# The Role of the PGC1α Gly482Ser Polymorphism in Weight Gain due to Intensive Diabetes

Authors: Samir S. Deeb; John D. Brunzell
Journal: PPAR Research
 (2009)

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## Abstract

The Diabetes Control and Complications Trial (DCCT) involved intensive diabetes therapy of subjects with type 1 diabetes mellitus (T1DM) for an average period of 6.5 years. A subset of these subjects gained excessive weight. We tested for association of polymorphisms in 8 candidate genes with the above trait. We found the Gly482Ser polymorphism in the peroxisome proliferator-activated receptorγ coactivator-1α (PGC1α) to be significantly associated with weight gain in males (P=.0045) but not in females. The Ser allele was associated with greater weight gain than the Gly allele (P=.005). Subjects with a family history of type 2 diabetes mellitus (T2DM) were more common among those who gained excessive weight. We conclude that T2DM and the Gly482Ser polymorphism in PGC1α contribute to the effect of intensive diabetes therapy on weight gain in males with T1DM.


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## Body

## 1. Introduction

The major complications of T1DM are microvascular disease leading to visual impairment and nephropathy, as well as premature cardiovascular disease [1]. The DCCT was designed as a
randomized prospective trial to evaluate the benefits of intensive diabetes
therapy compared with conventional therapy [2].
After a mean of 6.5 years of intensive diabetes therapy there was a reduction
in retinopathy, nephropathy, and neuropathy. Two negative outcomes of intensive
diabetes therapy were a threefold increase of episodes of severe hypoglycemia [2]
and marked weight gain in a subset of individuals [3].
However, it was shown that the increase in incidence of severe hypoglycemia did
not account for excessive weight gain [4].
The top quartile (Q4) of increase in body mass index (BMI) in the intensive
therapy had a mean weight gain of 17 kg and their average BMI increased from 24
to 31 [3].
Concomitant with their excess weight gain, they developed components of the
metabolic syndrome, including central obesity and insulin resistance.The
aim of this study was to identify genetic factors that contribute to excess
weight among T1D patients upon intensive insulin therapy. We tested for association
of this trait with sequence variants in 8 candidate genes that are implicated
in obesity and/or T2DM (Table1).
The DCCT study offers a unique opportunity to find such genes since it is a
large and very well characterized prospective study of subjects with T1DM that
have been exposed to intensive therapy and experienced well-documented changes
in weight. Whereas there are many pathways to central obesity, the weight gain
due to intensive diabetes therapy in this well-characterized DCCT cohort,
represents a homogeneous phenotype in which to search for central obesity
genes.Table 1
Candidate genes and SNPs analyzed
for association with excess weight gain with intensive diabetes therapy. Genotyping
was performed using ABI TaqMan assays with the indicated ABI identification
numbers (custom, made to order). Minor allele frequencies are for Caucasian
populations derived from published data and the SNP database
(http://www.ncbi.nlm.nih.gov/SNP/). PPARG, peroxisome proliferator
activated receptor γ; PPARGC1A, peroxisome proliferator-activated receptor γ coactivator-1α; HSD11B1, 11β-hydroxysteroid dehydrogenase 1; GR (NRC31), glucocorticoid receptor; GNB3, G protein β3 subunit; ADIPOQ, adiponectin; NPY,
neuropeptide y, FTO (aliases KIAA1752, MGC5149),
fat mass, and obesity-associated protein.
GeneChromosomal locationNCBI refSNP  numberTaqMan ABI assay IDPolymorphismMinor allele frequencyPPARγ23p25rs18012821129864Pro12Ala (exon 1)0.10PPARGC1A4p15.1rs8192678Custom 1643184Gly482Ser T/C (5′ UTR)0.34rs22795250.3111-β HSD11q32rs28840902502457C/T; intron 40.21GR (NRC31)5q34rs61881046353G/T; intron  50.31rs11749561178285T/C; intron 3GNB312p13rs54432184734C825T Silent/AS0.30ADIPOQ3q27rs266729Custom
2412786C-377G; promoter0.29NPY7p15.1rs557311164468Ser22Ser0.44FTO16q12.2rs9939609Custom 30090620T to A in 3′ UTR0.43


