INSIG2 variants, dietary patterns and metabolic risk in Samoa

Abstract

Background/Objectives Association of insulin-induced gene 2 (INSIG2) variants with obesity has been confirmed in several but not all follow-up studies. Differences in environmental factors across populations may mask some genetic associations and therefore gene-environment interactions should be explored. We hypothesized that the association between dietary patterns and components of the metabolic syndrome could be modified by INSIG2 variants. Subjects/Methods We conducted a longitudinal study of adiposity and cardiovascular disease risk among 427 and 290 adults from Samoa and American Samoa (1990–95). Principal component analysis on food items from a validated FFQ was used to identify neo-traditional and modern dietary patterns. We explored gene-dietary pattern interactions with the INSIG2 variants rs9308762 and rs7566605. Results Results for American Samoans were mostly non-significant. In Samoa, the neo-traditional dietary pattern was associated with lower triglycerides, BMI, waist circumference, systolic and diastolic blood pressure, and fasting glucose (all p-for-trend<0.05). The modern pattern was significantly associated with higher triglycerides, BMI, waist circumference, and lower HDL cholesterol (all p-for-trend<0.05). A significant interaction for triglycerides was found between the modern pattern and the rs9308762 polymorphism (p=0.04). Those from Samoa consuming the modern pattern have higher triglycerides if they are homozygous for the rs9308762 C allele. Conclusions The common INSIG2 rs9308762 variant was associated with poorer metabolic control and a greater sensitivity of trigylcerides to a modern dietary pattern. Environmental factors need to be taken into account when assessing genetic associations across and within populations.

Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations

“…Environmental factors that distinguish higher vs. lower triglycerides (e.g., obesity, physical inactivity, smoking, alcohol, high-carbohydrate diets, T2DM) are predicted to produce different genetic estimates under quantile-dependent expressivity. Traditionally, these differences have been attributed to gene-environment interactions, where: 1) the effect of the genotype on the phenotype differs by environment [26][27][28][29][30][31][32][33][34][35][36][37][41][42][43][45][46][47][48][49][50][51][52][53][54][55][58][59][60][61][62][63][65][66][67][74][75][76][77][78][79][80] , or equivalently: 2) the effect of the environment on the phenotype differs by genotype 36,[38][39][40]44,…”

Western Dietary Pattern Interaction with APOC3 Polymorphism in the Risk of Metabolic Syndrome: Tehran Lipid and Glucose Study

“…The WDP increases diabetes risk, especially among those with an increased genetic susceptibility to this disease [38]. Samoans adhering to the modern pattern characterized by a high intake of processed foods had higher TG if they were homozygous for the rs9308762 C allele [14]. …”

“…The gene-diet interaction has helped to shed more light on an understanding of this variability, an area that has been poorly explored [6,13]. Baylin et al [14] reported that Samoans adhering to modern dietary patterns have high TG levels if they are homozygous for the rs9308762 C allele. In both Japanese and Chinese Malaysian subjects, significant interactions were found between selected dietary patterns and VEGFR2 single nucleotide polymorphisms (SNP) and blood lipids [15].…”

Prevalence of adiposity and associated cardiometabolic risk factors in the samoan genome‐wide association study

“…While modernization and consequent changes in lifestyle patterns are clearly associated with the increase in adiposity and abnormalities in cardiometabolic traits, there is strong evidence from numerous studies that genetic factors influence our response to environmental exposures such as changing lifestyle and diet (Bouchard, ; Qi and Cho, ). Our prior research in Samoans showed that genetic variation is associated with adiposity and cardiometabolic risk factors (Åberg et al, ) as well as modulation of those risks by gene–environment interaction (Baylin et al, ). Precise identification of loci involved in these complex traits has, however, been difficult, with candidate gene and family‐based linkage studies having limited success.…”