Antti Huuskonen
The Role of Adiposity, Growth and
Inflammation Related Gene Variants
in Physical Performance and Body
Composition
Publications of the University of Eastern Finland
Dissertations in Health Sciences
ANTTI HUUSKONEN
The role of adiposity, growth and
inflammation related gene variants in
physical performance and body composition
To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for
public examination in Mediteknia Auditorium, Kuopio, on Saturday, March 23rd 2013, at 12 noon
Publications of the University of Eastern Finland
Dissertations in Health Sciences
155
Institute of Biomedicine, Physiology, School of Medicine, Faculty of Health Sciences,
University of Eastern Finland
Kuopio
2013
Kopijyvä Oy
Kuopio, 2013
Series Editors:
Professor Veli-Matti Kosma, M.D., Ph.D.
Institute of Clinical Medicine, Pathology
Faculty of Health Sciences
Professor Hannele Turunen, Ph.D.
Department of Nursing Science
Faculty of Health Sciences
Professor Olli Gröhn, Ph.D.
A.I. Virtanen Institute for Molecular Sciences
Faculty of Health Sciences
Distributor:
University of Eastern Finland
Kuopio Campus Library
P.O.Box 1627
FI-70211 Kuopio, Finland
http://www.uef.fi/kirjasto
ISBN (print): 978-952-61-1035-6
ISBN (pdf): 978-952-61-1036-3
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
III
Author’s address:
Institute of Biomedicine, Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Supervisors:
Docent Mustafa Atalay, M.D., M.P.H., Ph.D.
Institute of Biomedicine, Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Docent Niku Oksala, M.D., Ph.D., D.Sc.
Tampere University Hospital, Surgery, School of Medicine
University of Tampere
TAMPERE
FINLAND
Professor Timo Lakka, M.D., Ph.D.
Institute of Biomedicine, Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Jani Lappalainen, MSc.
Institute of Biomedicine, Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Reviewers:
Docent Arto Hautala, Ph.D.
Department of Exercise and Medical Physiology
Verve Research
OULU
FINLAND
Docent Karri Silventoinen, Ph.D.
Department of Sociology
University of Helsinki
HELSINKI
FINLAND
Opponent:
Docent Olavi Ukkola, M.D., Ph.D.
Institute of Clinical Medicine, Department of Internal Medicine
Oulu University Hospital
OULU
FINLAND
IV
V
Huuskonen, Antti
The role of adiposity, growth and inflammation related gene variants in physical performance and body
composition
University of Eastern Finland, Faculty of Health Sciences
Publications of the University of Eastern Finland. Dissertations in Health Sciences 155. 2013. 93 p.
ISBN (print): 978-952-61-1035-6
ISBN (pdf): 978-952-61-1036-3
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
ABSTRACT
Poor aerobic fitness and obesity are significant risk factors for morbidity and premature
mortality. Neuromuscular fitness is of crucial importance in maintaining daily activities
and preserving life quality in the elderly. The effects of physical activity and nutrition on
physical fitness and obesity are well known. In contrast, the effects of specific genes and
their variants on physical performance and body composition are largely unknown.
Associations of selected variants in IL6, IL6R, IGF1, IGFBP3, LEP, LEPR and FTO genes
with physical performance, body composition and cytokine levels were investigated in a
cross-sectional study setting in 846 young Finnish men during military refresher courses,
and the training responses were studied during eight weeks of basic military training in 56
conscripts. The studied genes produce key metabolic regulators which have an effect on
both body composition and on the physiological responses to physical training.
The FTO SNP rs8050136 was found to be associated with body mass index (BMI) and
waist circumference. Although aerobic fitness did not modify the effect of FTO on BMI or
waist circumference, it did have, however, a favorable effect on body composition
regardless of the FTO genotype. The IL6R SNP rs4537545 was associated with circulating
IL-6 levels and with BMI in the sedentary state. The IL6 rs1800795 associated with increased
VO2max (both absolute L·min-1 and relative mL·kg-1·min-1) in response to 8 weeks of physical
training. The IGF1 SNP rs7136446 associated with maximal isometric force production. The
LEP SNP rs7799039 associated with changes in body composition as they responded to 8
weeks of physical training. The AA genotype carriers lost significantly less fat mass as
compared to the G allele carriers during the training.
The present findings suggest that the effect of FTO genetic variation on body
composition is not modified by aerobic fitness. IL6R variants may be associated with
circulating IL-6 levels and BMI. Furthermore, IL6 variants may be associated with gains in
aerobic performance in response to physical training. IGF1 variants may associate with
force production and LEP variants with changes in body composition in response to
physical training. These observations can be applied in the prevention of obesity and in
improving physical performance, both being factors which can decrease the risk for chronic
diseases and improve the quality of life. The results provide novel information about the
biological mechanisms behind body composition and physical performance. However, it
should be noted, that these findings need to be replicated in larger cohorts, including
children, women and the elderly in order to confirm the putative associations.
National Library of Medical Classification: QZ 50, QT 261, QU 500, WD 210
Medical Subject Headings: Adiposity; Body Composition; Body Mass Index; Exercise; Genetic Variation;
Leptin; Muscle Strength; Obesity; Polymorphism, Single Nucleotide
VI
VII
Huuskonen, Antti
Lihavuuteen, kasvuun ja tulehdusvasteeseen liittyvien geenivarianttien vaikutus fyysiseen suorituskykyyn ja
kehon koostumukseen
Itä-Suomen yliopisto, terveystieteiden tiedekunta
Publications of the University of Eastern Finland. Dissertations in Health Sciences 155. 2013. 93 s.
ISBN (print): 978-952-61-1035-6
ISBN (pdf): 978-952-61-1036-3
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
TIIVISTELMÄ
Huono fyysinen kunto ja lihavuus ovat merkittäviä sairastavuuden ja ennenaikaisen
kuolleisuuden riskitekijöitä. Lihasvoima on vastaavasti tärkeä tekijä fyysisen
toimintakyvyn ja elämän laadun kannalta erityisesti vanhuksilla. Fyysisen harjoittelun
vaikutuksia fyysiseen kuntoon ja kehon koostumukseen on tutkittu paljon, mutta tietoa
spesifisten geenivarianttien vaikutuksesta fyysiseen kuntoon ja kehonkoostumukseen on
toistaiseksi vähän.
Valittujen geenivarianttien IL6, IL6R, IGF1, IGFBP3, LEP, LEPR ja FTO yhteyttä fyysiseen
kuntoon, kehon koostumuksen ja veren sytokiinitasohin tutkittiin 892 suomalaisen nuoren
miehen aineistolla, johon kuului poikkileikkaustutkimus 846 henkilön aineistolla ja
kahdeksan viikon harjoitus-interventio 56 miehen aineistolla. Aineisto kerättiin
puolustusvoimissa varusmiespalveluksen ja kertausharjoitusten yhteydessä.
FTO-geenin variantti rs8050136 oli yli yhteydessä painoindeksiin (BMI) ja vyötärön
ympärysmittaan. Hyvä aerobinen kunto ei pienentänyt FTO-geenin vaikutusta BMI:in tai
vyötärönympärykseen, mutta aerobisella kunnolla oli FTO-geenistä riippumaton suotuisa
vaikutus kehon koostumukseen. IL6 reseptorin geenin variantti rs4537545 oli yhteydessä
korkeampiin veren IL-6 pitoisuuksiin ja BMI:in. IL6-geenivariantti rs1800795 oli yhteydessä
maksimaalisen hapenottokyvyn (sekä absoluuttisen L·min-1 että suhteellisen mL·kg-1·min-1)
paranemiseen kahdeksan viikon harjoittelujakson jälkeen. IGF1-geenivariantti rs7136446 oli
yhteydessä alaraajojen maksimaaliseen isometriseen voimaan. LEP-geenivariantti rs7799039
oli yhteydessä kehon koostumuksen muutoksiin kahdeksan harjoitteluviikon jälkeen.
Henkilöillä, joilla oli LEP-geenin varianttina AA-genotyyppi, menettivät merkittävästi
vähemmän kehon rasvakudosta 8 viikon harjoittelun aikana verrattuna henkilöihin, joilla
oli tämän variantin G-alleeli.
Tämän tutkimuksen tulokset viittaavat siihen, että aerobinen kunto ei muokkaa FTOgeenin vaikutusta kehon koostumukseen, IL6R voi vaikuttaa veren IL-6-pitoisuuteen ja
BMI:in, IL6 voi vaikuttaa maksimaalisen hapenottokyvyn paranemiseen harjoittelun
seurauksena, IGF1 voi vaikuttaa voimantuottoon, ja että LEP voi vaikuttaa harjoittelun
aiheuttamiin muutoksiin kehon koostumuksessa. Tutkimuksen tuloksia voidaan soveltaa
lihavuuden ehkäisyssä ja fyysisen suorituskyvyn ylläpitämisessä, mikä voi vähentää
kroonisten sairauksien riskiä ja parantaa elämän laatua. Lisäksi tulokset tuottavat uutta
tietoa fyysisen suorituskyvyn ja kehon koostumuksen biologisesta taustasta, ja toimivat
pohjana jatkotutkimuksille. Tämän tutkimuksen tulokset tulisi vahvistaa suuremmissa
aineistoissa, jotka sisältäisivät myös naisia, lapsia ja iäkkäämpiä ihmisiä.
Luokitus: QZ 50, QT 261, QU 500, WD 210
Yleinen Suomalainen asiasanasto: epidemiologia, fyysinen kunto, geenit, lihasvoima, lihavuus, painoindeksi,
sairastavuus
VIII
IX
Acknowledgements
This work was carried out in the Unit of Physiology of the Institute of Biomedicine,
University of Eastern Finland, in collaboration with the Department of Biology of Physical
Activity, University of Jyväskylä during the years 2005-2012. I owe my deepest gratitude to
all the people who have contributed to this work. In particular, I would like to express my
thanks to:
Docent Mustafa Atalay, my principle supervisor, for his excellent guidance, feedback and
support, not only in this project but also in many other aspects of life. I also owe Mustafa
my deepest gratitude for his expertise in exercise physiology, the valuable time we spent
discussing the details of this thesis, and his willingness to address my questions almost
anytime.
Docent Niku Oksala, my second supervisor, for his expertise in genetics and statistics and
his valuable contribution to the writing of the manuscripts, for the grants and for bringing a
clinical point of view to this work.
Professor Timo Lakka, my third supervisor, for his invaluable comments, feedback and
contribution, especially in the writing of this thesis. I also thank Timo for his statistical
expertise and knowledge of genetics.
Jani Lappalainen, MSc, my technical supervisor, for his skillful guidance in genotyping, and
contribution to writing the manuscripts during this study.
Professor Heikki Kyröläinen and Professor Keijo Häkkinen from the University of
Jyväskylä, and Matti Santtila, PhD, from Finnish Defense Forces, for making this study
possible through the collaboration with the Reservist study. In addition, I owe you thanks
for planning the study and for your expertise in exercise physiology, assesment of physical
performance and body composition. I also thank Minna Tanskanen, PhD, for her
contribution to the manuscripts.
Docent Arto Hautala and Docent Karri Silventoinen, my official reviewers, for the
constructive comments and valuable critique which helped me to improve this work.
Tuomas Kilpeläinen PhD, for his expertise in genetics and his guidance in working with
genetic databases and genetic software tools. I also thank Associate Professor Tuomo
Rankinen from Pennington Biomedical Research Center, Louisiana State University, for
fruitful ideas.
Satu Marttila, Taina Vihavainen and Taija Hukkanen in the Unit of Physiology for the
pleasant collaboration and skillful work in the laboratory.
X
Statisticians Vesa Kiviniemi and Elina Kokkonen, for their support in statistical issues and
for the pleasant collaboration.
Dr. Ewen MacDonald, for reviewing the English language of this thesis.
All reservists and military conscripts who participated in this study.
My parents and my brother for their support and encouragement.
And finally all my friends, relatives and colleagues for their friendship and support. For
special contributions to the present PhD work, I would particularly like to thank you,
Jaakko, Jari and Risto.
For financial support, I would like to thank the Ministry of Education of Finland and TBGS
graduate school.
Jyväskylä, January 2013
Antti Huuskonen
XI
List of original publications
This dissertation is based on the following original publications:
I
Huuskonen A, Tanskanen M, Lappalainen J, Oksala N, Kyröläinen H, Atalay M.
A common variation in the promoter region of interleukin-6 gene shows
association with exercise performance. Journal of Sports Science and Medicine 8(2):
271-7, 2009.
II
Huuskonen A, Lappalainen J, Tanskanen M, Oksala N, Kyröläinen H, Atalay M.
Genetic variations of leptin and leptin receptor are associated with body
composition changes in response to physical training. Cell Biochemistry and
Function 28(4): 306-12, 2010.
III
Huuskonen A, Lappalainen J, Oksala N, Santtila M, Häkkinen K, Kyröläinen H,
Atalay M. Common genetic variation in the IGF1 associates with maximal force
output. Medicine and Science in Sports and Exercise 43(12): 2368-74, 2011.
IV
Huuskonen A, Lappalainen J, Oksala N, Santtila M, Häkkinen K, Kyröläinen H,
Atalay M. Aerobic fitness does not modify the effect of FTO variation on body
composition traits. PLoS ONE 7(12): e51635, 2012.
The publications were adapted with the permission of the copyright owners.
In addition, some unpublished data are presented.
XII
XIII
Contents
1 INTRODUCTION ..............................................................................
1
2 REVIEW OF THE LITERATURE .....................................................
2
2.1 Physical performance and body composition ............................
2
2.1.1 Cardiorespiratory fitness and health .................................
2
2.1.2 Neuromuscular performance and health ..........................
3
2.1.3 Body composition and obesity ...........................................
4
2.1.4 Cardiorespiratory responses to physical training ............
5
2.1.5 Metabolic responses to physical training ..........................
6
2.1.6 Physical activity and metabolic risk factors ......................
9
2.2 Genetics ............................................................................................
9
2.2.1 Genetic study designs ..........................................................
9
2.2.2 Genetic variation and single nucleotide polymorphisms
10
2.3 Genetic background of physical performance and body composition 11
2.3.1 Physical activity, genetic variation and gene expression
11
2.4 Genetics of cardiorespiratory performance ................................
13
2.4.1 General considerations ........................................................
13
2.4.2 The ACE gene .........................................................................
14
2.4.3 The PPAR and ACTN3 genes ...............................................
15
2.4.4 The ADRA and ADRB genes ................................................
15
2.4.5 The IL6 and IGF1 genes.........................................................
16
2.4.6 Polygenic profiles of athletes ...............................................
16
2.5 Genetics of neuromuscular performance ....................................
18
2.5.1 General considerations ........................................................
18
2.5.2 The ACTN3 gene ...................................................................
18
2.5.3 The ACE gene ........................................................................
19
2.5.4 The VDR gene ........................................................................
19
2.5.5 The CNTF and CNTFR genes ..............................................
19
2.5.6 The IGF-1 and IGF-2 pathway genes .................................
20
2.6 Genetics of body composition ......................................................
22
2.6.1 General considerations ........................................................
22
2.6.2 The LEP and LEPR genes .....................................................
23
2.7 Genetic modification of responses to physical training............
23
2.7.1 General considerations ........................................................
23
2.7.2 The PPAR genes ....................................................................
23
2.7.3 The ACE gene ........................................................................
24
2.7.4 The CKM and ATP1A2 genes ..............................................
24
2.7.5 The APOE and LPL genes .....................................................
25
2.7.6 The ACTN3 gene ...................................................................
25
2.7.7 The IGF1 gene ........................................................................
25
2.7.8 The RETN gene .....................................................................
25
2.7.9 The ADRA and ADRB gene .................................................
26
XIV
3 AIMS OF THE STUDY ......................................................................
29
4 METHODS ...........................................................................................
4.1 Subjects and study design .............................................................
4.1.1 Studies I and II ......................................................................
4.1.2 Studies III and IV ..................................................................
4.2 Selection of candidate genes and their polymorphisms ...........
4.3 Genotyping .....................................................................................
4.4 Assessment of cardiorespiratory performance ..........................
4.4.1 Treadmill test ........................................................................
4.4.2 Bicycle ergometer test ..........................................................
4.5 Assessment of neuromuscular performance ..............................
4.6 Assessment of body composition ................................................
4.6.1 Anthropometric measurements .........................................
4.6.2 Skinfolds ................................................................................
4.6.3 Bioimpedance .........................................................................
4.6.4 Energy intake .........................................................................
4.7 Biochemical measurements ..........................................................
4.7.1 Sub-maximal exercise test ...................................................
4.8 Assessment of physical activity ...................................................
4.9 Statistical methods .........................................................................
30
30
30
30
31
32
35
35
35
35
36
36
36
36
36
37
37
37
38
5 RESULTS ..............................................................................................
5.1 Descriptive data (Studies I, II, III and IV) ...................................
5.2 IL6 and IL6R genes (Studies I and III) .........................................
5.3 IGF1 and IGFBP3 genes (Study III) ..............................................
5.4 LEP, LEPR and FTO genes (Studies II and IV) ...........................
40
40
42
44
46
6 DISCUSSION ......................................................................................
6.1 Study designs and subjects ...........................................................
6.2 Cardiorespiratory performance ...................................................
6.3 Neuromuscular performance .......................................................
6.4 Body composition ..........................................................................
6.5 Plasma and serum cytokine levels ...............................................
6.6 Methodological considerations ....................................................
49
49
49
51
52
54
56
7 CONCLUSIONS .................................................................................
63
8 REFERENCES ......................................................................................
64
ORIGINAL PUBLICATIONS (I-IV)
XV
Abbreviations
5-HTT
Serotonin transporter
ACE
Angiotensin-converting
Northern
enzyme
European ancestry
ACSL1
CEU
Utah residents (CEPH) with
and
Western
Long-chain-fatty-acid—
CK
Creatine kinase
Coenzyme A ligase 1
CKMM
Muscle-specific creatine
ACTN3
Alpha-actinin-3
ACVR1B
Activin receptor type-1B
CNS
Central nervous system
ACVR2B
Activin receptor type-2B
CNTF
Ciliary neurotrophic factor
ADP
Adenosine diphosphate
CNTFR
Ciliary neurotrophic factor
ADRB2
Beta-2-adrenergic receptor
AGT
Angiotensinogen precursor
COL1A1
Collagen, type I, alpha 1
AMPD1
Adenosine monophosphate
COMT
Catechol-O-methyltransferase
deaminase 1
COPD
Chronic obstructive
AMPK
kinase
receptor
Adenosine monophosphate-
pulmonary disease
activated protein kinase
CYP19
Cytochrome P450 19
ANG
Angiogenin
DIO1
Deiodinase, iodothyronine,
APOB
Apolipoprotein B
APOE
Apolipoprotein E
DNA
Deoxyribonucleic acid
ATP
Adenosine triphosphate
ENPP1
Ecto-nucleotide
ATP1A2
ATPase, Na+/K+ transporting,
pyrophosphatase/phospho
alpha 2 (+) polypeptide
diesterase
type I
BDKRB2
Bradykinin receptor B2
EPHX1
Epoxide hydrolase 1
BDNF
Brain-derived neurotrophic
ETV5
Ets variant 5
factor
FAIM2
Fas apoptotic inhibitory
BMI
Body mass index
Ca2+-
Calcium ion
CAD
Coronary artery disease
CADM2
Cell adhesion molecule 2
molecule 2
FANCL
Fanconi anemia,
complementation group L
FAT%
Body fat percent
XVI
FFM
Fat-free mass
FLJ35779
Hypothetical
protein
IGF-1
Insulin like growth factor -1
IGFBP-3
Insulin like growth factor
LOC134359
binding protein -3
FM
Fat mass
IL15RA
Interleukin-15 receptor, alpha
FTO
Fat mass and obesity
IL-1β
Interleukin-1 beta
associated gene
IL-6
Interleukin-6
Growth differentiation factor
IPAQ
International physical activity
GDF-8
8, myostatin
GIPR
GNB3
Gastric inhibitory
IRS1
Insulin receptor substrate 1
polypeptide receptor
KCNH8
Potassium inwardly-
Guanine nucleotide-binding
rectifying channel, subfamily
protein G(I)/G(S)/G(T)
J, member 8.
subunit beta-3
GNPDA2
GPRC5B
questionnaire
KCTD15
Potassium channel
Glucosamine-6-phosphate
tetramerisation domain
deaminase 2
containing 15
G-protein coupled receptor
LDL
Low density lipoprotein
family C group 5 member B
LEP
Leptin
GR
Glucocorticoid receptor
LEPR
Leptin receptor
GRIN3A
Glutamate receptor,
LPL
Lipoprotein lipase
ionotropic, N-methyl-D-
LRP1B
Low-density lipoprotein
aspartate 3A
GWAS
Genome-wide association
receptor-related protein 1B
LRRN6C
Leucine rich repeat neuronal
study
6C ZNF608 (zinc
HBB
Hemoglobin, beta
protein 608)
HDL
High density lipoprotein
HFE
Hemochromatosis protein
HIF1A
Hypoxia-inducible factor 1
LYPLAL1
Lysophospholipase-like 1
HLA-A
Human leukocyte antigen
MAF
Minor allele frequency
HMGA1
High-mobility group protein
MAP2K5
Mitogen-activated
LTBP4
Homeobox A13
Latent transforming growth
factor beta binding protein 4
HMG-I/HMG-Y
HOXC13
finger
protein
kinase kinase 5
MC4R
Melanocortin receptor 4
XVII
MCT1
Monocarboxylate transporter
POMC
Pro-opiomelanocortin
MET
Metabolic equivalent
PPARs
Peroxisome proliferator-
MLCK
Myosin light-chain kinase
MRI
Magnetic resonance imaging
miRNA
Micro ribonucleic acid
mRNA
Messenger ribonucleic acid
MSRA
Methionine sulfoxide
activated receptors
PPP3R1
Protein phosphatase 3,
regulatory subunit B, alpha
PRDM1
PR domain zinc finger protein
1
reductase A
PRKD1
Protein kinase D1
MSTN
Myostatin
PTBP2
Polypyrimidine tract binding
MTCH2
Mitochondrial carrier
homolog
MTIF3
Mitochondrial
translation
protein 2
PTER
Phosphotriesterase related
PWCmax
Maximal physical working
initiation factor 3
N
Newton
NCR3
Natural cytotoxicity
triggering receptor 3
capacity
QPCTL
Glutaminyl-peptide
cyclotransferase-like
RBJ
DnaJ
(Hsp40)
homolog,
NERG1
Neuregulin 1
NFATC4
Nuclear factor of activated T-
RER
Respiratory Exchange Ratio
cells
RETN
Ret proto-oncogene
NOS3
Nitric oxide synthase 3
RPE
Ribulose-5-phosphate-3-
NPC1
Niemann-Pick disease, type
NR3C1
subfamily C, member 27
epimerase
C1, gene
RPL27A
Nuclear receptor subfamily 3,
SDCCAG8 Serologically defined colon
group C, member 1
NRF2
Nuclear factor (erythroidderived 2)-like 2
Ribosomal protein L27a
cancer antigen 8
SEC16B
SEC16 homolog B
SFTBP
Surfactant pulmonary-
NRXN3
Neurexin 3
NRXN3A
Neurexin-3-alpha
SH2B1
SH2B adaptor protein 1
NUDT3
Nudix motif 3
SLC39A8
Solute carrier family 39 (metal
O2
Oxygen molecule
PCR
Polymerase chain reaction
associated protein B
ion transporter), member 8
XVIII
SNP
Single nucleotide
VO2max
polymorphism
Maximal oxygen
consumption
SPRY2
Sprouty homolog 2
TBX15
T-box 15
C2H2-type zinc finger
TFAM
Transcription factor A,
proteins
mitochondrial
TFAP2B
Transcription factor
activating enhancer binding
protein 2 beta
TFAP2B
Transcription factor AP-2 beta
TMEM160 Transmembrane protein 160
TMEM18
Transmembrane protein 18
TNF- α
Tumor necrosis factor alpha
TNFA
Tumor necrosis factor alpha
gene
TNKS
Tankyrase, TRF1-interacting
ankyrin-related ADP-ribose
polymerase
Transcription factor Maf
TNNI3K
Troponin 3 type I interacting
kinase
TRF1
Telomeric repeat-binding
factor 1
UCP2
Mitochondrial uncoupling
protein 2
UCP3
Mitochondrial uncoupling
protein 3
VDR
Vitamin D receptor
VEGFA
Vascular endothelial growth
factor A
ZIC4
ZNF
ZIC family member 4 of
Zinc finger protein
1 Introduction
Poor physical fitness and excess body fat are risk factors for several chronic diseases, e.g.
cardiovascular diseases, type II diabetes and cancer (1). These are major health problems in
the developed countries, being responsible for enormous economical costs and reduced
quality of life. Although there is compelling evidence that poor cardiorespiratory fitness is a
strong and independent predictor of all-cause and cardiovascular mortality (2), it is often
overlooked from the clinical perspective as compared with traditional or other risk factors
such as smoking, hypertension, diabetes and obesity (3). These risk factors are easy to
assess and to diagnose, thus making them more attractive for clinical intervention than
physical fitness. Physical training has been shown to be an effective and low-cost method
for enhancing health (4-6). Physical activity has been found to decrease the risk of
developing overweight, obesity and type II diabetes and to delay the related complications
(7). Furthermore, adequate muscular strength has been observed to maintain independent
living and to decrease the risk of falls and fractures in the elderly population (8). Actual
physical performance appears to be a stronger risk factor for chronic diseases than physical
activity alone (9,10). An awareness of physiological mechanisms and other factors affecting
physical performance is essential for understanding its effects on health, and how it could
be utilized more efficiently.