## 2. Methods

### 2.1. Subjects

The DCCT was designed as a randomized prospective trial to evaluate the
benefits of intensive diabetes therapy with multiple insulin injections
compared with conventional therapy. A total of 1441 subjects, aged
13–39 years at
baseline, 50% females and over 95% Caucasian, were randomized to conventional or intensive therapy and followed for 3.5–9 years (mean 6.5 years). Subjects in the conventional therapy group [5] typically received one or
two insulin injections per day and had a quarterly follow-up at
their DCCT clinic. Intensive therapy subjects practiced more
rigorous diabetes management by taking three or more insulin injections
per day or using an insulin infusion pump, self-monitored their
blood glucose four or more times per day, and visited their DCCT
care providers monthly to achieve HbA1c levels at least one
percentage point lower than the conventional therapy group. All enrolled
subjects were in good general health.The
582 (48% females and 52% males) subjects in this study were in the intensive
therapy group and were 18 years of age or older at DCCT entry [2].
They had previously been divided into quartiles of change in BMI over the 6.2
years of the DCCT [3].
The top quartile (Q4) of increase in BMI in the intensive therapy had a mean
weight gain of 17 kg and their average BMI increased from 24 to 31. Concomitant
with their excess weight gain, they developed components of the metabolic
syndrome, including central obesity and insulin resistance, which are strong
risk factors for T2DM and for familial combined hyperlipidemia [5].


### 2.2. Selection of Candidate Genes

Previous reports of linkage and
association of gene variants with obesity indices and/or T2DM were the primary
criteria in prioritizing the candidate genes for this association study. In
addition, priority was given to genes that contain common variants with proven
functional consequences. Originally, 7 candidate genes were chosen, and more
recently theFTO (fat and obesity
associated protein) gene was added to this study because of the recent findings
that polymorphisms in this gene were associated with obesity (see what follows).The 8 candidate genes and the
respective single nucleotide polymorphisms that were tested for association in
this study are listed, in order of decreasing priority, in Table1. We previously showed that the Pro12Ala polymorphism in the
peroxisome proliferator-activated receptorγ2 (PPARγ2), a transcription factor that is
critical for adipogenesis, was associated with obesity and T2DM [6].
This polymorphism is among a few that have been confirmed to be associated with
T2DM [7].
A second good candidate gene (PPARGCA1)
encodes the PPRγ coactivator-1α (PGC-1α), common variants which,
particularly the Gly428Ser substitution, were observed in some studies to be
associated with obesity and T2DM [8–10].
PGC-1α controls the expression of a number of
genes involved in energy homeostasis [11, 12]
that were shown to be coordinately reduced in individuals with insulin
resistance and T2DM [13, 14].
Polymorphisms in the glucocorticoid receptor (GR) gene have been reported to be
associated with altered glucocorticoid sensitivity and central obesity [15–17].
11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) which plays an important role in
determining tissue glucocorticoid levels has also been implicated in features
of the metabolic syndrome in both humans and transgenic mice by amplifying the
effects of glucocorticoid action [18–20].
The gene encoding the G protein β3 subunit (GNB3) was considered as a good candidate for the metabolic syndrome
and T2DM because it contains a common polymorphism (C825T) that was observed to
be associated in some studies with development of obesity in young individuals
across different ethnic groups, body fat distribution and hypertension.
Furthermore, the T allele causes alternate splicing with functional
consequences [21–23].
A genome wide scan for the metabolic syndrome detected significant linkage to
chromosomal region 3q27 where the APM1 gene encoding adiponectin is located [24].
Furthermore, genetic variation in APM1 was observed to be associated with obesity, insulin resistance [25], and T2DM [26].  Neuropeptide Y (NPY) is a major regulator of
food consumption and energy homeostasis. Increased NPY signaling in the
hypothalamus leads to obesity and its complications such as T2DM and
cardiovascular disease. Polymorphisms in the FTO gene were shown by a genome-wide association analysis [27] to be associated with BMI and with
predisposition to childhood and adult obesity (reviewed in [28]).


