1471-2350-9-45 1471-2350 Research article <p>The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies</p> Cauchi Stéphane Stephane.Cauchi@good.ibl.fr Nead T Kevin knuf08@ufl.edu Choquet Hélène helene@good.ibl.fr Horber Fritz fritz@horber.org Potoczna Natascha natascha.potoczna@horber.org Balkau Beverley balkau@vjf.inserm.fr Marre Michel michel.marre@bch.ap-hop-paris.fr Charpentier Guillaume guillaume.charpentier@ch-sud-francilien.fr Froguel Philippe P.Froguel@imperial.ac.uk Meyre David meyre@good.ibl.fr

CNRS UMR8090, Institut de Biologie de Lille, Génomique et Physiologie Moléculaire des Maladies Métaboliques, Lille, France

Department of Surgery and Internal Medicine, Hirslanden Clinics, Bern and Zurich, Switzerland

INSERM U780-IFR69, Hôpital Paul Brousse, Villejuif, France

Université Paris-Sud, Paris, France

INSERM U695, Hôpital Bichat, Paris, France

Hôpital de Corbeil, service d'endocrinologie et de diabétologie, Corbeil-Essonnes, France

Genomic Medicine, Hammersmith Hospital, Imperial College London, UK

 BMC Medical Genetics 1471-2350 2008 9 1 45 http://www.biomedcentral.com/1471-2350/9/45 18498634 10.1186/1471-2350-9-45 30 1 2008 22 5 2008 22 5 2008 2008 Cauchi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Considering that a portion of the heterogeneity amongst previous replication studies may be due to a variable proportion of obese subjects in case-control designs, we assessed the association of genetic variants with type 2 diabetes (T2D) in large groups of obese and non-obese subjects.

Methods

We genotyped RETN, KCNJ11, HNF4A, HNF1A, GCK, SLC30A8, ENPP1, ADIPOQ, PPARG, and TCF7L2 polymorphisms in 1,283 normoglycemic (NG) and 1,581 T2D obese individuals as well as in 3,189 NG and 1,244 T2D non-obese subjects of European descent, allowing us to examine T2D risk over a wide range of BMI.

Results

Amongst non-obese individuals, we observed significant T2D associations with HNF1A I27L [odds ratio (OR) = 1.14, P = 0.04], GCK -30G>A (OR = 1.23, P = 0.01), SLC30A8 R325W (OR = 0.87, P = 0.04), and TCF7L2 rs7903146 (OR = 1.89, P = 4.5 × 10-23), and non-significant associations with PPARG Pro12Ala (OR = 0.85, P = 0.14), ADIPOQ -11,377C>G (OR = 1.00, P = 0.97) and ENPP1 K121Q (OR = 0.99, P = 0.94). In obese subjects, associations with T2D were detected with PPARG Pro12Ala (OR = 0.73, P = 0.004), ADIPOQ -11,377C>G (OR = 1.26, P = 0.02), ENPP1 K121Q (OR = 1.30, P = 0.003) and TCF7L2 rs7903146 (OR = 1.30, P = 1.1 × 10-4), and non-significant associations with HNF1A I27L (OR = 0.96, P = 0.53), GCK -30G>A (OR = 1.15, P = 0.12) and SLC30A8 R325W (OR = 0.95, P = 0.44). However, a genotypic heterogeneity was only found for TCF7L2 rs7903146 (P = 3.2 × 10-5) and ENPP1 K121Q (P = 0.02). No association with T2D was found for KCNJ11, RETN, and HNF4A polymorphisms in non-obese or in obese individuals.

Conclusion

Genetic variants modulating insulin action may have an increased effect on T2D susceptibility in the presence of obesity, whereas genetic variants acting on insulin secretion may have a greater impact on T2D susceptibility in non-obese individuals.

Background

Type 2 diabetes (T2D) is characterized by defects in both insulin sensitivity and beta-cell dysfunction 1. This disease involves a complex interaction between genetic variants and environment with obesity established as a primary risk factor 2. In this regard, correlations between increased body fat and insulin resistance have been reliably demonstrated 3. Recently, associations between polymorphisms and T2D were shown to be modulated according to obesity status.

ENPP1, ADIPOQ, PPARG and TCF7L2 single nucleotide polymorphisms (SNPs) have previously been associated with T2D 4567 and have shown variable significance amongst different classes of obesity. It has been suggested that the ENPP1 K121Q and ADIPOQ -11,391G>A and -11,377C>G variants may confer greater T2D susceptibility in obese populations 8910. PPARG Pro12Ala, though controversial, was more associated with T2D amongst obese subgroups in our previous study 111213. Conversely, the TCF7L2 rs7903146 T allele has been shown to be more prevalent in T2D non-obese individuals compared to T2D obese subjects 7. Interestingly, the variants in PPARG, ADIPOQ, and ENPP1, which elevate risk in obese populations, have been categorized as acting on insulin resistance while the TCF7L2 variant elevating risk in lean populations has been categorized as acting on insulin secretion 14151617. These preliminary results suggest that different genetic architectures could increase T2D susceptibility according to the presence or absence of obesity.

Based on these observations, we have chosen to confirm previous findings and evaluate additional genetic variants which have a reported effect on either insulin action (RETN -420C>G) 18 or insulin secretion (KCNJ11 E23K, HNF4A rs1884614 and rs2144908, HNF1A I27L, GCK -30G>A, and SLC30A8 R325W) 19202122232425 and which have previously been associated with T2D in large scale studies or meta-analysis 212627282930. The evaluated variants were tested for their differential association with T2D in obese [1,283 normoglycemic (NG) and 1,581 T2D] and non-obese subjects (3,189 NG and 1,244 T2D) of European descent.

Methods

Study population

All NG and T2D subjects were recruited in three different centers, two in France and one in Switzerland. The clinical characteristics of the studied groups are presented in Table 1.

