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Paradoxical Lower Serum Triglyceride Levels and Higher
Type 2 Diabetes Mellitus Susceptibility in Obese
Individuals with the
PNPLA3
148M Variant
Colin N. A. Palmer
1.
, Cristina Maglio
2.
, Carlo Pirazzi
2
, Maria Antonella Burza
2
, Martin Adiels
2
,
Lindsay Burch
1
, Louise A. Donnelly
1
, Helen Colhoun
1
, Alexander S. Doney
1
, John F. Dillon
1
,
Ewan R. Pearson
1
, Mark McCarthy
3
, Andrew T. Hattersley
4
, Tim Frayling
4
, Andrew D. Morris
1
,
Markku Peltonen
5
, Per-Arne Svensson
2
, Peter Jacobson
2
, Jan Bore
´n
2
, Lars Sjo
¨stro
¨m
2
,
Lena M. S. Carlsson
2
, Stefano Romeo
2,6
*
1Medical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, United Kingdom, 2Department of Molecular and Clinical
Medicine and Center for Cardiovascular and Metabolic Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, 3Oxford Centre for Diabetes,
Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom, 4Peninsula NIHR Clinical Research Facility, Peninsula College of Medicine and Dentistry,
University of Exeter, Exeter, United Kingdom, 5Chronic Disease Epidemiology and Prevention Unit, Department of Chronic Disease Prevention, National Institute for
Health and Welfare, Helsinki, Finland, 6Department of Clinical and Experimental Medicine, University of Catanzaro, Catanzaro, Italy
Abstract
Background:
Obesity is highly associated with elevated serum triglycerides, hepatic steatosis and type 2 diabetes (T2D). The
I148M (rs738409) genetic variant of patatin-like phospholipase domain-containing 3 gene (PNPLA3) is known to modulate
hepatic triglyceride accumulation, leading to steatosis. No association between PNPLA3 I148M genotype and T2D in
Europeans has been reported. Aim of this study is to examine the relationship between PNPLA3 I148M genotypes and
serum triglycerides, insulin resistance and T2D susceptibility by testing a gene-environment interaction model with severe
obesity.
Methods and Findings:
PNPLA3 I148M was genotyped in a large obese cohort, the SOS study (n = 3,473) and in the Go-
DARTS (n = 15,448), a T2D case-control study. Metabolic parameters were examined across the PNPLA3 I148M genotypes in
participants of the SOS study at baseline and at 2- and 10-year follow up after bariatric surgery or conventional therapy. The
associations with metabolic parameters were validated in the Go-DARTS study. Serum triglycerides were found to be lower
in the PNPLA3 148M carriers from the SOS study at baseline and from the Go-DARTS T2D cohort. An increased risk for T2D
conferred by the 148M allele was found in the SOS study (O.R. 1.09, 95% C.I. 1.01-1.39, P = 0.040) and in severely obese
individuals in the Go-DARTS study (O.R. 1.37, 95% C.I. 1.13-1.66, P = 0.001). The 148M allele was no longer associated with
insulin resistance or T2D after bariatric surgery in the SOS study and no association with the 148M allele was observed in the
less obese (BMI,35) individuals in the Go-DARTS study (P for interaction = 0.002). This provides evidence for the obesity
interaction with I48M allele and T2D risk in a large-scale cross-sectional and a prospective interventional study.
Conclusions:
Severely obese individuals carrying the PNPLA3 148M allele have lower serum triglyceride levels, are more
insulin resistant and more susceptible to T2D. This study supports the hypothesis that obesity-driven hepatic lipid
accumulation may contribute to T2D susceptibility.
Citation: Palmer CNA, Maglio C, Pirazzi C, Burza MA, Adiels M, et al. (2012) Paradoxical Lower Serum Triglyceride Levels and Higher Type 2 Diabetes Mellitus
Susceptibility in Obese Individuals with the PNPLA3 148M Variant. PLoS ONE 7(6): e39362. doi:10.1371/journal.pone.0039362
Editor: Anita Magdalena Hennige, University of Tu
¨bingen, Germany
Received January 17, 2012; Accepted May 18, 2012; Published June 18, 2012
Copyright: ß2012 Palmer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by grants from the Swedish Research Council (K2010-55X-11285-13, K2008-65X-20753-01-04), the Swedish Foundation for
Strategic Research to Sahlgrenska Center for Cardiovascular and Metabolic Research and the Swedish federal government under the LUA/ALF agreement. The
strategic initiative for foreigner researchers from the Institute of Medicine at the University of Gothenburg supported Cristina Maglio and Carlo Pirazzi. The
Wellcome Trust provides support for Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (Go-DARTS) and the Scottish Health Informatics
Programme. Further informatics support is provided by the Chief Scientist Office of Scotland. This work was also supported by the UK Medical Research Council
(G0601261). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: stefano.romeo@wlab.gu.se
.These authors contributed equally to this work.