It is well known that exercise training can improve physical performance. On the other
hand, there are several factors modifying the adaptation to exercise training and the
response of physical performance, including genetic variation between individuals. Indeed,
it has been claimed for the heritability of aerobic performance can be as much as 50% (11).
According to twin studies, about 60% of athletic status, about 90% of a basal level of
skeletal muscle mass, about 60% of the basal muscle force and 40-70% of obesity is heritable
(12-14). These twin studies indicate that physical performance and body composition are
strongly affected by genetic variation. However, very little is known about specific gene
variants affecting physical performance. Aerobic performance, muscular strength and body
composition are typical quantitative traits, where numerous common and rare variants in a
large number of genes interact to have additive effects modifying the phenotype (15). The
effects of these genetic variants are further modified by gene-gene and gene-behavior
interactions. Even though, interest in research of performance enhancing genotypes has
increased (15), knowledge in the field of performance genetics is still limited.
This doctoral thesis examines the associations of potentially important candidate gene
variants with cardiorespiratory capacity, muscular performance, body composition,
plasma/serum circulating cytokine levels and responses to physical training. These results
provide new information on genetic variants affecting physical performance and body
composition and may be useful in identifying individuals at increased risk of poor physical
performance and of becoming overweight. In addition, new physiological mechanisms
behind adaptations to exercise training may be found by studying the genes affecting
physical performance. This information may be applied to achieve more effective use of
exercise training as a tool for enhancing health, decreasing the risk of chronic diseases and
premature death, and increasing the quality of life.
2
2 Review of the literature
2.1 PHYSICAL PERFORMANCE AND BODY COMPOSITION
2.1.1 Cardiorespiratory fitness and health
Poor cardiorespiratory fitness is reported to be a strong and independent risk factor for
premature all-cause and cardiovascular mortality (2). However, it is not widely used in
clinical decision making when compared with more traditional or other risk factors such as
smoking, hypertension, diabetes and obesity (3). This is also related to fact that these other
mentioned risk factors are easy to assess and diagnose, making them more attractive for
potential clinical intervention than physical fitness. In a dose-response analysis including
healthy men and women, every one metabolic equivalent/metabolic unit (MET) increase in
exercise capacity (approximately equivalent to 1 km/h running/jogging speed) was
associated with a 13% decrease in premature all-cause mortality and a 15% decrease in
cardiovascular mortality (2). Interestingly, cardiorespiratory fitness is considered to be a
more powerful predictor of mortality than physical activity alone (9,10). One explanation
for this phenomenon is that physical performance can be assessed objectively and more
accurately than physical activity. In earlier studies the assessment of physical activity has
based on a subjective evaluation. However, nowadays physical activity can be assessed
objectively by using accelerometers for example (16,17).
Maximal oxygen uptake (VO2max) is considered as the golden standard measure of
cardiorespiratory fitness. It represents the maximal amount of oxygen per unit of time that
can be delivered to peripheral organs, particularly to skeletal muscle, where it is used in the
synthesis of adenosine triphosphate (ATP) and furthermore to sustain muscular contraction
at peak exercise (18). The VO2max is normally defined by the Fick equation:
[(left ventricular end diastolic volume – left ventricular end systolic volume) · heart rate ·
(arterial oxygen content – venous oxygen content)] = O2mL·min-1
The relative VO2max is determined as mL·kg-1·min-1. However, the relative VO2max may
underestimate the aerobic performance in obese subjects and thus it is has been proposed
that mL·kg-1 (lean body mass) ·min-1 would provide a more precise estimation of aerobic
performance (19). It has been claimed that he traditional per weight measurement may
inflate the associations between poor cardiorespiratory fitness and comorbidities of obesity
(19).
The strongest determinants of VO2max are frequency, duration and intensity of physical
activity (20). However, up to 50% of VO2max may be inherited (11). Small stroke volume,
low maximal heart rate and a small arterio-venous difference at exercise partly explain low
cardiorespiratory fitness (20,21). At the population level, chronic diseases, such as coronary
artery disease and asthma, are inversely associated with VO2max (22). Although VO2max
declines by with age approximately 50 ml/min per year (20,22), the rate of decline is
significantly smaller in physically active individuals (20). Men tend to have higher VO2max
values than women (22). The use of medication, such as beta-blockers, is inversely
associated with cardiorespiratory performance (22). In addition, there is an inverse
relationship between heart rate at rest and VO2max, this being related to increased stroke
volume and increased parasympathetic activity (23). The blood hemoglobin, which
3
transports oxygen to tissues, shows a weak relationship to VO2max (20). VO2max is also
related to nutrition i.e. being directly associated with carbohydrate intake, which may be
related to the ability of the skeletal muscle tissue to oxidize carbohydrates at a high rate
during peak exercise (20). A low fat-free weight and high adipose tissue mass are
associated with low VO2max. The fasting serum insulin level is inversely associated with
VO2max, which may be related to increased insulin sensitivity in response to physical
activity (20).
There are several physiological mechanisms by which physical activity and performance
could reduce all-cause and cardiovascular mortality. The main pathways are shown in
figure 1. It is known that poor cardiorespiratory fitness correlates with impaired insulin
sensitivity (24), with consistent findings having been obtained in older adults (25),
postmenopausal women (26) and young individuals (27). Individuals with low
cardiorespiratory fitness also have higher levels of plasma triglycerides, apolipoprotein B
and total cholesterol- high density lipoprotein (HDL) cholesterol ratio than the individuals
with high cardiorespiratory fitness, after matching individuals with similar body mass
index (BMI) (28). In a randomized controlled trial, the plasma apolipoprotein B level
decreased and low-density lipoprotein (LDL) cholesterol-apolipoprotein B ratio increased
(LDL·ApoB-1) as cardiorespiratory fitness increased (29). In middle-aged Finnish men,
concentrations of plasma saturated fatty acids were lower and those of polyunsaturated
fatty acids were higher in the most-fit tertile compared with the least-fit tertile (30). Physical
activity has been associated with a reduced incidence of many different cancers (31). The
mechanisms behind risk reduction may include beneficial changes in circulating levels of
insulin, insulin related pathways, inflammation and possibly also immunity (32). In
addition in an experimental study physical activity related transient systemic acidosis has
been postulated to arrest or delay transition from in situ to invasive cancer in experimental
study (33).
The main cardiovascular and metabolic factors related to cardiorespiratory performance
include insulin sensitivity, blood lipid and lipoprotein profile, body composition,
inflammation, autonomic nervous system function and oxidative stress (figure 1) (9). Poor
cardiorespiratory fitness appears to be an important risk factor for weight gain, excess body
fat, overweight and obesity (28,34-37). In addition, good cardiorespiratory fitness may
decrease mortality through a reduction in the lower systemic low-grade inflammation
regardless of body composition (3). Cardiorespiratory fitness has been inversely associated
with the circulating levels of C-reactive protein (38-41), fibrinogen, leukocytes and uric acid
(42). Large prospective studies have shown an inverse association between
cardiorespiratory fitness and blood pressure after adjustment of potential confounders (4345). Moreover, it is possible that fit individuals have a better autonomic nervous system
balance compared with their unfit counterparts as assessed by heart rate variability (46).
Physical training decreases heart rate at rest (47) and a low resting heart rate decreases risk
for premature cardiovascular mortality (48). Some health benefits of high aerobic
performance may be gained through the antioxidative effects of training (49).
In summary, low cardiorespiratory fitness associates with a cluster of known metabolic
and cardiovascular risk factors, such as changes in body composition, inflammation, blood
pressure and autonomic nervous system regulation, but it may also increase the risk of
diseases through other physiological pathways, and it is also an independent risk factor for
morbidity and premature mortality.
2.1.2 Neuromuscular performance and health
One aspect of physical health is the fitness of skeletal muscle system, consisting of three
components: muscular strength, endurance and flexibility (50). Skeletal muscle force
production depends on multiple factors, including muscle cross-sectional area, plasticity
(51), myosin heavy chain content (52), proportion of fast-twitch fibers, connective tissue
4
properties (51,53), and neural factors (54), and all of these factors may be modified by
genetic variation.
Musculoskeletal fitness is associated with increased muscular strength and endurance,
which can improve both basic and instrumental activities of daily living and this can
translate into a greater opportunity for independent living in the elderly (50). During
ageing, there is a reduction in the number of motor units and muscle mass (50). These
unfavorable changes are called as sarcopenia (55). Sarcopenia and skeletal muscle weakness
are known to trigger the risk for disability, chronic diseases, such as osteoporosis,
dementia, depression and fall-related accidents causing fractures, (56) and diminished
independence in aging adults (55). The force production of the lower limb skeletal muscles
has been a particularly interesting research topic, because of its clinical relevance to fall
accidents (57). It has been suggested that high risk fallers could be identified by assessing
their maximal force capacity with a leg press push test (57). Resistance training has been
found to improve muscle strength markedly in the elderly (58). The strength of local
skeletal muscles is also directly related to bone mineral density at the lumbar spine and the
proximal femur (59). Thus, since physical activity can improve the strength of skeletal
muscles, this is an important factor in the prevention of osteoporosis in older individuals
(60,61).
Resistance training has beneficial effects on the cardiovascular system and metabolism
(50). The beneficial effects include reduced resting heart rate (62-64), increased stroke
volume at rest and exercise (65,66), maintenance of cardiac output at rest (62,67), decline in
resting systolic and diastolic blood pressure (68,69), improved plasma lipid and lipoprotein
levels, improved glucose tolerance and insulin sensitivity (70). Furthermore, myocardial
performance is more economical, supplying more blood per beat to working skeletal
muscles (50). Strength training also enhances at least moderately cardiorespiratory
performance (50). These improvements decrease the risk of type 2 diabetes (71) and
cardiovascular diseases (50).
2.1.3 Body composition and obesity
Excess body fat is one of the main health problems encountered in the developed countries
(72). In 2008, 49% of Finnish adults were overweight, 16% were even obese (72). There are
some studies even postulating that children born in the 21st century will have a lower life
expectancy than their parents due to obesity (73). Body composition depends on the
proportion of lean body mass, e.g. muscle mass, and body fat (50). Traditionally, obesity
has been defined as weight in excess of 20% of one’s ideal body weight, which takes into
account person’s age, gender and body height (72). The National Institute of Health has
more precise limits of overweight and obesity based on BMI (72). However, one limitation
of BMI is that it does not assess only body fat mass but also muscle, bone and other tissue
mass (74). In addition, some studies have suggested that increased visceral fat, which
cannot be assessed by BMI, is more detrimental to health than total body fat (75). BMI may
underestimate overweight and obesity at the population level, whereas it causes
overestimation in athletes who have a high skeletal muscle mass (76).
In the simplest terms, overweight and obesity are the outcome of positive energy balance
(72), hence a restriction of energy intake is the key factor in body weight control (77). In
clinical practice, it is possible to divide energy-intake determinants into four domains:
socio-cultural, biomedical or physiological factors, psychological factor and medication
(78). Physical activity can markedly increase energy consumption, and thus it is another
key factor in successful weight control. On the other hand, overweight and obesity are
strongly associated with low physical performance (79). Aerobic and strength training are
both beneficial in weight control. Aerobic exercise increases energy consumption, whereas
resistance training decreases the relative fat percentage by increasing the fat-free mass (55).
The strongest predictors of obesity are obesity of the parents and obesity in childhood (80).
5
When both parents are obese, the offspring may have 15-fold increased risk for obesity in
adulthood (80). It has been claimed that parental obesity and childhood obesity are good
predictors of obesity most likely because they capture genetic background and lifestyle
simultaneously (80). In addition, the metabolic rate can influence the energy expenditure.
Women have approximately 20% lower metabolic rate than men, mainly accounted by
differences in fat-free mass (FFM) (78). Aging is an important determinant of the decline in
metabolic rate and the decline is approximately 150 kcal per decade, and related to changes
in fat-free mass, physical activity and neuro-endocrinological changes (81). Certain diseases
(e.g. Cushing’s syndrome) can reduce energy requirements and increase the deposition of
truncal fat and some drugs (beta-blockers) may induce a weight gain (82). Weight loss also
results in a lower metabolic rate (78).
The effects of obesity on health are wide and complex. The traditional concept that
adipose tissue was simply a fat storage has been replaced over the last years by the
realization that adipose tissue has a central role in lipid and glucose metabolism and it
produces a large number of hormones and cytokines such as tumor necrosis factor α (TNFα), interleukin 6 (IL-6), adiponectin, leptin (LEP), and plasminogen activator inhibitor-1
(PAI-1) (75). Low-grade inflammation of adipose tissue is one reason for obesity-associated
diseases (75). Several studies have shown that the risk of suffering type 2 diabetes is
elevated with increasing BMI (72). The risk of coronary artery disease starts to increase with
BMI of 23 in men and above 22 in women (72). Obesity is associated with coronary artery
disease both directly and indirectly. The risk for coronary artery disease is indirectly
increased by many of the known risk factors i.e. hypertension, dyslipidemia and type II
diabetes. On the other hand, obesity is also an independent risk factor for coronary artery
disease (83-85). The known risk factors, including elevated levels of plasma triglycerides,
low levels of high-density lipoprotein (HDL) cholesterol and high levels of low-density
lipoprotein (LDL) cholesterol, which are commonly encountered in obese individuals (86).
Overweight and obesity have also been associated with an increased risk of ischemic and
hemorrhagic stroke (87,88). Overweight and obesity increase mechanical stress on joints
and thereby promote development of osteoarthritis (89). However, the true mechanisms
underlying the association of overweight and obesity with osteoarthritis are more complex,
and also metabolic conditions are linked to the incidence of osteoarthritis (90). There are
reports that obese individuals have impaired qualities of life (73). The reasons for the low
reported life quality include increased psychological stress, depression, social
stigmatization and increased overall morbidity among obese individuals (91,92).
Altogether, overweight and obesity are associated with a cluster of metabolic and
cardiovascular diseases and abnormalities, where excess adipose tissue is the key factor.
2.1.4 Cardiorespiratory responses to physical training
Physiological responses to physical activity seem to be very important in the reduction of
premature cardiovascular morbidity and mortality. At present, the majority of people in
western countries do not undertake the levels of physical activity required for the
prevention of chronic diseases (93).
VO2max is mainly limited by the amount of oxygen (O2) delivered to working muscles
and not by the muscle’s ability to extract O2 from blood (94). Overall gains in VO2max in
response to endurance training depend on numerous factors, including the baseline fitness
status of an individual, genetic factors, the duration and intensity of the training program
and single workout (95). There are no clear optimal training programs for increasing
VO2max. However, there is some evidence that a short-term high-intensity interval training
(80% to 100% of VO2max) is the most effective way of developing aerobic performance (96).
On the other hand, there is no evidence for any long-term benefits of such high-intensity
training, and the long-term improvement in VO2 seems to be attributed to changes that
6
occur at sub-maximal levels, such as increased lactate threshold and exercise economy (97).
The adaptations to physical training are specific for training type and sport (98).
VO2max is strongly related to cardiac output, and in trained individuals maximal stroke
volumes are increased while maximal heart rate remains similar to the rate in untrained
individuals (99,100). Left ventricular size, end-diastolic volume and contractility increase
sensitivity to catecholamines decreases in response to physical training and result in
decreased heart rate during sub-maximal exercise (23). After physical training, exercising
muscle needs less blood flow to do the same sub-maximal exercise because there is an
increased arterio-venous oxygen difference (101). During maximal exercise these, changes
increase VO2max. Total hemoglobin content of the blood increases while relative amount of
hemoglobin decreases because of increased circulating plasma volume (102).
Long-term endurance training enhances exercise economy (94). An improvement in the
exercise economy results from an increased oxidative capacity of skeletal muscles,
improved pattern of motor unit recruitment, reduced ventilation during exercise, decreased
heart rate for the same exercise intensity, and improved technique (103-105). It is known
that endurance training can increase both the lactate and the ventilatory threshold (94).
These changes allow a higher absolute and relative exercise intensity to be sustained
without the accumulation of lactate in blood after training (94). Trained individuals have a
higher proportion of Type I skeletal muscle fibers and a greater capillary network in the
muscles (106) compared to untrained individuals. Furthermore, the uptake of free fatty
acids from blood and numbers of enzymes involved in mitochondrial β-oxidation are
increased (107). This leads to spared muscle glycogen during exercise, and less blood
glucose is used in ATP synthesis. In conjunction with increased muscle glycogen content
these are important changes induced by endurance training, because the depletion of
muscle glycogen stores is connected to fatigue (108).
2.1.5 Metabolic responses to physical training
Physical exercise increases energy expenditure and thus would be predicted to exert
favorable effects on body weight, fat mass and lean body mass. However, aerobic physical
training alone seems to have only modest effects on body composition (109). Dietary energy
restriction is more effective than exercise in achieving weight loss (77). When energy
restriction and exercise interventions are combined, the best results are gained (77). Dietinduced weight loss results in a decrease of body weight with approximately 75%
originating from fat mass and approximately 25% from fat-free mass (109,110). On the other
hand, aerobic training attenuates the loss of FFM (77) and resistance training even increases
FFM. Even aerobic exercise is moderately favorable in preserving FFM (77). Resistance
training increases FFM within two months and indirectly decreases fat percent provided
that energy intake has been kept at a reasonable level (111).
Resistance training programs are used in increasing force production and lean muscle
mass. In untrained individuals, skeletal muscle hypertrophy is virtually non-existent and
the majority of gains in strength result from neural adaptations in the initiation stages of
resistance training (111). However, within a couple of months of training, hypertrophy will
become the dominant factor in the strength gains. The upper extremities seem to gain
muscle mass earlier than lower extremities (112). Genetic background, age, and other
factors affect the gain in muscle mass in response to resistance training (113). There are
three key factors in the initiation of the hypertrophic response to resistance exercise:
mechanical tension, muscle damage and metabolic stress (111). Traditional resistance
training programs result in a parallel in sarcomers and myofibrils (114,115). Hypertrophy is
also augmented by an increase in non-contractile elements and fluid (116). This
sarcoplasmic hypertrophy is training specific and explains the finding that hypertrophy is
different in body builders than in power lifters (115). Indeed, body builders tend to have a
greater proliferation of fibrous endomysial connective tissue and muscle glycogen content
7
when compared to power lifters (117). Furthermore, satellite cells play a major role in
muscle hypertrophy (111).
Biochemical responses to exercise training programs are well studied. However, the role
of these responses is somewhat unclear. After strenuous exercise, IL-6 is secreted from
skeletal muscles (118) and the circulating IL-6 level may increase by up to 100 fold (119). It
has been speculated that IL-6 may be an important mediator of the increased metabolism
during physical exercise (119,120), since it increases liver glycogenolysis, muscle glucose
uptake and release of free fatty acids to circulation. Furthermore, muscle energy availability
may be improved (120,121). It has been hypothesized that IL-6 is a mediator of the antiinflammatory effects of exercise and thus it plays a role in the beneficial health effects of
physical training by reducing systemic low-grade inflammation (120,122).
Leptin is a peptide hormone secreted mainly from the white adipose tissue. Leptin
regulates hunger, body temperature and energy metabolism, and has been used as a
biomarker of energy deficiency (123,124). In addition, leptin can activate haematopoiesis
(125), angiogenesis (126,127) and hypertrophy of cardiomyocytes (128). In skeletal muscle,
leptin increases glucose and fatty acid oxidation (129) via the enzyme adenosine
monophosphate (AMP)-activated protein kinase (AMPK) (130). Circulating levels of leptin
decrease after a period of physical training (131) or even after a single bout of strenuous
exercise (129). Recent studies have reported that obese individuals are, in fact, leptin
resistant (124), but physical exercise has been claimed restore leptin sensitivity (132,132).
Moreover, it has been proposed that decreased levels of leptin could lead to decreased
secretion of insulin like growth factor-1 (IGF-1), which is an important mediator of skeletal
muscle growth and hypertrophy (124).
Hormonal and cytokine responses to strength training are well known. Testosterone and
growth hormone are classical anabolic hormones (133). However, also hepatocyte growth
factor, IL-5, IL-6, fibroblast growth factor and leukemia inhibitory factor have been shown
to promote anabolism (134-136). The long-term efflux of IL-6 may, however, cause atrophy
of skeletal muscle (137). Insulin activates satellite cells and inhibits protein degradation
(136). The role of IGF-1 in muscle hypertrophy has been a focus of research effort. IGF-1 has
hypertrophic effects in an autocrine and paracrine manner and has many anabolic
pathways (138). It has direct anabolic effects by increasing protein synthesis in
differentiated myofibers (138,139). In addition, locally expressed IGF-1 isoform, IGF-1Ec,
activates satellite cells and mediates their proliferation and differentiation (140,141).