<p>Table 1</p>

Clinical characteristics and cohort distribution of the study population

Obesity status

Glycemic status

Population

N

Sex ratio (men:women)

Age at examination (years)

BMI (kg/m2)

Non-obese

NG

French case-control group

1,067

477:590

46 ± 15

24.3 ± 3.0

French D.E.S.I.R. cohort

2,122

880:1,242

47 ± 10

22.7 ± 2.3

Swiss group

na

na

na

na

Overall

3,189

1357:1832

47 ± 12

23.3 ± 2.6

T2D

French case-control group

1,244

805:439

60 ± 11

26.2 ± 2.6

French D.E.S.I.R. cohort

na

na

na

na

Swiss group

na

na

na

na

Overall

1,244

805:439

60 ± 11

26.2 ± 2.6

Obese

NG

French case-control group

678

181:497

41 ± 11

41.0 ± 8.2

French D.E.S.I.R. cohort

na

na

na

na

Swiss group

605

128:477

42 ± 10

43.7 ± 7.0

Overall

1,283

309:974

42 ± 11

42.2 ± 7.8

T2D

French case-control group

1,347

645:702

56 ± 11

36.8 ± 7.1

French D.E.S.I.R. cohort

na

na

na

na

Swiss group

234

66:168

46 ± 11

42.7 ± 7.2

Overall

1,581

711:870

54 ± 12

37.1 ± 7.4

P-values are from linear regression models adjusted for gender and age

NG: Normoglycemic

T2D: Type 2 Diabetic

na: Not available

Non-obese: BMI < 30 kg/m2

Obese: BMI ≥ 30 kg/m2

Age and BMI data presented as 'mean ± standard deviation'

Unrelated NG and T2D French white adults were recruited using a multimedia campaign run by the "Centre National de la Recherche Scientifique" (CNRS), the Department of Nutrition of the Paris Hôtel-Dieu Hospital and the Pasteur Institute of Lille.

Individuals both non-obese and NG (n = 2,122) of the French general population, aged between 30 and 65 years, participated in the cohort for the Data From an Epidemiological Study on the Insulin Resistance syndrome (D.E.S.I.R.), a 9-year follow-up study that aims to clarify the development of the insulin resistance syndrome and described previously 31. Ethnic origin cannot be legally documented in France. We estimated the proportion of subjects having non-European ancestry from a subgroup of 654 subjects selected in the D.E.S.I.R. cohort, as previously described 32. We genotyped 328 SNPs which were spaced by at least 5 Mb and highly differentiated among individuals from different continents (Fst > 0.2 based on the Perlegen dataset) 33. Analysis using the STRUCTURE software identified only two individuals of non-european ancestry in a total of 654 individuals. From this analysis, the proportion of subjects having non-European ancestry was estimated to be 0.30% in the D.E.S.I.R. cohort. Additionally, all individuals born outside France were excluded from the study.

All NG and T2D Swiss white obese individuals were from Bern and Zurich as described previously 34. They were added to the French samples of the same corpulence category. No heterogeneities were found between these two population groups for all SNPs [see Additional file 1].

<p>Additional file 1</p>

Supplementary table 1. Homogeneity in genotypic distributions between Swiss and French subjects

Click here for file

In all centers, two classes of glycemic status were defined according to the 1997 American Diabetes Association criteria: NG, defined as fasting glucose < 6.1 mmol/l and T2D, defined as fasting plasma glucose ≥ 7.0 mmol/l. Two separate BMI categories were analyzed in this study, according to WHO international classification: non-obese (BMI < 30 kg/m2), and obese (BMI ≥ 30 kg/m2).

This genetic study was approved by the Ethical Committee of Hotel-Dieu in Corbeil-Essonnes and CHRU in Lille and informed consent was obtained from all participants.

SNP genotyping

High-throughput genotyping of all genetic variants was performed using the TaqMan® SNP Genotyping Assays (Applied Biosystems, Foster City, Calif. USA). The PCR primers and TaqMan probes were designed by Primer Express and optimized according to the manufacturer's protocol. There was a 96–99% genotyping success rate for all SNPs and the genotyping error rate was assessed by randomly genotyping 384 control and 384 T2D individuals. No difference was found from the first genotyping results, thus the genotyping error rate was 0% for each SNP. The genotypic distributions for all SNPs were in Hardy-Weinberg equilibrium (P ≥ 0.01) [see Additional file 2].

<p>Additional file 2</p>

Supplementary table 2. Hardy-Weinberg equilibrium for each studied SNP

Click here for file

Statistical analysis

The association with T2D was calculated using a logistic regression model adjusted for age, gender, and BMI. The minimum detectable effect size with a statistical power of 80% was assessed using Quanto [see Additional file 3]. The Woolf test was applied to asses the genotypic heterogeneity between obese and non-obese groups. No Bonferroni correction was applied as, in the case of replication studies, it is unlikely to detect effects due to statistical fluctuation only. All P values are two-sided. SPSS (version 14.0.2) and R statistics (version 2.5.0) software were used for general statistics.

<p>Additional file 3</p>

Supplementary table 3. Minimum detectable effect size with a statistical power of 80%.

Click here for file

Results

All SNPs were evaluated for their contribution to T2D in the complete sample population (4,472 NG and 2,825 T2D), then in obese individuals (1,283 NG and 1,581 T2D) and non-obese subjects (3,189 NG and 1,244 T2D). Clinical characteristics and allelic distributions of the study population are summarized in Table 1 and Table 2, respectively. The best fitting genetic models were selected from previous studies 56713262728293035 and were in agreement with what we found in our analyses (data not shown).

