Dietary fat intake and polymorphisms at the PPARG locus modulate BMI and type 2 diabetes risk in the D.E.S.I.R. prospective study

Lamri, Amel; Khalil, Charbel Abi; Jaziri, Riphed; Velho, Gilberto; Lantieri, Olivier; Vol, Sylviane; Froguel, Philippe; Balkau, Beverley; Marre, Michel; Fumeron, Frédéric

doi:10.1038/ijo.2011.91

Cited by 51 publications

(48 citation statements)

References 29 publications

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“…Luan et al showed that a low dietary polyunsaturated/saturated fatty acids ratio is associated with a greater BMI in Ala12 carriers, but when this ratio is high, the opposite is found (41). These observations are in line with the results from a large prospective study in a French population (42) and a study in Italian diabetic patients (43). Two other studies reported opposite findings: total dietary fat intake was associated with higher BMI in Pro/Pro individuals, but not among Ala12 carriers in a French Canadian po pulation (44) and healthy American women (45).…”

Section: Discussionsupporting

confidence: 81%

Effects of the PPARG Gene Polymorphisms on Markers of Obesity and the Metabolic Syndrome in Bosnian Subjects

Dujić¹,

Bego²,

Mlinar³

et al. 2014

Journal of Medical Biochemistry

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SummaryBackground: Peroxisome proliferator-activated receptor gamma (PPARg) is a key transcription factor in adipogenesis, and also regulates a number of genes associated with lipid storage and insulin sensitivity. Single nucleotide polymorphisms (SNPs) in the PPARG gene have been associated with obesity and diabetes. In this study, we explored the relationship of three PPARG gene variants with the metabolic syndrome (MetS) and related traits in a population from Bosnia and Herzegovina. Methods: Anthropometric and biochemical parameters were measured in 43 patients with MetS and 43 healthy controls. Subjects were genotyped for Pro12Ala (rs1801282) and 1431C>T (rs3856806) SNPs by classic PCR-restriction fragment length polymorphism analysis, and for -681C>G (rs10865710) variant by real-time PCR. Results: The genotype distributions for the three polymorphisms were not significantly different between MetS patients and controls. The Pro12Ala and 1431C>T variants were associated with lower body mass index in the control subjects (p=0.012 and p=0.049, respectively). In this group, the carriers of Pro12Ala had also lower waist circumference compared to the wild-type homozygotes (p=0.045).

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Section: Discussionsupporting

confidence: 81%

Effects of the PPARG Gene Polymorphisms on Markers of Obesity and the Metabolic Syndrome in Bosnian Subjects

Dujić¹,

Bego²,

Mlinar³

et al. 2014

Journal of Medical Biochemistry

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“…The Pro12Ala variant of PPARG was shown to modify the association between physical activity and glucose regulation in people with (Adamo et al, 2005) and without diabetes (Kahara et al, 2003). Evidence from GEI studies on nutrition and T2D also demonstrated that the carriers of this PPARG variant are more responsive to the beneficial effects of unsaturated fat and less susceptible to the adverse effects of saturated fat on glucose regulation and/ or body mass index (Lamri et al, 2012). Carriers of a TCF7L2 risk variant had a lower T2D risk when they were on low glycemic diet (Cornelis et al, 2009a).…”

Section: Introductionmentioning

confidence: 91%

Air pollution and diabetes association: Modification by type 2 diabetes genetic risk score

Eze

Imboden

Kumar

et al. 2016

Environment International

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Exposure to ambient air pollution (AP) exposure has been linked to type 2 diabetes (T2D) risk. Evidence on the impact of T2D genetic variants on AP susceptibility is lacking. Compared to single variants, joint genetic variants contribute substantially to disease risk. We investigated the modification of AP and diabetes association by a genetic risk score (GRS) covering 63 T2D genes in 1524 first follow-up participants of the Swiss cohort study on air pollution and lung and heart diseases in adults. Genome-wide data and covariates were available from a nested asthma case-control study design. AP was estimated as 10-year mean residential particulate matter <10μm (PM10). We computed count-GRS and weighted-GRS, and applied PM10 interaction terms in mixed logistic regressions, on odds of diabetes. Analyses were stratified by pathways of diabetes pathology and by asthma status. Diabetes prevalence was 4.6% and mean exposure to PM10 was 22μg/m(3). Odds of diabetes increased by 8% (95% confidence interval: 2, 14%) per T2D risk allele and by 35% (-8, 97%) per 10μg/m(3) exposure to PM10. We observed a positive interaction between PM10 and count-GRS on diabetes [ORinteraction=1.10 (1.01, 1.20)], associations being strongest among participants at the highest quartile of count-GRS [OR: 1.97 (1.00, 3.87)]. Stronger interactions were observed with variants of the GRS involved in insulin resistance [(ORinteraction=1.22 (1.00, 1.50)] than with variants related to beta-cell function. Interactions with count-GRS were stronger among asthma cases. We observed similar results with weighted-GRS. Five single variants near GRB14, UBE2E2, PTPRD, VPS26A and KCNQ1 showed nominally significant interactions with PM10 (P<0.05). Our results suggest that genetic risk for T2D may modify susceptibility to air pollution through alterations in insulin sensitivity. These results need confirmation in diabetes cohort consortia.

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“…Although several candidate gene studies have focused on gene-diet interactions (e.g. [43][44][45][46][47] ) are marked with an asterisk (SBP for class 1 interactions, and weight for class 2 interactions). α G , γ G and λ parameters were calculated based on individuals 30-60 years of age, as this was the age range in the dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Approaches include quantitative genetics studies, usually undertaken in twin or familybased cohorts [4,6,7,[34][35][36][37][38] and candidate gene studies, focused on individual genetic variants, haplotypes, or genetic risk scores constructed from variants with high biological priors for interactions or those conveying genome-wide < p < 5x10 TC, total cholesterol; TG, triacylglycerol significant marginal effects [39][40][41][42][43][44][45][46][47]. Several quantitative genetic studies have shown that physical activity attenuates the influence of genetic effects on cardiometabolic traits [4,6,34,35,37,38].…”

Section: Discussionmentioning

confidence: 99%

The heritable basis of gene–environment interactions in cardiometabolic traits

et al. 2016

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Aims/hypothesis Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. Methods Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.Results All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2 ) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. Conclusions/interpretation Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

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Dietary fat intake and polymorphisms at the PPARG locus modulate BMI and type 2 diabetes risk in the D.E.S.I.R. prospective study

Cited by 51 publications

References 29 publications

Effects of the PPARG Gene Polymorphisms on Markers of Obesity and the Metabolic Syndrome in Bosnian Subjects

Effects of the PPARG Gene Polymorphisms on Markers of Obesity and the Metabolic Syndrome in Bosnian Subjects

Air pollution and diabetes association: Modification by type 2 diabetes genetic risk score

The heritable basis of gene–environment interactions in cardiometabolic traits

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