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

• Published in: Nutrition, metabolism, and cardiovascular diseases Vol. 22; no. 5; pp. 449 - 455
• Main Authors: Smith, C.E, Arnett, D.K, Corella, D, Tsai, M.Y, Lai, C.Q, Parnell, L.D, Lee, Y.C, Ordovás, J.M
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-05-2012
• Subjects: adipocytes; Adolescent; Adult; Aged; Aged, 80 and over; alleles; carbohydrate intake; carbohydrates; Cardiovascular; Carrier Proteins - genetics; Carrier Proteins - metabolism; Dietary Carbohydrates - adverse effects; Dietary Fats - adverse effects; European Continental Ancestry Group; Female; Gene-nutrient interaction; Genetic Association Studies; genotype; Heterozygote; homeostasis; Humans; insulin; Insulin - blood; Insulin Resistance; linear models; Male; men; metabolism; Middle Aged; Minnesota; Nutrigenomics - methods; Perilipin; Perilipin-1; Phosphoproteins - genetics; Phosphoproteins - metabolism; Polymorphism, Single Nucleotide; regression analysis; Replication; Sex Characteristics; Utah; women; Young Adult; United States; Gene-nutrient interaction; Insulin resistance; Replication; Perilipin
• ISSN: 0939-4753; 1590-3729
• DOI: 10.1016/j.numecd.2010.09.003

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 g enotype for insulin resistance reinforces the potential usefulness of applying genotype information in the dietary management of insulin resistance.