<p>Table 2</p>

Genotypic distributions in obese and non-obese subjects by glycemic status

Non-obese

Obese

Gene

SNP

Genotype 1-1

Genotype 1-2

Genotype 2-2

Genotype 1-1

Genotype 1-2

Genotype 2-2

T2D

NG

T2D

NG

T2D

NG

T2D

NG

T2D

NG

T2D

NG

ADIPOQ

rs17300539

0.82 (1,011)

0.82 (2,579)

0.16 (199)

0.17 (541)

0.01 (16)

0.01 (37)

0.81 (1238)

0.84 (1059)

0.18 (274)

0.15 (189)

0.01 (15)

0.01 (15)

ADIPOQ

rs266729

0.56 (682)

0.56 (1,753)

0.38 (461)

0.37 (1164)

0.07 (83)

0.07 (228)

0.55 (860)

0.6 (761)

0.39 (611)

0.33 (425)

0.06 (89)

0.07 (85)

ENPP1

rs1044498

0.73 (893)

0.72 (2,227)

0.25 (308)

0.25 (785)

0.02 (29)

0.03 (79)

0.7 (1086)

0.74 (917)

0.27 (414)

0.23 (286)

0.03 (53)

0.03 (32)

PPARG

rs1801282

0.82 (1010)

0.79 (2498)

0.17 (209)

0.2 (638)

0.01 (9)

0.01 (30)

0.81 (1232)

0.76 (920)

0.18 (270)

0.22 (270)

0.01 (19)

0.02 (20)

RETN

rs1862513

0.48 (590)

0.49 (1499)

0.43 (533)

0.41 (1249)

0.09 (107)

0.1 (291)

0.5 (775)

0.48 (572)

0.4 (626)

0.44 (530)

0.1 (150)

0.08 (102)

GCK

rs1799884

0.64 (747)

0.68 (2045)

0.32 (369)

0.29 (858)

0.04 (48)

0.03 (97)

0.65 (960)

0.67 (775)

0.32 (466)

0.3 (347)

0.03 (47)

0.03 (37)

HNF1A

rs1169288

0.44 (532)

0.47 (1373)

0.44 (539)

0.43 (1270)

0.12 (151)

0.1 (295)

0.47 (718)

0.44 (537)

0.43 (658)

0.44 (538)

0.11 (168)

0.12 (143)

HNF4A

rs1884614

0.68 (840)

0.69 (2163)

0.29 (354)

0.28 (873)

0.03 (35)

0.03 (87)

0.7 (1078)

0.69 (825)

0.28 (429)

0.28 (340)

0.03 (44)

0.03 (31)

HNF4A

rs2144908

0.68 (814)

0.69 (2153)

0.3 (355)

0.28 (873)

0.03 (34)

0.03 (91)

0.69 (1051)

0.68 (811)

0.28 (429)

0.29 (348)

0.03 (43)

0.03 (31)

KCNJ11

rs5219

0.41 (489)

0.38 (1145)

0.45 (537)

0.48 (1438)

0.15 (180)

0.15 (439)

0.41 (623)

0.4 (480)

0.45 (683)

0.47 (568)

0.15 (222)

0.14 (164)

SLC30A8

rs13266634

0.55 (661)

0.49 (1497)

0.38 (455)

0.42 (1264)

0.08 (94)

0.09 (275)

0.53 (792)

0.51 (617)

0.4 (599)

0.42 (509)

0.08 (114)

0.08 (93)

TCF7L2

rs7903146

0.3 (366)

0.48 (1,512)

0.5 (607)

0.42 (1326)

0.2 (243)

0.09 (290)

0.41 (626)

0.5 (614)

0.46 (715)

0.41 (502)

0.13 (203)

0.1 (119)

Data presented as Frequency (n)

T2D: Type 2 diabetic

NG: Normoglycemic

Non-obese: BMI < 30 kg/m2

Obese: BMI ≥ 30 kg/m2

1: Major allele

2: Minor allele

All NG and T2D Individuals

We first evaluated the genetic variants amongst all NG and T2D individuals regardless of obesity status (Table 3). As previously reported in a more modestly-sized French set, T2D associations were confirmed in PPARG Pro12Ala [odds ratio (OR) = 0.81, P = 0.004], TCF7L2 rs7903146 (OR = 1.59, P = 7.9 × 10-27), and ENPP1 K121Q (OR = 1.15, P = 0.01) 78911. Additionally, GCK -30G>A (OR 1.20, P = 0.001) and SLC30A8 R325W (OR = 0.91, P = 0.03) were also found to increase T2D risk.

<p>Table 3</p>

Type 2 diabetes case-control study by obesity status

Functional relevance

Gene name

SNP rs ID

Genetic model

Obesity status

OR [95%CI]

P value

Woolf test

Risk allele frequency

T2D

NG

Insulin action

ADIPOQ

rs17300539

Dominant

Non-obese

0.96 [0.77–1.20]

0.71

0.5

0.09

0.10

Obese

1.07 [0.84–1.35]

0.58

0.10

0.09

All together

0.99 [0.86–1.15]

0.91

0.10

0.09

ADIPOQ

rs266729

Dominant

Non-obese

1.00 [0.85–1.19]

0.97

0.07

0.26

0.26

Obese

1.25 [1.04–1.49]

0.015

0.25

0.24

All together

1.08 [0.96–1.21]

0.18

0.25

0.25

ENPP1

rs1044498

Additive

Non-obese

0.99 [0.84–1.17]

0.94

0.02

0.15

0.15

Obese

1.30 [1.10–1.54]

0.003

0.17

0.14

All together

1.15 [1.03–1.28]

0.014

0.16

0.15

PPARG

rs1801282

Recessive

Non-obese

0.85 [0.69–1.05]

0.136

0.24

0.91

0.89

Obese

0.73 [0.58–0.90]

0.004

0.90

0.87

All together

0.81 [0.71–0.94]

0.004

0.90

0.89

RETN

rs1862513

Recessive

Non-obese

0.81 [0.60–1.09]

0.16

0.11

0.30

0.30

Obese

1.14 [0.84–1.55]

0.42

0.30

0.31

All together

1.00 [0.82–1.22]

0.99

0.30

0.30

Beta-cell defect

GCK

rs1799884

Additive

Non-obese

1.23 [1.06–1.45]

0.009

0.55

0.20

0.17

Obese

1.15 [0.97–1.35]

0.12

0.19

0.18

All together

1.20 [1.08–1.34]

0.001

0.19

0.18

HNF1A

rs1169288

Additive

Non-obese

1.14 [1.01–1.30]

0.042

0.06

0.34

0.32

Obese

0.96 [0.84–1.10]

0.53

0.32

0.34

All together

1.02 [0.93–1.11]

0.71

0.33

0.32

HNF4A

rs1884614

Additive

Non-obese

1.06 [0.90–1.24]