Circulating systemic isoform IGF-1Ea is shown to increase the fusion of satellite cells and
myofibers, facilitating the donation of myonuclei in muscle tissue (135). IGF-1 also increases
intracellular calcium ion (Ca2+)-concentration by activating L-type calcium channel gene
expression (142).
8
Figure 1. The main evidence-based pathways on how physical activity or exercise therapy can
delay progression of diseases and occurrence of disability and deaths. Modified from Kujala et
al. (7)
9
2.1.6 Physical activity and metabolic risk factors
Physical activity prevents obesity and is effective in the treatment of obesity, enhancing fat
distribution, improving insulin sensitivity, improving blood lipids and lipoprotein profile,
reducing inflammation, improving vascular endothelial function and decreasing depressive
symptoms and anxiety (143,144). Together these favorable physiological changes decrease
the risk of chronic diseases such as type II diabetes, metabolic syndrome and cardiovascular
disease (7). Physical activity decreases abdominal and subcutaneous fat, without changes in
dietary energy intake (145). The decrease in visceral fat may be greater than decrease in
total or subcutaneous fat (145,146) and a greater amount of physical activity appear to
result in a greater reduction in the amount of abdominal fat (146). A single bout of exercise
increases insulin sensitivity for 48-72 hours (147). In addition, physical activity increases
insulin sensitivity over the long-term, which is partly related to alteration of body
composition and fat distribution (148). Physical activity increases plasma HDL and
decreases plasma LDL levels and triglycerides. The effect on the plasma lipid and
lipoprotein concentration is approximately 3-5 % (149). Physical activity decreases systolic
blood pressure by 3.8 mmHg and diastolic blood pressure by 2.6 mmHg in individuals with
normal or elevated blood pressure, reduction is slightly greater in individuals with
hypertension (150). A single bout of physical activity triggers an acute pro-inflammatory
reaction, and increases C reactive protein (CRP) levels and release of pro-inflammatory
cytokines (151). However, long-term physical activity is associated with decreased levels of
CRP and pro-inflammatory markers (151). Endothelial function may be increased in
response to physical activity, at least in individuals with endothelial dysfunction (152).
2.2 GENETICS
2.2.1 Genetic study designs
The role of genetics in different traits or diseases can be studied many ways. Every method
has its strengths and weaknesses and they produce different kinds of information. Among
the first study designs were the twin studies. In this type of study, the heritability of a trait
can be estimated. Monozygotic twins share 100% of their genes, whereas dizygotic twins
share 50% (153). The variance in a trait is caused by additive genetic components, the
common environment of the twins and also unique environmental effects. Therefore,
phenotypic resemblance can be compared between monozygotic and dizygotic twins. If
monozygotic twins resemble each other more than dizygotic twins, the narrow heritability
of the phenotype can be estimated from twice of the difference between the correlations
found in monozygotic and dizygotic twins (153). There are several inherent assumptions in
twin studies e.g. random mating, equal environments between monozygotic and dizygotic
twins, genes and the environment have separate effects and that only one genetic
mechanism, typically an additive mechanism, underlies the effect. In addition, twins are
selected and do not represent the general population (153).
Artificial breeding in rat models is applied to accumulate the gene variants associated
with a selected trait, such as aerobic fitness. In one such program, there were 20 generations
of breeding, with rats being selected for low aerobic capacity or high aerobic capacity based
on running distance (154). The high aerobic capacity rats had a 4.7-times greater running
distance than their low aerobic capacity counterparts (154). The main finding from this type
of animal model was that the adult low aerobic capacity rats displayed cardiovascular risk
factors typical of the metabolic syndrome, including visceral fat accumulation, increased
blood pressure, dyslipidemia, increased blood pressure, endothelial dysfunction in carotid
arteries and insulin resistance. In addition, the life expectancy was higher in the high
10
aerobic capacity rats (154). However, the low aerobic capacity rats improved their running
distance and cardiovascular risk factors in response to physical training. It can be
concluded that the genes associated with aerobic capacity are also associated with
cardiovascular risk factors and that physical activity can attenuate the effect of genes
associated with low aerobic capacity and cardiovascular risk factors.
Genetic association studies are used in investigating the relationships of specific gene
variants with different traits or diseases and in examining gene-environment interactions.
In the classical candidate gene study design, selected variants in predetermined genes are
studied (155). Frequently the candidate genes are selected based on the role of their protein
product in the biology of the studied trait, often based on gene expression studies or
genome-wide association studies (GWAS). The variance is compared between individuals
with different variants of the same gene. In a GWAS, the associations of specific genetic
markers across the entire genome with the trait are investigated with chip technology,
rather than focusing on a specific gene (156). It is not necessary to read through all 3.3 base
pairs of each individual. GWAS takes advantage of the fact that segments of the genome
(haplotypes) are likely to be inherited together. By selecting one nucleotide within a
haplotype, the make-up of the entire segment can be estimated. The associations of gene
variants found in GWAS, can identify those genetic regions responsible for a variation in
the phenotype, but these are not necessarily the causative gene or mutation (156).
Gene expression studies investigate which genes are resided in cells and when they are
activated after a known stimulus, such as resistance or aerobic exercise. Chip technology
makes it possible to study the expression of thousands of genes at a time. Messenger RNA
from cells is collected before and after the exercise session. RNA is converted to cDNA and
hybridized on the chip with specific DNA probes representing the genes of interest. After
hybridization, the specific fluorescent dye reveals the amount of mRNA in pre-and postexercise samples (157).
In addition, traits can be inherited without there being any changes in DNA, due to
epigenetic variation between individuals. Epigenetic regulatory factors can activate or
make genes passive. It is well known that maternal nutrition can exert long-lasting
modifications to gene expression patterns in embryo through epigenetic mechanisms (158).
For example, histone modification, aberrant miRNA expression and DNA methylation all
serve to regulate gene expression without altering the DNA sequence (159). Epigenetics
enable the heritance of some acquired traits. The epigenetic mechanisms have been
reported to play a major role in many cancers (158). So far, however, only a few reports are
available on the associations of physical activity with epigenetic factors (160).
2.2.2 Genetic variation and single nucleotide polymorphisms
The human genome is the complete DNA sequence and the repertoire of proteins is the
proteome. The haploid human genome is about 3.3 billion base pairs (adenosine, thymine,
guanine, cytosine, and urasil in mRNA sequence). Roughly 1-2% of the human genome
consists of coding sequences for about 20 000 to 25 000 protein coding genes. The genome of
two non-related individuals is 99.9% identical, but the DNA sequence can vary between
two versions of chromosome in several ways (161).
Many forms of DNA sequence variations exist and they can be classified in different
ways. The two most important structural classes are micro-satellites and single nucleotide
polymorphisms (SNP). In microsatellites, the alleles are differentiated by the number of
repeats e.g. for: CA12 and CA19, where the first contains 12 repeats of CA and the second
contains 19, respectively. Coding regions tend to have no microsatellites. SNPs represent a
variation in a single nucleotide and are more frequent being present in 1% of the
population (161). When the frequency of the genetic variation in the population is rarer
than 1%, the issue at stake is a mutation. In the year 2005, the number of known SNPs in the
human genome exceeded 10 million. In the latest National Center of Biotechnology
11
Information (NCBI) SNP database the number of known validated SNPs, containing all
variation in human genome, had reached over 41 million. It is estimated that if one
compares two non-related individuals then one SNP will be found in every 300-1200 base
pairs (162). SNPs in protein coding regions can be either synonymous or non-synonymous
depending on whether they do or do not affect amino acid sequence of the protein product.
Intronic and intergenic SNPs lie in the non-coding regions of the DNA sequence. However,
SNPs when located in the promoter region can affect gene regulation. A non-synonymous
SNP in an exon (coding region) is more likely than other SNPs to affect the function or the
availability of the protein. However, all types of SNPs can cause disease or affect a trait by
modifying the regulation of transcription (161). SNPs in non-coding regions may be linked
(tag SNP) to a SNP in coding regions. In this way, analysis of every SNP is not necessary. If
two SNPs are located close to each other, they are also more likely to be inherited together.
When the distance between SNPs increases, the likelihood for recombination increases
(161). The copy number variation indicates the duplication of a large DNA sequence, not
single nucleotides (163).
If one wishes to detect genetic variation based inter-individual differences in
phenotypes, then a large sample size is often needed due to the small effect of a single SNP.
On average one SNP can, account for 1-3% of the change in the phenotype, for example in
blood pressure. Even more subjects are needed in studies in which lifestyle-gene
interactions are to be investigated. Another reason for the need of large sample sizes is to
avoid false positive findings when testing statistical significance of a large number of
associations (multiple testing problem). In GWAS studies even, 1 million SNPs can be
assessed in a single experiment. For these reasons, large meta-analyses combining data
from many studies are often published by consortiums.
2.3 GENETIC BACKGROUND OF PHYSICAL PERFORMANCE AND BODY
COMPOSITION
2.3.1 Physical activity, genetic variation and gene expression
Phenotype variability is affected by environmental and behavioral factors, as well as the
genetic component. Indeed, many behavioral factors such as physical activity and nutrition
are important determinants. Often the variation caused by environmental factors can be, at
least partly, taken into account in the study design and statistical methods. A large
proportion of the phenotype variation is attributable to genetic variation.
Adaptations to physical exercise differ significantly while some individuals are more
responsive than others to physical exercise intervention (164). Individual variation in the
response to exercise can be observed in many parameters such as blood pressure, plasma
lipids and lipoproteins, insulin sensitivity, body composition, VO 2max, and muscle force, to
name but a few (164). However, an individual may gain improvements in one risk marker,
while changes are not seen in others. In other words, there is no strong correlation between
the responses of individual risk factors (165). For example in sedentary individuals, the
mean increase in VO2max after standardized exercise programs has been reported to be
25%, ranging from zero improvement to doubling of the VO2max in different individuals
(166). This finding indicates that there is an interaction between the environment and the
genes (166). The same environmental or behavioral factor does not lead to the same
responses between individuals. The interaction is specific for phenotype and environmental
factors. In the case of a physical activity, a bout of exercise may increase expression of
12
specific genes and a variation in that specific gene may become evident in health benefits,
anti-inflammatory effects or improvements in fitness.
Acute exercise induces upregulation of expression of numerous genes in skeletal muscle
tissue and results in increased energy metabolism (167,168). The upregulated genes can be
divided into three groups; 1) genes that are involved in the acute stress reaction, such as
heat shock proteins and exercise-modulated transcription factors, whose effects last for a
couple of hours, 2) genes that respond to depletion of energy stores and are activated by the
depletion of the glycogen content of the skeletal muscle cells at the end of exercise and that
regulate the availability of glucose as energy source in the skeletal muscle cells and whose
effects last for a couple of hours to one day, and 3) genes that are involved in the conversion
of nutrients to energy and are activated less intensively and which regulate the
regeneration and function of mitochondria and capillaries and whose effects last for one
day to one week. The effects of most of the genes activated by acute exercise last
approximately for one day, which highlights the importance of regular daily exercise.
In most cases, obesity is a multifactorial complex phenotype. The current theory states
that exposure to an obesogenic environment in individuals with latent genetic
predisposition causes obesity (166). These genes tend to have only a small effect but the
variant allele is common at the population level. According to overfeeding and energyintake deficit studies, large inter-individual differences are seen in accumulation of adipose
tissue or on the other hand, in weight reduction. One example of gene-behavior interaction
is FTO-physical activity interaction. First, the FTO region was found to associate with type
II diabetes (169). However, the adjustments for BMI abolished the association. This led to
concept that the FTO genotype could be a candidate for obesity, and this finding has been
confirmed in several cohorts (170). Subsequently it was noted that physical activity
attenuates the influence of FTO variants on the risk of obesity (171). In addition, FTOphysical activity interaction is an example of complexity on gene-behavior interactions. The
FTO variants increase the risk for obesity that increases risk for type 2 diabetes in
individuals with genetic predisposition, creating an endophenotype (obesity) and a chain of
interactions (156) (figure 2).
The identification of genetic variants responsible for difference in physical performance
and training responses will clarify the etiology of physical fitness related health benefits.
This could lead to new strategies in decreasing morbidity and mortality, improving
independent living in elderly and increasing life quality. In addition, by taking into account
genetic background, targeted exercise therapies could be tailored to increase efficiency of
prevention or treatment of diseases including type 2 diabetes and cardiovascular disease.
Genotype information is also useful in identification of individuals at high risk for these
diseases. In the last decade, many associations have been found between genetic
polymorphisms and physical performance traits. In this section, only the most significant
associations from most well conducted trustworthy studies and replicated results are
described.
This doctoral thesis is focused on genetic association studies. There are other study
designs with which to investigate physical performance and body composition related
genetic factors, such as gene expression and epigenetic studies. In the field of exercise
genomics, many studies have low quality, and so far only few GWAS have been published.
The main reason for the low quality is the small number of subjects in studies, poorly
defined physical performance traits and the candidate gene study setting. In addition, few
studies on the physical activity related gene expression have been published with scanty
results. However, it might be that physical activity induced transcripts are involved in
development, tumor biology- and immunology related pathways (172).
13
Figure 2. A schematic model of gene-physical activity interaction highlights the complex
interactions between physical activity, intermediate phenotypes, other environmental factors,
genes and health status. Modified from Bray (173).
2.4 GENETICS OF CARDIORESPIRATORY PERFORMANCE
2.4.1 General considerations
There is large variability between individuals with respect to cardiorespiratory
performance. Physical activity accounting for a great proportion of this difference.
However, approximately 50% of VO2max differences between sedentary individuals can be
traced to genetic variation (11). Genetic factors affecting the levels of physical performance
before exercise training are not necessarily the same as those influencing the actual
responses to physical training. In this section, variations affecting levels of aerobic
performance without exercise training are described. However, many of the studies are
limited by small sample sizes. Furthermore, in some studies, the populations have
consisted of healthy individuals, even athletes, whereas in others patients with physical
performance decreasing diseases, such as heart failure or chronic obstructive pulmonary
disease (COPD), have been examined. Furthermore, different study designs have been used
for the investigation of the associations of genetic variation with physical performance at
the basal level. The two most common types are cross-sectional case-control studies, where
athletes are used as cases and sedentary individuals as controls, and cross-sectional
association studies (table 1).
14
2.4.2 The ACE gene
The angiotensin-converting enzyme (ACE) is a circulating enzyme which plays a role in the
renin-angiotensin system and participates in the regulation of extracellular volume and
vasoconstriction (174). ACE is expressed in lungs and renal endothelium and catalyses the
conversion of Angtiotensin I into Angiotensin II (174). The ACE insertion deletion
polymorphism I/D has been one of the most extensively studied polymorphisms in the field
of performance genetics. In case-control studies, the D allele of the ACE insertion deletion
polymorphism I/D was found frequently in marathon runners as compared to sedentary
individuals and even more often than in sprint athletes (175,176). Furthermore, the D allele
was more common in Caucasian elite swimmers (177) and in Russian short distance
athletes than in controls (178). The frequency of D allele was also found to be higher in
Spanish cyclists and controls compared to runners (179). In a small sample of non-elite
athletes, DD genotype was more frequent in a superior group tested with a 2000 meter
running test when they were compared with other genotypes (180). In contrast, the I allele
was more frequent in middle distance athletes (178), in Turkish athletes (181) and in the 100
fastest South-African finishers in a triathlon (182). In soccer players, the I allele was more
rare than in endurance athletes, and the authors concluded that soccer players tend to have
a so called strength/power genotype (183). In British Olympic runners, an increasing
number of I alleles was observed as a function of running distance (184). However, not all
studies have confirmed these associations (185). On the other hand, there were racial
differences in the different studies. Altogether, these reports seem to indicate that the D
might be more common in power athletes and athletes competing over short distances,
whereas the I allele is more common in endurance athletes. However, according to these
case-control studies, one cannot conclude that the ACE I/D is actually associated with
physical performance.
In cross-sectional association studies conducted in congestive heart failure patients, II
genotype of the ACE I/D polymorphism associated with higher VO2peak and longer
exercise time on a treadmill than the D allele (186). The same association was found in
postmenopausal women. The D allele associated with lower VO2max as compared to II
genotype (187,188). In COPD patients, the II genotype associated with lower post-exercise
lactate levels and the DD genotype associated with lower oxygen delivery after a graded
exercise test as compared to the other genotypes (189-191). However, this finding was not
supported by another study in chronic heart failure patients (192). In COPD patients, the
individuals with the I-allele had higher endurance efficiency than individuals with the DD
genotype and individuals with the II genotype had a longer walking distance in the 6minute walking test than individuals with the D-allele (193). On the contrary, in a small
sample of wrestlers and age matched controls, carriers of DD genotype had higher VO2max
values in both groups as compared to carriers of the II genotype (194) and in a small sample
of Asian men, the D allele associated with higher VO2max values than in those with the II
genotype (195). Thus according to the research done so far, the D-allele of the ACE I/D
polymorphism seems to associate with enhanced power performance, whereas the I allele
appears to improve aerobic performance and to increase the VO2max value.
2.4.3 The PPAR and ACTN3 genes
Many SNPs have not been so widely studied as ACE I/D, and only few of the putative
associations have been replicated in subsequent studies. The peroxisome proliferatoractivated receptors (PPARs) are a group of endogenous receptor proteins which function as
transcription factors and regulate gene expression. PPARs play a role in the regulation of
development as well as cellular differentiation and metabolism (196). There are three subtypes of PPARs, PPARα, PPARβ/δ and PPARγ. Saturated fatty acids are endogenous
ligands for PPARα and PPARβ/δ, whereas unsaturated fatty acids are endogenous ligands
for PPARγ. PPARα is activated and provides energy from fatty acid catabolism during the
15
times of starvation and cold acclimatization, PPARγ is activated in the well fed state and
regulates the synthesis of fatty acids and related lipids, while PPARβ/δ ensures that fatty
acids can provide energy for working muscles (197). The PPARs are essential in the
regulation of inflammation and are involved in several morbidities such as obesity, type 2
diabetes and atherosclerosis (197). Significantly different 7C/G genotype frequencies of the
peroxisome proliferator-activated receptor-alpha (PPARA) have been found in endurance
athletes compared to power athletes, and GG carriers tend to have a higher percentage of
slow-twitch fibers than C allele carriers (198). In addition, the frequency of the Ser482 allele
of the PPARG Gly482Ser polymorphism was significantly lower in athletes compared to
exceptionally unfit controls (179). The Gly allele of the Gly482Ser in the PPARG and GG
genotype of rs4253778 C/G in the PPARGCIA may be associated with the endurance type
phenotype (199). The PPARD CC + PPARGCIA GlyGly genotype was more frequent in elite
endurance athletes than in national level endurance athletes (200).
In type II muscle fibers, actinin proteins 2 and 3 stabilize actin proteins. In Western
European populations, 18% of all individuals have a complete deficiency of the alphaactinin-3 protein owing to homozygosity for a stop codon mutation (R577X) in the ACTN3
gene (201). The distributions of ACTN3 alleles and haplogroups were significantly different
between endurance and sprint athletes (202). The XX genotype trend of the ACTN3
polymorphism was seen in endurance athletes (202). The XX genotype of the ACTN3 gene
nonsense mutation was more frequent in controls than in elite sprint athletes or power
athletes (203). Thus the XX genotype may be more frequent in endurance athletes than
sprint or power athletes.
2.4.4 The ADRA and ADRB genes
The adrenergic receptors are a group of G-protein coupled receptors that are targets of the
endogenous catecholamines, adrenaline and noradrenalin. Binding of an agonist will wake
a sympathetic response. The alpha-adrenoceptors cause smooth muscle contraction and
cardiac muscle relaxation, whereas beta-adrenoceptors are involved in cardiac muscle
contraction and smooth muscle relaxation (204). Sedentary individuals have been reported
to have a higher frequency of the Gly allele of Arg16Gly polymorphism in the beta-2
adrenergic receptor (ADRB2) gene than elite athletes (205). Moreover, significant
differences have been reported in allele and genotype frequencies of the ADRA2A gene
marker between elite endurance athletes and sedentary individuals (206). In
postmenopausal women, Glu homozygotes of the ADRB2 Gln27Glu polymorphism had
lower body weight adjusted VO2max compared to Gln allele carriers (207). In addition in
postmenopausal women, the Gln allele carriers had higher VO2max values than the Glu
allele carriers of the Gln27Glu polymorphism (208). In heart failure patients, ADRB2 was
associated with VO2max (209). An association with aerobic power was found with the
Ser49Gly and haplotypes of Ser49Gly and Gly389Arg of the beta adrenergic receptor
(ADRB1) (210). In patients with idiopathic or ischemic cardiomyopathy, the Arg389 allele of
the ADRB1 associated with higher peak VO2max than Gly389 (211) and in a sample of heart
failure patients, the 389Arg allele carriers of the ADRB1 polymorphism had higher VO2peak
and longer exercise time than 389Gly homozygotes (212). According to these studies, the
Gln allele of the ADRB2 Gln27Glu polymorphism may associate with higher aerobic
performance in women compared with Glu homozygotes and the 389Arg allele of the
Gly389Arg of ADRB1 may associate with higher VO2peak as compared with Gly
homozygotes.
2.4.5 The IL6 and IGF1 genes
IGF-1 has hypertrophic effects in an autocrine and paracrine manner and it interacts with
many anabolic pathways (138). It has direct anabolic effects by increasing protein synthesis
in differentiated myofibers (138,139). In postmenopausal sedentary women, the 189 bp CT
16
allele repeats of the IGF1 gene were associated with better exercise economy and endurance
performance (213).
IL-6 acts as both an anti-inflammatory and pro-inflammatory cytokine. It is also
considered as a myokine. After strenuous exercise, IL-6 is secreted from skeletal muscles
(118) and the circulating IL-6 level may be elevated up to 100 fold (119). It has been
speculated that IL-6 may be an important mediator of metabolism during physical exercise
(119,120) as it can increase liver glycogenolysis, muscle glucose uptake and the release of
free fatty acids into the circulation. In a moderate sample size of young male smokers, IL6
SNP (rs1800795) was associated with maximal working capacity (214). The C allele was
associated with low maximal physical work capacity only in smokers, and it was concluded
that smoking could be particularly harmful to these individuals (214). Inconsistent results
have been published regarding the effects of the SNP rs1800795 on plasma IL-6 levels
(215,216). In a recent replication study of Israeli subjects, the IL6 genotype did not associate
with elite power performance (217).
2.4.6 Polygenic profiles of athletes
There are a few reports about the polygenic profiles of athletes. The prevalence of ten
endurance–related alleles (Gly160 of NFATC4, allele G of PPARA SNP rs4253778, allele C of
PPARD SNP rs2016520, Gly482 of PPARGC1A, 203Pro of PPARGC1B, promoter 5I of
PPP3R1, 12Thr of TFAM, 55Val of UCP2, allele T of UCP3 SNP rs1800849, and allele C of
VEGFA SNP rs2010963) was studied in Russian athletes and controls (218). In that study,
high numbers of endurance alleles were found in the most successful athletes. In addition,
the number of endurance alleles was positively correlated with fatigue-resistant type I
muscle fibers, and VO2max values (218). The more favorable combination of endurance
genotypes (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170 bp/985 + 185
bp, HFE His63Asp, GDF-8 Lys153Arg and PPARGC1A Gly482Ser) was found in elite
Spanish endurance athletes than in controls, which indicates that endurance athletes
opposes more favorable polygenic profile (219).
