitivity
resistance
Insulin
Blood
Insulin
in male
Dyslipidaemia
subjects
in obese
Dyslipidaemia
No difference
profiles
lipid
Lower
Lower
No difference
level
Adiponectin
in duration
No difference
DM2
2.3.7 Adiponectin gene
The adiponectin gene (APM1) is located on chromosome 3q27, which has also
been found to be the locus for other candidate genes with phenotypes related to
MetS (Kissebah et al. 2000). Therefore it was speculated that the adiponectin
gene may be a “susceptibility gene” for DM2. Several genetic variations of the
human APM1 have been reported. Among two ethnic groups of French and
Japanese, > 10 relatively frequent polymorphisms in the adiponectin gene have
been detected (Hara et al. 2002) (Figure 8).
I164T
R211S
G84R
-11365
-11379
45
-4034
H241P
267
-3964
-11414
3
2
1
712
349
2019
Fig. 8. Schema of genomic structure and polymorphic variants of the adiponectin gene
(Modified from Hara et al. 2002).
The more prevalent variants of the adiponectin gene are 45T > G and
276G > T polymorphisms, which have been extensively demonstrated in various
studies to be associated with the components of MetS (Table 6). The association
of common polymorphisms of the adiponectin gene with insulin sensitivity was
first reported by Stumvoll and colleagues (Stumvoll et al. 2002).
An association between frequent single nucleotide polymorphisms at
positions 45 and 276 in the adiponectin gene and DM2 was shown for the
Japanese population (Hara et al. 2002). Subjects with the G/G genotype at
position 45 or the G/G genotype at position 276 had a significantly increased risk
of type 2 diabetes compared with those having the T/T genotype at positions 45
and 276, respectively. In addition, subjects with the G/G genotype at position 276
had a higher insulin resistance index than those with the T/T genotype.
The G allele at position 276 was linearly associated with lower serum
adiponectin levels in subjects with higher BMIs (Hara et al. 2002). Subjects with
the T/T genotype at positions 45 had higher post-glucose-load insulin levels,
49
suggesting an association with insulin resistance (Yang et al. 2003). In nondiabetic Japanese the G allele at position 276 was reported to associate with
higher insulin resistance and lower serum adiponectin level (Hara et al. 2002).
In an Italian “non-diabetic cohort”, subjects with the G/G genotype at
position 276 and the TG-haplotype homozygote at positions 45 and 276 had
higher fasting insulin levels, elevated HOMA index, higher total cholesterol to
high-density lipoprotein (HDL)-cholesterol ratio as well as higher triglyceride
levels with a borderline statistical significance (Menzaghi et al. 2002). In
contrast, in another group of 253 non-diabetic Italians, the T allele at position 276
was associated with higher fasting insulin and elevated HOMA index but lower
serum adiponectin concentrations (Filippi et al. 2004). In Greek PCOS patients,
subjects with the T/T genotype at position 45 or G/X genotype at position 276
displayed higher insulin resistances and higher triglyceride levels (Xita et al.
2005).
The effect of adiponectin genetic polymorphisms on blood pressure has not
been extensively addressed. In the Italian nondiabetic cohort, the T45 allele was
associated with higher systolic and diastolic blood pressures (Menzaghi et al.
2002). In Swedish obese subjects, the subjects with G276 allele had higher
diastolic blood pressure (Ukkola et al. 2003).
50
51
FrenchCanadian
Caucasian
Berthier et al.
(2005)
Filippi et al.
Asian
American
Caucasian
Hara et al. (2002)
Qi et al. (2005)
Schaffler et al.
(2002)
Stumvoll et al.
German
Caucasian
(2004)
(2004)
French
Fumeron et al.
(2004)
Population
References
371
892
879
864
5200
253
270
No
40–75
48–78
30–64
Age
Mixed
Mixed
Males
Mixed
Mixed
Mixed
Males
Sex
pressure
sensitivity
in 45 G
Non diabetics Higher
Controls
and 2 diabetes
Type 1
Diabetics
in 45 G
Lower
in 276G
DM2 patients
diabetics
in 276T
Lower
Blood
Insulin
Lower
in 45 G
Higher
in 276T
Higher
BMI
Non
glycaemic
Normo-
diabetics
Non
Random
Healthy
Comments
pressure, Lipids, adiponectin level and development of diabetes in different studies.
in 276T
G45 and G276
45G
association with
Not influence
in T276T
Higher
276G
DM2
in obese with associated with
Lower
In 276T
Lower
in 45 G
Positive
Adiponectin DM2
level
Dyslipidaemia Higher
Lipid profiles
Table 6. Association of common polymorphisms in adiponectin gene (45T > G and 276G > T) with BMI, Insulin sensitivity, Blood
52
Asian
Takahashi et al.
(2005)
40–70
Mixed
International
inT45
in G45
Weight gain
in T45
IGT
Lower
Non diabetics Higher
G276X
and lower in
lower in T45G
Zacharova et al.
245
andG276
in G276X
Asians
Poor in T45
Increase
Yang et al. (2003)
Females PCOS
Increase
Tended to be
16–37
in G276
Mixed
G276
in 45G
100
45–71
blood pressure
in T45 and
non obese
with G45T
Higher diastolic
No
level
Adiponectin
association
profiles
Blood pressure Lipid
with G45T
sensitivity
Insulin
association
No
BMI
Increased
Comments
Females Obese and
Mixed
Sex
Greek
1373
20–83
Age
Xita et al. (2005)
French
Vasseur et al.
192
219
No
(2002)
Swedish
Ukkola et al. (2003)
(2000)
Population
References
Table 6. (Continued).
45G+276T
diabetes in
IGT convert to
DM2
2.3.8 Insulin receptor substrate (IRS) molecules and IRS-1 gene
Insulin binds to its receptor on the surface of most cells after release from the
pancreas. The insulin receptor is composed of two α-subunits, each linked to
β-subunit and to each other by disulphide bonds (Figure 9). The insulin receptor
acts as a tyrosine kinase enzyme that transfers phosphate groups from ATP to
tyrosine residues on intracellular target proteins. Binding of insulin to the
α-subunits
causes
the
β-subunits
to
phosphorylate
themselves
(autophosphorylation), thus activating the catalytic activity of the receptor. The
activated receptor then phosphorylates a number of intracellular proteins such as
insulin receptor substrates (IRS), which in turn alters their activity, thereby
generating a biological response (White 1997, Baudry et al. 2002) (Figure 9).
Insulin
α
β
α
β
Phosphorylation of docking proteins (e.g. IRS-1)
Activation of signalling pathways (e.g. PI3-Kinase)
Glucose Glycogen Lipogenesis Protein
transport synthesis
lipolysis synthesis
Cell
Gene
expression growth
Fig. 9. Insulin signalling pathway (Modified from Baudry et al. 2002)
Four members of the insulin receptor substrate family have been identified,
including IRS-1, 2, 3 and 4. It has been shown for human adipocytes that IRS-1 is
the main docking protein for the binding and activation of Phosphatidylinositol 3
(PI3)-kinase in response to insulin (Roundinone 1997). The human IRS-1 gene is
localized on chromosome 2q36–37 (White 1997). IRS-1 is considered to play a
role in the insulin-signalling pathway and in mediating both metabolic and
mitogenic effects of insulin in peripheral tissues like muscle and adipose tissue. It
suggests a novel important role for IRS-1 in ß-cell function (White 1997, Giorgio
et al. 2001). A glycine to arginine substitution in codon 972 (Gly972Arg) of the
IRS-1 gene has been shown to be associated with a high prevalence of DM2 due
to insulin resistance and impaired insulin secretion (Porzio et al. 1999, Federici et
al. 2001). Significantly higher insulin sensitivity in X/Ala (PPARγ2) + X/Arg
(IRS-1) contrary to X/Ala + Gly/Gly, Pro/Pro +X/Arg and Pro/Pro + Gly/Gly has
previously been reported (Stumvoll et al. 2001). A meta-analysis of 27 studies
revealed that carriers of the 972Arg variant of the IRS-1 gene are at a 25%
increased risk of having DM2 compared with non-carriers (Jellema et al. 2003).
2.4
Genetic Epidemiology
Genetic epidemiology, a field that started in the 1960s, is an epidemiological
evaluation of the role of inherited causes of disease in populations and families
and focuses on the joint effects of genes and non-genetic determinants.
Conventional genetic analysis focuses on genes that account for specific
phenotypes and traditional epidemiology is more concerned with environmental
causes and risk factors related to traits. Genetic epidemiology is an alliance of the
two fields that focuses on both genetics, including allelic variants in different
populations, and the environment. Its aims are the detection of inheritance
patterns of a disease and to localize the gene and find a marker associated with
disease susceptibility (Burton et al. 2005). It explains how genes are effective in
different environmental contexts and arrive at a more complete comprehension of
the aetiology of complex traits and thus deals with the aetiology, distribution, and
control of diseases in groups of relatives and inherited causes of diseases in
populations (Kaprio 2000, Permutt et al. 2005). Genetic epidemiology today is
increasingly focusing on complex diseases, which are characteristically caused by
several interacting genetic and environmental determinants (Burton et al. 2005).