0.51

0.94

0.17

0.17

Obese

1.05 [0.89–1.25]

0.54

0.17

0.17

All together

1.03 [0.93–1.15]

0.58

0.17

0.17

HNF4A

rs2144908

Additive

Non-obese

1.08 [0.92–1.27]

0.33

0.75

0.18

0.17

Obese

1.04 [0.88–1.24]

0.63

0.17

0.17

All together

1.04 [0.94–1.16]

0.45

0.17

0.17

KCNJ11

rs5219

Dominant

Non-obese

0.93 [0.78–1.10]

0.38

0.8

0.37

0.38

Obese

0.96 [0.80–1.15]

0.68

0.37

0.37

All together

0.90 [0.80–1.01]

0.085

0.37

0.38

SLC30A8

rs13266634

Additive

Non-obese

0.87 [0.76–0.99]

0.043

0.37

0.73

0.70

Obese

0.95 [0.82–1.09]

0.44

0.73

0.71

All together

0.91 [0.83–0.99]

0.033

0.73

0.70

TCF7L2

rs7903146

Additive

Non-obese

1.89 [1.67–2.14]

4.5 × 10 -23

3.2 × 10 -5

0.45

0.30

Obese

1.30 [1.14–1.48]

1.1 × 10 -4

0.36

0.30

All together

1.59 [1.46–1.73]

7.9 × 10 -27

0.40

0.30

Odds ratios were adjusted for age, gender and BMI

Woolf test: Test for heterogeneity in genotypic distribution between obese and non-obese subjects

Non-obese: BMI < 30 kg/m2

Obese: BMI ≥ 30 kg/m2

In contrast, in our whole sample analysis, no evidence for association with T2D was found for the ADIPOQ variants -11,391G>A (OR = 0.99, P = 0.91) and -11,377C>G (OR = 1.08, P = 0.18), HNF1A I27L (OR = 1.01, P = 0.71), HNF4A variants rs1884614 (OR = 1.03, P = 0.58) and rs2144908 (OR = 1.04, P = 0.45), KCNJ11 E23K (OR = 0.9, P = 0.09), or RETN -420C>G (OR = 1.00, P = 0.99).

Non-Obese T2D Association

We then assessed the genetic variants amongst the non-obese population (BMI < 30 kg/m2) (Table 3). The risk alleles of HNF1A I27L (OR = 1.14, P = 0.04), GCK -30G>A (OR = 1.23, P = 0.01), SLC30A8 R325W (OR = 0.87, P = 0.04), and TCF7L2 rs7903146 (OR = 1.89, P = 4.5 × 10-23) conferred a significantly increased risk of T2D.

The ADIPOQ variants -11,391G>A (OR = 0.96, P = 0.71) and -11,377C>G (OR = 1.00, P = 0.97), ENPP1 K121Q (OR = 0.99, P = 0.94), PPARG Pro12Ala (OR = 0.85, P = 0.14), RETN -420C>G (OR = 0.41, P = 0.14), KCNJ11 E23K (OR = 0.96, P = 0.68), and HNF4A variants rs1884614 (OR = 1.05, P = 0.54) and rs2144908 (OR = 1.08, P = 0.33) were not associated with T2D amongst the non-obese group.

Obese T2D Association

In the obese group (BMI ≥ 30 kg/m2), associations with T2D were detected in PPARG Pro12Ala (OR 0.73, P = 0.004), ADIPOQ -11,377C>G (OR = 1.25, P = 0.015), ENPP1 K121Q (OR = 1.30, P = 0.003) and TCF7L2 rs7903146 (OR = 1.30, P = 1.1 × 10-4) (Table 3).

We did not find evidence for increased T2D risk for GCK -30G>A (OR = 1.15, P = 0.12), ADIPOQ -11,391G>A (OR = 1.07, P = 0.58), HNF1A I27L (OR = 0.96, P = 0.53), HNF4A variants rs1884614 (OR = 1.05, P = 0.54) and rs2144908 (OR = 1.04, P = 0.63), KCNJ11 E23K (OR = 0.96, P = 0.68), or SLC30A8 R325W (OR = 0.95, P = 0.44) amongst obese subjects.

Genotypic heterogeneity

Discrepancies in genotypic distribution were assessed by Woolf test between the obese and non-obese categories (Table 3). A genotypic heterogeneity was found for TCF7L2 rs7903146 (P = 3.2 × 10-5) and ENPP1 K121Q (P = 0.02). Their association with BMI change was then assessed in all studied subgroups. Significant effects on BMI were only detected in the T2D group for the TCF7L2 genetic variant [see Additional file 4]. Trends towards heterogeneity were found for ADIPOQ 11377C>G (P = 0.07) and HNF1A I27L (P = 0.06).

<p>Additional file 4</p>

Supplementary table 4. BMI distribution by ENPP1 and TCF7L2 genotypes.

Click here for file

Discussion

This large case-control study further supports the hypothesis that the T2D risk contribution of ENPP1, ADIPOQ, PPARG and TCF7L2 SNPs may be modulated by obesity status 67811. Notably, we made the novel finding that T2D risk, as conferred by HNF1A, GCK, and SLC30A8 genetic variants, may also be modified in the presence/absence of obesity. Despite the stringent standards exacted by the Woolf test, genotypic heterogeneity between obese and non-obese individuals was detected for TCF7L2 rs7903146 and ENPP1 K121Q, and suggested for ADIPOQ 11377C>G and HNF1A I27L SNPs. Future study designs containing more individuals will be needed to confirm all apparent genotypic heterogeneities.

The analysis of HNF4A, KCNJ11 and RETN polymorphisms did not support their role as T2D risk factors despite sufficient statistical power [see Additional file 3] to detect the effect sizes (1.08, 1.09 and 1.16, respectively) previously established by meta-analysis 212627. The inability of the current study to replicate previous findings is likely the result of true but modest contributions to T2D risk in the European population.

