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)

• Autores: Wuni, Ramatu, Curi-Quinto, Katherine, Liu, Litai, Espinoza, Dianela, Aquino, Anthony I., del Valle-Mendoza, Juana, Aguilar-Luis, Miguel Angel, Murray, Claudia, Nunes, Richard, Methven, Lisa, Lovegrove, Julie A., Penny, Mary, Favara, Marta, Sánchez, Alan, Vimaleswaran, Karani Santhanakrishnan
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/684182
• Enlace del recurso: http://hdl.handle.net/10757/684182
• Nivel de acceso: acceso abierto
• Materia: Carbohydrate intake; Genetic risk score; Gene–diet interaction; HDL-C; Lipids; Peru

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 (Pinteraction = 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 g/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 (Pinteraction = 0.002). For participants with a high GRS, there were lower concentrations of HDL-C across tertiles of GL (Ptrend = 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.