17
Table 1. Associations of genetic variants with aerobic or endurance performance traits
Gene
Genetic
variation
CKMM
NcoI
80
women,
80 men
and 80
unrelated
adult
offsprings
EPHX1
rs1877724 and
rs1051740
HIF1A
Subjects
Study
design
Result
Reference
Crosssectional
VO2max was significantly
associated with the CKMM
genotype in the parents in
sedentary state
(220)
304 COPD
patients
Crosssectional
Rs1877724 and rs1051740 were
associated with maximum work
and exercise capacity
(221)
A-2500T
155
Caucasian
and
AfricanAmerican
Crosssectional
T allele associated with lower
VO2max compared to AA
genotype
(222)
HLA-A
A, B and C loci
8 monoCrosszygotic
sectional
and 8
dizygotic
pairs of
twin
sportsmen
The A2A11 group had higher
VO2max than other groups
(223)
LTBP4
rs2303729,
rs1131620,
rs1051303 and
rs2077407
304 COPD
patients
Crosssectional
Rs2303729, rs1131620,
rs1051303, and rs2077407
associated with maximum work,
low exercise capacity and 6-min
walking test distance
(221)
MCT1
A1470T
10 men
Crosssectional
Carriers of the A1470T seemed
to exhibited worse lactate
transports capacity in to the less
active muscle cells for oxidation
(224)
NOS3
G894T
443
triathlon
athlete
men and
203
Caucasian
men
controls
Case-Control
The significant increasing linear
trend was found with genotype
GG and finishing time in
triathlon race
(225)
SFTBP
D2S388
304 COPD
patients
Crosssectional
A short tandem repeat marker
associated with low exercise
capacity and 6-min walking test
distance
(221)
TNFA
G-238A
600 twin
Crossindividuals sectional
The GG genotype associated
with lower physical performance
compared to A allele carriers
(226)
VEGFA
-2578/-1154/634 promoter
region haplotype
148 white
and black
Individuals with at least one
copy of the AAG or CGC
haplotype had higher VO2 max
before and after aerobic
exercise training than did
subjects with only the AGG
and/or CGG haplotype
(227)
Crosssectional
To be
continued
18
Table 1
continues
Gene
Genetic
variation
VEGF2R
His472Gln
Subjects
471
athletes
and 603
controls,
men and
women
Study
design
Case-control
Result
Reference
VO2max was higher in Gln allele
carriers than in His
homozygotes, however only in
women
(228)
2.5 GENETICS OF NEUROMUSCULAR PERFORMANCE
2.5.1 General considerations
According to twin studies, about 60% of athletic status, about 90% of skeletal muscle mass
and about 60% of skeletal muscle force is heritable (13,14). Genetic variations associated
with neuromuscular performance before exercise training are not necessarily the same as
those linked with gains in neuromuscular performance in response to physical training. In
this section, variations which affect levels of the neuromuscular performance without
exercise training are described. Regretfully, many studies are limited by the small sample
sizes and poor standardization of neuromuscular traits. Furthermore, the study
populations exhibit great differences. The two most common types are cross-sectional casecontrol type studies, where athletes are used as cases and sedentary individuals as controls,
and the cross-sectional association studies. At present, there are no GWAS available for
strength and power traits. Many candidate gene studies have been published, but in only
minority have the findings been replicated (table 2).
2.5.2 The ACTN3 gene
In Western European populations, approximately 18% have a complete deficiency of the
alpha-actinin-3 protein owing to homozygosity for a stop codon variation (R577X) in the
ACTN3 gene (201). Actin proteins are stabilized by actinin proteins 2 and 3 in type II muscle
fibers. Thus, this polymorphism is considered as a candidate gene for muscle performance.
The XX genotype of the nonsense X577R polymorphism of ACTN3 associated with slower
40-meter sprint times in adolescent men (229). But this association was not found in
women. Young men with the XX genotype had a lower peak torque at 300 degrees per
second (°s-1) than others, when isometric and isokinetic knee extension strengths were
studied (230). Young men with the XX genotype also had a lower proportion of type IIx
muscle fibers compared to young men with the RR genotype (230). In addition, women
with the XX genotype had a lower knee extension shortening torque and total body fat-free
mass than women carrying the R allele, but no such association was found in men (231).
The XX genotype was associated with a greater baseline relative peak power at 70% of one
repetition maximum knee extension exercise only in women (232). The R allele of the
ACTN3 R577X polymorphism was more frequently detected in Israeli sprinters compared
to the XX genotype, which was also more common in endurance athletes (233). On the
contrary, the XX genotype was also more frequent in Russian elite power athletes than in
controls (234). In addition, the HIF1A Pro homozygotes having ACTN3 RR genotype had
increased odds for being sprinter as compared to endurance athletes and controls (235). The
X allele and the XX genotype of the ACTN3 R/X polymorphism were more frequent in
Chinese female endurance athletes compared to controls (236). The same finding was not
confirmed in endurance athlete men (237). In conclusion, the results of the associations of
19
ACTN3 gene variants with neuromuscular performance are somewhat conflicting.
However, the R allele of the ACTN3 R577X polymorphism may associate with high power
and strength performance. However, a higher R allele frequency of the ACTN3 in power
athletes does not necessarily exhibit a clear association with performance traits.
2.5.3 The ACE gene
Genetic variations of ACE in relation to muscle force phenotypes have been extensively
studied. In women, the II genotype of the ACE I/D polymorphism associated with a higher
hand grip force and vertical jump scores than the ID or DD genotypes (238). But no such
associations were found in men. Furthermore, no association with elbow flexion force was
found in another study (239). In men and women, the II homozygotes had a lower
maximum and optimum contraction velocity when they were compared with the ID or DD
groups when leg press strength variables were evaluated (240). In the cohort of COPD
patients, the D allele carriers had greater quadriceps force compared to II homozygotes
(241). In a small sample of Caucasian men, the II homozygotes had a lower quadriceps
strength as compared to men with other genotypes (242). The D allele was also found more
frequently in short distance swimmers as compared to controls (243). The frequency of
allele I was higher in Polish rowers than in controls (244). Individuals with the ACTN3 RR
genotype who were ACE I allele carriers had 2.3-fold higher odds of being a sprinter than
controls, and the individuals with ACTN3 R allele and the ACE II genotype had a 3.6-fold
odds of being a sprinter than controls (245). These findings together suggest that the D
allele of the ACE gene is associated with a higher power and strength performance.
2.5.4 The VDR gene
Vitamin D receptor (VDR) is a member of a nuclear receptor family of transcription factors
(246). After its activation by vitamin D, the receptor binds to hormone response elements on
DNA resulting in expression of specific gene products (246). In women, the ff homozygotes
of the VDR FokI (F and f alleles) polymorphism had a higher quadriceps concentric and
isometric strength than F allele carriers. In addition, the AA carriers of the ApaI locus had a
lower elbow flexor concentric peak torque than the aa carriers and a lower knee extensor
eccentric peak torque than the a-allele carriers (247). No associations with TaqI were found.
In older Caucasian men, FokI associated with knee extensor isometric strength. However,
the association was explained by differences in muscle mass (248). In postmenopausal
women, the VDR BsmI polymorphism associated with grip and quadriceps strength (249).
The bb carriers of the BsmI polymorphism had a lower knee flexor concentric peak torque
than the B allele carriers (247). In men, the BsmI/TaqI haplotype was associated with
isometric quadriceps strength, with the Bt/Bt carriers having more strength than the bT
carriers (250). In young women, homozygotes for the shorter poly-A repeat of the VDR
were noted to posses with a greater isokinetic hamstring muscle strength as compared to
homozygotes for the longer repeat poly-A repeat (251). According to these reports,
polymorphisms in the vitamin D receptor gene may associate with muscle strength traits.
However, only a limited numbers of these findings have been replicated.
2.5.5 The CNTF and CNTFR genes
Ciliary neurotrophic factor (CNTF) is a nerve growth factor, which is coded by CNTF gene.
In the nervous system it has a neuroprotective role (252). Homozygous individuals for the
null allele (A/G, A=null allele) of the CTNF gene rs1800169 nonsense polymorphism had a
lower hand-grip strength than those with the AG or GG genotypes (253). However, this
finding was not supported by another study where multiple variables of knee extension
and flexion measurements were analyzed (254). Healthy individuals with the GA genotype
displayed better muscle strength and muscle performance at relatively fast contraction
20
speeds as compared to those with the GG genotype (255). However, it is challenging to
provide a biological basis for an association between individuals with heterozygote
genotype and the studied trait. In addition, no CNTF X CNTF receptor (CNTFR) gene
variant interactions were observed. The individuals with allele T of the CNTFR C-1703T
polymorphism showed higher knee flexor torque values than individuals with genotype
CC (254). In women, allele A carriers of the CNTFR SNP T1069A gene had lower concentric
knee flexor peak torque at multiple speeds and isometric torque at 120°s-1 as compared with
the TT genotype carriers (254). Carriers of the allele T of the CNTFR gene polymorphism
C174T had increased high and low velocity knee extensor peak torque than those with the
CC genotype, and the authors explained these results as being due to the increased muscle
mass in the T allele carriers (256). In summary, the CNTF and CNTFR receptor
polymorphisms may play a role in muscle strength, but the quality and number of studies
is limited.
2.5.6 The IGF-1 and IGF-2 pathway genes
There is also a limited number of studies examining the effects of insulin-like growth factor
(IGF) pathway gene variations in relation to muscle force. No associations of genetic
variations in IGF1, calcineurin (PPP3RI) and insulin-like growth factor binding protein 3
(IGFBP3) genes with muscle strength were found. However, the IGF1 gene variation did
associate with gains in one-repetition maximum strength and muscle volume in response to
strength training (257). The 192-allele carriers of the microsatellite in the promoter of IGF1
exhibited greater gains in quadriceps muscle strength after 10 weeks of resistance training
(258). On the contrary, the IGF2 gene polymorphisms were associated with a loss of
isometric strength after a damaging exercise protocol of the elbow flexors (259). In earlier
studies, the A/G SNP in the IGF2 gene associated with grip strength in men but not in
women (260). However in another study, the ApaI polymorphism (A/G) in IGF2 seemed to
associate with isokinetic strength measures in women. I.e. The GG homozygotes displayed
higher values than the A allele carriers (261). These reports indicate that some IGF1 and
IGF2 gene variants may associate with strength performance; however more studies are
needed to confirm these findings.
21
Table 2. Associations of genetic variants with strength and power performance traits
Gene
Genetic
variation
Study
design
Result
Reference
ACVR1B
rs2854464
500
Caucasian
brothers
Crosssectional
AA individuals were 2% stronger
than G-allele carriers
(262)
ACVR2B
3 haplotype
groups
315 men
and 278
women
Crosssectional
Haplotype was associated with
knee extensor concentric peak
torque
(263)
AMPD1
c.34C>T and
c.404delT
139 men
and
women
Crosssectional
AMP deaminase deficiency is
associated with more rapid
decline in power output and
lower mean power compared to
other phenotypes
(264)
BDKRB2
-9/+9
110 COPD
patients
Crosssectional
+9/+9 carriers had higher
quadriceps isometric strength
than-9 allele carriers
(241)
COL1A1
Sp1
352
elderly
men
Crosssectional
The presence of s-allele was
associated with low grip and
biceps strength
(265)
DIO1
D1a-C/T
350
elderly
men
Crosssectional
The D1a-T allele carriers had
higher isometric grip and leg
extensor strength than D1a-C
homozygotes
(266)
MLCK
C49T and
C37885A
157
Crosssectional
Polymorphisms associated with
baseline strength, creatine and
myoglobin responses after
single bout of eccentric exercise
and with strength loss after
exercise bout
(267)
MSTN
K153R
281 (214
men)
Crosssectional
KR genotype associated with
better vertical jump results
compared to KK genotype and
authors concluded that this
variant affects ability to produce
“peak power”
(268)
MSTN
K153R
286
Crosswomen
sectional
Caucasian,
AfricanAmerican
and Asian
R153 allele of the myostatin
gene polymorphism associated
with lower strength
(269)
NR3C1
ER22/23EK
350 men
and
women
ER22/23EK associated with arm
and leg muscle strength in men.
(270)
Subjects
Crosssectional
22
2.6 GENETICS OF BODY COMPOSITION
2.6.1 General considerations
The knowledge of genetic background of obesity has moderately increased during the last
few years. In GWAS, fat mass and obesity associated (FTO) gene variants have been shown
to modify body weight and fat mass (170). Physical activity attenuates the increasing effect
of the FTO gene variant on body and fat mass (171). Despite intensive research, the function
of the FTO gene product has not been clearly defined. More than 30 other loci are believed
to associate with BMI in GWAS (271) and overall, 52 loci have been shown to associate with
obesity traits (271). Associations of SNPs with obesity include genetic variants in brainderived neurotrophic factor (BDNF), SH2B adaptor protein 1 (SH2B1) and neuregulin
(NERG1) genes, indicating that neuronal regulation may play a major role in food intake
and obesity. Monogenic heritable obesity syndromes are not the focus of the present
doctoral thesis (271). Instead this review is will focus on available GWAS investigating
association with body composition.
Genetic variants near the melanocortin-4 receptor (MC4R) can influence fat mass, weight
and obesity risk at the population level (272). In addition, genetic variants near the MC4R
have been associated with waist circumference (273). Homozygotes for the risk allele of the
FTO had a body weight approximately three kilograms heavier and an 1.67-fold risk for
obesity as compared with individuals without the risk allele (170). This finding has been
confirmed in several studies (274). Willer et al. conducted in a meta-analysis of six new
obesity-associated loci. Transmembrane protein 18 (TMEM18),
potassium channel
tetramerisation domain containing 15 (KCTD15), glucosamine-6-phosphate deaminase 2
(GNPDA2), SH2B1, mitochondrial carrier homolog (MTCH2) and NEGR1 which are all
expressed or known to act in central nervous system (CNS), which highlights the central
role of the CNS in weight regulation (275). In an Icelandic population, seven new loci
associating with BMI or body weight were found near TMEM18, NEGR1, ets variant 5
(ETV5), BDNF, fas apoptotic inhibitory molecule 2 (FAIM2), KCTD15, SH2B1, natural
cytotoxicity triggering receptor (NCR3) and SEX16 homolog B (SEC16B) genes (276). In a
case control study, signals near Niemann-Pick disease type C1 gene (NPC1), transcription
factor Maf (MAF) and phosphotriesterase related (PTER) genes were associated with severe
obesity (277). Variants near the transcription factor-activating enhancer binding protein 2
beta (TFAP2B) and methionine sulfoxide reductase A (MSRA) associated with waist
circumference and near to the lysophospholipase-like 1 (LYPLAL1) with an elevated waist
to hip ratio (278). In a large sample of Caucasian descents, a variant in neurexin 3 (NRXN3)
gene was also associated with waist circumference and BMI (279). Serologically defined
colon cancer antigen (SDCCAG8) and tankryase (TNKS)-MSRA gene variants have been
linked with early onset extreme children obesity (280). In large cohort with over 128 000
subjects, pro-opiomelanocortin (POMC), gastric inhibitory polypeptide receptor (GIPR) and
high mobility group protein HMGA1 loci were associated with BMI (281). A meta-analysis
confirmed the association of the vascular endothelial growth factor A (VEGFA), T-box 15
(TBX15), and homeo-box C 13 (HOXC13) gene variants with waist to hip ratio, this being
independent from BMI (282). In the study conducted by GIANT (Genetic Investigation of
ANthropometric Traits) consortium, 18 more loci were found to associate with BMI:
NUDT3, near LRP1B, MTIF3, near RBJ, SLC39A8, near TMEM160, QPCTL, LRRN6C, near
ZNF608, near FLJ35779, near FANCL, near PRKD1, near RPL27A, TNNI3K, MAP2K5,
CADM2, near GPRC5B, near PTBP2, TFAP2B and NRXN3 (80). The variants near IRS1 and
SPRY2 associated with type 2 diabetes and fat percent. Interestingly, the allele which
associated with lower fat percent near IRS1 was found to be associated with an increased
23
risk for type II diabetes. Further analysis revealed that variants near IRS1 did not have any
effect on visceral fat (80).
2.6.2 The LEP and LEPR genes
Leptin is an adipocine, which regulates energy intake (124), can activate haematopoiesis
(125), trigger angiogenesis (126,127) and promote hypertrophy of cardiomyocytes. There
are also reports of decreased circulating levels of leptin after a period of physical training
(131) or even after a single bout of strenuous exercise (129). Moreover, it has been suggested
that decreased levels of leptin could lead to reduced secretion of IGF-1, which is an
important mediator of skeletal muscle hypertrophy and growth (124). Leptin gene
polymorphism LEP -2548 A/G rs7799039 has been reported to associate with BMI (283). The
GG genotype was found more frequently in severely obese subjects than in normal weight
or slightly overweight subjects. Similarly, the G allele was more common in overweight
people and was associated with lower plasma leptin levels in women than the AA genotype
(284). On the other hand, AA genotype of the rs7799039 has been claimed to increase gene
expression and leptin secretion from adipose tissue (285).
In the previous studies, the Arg/Arg subjects of the LEPR SNP rs1137101 had higher fat
mass than subjects with other genotypes, and with this genotype being also associated with
obesity (286-288). In one study, the subjects were healthy men (286), whereas the cohorts
examined in two studies consisted of women and COPD patients (287,288). Thus it is not
surprising that conflicting results have reported (289,290). In a study with nearly 700
subjects, carriers of Gln allele had lower BMI and leptin levels compared to Arg
homozygotes (291).
The polymorphism Lys656Asn (rs8179183) of leptin receptor gene (LEPR) has been
reported to associate with substrate oxidation during physical activity, basal metabolic rate
(292), fatty acid oxidation (293) and abdominal and subcutaneous fat in obese women (294).
However, no associations with BMI or other body composition traits have been found
(289,295-297). In addition, the latest GWAS studies have not confirmed the earlier reported
associations between LEP and LEPR gene variants and body composition traits (80).
2.7 GENETIC MODIFICATION OF RESPONSES TO PHYSICAL TRAINING
2.7.1 General considerations
At present, there is only one published GWAS study examining the effects of 324,611 SNPs
and a 20 week training program on responses to cardiorespiratory performance in adults.
In the HERITAGE cohort, up to 47% of the gains in VO 2max were inherited (18). In the
study, 39 SNPs were associated with gains in cardiorespiratory performance, with the most
significant association being found with respect to SNP rs6552828 in the acyl-CoA synthase
long-chain member 1 (ACSL1) gene, which accounted by itself for approximately 6% of the
training response of VO2max (18). The other genes nearest to the SNPs associated with
VO2max gains were PR domain zinc finger protein 1 (PRDM1), glutamate receptor
ionotropic N-methyl-D aspartate 3A (GRIN3A), potassium inwardly-rectifying channel
subfamily J member 8 (KCNH8) and ZIC family member 4 of C2H2-type zinc finger
proteins ZIC4 (18). However, none of the findings displayed genome-wide significance.
2.7.2 The PPAR genes
Earlier in the HERITAGE cohort, individuals with CC genotype in the exon 4 + 15 of the
peroxisome proliferator activated receptor delta (PPARD) was associated with a lower
increase in oxygen consumption and maximal power output in response to 20 weeks of
training in black subjects compared to individuals with allele T (298). A similar trend was
also found in white subjects. A lower anaerobic threshold response to exercise was found in
carriers of the G allele of the PPARD gene SNP rs2267668 compared with individuals with
the AA genotype (299). In the same study, a reduced increase in the anaerobic threshold
24
was observed in individuals with the Ser482-encoding allele of the Gly482Ser
polymorphism in the PPARG, coactivator 1 alpha (PPARC1A) gene in comparison with Gly
homozygotes. In addition, additive effects for PPARD and PPARC1A SNPs on the
effectiveness of aerobic training on the increase in anaerobic threshold were reported (299).
In the CARDIA fitness study with a 20-year follow-up, genetic markers in angiotensinogen
precursor (AGT), AMDP, angiogenin (ANG) and PPARGC1A were associated with a decline
in performance in a symptom limited exercise test (300). The Ala homozygotes of the
PPARG Pro12Ala polymorphism underwent a greater decrease in body weight after a
physical training intervention than individuals with the Pro homozygotes (301). In the
control group (no training intervention), no such association was found. Pro12Pro genotype
carriers had achieved less weight loss after a 10-week training intervention than Ala12
allele carriers (302). Variants in the PPARG and PPARC1A gene may associate with gains in
aerobic performance and body composition changes after physical training. However,
further research and replication studies are needed.
2.7.3 The ACE gene
Individuals with the II genotype of the ACE I/D polymorphism displayed better anaerobic
power responses to training compared to individuals with the D allele, in a modest size
cohort of coronary artery disease patients (210). In COPD patients, genotype DD carriers
experienced less increase in maximal workload than I allele carriers after 8-week endurance
training rehabilitation program (303). In women after 6 weeks of running training,
individuals with the II genotype enjoyed better improvements in medium duration aerobic
exercises, and individuals with DD genotype had greater improvements in short duration
and high intensity exercises, as compared to individuals with DD and II genotypes
respectively (304). In the HERITAGE family study, the DD carriers tended to show greater
improvements in power phenotypes in response to endurance training than the I allele
carriers (305). In a small group of healthy men, individuals with D allele showed
significantly greater training responses to strength training than individuals with the ACE
II genotype, when this was assessed through quadriceps force measurements (306). In
American army recruits, no associations were found between the ACE genotype and pushup or sit-up results after 8 weeks of army basic training (307). After 18 months of training,
individuals with the DD genotype had higher gains in knee extension force when
compared to individuals II genotype (308). However, conflicting results have also been
published. Greater improvements were observed in I allele carriers of the ACE I/D
polymorphism in isometric strength after unilateral elbow flexion strength training
compared to individuals with the DD genotype (239) and the strength of untrained hand
increased only in I allele carriers (239). A significant association was found with ACE I/D
polymorphism and triceps and subscapular skinfolds in physically inactive young women
(309). In large cohort of aged subjects, II genotype carriers had higher body fat percentage
and intermuscular fat than the D allele carriers (310). However, an association was found
only in physically active individuals. Altogether, these studies suggest that the I allele of
the ACE I/D polymorphism associates with grater gains in aerobic performance and the D
allele with greater gains in strength performance in response to physical training. However
there are conflicting, results in the association studies conducted examining body
composition traits.
2.7.4 The CKM and ATP1A2 genes
Muscle creatine kinase is an enzyme, which catalyses the conversion of creatine to
phosphocreatine and ATP to ADP in muscle cell (311). Individuals with the AG genotype of
the muscle creatine kinase gene (CKM) NcoI polymorphism, had a higher increase in
running economy and a better adaptation in response to a 5000 meter training program
than carriers of the AA or GG genotype (312). However, it is hard to discern any reasonable
biological mechanism to explain why the heterozygote genotype would have an effect on
25
phenotype and thus this result needs to be interpreted with caution. Na+/K+-ATPase
(ATP1A2) is an integral membrane protein responsible for maintaining and establishing
electro-biochemical gradients of Na† and K† across the plasma membrane; these gradients
are essential for excitability of nerve and muscle cells (313). In the HERITAGE study, CKM
polymorphism associated with VO2max (220), and the ATP1A2 markers showed an
association with VO2max and maximal power output training responses (314).