While it is known that defects in insulin action and insulin secretion are critical in T2D pathogenesis, their interaction with genetic and environmental factors is less clear. Interestingly, SLC30A8, GCK, and HNF1A genetic variants, all of which have been associated with insulin secretion 22232425, were only associated with T2D in non-obese individuals. The TCF7L2 genetic variant, another SNP modulating beta cell function 1736, was more associated with T2D in non-obese subjects than in obese individuals. Conversely, insulin resistance associated genetic variants of PPARG, ADIPOQ and ENPP1 1415163738 were found to be only associated in obese subjects. While obesity has been strongly associated with increased insulin resistance 3, our data suggests that insulin secretory variants confer a greater T2D risk in non-obese individuals while insulin sensitivity variants more significantly modulate T2D risk in obese subjects.

Currently, some dissension exists regarding the BMI modulation of the Pro12Ala SNP in T2D risk. Contrary to what was reported by Ghoussaini and colleagues 11, other studies of the PPARG Pro12Ala SNP suggest that the protective effect of the Ala allele may be greater when control BMI is lower 1213. The present analysis was specifically designed to detect interactions by analyzing a large number of European subjects with no genotypic heterogeneity and characterized by a wide range of BMI. In the "Diabetes Prevention Program", 55 percent of participants were Caucasian, 45 percent were minorities, most were obese, and most had a family history of T2D 12. Similarly, Ludivico and colleagues performed a combined analysis of Asian, North American, and European populations. Interestingly, the genetic effect on T2D was found to be ~30% stronger in Asians than in the two other more corpulent populations 13. We therefore suggest that population heterogeneity may be the primary contributor to the overall observed BMI effect as supported by the ability of BMI to statistically explain the heterogeneity between these populations but not within Europeans 13. Large ethnically-matched studies would be necessary to know if such interaction is found in non-European subjects.

These results illustrate the importance of BMI in population design as obesity mediated T2D associations may account for part of the difficulty encountered in replication studies. The ENPP1 K121Q variant may be illustrative of this point. Studies exploring the T2D risk contribution of the K121Q SNP amongst obese individuals 810 have found an enhanced association in this subgroup. In the present study, we supported these results in finding no significant effect on T2D in non-obese individuals (OR = 0.99, P = 0.94), an association with T2D in obese subjects (OR = 1.30, P = 0.003) and a subsequent modest effect when obese and non-obese samples were analyzed together (OR = 1.15, P = 0.01). A portion of the heterogeneity amongst previous replication studies may be due to a variable fraction of obese subjects in study designs 583940. Considering these results, future association studies should take into account obesity status when interpreting their data.

A representation of the possible relationship between T2D risk variants and obesity status is modeled in Figure 1. This study suggests that the magnitude of a variant's effect on T2D susceptibility is modulated by both obesity status and SNP function. Specifically, variants effecting insulin action more significantly increase T2D susceptibility in obese individuals while variants effecting insulin secretion confer greater T2D risk in non-obese individuals. In our study, data on waist/hip ratio were not available in most T2D cases. However, further analyses of the issue should consider this anthropometric parameter when analyzing interactions between obesity and T2D.

<p>Figure 1</p>

Functional relevance of genetic variants may affect their association with T2D by obesity status

Functional relevance of genetic variants may affect their association with T2D by obesity status. The present figure does not take into account variants associated with T2D through their effect on BMI.

These data are concordant with results from previous epidemiological and physiological studies. Prospective data indicates that impaired beta cell function, not insulin resistance, predicts future T2D in non-obese subjects 41. Furthermore, it was recently reported that interactions between TCF7L2 genetic variants and adiposity may lead to reduced beta-cell compensation in leaner individuals but facilitate improved compensation in more obese subjects 42. Increased beta-cell mass has indeed been observed in the pancreata of obese compared to lean non-diabetic subjects 43. These results support a primary role of insulin secretory modulating variants in the absence of obesity. Conversely, the hyperbolic relationship of insulin resistance and insulin secretion enables the beta-cell to adequately maintain glucose homeostasis for a large range of insulin resistance levels 44. In non-obese subjects not affected by adiposity-induced insulin resistance, this mechanism may sufficiently compensate the effects of genetic variants increasing insulin resistance.

Obesity-induced insulin resistance is characterized by a series of events: i) impaired regulation of lipolysis in adipocytes, ii) increased circulating levels of free fatty acids 45, iii) ectopic fat storage in muscle and liver, iv) impaired ability of insulin to inhibit hepatic glucose production and muscular glucose uptake 46, v) increased beta cell death by apoptosis (glucolipotoxicity) 4347. In obesity-prone populations, insulin resistance more accurately predicts development of T2D than does insulin secretory dysfunction 48. Previous studies on lipodistrophy in both human and murine models have shown that adipose tissue plays a key role in insulin resistance 49. Interestingly, PPARG, ADIPOQ, and ENPP1 SNPs only associated with T2D in obese subjects, and have a pivotal role in adipocyte differentiation, maturation, and action 505152. The effects of genetic polymorphisms leading to insulin resistance may therefore be worsened by obesity.

In the present study, we suggested that the genetic architecture of T2D may be different in obese and non-obese individuals. In a given ethnic group, the lack of replication in association studies may be, in part, due to different fractions of obesity in case-control designs. Novel SNPs recently identified in genome-wide association studies will prove important in the confirmation of these findings once their functions have been reliably demonstrated.

Abbreviations

T2D: Type 2 Diabetes; NG: Normoglycemic; BMI: Body Mass Index; OR: Odds ratio; SNP: Single Nucleotide Polymorphism.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

SC managed the study, carried out the genetic analyses and drafted the manuscript. KTN carried out the genetic analyses, drafted the manuscript and carried out the genotyping experiments. HC carried out the genotyping experiments. FH participated in the design of the study. NP participated in the design of the database. BB participated in the design of the study. MM participated in the design of the study. GC participated in the design of the study. PF coordinated the study. DM conceived the study, and participated in its design and coordination. All authors read and approved the final manuscript.

Acknowledgements

This work was partly supported by the French Governmental "Agence Nationale de la Recherche", and the charities: "Association Française des Diabétiques" and "Programme national de recherche sur le diabète". We thank Marianne Deweider and Frederic Allegaert for the DNA bank management and Stefan Gaget for his help on phenotype databases. We are indebted to all subjects who participated to this study.