### 2.3. Genotyping

DNA samples from
the subjects were prepared from peripheral white blood cells at the
Diabetes/Endocrine Research Center at the University of Washington, USA. Single nucleotide polymorphism (SNP)
genotyping was performed by real-time PCR amplification on an Applied
Biosystems (ABI) 3500 apparatus using ABI TaqMan assays purchased from the
manufacturer. The ABI assay identification numbers are given in Table1.


### 2.4. Statistical Analysis

The frequency of alleles and genotypes were compared in Q1
and Q4 by the chi-square (χ2) test. Bonferroni
corrections to the significance of associations of the PGC1α Gly482Ser polymorphism with weight
gain were not made because of our study design that placed
PPARγ2 and PGC1α as top priority candidate genes (Table 1). All of the genes we tested
had already been reported to be associated with either T2DM or obesity.


## 2.1. Subjects

The DCCT was designed as a randomized prospective trial to evaluate the
benefits of intensive diabetes therapy with multiple insulin injections
compared with conventional therapy. A total of 1441 subjects, aged
13–39 years at
baseline, 50% females and over 95% Caucasian, were randomized to conventional or intensive therapy and followed for 3.5–9 years (mean 6.5 years). Subjects in the conventional therapy group [5] typically received one or
two insulin injections per day and had a quarterly follow-up at
their DCCT clinic. Intensive therapy subjects practiced more
rigorous diabetes management by taking three or more insulin injections
per day or using an insulin infusion pump, self-monitored their
blood glucose four or more times per day, and visited their DCCT
care providers monthly to achieve HbA1c levels at least one
percentage point lower than the conventional therapy group. All enrolled
subjects were in good general health.The
582 (48% females and 52% males) subjects in this study were in the intensive
therapy group and were 18 years of age or older at DCCT entry [2].
They had previously been divided into quartiles of change in BMI over the 6.2
years of the DCCT [3].
The top quartile (Q4) of increase in BMI in the intensive therapy had a mean
weight gain of 17 kg and their average BMI increased from 24 to 31. Concomitant
with their excess weight gain, they developed components of the metabolic
syndrome, including central obesity and insulin resistance, which are strong
risk factors for T2DM and for familial combined hyperlipidemia [5].