The successful identification of genes for monogenic diseases has led to
interest in investigating the genetic component of more complex diseases (Kaprio
2000). Investigations of genetic causes of disease aetiologies making use of the
human genome project (Collins 1999) and have allowed researchers to confirm
the genetic basis of diseases, to identify the genes responsible for various diseases
and to provide the best basis for understanding human disease (Figure10). So far,
over 100 disease-linked genes have been isolated with the use of the positionalcloning technique. This method has been used to isolate many altered genes
associated with human diseases (Collins 1999). Another rapidly developing
application of diagnostics is pharmacogenomics, which deals with predictions of
responsiveness to drugs. The advantage of the genetic approach lie in the
54
development of new gene and drug therapies, but they are likely to require many
more years of intensive research to become effective (Collins 1999).
Disease with genetic
component
Diagnostic
Time
Gene Understand
cloning
basic
biologic
defect
Use of
Human
genome
project
Preventive
medicine
Pharmacogenomics
Gene
therapy
Drug
therapy
Fig. 10. Steps in the genetic revolution in medicine (Modified from Collins 1999).
The next step after obtaining evidence of a likely genetic component in the
cause of a complex disease is to locate and identify the causative genes. Genetic
linkage studies can be used to identify regions of the genome that contain genes
that predispose to disease. A linkage analysis is often the first stage in the genetic
investigation of a trait, since it can be used to identify broad genomic regions that
might contain a disease gene (Teare & Barrett 2005). Genetic association studies
aim to detect a statistical relationship between genomic variations at one or more
sites with a trait. Association studies differ from linkage studies, in that the same
allele (or alleles) is associated with the trait, while linkage allows different alleles
to be associated with the trait. Association analyses have greater power than
linkage studies to detect small effects, but require many more markers to be
examined. The association operates only over short distances in the genome.
Genetic susceptibility to common complex disorders probably involves many
genes, most of which have small effects. This fact, together with the identification
of large numbers of single nucleotide polymorphisms (SNPs) throughout the
genome as well as rapidly falling genotyping-costs, have led to the importance of
association studies in genetic epidemiology. It is now possible to search for
disease susceptibility genes by screening large numbers of SNPs across the whole
genome (Cordell & Clayton 2005).
In any population, two unrelated individuals share more than 99% of their
DNA sequence and what makes an individual unique is the remaining less than
55
1%, containing genetic variation. Among the variations are alterations that affect
susceptibility to disease and environmental conditions. A single nucleotide
polymorphism (SNP) refers to a variation in a single nucleotide (A, T, C, and G)
in the DNA sequence of the genome (Nomikos 2006). SNPs are thought to be the
genetic basis of most human diseases, or at least positional markers of our genetic
heritage. SNPs can predispose individuals to a particular disease or affect drug
response or metabolism. In contrast to monogenic diseases, complex diseases
require the simultaneous testing of hundreds of genes with thousands of SNPs
(Matthias 2004). Understanding these variations can lead to understanding the
causes of the disease as well as possible treatment options (Nomikos 2006). More
than 5 million single-nucleotide polymorphisms (SNPs) with minor-allele
frequencies greater than 10% are expected to exist in the human genome
(Kruglyak & Nickerson 2001). The presence of particular SNP alleles in an
individual is determined by testing (genotyping) a genomic DNA sample.
Because association studies to identify genomic loci associated with particular
phenotypic traits have focused primarily on genotyping SNPs, it is important to
determine whether common structural polymorphisms are in linkage
disequilibrium (LD) with common SNPs, and thus can be assessed indirectly in
SNP-based studies (Hinds et al. 2006).
The specific set of alleles observed on a single chromosome, or part of a
chromosome, is called a haplotype. The coinheritance of SNP alleles on these
haplotypes leads to associations between these alleles in the population and is
known as linkage disequilibrium. Linkage disequilibrium (LD) therefore refers to
associations between tightly linked SNPs on the chromosome (Terwilliger &
Weiss 1998). If one locus has a disease-predisposing allele and this allele is in LD
with the alleles of a nearby locus, these can be inherited together (Terwilliger &
Weiss 1998). If a chromosomal region has been linked to the disease, the next
step would be the search for attractive candidate genes in the region or narrowing
the region by linkage-disequilibrium mapping (Groop 2000).
The strong associations between SNPs in a region allow genotyping of only a
few, carefully chosen SNPs in regions (so called “tag” SNPs), which provide
enough information to predict much of the information about the remainder of the
common SNPs in the region in question. As a result, only a few of these “tag”
SNPs are required to identify each of the common haplotypes of a specific region.
Observations like these are the foundation for developing a haplotype map of the
human genome, known as the 'HapMap' project. This map will describe the
common patterns of variation, including associations between SNPs, and will
56
include those “tag” SNPs selected to most efficiently and comprehensively
capture this information. The International HapMap Project was launched in
October 2002 to create a public, genome-wide database of common human
sequence variation, in order to provide information needed as a guide to genetic
studies of clinical phenotypes. The aim of this Project is to determine the common
patterns of DNA sequence variation in the human genome, by characterizing
sequence variants, their frequencies, and correlations between them, in DNA
samples from populations with ancestry from parts of Africa, Asia and Europe.
The project will thus provide tools that will allow indirect associations to be
applied readily to any functional candidate gene in the genome, at any region
suggested by family-based linkage analysis, or ultimately to the whole genome
for scans for disease risk factors. Common variants responsible for disease risk
will be most readily approached by this strategy, but not all predisposing variants
are common (The International HapMap Project 2003).
2.4.1 Genetic epidemiology of the metabolic syndrome
MetS is considered to result from the interaction of environmental factors with
genetic susceptibility factors. It has been shown that insulin resistance clusters in
families and 45% of first degree relatives of patients with DM2 are insulin
resistant compared with 20% of subjects without a family history of diabetes
(Groop et al. 1996). A transition from rural to urban lifestyle was also shown to
be correlated with obesity, DM2 and MetS. The “thrifty genes” hypothesis (Neel
1962) proposes that energy conserving genotypes selected by a harsh environment
are associated with survival disadvantage when there is an abundance of food.
Thus thrifty genes contribute to the phenotype of MetS. By use of the candidate
gene approach, thrifty genes have been sought for amongst genes regulating body
weight, lipolysis, fat metabolism and insulin resistance.
The origins of the metabolic syndrome lie not only in the interaction between
genes and traditional risk factors in adults, such as unbalanced diet, physical
inactivity, etc. but also in the interplay between genes and the embryonic, foetal
and early postnatal environment. Barker and Hales (Hales et al. 1991, Barker
1992) postulated that impaired foetal growth might have predisposed the
survivors to heart disease in later life. Noting the highest risks of heart disease
and of type 2 diabetes, the insulin resistance syndrome, or impaired glucose
tolerance are in those who were small at birth but became overweight adults, led
to the second part of the hypothesis proposed by them: the idea of the "thrifty
57
phenotype" (Hales & Barker 1992, Hales & Barker 2001). As an adaptation to
under nutrition in foetal life permanent metabolic and endocrine changes occur it
would be beneficial if nutrition remained scarce after birth. If nutrition becomes
plentiful, these changes predispose to obesity and impaired glucose tolerance. This
phenomenon proposes that foetal malnutrition results in impaired pancreatic ßcell development and insulin resistance. Offspring are subsequently more prone to
MetS when exposed to abundant nutrition later in life. However fetal insulin
hypothesis suggests that the association between low birthweight and adult
insulin resistance is principally genetically mediated. In this regard, the increased
prevalence of MetS in offspring of diabetic mothers may be a consequence of
environmental factors operating on a genetic background in the prescence of an
altered intrauterine environment superimposed on a genetic predisposition of the
foetus (Hattersley & Tooke 1999).
2.5
Summary of the literature review
The metabolic syndrome is a cluster of metabolic risk factors including central
obesity, diabetes or prediabetes, raised TG and reduced HDL and high blood
pressure in one person. People with MetS are twice as likely to die from, and
three times more likely to experience a heart attack or stroke than people without
the syndrome. They also have a five-fold greater risk of developing DM2 (Isomaa
et al. 2001, Stern et al. 2004). Therefore, there is an overwhelming moral,
medical and economic need to identify people with MetS, so that lifestyle
interventions and treatment may prevent the development of DM2 and CVD.