 <p>Type 2 diabetes: principles of pathogenesis and therapy</p> Stumvoll M Goldstein BJ van Haeften TW Lancet 2005 365 9467 1333 1346 10.1016/S0140-6736(05)61032-X 15823385 <p>Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001</p> Mokdad AH Ford ES Bowman BA Dietz WH Vinicor F Bales VS Marks JS Jama 2003 289 1 76 79 10.1001/jama.289.1.76 12503980 <p>Mechanisms linking obesity to insulin resistance and type 2 diabetes</p> Kahn SE Hull RL Utzschneider KM Nature 2006 444 7121 840 846 10.1038/nature05482 17167471 <p>The ENPP1 K121Q polymorphism is associated with type 2 diabetes in European populations: evidence from an updated meta-analysis in 42,042 subjects</p> McAteer JB Prudente S Bacci S Lyon HN Hirschhorn JN Trischitta V Florez JC Diabetes 2008 57 4 1125 1130 10.2337/db07-1336 18071025 <p>Studies of the relationship between the ENPP1 K121Q polymorphism and type 2 diabetes, insulin resistance and obesity in 7,333 Danish white subjects</p> Grarup N Urhammer SA Ek J Albrechtsen A Glumer C Borch-Johnsen K Jorgensen T Hansen T Pedersen O Diabetologia 2006 49 9 2097 2104 10.1007/s00125-006-0353-x 16865358 <p>Single-nucleotide polymorphism haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians</p> Vasseur F Helbecque N Dina C Lobbens S Delannoy V Gaget S Boutin P Vaxillaire M Lepretre F Dupont S Hara K Clement K Bihain B Kadowaki T Froguel P Hum Mol Genet 2002 11 21 2607 2614 10.1093/hmg/11.21.2607 12354786 <p>Effects of TCF7L2 Polymorphisms on Obesity in European Populations</p> Cauchi S Choquet H Gutierrez-Aguilar R Capel F Grau K Proenca C Dina C Duval A Balkau B Marre M Potoczna N Langin D Horber F Sorensen TI Charpentier G Meyre D Froguel P Obesity (Silver Spring) 2008 16 2 476 482 10.1038/oby.2007.77 18239663 <p>Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes</p> Meyre D Bouatia-Naji N Tounian A Samson C Lecoeur C Vatin V Ghoussaini M Wachter C Hercberg S Charpentier G Patsch W Pattou F Charles MA Tounian P Clement K Jouret B Weill J Maddux BA Goldfine ID Walley A Boutin P Dina C Froguel P Nat Genet 2005 37 8 863 867 2000804 16025115 10.1038/ng1604 <p>Hypoadiponectinaemia and high risk of type 2 diabetes are associated with adiponectin-encoding (ACDC) gene promoter variants in morbid obesity: evidence for a role of ACDC in diabesity</p> Vasseur F Helbecque N Lobbens S Vasseur-Delannoy V Dina C Clement K Boutin P Kadowaki T Scherer PE Froguel P Diabetologia 2005 48 5 892 899 10.1007/s00125-005-1729-z 15830179 <p>New polymorphism of ENPP1 (PC-1) is associated with increased risk of type 2 diabetes among obese individuals</p> Bochenski J Placha G Wanic K Malecki M Sieradzki J Warram JH Krolewski AS Diabetes 2006 55 9 2626 2630 10.2337/db06-0191 16936213 <p>Implication of the Pro12Ala polymorphism of the PPAR-gamma 2 gene in type 2 diabetes and obesity in the French population</p> Ghoussaini M Meyre D Lobbens S Charpentier G Clement K Charles MA Tauber M Weill J Froguel P BMC Med Genet 2005 6 11 1084346 15784141 10.1186/1471-2350-6-11 <p>Effects of the type 2 diabetes-associated PPARG P12A polymorphism on progression to diabetes and response to troglitazone</p> Florez JC Jablonski KA Sun MW Bayley N Kahn SE Shamoon H Hamman RF Knowler WC Nathan DM Altshuler D J Clin Endocrinol Metab 2007 92 4 1502 1509 2267936 17213274 10.1210/jc.2006-2275 <p>Heterogeneous effect of peroxisome proliferator-activated receptor gamma2 Ala12 variant on type 2 diabetes risk</p> Ludovico O Pellegrini F Di Paola R Minenna A Mastroianno S Cardellini M Marini MA Andreozzi F Vaccaro O Sesti G Trischitta V Obesity (Silver Spring) 2007 15 5 1076 1081 17495182 <p>A Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity</p> Deeb SS Fajas L Nemoto M Pihlajamaki J Mykkanen L Kuusisto J Laakso M Fujimoto W Auwerx J Nat Genet 1998 20 3 284 287 10.1038/3099 9806549 <p>The fat-derived hormone adiponectin reverses insulin resistance associated with both lipoatrophy and obesity</p> Yamauchi T Kamon J Waki H Terauchi Y Kubota N Hara K Mori Y Ide T Murakami K Tsuboyama-Kasaoka N Ezaki O Akanuma Y Gavrilova O Vinson C Reitman ML Kagechika H Shudo K Yoda M Nakano Y Tobe K Nagai R Kimura S Tomita M Froguel P Kadowaki T Nat Med 2001 7 8 941 946 10.1038/90984 11479627 <p>Membrane glycoprotein PC-1 inhibition of insulin receptor function occurs via direct interaction with the receptor alpha-subunit</p> Maddux BA Goldfine ID Diabetes 2000 49 1 13 19 10.2337/diabetes.49.1.13 10615944 <p>Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes</p> Lyssenko V Lupi R Marchetti P Del Guerra S Orho-Melander M Almgren P Sjogren M Ling C Eriksson KF Lethagen AL Mancarella R Berglund G Tuomi T Nilsson P Del Prato S Groop L J Clin Invest 2007 117 8 2155 2163 1934596 17671651 10.1172/JCI30706 <p>The hormone resistin links obesity to diabetes</p> Steppan CM Bailey ST Bhat S Brown EJ Banerjee RR Wright