2.7.5 The APOE and LPL genes
Apolipoprotein E is essential for the normal catabolism of triglyceride-rich lipoprotein
constituents. This protein is also involved in immunoregulation, transport of lipoproteins,
fat-soluble vitamins, cholesterol in lymph system and then to blood (315). The lipoprotein
lipase (LPL) is an enzyme that hydrolyzes triglycerides into two free fatty acids and one
monoglycerol molecule (316). The APOE polymorphism APOE 3/3 carriers had a lower
increase in VO2max after 6 months of supervised aerobic training intervention than
individuals with the other genotypes (317). However this finding was not confirmed in the
HERITAGE cohort (318). In white women, the individuals with X447 allele of LPL gene was
achieved a greater reduction in BMI, fat mass and percent body fat than women with the
S447 allele (319). In addition, association was found with visceral body fat in black women
(319).
2.7.6 The ACTN3 gene
In women, carriers of the X allele of ACTN3 R/X polymorphism had a lower change in a
relative peak power in response to 10-week unilateral knee extension strength training than
the RR genotype carriers (232). However, baseline strength measurements were higher in
XX carriers than R allele carriers (232). In a 12-week strength training intervention study
conducted with women, the X allele carriers displayed greater improvements in one
repetition maximum than the RR homozygotes (320). However, the baseline strength was
lower in the XX women than in the RR women (320). The results are conflicting and at the
moment there is no strong evidence of for a role for ACTN3 R/X in the strength responses to
training.
2.7.7 The IGF1 gene
The IGF1 gene CA repeat polymorphism was associated with one repetition maximum
improvement in response to strength training (257). An interaction was noted with PPP3R1
gene I/D polymorphism (257). The 192-allele carriers of the microsatellite in the promoter of
IGF1 had greater gains in quadriceps muscle strength after a 10-week resistance training in
men than other genotypes (258). In the HERITAGE study, the 189 bp repeat polymorphism
of IGF1 was significantly associated with FFM gains in response to a 20-week endurance
training compared with the heterozygotes and 189bp noncarriers (321). Thus there is some
evidence that IGF1 variants may associate with muscle strength improvements in response
to training.
2.7.8 The RETN gene
Resistin (RETN) is a cytokine and its physiological role may include involvement in
inflammation related insulin resistance. Associations with both obesity and type II diabetes
have been reported (322). In women, the T allele carriers of the RETN C/T polymorphism
achieved better strength training induced improvement in one repetition maximum and in
maximum voluntary contraction compared to C allele carriers (323). In men, individuals
with the CC genotype of the -420 C/G polymorphism of RETN had greater training-induced
improvement in one repetition maximum and maximum voluntary contraction than GG
genotype carriers (323). Moreover, the 540 G/A polymorphism was associated with the
improvement in one repetition maximum result after a training period, with GG carriers
26
experiencing greater gains than the AA carriers (323). The RETN gene polymorphisms may
associate with resistance exercise induced gains in muscle strength. However, the role of
resistin in human physiology is unclear and more studies are needed to confirm RETN gene
associations with strength gains.
2.7.9 The ADRA and ADRB genes
Decreases in intermuscular fat, after a 10-week single leg resistance training intervention,
were significantly different in between carriers and noncarriers of the ADRA2B Glu(9)
polymorphism (324). The Arg16Arg genotype carriers of the ADRB2 polymorphism had
greater reductions in BMI, body fat mass and body fat percentage after a 20 week
endurance training period in the HERITAGE study compared to the Gly16Gly carriers; this
was found in the white, but not in the black female subjects (325). The obese men, who were
Glu27Glu carriers of the ADRB2 polymorphism, lost greater amounts of body fat than the
obese Gln27Gln carriers in the same study (325). In obese women, the individuals with
27Glu allele of the ADRB2 polymorphism Glu/Gln displayed a lower reduction in BMI after
increasing physical activity level in comparison to the 27 Gln allele carriers (326). In a 3
month combined diet and exercise intervention study conducted in middle-aged women,
only the Trp64Trp carriers managed to reduce body weight, BMI and waist circumference
compared to individuals with other genotypes of the Trp64Arg polymorphism of ADRB3
gene polymorphism (327). The body fat changes occurring after 24-week endurance
training intervention were associated with ADRB2 and ADRB3 genes (328). The ADRB2 and
ADRB3 gene variants may associate with reduction in BMI and body fat in the response to
physical training. However, the number of replication studies is limited and further
research is needed.
27
Table 3. Associations of genetic variants with training responses in aerobic and neuromuscular
performance and body composition traits
Gene
Genetic
variation
5-HTT
L/S
428
Caucasian
triathlon
athlete
men
AMPD1
C34T
AMPD1
Subjects
Study
design
Result
Reference
Exercise
intervention
SS genotype was associated
with larger weight losses during
the Ironman Triathlon than LS
or LL genotype
(329)
503 white
men and
women
Exercise
intervention
TT genotype was associated
with higher increase in oxygen
uptake, ventilation and carbon
dioxide production at maximal
exercise than other genotypes
(330)
C34T
935 CAD
patients
Exercise
intervention
Carriers of the minor allele had
a significantly lower relative
increase in peakVO2
(331)
BDKRB2
+9/-9
428
Caucasian
triathlon
athlete
men
Exercise
intervention
+9/+9 genotype was associated
with larger weight losses during
the Ironman Triathlon than
other genotypes
(225)
COMT
Val108/158Met
173
Exercise
intervention
Met homozygotes had a smaller
decrease in percentage fat than
Val homozygotes
(332)
CYP19
Intron 4
(TTTA)n; n = 7
to 13 and a 3base pair
deletion
173
Exercise
intervention
Exercisers with two vs. no 11repeat alleles had a larger
decrease in total fat and
percentage body fat. At least
one copy of the 11-repeat allele
vs. those with neither
genotype/allele had a larger
decrease in BMI, total fat, and
percentage body fat
(332)
CNTF
A1537G
754
Caucasian
men and
women
Exercise
intervention
The GG genotype was
associated with higher increases
in absolute isometric strength in
women than other genotypes
(333)
CNTF
R23K
935 CAD
patients
Exercise
intervention
Carriers of the minor allele had
a significantly higher increase in
peakVO2 after training
(331)
ENPP1
K121Q
84 obese
Asian
women
Exercise
intervention
Women carrying Q allele had
lower decrease in BMI and body
weight after 12 week training
compared to KK homozygotes
(334)
FTO
rs9939609
218,166
adults and
19,268
children
Meta-analysis The association of the FTO risk
allele with the odds of obesity is
attenuated by 27% in physically
active adults
(171)
GNB3
825 C/T
14,716
AfricanAmericans
Crosssectional
(335)
T allele carriers had increased
risk for obesity in low physical
activity group and decreased
risk in high physical activity
group
To be
continued
28
Table 3
continues
Gene
Genetic
variation
GNB3
825 C/T
473 white
and 255
black
GR
R23K
HBB
Subjects
Study
design
Result
Reference
Exercise
intervention
In blacks, the TT genotype was
associated with a greater
training-induced decrease in fat
mass and percent body fat
(336)
935 CAD
patients
Exercise
intervention
Carriers of the minor allele had
a significantly higher increase in
peakVO2 after training
(331)
-551C/T,
intron2,+16C/G
and +340 A/T
108 Asian
Exercise
intervention
551C/C or intron2,+16C/C
genotype might explain part of
the individual variation in the
cardiorespiratory adaptation to
endurance training
(337)
HIF1A
P582S
155
Caucasian
and
AfricanAmericans
Exercise
intervention
The 582S showed association
with lower gains in VO2max
compared to other genotypes.
The association was found only
in age-groups 60 and 65 yr, but
not in 50 yr
(222)
IL15RA
Exon 7 A/C and
Exon 4 C/A
76 men 76 Exercise
women
intervention
SNPs in exon 7 and exon 4 were
strongly associated with muscle
hypertrophy
(338)
NRF2
rs12594956,
rs8031031 and
rs7181866
108 Asian
Response of VO2, was
associated with rs12594956,
rs8031031 and rs7181866.
When the three SNPs were
considered together, those
carrying the ATG haplotype had
57.5 % higher training response
in VO2 than non-carriers.
(339)
PPARA
L162V
610 young Exercise
adults
intervention
In men, resistance training
increased subcutaneous fat
volume in V-allele carriers and
decreased subcutaneous fat
volume in LL-genotype carriers
(340)
TNFA
A-308G
214 knee
osteoarthritis
patients
Exercise
intervention
The carriers of the A-allele
enhanced stair climb
performance more than GG
genotype carriers
(341)
UCP3
GAIVS6
dinucleotide
repeat
276 white
and 503
black
subjects
Exercise
intervention
GAIVS6 was associated in
whites with changes in fat mass,
percent body fat and changes in
the sum of eight skinfold
thicknesses
(342)
Exercise
intervention
29
3 Aims of the study
The main aim of the present study was to identify genetic variants contributing to
individual differences in aerobic and neuromuscular performance, body composition and
responses to physical exercise.
The following hypotheses were tested:
1. Do variants in the IL6, IL6R, LEP, LEPR, IGF1, IGFBP3 and FTO genes associate with
aerobic and neuromuscular performance and body composition
2. Do variants in the IL6, LEP and LEPR genes associate with the magnitude of changes
in aerobic performance and body composition in response to exercise intervention
3. Do variants in the IL6, IL6R, LEP, LEPR, IGF1, IGFBP3 and FTO genes associate with
plasma cytokine and hormone levels, and their responses to a single bout of aerobic
exercise and exercise intervention
4. Does aerobic fitness modify the weight-increasing effect of the obesity-associated
variant in the FTO gene
30
4 Methods
4.1 SUBJECTS AND STUDY DESIGN
All subjects were men and military conscripts or reservists. Altogether 892 subjects (N=56 in
studies I and II, N=846 in studies III and IV) were examined. In Finland, military service is
compulsory for men, and 80% of each age group undergoes this training. Thus military
conscripts and reservists form a representative sample of the young (18 to 30 yr) Finnish
population (343). Furthermore, military training takes place in a controlled environment,
which reduces the number of confounding factors.
4.1.1 Studies I and II
In these studies, 56 healthy conscript men, who were performing their regular military
training, and who volunteered to take part, were chosen as subjects. Altogether 48 subjects
performed all of the assessments. The use of any nutritional supplements was not allowed
during the 8-week study. The subjects performed physically demanding activities such as
marching and combat training, occasionally also burdened by a full combat gear of 25 kg
total weight including clothing. The study took place during winter in Finland, when
outdoor temperatures ranged from -31°C to +1°C, with average temperature of -13°C.
During the basic training season, the overall physical load of the subjects was in accordance
with the standard direction of the Finnish Defense Forces. The intensity of physical activity
in the daily program was planned to be low in the first week of the training intervention
and it then increased thereafter. Food and fluid intake were in accordance with the
standard army diet, and water intake was not restricted. The Military training included
physically demanding activities such as marching, combat training and sports-related
physical training. In addition, the training program included an overnight field exercise.
Garrison training included theoretical instruction in classroom settings, material handling,
shooting and general military education, such as close order drills. The subjects marched in
total approximately 5 km prior to meals. At the beginning of study period, the amount of
physical training was approximately two hours per day, increasing to 3 to 4 hours during
weeks 4 to 7. The intervention included four longer exercises which involved, from two to
eight hours, marching drills with battle equipment. However, even daily outdoor exercises,
including transportation, were often performed by marching. The ethical committee of the
University of Jyväskylä approved the research plan. All subjects provided an informed
consent prior to inclusion in the first two studies.
4.1.2 Studies III and IV
Studies III and IV were cross-sectional. Eight hundred forty-six healthy Finnish (Caucasian)
male volunteers, 824 of who participated in all assessments, with mean age of 25 years (SD
±5 years) were chosen as subjects. The subjects were participating in military reservist
refresher courses in Finland during year 2008. The reservists were gathered from the
nationwide compulsory military service. Thus out of the 1155 invited reservists, 922
31
participated in the courses, and 846 of them volunteered to take part in the present study.
All subjects were informed about the purpose of the study and gave written informed
consent before performing the tests which took place during military refresher programs.
The ethics committee of Central Finland Health Care District approved the research plan.
Anthropometric data and blood samples were collected after an overnight fast. Drinking
water was not restricted. The subjects ate a light breakfast 1 to 2 hours before the fitness
tests.
4.2 SELECTION OF CANDIDATE GENES AND THEIR POLYMORPHISMS
The genes and SNPs used in the analyses were selected based on their reported biological
significance in exercise physiology. Some of these genes have been previously reported to
associate with physical performance or body composition. However, in an attempt to
generate novel information, the chosen genes or SNPs had not been previously extensively
studied. In addition, SNPs in addition to those previously reported to associate with
physical performance or body composition in the same gene, were selected. Previous
results of the associations of LEP and LEPR SNPs with body composition have been
conflicting.
It has been hypothesized that IL-6 is a mediator of the anti-inflammatory effects of
exercise and thus this cytokine can induce favorable metabolic changes related to physical
training by decreasing the extent of the systemic low-grade inflammation (119). The IL6
SNP rs1800795 has been shown to associate with aerobic performance in smokers (214) and
also with power performance (344). Thus IL6 and its receptor (IL6R) were selected as the
candidate genes. The IL6 SNP rs1800795 is located in the promoter region of the IL6 and
may have an effect on gene regulation and has been associated with IL-6 levels (345). The
IL6R SNP rs4537545 tags the functional nonsynonymous Asp358Ala variant (rs8192284) in
the IL6R gene and this variant has also been claimed to increase circulating IL-6 receptor
levels (346).
The LEP and LEPR were selected due to their role in the regulation of body composition
and energy homeostasis. In addition, leptin can activate haematopoiesis (125), angiogenesis
(126,127) and hypertrophy of cardiomyocytes. There are also reports of decreased
circulating levels of leptin after a period of physical training (131) or even after a single
bout of strenuous exercise (129). Moreover, it has been postulated that decreased levels of
leptin could lead to decreased secretion of IGF-1, which is an important mediator of skeletal
muscle hypertrophy and growth (124). The LEP and LEPR SNPs have been associated with
body composition (283,286) but not with physical performance. The LEP SNP rs7799039 is
located in the promoter region of the LEP and may thus have an effect on gene regulation.
The AA genotype of the rs7799039 has been reported to increase gene expression and leptin
secretion from adipose tissue (285). The LEPR rs1137101 is located in the fifth exon (coding
region) of the LEPR and it leads to a functional (missense) change from Gln to Arg in the
protein. The LEPR SNP rs8179183 is located in the 11th exon of the LEPR and causes a
functional (missense) change from Lys to Asn in the protein. Since these SNPs ewoke
structural changes in protein product, they could well be linked with a solid biological
mechanism to confirm the positive finding.
32
IGF1 and IGF1BP3 were selected due to the role of the anabolic, neurotrophic and
neuroprotective effects of IGF-1. IGF-1 has hypertrophic effects in autocrine and paracrine
manner and it has many anabolic pathways (138). IGF-1 is bound to IGF-1 binding proteins
in plasma (347). Furthermore, increased levels of IGFBP-3 may decrease the proportion of
active IGF-1 in blood (348). Other SNPs in IGF1 gene have been found to associate with
strength gains in response to resistance exercise (257) and exercise performance in postmenopausal women (213). The IGF1 SNP rs7136446 is located in the intron of the IGF1. The
IGF1 haplotype labeled as “TCC” was associated with prostate cancer risk and circulating
IGF-1 levels (349). The rs7136446 and the rs6220 tag the “TCC” haplotype (349). The rs6220
is situated in the 3’ untranslated region of the IGF1. The IGFBP3 rs2854744 is located in the
promoter region of the IGFBP3 gene and it has been reported to associate with circulating
IGFBP-3 levels and IGF-1 activity (350). The variation in the 3’ UTR region may increase or
decrease the half-life of messenger RNA, leading to increased or decreased protein levels
(351). The intronic region of the gene may also encode themselves specific proteins or
modify gene product by alternative splicing. SNPs in the intronic regions may be linked to
SNPs in exonic regions responsible for structural changes in protein product. The majority
of the SNPs associated with diseases and traits are located in the intronic region (351).
The role and function of the FTO gene product in biology are not known precisely.
However, GWAS have identified the FTO gene as the first susceptibility locus for common
obesity (170,274). In a recent meta-analysis, physical activity was confirmed to attenuate the
weight increasing effect of the risk allele of the rs8050136 (171). However, knowledge on the
associations of FTO variants with physical performance and their potential interactions on
body composition are limited. It was intended to clarify whether FTO variants associate
with physical performance and whether there is a genotype – aerobic fitness interaction
affecting body composition. The rs8050136 is located in the intronic region of the FTO, and
it is in linkage disequilibrium with the commonly studied rs9939609. The positive
associations of rs8050136 according to body can be interpreted with confidence because the
role of rs9939609 in predisposition to obesity has been confirmed (170,274).
4.3 GENOTYPING
The SNP analysis was performed by using allele-specific polymerase chain reaction assays
(PCR). No-template controls and positive controls were used in every run for genotyping,
and all samples were run in duplicate. Briefly, genomic DNA was first isolated from the
peripheral blood mononuclear cells using QIAamp DNA Blood kit (Qiagen, Hilden,
Germany). Next, 50 ng of the DNA was amplified with Brilliant QPCR Master Mix
(Stratagene, La Jolla, CA) and allele specific SNP assays on a Mx3000P Real-time PCR
System (Stratagene). For IGF1 (rs6220), IL6R (rs4537545), FTO (rs8050136) and LEPR SNP
Lys656Asn (rs8179183), the commercially available TaqMan SNP assays were used
(Applied Biosystems, Foster City, CA), and for IGF1 (rs7136446), IGFBP3 (rs2854744), and
IL6 (rs1800795), LEP (rs7799039) and LEPR (rs1137101), self developed molecular beacons
assays were used (table 4).
33
Table 4. IGF1 (rs7136446), IGFBP3 (rs2854744), and IL6 (rs1800795), LEP (rs7799039) and
LEPR (rs1137101) beacons assays
Fluorogenic
beacons
SNP
Forward primer
Reverse primer
IGF1 (rs7136446)
5’AATTGGTTACCTGCTACATTG
A-3’
5’5’(FAM/HEX)GAGTTAACGCATCTCCTTACT CGCTCGCTGCCCTAAG
G-3’
TGC(T/C)GCGTAGTCG
AGCG-BHQ1-3’
IGFBP3 (rs2854744)
5’CACCTTGGTTCTTGTAGACGA
-3’
5’-CGTGCAGCTCGAGACTC3’
5’- (FAM/HEX)CCTCGCGTG(C/A)GCA
CGAGGAGCACGAGGBHQ1-3’
IL6 (rs1800795)
5’AAGAGTGGTTCTCGTTCTTAC
G-3’
5’GTGAGGGTGGGCGCAGAG3’
5’- (FAM/HEX)CCGGATCAGTTGTGTC
TTCG
(C/G)ATCGTAAAGGAC
GATCCGG-BHQ1-3’
LEP (rs7799039)
5’CCTGTAATTTTCCCATGAGAA
C-3’
5’TGCAACATCTCAGCACTTAG3’
5’-FAM/HEXCGTGCCCGACAGGGTT
GC(G/A)CTGATCGGCA
CG -BHQ1-3’
LEPR (rs1137101)
5’TCAACGACACTCTCCTTATG3’
5’TTATGGGCTGAACTGACAT3’
5’-FAM-CGG
ACGTGGAGTAATTTTC
CAGTCACCTCCGTCCG
-BHQ1-3’, and Allele
G (Arg) Beacon 5’HEXCGGGACTGGAGTA
ATTTTCCGGTCACCTC
GTCCCG-BHQ1-3’
All SNPs were called after the PCR program consisting of 10 min polymerase activation at
95 C°, followed by 40 PCR cycles of 30 s template denaturation at 95 C°, 25 s annealing at 55
C° for primers and molecular beacons, and 30 s polymerase extension at 72 C°.
Fluorescence signals were measured at the end of the annealing step. Genotyping call rates
were >99% with duplicate concordance rates of >99.5%. All studied SNPs conformed to
Hardy-Weinberg’s equilibrium and genotype frequencies were validated against known
Utah residents (CEPH) with Northern and Western European ancestry (CEU) population.
34
Figure 3. Schematic figure showing locations of studied SNPs in FTO, LEP and LEPR genes.
Untranslated regions are marked with black boxes and exons with white boxes.
Figure 4. Schematic figure showing the locations of the studied SNPs in IGF1, IGFBP3, IL6 and
IL6R genes. In addition, the location of CA –tandem repeat is shown in the IGF1 gene.
Untranslated regions are marked with black boxes and exons with white boxes.
35
4.4 ASSESSMENTS OF CARDIORESPIRATORY PERFORMANCE
4.4.1 Treadmill test
In studies I and II, the following protocol was used for the assessment of aerobic
performance. The conscripts performed a maximal treadmill test at week 1 (before
training), and after 5 and 8 weeks to determine peak VO2 consumption (mL·kg-1·min-1). The
start of the test involved walking for 3 minutes at 4.6 km·h-1 and walking/jogging at 6.3
km·h1 (1% slope) as a warm-up. Thereafter, exercise intensity was increased every 3
minutes to induce an increase of 6 mL·kg-1·min-1 in the theoretical VO2 peak demand of
running. This was achieved by increasing the initial running speed of 4.6 km·h-1 by a mean
of 1.2 km·h-1 (range 0.6 to 1.4 km·h-1), and by increasing the initial grade of 18 by a mean of
0.58 (range 0.0–1.08) up to the point of exhaustion (352). Pulmonary ventilation and
respiratory gas exchange data were measured on-line using the breath-by-breath method
(Jaeger Oxygen Pro, VIASYS Healthcare GmbH, Hoechberg, Germany), and mean values
were calculated at 1-min intervals for statistical analysis. The analyzer was calibrated before
each test according to the specifications of the manufacturer. Heart rate was continuously
recorded at 5 s intervals using a telemetric system (Polar810i, Polar Electro Oy, Kempele,
Finland). Rating of perceived exertion was assessed at the end of each workload. The
following criteria used for determining VO2 peak were: a lack of increase in peak VO2 and
heart rate despite an increase in grade and/or speed of the treadmill, a respiratory exchange
ratio higher than 1.1, and a post-exercise blood lactate value (determined 1 min after
exercise completion, not during the test, from a fingertip blood sample using a lactate
analyzer; LactatePro, Arkray, Japan) that was higher than 8 mmol·L-1 (352). All participants
fulfilled these criteria. According to the manufacturers’ manuals, technical errors of
VO2max and HR determinations are reported to vary between 1 to 2 %. Repeatability of
VO2max and HRmax in an exercise test among athletes has reported to be 4% and 5%,
respectively (353).