An interplay between the genetic and environmental factors plays a role in
the development of metabolic syndrome. Obesity and obesity-linked insulin
resistance are thought to be important underlying factors of MetS. White adipose
tissue has been increasingly recognized as an important endocrine organ that
secretes a number of "adipokines" (Ahima et al. 2000). Of these adipokines,
adiponectin has recently received much attention, because of its anti-diabetic and
anti-atherogenic effects. It is expected to become a novel therapeutic tool in MetS
and DM2 (Kadowaki & Yamauchi 2005). Indeed, a decrease in the circulating
levels of adiponectin as a consequence of genetic (such as adiponectin gene
variation) or environmental factors (such as abdominal obesity) has been
suggested to contribute to the development of MetS, DM2 and CVD. Therefore,
increased plasma adiponectin and enhancement of its action by up-regulation of
serum adiponectin or its receptors, or the development of adiponectin receptors
58
agonists may serve as mitigators in connection with MetS. The TZDs class of
anti-diabetic drugs, which are PPARγ agonists, is known to exert its effects partly
through increasing the levels of the active form of adiponectin. Consideration of
adiponectin as a novel therapy agent and a potentially promising target for the
prevention and treatment of the MetS and therefore prevention of DM2 and CVD
will require further rigorous clinical and experimental investigations. The present
research will then serve as a pilot-study for further and more involved
investigations in this field.
59
60
3
Purpose of the present study
The overall aim of this study was to obtain new information about the roles that
lifestyle risk factors and the polymorphisms of candidate genes play in connection
with the regulation of serum adiponectin level as a novel marker of the metabolic
syndrome (MetS).
More specifically this study was to address the following questions:
1.
2.
3.
4.
The effect of Finnish military service lifestyle and weight changes on serum
adiponectin levels over a six-month follow-up period.
The interaction of weight change and common variations of PPARγ2 gene on
serum adiponectin levels over a six-month follow-up.
The effects of common polymorphisms of the PPARγ2 and IRS-1 genes and
their interactions on the serum adiponectin level.
The effect of the adiponectin gene common variations on the serum
adiponectin levels.
61
62
4
Subjects and Methods
4.1
Study Cohort
The northern Finland military service cohort study involved recruits from the
1995 and 1997 intake. It was based on two cohorts of Finnish servicemen, 17–29
years of age. The first started military service in January 1995 in the
Ostrobothnian Brigade garrison; the second began their service with the First
Signal Company in January 1997. A prospective study with a six-month followup investigated the impact of the military lifestyle on insulin resistance
cardiovascular-associated risk factors among these two groups of servicemen. All
the conscripts were from central and northern parts of Finland. In January 1995,
altogether 1268 men started their military service at the Ostrobothnian Brigade
garrison in Northern Finland. All servicemen were invited to participate in the
study. Those servicemen, who were willing to take part in this study, gave their
written consent. Before beginning of the study in 1995, 45 subjects left (relieved
from the military service or changed their garrison) and 9 persons denied to
participate, therefore participation rate was 1214/1268 (96%). The first phase of
the study included anthropometric measurements (height, weight, waist and hip
circumferences), blood pressure measurements and a questionnaires consisting of
the history of diabetes, blood pressure, cardiovascular disease in their family and
blood pressure, smoking, food consumption and amount of exercise among
themselves. Men with at least one of the following criteria: (1) BMI > 27 kg/m²,
(2) WHR > 0.98, (3) diastolic blood pressure > 85 mmHg, were considered as
screen positives (Tähtinen 2006).
There was no official definition for MetS in 1995 to use for the screening of
the subjects. Therefore the researchers´ own definition based on the abovementioned criteria was used. According to this screening, 160 subjects were
identified as positive and out of them 144 subjects participated in the blood tests.
In addition 132 randomly selected subjects were invited to the test, out of which
109 subjects participated in the blood tests. Altogether 253 subjects attended the
baseline blood tests. Of these 253 subjects only 127 males took part in the followup study six months later (Figure 11). In January 1997, 106 men started their
service with the First Signal Company in Northern Finland. All 106 conscripts
were included in the first part of the study but only 78 men completed baseline
and follow-up tests (Figure 11).
63
The Northern Finland Military Service Cohort
Study (1995)
Study (1997)
Ostrobothnian Brigade
1268 subjects (1995)
9 subjects denied
participating in
the study
First Signal Company
106 subjects (1997)
45 subjects left
before beginning
of the study
1214 subjects participated in the study
160 screen positive
subjects (BMI>27 or
WHR>0.98 or
DBP>85)
132 randomly
selected
144 screen positives
who participated in
the blood test at
baseline
109 participated
in the blood test
at baseline
78 subjects
participated in the
follow-up test
49 subjects
participated in
the follow-up test
127 subjects
participated in the
follow-up test
All 106 subjects participated
in the baseline study
78 subjects participated in the
six month follow-up study
205 subjects with
data from baseline
and follow-up of
both garrisons
Fig. 11. Northern Finland military service cohort study 1995–1997
For the present study, genotyping for the common variations of the metabolic
syndrome candidate genes were done at the University of Kuopio involving 266
subjects (175 from the Ostrobothnian Brigade and 91 from the First Signal
Company). Serum adiponectin levels of the frozen blood samples were measured
from subjects of both garrisons. For 205 subjects baseline data as well as data
from the six month follow-up (Figure 11 & 12) were available and analysed as
part of the first study. Among these 205 subjects were 170 subjects, who had
already been genotyped and were used as material for the second paper. A total of
64
252 subjects, with serum adiponectin values and genotyping data present, formed
the basis for analyses of the third and fourth papers (Figure 12).
266 subjects were genotyped (175 from
Ostrobothnian Brigade and 91 from First
Signal Company)
205 subjects with complete
data at the baseline and
follow up + adiponectin
level
(First study)
170 subjects having
complete data at the
beginning and follow up
and adiponectin level +
genotyping
(Second study)
252 subjects with adiponectin
levels at the baseline +
genotyping
(Third and fourth study)
Fig. 12. Present study cohort in individual articles
4.2
Measurements
4.2.1 Anthropometric measurements, medical history and lifestyle
assessments
Systolic and diastolic blood pressure, weight, height, body mass index (BMI),
waist-to-hip ratio (WHR), were measured once at the beginning of the study and
then after six months (Tähtinen et al. 2001). Blood pressures were measured by a
trained medical assistant (physicians or nurses). If systolic blood pressure was
> 140 mmHg or diastolic blood pressure > 80 mmHg the measurement was
repeated twice after a 5–10 minutes rest in a sitting position. The mean of the two
measurements was then used as the subject's blood pressure. Subjects were
examined in light clothing for their weight. Body mass index (BMI) was
calculated as weight (kg) divided by the square of the height (m²). Waist
circumference was defined as the smallest girth midway between the lowest rib
margin and the iliac crest. Hip circumference was measured at the level of greater
trochanter. Waist to hip ratio (WHR) was calculated as waist circumference
divided by hip circumference. Using questionnaires, conscripts had been asked
65
about the history of type 2 diabetes, hypertension and cardiovascular diseases
among parents, brothers and sisters and grandparents and also about their own
history of high blood pressure, smoking, food consumption and exercise.
4.2.2 Laboratory measurements
Fasting serum total cholesterol, LDL and HDL cholesterol, triglyceride (TG),
fasting plasma insulin, and fasting glucose were measured at the beginning of the
study and six months later (Tähtinen et al. 2001). All blood samples were taken in
the morning after a 9 hour fast and in a same way for all the participants.
Immediately afterwards, venous blood samples were put on ice and transferred to
the laboratory of the Oulu Deaconess Institute (ODL) for a variety of laboratory
measurements. Thereafter, the samples were stored at -70°C in the Department of
Public Health Science and General Practice, University of Oulu, until the
measurement of the serum adiponectin level. Additional frozen samples of the
study subjects were transferred to the university hospital of the University of
Kuopio to measure fasting plasma insulin and glucose and genotyping (Tähtinen
2006).
Measurement of adiponectin
Present study started in 2003 when frozen samples were collected and delivered
to the laboratory of the Department of Public Health Science and General
Practice, University of Oulu, for measurement of the serum adiponectin levels.
Adiponectin concentration was determined by the Human Adiponectin ELISA Kit
(B-Bridge international, Inc., CA, and USA). This ELISA is a sandwich type
enzyme-linked immunoassay consisting of a primary (mouse anti-adiponectin
monoclonal) antibody-coated plate, a secondary (rabbit anti-human adiponectin
polyclonal) antibody, detection (Horseradish peroxidase (HRP)-conjugated goat
anti-rabbit IgG) antibody, a substrate for HRP and a (recombinant human)
adiponectin standard. According to the laboratory, one human serum sample with
a known adiponectin concentration was used as the reference.
4.2.3 Estimation of insulin resistance
Insulin resistance in the present study was estimated with the quantitative insulin
sensitivity check index (QUICKI), which can be determined from fasting insulin,
66
and glucose values according to the equation: 1/ [log (I0)] + [log (G0)], in which I0
is fasting insulin and G0 is fasting glucose ( Katz et al. 2000) .