Introduction
Hepatic steatosis, serum triglycerides, insulin resistance and type
2 diabetes are tightly associated [1–5]. The most widely replicated
genetic variant associated with hepatic steatosis is an isoleucine to
methionine substitution at position 148 (I148M, rs738409) in the
patatin-like phospholipase domain-containing 3 gene (PNPLA3)
[6–11]. To date, no association between PNPLA3 I148M genotype
PLoS ONE | www.plosone.org 1 June 2012 | Volume 7 | Issue 6 | e39362
and type 2 diabetes in Europeans has been reported [9,12–14].
However, a recent study showed that hepatic Pnpla3 over-
expression leads to changes in the glucose and triglycerides
homeostasis in mouse models of obesity and diabetes [15].
Moreover, genetic reports in Europeans indicate that obesity
modifies signals arising from the genome, showing that the
PNPLA3 I148M genotype association with increased alanine
transferase (ALT) is greatly exacerbated in obesity [12,16,17].
To test a gene-environment interaction model with severe
obesity in determining metabolic traits, we examined the relation-
ship between PNPLA3 I148M genotypes, serum triglycerides,
insulin resistance and type 2 diabetes susceptibility in a large obese
cohort, the Swedish Obese Subjects (SOS) study, before and after
bariatric surgery. We also validated our findings in the Genetics of
Diabetes Audit and Research Tayside Scotland (Go-DARTS)
study, a type 2 diabetes case-control study.
Methods
Study design
This genetic study involved two independent European cohorts:
the SOS and the Go-DARTS studies (Figure 1). The SOS is
a prospective controlled intervention trial that compares the long
term effects of bariatric surgery and conventional care in obese
subjects (Figure 1A). The Go-DARTS is a cross sectional
population case control study for type 2 diabetes comprising
normal weight to morbidly obese individuals (Figure 1B).
The hypothesis of an association between PNPLA3 I148M
genotype and serum triglycerides, insulin resistance (assessed by
homeostasis model assessment for insulin resistance, HOMA-IR)
and type 2 diabetes was tested in obese participants of the SOS
study before any treatment (baseline, Figure 1A). Subsequently,
the associations found at baseline were investigated at 2- and 10-
year follow up by analyzing separately the control (individuals who
did not have substantial changes in body weight), and the surgery
group (individuals who underwent bariatric surgery) to test if the
associations were preserved at different time points and if they
were affected by severe weight loss.
Next, the Go-DARTS study (Figure 1B) participants were
examined to confirm the SOS study findings in an independent
study cohort. Specifically, the PNPLA3 I148M association with
serum triglycerides and type 2 diabetes risk in severely obese
individuals (Body-mass index $35) was tested.
Ethic statement
Written Informed consent has been obtained by all study
participants. All clinical investigations have been conducted
according to the principles expressed in the Declaration of
Helsinki. The SOS study protocol was approved the following
Swedish ethics committees: Regional Institutional Review Board
of Gothenburg University, Regional Institutional Review Board of
Linko¨ping University, Regional Institutional Review Board of
Lund University, Regional Institutional Review Board of Kar-
olinska Intitute, in Stockholm, Regional Institutional Review
Board of Umea˚ University, Regional Institutional Review Board
of O
¨rebro University and Regional Institutional Review Board of
Uppsala University. SOS trial has been registered in the
ClinicalTrials.gov registry (NCT01479452, http://clinicaltrials.
gov/ct2/show/NCT01479452?term = NCT01479452&rank
= 1). The Go-DARTS study has been approved by the Tayside
Medical ethics Committee at the Ninewells Hospital and Medical
School, Dundee, UK.
The SOS study
The SOS study has been previously described [18,19]. Briefly,
the study enrolled 4,047 obese individuals in Sweden between
September 1987 and January 2001. A total of 2,010 individuals
constituted the bariatric surgery arm and a matched control group
of 2,037 individuals was enrolled based on 18 matching variables.