## 2.2. Selection of Candidate Genes

Previous reports of linkage and
association of gene variants with obesity indices and/or T2DM were the primary
criteria in prioritizing the candidate genes for this association study. In
addition, priority was given to genes that contain common variants with proven
functional consequences. Originally, 7 candidate genes were chosen, and more
recently theFTO (fat and obesity
associated protein) gene was added to this study because of the recent findings
that polymorphisms in this gene were associated with obesity (see what follows).The 8 candidate genes and the
respective single nucleotide polymorphisms that were tested for association in
this study are listed, in order of decreasing priority, in Table1. We previously showed that the Pro12Ala polymorphism in the
peroxisome proliferator-activated receptorγ2 (PPARγ2), a transcription factor that is
critical for adipogenesis, was associated with obesity and T2DM [6].
This polymorphism is among a few that have been confirmed to be associated with
T2DM [7].
A second good candidate gene (PPARGCA1)
encodes the PPRγ coactivator-1α (PGC-1α), common variants which,
particularly the Gly428Ser substitution, were observed in some studies to be
associated with obesity and T2DM [8–10].
PGC-1α controls the expression of a number of
genes involved in energy homeostasis [11, 12]
that were shown to be coordinately reduced in individuals with insulin
resistance and T2DM [13, 14].
Polymorphisms in the glucocorticoid receptor (GR) gene have been reported to be
associated with altered glucocorticoid sensitivity and central obesity [15–17].
11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) which plays an important role in
determining tissue glucocorticoid levels has also been implicated in features
of the metabolic syndrome in both humans and transgenic mice by amplifying the
effects of glucocorticoid action [18–20].
The gene encoding the G protein β3 subunit (GNB3) was considered as a good candidate for the metabolic syndrome
and T2DM because it contains a common polymorphism (C825T) that was observed to
be associated in some studies with development of obesity in young individuals
across different ethnic groups, body fat distribution and hypertension.
Furthermore, the T allele causes alternate splicing with functional
consequences [21–23].
A genome wide scan for the metabolic syndrome detected significant linkage to
chromosomal region 3q27 where the APM1 gene encoding adiponectin is located [24].
Furthermore, genetic variation in APM1 was observed to be associated with obesity, insulin resistance [25], and T2DM [26].  Neuropeptide Y (NPY) is a major regulator of
food consumption and energy homeostasis. Increased NPY signaling in the
hypothalamus leads to obesity and its complications such as T2DM and
cardiovascular disease. Polymorphisms in the FTO gene were shown by a genome-wide association analysis [27] to be associated with BMI and with
predisposition to childhood and adult obesity (reviewed in [28]).


## 2.3. Genotyping

DNA samples from
the subjects were prepared from peripheral white blood cells at the
Diabetes/Endocrine Research Center at the University of Washington, USA. Single nucleotide polymorphism (SNP)
genotyping was performed by real-time PCR amplification on an Applied
Biosystems (ABI) 3500 apparatus using ABI TaqMan assays purchased from the
manufacturer. The ABI assay identification numbers are given in Table1.


## 2.4. Statistical Analysis

The frequency of alleles and genotypes were compared in Q1
and Q4 by the chi-square (χ2) test. Bonferroni
corrections to the significance of associations of the PGC1α Gly482Ser polymorphism with weight
gain were not made because of our study design that placed
PPARγ2 and PGC1α as top priority candidate genes (Table 1). All of the genes we tested
had already been reported to be associated with either T2DM or obesity.