4.2.4 Genotyping
Genomic DNA was obtained from whole blood leukocytes. The polymorphisms
of the genes were determined by the TaqMan polymerase chain reaction (PCR)
method at the University of Kuopio. The following primers and probes have been
used:
For PPARγ2 gene: Primers 5´-GACAAAATATCAGTGTGAATTACAGC
3´and5´CCCAATAGCCGTATCTGGAAGG-3´ was used to amplify exon B of
the gene encoding PPARγ2. The PCR conditions were: an initial denaturation step
at 94 °C for 2 min, followed by 30 cycles of denaturation at 94 °C for 15 s,
annealing at 60 °C for 30 s, and extension at 72 °C for 30 s, with a final extension
of 4 min at 72 °C. SSCP (Single strand conformation polymorphism) analysis was
done after PCR. PCR products were electrophoresed on a non-denaturing 6%
polyacrylamide gel containing 10% glycerol for 5 h at 28–32 °C. PCR products
that displayed variant conformers were directly sequenced.
The SNaPshot ddNTP Primer Extension Kit technique was used to genotype
SNP 45 T/G and SNP 276 G/T of the adiponectin gene. Forward
(5'-GGCTCAGGATGCTGTTGCTGG-3') and reverse (5'-GCT TTG CTT TCT
CCC TGT GTC T-3') primers were used to amplify a 328-bp DNA fragment. The
following cycling conditions were used for the PCR: 94 C for 4 min and 35 cycles
of 94 C for 30 sec, 57 C for 30 sec, 72 C for 30 sec, and 72 C for 4 min. The PCR
product was purified with 1 U shrimp alkaline phosphatase and 2 U exonuclease I
incubated at 37 C for 60 min and at 75 C for 15 min. Primers used to determine
the genotypes were 5'-CTGCTATTAGCTCTGCCCGG-3' for the SNP 45 T/G
polymorphism and 5'-ACCTCCTACACTGATATAAACTAT-3' for the SNP 276
G/T polymorphism.
The single exon of the IRS-1 gene was amplified in 10 overlapping fragments
ranging in size from 334 to 566 bp. Each fragment was amplified with PCR using
different primers (Laakso et al. 1994). PCR conditions were denaturation at 94°C
for 2–4 minutes followed by 35 cycles of denaturation at 92–94 °C for 45–60
seconds, annealing at 62–66 °C for 1 min, and extension at 72 °C for 45–60 s,
with a final extension of 4 min at 72 °C. All genotype frequencies were in HardyWeinberg equilibrium.
67
4.3
Statistical Analyses
Statistical analyses and data processing were performed using SPSS (v. 11.5) and
SAS (v. 8.2) for Windows. The normality of the variables studied was tested with
the Kolmogorov-Smirnov test. Because of the skewness of the distribution of
triglycerides, insulin and glucose, their values were log-transformed to achieve a
normal distribution and median (interquartile range) were used.
The paired t-test was used to evaluate the significance of the difference
between the means of the parameters at the baseline and six months follow-up
study as well as to evaluate differences of means between subjects with data at
baseline and follow-up compare with those having only baseline data. Paired
t-test were also used to compare the baseline serum adiponectin levels and the
levels recorded six months later among the different groups of PPARγ2 genotypes.
The potential univariate relationships between adiponectin levels and other
values (the biological or anthropometric parameter recorded) were assessed by
Pearson´s or Spearman’s correlation coefficients. Thereafter, stepwise (with
backward elimination) linear regression analysis was performed to assess the
variables independently related to the change of serum adiponectin levels during
the military service. For this purpose, only the variables showing a significant
linear regression univariate association with adiponection concentration were
considered for multivariate analysis.
Analysis of variance (ANOVA) was used to compare the means of the
variables among the genotypes of PPARγ2, IRS-1, and their genotype combination
groups. Post hoc comparison of “Fisher’s least significant difference” was used to
test the significance of the mean difference between the groups. Hochburg–
Benjamin method was applied to adjust P values for multiple comparisons in
genotype combination groups of PPARγ2 and IRS-1 (Hochburg & Benjamin 1990,
Benjamin & Hochburg 1995).
In univariate analysis, differences between the means of continuous
dependent variables, adiponectin concentration, and anthropometric variables
among the adiponectin genotype groups were tested by ANOVA. To control for
potential confounding factors in comparisons of adiponectin concentrations
among the genotype groups, ANCOVA was applied. QUICKI, WHR, DBP and
triglyceride level were included as covariates into the model, because they
showed statistically significant associations with serum adiponectin levels.
Interactions between the SNP+276 genotypes of adiponectin gene and covariates
were assessed with ANCOVA. Analysis of variance used to detect interactions
68
between the PPARγ2 and IRS-1 genes variations as well as interaction of these
genotypes with screening classes, with respect to the adiponectin level.
To evaluate the differences in glucose and insulin between the PPARγ2 and
IRS-1 genotype groups, Kruskal-Wallis and Mann-Whitney U tests were used.
Wilcoxon and Mann-Whitney U tests were also used to evaluate the differences in
glucose and insulin between subjects with baseline data and values from the
follow-up compared with those for whom only baseline data were available. Pvalues < 0.05 were considered statistically significant in all studies.
4.4
Ethical considerations
Before the beginning of the study, all servicemen in this study, having been
briefed and informed on the aims and technicalities of the study, had given their
written consent to participate. Questionnaires and data are confidentially stored in
the Department of Public Health Science and General Practice (KTTYL)
University of Oulu. The Ethics Committees of the Oulu Deaconess Institute and
the Finnish Defence Forces approved the study protocol.
69
70
5
Results
5.1
Baseline characteristics of study subjects and changes of
values during six months in military service (I)
Clinical and laboratory characteristics of the study subjects at baseline and after
six months of military service are shown in Table 7. The results show that
concentrations of serum adiponectin statistically significantly decreased from
baseline levels during the six-months follow up in military service (Table 7). Total
cholesterol, low and high density lipoprotein cholesterol, triglycerides and fasting
insulin significantly increased while body weight, systolic blood pressure and
diastolic blood pressure significantly decrease during military service (Table 7).
5.1.1 Loss analysis and sample selection effects
Differences between the values in the group of subjects for whom baseline and
follow-up data were available (N = 205), compared with subjects with only
baseline data (N = 154) were investigated. It was shown that serum adiponectin
levels were significantly lower and total cholesterol levels were significantly
higher in subjects for whom only baseline data had been available (Table 7).
To investigate whether sample selection based on the criteria used in 1995 for
screening of MetS (BMI > 27 or WHR > 0.98 or DBP > 85) influenced the results
of the present study, clinical and laboratory characteristics of subjects with
baseline and six months follow-up data available were compared between screen
negative (N = 110) and screen positive (N = 95) subjects. Significant changes of
similar magnitude were seen in serum adiponectin and lipid profiles in both
groups (serum adiponectin level significantly decreased and lipids levels
significantly increased during the follow-up in both groups) (Table 8).
In screen negative subjects WHR and fasting-insulin significantly increased
while SBP significantly decreased. Weight, BMI, glucose and DBP did not
change significantly. In screen positives, weight, WHR, BMI, SBP and DBP
significantly decreased (Table 8).
71
72
25.1 (4.5)
BMI (kg/m²)
7.9 (6–11.1)
4.9 (4.6–5.2)
TG (mmol/l)§
F. Ins (mU/l) §
F. Glu (mmol/l) §
(9.3)
129.3 (12.9)
74
P-value$
(0.7–1.3)
72
(9.2)
120.6 (12.9)
10.3 (3.7)
4.9 (4.7–5.2)
8.3 (6.6–11.5)
1
2.6 (0.7)
1.3 (0.3)
4.4 (0.8)
24.7 (3.9)
0.88(0.0)
78.3 (13.8)
# 28 subjects from First Signal Company and 126 subjects from Ostrobothnian Brigade
£Comparison of mean values between subjects with complete data and subjects with baseline data,
0.007
<0.001
<0.001
0.062*
0.010*
<0.001*
<0.001
<0.001
<0.001
<0.001
0.354
<0.001
*Wilcoxon Signed Ranks Test, §Median (Q1–Q3), $ Comparison of mean values at baseline and follow-up,
DBP (mmHg)
SBP (mmHg)
11.3 (3.6)
0.7 (0.5–1)
LDL (mmol/l)
Serum adiponectin (µg/ml)
1.1 (0.3)
2.2 (0.6)
HDL (mmol/l)
3.7 (0.7)
0.89(0.0)
Cholesterol (mmol/l)
79.6 (15.5)
(SD)
WHR (cm)
Mean at follow-up
(SD)
73.3 (9.4)
127.0 (11.7)
10.0 (3.8)
4.9 (4.6–5.3)
8.4 (6.4–12.1)
0.7 (0.6–1)
2.3 (0.6)
1.1 (0.3)
3.9 (0.7)
25.1 (3.8)
0.88(0.0)
81.0 (14.3)
Mean
0.458
0.096
0.010
0.508
0.482
0.475
0.051
0.792
0.032
0.945
0.591
0.370
P-value£
N = 154 #
N = 205
Mean at baseline
Subjects with baseline data
Subjects with baseline and follow up data
Weight (kg)
Value
Table 7. Clinical and laboratory characteristics of study subjects.
