Interaction between genetic risk score and dietary carbohydrate intake on high- density lipoprotein cholesterol levels: Findings from the Study of Obesity, Nutrition, Genes and Social factors (SONGS)

Abstract

Background & aims: Cardiometabolic traits are complex interrelated traits that result from a combination of genetic and lifestyle factors. This study aimed to assess the interaction between genetic variants and dietary macronutrient intake on cardiometabolic traits [body mass index, waist circumference, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triacylglycerol, systolic blood pressure, diastolic blood pressure, fasting serum glucose, fasting serum insulin, and glycated haemoglobin].

Methods: This cross-sectional study consisted of 468 urban young adults aged 20 ± 1 years, and it was conducted as part of the Study of Obesity, Nutrition, Genes and Social factors (SONGS) project, a sub-study of the Young Lives study. Thirty-nine single nucleotide polymorphisms (SNPs) known to be associated with cardiometabolic traits at a genome-wide significance level (P<5×10^-8) were used to construct a genetic risk score (GRS).

Results: There were no significant associations between the GRS and any of the cardiometabolic traits. However, a significant interaction was observed between the GRS and carbohydrate intake on HDL-C concentration (P_interaction=0.0007). In the first tertile of carbohydrate intake (≤327 g/day), participants with a high GRS (>37 risk alleles) had a higher concentration of HDL-C than those with a low GRS (≤37 risk alleles) [Beta=0.06 mmol/L, 95% confidence interval (CI), 0.01-0.10; P=0.018]. In the third tertile of carbohydrate intake (>452 grams/day), participants with a high GRS had a lower concentration of HDL-C than those with a low GRS (Beta= -0.04 mmol/L, 95% CI -0.01 to -0.09; P=0.027). A significant interaction was also observed between the GRS and glycaemic load (GL) on the concentration of HDL-C (P_interaction=0.002). For participants with a high GRS, there were lower concentrations of HDL-C across tertiles of GL (P_trend=0.017). There was no significant interaction between the GRS and glycaemic index on the concentration of HDL-C, and none of the other GRS*macronutrient interactions were significant.

Conclusions: Our results suggest that young adults who consume a higher carbohydrate diet and have a higher GRS have a lower HDL-C concentration, which in turn is linked to cardiovascular diseases, and indicate that personalised nutrition strategies targeting a reduction in carbohydrate intake might be beneficial for these individuals.

Keywords: HDL-C; Peru; carbohydrate intake; gene-diet interaction; genetic risk score; lipids.