## 3. Results

A total of 252 DCCT
non-Hispanic white North American subjects (141 females and 111 males) comprising
the upper and lower quartiles of weight gain with intensive therapy were
initially genotyped, in a double-blinded manner, for a single common SNP in
each of 7 candidate genes (Table1).
The subjects were originally stratified into quartiles regardless of gender.
Quartile 1 (Q1) had 64 females and 63 males gained the least amount of weight
and quartile 4 (Q4) had 85 females and 51 males and gained the highest amount
of weight. Allelic and genotypic frequencies were initially compared between Q1
and Q4 regardless of gender. The results of this analysis revealed a
significant difference in allelic and genotypic frequencies of the Gly482Ser
polymorphism (rs8192678) in PPARGC1A.
The Gly/Gly genotype frequency was 0.49 and 0.32 in Q1 and Q4, respectively, (P  =  .028) and the Gly/Ser genotype was 0.42 and 0.58 in Q1 and Q4, respectively,
(P  =  .028). The genotypic distributions were in Hardy-Weinberg
equilibrium (P  =  .25). The frequency of the Ser allele was 0.30 and 0.39
in Q1 and Q4, respectively, (P  =  .08). The frequencies of alleles and
genotypes in all other 6 candidate genes were not significantly different
between Q1 and Q4.Next, we asked whether
gender plays a role in the observed association. Interestingly, we found that
the Gly482Ser polymorphism inPPARGC1A was associated with weight gain on intensive diabetes therapy only in males (Table 2). The frequency of the Ser
allele in males of Q1 was 0.23 compared to 0.45 in males of Q4 (P  =  .005).
The Ser allele frequency was almost identical in females of Q1 and Q4. As
expected, the frequencies of genotypes in males were also significantly
different between the two quartiles (Table 2). No other covariates were tested for association of the polymorphisms
with weight gain.Table 2
Genotype and allele frequencies of the variants in
candidate genes examined for association with weight gain by DCCT subjects on
intensive diabetes therapy.N, number of subjects; the P-values
compare genotype and minor allele frequencies between quartile 1 (Q1) and
quartile 4 (Q4) of weight gain; NS, statistically not significant. The brackets
indicate that heterozygotes and homozygotes for the minor allele were combined
for comparisons between quartiles.
Gene/polymorphismFrequency in femalesFrequency in malesQ1 (N, %)Q4 (N, %)PQ1 (N, %)Q4 (N, %)PPPARG2Pro/Pro52 (82.5)62 (78.5)NS45 (75.0)42 (84.2)NSPro/Ala11 (17.5)17 (21.5)NS15 (25.0)9 (17.6)NSAla/Ala0  (0)0  (0)0  (0)0  (0)NSAllele frequency0.090.11NS0.130.09NSPPARGC1AGly/Gly25 (40.3)30 (38.0)NS35 (58.3)12 (23.5).0054Gly/Ser[28(45.2)9(14.5)43(54.4)6(7.6)]NS[22(36.7)3(5.0)32(62.8)7(13.7)].0131Ser/SerFrequency of Ser0.370.35NS0.230.45.0050PPARGC1AT/T˜28 (45.2)35 (44.9)NS25 (42.3)28 (54.9)NST/C˜[27(43.5)7(11.3)38(48.7)5(6.4)]NS[26(44.1)8(13.6)19(37.3)4(7.8)]NSC/CFrequency