73
22.1 (2.4)
BMI (kg/m²)
6.7 (5.5–8.8)
4.8 (4.5–5.2)
TG (mmol/l)§
F. Ins (mU/l)§
F. Glu (mmol/l)§
71.3 (7.8)
DBP (mmHg)
*Wilcoxon Signed Ranks Test, §Median (Q1–Q3)
125.8 (10.9)
SBP (mmHg)
11.7 (3.6)
0.6 (0.5–0.8)
LDL (mmol/l)
S. adiponectin (µg/ml)
1.2 (0.3)
2.2 (0.6)
HDL (mmol/l)
3.7 (0.7)
0.84(0.0)
Cholesterol(mmol/l)
69.8 (9.6)
70.5 (8.4)
115.5 (9.6)
10.4 (3.3)
4.8 (4.6–5.0)
7.6 (6.1–9.8)
1.0 (0.7–1.3)
2.5 (0.7)
1.3 (0.3)
4.3 (0.7)
22.3 (2.0)
0.86(0.0)
70.2 (8.7)
(SD)
WHR (cm)
Mean at follow-up
0.406
<0.001
<0.001
0.881*
0.046*
<0.001*
<0.001
<0.001
<0.001
0.320
0.026
0.351
P-value
(7.8–14.7)
77.2 (9.9)
133.3 (13.9)
10.8 (3.6)
4.9 (4.7–5.3)
11
0.8 (0.6–1.1)
2.2 (0.6)
1.0 (0.2)
3.7 (0.7)
28.6 (3.8)
0.92(0.0)
90.8 (13.2)
(SD)
Mean at baseline
(12.6)
74.1
126.3
10.2
5
11.1
1.1
2.7
1.2
4.5
27.5
(9.7)
(13.7)
(3.9)
(4.8–5.3)
(8.1–15.2)
(0.8–1.6)
(0.7)
(0.3)
(0.8)
(3.6)
0.90 (0.0)
87.6
(SD)
Mean at follow-up
N = 95
N = 110
(SD)
Screen positives
Screen negatives
Mean at baseline
Weight (kg)
Value
P-value
0.003
<0.001
0.019
0.005*
0.118*
<0.001*
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Table 8. Clinical and laboratory characteristics of screen negative and screen positive subjects at baseline and follow-up.
5.2
Influence of weight changes over the six-month follow-up on
serum adiponectin level (I)
The influence of the changes in body weight during military service on serum
adiponectin level was investigated in the first study. Depending on the change in
weight during the period of military service, subjects of this study were classified
into the following four groups: subjects who gained 5–10% weight, those with no
weight change, subjects with 5–10% weight loss and subjects who lost > 10% of
their weight during the six months of military service. The results (Table 9) show
a significant decrease in adiponectin concentrations in subjects that experienced a
gain in weight, or recorded no weight change or lost 5–10 % of their weight.
Serum adiponectin concentrations did not change significantly in subjects who
lost > 10% of their body weight.
Statistically significant increase in total cholesterol, LDL cholesterol and
HDL cholesterol was found in all groups. TG level increased in the group that had
no body weight change and in subjects with 5–10% weight loss. Fasting-insulin
significantly increased in subjects who gained 5–10% weight. Subjects with
> 10% body weight reduction had a statistically significant decline in insulin but
total and LDL cholesterol significantly increased in the follow-up (Table 9).
Obese subjects with BMI ≥ 30 kg/m² who had also lost > 10% of their body
weight (N = 5 mean BMI = 32.3), had a statistically significant decrease in the
fasting-insulin levels (15.50 mU/l vs. 9.56 p = 0.015). However, total cholesterol
and LDL cholesterol significantly increased (3.38 mmol/l vs. 4.38 p = 0.004 and
2.0 mmol/l vs. 2.58 p = 0.036 respectively) in the subjects of this group. There
was no statistically significant interaction with regard to serum adiponectin levels
between body weight change and belonging to the screen-positive or screennegative groups.
74
Table 9. Association of weight change with changes of laboratory values during the
follow-up.
Value
N = 34
N = 110
N = 38
N = 23
ANOVA
> 5–10%▲
No change
> 5–10%▼
> 10%▼
P-Value
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Baseline
11.90 (3.3)
11.50 (3.4)
10.3 (3.7)
Six months later
10.90 (2.8)†
10.50 (3.7)*
9.4 (3.5)†
Baseline
3.8 (0.7)
3.79 (0.7)
3.60 (0.7)
3.39 (0.6)
0.035
Six months later
4.5 (0.8)*
4.45 (0.8)*
4.27 (0.7)*
4.01 (0.9)*
0.059
Baseline
2.3 (0.7)
2.25 (0.6)
2.13 (0.6)
1.91 (0.6)
0.079
Six month later
2.8 (0.8)*
2.57 (0.7)*
2.47 (0.6)*
2.34 (0.8)*
0.074
Baseline
1.2 (0.3)
1.16 (0.3)
1.1 (0.3)
1.0 (0.2)
0.253
Six month later
1.3 (0.3)†
1.32 (0.3)*
1.2 (0.3)†
1.1 (0.2) *
0.074
Adiponectin(µg/ml)
10.1 (4.6)
0.189
10.4 (4.6)
0.321
Cholesterol(mmol/l)
LDL (mmol/l)
HDL (mmol/l)
TG (mmol/l) §
Baseline
0.7 (0.6–0.9)
0.7 (0.5–1)
0.7 (0.5–1)
0.8 (0.6–1.3)
0.575
Six month later
0.8 (0.6–1)
1.0 (0.8–1.4)*
1.1 (0.8–1.6)*
1.0 (0.7–1.1)
0.008
Baseline
6.4 (5.3–8.4)
7.2 (5.9–10.6)
8.7 (6.0–12.3)
11.4 (9–15.6)
<0.001
Six month later
8.1(6.8–9.9)*
7.9 (6.4–11.1)
9.7 (7.6–12.9)
9.1 (6.4–12.1)*
F. Ins (mU/l) §
0.139
BMI (kg/m²)
<0.001
Baseline
21.6 (3.2)
24.8 (4.3)
27.3 (4.3)
28.6 (2.9)
Six months later
23.3 (3.5)*
24.9 (4.2)
25.5 (3.8)*
24.9 (2.6)*
0.088
<0.001
WHR (cm)
Baseline
0.85 (0.0)
0.88 (0.0)
0.92 (0.0)
0.91 (0.0)
Six months later
0.86 (0.0)
0.88 (0.0)
0.90 (0.0)†
0.87 (0.0)†
0.015
SBP (mmHg)
Baseline
125.0 (13.7)
129.6 (12)
132.7 (12.9)
128.5 (15.3)
0.092
Six months later
119.7 (12.3)†
120.8 (11.8)*
122.1 (15.0)*
118.4 (14.9)*
0.706
DBP (mmHg)
Baseline
74.2 (11)
73.8 (9.3)
75.2 (8.4)
73.0 (8.0)
0.813
Six months later
73.2 (10)
73.2 (8.7)
71.2 (9.5)†
67.6 ( 9.1)†
0.042
*P< 0.001, † P<0.05, § Median (Q1–Q3), ▲ weight gain, ▼ weight loss
75
5.3
Influence of common variations of PPARγ2 gene on baseline
serum adiponectin levels and interaction of these variations
with weight changes on serum adiponectin level (II & III)
The association of serum adiponectin levels with the common variants of the
PPARγ2 gene was investigated in the second and third studied. When looking at
individual genotype pairs it appeared that (Table 10) subjects having the
Ala12Ala genotype of the PPARγ2 gene had statistically significantly higher
levels of serum adiponectin compared with subjects having the Pro12Ala
(p = 0.02) and Pro12Pro genotypes (p = 0.02). Total cholesterol and LDL
cholesterol levels were significantly higher in subjects with the Pro12Pro
genotype compared with subjects having the Pro12Ala genotype of the PPARγ2
gene (Table 10).
The overall serum adiponectin and lipid levels however did not differ
between the genotypes of the PPARγ2 gene (all ANOVA P-values > 0.05). When
P values were corrected for multiple testing (Bonferroni), serum adiponectin level
was significantly higher in Ala12Ala genotype compared with subjects with the
Pro12Pro (not shown in the original article).
Table 10. Adiponectin level according to the Pro12Ala polymorphism of the PPARγ2
gene.