Inclusion and exclusion criteria have been presented previously
[18]. A total of 3,585 DNA samples from SOS study participants
(SOS Version 1.0) was available for analyses. PNPLA3 was
successfully genotyped in 3,473 individuals: 1,719 from the control
group and 1,754 from the surgery group (success rate: 97%).
The surgery patients and the conventionally treated controls
both started the study 4 weeks before the date of bariatric surgery
(termed baseline). Biochemical parameters were measured at
baseline and after 2 and 10 years in both the control and the
surgery group. All blood samples were obtained in fasting
conditions [18,19]. Type 2 diabetes (n = 521 at baseline) was
classified as fasting blood glucose $6.1 mmol/L (corresponding to
fasting plasma glucose $7.0 mmol/L or 126 mg/dL) and/or
therapy with glucose-lowering medications. At each examination,
weight, height and blood pressure were measured.
The Go-DARTS study
The Go-DARTS study is a Wellcome Trust-sponsored ongoing
cohort study examining the genetic factors that contribute to type
2 diabetes and related conditions [20–22]. Recruitment was
performed in the Tayside region of Scotland. All participants are
of European ancestry. Between 1997 and 2009, Go-DARTS
recruited 9,000 individuals with type 2 diabetes and 8,187
individuals without type 2 diabetes. PNPLA3 I148M genotyping
was available for a total of 7,691 and 7,757 individuals in the case
and control group, respectively.
Retrospective and follow up data are ascertained from the
Tayside region electronic medical record. Biochemical parameters
were measured at the study visit. ALT levels were available from
routine clinical database.
Genotyping of the PNPLA3 I148M genetic variant
(rs738409)
Fluorogenic 59-nucleotidase (Taqman) allelic discrimination
assay for the PNPLA3 rs738409 sequence variant was used to
Figure 1. Study design. PNPLA3 I148M was genotyped in the
Swedish Obese Subjects (SOS) study (A) and Genetics of Diabetes Audit
and Research Tayside Scotland (Go-DARTS) study (B) participants
(n = 3,473 and 15,448 respectively).
doi:10.1371/journal.pone.0039362.g001
PNPLA3 I148M, Lipid/Glucose Metabolism and Obesity
PLoS ONE | www.plosone.org 2 June 2012 | Volume 7 | Issue 6 | e39362
genotype all participants, as previously described [12,16], with
a success rate of 97%. To ensure for the quality and consistency of
genotyping, three positive controls (one for each PNPLA3
rs738409 I148M genotype) which were previously genotyped by
direct Sanger sequencing were included in each plate of the allelic
discrimination assay. Furthermore, each DNA sample was run in
triplicate and genotype calls with a 100% consistency were
included in the analyses.
Statistical analyses
Variables are shown as means 6standard deviations. HOMA-
IR was calculated only in non-diabetic participants as: [fasting
insulin mIU/L x fasting glucose mmol/L]/22.5 [23]. Genotype
and allele as well as categorical variable distributions across the
genotype classes were compared using x
2
test. Linear regression
analysis was used to assess the effect of the three different PNPLA3
genotypes on continuous dependent variables and P values were
adjusted for age, gender and body-mass index (BMI). P values
were similar after adjustment of triglycerides for HOMA-IR and
ALT or after adjustment of HOMA-IR for ALT and triglycerides.
Changes at 2- and 10- year follow up have been calculated as
follows: [(follow up value – baseline value)/ baseline value] 6100.
Binary logistic regression analysis was used to calculate allelic odds
ratio and 95% confidence interval for insulin resistance, type 2
diabetes and combined risk; age, gender, and BMI were included
in the model as covariates.
Non-normally distributed variables were log transformed before
entering the statistical models. Statistical analyses were carried out
using the Statistical Package for Social Sciences (SPSS, version
18.0.0, Inc. Chicago, IL, USA). P values ,0.05 were considered
significant.