ofC0.330.30NS0.350.27NSHSD11B1C/C41 (66.1)50 (59.5)NS42 (66.7)32 (62.7)NSC/T[18(29.0)3(4.9)28(33.3)6(7.2)]NS[16(25.4)5(7.9)17(33.3)2(4.0)]NST/TFrequency ofT0.190.23NS0.210.21NSGNB3C/C34 (54.8)35 (44.3)NS30 (50.0)28 (54.9)NSC/T[23(37.1)5(8.1)33(41.8)11(13.9)]NS[23(38.3)7(11.7)19(37.3)4(7.8)]NST/TFrequency
ofT0.270.35NS0.310.27NSNRC31G/G27 (42.2)41 (52.6)NS29 (48.3)25 (49.0)NSG/T[29(45.3)8(12.5)34(43.6)3(3.8)]NS[22(36.7)9(15.0)25(49.0)1(2.0)]NST/TFrequency ofT0.350.26NS0.330.27NSADIPOQC/C32
(52.5)49 (62.0)NS32 (53.3)30 (58.8)NSC/G[22(36.1)7(11.4)24(30.4)6(7.6)]NS[26(43.4)2(3.3)17(33.3)4(7.9)]NSG/GFrequency
ofG0.300.23NS0.250.25NSNPYG/G19
(30.6)21 (26.6)NS15 (25.0)10 (19.6)NSG/A[29(46.8)14(22.6)34(43.0)24(30.4)]NS[32(53.3)13(21.7)27(52.9)14(27.5)]NSA/AFrequency
ofA0.460.52NS0.480.54NSFTOTT23
(36.0)29 (35.0)NS16 (25.4)13 (27.1)NSTA32
(50.0)40 (48.2)NS35 (55.6)25 (52.1)NSAA9
(14.0)14 (17.0)NS12 (19.0)10 (20.8)NSFrequency ofA0.390.41NS0.470.47NSIn an attempt to validate
the above association, we genotyped male subjects belonging to Q2 and Q3 for
the Gly482Ser polymorphism and examined the association with the extent of
weight gain on intensive diabetes therapy in all quartiles. The results are
plotted in Figure1. The frequency
of the Ser allele in Q1 (N  =  126) was 0.23; Q2 (N  =  172) was 0.34; Q3 (N  =  158) was
0.38; Q4 (N  =  102) was 0.45. As observed with Q1 and Q4, the Ser allele was
significantly associated with a trend for higher weight gain with a P-value
of .004 (Chi-square with 3 degrees of freedom), and a P-value of .0004 (trend, Cochran-Armitage).Figure 1
Association of thePPARGC1A
Ser482
allele with weight gain
in all quartiles of males with T1DM on intensive diabetes therapy. The
frequency of the Ser allele in Q1 (N  =  126) was 0.23; Q2 (N  =  172) was 0.34; Q3
(N  =  158) was 0.38; Q4 (N  =  102) was 0.45. The Ser allele was significantly
associated with a trend for higher weight gain with a P-value of .004
(Chi-square with 3 degrees of freedom), and a P-value of .0004 (trend, Cochran-Armitage).No significant differences
in allele and genotype differences between Q1 and Q4 in either males or females
were found for the rest of the candidate genes. The Gly482Ser polymorphism is
widespread among various ethnic groups. The frequency of the Ser allele was
reported to be 0.34 among Germans [10], 0.30 among Danes [8], 0.34 among European Americans (this study), 0.13 among African Americans (unpublished data from our laboratory), 0.18 among Pima Indians [29], 0.43 among Japanese [9],
and 0.39 among Chinese (dbSNP at http://www.ncbi.nlm.nih.gov/SNP/).Subsequently, we assessed
the association of a second polymorphism (rs 2279525) located in the
5′ untranslated region (5′ UTR) of the PGC-1α gene (Figure 2). No association with weight gain was observed in either
males or females (Table 2). This
polymorphism is approximately 46.6 KB upstream of the Gly482Ser polymorphism [30], and the two polymorphisms
are in linkage equilibrium (http://www.hapmap.org/ and our data).Figure 2
Structure of thePPARGC1A gene showing location of
linkage disequilibrium (LD) blocks and sequence polymorphisms.