Value
Pro12Pro
Pro12Ala
Ala12Ala
N = 173
N = 73
N=6
Mean (SD)
Adiponectin
10.4
(3.5)
Mean (SD)
10.5
(3.9)
P-value
Mean (SD)
14.1*† (3.9)
0.054
(µg/ml)
Total cholesterol
3.80 (0.7)
3.57 (0.6)*
3.86 (0.8)
0.052
2.24 (0.6)
2.06 (0.5)*
2.38 (0.7)
0.074
0.91 (0.6)
0.82 (0.4)
0.60 (0.1)
0.187
(mmol/l)
LDL cholesterol
(mmol/l)
Triglycerides
(mmol/l)
† p<0.05, compared to the Pro/Ala genotype group *P<0.05, compared to the Pro/Pro genotype group
With regard to the levels of the serum adiponectin, the data of the present study
also showed a relation between the common variants of the PPARγ2 gene and
weight changes (Figure 13). Because there was only one homozygous subject of
Ala12Ala, the subject was combined with subjects of the Pro12Ala genotype,
collectively named X12Ala. Subjects, who possessed the X/Ala allele of the
76
PPARγ2 gene and had lost > 10% of their body weight during the six months
period of military service (N = 5), exhibited a significant increase in serum
adiponectin levels compare with the subjects who had this allele, but did not show
the same amount of body weight decrease or those who did not have this allele,
even if their body weight became reduced by > 10% (10.66 μg/ml vs.12.40
P = 0.002). No interactive effect was noticed between the PPARγ2 genotypes and
screen-positiveness or negativeness with either these genotypes or possessing
complete data, with regard to the serum adiponectin level (data not shown).
Fig. 13. Change in serum adiponectin levels in relation to different categories of
weight change and according to the Pro12Ala polymorphism of the PPARγ2.
77
5.4
Interactive effect of the common variations of the PPARγ2 and
IRS-1 genes on serum adiponectin level at baseline (III)
Putative interactive effects of the common variants of the PPARγ2 and IRS-1
genes on serum adiponectin levels were investigated in the third study. The twoway ANOVA interaction between the PPARγ2 and IRS-1 genes with respect to the
level of serum adiponectin was statistically significant (p = 0.02).
The effect of different genotype combinations on the serum adiponectin level
was investigated. As shown in Figure 14, serum adiponectin concentrations
differed amongst the five combination groups of the PPARγ2 and IRS-1 genotypes
(ANOVA, unadjusted p = 0.05, adjusted for BMI, p = 0.07). Statistically
significantly higher levels of serum adiponectin (p < 0.05) were found in subjects
who carried the Ala12Ala + Gly972Gly genotype combination of these two
genes, compared with subjects having the Pro12Pro + Gly972Gly or those who
carried Pro12Ala + Gly972Gly genotype combinations (Figure 14). Subjects with
the Pro12 Pro + X972Arg showed significantly higher total (P<0.05) and LDL
cholesterol (P<0.05) levels than subjects with the Pro12Pro + Gly972Gly and
Pro12Ala + Gly972Gly genotype combinations (Figures 2 and 3 of the third
original article). Differences in adiponectin, total cholesterol and LDL cholesterol
remained statistically significant (P<0.05) after adjustment for multiple
comparisons. No subject with the Ala12Ala + X972Arg formed part of this study.
No difference in serum adiponectin levels were observed among the IRS-1
genotype groups, but total cholesterol and LDL cholesterol levels were
significantly higher in the subjects with X972Arg genotype (4.06 &2.45 vs. 3.70
&2.16 p <0.05). There was no interactive effect between either IRS-1 genotypes
or screening class or with the genotypes and availability of complete data on
serum adiponectin level (data not shown).
78
Fig. 14. Comparison of serum adiponectin levels in the combined genotype groups of
the PPARγ2 and IRS-1 genes
5.5
Association of common variations of the adiponectin gene on
serum adiponectin level at baseline (IV)
A possible association of the genotype groups of SNP+276 of the adiponectin
gene with adiponectin concentrations was investigated in the fourth study. The
results showed that (Table 11) serum adiponectin levels (p < 0.001) as well as
diastolic blood pressure (p = 0.031) were statistically significantly higher in
subjects who carried the T276T genotype compared with subjects with the G276T
(p = 0.001 and 0.009) or those possessing the G276G (p < 0.001 and 0.035)
genotypes of adiponectin gene. Mean diastolic blood pressure in subjects with the
79
T276T genotype was 80 mmHg, while that in those who carried the G276G and
G276T genotypes was <75 mmHg. A significant two way interaction of
triglycerides with SNP+276 genotype was found with respect to the serum
adiponectin level (p = 0.009). In subjects with the T276T genotype, an increase in
triglycerides was associated with a decrease in adiponectin level, and this result
was not observed among subjects with G276G or T276G (Figure 1 of the fourth
original article).
Examinations of the polymorphisms at position 45 of the adiponectin gene
revealed that subjects with the T45T genotype tended to have higher serum HDL
cholesterol (p = 0.051), than subjects with the X45G genotype (Table 11). No
interaction with these genotypes and screening class or having complete baseline
and follow-up data was found.
Table 11. Cardiovascular risk factor levels in young Finnish men according to the
G276T and T45G polymorphisms of the adiponectin gene.
Value
Adiponectin
G276G
T276G
T276T
ANOVA
T45T
X45G
N = 137
N = 95
N = 20
P- value
N = 226
N = 26
P- value
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
9.1 (3.3)
10.7 (3.5)
13.7 (5.0)*
<0.001
10.55 (3.8)
10.50 (2.7)
0.93
3.8 (0.7)
3.7 (0.6)
3.8 (0.6)
0.63
3.7 (3.7)
3.8 (3.7)
0.88
1.1(0.3)
1.1 (0.3)
1.1 (0.3)
0.68
1.14 (0.2)
1.03 (0.2)
0.05
2.2 (0.6)
2.2 (0.6)
2.2 (0.5)
0.94
2.18 (2.2)
2.30 (2.3)
0.34
0.90 (0.6)
0.83 (0.4)
0.99 (0.5)
0.31
0.88 (0.5)
0.93 (0.5)
0.57
74.1(10.7)
80.2 (7.6) *
0.31
74.9 (10.0)
(μg/ml)
Total cholesterol
(mmol/l)
HDL cholesterol
(mmol/l)
LDL cholesterol
(mmol/l)
Triglycerides
(mmol/l)
DBP
(mmHg)
*P<0.05
80
73.9 (9.6)
74.3 (9.6)
0.78
6
Discussion
6.1
Evaluation of the study cohort and methods
The present study involved young and healthy Finnish men entering the military
service. Persons serving with the Finnish military service provide a good study
cohort on account of their very similar living environment during military service
including physical activities, duration of sleep and diet. This study group was
limited to males, because of the low number of female conscripts and of avoiding
the confounding effect of sex on the results and findings.
In the process of study, some of the conscripts took part only in baseline tests
because they had changed their garrison or transferred to other classes of the
military service after the first part of the investigation. Also a number of the
subjects declined to participate in the study. This loss of subjects might pose a
problem in attempts to elevate the results to population level. As it is shown in the
Table 7, serum adiponectin level was significantly lower and total cholesterol
significantly higher in subjects for whom only baseline data were available. The
effect of this selection is difficult to estimate, but it might be justified to suppose
that this selection does not lead to significantly wrong evaluations of the results;
however, one needs to be careful in generalizing the study results to all young
men.
Subjects of the study had been selected with the help of screening method
which was developed for the purposes of study in order to identify the subjects
with markers of MetS in 1995. The following set of criteria (with at least one of
these, i.e. BMI > 27 kg/m² or WHR > 0.98 or diastolic blood pressure > 85
mmHg in order to be considered screen positives), were used to select subjects for
the blood test. This might have caused a selection bias in the study. In order to
control for the influence of this sample selection, some subgroup comparisons
between screen-positives and screen-negatives were made. The results showed
that in both groups, serum adiponectin level significantly decreased and lipids
levels significantly increased during the follow-up period. In screen-negative
subjects WHR and fasting-insulin significantly increased and SBP significantly
decreased. Weight, BMI, glucose and DBP did not change significantly in this
group. In screen-positive subjects, weight, WHR, BMI, SBP and DBP
significantly decreased. This could in theory be the result of regression towards
the mean because screen positive subjects had the extreme values of above
mentioned measurements at the baseline. Because the changes in the pre-screened
81
group (so called screen-positives) were in most study variables similar to those of
the non-selected cohort or screen negatives, it is obvious that screening method
did not cause the kind of selection bias that could affect the main results. No
interactive effects were found between gene polymorphism associated laboratory
changes and belonging to screen-positive or negative groups. The sample sizes
were quite modest and power to detect interactions was limited. Analyses
weighting the samples to reflect the characteristics of the entire baseline cohort,
i.e. giving more weight to the unselected subgroup, would have permitted a
stronger generalization to the entire study base of young Finnish men.
The power calculations to define sample size were not done at the beginning
of the study, because any estimates to be used for these calculations were not
available at that time. Laboratory and genetic analyses were performed with
standardized methods in the research laboratories of the Universities of Oulu and
Kuopio. The baseline and follow-up serum adiponectin levels were analysed at
the same time and with the same kit at the University of Oulu. Normal quality
analyses were done with standard serum in each kit. The plasma lipid and glucose
values were analysed in laboratory of Oulu Deaconess Institute, which was a
leading laboratory unit in northern Finland and the quality control system was
followed in that laboratory. The methods did not change during the time of the
study. Further studies with a larger number of subjects are needed to confirm the
results, but the main value of the present research is undoubtedly that it could
serve as a helpful pilot-study and yardstick for future work.