Results
PNPLA3 148M allele is associated with metabolic traits in
obese subjects in the SOS study before bariatric surgery
To assess the effect of the PNPLA3 I148M genotype on serum
lipids and insulin resistance in obese subjects, DNA from SOS
participants was genotyped (Figure 1A). Clinical characteristics of
participants at baseline (n = 3,473; combined control and surgery
groups) are shown in Table S1. Genotype and allele frequencies in
these subjects were in Hardy-Weinberg equilibrium (Table S2)
and they were not different from those previously observed in
Europeans [24]. The clinical characteristics at baseline stratified
for PNPLA3 I148M genotypes are presented in Table 1. All
participants were severely obese at baseline (mean BMI 4165,
Table S1). As a positive control for the genetic analyses ALT were
examined. Increased serum levels of ALT were found in carriers of
the 148M allele at baseline (P,0.001; Table 1). Triglycerides were
found to be lower (P,0.001; Table 1) and HOMA-IR (P = 0.004;
Table 1) to be higher in the PNPLA3 148M carriers at baseline.
Next the risk of being insulin resistant was examined in non
diabetic individuals at baseline. The individuals were divided into
insulin resistant and sensitive according to the median of the
HOMA-IR (in men 3.9 and in women 2.9 U). The PNPLA3 148M
variant was associated with a higher risk of being insulin resistant
(Odds ratio, O.R.: 1.10, 95% Confidence interval, C.I. 1.02-1.35,
P =0.038; Panel A, Table S3). Next, the risk of type 2 diabetes was
examined and the PNPLA3 148M allele was also associated with
a significantly higher risk of type 2 diabetes at baseline after
adjusting for age, gender, and BMI (O.R. 1.09, 95% C.I. 1.01-
1.39, P = 0.040, Panel B, Table S3). The combined risk of
developing insulin resistance or diabetes was consistently increased
(O.R. 1.11, 95% C.I. 1.03-1.37, P = 0.021, Panel C, Table S3).
Associations between PNPLA3 148M allele and metabolic
traits are abolished by surgically induced weight loss
The clinical characteristics at 2- and 10- year follow up in the
control and the surgery group separately, stratified for PNPLA3
I148M genotypes are shown in table S4 and S5, respectively.
Weight changes in the surgery and the control group from baseline
to 10-year follow up have been previously described [19]. Briefly,
there was almost no weight loss in the control group at the 2- and
10-year follow up, whereas the mean BMI in the surgery group
was reduced by 24% at 2- year and 17% at 10- year follow up.
Serum triglycerides were lower in PNPLA3 148M carriers in the
control at 2- and 10-year follow up (P,0.001 in both, Figure 2A)
but not in the surgery group (Figure 2B). HOMA-IR was greater
in carriers of the 148M allele in the control group at the 2-year
(P = 0.001) and 10-year (P = 0.045) follow up (Figure 2C), but no
differences were found across PNPLA3 genotypes in the surgery
group (Figure 2D). In the 2- and 10- year follow up there was no
difference in the PNPLA3 I148M genotype distribution in
participants with and without type 2 diabetes in both the control
and surgery group (Table S4 and S5).
As a positive control for the genetic analyses, ALT levels were
examined. Increased serum levels of ALT were found in carriers of
Table 1. Baseline Clinical Characteristics of SOS Study
Participants Stratified by PNPLA3 I148M Genotype.
PNPLA3 genotype
Characteristic II IM MM P Value*
N2,139 1,179 155 -
Male (%) 30 30 26 0.509
Age (years) 486648664867 0.924
Body-mass index 416541654264 0.139
Systolic blood pressure
(mmHg)
141618 142619 139620 0.954
Diastolic blood pressure
(mmHg)
88611 88611 87611 0.682
Glucose (mg/dL) 90633 93637 90640 0.092
Insulin (mIU/L){18610 19612 19611 0.002
HOMA-IR{3.662.2 3.962.7 3.962.3 0.004
Total cholesterol (mg/dL) 223643 220641 216639 0.020
HDL cholesterol (mg/dL) 52612 52613 51611 0.582
Triglycerides (mg/dL) 1936134 1906135 167680 ,0.001
AST (IU/L) 23611 26616 29613 ,0.001
ALT (IU/L) 34621 39630 45628 ,0.001
Alcohol intake (g/week) 568568567 0.951
Lipid-lowering medications
(%)
2 2 0 0.077
Type 2 diabetes (%) 14 17 12 0.046
Abbreviations: SOS, Swedish obese subjects; PNPLA3, patatin-like
phospholipase domain-containing 3; II, individuals with two 148I alleles; MM,
individuals with two 148M alleles; IM, heterozygotes; n, number; HOMA-IR,
homeostasis model assessment for insulin resistance; HDL, high-density
lipoprotein; AST, aspartate transferase; ALT, alanine transferase.