## 4. Discussion

We found that the common and
widespread Gly482Ser polymorphism in PGC1α is significantly associated with
excessive weight gain in male but not female subjects with T1DM who had
undergone intensive diabetes therapy. The less common Ser allele was associated
with a higher level of weight gain. Subjects who gained excessive weight had a
higher frequency of T2DM among their parents. Therefore, we hypothesized that
the Gly482Ser polymorphism may lead to excessive weight gain in DCCT via its
association with the metabolic syndrome and T2DM.PGC1α is a powerful transcriptional coactivator
of several nuclear receptors, including PPARγ, that regulate key metabolic steps in
energy homeostasis and glucose metabolism. The level of PGC1α mRNA in skeletal muscle was observed
to be lower in individuals with insulin resistance and T2DM [31].
In addition, the expression of PGC1α-responsive genes involved in oxidative
phosphorylation is reduced in skeletal muscle of individuals with T2DM, their
healthy first degree relatives and even those with impaired glucose tolerance [13, 14]. Importantly, PGC1α mRNA levels in skeletal muscle were
lower among carriers of the Ser482 allele [31].
Therefore, it is likely that PGC1α with Ser at position 428 is a less
efficient coactivator of transcription factors, including those that regulate
the PPARGC1A gene itself [32].
Therefore, lower PGC1α activity levels may lead to lower
levels of glucose and fatty acid oxidation and to fat accumulation [14].In
this study, the Ser482
allele predicted excessive weight gain
with intensive diabetes therapy in males, but not in females. Two studies have
also reported a gender-specific association of the Ser allele with obesity. Ridderstråle et al. reported
association of the Ser allele with increased risk of obesity in physically
inactive men over 50 years of age, but not in women [33]. However, in another study [10] the association was observed in
middle-aged women but not men.
The
reason for this conflicting result could be attributed to the older age of
subjects than those in DCCT, for example, the above middle-aged women could
have been postmenopausal. Also, our subjects had T1DM and their weight gain was
due specifically to intensive diabetes therapy. The Ser allele was also found
to be associated with T2DM in Northern Chinese, especially in males [34].
We do not have an explanation for the gender-specific association. One
possibility is that activity levels of PGC1α may be coregulated by estrogens. This
is compatible with the observation that the level of muscle PGC1α mRNA is lower in females than males [31]. Therefore, effects of the Gly482Ser
polymorphism on weight gain may be attenuated by the action of estrogens.The following genetic association studies support the hypothesis
that the PGC1α Gly482Ser polymorphism affects weight gain via its effect on the
metabolic syndrome (which includes insulin resistance and abdominal obesity) and
T2DM. First, a meta-analysis showed a modest association of the Ser482 allele with the risk for T2DM [35]. These case-control study
populations included both males and females (3,718 cases and 4,818 controls)
that were, unfortunately, not independently tested for association. Second, a
locus on chromosome 4p15.1, where the PPARGC1A is located, was found to be associated with abdominal obesity [36] and insulin resistance [37]. Third, the PPARγ2 Pro12Ala and the PGC1α Gly482Ser polymorphisms were associated with conversion from
impaired glucose tolerance to T2DM in the STOP-NIDDM trial [38]. The PPARγ2 Pro12Ala polymorphism has been consistently shown to be
associated with risk for T2DM [6, 7, 39]. Fourth, The Gly482Ser
polymorphism is located in a highly conserved domain that was shown to interact
with and coactivate the muscle enhancer factor 2C that activates the GLUT4 gene
[40].
Importantly, the Ser482 allele was consistently observed to be associated with insulin resistance, T2DM
or obesity. This monoallelic association in different populations represents a
strong validation for this association.Haplotype analysis at thePPARGC1A gene locus in Caucasians
revealed the existence of 4 adjacent multi-SNP linkage disequilibrium (LD) blocks
with a D′ of close to 1 (Figure 2) (http://www.hapmap.org/). The Gly482Ser
polymorphism lies within the 16.2 KB LD block. This block contains 10 haplotypes based on 8
single-SNP genotypes (http://www.broad.mit.edu/mpg/haploview/). Only one haplotype contains the Ser
allele and is the most common one (0.367). The other seven haplotypes have Gly,
the most common (0.224) differs from the Ser-containing haplotype at 8 of the
13 SNPs. Therefore, it is possible that variants in LD with the Ser allele
could contribute to the observed association. Another polymorphism in exon 9
(Thr612Met) (Figure 2)
that lies within the 16.2 KB LD block could also influence the activity
of PGC1α [8].
However, its frequency is quite low in the general population (Met allele
frequency of 0.03) and, therefore, is unlikely to have a major impact on the
observed association with the Ser482 allele.The polymorphism in the 5′ untranslated region ofPPARGC1A (Figure 2) was not associated with
weight gain in either sex in our study. Since this polymorphism is in LD with
those in exon 1, our result is consistent
with lack of association between the Ser74Leu polymorphism in exon 2 (Figure 2) and T2DM [8].No significant association
of variants in the other 7 candidate genes was observed. This does not mean
that these genes do not contribute to excessive weight gain. The relatively
small sample size and the low frequency of some of the polymorphisms may have
resulted in low power to detect an association. A more thorough analysis of
other variants in these genes is necessary using a larger population sample in
order to detect moderate effects on weight gain.


## 5. Conclusions

The common
PGC1α-Gly482Ser polymorphism was
significantly associated with excessive weight gain in male, but not female
subjects with T1DM who had undergone intensive diabetes therapy. The less
common Ser allele was associated with a higher level of weight gain. PGC1α Ser is a less efficient coactivator of
transcription factors. Therefore, lower PGC1α activity levels may lead to lower
levels of glucose and fatty acid oxidation and to fat accumulation.



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*Source: 649286-2009-04-07.xml*