6.2
Major results of the study
6.2.1 Military service lifestyle and changes of serum
adiponectin level (I)
The results of the present study demonstrated that over a six-month period of
military service, serum adiponectin levels of the conscripts significantly
decreased and lipid levels significantly increased. The reasons for the decrease of
adiponectin levels are not clear, but they are in line with the changes in lipids and
insulin resistance findings, except for the increase in HDL cholesterol, which
represent a reflection of the increase in physical exercise during the military
service. There might be several possible explanations for decline in serum
adiponectin during the follow-up. One reason might be an increase in plasma
82
levels of lipids owing to high calorie diet during military service. It has been
shown that food habits will change during the period of military service. In one
study amongst Norwegian servicemen (Schei 1995), increases in the consumption
of fast foods, sweets and soft drinks by 57% after 3 months and by 80% after 10
months could be demonstrated. It has also been reported that in this particular
cohort, a high caloric diet induced increases in insulin resistance risk factors
during the follow-up (Tähtinen et al. 2001). The caloric content of ordinary meals
served to the Finnish army population has been 3200–3600 kcal per day
(Tähtinen et al. 2002), and additional energy could obtain from snacks, which are
available for sale in cafeterias during daytime and evenings (Tähtinen et al.
2001). According to the questionnaire that surveyed the cohort of the First Signal
Company in the current study, it was noticed that the consumption of
confectionary, doughnuts and soft drinks significantly increased during the
service. On the other hand 63% of conscripts claimed that their physical activity
had increased during the service (Tähtinen et al. 2001).
Amount and type of dietary fats seem to be the reason for an increase in
plasma lipids during the six month follow-up in the present study. Associations of
hyperlipidaemia with low plasma adiponectin level and an increase in adiponectin
concentrations with an improvement in plasma lipid profiles have been
demonstrated (Yamamoto et al. 2002 & Matsubara et al. 2002). Increases in
serum adiponectin through taking insulin-sensitizing agents such as rosiglitazone,
which decrease the lipids levels have been shown (Motoshima et al. 2002, Dubois
et al. 2002, Combs et al. 2002, Yang et al. 2002). Fernández-Real et al. (2005)
showed that adiponectin concentration was negatively associated with the
saturated acids in the diet. The mechanisms of the action of the dietary fat on
peripheral adiponectin concentration or vice versa are unknown. Activation of the
PPARγ by fatty acids (Krey et al. 1997) or pharmacologic activation with PPARγ
agonists lead to increased plasma adiponectin concentrations (Maeda et al. 2001,
Miyazaki et al. 2004). It has also been shown that dietary saturated fatty acids and
Trans-fatty acids alter the expression of different genes associated with insulin
sensitivity in rat adipose tissue (Saravanan et al. 2005). In the same study,
adiponectin mRNA levels decreased (~35%) with saturated fatty acids diet when
compared with control diet. Quality, amount and proportion of fatty acids appear
to be of importance for the development of insulin resistance, related
inflammatory activity and adiponectin levels (Fernández-Real et al. 2003,
Fernández-Real et al. 2005). In the current study an increase in plasma lipid
levels might suppress the secretion of adiponectin. Whether lipids directly affect
83
the adiponectin gene expression, or indirectly act through the PPARγ, or whether
both or other mechanisms are involved, needs to be further investigated.
Another explanation for the decrease of serum adiponectin levels points to
other lifestyle risk factors of the MetS such as physical activity, smoking habits,
stress, depression and sleep loss during the military service. A six fold increase in
the prevalence of the clusters of metabolic abnormalities associated with insulin
resistance among the smokers rather than the non-smokers has been shown in this
cohort (Tähtinen et al. 1998). According to questionnaires, a few numbers of
conscripts started or quitted smoking during the military service. In one earlier
study (Iwashima et al. 2005) plasma adiponectin was reported to be significantly
lower in current smokers compared with never-smokers. The same study also
showed a significant decrease in plasma adiponectin level maximaly 12 hours
after acute smoking exposure (Iwashima et al. 2005).
An association of major depression with a decrease in serum adiponectin
level (Leo et al. 2006) as well as a relation of sleep loss with development of
DM2 (Spiegel et al. 2005) have been shown. In the present study the degree of
stress or depression or sleep patterns of the conscripts were not assessed, but
noticed that day after day servicemen had to wake up early in the morning. On the
other hand they also can go to bed earlier. Therefore additional studies with more
accurate information on specific life style risk factors seem to be needed.
6.2.2 Influence of weight change on regulation of serum
adiponectin level (I)
The results of this study showed that, serum adiponectin concentrations
significantly decreased during the follow-up in those servicemen who gained
weight or did not change in weight and surprisingly even in those who
experienced a moderate (5–10%) weight loss (probably obtained by exercise or
for other reasons like decrease in energy intake). Belonging to screen-positive or
screen-negative groups did not have any effect on this result since no statistically
significant interaction between body weight changes and screening class of the
subjects with respect to serum adiponectin levels was found. An increase in serum
adiponectin level with body weight loss was expected since previous studies
(Hotta et al. 2000, Yang et al. 2001 & Hulver et al. 2002) had reported that body
weight reduction resulted in a rise of serum adiponectin levels. The discrepancies
between these results and the results of the present study are not clear, but there
might be some explanations. In one of those studies (Hotta et al. 2000), serum
84
adiponectin rose after a 10 % BMI reduction in subjects on a calorie-restricted
diet in hospital but subjects of this study were not on a calorie-restricted diet. In
two other studies (Yang et al. 2001, Hulver et al. 2002), serum adiponectin levels
increased after severely obese patients underwent gastric surgery and a marked
decrease of body weight happened. Subjects of the present study were mostly
overweight (BMI mean = 27.9), not obese males. Other factors like age and
ethnic differences as well as the difference in sample size between our study and
those studies must be kept in mind. In agreement with the present results it has
been shown that moderate weight loss in obese and overweight women (Ryan et
al. 2003, Abbasi et al. 2004) was not associated with an increase in serum
adiponectin levels.
The current study also showed that despite an increase in physical activity
and > 10 % weight reduction, followed by an increase in HDL cholesterol level,
total cholesterol and LDL cholesterol increased. Although a large number of
studies have shown favourable plasma lipid profile changes after weight loss, the
negative lipid profile change, despite significant weight loss has also been
reported in subjects on a high-fat diet over a one year period (Fleming 2002).
Based on these results and the results of the present study it seems that apart from
a healthy diet, a substantial (> 10%) weight loss in obese (BMI ≥ 30) persons
might be necessary to increase serum adiponectin concentrations and lower the
lipid levels. However we did not have accurate detailed information on diet
before the conscripts started their military service and at the follow-up period.
6.2.3 Influence of PPARγ2 and IRS-1 common variations and their
interaction on regulation of serum adiponectin level (II & III)
In this study subjects with the Ala12Ala genotype of PPARγ2 gene showed
significantly higher serum adiponectin levels compared with the Pro12Ala or
Pro12Pro genotypes. Based on an overall ANOVA test (P = 0.054), the association
of the PPARγ2 with serum adiponectin level was not statistically significant, but it
might be due to the number of samples, since there were only six subjects with
the Ala12Ala genotype of PPARγ2 gene in this study. Increased serum
adiponectin levels with an induction of PPARγ by its agonists had already been
reported in a number of studies (Yang et al. 2002, Dubois et al. 2002, Combs et
al. 2002, Motoshima et al. 2002). Yamamoto et al. (2002) had reported that serum
adiponectin concentrations in Japanese middle-aged males and females were
significantly lower in subjects with the Pro12Ala genotype than in subjects with
85
the Pro12Pro genotype (Yamamoto et al. 2002). Another study involving a
healthy European population (Thamer et al. 2003), however did not find any
significant difference in serum adiponectin concentrations between the genotype
groups of PPARγ2 gene. Only subjects with the Ala12Ala genotype tended to
have higher serum adiponectin concentrations in the same study. Discrepancies
between the results of the present study with those of earlier studies are not
completely clear, but differences of sample size, age, ethnicity, and genetic
background of the individuals must be considered. The study by Yamamoto et al.
did not include any homozygous cases of PPARγ2, while in the present study the
subjects with Ala12Ala genotype of PPARγ2 (N = 6) had a slightly lower BMI
and WHR, which might at least partly explain higher adiponectin levels in this
group. These results could be due to the direct effect of this polymorphism on
serum adiponectin secretion or be related to the linkage disequilibrium of this
polymorphism with other loci responsible for the changes in serum adiponectin
level.
Subjects who had undergone a reduction > 10% of body weight and
possessed the Ala12 allele of PPARγ2 gene in this study, demonstrated a
significant increase in their serum adiponectin concentrations while their
cholesterol significantly elevated. In contrast, subjects with > 10% weight loss
without the Ala12 allele showed a decrease in serum adiponectin levels. This
result indicates that even a moderate weight reduction might have more
advantageous effects on the levels of the serum adiponectin in subjects with the
Ala12 allele of PPARγ2 gene and it might suggest a genetic–environmental
interaction on the regulation of serum adiponectin concentrations.