Plus-minus values are means 6SD.
*P values were calculated using linear regression model including age, gender
and body-mass index for all variables. HOMA-IR, triglycerides, ALT and AST were
log-transformed before entering the model. Male gender, lipid-lowering
medications and type 2 diabetes distribution were compared by x2 test. See
methods for more details on the statistical analyses.
{
Fasting insulin and HOMA-IR are shown only in non-diabetic individuals.
doi:10.1371/journal.pone.0039362.t001
PNPLA3 I148M, Lipid/Glucose Metabolism and Obesity
PLoS ONE | www.plosone.org 3 June 2012 | Volume 7 | Issue 6 | e39362
the 148M allele in the control group at the 2-year (P,0.001) and
10-year (P = 0.004) follow up (Table S4). In the surgery group no
differences in ALT levels were found across PNPLA3 genotypes
(Table S5) at 2- year follow up whereas at 10-year follow up ALT
increased across the PNPLA3 genotypes (P = 0.002; table S5).
Moreover, 2- and 10- year changes in metabolic parameters
were tested across the genotype classes. In the surgery group, the
Figure 2. Serum triglycerides and homeostasis model assessment for insulin resistance (HOMA-IR) at 2- and 10-year follow-up.
Serum triglycerides (panel A and B) levels and HOMA-IR (panel C and D) at 2- and 10- year follow up (FU) in the control (A, C) and the surgery (B, D)
group across the PNPLA3 genotypes. Values are means and standard deviations. * HOMA-IR values are shown only in non-diabetic individuals.
doi:10.1371/journal.pone.0039362.g002
PNPLA3 I148M, Lipid/Glucose Metabolism and Obesity
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PNPLA3 148M allele was associated with a higher reduction in
HOMA-IR and ALT levels and with a lower reduction in
triglyceride levels at 2- and 10-year follow up (Table S6). No
associations were observed in the individuals without surgery.
Association of PNPLA3 148M Allele with Metabolic Traits
in the Go-DARTS Study
The effect of the PNPLA3 148M allele on metabolic traits was
also examined in the Go-DARTS study, a large case (n = 7,691)
control (n = 7,757) study of type 2 diabetes based in the Tayside
region of Scotland (for clinical characteristics Table S7). Genotype
and allele frequencies were in Hardy-Weinberg Equilibrium
(Table S8). Lower levels of fasting serum triglycerides coupled
with an increase in ALT levels (both P,0.001) were observed in
148M diabetic carriers but not in the control group (Table 2).
Subsequently, the Go-DARTS diabetic participants with BMI
$35 (n = 1,842) and ,35 (n = 5,849) were analyzed separately.
The association with ALT levels was present in both groups (data
not shown), but the association with serum triglycerides was only
found in the severely obese (BMI$35) group (II: 2256131, IM:
2086132, MM: 1916131 mg/dL, P = 0.003) and not in those
with BMI,35 (II: 1906122, IM: 1846117, MM: 1846120 mg/
dL, P = 0.219), although the interaction term did not reach
statistical significance (P = 0.18). After stratifying the Go-DARTS
control group by BMI, no associations between serum triglyceride
levels and PNPLA3 were found in individuals either with BMI$35
or ,35. However, it should be pointed out that the severely obese
group without type 2 diabetes was small (n = 456).
Moreover, the PNPLA3 148M allele was associated with
a marginally increased risk of developing type 2 diabetes in the
overall study (O.R. 1.04, 95% C.I. 0.98-1.11, P = 0.104, table 3A).
After stratifying the cohort according to BMI, the 148M allele was
found to confer a significantly increased risk in those with BMI
$35 (n = 1,842 in the type 2 diabetes group and n = 456 in the
control group; O.R. 1.37, 95% C.I. 1.13-1.66, P = 0.001, table 3B)
but not in those with BMI ,35 (n = 5,849 in the type 2 diabetes
group and n = 7,301 in the control group; O.R. 1.01, 95% C.I.
0.94-1.07, P = 0.855, table 3B). A highly significant statistical
interaction term between PNPLA3 genotype and severe obesity
status was observed in determining risk of type 2 diabetes
(P = 0.002).
