The cluster of differentiation 36 ( CD36 ) rs1761667 polymorphism interacts with dietary patterns to affect cardiometabolic risk factors and metabolic syndrome risk in apparently healthy individuals.
Zeinab YazdanpanahAmin Salehi-AbargoueiMehdi MollahosseiniMohammad Hasan SheikhhaMasoud MirzaeiHassan Mozaffari-KhosraviPublished in: The British journal of nutrition (2023)
Several studies have examined the association between CD36 rs1761667 polymorphism with cardiometabolic risk factors and metabolic syndrome (MetS). This study aimed to investigate the interactions between rs1761667 polymorphism and dietary patterns on the cardiometabolic risk factors and the risk of MetS in apparently healthy individuals aged 20-70 years. Food consumption data were acquired using a validated semi-quantitative food frequency questionnaire. Dietary patterns were identified by factor analysis. CD36 rs1761667 was genotyped by polymerase chain reaction-restriction fragment length polymorphism. The gene-diet interaction was detected by the general linear model or logistic regression. Significant or marginally significant interactions were observed between healthy dietary pattern (HDP) and CD36 rs1761667 on weight (P=0.006), BMI (P=0.009), waist circumference (P=0.005), hip circumference (P=0.06), body muscle percentage (P=0.02), body fat percentage (P=0.09), triglyceride-glucose index (P=0.057), atherogenic index of plasma (P=0.07), the risk of MetS (P=0.02), risk of abdominal obesity (P=0.02), and elevated blood pressure (P=0.07). Besides, a gene-diet interaction was detected between the traditional dietary pattern (TDP) and rs1761667 variants on odds of hypertriglyceridemia (P=0.02). The adherence to HDP was associated with a lower weight, BMI, and higher odds of HDL-C only in A-allele carriers. In conclusion, adherence to HDP (a diet with high fiber, fish, and dairy products) can be more effective on some cardiometabolic risk factors and risk of MetS components in the A-allele carrier than the GG genotype of rs1761667 polymorphism. However, future studies are required to shed light on this issue.
Keyphrases
- risk factors
- body mass index
- metabolic syndrome
- weight loss
- physical activity
- weight gain
- blood pressure
- body weight
- copy number
- insulin resistance
- nk cells
- genome wide
- big data
- cross sectional
- cardiovascular risk factors
- gene expression
- dna methylation
- hypertensive patients
- machine learning
- cardiovascular disease
- amyotrophic lateral sclerosis
- psychometric properties
- artificial intelligence