Another finding of the present study revealed that Pro12Ala polymorphism of
PPARγ2 gene had an interaction with the Gly972Arg polymorphism of IRS-1 gene
with respect to serum adiponectin level. Subjects who had both Pro12Ala of
PPARγ2 gene and X972Arg of IRS-1 gene, and also those subjects with both
Ala12Ala of PPARγ2 gene and Gly 972 Gly of IRS-1 gene, showed the highest
serum adiponectin concentrations. However, any significant difference in serum
adiponectin levels, neither between subjects with Pro12Pro and Pro12Ala, nor
between the IRS-1 genotypes, was found, but subjects with Pro12Ala and
X972Arg showed low levels of total and LDL cholesterol as well as high level of
serum adiponectin. The X972Arg + Pro12Pro combination, which is assumed to
be the least favourable combination of alleles with respect to insulin resistance,
seems to be associated with high total and LDL cholesterol and low adiponectin
concentrations. A higher insulin sensitivity in subjects with both polymorphisms
86
of PPARγ2 (X12Ala) and IRS-1 (X972Arg) genes was reported (Stumvoll et al.
2002). According to the results of the present study, high serum adiponectin levels
found in subjects with both polymorphic alleles (X12Ala + X972Arg) could
partly explain higher insulin sensitivity showed in Stumvoll´s study. In the present
study weight, BMI and WHR tended to be lower in subjects with X972Arg of the
IRS-1 gene as well as in the subjects with X12Ala + X972Arg, which might be at
least one likely explanation for high serum adiponectin levels in these groups.
These two alleles could have a direct effect on adipose tissue to alter the release
of other hormones like leptin which would affect weight or affect the adiponectin
gene expression. Further studies with a larger sample size population however are
needed to clarify these speculations. These findings show that there might be a
gene–gene interaction between the PPARγ2 and IRS-1 genes in their effects on
serum adiponectin concentrations.
Considering that, sample screening used in 1995, might have caused a
selection bias towards these results, possible interactions between the PPARγ2 and
IRS-1 different genotypes and sample screening were analysed with regard to the
serum adiponectin level. Since no significant interactive effects were found, it
appears that screening selection had no major influence on these results.
6.2.4 Influence of the adiponectin gene common variations on
regulation of serum adiponectin level (IV)
Higher serum adiponectin concentrations in carriers of the T276T genotype of the
adiponectin (APM1) gene compared with subjects with the G276T or G276G
genotypes of this gene were revealed in the current study. Furthermore, an
interaction between levels of TG and SNP+276 was found. It may suggest that in
subjects with the T276T genotype, elevated TG levels were associated with low
serum adiponectin concentrations compared with subjects with the G276T or
G276G genotypes of the APM1 gene. In agreement with the results of the present
study, an association between the G allele of SNP+276 and increased risk of
diabetes and lower plasma adiponectin levels had been reported in Japanese
subjects (Hara et al. 2002). Also significantly higher levels of serum adiponectin
concentrations in subjects with the T276T genotype were reported in another
study involving European subjects (Menzaghi et al. 2004). Subjects with the
polycystic ovary syndrome and the G276G and G276T genotypes revealed higher
TG concentrations compared with subjects with the T276T genotype (Xita et al.
2005). Mechanisms of the relation between the T276T genotype and higher levels
87
of serum adiponectin, as well as negative association of triglyceride levels with
serum adiponectin levels, remain unclear. However high insulin sensitivity,
related to high adiponectin level, could contribute to these findings.
In the present study higher levels of diastolic blood pressure in carriers of the
T276T genotype of the adiponectin (APM1) gene compared with subjects with the
G276T or G276G genotypes of this gene were found. The reason for the increase
in the DBP in the T276T genotype of adiponectin gene is not clear when based
alone on the results of this study. The effect of sample selection could be
involved. However, no interaction between these genotypes and screening was
found in respect to the diastolic blood pressure. In order to avoid wrong
interpretations, it is worthwhile to carry out a 24-h ambulatory blood pressure
monitoring in future studies.
An overall protective effect was observed in carriers of the T45T genotype in
terms of anthropometric and metabolic parameters, such as an increase in HDL
level. However these differences did not reach to a statistical significance level
due to the relatively small sample size of this study cohort. Based on the results, it
appears that these two polymorphisms of APM1 gene influence biological
functions associated with insulin resistance. Even though the mechanisms remain
to be elucidated, however, it is possible that these polymorphisms affect the
adiponectin gene expression or are in linkage disequilibrium with other loci,
which could be responsible for changes in serum adiponectin or insulin
resistance-associated risk factors levels. No interaction with these genotypes of
the adiponectin gene and sample screening was found.
6.3
Summary and Conclusions
Lifestyle interventions including increased physical activity and healthy diet have
been shown to reduce the risk of MetS. However, response to lifestyle or dietary
interventions does differ between individuals, and the genetic or environmental
factors that may account for these differences are not yet precisely characterized.
With a full understanding of multiple gene–environment and gene-gene
interactions, the molecular basis of MetS might be solved in order to reduce the
risk and minimise the adverse health effects of obesity, DM2 and CVD.
The current study investigated the effects of living conditions during military
service, as well as the contribution of common variants in PPARγ2, APM1 and
IRS-1 genes and their interactions with environmental factors on the serum
adiponectin level, as a novel marker of MetS. Findings of this study show that:
88
1.
2.
3.
4.
Lifestyle during the military service may have decreased serum adiponectin
levels in young conscripts. The military lifestyles with its average highcaloric diet, and probably stress, sleep loss and smoking are likely reasons for
a significant decrease of serum adiponectin levels. (I). Furthermore weight
loss during the military service, most likely from increased physical activity
and dietary changes, did not increase serum adiponectin concentrations.
A small number (n = 5) of subjects with > 10% weight reduction and the
Ala12 allele of the PPARγ2 gene, displayed a significant increase in serum
adiponectin concentrations (subjects with > 10% weight loss without the
Ala12 allele showed a decrease in serum adiponectin levels) (I& II).
Although the result is based on a very small number of subjects, it might
suggest that, the moderate weight reductions have more advantageous effects
on serum adiponectin levels in subjects with the Ala12 allele of PPARγ2 gene
and indicates an interaction of genetic and environmental factors in the
regulation of serum adiponectin concentrations. This need to be replicated in
other studies.
Data of this study furthermore showed that at baseline the Ala12Ala variation
of the PPARγ2 was associated with significant higher levels of the serum
adiponectin compared with subjects without this variation. Subjects with
Ala12 Ala of PPARγ2 + Gly972Gly of IRS-1 genes also had the highest serum
adiponectin levels in this study (III).
Results of this study also showed that at baseline carriers of the T276T
variation of the APM1 gene had significant higher levels of the serum
adiponectin compared with subjects without this variation. Furthermore an
overall protective effect, even though not significant, was observed in carriers
of the T45T genotype of APM1 gene (IV).
In conclusion, the main findings of the present study showed a possible influence
of lifestyle, gene variations and their interactions on the regulation of the serum
adiponectin levels. However, given the moderate sample size and the lack of
information on specific dietary factors and physical activity, further investigations
are warranted before these findings can be the basis for policy changes.
89
90
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U N I V E R S I TAT I S
S E R I E S
SCIENTIAE RERUM NATURALIUM
Professor Mikko Siponen
HUMANIORA
TECHNICA
Professor Harri Mantila
Professor Juha Kostamovaara
MEDICA
Professor Olli Vuolteenaho
SCIENTIAE RERUM SOCIALIUM
Senior Assistant Timo Latomaa
ACTA
U N I V E R S I T AT I S O U L U E N S I S
Firoozeh Mousavinasab
E D I T O R S
Firoozeh Mousavinasab
A
B
C
D
E
F
G
O U L U E N S I S
ACTA
A C TA
D 936
EFFECTS OF LIFESTYLE
AND GENETIC FACTORS
ON THE LEVELS OF SERUM
ADIPONECTIN, A NOVEL
MARKER OF THE METABOLIC
SYNDROME, IN FINNISH
SERVICEMEN
SCRIPTA ACADEMICA
Communications Officer Elna Stjerna
OECONOMICA
Senior Lecturer Seppo Eriksson
EDITOR IN CHIEF
Professor Olli Vuolteenaho
EDITORIAL SECRETARY
Publications Editor Kirsti Nurkkala
ISBN 978-951-42-8503-5 (Paperback)
ISBN 978-951-42-8504-2 (PDF)
ISSN 0355-3221 (Print)
ISSN 1796-2234 (Online)
FACULTY OF MEDICINE,
DEPARTMENT OF PUBLIC HEALTH SCIENCE AND GENERAL PRACTICE,
UNIVERSITY OF OULU;
DEPARTMENT OF MEDICINE,
UNIVERSITY OF KUOPIO;
UNIT OF GENERAL PRACTICE,
OULU UNIVERSITY HOSPITAL;
OULU CITY HEALTH CENTRE AND OULU DEACONESS INSTITUTE
D
MEDICA