CM Patel HR Ahima RS Lazar MA Nature 2001 409 6818 307 312 10.1038/35053000 11201732 <p>The E23K variant of Kir6.2 associates with impaired post-OGTT serum insulin response and increased risk of type 2 diabetes</p> Nielsen EM Hansen L Carstensen B Echwald SM Drivsholm T Glumer C Thorsteinsson B Borch-Johnsen K Hansen T Pedersen O Diabetes 2003 52 2 573 577 10.2337/diabetes.52.2.573 12540638 <p>Mutation in the HNF-4alpha gene affects insulin secretion and triglyceride metabolism</p> Lehto M Bitzen PO Isomaa B Wipemo C Wessman Y Forsblom C Tuomi T Taskinen MR Groop L Diabetes 1999 48 2 423 425 10.2337/diabetes.48.2.423 10334325 <p>Studies in 3,523 Norwegians and meta-analysis in 11,571 subjects indicate that variants in the hepatocyte nuclear factor 4 alpha (HNF4A) P2 region are associated with type 2 diabetes in Scandinavians</p> Johansson S Raeder H Eide SA Midthjell K Hveem K Sovik O Molven A Njolstad PR Diabetes 2007 56 12 3112 3117 10.2337/db07-0513 17827402 <p>Common variants in HNF-1 alpha and risk of type 2 diabetes</p> Holmkvist J Cervin C Lyssenko V Winckler W Anevski D Cilio C Almgren P Berglund G Nilsson P Tuomi T Lindgren CM Altshuler D Groop L Diabetologia 2006 49 12 2882 2891 10.1007/s00125-006-0450-x 17033837 <p>Glucokinase and IRS-2 are required for compensatory beta cell hyperplasia in response to high-fat diet-induced insulin resistance</p> Terauchi Y Takamoto I Kubota N Matsui J Suzuki R Komeda K Hara A Toyoda Y Miwa I Aizawa S Tsutsumi S Tsubamoto Y Hashimoto S Eto K Nakamura A Noda M Tobe K Aburatani H Nagai R Kadowaki T J Clin Invest 2007 117 1 246 257 1716196 17200721 10.1172/JCI17645 <p>Primary pancreatic beta-cell secretory defect caused by mutations in glucokinase gene in kindreds of maturity onset diabetes of the young</p> Velho G Froguel P Clement K Pueyo ME Rakotoambinina B Zouali H Passa P Cohen D Robert JJ Lancet 1992 340 8817 444 448 10.1016/0140-6736(92)91768-4 1354782 <p>A variant in CDKAL1 influences insulin response and risk of type 2 diabetes</p> Steinthorsdottir V Thorleifsson G Reynisdottir I Benediktsson R Jonsdottir T Walters GB Styrkarsdottir U Gretarsdottir S Emilsson V Ghosh S Baker A Snorradottir S Bjarnason H Ng MC Hansen T Bagger Y Wilensky RL Reilly MP Adeyemo A Chen Y Zhou J Gudnason V Chen G Huang H Lashley K Doumatey A So WY Ma RC Andersen G Borch-Johnsen K Nat Genet 2007 39 6 770 775 10.1038/ng2043 17460697 <p>The G/G genotype of a resistin single-nucleotide polymorphism at -420 increases type 2 diabetes mellitus susceptibility by inducing promoter activity through specific binding of Sp1/3</p> Osawa H Yamada K Onuma H Murakami A Ochi M Kawata H Nishimiya T Niiya T Shimizu I Nishida W Hashiramoto M Kanatsuka A Fujii Y Ohashi J Makino H Am J Hum Genet 2004 75 4 678 686 1182055 15338456 10.1086/424761 <p>Common variants in the ATP-sensitive K+ channel genes KCNJ11 (Kir6.2) and ABCC8 (SUR1) in relation to glucose intolerance: population-based studies and meta-analyses</p> van Dam RM Hoebee B Seidell JC Schaap MM de Bruin TW Feskens EJ Diabet Med 2005 22 5 590 598 10.1111/j.1464-5491.2005.01465.x 15842514 <p>Common variants of the hepatocyte nuclear factor-4alpha P2 promoter are associated with type 2 diabetes in the U.K. population</p> Weedon MN Owen KR Shields B Hitman G Walker M McCarthy MI Love-Gregory LD Permutt MA Hattersley AT Frayling TM Diabetes 2004 53 11 3002 3006 10.2337/diabetes.53.11.3002 15504983 <p>Association of common variation in the HNF1alpha gene region with risk of type 2 diabetes</p> Winckler W Burtt NP Holmkvist J Cervin C de Bakker PI Sun M Almgren P Tuomi T Gaudet D Hudson TJ Ardlie KG Daly MJ Hirschhorn JN Altshuler D Groop L Diabetes 2005 54 8 2336 2342 10.2337/diabetes.54.8.2336 16046299 <p>Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type 2 diabetes</p> Winckler W Weedon MN Graham RR McCarroll SA Purcell S Almgren P Tuomi T Gaudet D Bostrom KB Walker M Hitman G Hattersley AT McCarthy MI Ardlie KG Hirschhorn JN Daly MJ Frayling TM Groop L Altshuler D Diabetes 2007 56 3 685 693 10.2337/db06-0202 17327436 <p>[An epidemiologic survey from a network of French Health Examination Centres, (D.E.S.I.R.): epidemiologic data on the insulin resistance syndrome]</p> Balkau B Rev Epidemiol Sante Publique 1996 44 4 373 375 8927780 <p>A genome-wide association study identifies novel risk loci for type 2 diabetes</p> Sladek R Rocheleau G Rung J Dina C Shen L Serre D Boutin P Vincent D Belisle A Hadjadj S Balkau B Heude B Charpentier G Hudson TJ Montpetit A Pshezhetsky AV Prentki M Posner BI Balding DJ Meyre D Polychronakos C Froguel P Nature 2007 445 7130 881 885 10.1038/nature05616 17293876 <p>Whole-genome patterns of common DNA variation in three human populations</p> Hinds DA Stuve LL Nilsen GB Halperin E Eskin E Ballinger DG Frazer KA Cox DR Science 2005 307 5712 1072 1079 