4.4.2 Bicycle ergometer test
In studies III and IV the following protocol was applied for the assessment of aerobic
performance. Aerobic performance was measured by a maximal bicycle ergometer test
(Ergoline 800S, Ergoselect 100K, Ergoselect 200K, Bitz, Germany) as previously described
(343). The test was performed 30 minutes after assessment of neuromuscular performance.
The protocol involved increasing the workload until exhaustion. The first load was 50W
with 25W increase in 2-minute intervals. HR was monitored throughout the test (Polar T31; Polar Vantage, Kempele, Finland). The analyzed variables maximum HR, maximal
workload, and maximal oxygen consumption (mL·kg -1·min-1) were calculated by computer
software (Milfit4/ Fitware, Helsinki, Finland) [(11.016 × maximum workload) × (body
weight-1) + 7.0]. The test was terminated when the subject was not able not maintain the
required cycling speed at pedaling level of 60 to 90 rpm.
4.5 ASSESSMENTS OF NEUROMUSCULAR PERFORMANCE
In studies III and IV, the following protocol was used for the assessment of neuromuscular
performance. Maximal isometric force of the bilateral leg extensors was measured by a
strain-gauge dynamometer developed in the University of Jyväskylä, Department of
36
Biology of Physical Activity. This method has been described earlier in more detail (354),
and its reproducibility has been reported to be high (r=0.98, coefficient of variation CV%
=4.1%) (355). The force tests were performed in the morning before the aerobic performance
test. The test was performed in a sitting position with a knee angle of 107°. The subjects
were instructed to exert maximal force as quickly as possible and to maintain the force for 3
seconds. Data were analyzed with a 16-bit AD converter (CED power 1401; Cambridge
Electronic Design Ltd., Cambridge, United Kingdom) and a Signal (2.16) program. A
minimum of three trials was completed for each subject, and the best performance about
maximal force was selected for the subsequent statistical analysis. The tested muscle groups
were quadriceps femoris (knee extensor), hamstrings, and gluteus muscles (hip extensors).
4.6 ASSESSMENTS OF BODY COMPOSITION
4.6.1 Anthropometric measurements
In all studies, body height, weight, and waist circumference were recorded, and values of
body mass index (BMI) were calculated. Body mass was recorded by a precision scale
(Sartorius F150S-D2, Goettingen, Germany). Body mass index (BMI, kg·m-2) was calculated
as body mass divided by the body height squared.
4.6.2 Skinfolds
In studies I and II, body fat percentage was assessed with skinfold measurement. Body fat
percentage (FAT%) was estimated from the thickness of four skinfolds (triceps, biceps,
subscapular, and supraspinale) using skinfold calipers (Model 98.610, Holtain Ltd., Dyfed,
Wales, UK) to the nearest 2 mm. All measurements were performed in duplicate by the
same anthropometrist and the mean values were used. FAT% was estimated by using the
Equation of Durnin and Rahaman (356). FFM was calculated using the following formula:
body mass × (100 FAT%) × 100-1. Body height was measured to the nearest 0.5 cm using a
wall-mounted stadiometer.
4.6.3 Bioimpedance
In studies III and IV, body fat percentage was assessed with bioimpedance method. FAT%
and lean mass of legs were recorded using an eight-polar bioimpedance method with
multifrequency current (InBody 720; Biospace Company, Seoul, Korea). Bioimpedance was
recorded after an overnight fast and at least 1 day after any intensive physical activity. Lean
body mass was calculated by subtracting body fat mass from total body mass.
4.6.4 Energy intake
In studies I and II, the following protocol was applied to estimate possible variations in
energy intake during the study. The subjects kept pre-filled dietary records for 3-4 days in
four phases. In this way, habitual food intake was monitored for 15 days. The pre-filled
dietary records provided detailed information regarding the food ingested. Any questions,
ambiguities or omissions were individually resolved and controlled via personal
interviews, where under-eating or mis-recordings were questioned. Nutrient consumption
was calculated using Nutrica® software (version 3.11, The Social Insurance Institution of
Finland, Helsinki, Finland). In addition, energy expenditure was measured by the doubly
labeled water method during last two weeks of 8 the week study period.
37
4.7 BIOCHEMICAL MEASUREMENTS
In studies I and II, plasma and whole blood samples were stored at -80°C until analysis.
Plasma levels of IL-6, TNF-α and IL-1β were measured by immunoassay (ELISA) according
to manufacturer’s protocol (Sanquin Reagents, Amsterdam, The Netherlands). Assay
specifications for IL-6, TNF-α, IL-1β were as follows: sensitivity limits; 0.4 pg/ml, 1.4 pg/ml
and 1.5 pg/ml, respectively, intra-assay CV% were 5.6%, 7.2% and 5.3%, respectively, and
those of inter-assay were 8.4%, 7.2% and 9.7%, respectively. Creatine kinase (CK) was
measured using Vitros CK DT Slides and a Vitros DT60 Analyzer (Ortho-Clinical
Diagnostics, Inc., Rochester, NY, USA). At high and low concentrations, the intra-assay
CV% were 3.1% and 1.7%, respectively, and inter-assay CV% were 6.1% and 3.0%,
respectively. The leptin levels were analyzed with commercial ELISA kit (R&D Systems,
Minneapolis, MN, USA). The assay specifications for plasma leptin were as follows:
sensitivity limit was 7.8 pg·mL-1, maximum intra- and interassay CV% was 3.3% and 5.4%,
respectively. The percentage change in plasma volume (%ΔPV) was calculated from
changes in hemoglobin and hematocrit according to the method of Dill and Costill (357). In
order to adjust for hemoconcentration, IL-6, TNF-α, IL-1β and CK post-exercise values were
normalized as follows: Post-EXad = Post-EX + (Post-EX*%ΔPV ·100-1).
In studies III and IV, the following biochemical measurements were applied. The fasting
blood samples were collected and analyzed immediately with a hemacytometer (Sysmex
Co., Kobe, Japan). Plasma was separated from the whole blood and stored at -80C° until
analysis. Plasma IL-6 concentrations were assayed with a commercial high-sensitivity
ELISA kit according to the manufacturer’s instructions (Quantikine HS; R&D Systems,
Minneapolis, MN, USA). The assay specifications for IL-6 were as follows: sensitivity limit
0.16 pg·mL-1, maximum intra-assay and inter-assay coefficient of variation 5.9% and 9.8%,
respectively. Serum IGF-1 concentrations were measured with Immulite 100 system
(Siemens Healthcare Diagnostics Products Ltd., Gwynedd, UK) with assay sensitivity of 2.6
mmoI·L-1, and the intra-assay and inter-assay variances were both under 5%. Plasma leptin
levels were assayed by commercial ELISA test according to the manufacturer’s instructions
(Quantikine, R&D Systems, Minneapolis, MN, USA). Assay specifications for plasma leptin
were as follows: sensitivity limit 7.8 pg·mL-1, maximum intra- and inter-assay CV% 3.3%
and 5.4%, respectively.
4.7.1 Sub-maximal exercise test
In studies I and II, the responses of biochemical markers to sub-maximal exercise was
studied with the following protocol. Blood was sampled at rest and immediately after a 45
min sub-maximal marching test performed on an indoor track at the level of 70% of each
subject’s maximal workload at week 2, 4, and 7 of the 8-week study period.
4.8 ASSESSMENT OF PHYSICAL ACTIVITY
In studies III and IV, the International Physical Activity Questionnaire (IPAQ) (358) was
applied to determine subjects’ physical activity prior to entering military training and
38
military refresher programs. An additional questionnaire was used for the determination of
participant’s physical strenuousness of work, subjective health and smoking habits.
4.9 STATISTICAL METHODS
The calculations were performed using SPSS software (SPSS Inc, Chicago, IL, USA). In
studies I and II, genotype frequencies were analyzed by chi-square with Yates’ correction
between the different groups. Analysis of variance (ANOVA) was used to compare the
effect of IL6, LEP and LEPR SNPs on continuous variables. Interactions between exercise,
training, and genotype were examined using repeated measures ANOVA. Bonferroni
adjustment was used when more than two groups were analyzed simultaneously. Leptin
values were Log-transformed to achieve normality. Correlations were analyzed with
Pearson’s product moment method. Statistical significance was set at p<0.05. Data are
presented as mean values with 25-75% percentile range.
In study III, one-way ANOVA, Analysis of covariance (ANCOVA), linear regression or
nonparametric statistics (Kruskal-Wallis test) were used to compare the effect of IGF1,
IGFBP3, IL6 and IL6R SNPs on continuous variables, when appropriate. Deviation from
Hardy–Weinberg equilibrium was tested by X2 statistics. Linkage disequilibrium of the
IGF1 SNPs was evaluated by using the MIDAS software package (359). In ANOVA
analysis, genotype was set as a discrete variable and the other variables were handled as
continuous. In linear regression analysis, also genotype was set as a continuous variable. In
ANCOVA and linear regression age, smoking and earlier physical activity were used as
covariates. Smoking (smoker or non-smoker) and physical activity (vigorous physical
activity more than 3 times per week) were categorized as dichotomous variables, whereas
age, VO2max, BMI, and waist circumference were considered as continuous variables.
Logistic regression was used to evaluate the genetic variation–related odds for increased
maximal extension force of the lower limbs and body fat percent. A recessive model was
applied in the analysis (minor allele homozygotes vs. major allele carriers). Age, smoking
(smoker or non-smoker) and physical activity (vigorous physical activity at least 3 times per
week) were used as covariates. The subjects were divided into quartiles according to
maximal isometric extension force of the lower limbs and body fat percent. The genotype
related odds for high maximal extension force and fat percent were analyzed as follows.
The subjects in the highest 25% quartile according to fat percent and maximal isometric
extension force of legs were compared to the subjects under 75% percentile according to fat
percent and maximal isometric extension force of legs to analyze odds for increased muscle
force or fat percent respectively. The genotype related risk for low maximal extension force
and fat percent were analyzed as follows; the subjects in the lowest 25% quartile according
to fat percent and maximal isometric extension force of legs were compared to remaining
75% of the subjects according to fat percent and maximal isometric extension force of legs to
analyze odds for decreased muscle force or fat percent. The 75% percentile limit for
maximal isometric extension force of the lower limbs was 3372 N and the percentile limit
for 25% percentile was 2371 N. The 75% percentile limit for body fat percent was 22.1% and
the percentile limit for 25% percentile was 12.6%. Statistical power and sample sizes were
estimated with a SISA Web calculator (360). A 90% level was chosen in the power
39
calculations, the alpha was 0.05, and this permitted an estimation of the number of subjects
needed to reveal a 5% difference in continuous parameters. Data are presented as mean ±
standard deviation unless otherwise stated. Statistical significance was set at p<0.05.
Bonferroni correction was applied in the multiple post-hoc tests.
In study IV, the calculations were performed with SPSS software (Illinois, Chicago, USA)
by using one-way ANCOVA or non-parametric statistic tests (Kruskal-Wallis test), when
appropriate. Age, smoking and earlier physical activity were introduced as covariates.
Smoking (smoker or non-smoker) and physical activity (vigorous physical activity more
than 3 times per week) were set as dichotomous variables, whereas age, VO2max, BMI, and
waist circumference were designated as continuous variables. Genotype-VO2max
interaction effects on BMI, waist circumference and fat percent were analyzed by using the
general linear model. The general linear model included the main effects terms for earlier
physical activity, smoking, age, genotype, VO2max and genotype-VO2max interaction.
Physical activity and smoking were set as dichotomous variables, and VO2max, age and
body composition traits were designated as continuous variables. Genotype was set as a
discrete variable in every analysis. Additionally, the FTO genotype and FTO genotypeVO2max interaction related odds for overweight (BMI more than 25 kg·m-2) and abdominal
obesity (waist circumference greater than 90 cm) were analyzed by logistic regression
models. A recessive model was applied in the analysis (minor allele homozygotes vs. major
allele carriers). Age, smoking and physical activity were used as covariates. The subjects
were divided into quartiles according to VO2max. The 75% percentile limits for VO2max
were 3.7 L·min-1 and 46.9 mL·kg-1·min-1. The subjects in the highest 25% quartile were
defined as the high aerobic fitness group and compared with the remaining 75% of subjects
(VO2max <3.7 L·min-1 or <46.9 mL·kg-1·min-1). Data are presented as mean ± standard
deviation unless otherwise stated. Statistical significance was set at p<0.0125 to account for
multiple testing. Bonferroni correction was applied for multiple post-hoc comparisons.
Statistical power and sample sizes were estimated with SISA web calculator (360). A 90%
level was chosen in the power calculations, the alpha was 0.05, and this permitted an
estimation of the number of subjects needed to reveal a 5% difference in continuous
parameters.
40
5 Results
5.1 DESCRIPTIVE DATA (STUDIES I, II, III AND IV)
In studies I and II, the subjects were young healthy men with normal body weight and
average cardiorespiratory performance (table 5). In studies III and IV, the subjects were
young men with normal body weight and average cardiorespiratory and neuromuscular
performance (table 6).
Table 5. Demographic data of 56 men in studies I and II at week 1 (pre-training) and week 8
(post-training). The post-training leptin and IL-6 concentrations were measured at week 7.
Mean ± SD
Mean ± SD
Pre Training
Post Training
Age (yr)
19 ± 0.2
Body weight (kg)
79 ± 17
Body height (cm)
p-value
75 ± 11
<0.001
178 ± 7
-2
Body mass index (kg·m )
25 ± 4
24 ± 3
<0.001
Percent body fat (%)
17 ± 6
16 ± 4
<0.001
88 ± 12
84 ± 7
<0.001
Peak maximal oxygen uptake (mL·kg ·min )
43 ± 7
48 ± 5
<0.001
Peak maximal oxygen uptake (L·min-1)
3.3 ± 0.6
3.6 ± 0.5
<0.001
Plasma leptin concentration (ng·mL )
7272 ± 10782
4151 ± 5703
<0.001
Plasma IL-6 concentration (pg·mL-1)
4.4 ± 3.2
4.8 ± 4.0
0.394
Waist circumference (cm)
-1
-1
-1
Table 6. Demographic data of 824 men in studies III and IV.
Mean ± SD
Age (yr)
25 ± 4
Body weight (kg)
80 ± 13
Body height (cm)
180 ± 6
-2
Body mass index (kg·m )
25 ± 4
Percent body fat (%)
19 ± 8
Waist circumference (cm)
86 ± 10
-1
-1
Peak maximal oxygen uptake (mL·kg ·min )
42 ± 8
Peak maximal oxygen uptake (L·min-1)
3.3 ± 0.6
-1
Plasma IL-6 concentration (pg·mL )
1.1 ± 1.2
Serum IGF-1 concentration (mmol·mL-1)
31.1 ± 7.6
Plasma leptin concentration (pg·mL-1)
3755 ± 3948
Maximal force of leg extensors (N)
2939 ± 876
41
Genotype frequencies in the study population in the studies I and II are shown in table 7,
and genotype frequencies in the studies III and IV are shown in table 8.
Table 7. Genotype frequencies in studies I and II
SNP
Genotype
frequencies
IL6 rs1800795
GG 22% (n=12)
CG 54% (n=29)
CC 24% (n=13)
LEP rs7799039
GG 25% (n=12)
GA 60% (n=29)
AA 15% (n=7)
LEPR rs1137101
AA 19% (n=9)
AG 33% (n=16)
GG 48% (n=23)
Table 8. Genotype frequencies in studies III and IV
SNP
Genotype frequencies
IL6 rs1800795
GG 22% (n=181)
CG 50% (n=414)
CC 28% (n=232)
LEP rs7799039
GG 25% (n=176)
GA 54% (n=384)
AA 21% (n=153)
LEPR rs1137101
AA 18% (n=125)
AG 49% (n=350)
GG 33% (n=238)
IL6R rs4537545
TT 9% (n=75)
CT 45% (n=370)
CC 46% (n=384)
IGF1 rs6220
GG 12% (n=103)
GA 47% (n=390)
AA 41% (n=336)
IGF1 rs7136446
TT 45% (n=376)
TC 45% (n=370)
CC 10% (n=83)
IGFBP3 rs2854744
AA 15% (n=128)
CA 51% (n=427)
CC 34% (n=278)
FTO rs8050136
AA 35% (n=269)
CA 49% (n=380)
CC 16% (n=124)
LEPR rs8179183
Lys/Lys 77% (n=549)
Lys/Asn 21% (n=152)
Asn/Asn 2% (n=12)
42
5.2 IL6 AND IL6R GENES (STUDIES I AND III)
In study I, VO2max values were calculated as mL·kg-1·min-1 in relation to total body mass
and fat-free mass. The results were comparable and the thus traditional VO2max values in
relation to total body mass results were utilized. Furthermore, L·min-1 values were
analyzed. At baseline, the VO2max values were different between genotype groups of IL6
rs1800795 with the highest values in subjects with genotype CC and the lowest with
genotype CG (p=0.05, p=0.037 for main effect). VO2max values increased in all genotype
groups during the study period. However, only G allele carriers improved their VO2max
significantly (figure 5). After 8 weeks of training, subjects with genotype CG made the
greatest gains in their VO2max (p<0.001), followed by subjects with genotype GG (p=0.02),
and the smallest improvements were observed in subjects with genotype CC (p=0.17).
When VO2max was defined in absolute values (L·min-1), the rank-order for gains was as
follows: genotype CG (p<0.001), genotype GG (p=0.015), and genotype CC (p = 0.19) (figure
5). In study III, IL6 rs1800795 or IL6R rs4537545 genotypes did not associate with VO2max in
the sedentary state. In study III, IL6 rs1800795 or IL6R rs4537545 genotypes did not
associate with maximal force of leg extensors.
Figure 5. Effect of IL6 rs1800795 on VO2max during eight weeks of training (repeated measures
ANOVA). Values are means. Difference to baseline (Week 1): *p<0.05, ***p<0.001; difference
between the genotypes CC and CG at baseline: #p=0.05. p=0.016 for genotype-exercise
interaction.
43
In study III, TT carriers of the IL6R SNP rs4537545 had higher plasma IL-6 levels compared
to the genotype CC (p<0.01) and CT (p<0.05) carriers (p=0.009 for the main effect) (figure 6).
The result was also significant (p=0.002 for main effect) after adjustments with covariates
(p=0.01 between CC and TT, and p=0.024 between TT and TC). In the linear regression
model IL6R SNP rs4537545 associated with BMI (p=0.015) after adjustments with covariates.
The BMI values and SDs were 24.6 ± 3.4 kg·m-2, 25.0 ± 4.1 kg·m-2 and 25.6 ± 4.3 kg·m-2 for the
CC, CT and TT genotype carriers, respectively. The IL6 SNP rs1800795 did not associate
with plasma IL-6 levels in the sedentary state in studies I and III. However, in study I,
plasma IL-6 levels increased in the CG carriers at weeks 1 and 7 in response to sub-maximal
exercise (p=0.037 and p=0.042, respectively). In study III, the CG carriers of the IL6
rs1800795 had higher IGF-1 levels than the CC carriers, however the main effect was
insignificant (p=0.078).
In study I, only subjects with the genotype CG of the IL6 rs1800795 significantly reduced
their BMI during the exercise intervention (p=0.02 from week 1 to week 5, p <0.001 from
week 1 to week 8 and p < 0.001 from week 5 to week 8). In study III, IL6 SNP rs1800795 did
not associate with BMI, body mass, lean mass or lean mass of legs.
There were no significant differences in physical activity assessed by the IPAQ
questionnaire or physical strenuousness of work, subjective health status, and smoking
habits between genotype groups before the military training or military refresher
programmes. In addition, no major differences were found in the dietary habits of the
subjects.
Figure 6. Box-plot showing the effect of IL6R rs4537545 genotype on plasma IL-6 concentration
in young healthy men in the sedentary state with median values and 95% CI shown (N=824).
p=0.009 for the main effect, *p<0.05 and **p<0.01 (ANOVA).
44
5.3 IGF1 AND IGFBP3 GENES (STUDY III)
In study III, the CC homozygotes of the IGF1 SNP rs7136446 showed higher maximal force
production (p=0.042 for the main effect) compared to the other genotypes in ANOVA. The
difference was significant between the genotypes CC and CT (Bonferroni corrected
p=0.045), and the result was borderline significant between the genotypes CC and TT
(Bonferroni-corrected p=0.056) (figure 7). After adjustments for the covariates, this result
was borderline significant (p=0.057) for main effect. Carriers of the genotype GA of IGF1
SNP rs6220 had significantly higher maximal force production compared with AA
homozygotes (p=0.047). However, the result for the main effect was insignificant (p=0.136)
and in logistic regression no such association was found. No associations with aerobic
performance were found.
Figure 7. Effect of IGF1 SNP rs7136446 on maximal force production. The CC homozygotes had
higher force production compared to the other genotypes. Data are box plots with median
values and 95% CI shown. *p=0.045.
45
The CC homozygotes of IGF1 SNP rs7136446 showed higher body fat values compared
to other genotypes (p=0.035 for the main effect). The difference was significant between the
CC and CT genotypes (Bonferroni-corrected p=0.03) (figure 8). The result was insignificant
after adjustment to covariates. In addition, the CC homozygotes had higher BMI values
compared with the other genotypes. However, the result was insignificant for the main
effect. In logistic regression analysis, T allele decreased the odds for maximal force
production >3372N (OR 0.6, 95% CI 0.36-0.98, p=0.041) and body fat >22.1% (OR 0.61, 95%
CI 0.37-0.99, p=0.047), but no differences in lean mass were found in ANOVA or logistic
regression. The C allele of IGFBP3 SNP rs2854744 decreased the odds for body fat percent
<12.6% (OR 0.69, 95% CI 0.49-0.97, p=0.033). None of the studied SNPs associated with lean
body mass.
Figure 8. Effect of IGF1 SNP rs7136446 on body fat percent. CC homozygotes had higher body
fat compared to the other genotypes. Data are box plots with median values and 95% CI
shown. *p=0.03.
Plasma IL-6 levels were significantly lower in AA homozygotes of the IGFBP3 SNP
rs2854744 compared with CC homozygotes (p=0.05). However, the result for the main effect
was insignificant (p=0.092). In addition, AA homozygotes of the IGF1 SNP rs6220 had lower
levels of serum IGF-1 compared with genotype GA carriers. Again, the result was
insignificant for the main effect (p=0.130).
There were no significant differences in the physical activity assessed by IPAQ
questionnaire or physical strenuousness of work, subjective health status, and smoking
46
habits between genotype groups before the military training or military refreshers. In
addition, no major differences were found in dietary habits of the subjects.
5.4 LEP, LEPR AND FTO GENES (STUDIES II AND IV)
In study II, the subjects with the genotype AA of the LEP SNP rs7799039 had the lowest
VO2peak values (mL·kg-1·min-1) when compared to the genotype GA or GG at weeks 5 and
8 (p<0.05). However, when VO2peak was expressed as absolute values (L·min-1), no
statistically significant differences were found. Nevertheless, VO 2peak increased
significantly in all subjects during the 8-week study. In study IV, FTO SNP rs8050136, LEPR
SNP rs1137101 or rs8179183, and LEP SNP rs7799039 did not associate with aerobic or
neuromuscular performance at sedentary state.