Discussion
This is the first study showing that the PNPLA3 148M allele is
associated with insulin resistance and increased type 2 diabetes risk
specifically in severely obese individuals despite relatively lower
serum triglycerides. The results of this study are supported by
extensive prior research that has established the robust gene/
environment interaction between the PNPLA3 148M allele and
obesity in determining individual susceptibility to hepatocyte lipid
accumulation and damage [10,12,16,24]. This interaction is
clearly evident in our study, as the obese PNPLA3 148M carriers
in the SOS study had higher ALT levels before, but not after,
weight loss caused by bariatric surgery. We have now shown that
the association of the PNPLA3 148M variant with other
phenotypes is also dependent on increased body mass per se or
on factors related to it.
Lower serum triglycerides and higher insulin resistance in 148M
carriers were found before but not after surgery. This difference in
insulin resistance was seen in the individuals without diabetes but
was also reflected in an observed 15% increased risk for type 2
diabetes in the pre-surgery SOS study participants for each
PNPLA3 148M allele. The absence of the association with type 2
Table 2. Clinical Characteristics of Go-DARTS Study Participants Stratified by PNPLA3 I148M Genotype.
Type 2 Diabetes Control
PNPLA3 genotype PNPLA3 genotype
Characteristic II IM MM P Value* II IM MM P Value*
N4,798 2,553 340 - 4,947 2,476 334 -
Age (years) 66611 66611 66612 ns 61613 61613 61613 ns
Body-mass index 316632663166ns 276527642764ns
Systolic blood pressure
(mmHg)
142619 142619 140618 ns 136620 136620 135618 ns
Diastolic blood pressure
(mmHg)
76611 76611 77612 ns 80610 79610 79610 ns
Glucose (mg/dL) ----89615 89611 90614 ns
Insulin (mIU/L) ----116811671069ns
HOMA-IR ----1.761.4 1.761.2 1.761.7 ns
Total cholesterol (mg/dL) 169636 168637 169636 ns 205641 204641 203641 ns
HDL cholesterol (mg/dL) 52615 52615 51614 ns 63618 63618 62617 ns
Triglycerides (mg/dL) 1986132 1896114 1886111 ,0.001 138687 139698 132672 ns
ALT (IU/L) 31627 34625 42630 ,0.001 27634 30670 28617 ns
HbA1c (%) 7.561.4 7.561.5 7.461.4 ns 5.560.4 5.560.4 5.560.5 ns
Abbreviations: Go-DARTS, Genetics of Diabetes Audit and Research Tayside Scotland; PNPLA3, patatin-like phospholipase domain-containing 3; II, individuals with two
148I alleles; MM, individuals with two 148M alleles; IM, heterozygotes; n, number; HOMA-IR, homeostasis model assessment for insulin resistance HDL, high-density
lipoprotein; ALT, alanine transferase; HbA1c, glycated hemoglobin; ns, P value$0.05.
Plus-minus values are means 6SD.
*P values were calculated using linear regression model including age, body-mass index and gender for all variables. HOMA-IR, triglycerides and ALT were log-
transformed before entering the model. See methods for more details on the statistical analyses.
doi:10.1371/journal.pone.0039362.t002
PNPLA3 I148M, Lipid/Glucose Metabolism and Obesity
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diabetes in the SOS control group at 2- and 10- year follow up is
likely due to a lack of statistical power given the reduced sample
size.
Furthermore, we examined if PNPLA3 I148M variant affects
changes in insulin resistance, triglyceride and transaminase levels.
In the surgery group, the PNPLA3 148M allele was associated with
a higher reduction in HOMA-IR and ALT levels and with a lower
reduction in triglyceride levels at 2- and 10-year follow up. No
associations were present in the control group. This result reflects
the association of PNPLA3 with HOMA-IR, triglyceride and ALT
levels before but not after weight loss.
Similar observations were made in the Go-DARTS study,
where the association with triglycerides was dependant on obesity,
with the lean group showing no association. The association with
diabetes was also consistent between the two studies. While the
overall increased risk of the PNPLA3 148M allele for type 2
diabetes in the Go-DARTS study was only 4%, in the severely
obese a 37% increased risk per 148M allele for type 2 diabetes was
found. Again, no association with type 2 diabetes was seen in those
with a lower BMI.
The mean BMI of the Go-DARTS diabetic cohort is similar to
that of the SOS surgery group in the follow up. However, no effect
of the PNPLA3 genotype on serum parameters was observed in the
SOS surgery group. Surgery may lead to a great improvement of
the metabolic profile due to profound reductions in calorific intake
and thus blunt the effect of the PNPLA3 genotype. Indeed, despite
the similar BMI, the SOS surgery group shows an overall healthier
metabolic profile compared to the Go-DARTS diabetic cohort.