10.1126/science.1105436 15718463 <p>Binge eating as a major phenotype of melanocortin 4 receptor gene mutations</p> Branson R Potoczna N Kral JG Lentes KU Hoehe MR Horber FF N Engl J Med 2003 348 12 1096 1103 10.1056/NEJMoa021971 12646666 <p>Genetic variation near the hepatocyte nuclear factor-4 alpha gene predicts susceptibility to type 2 diabetes</p> Silander K Mohlke KL Scott LJ Peck EC Hollstein P Skol AD Jackson AU Deloukas P Hunt S Stavrides G Chines PS Erdos MR Narisu N Conneely KN Li C Fingerlin TE Dhanjal SK Valle TT Bergman RN Tuomilehto J Watanabe RM Boehnke M Collins FS Diabetes 2004 53 4 1141 1149 10.2337/diabetes.53.4.1141 15047633 <p>Variants of the TCF7L2 gene are associated with beta cell dysfunction and confer an increased risk of type 2 diabetes mellitus in the ULSAM cohort of Swedish elderly men</p> Dahlgren A Zethelius B Jensevik K Syvanen AC Berne C Diabetologia 2007 17618413 <p>Plasma adiponectin concentration is associated with skeletal muscle insulin receptor tyrosine phosphorylation, and low plasma concentration precedes a decrease in whole-body insulin sensitivity in humans</p> Stefan N Vozarova B Funahashi T Matsuzawa Y Weyer C Lindsay RS Youngren JF Havel PJ Pratley RE Bogardus C Tataranni PA Diabetes 2002 51 6 1884 1888 10.2337/diabetes.51.6.1884 12031977 <p>The K121Q polymorphism of the PC-1 gene is associated with insulin resistance but not with dyslipidemia</p> Kubaszek A Pihlajamaki J Karhapaa P Vauhkonen I Laakso M Diabetes Care 2003 26 2 464 467 10.2337/diacare.26.2.464 12547881 <p>The K121Q polymorphism of the ENPP1/PC-1 gene is associated with insulin resistance/atherogenic phenotypes, including earlier onset of type 2 diabetes and myocardial infarction</p> Bacci S Ludovico O Prudente S Zhang YY Di Paola R Mangiacotti D Rauseo A Nolan D Duffy J Fini G Salvemini L Amico C Vigna C Pellegrini F Menzaghi C Doria A Trischitta V Diabetes 2005 54 10 3021 3025 10.2337/diabetes.54.10.3021 16186408 <p>No evidence of association of ENPP1 variants with type 2 diabetes or obesity in a study of 8,089 U.K. Caucasians</p> Weedon MN Shields B Hitman G Walker M McCarthy MI Hattersley AT Frayling TM Diabetes 2006 55 11 3175 3179 10.2337/db06-0410 17065358 <p>K-value and low insulin secretion in a non-obese white population: predicted glucose tolerance after 25 years</p> Alvarsson M Wajngot A Cerasi E Efendic S Diabetologia 2005 48 11 2262 2268 10.1007/s00125-005-1929-6 16160865 <p>Transcription factor 7-like 2 (TCF7L2) is associated with gestational diabetes mellitus and interacts with adiposity to alter insulin secretion in Mexican Americans</p> Watanabe RM Allayee H Xiang AH Trigo E Hartiala J Lawrence JM Buchanan TA Diabetes 2007 56 5 1481 1485 10.2337/db06-1682 17317761 <p>Beta-cell deficit and increased beta-cell apoptosis in humans with type 2 diabetes</p> Butler AE Janson J Bonner-Weir S Ritzel R Rizza RA Butler PC Diabetes 2003 52 1 102 110 10.2337/diabetes.52.1.102 12502499 <p>Islet dysfunction in insulin resistance involves impaired insulin secretion and increased glucagon secretion in postmenopausal women with impaired glucose tolerance</p> Larsson H Ahren B Diabetes Care 2000 23 5 650 657 10.2337/diacare.23.5.650 10834425 <p>Role of fatty acids in the pathogenesis of insulin resistance and NIDDM</p> Boden G Diabetes 1997 46 1 3 10 10.2337/diabetes.46.1.3 8971073 <p>Insulin signalling and the regulation of glucose and lipid metabolism</p> Saltiel AR Kahn CR Nature 2001 414 6865 799 806 10.1038/414799a 11742412 <p>Islet beta cell failure in type 2 diabetes</p> Prentki M Nolan CJ J Clin Invest 2006 116 7 1802 1812 1483155 16823478 10.1172/JCI29103 <p>Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus. Prospective studies of Pima Indians</p> Lillioja S Mott DM Spraul M Ferraro R Foley JE Ravussin E Knowler WC Bennett PH Bogardus C N Engl J Med 1993 329 27 1988 1992 10.1056/NEJM199312303292703 8247074 <p>Lipodystrophies</p> Garg A Am J Med 2000 108 2 143 152 10.1016/S0002-9343(99)00414-3 11126308 <p>Dominant negative mutations in human PPARgamma associated with severe insulin resistance, diabetes mellitus and hypertension</p> Barroso I Gurnell M Crowley VE Agostini M Schwabe JW Soos MA Maslen GL Williams TD Lewis H Schafer AJ Chatterjee VK O'Rahilly S Nature 1999 402 6764 880 883 10622252 <p>Adiponectin stimulates glucose utilization and fatty-acid oxidation by activating AMP-activated protein kinase</p> Yamauchi T Kamon J Minokoshi Y Ito Y Waki H Uchida S Yamashita S Noda M Kita S Ueki K Eto K Akanuma Y Froguel P Foufelle F Ferre P Carling D Kimura S Nagai R Kahn BB Kadowaki T Nat Med 2002 8 11 1288 1295 10.1038/nm788 12368907 <p>Role of ENPP1 on adipocyte maturation</p> Liang J Fu M Ciociola E Chandalia M Abate N PLoS ONE 2007 2 9 e882 1964519 17849011 10.1371/journal.pone.0000882

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