Perilipin polymorphism interacts with saturated fat and carbohydrates to modulate insulin resistance

Abstract

Background and aims: Macronutrient intakes and genetic variants have been shown to interact to alter insulin resistance, but replications of gene-nutrient interactions across independent populations are rare, despite their critical importance in establishing credibility. We aimed to investigate a previously demonstrated saturated fat and carbohydrate interaction for insulin resistance for perilipin (PLIN1), a regulator of adipocyte metabolism.

Methods and results: We investigated the previously shown interaction for PLIN1 11482G > A (rs894160) on insulin resistance in US men (n = 462) and women (n = 508) (mean ± SD, 49 ± 16 years). In multivariable linear regression models, we found an interaction (P < 0.05) between the ratio of saturated fat to carbohydrate intake as a continuous variable and PLIN1 11482G > A for HOMA-IR (homeostasis model assessment of insulin resistance) in women. For carriers of the minor allele but not for non-carriers, as the ratio of saturated fat to carbohydrate intake increased, predicted HOMA-IR increased (P = 0.002). By dichotomizing the ratio of saturated fat to carbohydrate intake into high and low, we found significant interaction terms for insulin and HOMA-IR (P < 0.05). When the ratio of saturated fat to carbohydrate was high, insulin and HOMA-IR were higher in minor allele carriers (P = 0.004 and P = 0.003, respectively), but did not differ when the ratio was low. Similar patterns or trends were observed when saturated fat and carbohydrate were dichotomized into high and low as individual macronutrients.

Conclusions: Replication of the previously reported interaction between macronutrient intakes and PLIN1 genotype for insulin resistance reinforces the potential usefulness of applying genotype information in the dietary management of insulin resistance.

Figure 1

Predicted values of HOMA-IR (natural log) by PLIN 11482G>A genotype (CC, n=254; CT+TT, n=254), plotted against saturated fat to carbohydrate ratio as a continuous variable. Predicted values for HOMA-IR (natural log) were calculated from the regression model after adjustment for age, waist, smoking, alcohol, family relationships, hormone replacement therapy and anti-glycemic medications. P value for interaction indicates the statistical significance of the interaction term for the ratio of saturated fat to carbohydrate ratio and PLIN genotype in the adjusted regression model. P values for CC and CT+TT indicate the statistical significance of the regression coefficients in the adjusted regression model.