The gene/obesity interaction, where the association is only seen
in a fairly extreme subgroup, may explain why the PNPLA3 148M
allele has not been widely associated with serum triglyceride levels,
insulin resistance and type 2 diabetes in other studies [6,24,25]. In
many population-based or healthy volunteer studies, the preva-
lence of severe obesity would be rather limited. Moreover, a low
statistical power may explain the lack of significant associations in
previous studies performed on obese subjects [10,12,16]. To date,
two well-powered genetic studies have shown an association of the
PNPLA3 148M allele with lower fasting serum triglycerides; this
association was specifically present in overweight/obese individ-
uals and no association was found in the other non-obese cohorts
[24,26]. Furthermore, a recent genetic report showed an
association of the 148M allele with insulin resistance in individuals
of Asian descent with NAFLD where the authors raised the
question regarding the possibility of an association of the 148M
allele and diabetes mellitus [27], however this study was
confounded by the fact that the cases were ascertained by hepatic
steatosis status, therefore the causal relationships between the
genotype and insulin resistance could not be established.
The cut off point of a BMI of 35 used in the analysis of the Go-
DARTS cohort was selected to match the severe obesity in the
SOS study, but further analysis showed that this was an optimal
split point with the associations with serum triglycerides and type 2
diabetes being limited to individuals with severe obesity. In fact,
none of these associations were apparent in the overweight or
moderately obese groups (25.1–34.9, data not shown), although
the association between 148M and ALT levels was only absent in
individuals with a BMI ,25 in the Go-DARTS cohort, pre-
sumably reflecting the more pronounced gene environment
interaction of this variant and hepatocyte damage. This notion is
supported by the fact that only the association with ALT levels was
apparent upon the 10 year weight rebound in the SOS surgery
group.
The novelty of our analysis resides not only in the PNPLA3
148M allele being associated with type 2 diabetes but also
simultaneously with lower serum triglycerides and increased
insulin resistance. Furthermore, it supports a recent study in
which serum triglyceride raising alleles were not found associated
with increased risk for type 2 diabetes [28]. In line with this, the
GCKR gene sequence variant (rs780094) has been found
consistently associated with increased serum triglycerides, de-
creased insulin resistance and protection from type 2 diabetes
[13,29,30]. Moreover, results of this study also suggest the
possibility of finding new genetic loci associated with type 2
diabetes, insulin resistance or fasting triglycerides levels uncovered
by obesity which would serve as a stressor acting on the genetic
background. In vivo genetically modified murine models often
reveal glucose or lipid metabolism changes after obesity is induced
by genetic manipulation or high fat diet [31].
Although no effect on lipid or glucose metabolism was found in
the Pnpla3 knock out mouse model [32,33], a recent study of mice
overexpressing hepatic Pnpla3 demonstrated marked changes in
Table 3. Multivariate regression analysis of Risk of Type 2
Diabetes Mellitus in the Go-DARTS study.
A: Unstratified analysis
Confidence Interval
P value* OR Lower Upper
Gender (F) ,0.0001 0.73 0.68 0.79
Age ,0.0001 1.05 1.05 1.05
BMI ,0.0001 1.19 1.18 1.20
PNPLA3 148M allele 0.104 1.04 0.98 1.11
B: Stratified by Severe Obesity
BMI,35
Confidence Interval
P value* OR Lower Upper
Gender (F) ,0.0001 0.77 0.71 0.83
Age ,0.0001 1.05 1.05 1.06
BMI ,0.0001 1.20 1.19 1.21
PNPLA3 148M allele 0.855 1.01 0.94 1.07
BMI$35
Confidence Interval
P value* OR Lower Upper
Gender (F) ,0.0001 0.56 0.45 0.70
Age ,0.0001 1.02 1.01 1.03
BMI ,0.0001 1.10 1.07 1.14
PNPLA3 148M allele 0.001 1.37 1.13 1.66
*P-values were calculated under an additive model using a binary logistic
regression model including gender as a categorical variable (Males = referent)
and age and BMI as continuous variables.
P value for interaction between severe obesity and I148M for type 2 diabetes
risk = 0.002.
