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Multicenter Study
. 2012 May;22(5):449-55.
doi: 10.1016/j.numecd.2010.09.003. Epub 2010 Dec 30.

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

C E Smith  1 , D K Arnett, D Corella, M Y Tsai, C Q Lai, L D Parnell, Y C Lee, J M Ordovás
Affiliations
Multicenter Study

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

C E Smith et al. Nutr Metab Cardiovasc Dis. 2012 May.

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.

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Conflict of interest statement

Author disclosures: C. E. Smith, D. K. Arnett, D. Corella, M. Y. Tsai, C. Q. Lai, L. D. Parnell, Y. C. Lee, J. M. Ordovás, have no conflicts of interest.

Figures

Figure 1
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.
See this image and copyright information in PMC

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