In study II, the AA carriers of LEP SNP rs7799039 had higher BMI values than the GG
(p<0.01) and the GA (p<0.01) carriers at sedentary state, at week 5 and week 8 (p=0.002 for
main effect). All subjects reduced their BMI significantly in response to physical training by
week 8. In terms of changes in body composition, the G allele carriers reduced their fat
mass significantly by week 5 (p=0.039), but did not occur in the subjects with the genotype
AA (p=0.076, p=0.034 for genotype-exercise interaction). Similarly, at baseline the fat
percentage was the highest in the AA subjects (21.1%), and the lowest in the GG subjects
(16.4%). This difference was significant (p=0.033, p=0.009 for main effect) (figure 9). There
were no significant differences in BMI between LEPR SNP rs1137101 genotypes. The BMI
values were significantly decreased from the sedentary state in the genotype group
Arg/Arg from week 1 to 8 (p<0.001). However, in the Gln/Arg subjects, the mean BMI loss
was insignificant, whereas in the Gln/Gln subjects the mean value decreased significantly
(p=0.04). There were no significant differences in body fat percent between LEPR SNP
rs1137101 genotypes. However, only the Arg/Arg carriers decreased their percent body fat
significantly by week 8 (p=0.017) (figure 10). The result was not significant for genotypeexercise interaction.
In study IV, in the ANCOVA analysis, the FTO SNP rs8050136 associated with BMI and
waist circumference. The AA carriers had significantly higher BMI (p=0.007 for main effect
and p=0.005 between CC and AA) and the waist circumference (p=0.012 for main effect and
p=0.012 between CC and AA) compared to the CC carriers. In the general linear model, the
main effects of relative and absolute VO2max on BMI and waist circumference were
significant (p<0.001). The association with relative VO2max was inverted and the
association with absolute VO2max was direct. However, no significant FTO genotyperelative VO2max (mL·kg-1·min-1) interaction on BMI (p=0.081) or waist circumference
(p=0.093) was found. In addition, the result was insignificant when the FTO genotypeabsolute VO2max (L·min-1) interaction on BMI (p=0.937) or waist circumference (p=0.262)
was analyzed. In the logistic regression, the AA genotype increased odds for waist
circumference over 90 cm. However, AA genotype-high VO2max interaction was
insignificant. No association was found with aerobic fitness (VO2max expressed as L·min-1
or mL·kg-1·min-1).
47
Figure 9. (a) Effect of LEP rs7799039 on BMI during 8 weeks of training. (b) Effect of LEP
rs7799039 on fat per cent (FAT%) during 8 weeks of training. Mean values are shown.
Repeated-measures ANOVA: * compared to week 1 of the same genotype, *p<0.05, **p<0.01,
***p<0.001; ¤between weeks 5 and 8, ¤p<0.05, ¤¤p<0.01, ¤¤¤p<0.001; #between
genotypes GG and AA at weeks 1, 5, and 8, #p<0.05,##p<0.01, ###p<0.001; § between
genotypes GA and AA at weeks 1, 5, and 8, §p<0.05, §§p<0.01, §§§p<0.001; non-significant
between the genotypes GA and GG.
Figure 10. (a) Effect of LEPR rs1137101 on BMI during 8 weeks of training. (b) Effect of LEPR
rs1137101 on fat per cent (FAT%) during 8 weeks of training. Mean values are shown.
Repeated-measures ANOVA: *compared to week 1 in genotype group Arg/Arg, *p<0.05,
**p<0.01, ***p<0.001; $compared to week 1 in genotype group Arg/Gln, $p<0.05, $$p<0.01,
$$$p<0.001; £compared to week 1 in genotype group Gln/Gln, £p<0.05, ££p<0.01,
£££p<0.001; ¤between weeks 5 and 8 in genotype group Arg/Arg, ¤p<0.05, ¤¤p<0.01,
¤¤¤p<0.001; non-significant between the genotypes.
48
In study IV, in ANCOVA analysis, the LEP SNP rs7799039 did not associate with any
variable. No interactions were observed between aerobic fitness and genotype. LEPR SNP
rs1137101 did not associate with BMI, body fat, leptin levels or physical performance in
ANCOVA, and no aerobic performance-genotype interactions were observed in the general
linear model. In the ANCOVA analysis, the LEPR Lys656Asn SNP rs8179183 did not
associate with any of the variables. In addition, no interactions were observed between
genotype and aerobic fitness in the general linear model analysis.
In studies II and IV, LEPR SNP rs1137101 or rs8179183, and LEP SNP rs7799039 did not
associate with leptin levels in the sedentary state. In addition, in study IV, FTO rs8050136
did not associate with leptin levels in the sedentary state. In study II, exercise acutely
increased leptin levels in subjects with the genotype GA and AA of the LEP SNP rs7799039
at week 2 (p<0.001 and p=0.001, respectively), and also at week 4 in the subjects with the
genotype AA (p=0.002). After the 7-week training period, the levels were decreased in the
genotype GG and GA subjects (p=0.003 and p=0.001). When the response to acute exercise
was examined, leptin levels increased in every genotype group of LEPR SNP rs1137101 at
week 2 (p<0.001 in the Arg homozygotes, p<0.001 in the ArgGln subjects, and p<0.001 in the
Gln homozygotes). The resting leptin levels decreased significantly from week 4 to week 7
in all genotype groups of LEPR SNP rs1137101.
49
6 Discussion
6.1 STUDY DESIGNS AND SUBJECTS
In the present thesis, complex associations between selected gene variants and physical
performance traits, as well as body composition and relevant cytokine levels were studied.
The physical performance and body composition are highly significant risk factors for
overall morbidity and premature mortality. The main incentive for studying the genetic
background of physical fitness is that new biological mechanisms may be identified to
explain the associations between physical fitness and morbidity and premature mortality.
Secondly, individuals with novel genetic predispositions for low physical performance and
related health risks may be recognized. Furthermore, studying interactions between genetic
risk variant and behavioral and environmental factors may provide new information, about
how the genetic factors related risk for morbidity may be attenuated.
A candidate gene study setting was used in an intervention study design and in crosssectional study design and the traits were well defined and measured. The study was based
on data collected during military basic training period in the Kainuu brigade in winter 2004
and during Finnish military refresher courses in 2008. The military environment is well
controlled and has advantage in reducing confounding factors. The subjects were young
men and they had no medications or diseases which could reduce physiological
performance, as may be the case in older populations. The present results include novel
findings and replicate some of the previous findings. The main findings indicate that IL6
variants may associate with gains in aerobic performance in response to physical training,
the effect of FTO genetic variation on body composition is not modified by aerobic fitness,
IL6R variants may associate with circulating IL-6 levels and BMI, IGF1 variants may
associate with fore production and LEP variants with changes in body composition in
response to physical training.
6.2 CARDIORESPIRATORY PERFORMANCE
The associations of genetic variants with cardiorespiratory performance were evaluated in
all studies (I-IV). In study I, subjects with genotype CC of the IL6 SNP rs1800795 had the
highest baseline VO2max. With respect to the training response in study I, subjects with
allele G had significant gains in VO2max, and this improvement can be explained to a great
extent by the fact that their baseline values were the lowest. Indeed, the baseline VO 2max
strongly determines the magnitude of an individual response to physical training (352).
Genotype frequencies were in Hardy-Weinberg equilibrium. The results of study I are
partially supported by previous findings in healthy male smokers, where the allele C of the
IL6 SNP rs1800795 was associated with reduced physical performance in smokers (214).
However in study I, smokers were not separately studied due to the small sample size.
Furthermore, the method for assessing aerobic fitness was different between study I and an
earlier study (214). Previous studies have not found any association between the present
50
SNP and physical performance (361,362). Furthermore, in the previously mentioned
studies, the genotype frequencies were not in Hardy-Weinberg equilibrium, and the
subjects were apparently older than those examined in the present study. No directly
comparable follow-up studies have been previously performed. In addition, in study III
with a larger sample size, no association of IL6 SNP rs1800795 with aerobic performance
was found in the sedentary state. The method for assessing aerobic performance was not
the same in studies I and III, and thus, the results are not fully comparable. Voluntary
physical activity before the military training or refreshment could not explain the observed
VO2max values between genotype groups in studies I and III, because the self-reported
physical activity prior to the study period was comparable in all genotype groups.
Moreover, in study I, during eight weeks of training, the genotype groups did not differ in
their dietary habits. Therefore, the different VO 2max values at baseline cannot be explained
by preceding physical activity or nutritional factors. Cigarette smoking and general
perceived health were also evaluated, but no factor identified that could explain the
difference in baseline VO2max measurement. However, the present results suggest a
possible association of the allele G with gains in VO2max. Whether the present IL6 promoter
SNP is an independent determinant of aerobic performance should be elucidated further in
a larger cohort. The first GWAS reporting genomic predictors of gains in VO2max was
performed in the HERITAGE Family Study and did not find any significant association of
IL6 genotype with VO2max gains in response to 20 weeks of exercise training (18). The
HERITAGE cohort is fairly comparable with the present sample, except that it included also
women. In conclusion IL6 rs1800795 is unlikely to associate with cardiorespiratory
performance, at least in the sedentary state. However, the G allele of the aforementioned
SNP may associate with higher gains in VO2max in response to a training intervention in
comparison to the situation in individuals with the CC genotype. This result needs to be
interpreted with caution due to the limited sample size in study I.
In study II, the observed differences in relative VO2peak values between genotypes
(expressed as mL·kg-1·min-1) during exercise intervention at weeks 5 and 8 may be evidence
of an association between cardiorespiratory performance and the LEP SNP rs7799039,
although the absolute VO2peak values (expressed as L·min-1) did not support this finding. It
is therefore likely that the higher body mass of subjects with the AA genotype explains
their lower VO2peak values (expressed as mL·kg-1·min-1). In study IV LEP, LEPR did not
associate with aerobic performance in the sedentary state. So far there are no reports of
associations of LEP or LEPR polymorphisms with aerobic performance. With regard to the
results of the present study, it seems that LEP and LEPR SNPs are unlikely to associate with
aerobic performance. However, some effect could be seen in relative aerobic performance
due to effect of LEP and LEPR gene variants on body mass.
The genetic variation in FTO did not associate with aerobic performance, expressed as
mL·kg-1·min-1 or L·min-1, this is in line with earlier reports (363,364). In the study of Mitchell
et al. (364) the subjects were postmenopausal women, whereas in the study of Berentzen et
al. (363) the subjects were obese and normal weight young males. Therefore, the latter
study sample is comparable with the present study sample. The FTO gene is unlikely to
associate with cardiorespiratory performance. However, there may be associations with
measures of aerobic performance standardized by body weight, such as VO2max in mL·kg 1·min-1due to the weight increasing effect of this gene variant.
51
In addition, IL6R, IGF1 or IGFBP3 gene variants did not associate with cardiorespiratory
performance. Although no effect was found for the IGF1 variants on aerobic performance,
the IGF1 promoter (CT)n repeat polymorphism has been associated with increased exercise
economy and aerobic capacity (213). However, that study is not comparable with the
present study due to different type of genetic variation. In addition, the reported finding
was made in post-menopausal women who only underwent exercise test on a treadmill
(213). There are no earlier reports on the SNPs in the present study, and they are unlikely to
associate with cardiorespiratory performance.
6.3 NEUROMUSCULAR PERFORMANCE
Associations with neuromuscular performance, i.e. maximal isometric bilateral leg
extension force in the sedentary state were investigated in studies III and IV. In study III,
the IGF1 gene variant rs7136446 was associated with maximal force production. In fact, the
CC homozygotes were able to generate approximately 9% greater maximal extension force
than the CT heterozygotes, and this difference was statistically significant. However, the
result between CC homozygotes and TT homozygotes was only a borderline significant
case. This is a novel finding and points an association of the minor allele with maximal
force production. However, the difference was no longer statistically significant after
controlling for confounding factors in the ANCOVA, although it did remain significant in
logistic regression. There are no earlier reports studying the association of rs7136446 and
muscle force production. However, the exact biological mechanism is unknown because
this SNP is located in the intron of IGF1 gene and potentially can alter its regulation or it is
alternatively linked with SNPs that may result in a structural change of the IGF-1 protein.
The CC homozygotes also tended to have the highest lean mass in the legs, but this
difference was statistically nonsignificant. Although this may be due to a lack of statistical
power, i.e. it is possible that greater sample size would be needed to detect minor-tomoderate changes in lean mass. In addition to hypertrophy, muscle force production is also
dependent on the level of voluntary neural drive to the muscle in question, in which IGF-1
shows protective effects on neurons, including myelination, stimulation of axonal
sprouting, antiapoptotic effects, and repair of axonal damage (365). On the other hand,
muscle force depends on many factors including skeletal muscle plasticity (366), myosin
heavy chain content (52), proportion of fast-twitch fibers, and connective tissue properties
(51,53). In an earlier study, carriers of the 192 allele of the IGF1 promoter CT repeat
polymorphism also displayed increased force after strength training without significant
effect on muscle volume compared to non-carriers of the 192 allele (258). Although, subjects
were seemingly older than in the present study and the study by Kostek et al. (258)
included strength training intervention. In conclusion, the IGF1 gene variant rs7136446 may
associate with muscle force production in a sedentary state.
According to the results of study III, the AA homozygotes of the IGF1 SNP rs6220 had
significantly lower maximal force production values compared with carriers of the GA
genotype, although the main effect was nonsignificant. Therefore it is likely that association
of GA genotype with increased muscle force is a false positive finding. As far is known, this
is the first study to investigate the association of the IGF1 SNP rs6220 with muscular
strength. This SNP is located in the 3’-UTR region of IGF1 gene, which contains regulatory
52
regions for mRNA expression, and may therefore alter the regulation of protein expression.
However, the IGF1 SNP rs6220 is still unlikely to associate with sedentary state muscle
force production in young men.
In study III, the IGFBP3 rs2854744 did not associate with muscle force production. There
is only one earlier study which has examined the association of the IGFBP3 SNP rs2854744
with muscle force production (257). In that study, the authors reported no effect of
rs2854744 on muscle force before or after 10-week single leg strength training intervention
in black and white men and women. This baseline finding has been replicated in the
present study, although the studies are not fully comparable due to differences in subjects
and study settings. The rs2854744 is located in the regulatory promoter region of the
IGFBP3 gene, and the allele A of this SNP has been reported to be associated with higher
IGFBP-3 levels than the CC genotype (367). Furthermore, increased levels of IGFBP-3 may
decrease the proportion of active IGF-1 in blood (367). In conclusion, according to the
results of the present and an earlier study, IGFBP3 rs2854744 is unlikely to associate with
neuromuscular performance in the sedentary state.
Earlier studies have suggested that the IL6 promoter SNP rs1800795 may associate with
maximal physical working capacity in smokers (214) and with increased muscle power
performance (344). This SNP is located in the promoter region of the IL6 gene, with
reported effects on IL-6 levels on plasma (345). However, we could not confirm the
association of aforementioned SNP with neuromuscular performance. Earlier it has been
reported that the allele G may favor power performance (344). In the aforementioned study
no actual force measurements were performed, however the frequency of G allele was
higher in power athletes compared to controls (344). In addition, a subsequent replication
study did not confirm the finding (217). Altogether, IL6 rs1800795 is unlikely to associate
with neuromuscular performance.
6.4 BODY COMPOSITION
In study IV, the AA genotype of the FTO SNP associated with higher BMI and waist
circumference compared with other genotypes. The present findings are in line with a
recent meta-analysis (368). However, one novel finding was that aerobic fitness does not
modify the effect of FTO variation on BMI and waist circumference. The present results are
partly conflicting with earlier reports. In a recent meta-analysis, physical activity level was
shown to modify the relationship between the FTO risk variant and body weight and the
risk of obesity (171). In addition, in women physical activity modified the genetic risk of
FTO rs8050136 on obesity phenotypes (369) and in the HERITAGE cohort CC homozygotes
had a greater fat mass and % fat mass losses compared to AA homozygotes (370). On the
other hand in post-menopausal women, no exercise intervention-FTO interaction was
found, though the weight reduction was significant (364). These previous studies are not
fully comparable because the present study is the first to describe an actual FTO- aerobic
fitness interaction. The questionnaire-based assessment of physical activity may have some
reporting bias (16,166). In addition, the present findings may be explained by the fact that
apart from physical activity, other factors can affect aerobic fitness. Approximately 20 to
50% of VO2max has been estimated to be heritable and thus other factors, such as physical
activity, intake of carbohydrates, smoking, body weight, blood hemoglobin levels, age and
presence of chronic disease account for the majority of this parameter (11,20). Furthermore,
53
our study included only young males, while the other studies mentioned above included
women and older individuals. Physical activity level was self reported in previous studies,
whereas in the present study physical fitness was objectively measured. However, in the
present study, aerobic fitness was associated with body composition traits, regardless of the
FTO genotype. Despite the lack of FTO genotype-aerobic fitness interaction, AA genotype
carriers who have susceptibility towards obesity will also benefit from aerobic fitness.
In study IV, the LEP promoter -2548 G/A SNP rs7799039 did not associate with body
composition traits. The present results are in conflict with earlier studies (283,284).
However, the conflicting results may be partly explained by the different study
populations, e.g. genders and ethnic origins. It has been speculated that lower leptin levels
in the G allele carriers compared with AA homozygotes, would be the mechanism
responsible for the increased accumulation of adipose tissue with this genotype (285).
However, in study IV, no differences were found in leptin levels between genotype groups.
Furthermore, there was no interaction between LEP promoter SNP rs7799039 and VO2max
in study IV. Study II, revealed that subjects with the genotype AA had significantly higher
BMI values at the baseline than other subjects. In addition, although eight weeks of physical
training significantly reduced the BMI values in all genotype groups, it did appear,
however, that the AA subjects mainly lost their FFM, whereas the other genotypes reduced
their fat mass. This may indicate that subjects with genotype AA do not benefit from
training in the same way as carriers of the G allele. However, it is possible that this finding
is a false positive and clearly requires further examination and replication in studies with
larger sample sizes. Nevertheless, some previous studies, as well as study IV, have
provided controversial data on the association of this SNP with obesity and plasma leptin
levels in the sedentary state. Wang et al. (283) reported that the GG genotype was more
frequent in substantially obese Taiwanese aboriginals compared to normal weight or
slightly overweight subjects. Similarly, in a study of 314 individuals, the G allele was more
frequent in overweight subjects and was associated with lower plasma leptin levels than in
individuals with the AA genotype (284). On the other hand, these were not exercise or
training studies and therefore need to be cautiously compared with the present study. The
latest obesity GWAS found no associations of LEP or LEPR SNPs with obesity, but the LEP
and LEPR mutations are believed to cause monogenic obesity syndromes (271). The present
study sample consisted of young and healthy non-obese males. Therefore, the contradictory
results between our and previous studies may be due to differences in the study
populations and selection bias. Altogether, the present results suggest that the LEP SNP
rs7799039 may be associated with weight loss responses to exercise training. The AA
genotype carriers may lose more fat-free mass in response to exercise intervention, however
due the small sample size and complex interactions this finding may be a false positive.
The results of study II, also reveal that homozygous subjects to the LEPR rs1137101
genotype Arg mostly reduced their BMI during eight weeks of training intervention,
compared to carriers of the Gln allele. On the other hand, the Arg homozygotes also had
the highest BMI values in the beginning of the study. Nonetheless, these results are partly
supported by previous studies where the Arg homozygotes had a higher fat mass than the
subjects with other genotypes, and this genotype was also associated with obesity (286-288).
In particular, the study of Masuo and coworkers was fairly comparable to the present study
despite the small sample size (286), whereas cohorts of the two other studies consisted of
women and COPD patients (287,288). Furthermore, Ali et al. (291) found a significant
54
relationship between the Arg allele and lower levels of leptin in obese middle-age subjects,
but only in women, in the study including 391 obese patients and 302 normal weight
subjects. However, the results of the present study revealed that the Arg homozygotes
responded as equally to physical training in terms of reduction in BMI and the fat
percentage as the subjects with other LEPR genotypes. In study IV, no associations were
found with BMI, waist circumference or body fat between LEPR rs1137101 genotype
groups. As mentioned earlier, the GWAS have not found that LEP or LEPR SNPs would be
associated with obesity (271). Altogether, the present results suggest that LEPR rs1137101 is
unlikely to associate with body composition traits or responses to exercise intervention.
In study III, the CC homozygotes of IGF1 SNP rs7136446 had higher body fat values than
the other genotypes. The T allele decreased risk for fat percent <12.6%. This is a novel
finding and points to an association of the minor allele with body composition. However,
adjustments for confounding factors eliminated this association. The associations of IGF1
variants with fat percent may be related to the anabolic effect of IGF-1, since individuals in
an over-nourished state, i.e. whose endogenous insulin levels are high and hepatic GH
receptors are activated, have increased IGF-1 levels (371). In addition, increased IGF-1
levels have been reported to associate with adiposity (372), fat mass and muscle mass (373).
A moderate sample size and multiple statistical testing may produce false positive findings
in the present study. These results suggest that IGF1 SNP rs7136446 is unlikely to associate
with fat percent. However, this finding needs to be replicated in a larger cohort study with
women and older subjects.
In study I, subjects with the CG genotype of the IL6 rs1800795 reduced BMI during 8
weeks of training. However, the number of subjects was greatest in this group and thus the
result was significant as the other genotype groups were lacking statistical power.
Therefore this finding may be a false positive. In study III, no associations with IL6 were
found with body composition traits, and thus this SNP is unlikely to associate with body
BMI, fat percent or waist circumference. However in study III, the IL6R variant rs4537545
associated with BMI when confounding factors were included in the linear regression
model. Obesity is associated with low-grade inflammation and BMI positively associates
with elevated IL-6 levels (374). However, whether the elevated IL-6 levels in the TT
genotype carriers of the rs4537545, as compared with C allele carriers, are the reason for the
increased BMI, remains unclear and warrants further research. This is a novel finding and
needs to be replicated with a larger cohort.
6.5 PLASMA AND SERUM CYTOKINE LEVELS
In study I, the IL6 promoter SNP rs1800795 polymorphism showed an association with
plasma IL-6 response to acute exercise in subjects with genotype CG. The finding that
elevated IL-6 levels were found only in CG carriers may be related to the fact that the
number of subjects was greatest in that group, and thus it could be due to the greater
statistical power. For example, in the homozygote groups statistical power was lacking due
to the small number of subjects. Furthermore, no additive effect of C or G allele was found
and the finding may be false positive. Although circulating IL-6 concentrations may not
precisely reflect the expression pattern and biological significance at the tissue level, earlier
55
studies partly support the present finding that allele C was associated with elevated IL-6
plasma levels only in response to acute stressors (375,376). On the other hand, elevated
levels of plasma IL-6 have been detected in type II diabetic patients with allele G (377),
while decreased levels were observed in patients with juvenile chronic arthritis with allele
C (378), although some other studies did not find evidence for this association (216). In the
study of Oberbach et al. (215), the subjects carrying the allele C, had significantly lower IL-6
levels in serum after long-term exercise training than the subjects with the GG genotype. It
is possible that genetic variations are important determinants for the individual response to
the anti-inflammatory properties of exercise training (215). Evidently, the net effect of this
SNP on circulating IL-6 remains unclear and may be attributable to the characteristics of the
population investigated. In study I, no evidence was found for increased skeletal muscle
damage between the genotype groups which could explain the observed differences in the
IL-6 response to acute exercise. Despite the fact that metabolic factors may induce IL-6
secretion in response to physical exercise (379), the possibility that the increase in plasma
IL-6 levels was associated with inflammation in subjects with genotype CG cannot be
completely ruled out. In study III, sedentary state IL-6 levels did not associate with IL6
promoter SNP rs1800795 genotype. In summary, the present results suggest that IL6
promoter SNP rs1800795 is unlikely to associate with plasma IL-6 levels in the sedentary
state or after exercise.