Abbreviations: Go-DARTS, Genetics of Diabetes Audit and Research Tayside
Scotland; OR, odds ratio; F, female; BMI, body mass index; PNPLA3, patatin-like
phospholipase domain-containing 3.
doi:10.1371/journal.pone.0039362.t003
PNPLA3 I148M, Lipid/Glucose Metabolism and Obesity
PLoS ONE | www.plosone.org 6 June 2012 | Volume 7 | Issue 6 | e39362
serum triglycerides and glucose [15]. Fasting serum triglycerides
are a reflection of the basal hepatic VLDL efflux as opposed to
postprandial serum triglycerides which consist mainly of chylomi-
crons [34]. PNPLA3 is a membrane bound protein highly
expressed in hepatocytes [35]. It is tightly associated with the
endoplasmic reticulum and lipid droplets [36] where nascent
VLDL are enriched in triglycerides. This protein shows an in vitro
hydrolytic lipase activity [36] on triglycerides which may be
responsible for the nascent VLDL lipidation. The PNPLA3 148M
mutant protein fails to hydrolyze triglycerides in vitro [36]. The
loss of function mutation could account for a reduction of the
VLDL lipidation and a subsequent impaired hepatic triglycerides
efflux resulting in lower serum triglycerides and liver fat retention.
Under these circumstances liver fat accumulation may lead to
insulin resistance and possibly to type 2 diabetes in obese
individuals. Further physiological studies are warranted to un-
derstand the exact role of PNPLA3 in mediating the observed
phenotypes.
A strength of this study is the evaluation of two large, well-
powered and phenotyped European study cohorts and the use of
a surgical intervention to prospectively modify the gene/obesity
interaction within individuals. A limitation of this study is the lack
of imaging data regarding the accumulation of lipids in the livers
of the obese subjects; however the role of PNPLA3 148M in
modulating hepatic triglyceride accumulation and the correspond-
ing relationships with altered ALT values is well established by
many previous studies. Another limitation of this study is the use of
HOMA-IR to assess insulin resistance; studies on selected severe
obese individuals using euglycaemic clamp to measure insulin
resistance are needed to confirm the association of the 148M allele
and insulin resistance.
In conclusion, this study demonstrates a replicated gene by
obesity interaction in determining type 2 diabetes risk, and
provides insight into the causal role of obesity-driven hepatic lipid
accumulation and type 2 diabetes.
Supporting Information
Table S1 Clinical Characteristics of SOS Study Partic-
ipants at Baseline.
(DOC)
Table S2 Genotype and Allele Frequencies of the
PNPLA3 I148M Sequence Variant (rs738409)in the SOS
Study Participants.
(DOC)
Table S3 Multivariate regression analysis of insulin
resistance, type 2 diabetes and combined risk in the SOS
study.
(DOC)
Table S4 Clinical Characteristics of SOS Study Control
Group Stratified by PNPLA3 I148M Genotype at 2- and
10-Year Follow Up.
(DOC)
Table S5 Clinical Characteristics of SOS Study Surgery
Group Stratified by PNPLA3 I148M Genotype at 2- and
10-Year Follow Up.
(DOC)
Table S6 Two- and ten-year changes in HOMA-IR,
triglyceride and ALT values in the surgery and the
control group from the SOS study.
(DOC)
Table S7 Clinical Characteristics of Go-DARTS Study
Participants.
(DOC)
Table S8 Genotype and Allele Frequencies of the
PNPLA3 I148M Sequence Variant in the Type 2 Diabetes
and Control Groups from the Go-DARTS Study.
(DOC)
Acknowledgments
We acknowledge the support of the Health Informatics Centre, University
of Dundee for managing and supplying the anonymised data and NHS
Tayside, the original data owner. We are grateful to all the participants
who took part in the Go-DARTS study, to the general practitioners, to the
Scottish School of Primary Care for their help in recruiting the
participants, and to the whole team, which includes interviewers, computer
and laboratory technicians, clerical workers, research scientists, volunteers,
managers, receptionists, and nurses.
Author Contributions
Conceived and designed the experiments: CNAP SR CM. Performed the
experiments: CM CP MAB. Analyzed the data: CNAP SR CM CP MAB.
Contributed reagents/materials/analysis tools: CNAP LMSC JB SR.
Wrote the paper: CNAP SR CM CP MAB MA LB LAD HC ASD JFD
ERP MM ATH TF ADM MP PAS PJ JB LS LMSC.
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