Study III, also revealed that the minor T allele of the IL6R SNP rs4537545 was associated
with higher IL-6 levels compared with CC genotype, which replicates the finding from a
previous study (346). The result was also significant after adjustments for confounding
factors. The rs4537545 tags the functional nonsynonymous Asp358Ala variant (rs8192284)
in the IL6R gene and this variant also affects circulating IL-6r levels (346). In conclusion, the
TT genotype of the IL6R SNP rs4537545 may associate with higher IL-6 levels than allele C.
The finding may be reflected in individual differences in low grade inflammation and
inflammation related diseases.
Eight weeks of training tended to reduce circulating leptin in the present study, in
agreement with previous studies (380,381), but in contrast to an exercise intervention in
overweight children (382). This may be explained by differences in the examined
populations of the two studies. However, it was apparent that leptin levels exhibited
diverse responses to acute exercise in the present study, which may be due to variable fat
mass and the effect of genotype. Acute exercise seemed to increase leptin levels in the A
allele carriers of LEP SNP rs7799039. However, this might result from the fact that the submaximal exercise probably was not sufficiently intense to result in decreased leptin levels
as previously reported (129). In the present study, plasma leptin levels decreased
significantly only in the G allele carriers after 7 weeks of training, suggesting that the G
allele carriers of the LEP SNP rs7799039 may respond better to training. Nevertheless, as
earlier described, some previous studies have presented controversial data on the
association of this SNP with obesity and plasma leptin levels (283,383). Nonetheless, these
separate studies must be cautiously compared with the present study due to differences in
the study population e.g. there could be some selection bias in the present study as the
subjects consisted of young and healthy non-obese men. Furthermore, the present study
was an exercise intervention study. Previously, the Arg allele of the LEPR rs1137101 has
been associated with higher plasma leptin levels, (286,297,384) higher blood pressure (385)
compared with Gln homozygotes and it has been proposed that this allele can also lead to
56
leptin resistance hence causing obesity. The population in the study of Masuo et al. (286)
was fairly comparable with the present study, despite the fact that relatively more obese
subjects were included and the number of subjects was lower than in the present study,
which may explain the different findings. The Quinton et al. study (384) included only postmenopausal women, whereas in the study by van Rossum et al. study (296), the association
of LEPR rs1137101 and leptin levels was only observed in the sub-group of subjects, who
gained weight during the follow-up. In the present study, training decreased leptin levels
in all subjects irrespective of their LEPR genotype. Our finding suggests that LEPR
genotype does not associate with leptin level responses to physical training. In study IV,
LEP and LEPR genotypes did not associate with leptin levels in the sedentary state. In
conclusion, LEP rs7799039, and LEPR rs8179183 and rs1137101 genotypes are unlikely to
associate with leptin levels in the sedentary state. However, some differences may be
observed due to the direct effect of adipose tissue on circulating leptin levels. The G allele
carriers of the LEP SNP rs7799039 may experience greater reductions in leptin levels in
response to training.
According to the results of study III, the IGF1 SNP rs6220 is unlikely to associate with
circulating IGF-1 levels. Elevated IGF-1 levels have been reported in individuals with CC
genotype compared to individuals with other genotypes of the IGF1 SNP rs7136446 e.g. in
elderly men in the prostate cancer study, and in post-menopausal women (349,386).
However, this association was not replicated in the present study, which examined young
healthy men. Due to differences in study-cohorts, the results are not directly comparable. It
is possible that local IGF-1 production may play a more significant role in exercise-induced
muscle hypertrophy than circulating levels of IGF-1 (54), and therefore, the elevated levels
of IGF-1 are not absolutely needed for the altered muscle phenotype.
Finally, in study IV, the FTO SNP did not associate with elevated leptin levels. However,
in earlier studies, another FTO SNP (rs9939609) has been shown to be associated with
increased leptin levels (387,388). However, conflicting results have been reported in
children and adolescents (387,388). The most likely reasons for FTO genotype associations
with leptin levels are the fact that leptin is produced in adipose tissue, and the FTO
genotype associates with body fat mass (389). The FTO SNP rs8050136 is unlikely to
associate with circulating leptin levels, however the AA genotype may associate indirectly
with higher leptin levels when compared to the levels in allele C carriers due to greater
amount of adipose tissue in the former subjects.
6.6 METHODOLOGICAL CONSIDERATIONS
Research on the genetic background of physical performance and body composition, and
their responses to exercise training is challenging. The relatively weak effects of single SNP
on physical performance and body composition, the interactions of these SNPs with
physical activity or exercise training and confounding by other SNPs, lifestyle and
environmental factors all highlight the need for a sufficient number of subjects (390-392).
The sample size is the most important determinant of the quality of a genetic association
study (393). The sample sizes of studies I and II were relatively small if one intended to
investigate how single SNPs could modify the effects of exercise training on physical
performance, body composition and cytokine levels, whereas the sample size of studies III
and IV were moderate for to investigating the associations of single SNPs with body
57
composition, physical performance and cytokine levels. Furthermore, multiple testing may
lead to false positive findings (type I error) that are quite common in genetic association
studies. On the other hand, statistical power due to the relatively small study sample is low
which may have hampered the detection of real biological effect (type II error, false
negative). However, these considerations about sample size are at least partially
compensated by the comprehensive phenotyping of the study subjects. Monitoring the
physical activity before fitness tests is often based on subjective evaluation using
questionnaires and this may be subject to some kind of reporting bias. The physically active
individuals may have been selected to a specific genotype group despite that fact that no
apparent differences in physical activity are revealed by the questionnaire.
In studies I and II, the subjects were 54 young healthy men performing compulsory
Finnish military service. Since the sample size was relatively low in studies I and II, the
results need to be interpreted with caution. Because the number of the subjects is limited
and effect of individual SNP on VO2max is small, the fittest individuals may have been
selected to one of the genotype groups by chance. Another limitation of the studies is that
the subjects were young men. If the age and gender of individuals are likely to modify the
effects of exercise training on aerobic performance and body composition, it is difficult to
generalize the results of the studies I and II to female or older populations. The study
population did not include elite athletes or morbidly obese subjects, who may have
different genotype frequencies of studied SNPs as compared to the control subjects. On the
other hand, due to the fact that military service is compulsory in Finland, conscripts are a
rather representative sample of young Finnish men (343).
Exercise training should be intensive enough to produce a training effect. During the
basic training period of two months, the overall physical load of the subjects was
determined according to the protocols of the Finnish Defense Forces. During the basic
training period of Finnish military service, the amount of physical exercise is initially two
hours per day, increasing to up to four hours (394). The physical activity of military training
is comparable to strenuous work or daily competitive athletic training (394). This means
that the physical load was sufficient to produce large changes in physical fitness. However,
the training regimen may not be well tolerated by all individuals (394). The strength of
studies I and II was the objectively measured physical performance change in response to
the 8-week training in a controlled environment, which decreases the possible effect of
confounding factors. Furthermore, the results obtained in the sedentary state could be
replicated in studies III and IV with a larger cohort of men. These similar results would
appear to confirm the baseline results obtained from studies I and II.
In studies III and IV, 846 young healthy men were studied in a cross-sectional
epidemiological study setting. The sample size was moderate for a genetic association
study. A truly sufficient sample size to confirm true positive findings would require
thousands of subjects. However, a strong association of a functional or otherwise
potentially significant SNP with a well-assessed phenotype even when identified in a
smaller study sample can represent a true finding. Nevertheless, it is important to replicate
the observations in other studies to obtain solid evidence for the true association. Residual
confounding due to factors that have not been assessed or have been assessed inaccurately
has always to be taken into account in a cross-sectional study. It is also important to
remember that cross-sectional studies cannot provide evidence for causal associations; only
intervention studies can identify possible causal relationships. Furthermore, the
58
phenotypes in the studies III and IV were well defined and measured. However, the results
of the present studies should be replicated in larger populations of men and women of
different age groups.
Real-time PCR was chosen as a genotyping method and unlikely represents a significant
source of error. Genotyping error rates of 0.5 to 1% have been reported depending on the
genotyping method (395,396). In the present study, all samples were run in duplicate and
genotyping call rates were >99% with duplicate concordance rates of >99.5%. It has been
also suggested that regenotyping should be done for 5-10% of subjects (397,398). The test of
Hardy-Weinberg’s equilibrium was done for each and every SNP analyzed. The deviation
from the Hardy-Weinberg’s equilibrium often indicates either a genotyping error or highly
deviant allele frequencies. In the present study, all studied SNPs conformed to HardyWeinberg’s equilibrium. In addition, genotype frequencies were validated against a known
CEU population. With a genome-wide association setting and the chip array method, over
500 000 SNPs could be genotyped from one subject. However, this method was not widely
available in the beginning of the present doctoral thesis.
Aerobic performance was evaluated with the analysis of VO2max, which is considered as
the golden standard for evaluating aerobic performance (18). Traditional relative VO2max
expressed in mL·kg-1·min-1 may underestimate aerobic fitness in obese subjects (19), and
thus in every study also absolute VO2max (L·min-1) values were analyzed. The assessment
of VO2max in studies I and II was based on direct gas exchange analysis during exercise test
on a treadmill. This method has high accuracy in its ability to estimate VO2max value (399).
The co-operation of the subjects in the exercise test is essential to achieve VO2max. The
success of the test performance can be objectively evaluated. In studies III and IV, VO2max
was assessed indirectly during the exercise test on a bicycle ergometer. First aerobic
performance was evaluated as in maximal working capacity (Watts) and the results were
converted to equivalent to VO2max using the Milfit4 software (Fitware, Helsinki, Finland).
The test was indirect, and thus it is not as precise as a direct measurement of gas exchange
taken during an exercise test. The indirect method also tends to overestimate VO2max
values (400), in addition, some subjects tend to have 10 to 20% lower peak VO2 in exercise
test performed on a bicycle ergometer than by treadmill because of quadriceps muscle
fatigue (400). However, the exercise test using bicycle ergometer is a cost-friendly and
sufficiently precise method for assessing large population.
Maximal isometric force of the bilateral leg extensors was measured by a strain-gauge
dynamometer developed in the Department of Biology of Physical Activity of the
University of Jyväskylä. This method has been described earlier in more detail (354), and its
reproducibility has been reported as being high (355). Although, it is not a standard method
for the measurement of maximal isometric extension force of the legs. The tested muscle
groups include gluteus, quadriceps femoris and hamstring muscles. However, the test is
more functional compared to unilateral leg extension tests, and thus the result may better
reflect the challenges of daily living.
Body composition was measured by skinfold thickness in studies I and II, or by multifrequency bioimpedance method in the studies III and IV. In the skinfold caliper method,
the thickness of four skinfolds (triceps, biceps, subscapular, and supraspinale) was
measured. This method is susceptible to measurement errors (401). Measurements may
vary depending on the force applied by the anthropometrist, measurement technique
differences between anthropometrists, hydration status of subject and the individual shape
59
of the body. The total body fat, including visceral fat, is estimated by calculations.
However, errors of approximately 3.5% have been reported in estimation of body fat %
(356), which are tolerable. In an effort to decrease errors, the same anthtropometrist
performed all the measurements in studies I and II. The advantages of the skinfold
measurement include portable its use, relatively low cost, minimal effort of the subject
required, and safety (356), thus making it useful for large-scale studies and when assessing
changes of body composition of the same subject. In studies III and IV, a 2-compartment
bioimpedance method was used, in which the body composition is divided into fat mass
and fat-free mass, consisting of water, protein and minerals. The values provided by
method may be inaccurate e.g. in dehydration, a full urinary bladder, pregnancy, ageing or
after weight reduction in obese people (402). It was attempted to minimize these
confounding factors by adjusting meal times and recovery time from prior physical activity.
The advantages of the multifrequency bioimpedance method include its portability, ease of
use, relatively low cost, minimal participation requirement, high reproducibility and safety,
except that it is not recommended for individuals with a pacemaker (402), thus making it
attractive for large-scale studies. At present, no golden standard has emerged for the
assessment of body composition, and therefore the accuracy of any particular method has
been claimed to be mediocre at best (401). However the Dual X-ray absorptiometry method
(DXA) is considered as the best when accuracy and cost-friendliness are taken into account.
DXA would be a more accurate method (402). Magnetic resonance imaging (MRI) would be
the most accurate way of measuring of body composition providing information on visceral
fat that may be the most hazardous fat compartment (402). However, magnetic resonance
imaging is more expensive and less widely available than other methods and this limit its
use in larger population studies.
In studies I and II, the subjects kept dietary records for 15 days in 4 periods of 3-4 days
during the intervention to provide a representative and accurate perspective of food
consumption and nutrient intake. The dietary records also provide an estimate of overall
energy intake, but it is known that records may underestimate energy intake (403).
Moreover, there is a possibility for reporting errors because of the subjective estimation of
food consumed. It was attempted to control for the reporting errors by interviewing the
subjects. The energy expenditure was assessed with DLW, which is considered as the
golden standard (404), and this was used for last two weeks of the study period. There were
no differences in energy intake between the genotype groups suggesting that energy intake
did not confound the effects of exercise training on VO2max, BMI or fat percent.
In studies I and II, plasma IL-6, TNF-α, IL-1β and leptin were measured by commercial
ELISA tests according to the manufacturer’s protocols. Regardless of the low plasma levels
of cytokines, IL-6 plasma levels have been reported to increase by up to 100-fold after
exercise and the sensitivity of the technique to detect such a change is sufficient (119,122).
In addition, the cytokine levels may increase in several inflammatory states, such as acute
infections or rheumatoid arthritis. The subjects were generally healthy and different
physiological stressors other than exercise and genetic variation were unlikely to affect
blood cytokine levels. Some subjects suffered from upper respiratory tract infections, which
may have increased the cytokine levels, during the study period. On the other hand, only
healthy subjects performed the marching drills. Exercise may cause dehydration and
hemoconcentration that would result in an overestimation of plasma protein concentrations
after exercise. Therefore post-exercise plasma protein concentrations were corrected by
60
standardizing them with plasma volume (%ΔPV) calculated from changes in hemoglobin
and hematocrit according to the method of Dill and Costill (357) and these corrected postexercise values were used in the analyses. In studies III and IV, plasma IL-6 concentrations
were analyzed by high-sensitivity ELISA that is a sensitive and specific method for
assessment of sedentary state plasma IL-6 levels. Serum IGF-1 concentrations were
measured on a Immulite 100 system, which has sufficient sensitivity and specificity to
permit the assessment of sedentary state IGF-1 levels.
In studies I and II, a sub-maximal exercise test was applied to investigate the responses
of biochemical markers to physical exercise. Blood was sampled at rest and immediately
after 45 minutes of marching at 70% of each subject’s maximal workload on week 2, 4, and 7
of the study period. Leptin levels have been found to decrease after a single bout of
strenuous exercise, if energy expenditure exceeds 800 kcal or after a longer physical
training period (129). In addition, IL-6 levels have been reported to increase after a single
bout of strenuous exercise by as much as 100-fold (119,122). It is possible that the 45-minute
regime of marching at 70% intensity corresponding to sub-maximal exercise was not
sufficient to evoke an intense physiological stress change in the plasma cytokine levels. In
addition, also moderate exercise has been observed to increase IL-6 levels although less
extensively than strenuous exercise (405). The 45-minute bout of exercise should be
sufficient to cause an increase in plasma IL-6 levels, because IL-6 levels have been found to
increase after 30 minutes of exercise and to be highest immediately after exercise (405).
The International Physical Activity Questionnaire (IPAQ) (358) was applied to assess
physical activity prior to military service and military refresher periods. Another
questionnaire was used to assess the physical strenuousness of their work, as well as
subjective health and smoking habits. Self-administered questionnaires are susceptible to
reporting errors, because it may be difficult to estimate the frequency of physical activity or
the duration of each session of physical activity. The IPAQ questionnaires produce
repeatable data (Spearman's rho around 0.8) and of moderate validity (Spearman's rho of
about 0.30 against computer science application accelometer) (358). The reliability and
validity of the IPAQ may depend on several factors such as age, gender and socioeconomic
status of individuals. However, it has been stated the IPAQ can be used with confidence in
the developed countries (358) and access with sufficient accuracy the physical activity of
young Finnish men.
In studies I and II, repeated measures ANOVA was used to analyze the development of
aerobic fitness and body composition during the exercise intervention. The data were
categorized into the three genotype groups. Bonferroni correction was used because of the
need for multiple comparisons. There was no difference in the levels physical activity
before exercise training between the genotype groups, and thus it was not adjusted in the
statistical models. Smoking is known to decrease aerobic performance, and there were 15
smokers in studies I and II. However, there was no difference in smoking between the
genotype groups. Furthermore, due to the relatively low number of subjects, the study
sample was not stratified according to smoking status. No adjustments in repeated
measures ANOVA for other confounding factors were used in study II because genotype
groups were homogenic according to sex, age, nutrition, smoking, physical activity and
prevalent diseases, and other factors have only a minor effect on body composition. We
used the absolute change in VO2max instead of relative VO2max change, because the
relative change can well be large even though the absolute change was small in some men
61
with a low initial VO2max. This may lead to false positive case if the genotype is associated
with low initial VO2max and the response to physical training results in relatively large
improvements in these subjects.
In study III, ANOVA, ANCOVA and linear regression were applied to reveal differences
in physical performance, body composition and biochemical markers in the sedentary state.
As in studies I and II, the genotype groups did not differ according to the physical activity
or smoking. Furthermore, logistic regression analysis was used to estimate the risk of
having a lower BMI, fat percent, VO2max and maximal force of leg extensors in two
genotype groups compared to one genotype group that was used as a reference group. This
may have decreased the statistical power to find differences between the genotype groups.
The subjects were divided into quartiles for a given continuous trait. The study sample
included very few obese men (BMI over 30) or those with a remarkably high aerobic
performance and thus the classification of subjects based on these factors would have lead
to small groups and loss of statistical power. A recessive model was applied in the analysis
in i.e. minor allele homozygotes were compared to major allele carriers. This increases
statistical power, but may reduce biological information which can be obtained from this
study cohort (406). The logistic regression model and ANCOVA were adjusted for age,
smoking and earlier physical activity. In addition, both gender and nutrition can have effect
on physical performance; here all of the subjects were males. However, nutritional factors
were not assessed in this study and in particular, the intake of carbohydrates may have a
modest effect on VO2max (20).
In study IV, ANCOVA was applied to investigate differences in physical performance,
body composition and biochemical markers in the sedentary state. In the ANCOVA
analysis age, smoking and earlier physical activity were set as covariates. GenotypeVO2max interaction effects on BMI, waist circumference and fat percent were analyzed
using the general linear model. The sample size was adequate for the general linear model
which provides a relatively good power for continuous traits in this study population.
Additionally, the FTO genotype and FTO genotype-VO2max interaction related odds for
being overweight (BMI over 25 kg·m-2) and for having abdominal obesity (waist
circumference over 90 cm) were analyzed by logistic regression. The limits were selected
because in the study population there were only a few obese men (BMI over 30) and thus
the statistical power was small. The recessive model was applied in the analyses. Age,
smoking and physical activity were set as covariates. Physical activity has been reported to
decrease the risk for obesity in minor-allele carriers of FTO (171), and thus the subjects in
the highest quartile of aerobic performance were compared with the others in the
interaction analysis. The sample sizes were sufficient for assessing the essential basic body
composition and physical performance variables, whereas the sample size was too low for
some of the biochemical measurements. In study IV, the number of the minor allele
homozygotes for the LEPR Lys656Asn SNP rs8179183 was small, that may have reduced
the possibility to observe a real effect of this SNP on examined traits.
Due to large numbers of statistical tests were performed, false positive findings may
have occurred. In addition, selection bias, especially in studies I and II, may have caused
false positive findings. Furthermore, the small number of subjects may have reduced
possibility to detect true biological effects due to inadequate statistical power. The present
association studies do not provide information of causality for any given trait. Therefore,
62
these results should be confirmed and replicated in larger and independent samples in
order to confirm these findings.
63
7 Conclusions
1. Carriers of the AA genotype of the FTO SNP rs8050136 have higher BMI and waist
circumference, and this genotype may associate with an increased risk of abdominal
obesity. As a novel finding, aerobic fitness did not modify the effect of FTO variant
on BMI or waist circumference. However, aerobic fitness associated with BMI and
waist circumference regardless of the FTO genotype.
2. The IL6 SNP rs1800795 unlikely associates with cardiorespiratory and
neuromuscular performance and with IL-6 levels at least in the sedentary state.
However, the G allele of this SNP may associate with greater gains in VO2max in
response to physical training as compared to the CC genotype. This preliminary
observation should be replicated with a larger cohort. Carrires of the TT genotype of
the IL6R SNP rs4537545 have higher IL-6 levels and may have higher BMI than C
allele carriers in the sedentary state.
3. Carriers of the CC genotype of the IGF1 gene variant rs7136446 may have greater
muscle force production than carriers of the T allele in the sedentary state, and it is
possible that the carriers of the T allele are less likely to exhibit high force
production compared to carriers of the CC genotype. This preliminary finding
should be confirmed with other cohorts. Two SNPs, i.e. IGF1 rs6220 and IGFBP3
rs2854744 are unlikely to associate with muscle force production in sedentary state.
The investigated IGF1 and IGFBP3 variants are not thought to associate with
circulating IGF-1 levels.
4. Carriers of the AA genotype of the LEP SNP rs7799039 tend to lose more fat-free
mass in response to physical training, whereas the allele G carriers may experience
greater decreases in fat mass and leptin levels in response to training. The LEP and
LEPR variants are unlikely to be associated with aerobic performance. However,
some differences may be seen in relative aerobic performance due to the effect of the
LEP and LEPR variants on body mass. The three SNPs (LEP rs7799039, LEPR
s8179183 and rs1137101) are unlikely to associate with leptin levels in the sedentary
state. However, some differences may be observed due to direct effects of adipose
tissue on circulating leptin levels.
64
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Antti Huuskonen
The Role of Adiposity,
Growth and Inflammation
Related Gene Variants in
Physical Performance and
Body Composition
Obesity and poor physical
performance are major risk factors
for premature mortality and
morbidity. The purpose of this study
was to investigate effects of the
selected gene variants on physical
performance and body composition
in young Finnish men. Variants in
studied genes had effects on physical
performance and body composition,
which may partly explain the
interindividual differences in
responses to physical training.
Publications of the University of Eastern Finland
Dissertations in Health Sciences
isbn 978-952-61-1035-6