Abstract

We examined the hypothesis that the genetic variants associated with abdominal obesity (AO) risk, excluding skeletal muscle mass impact, interact with lifestyle characteristics of adults aged >40 years in three cohorts from the Korean Genome and Epidemiology Study. Participants from a large city hospital-based cohort, excluding those with cancers, thyroid diseases, chronic kidney disease or brain-related diseases, were divided into AO (case; n=17 545) and control (n=36 283) using the cut-offs for waist circumference of 90 cm for men and 85 cm for women. The genetic variants affecting AO risk were chosen from a genome-wide association study in this cohort with obesity-related covariates, including and excluding skeletal muscle mass as a covariate. The genetic variants were confirmed in the other two cohorts. The interaction between the genetic variants was identified by generalised multifactor dimensionality reduction analysis (GMDR). The polygenic risk scores (PRS)-nutrient interactions were determined. Abdominal obesity was associated with SEC16B_rs543874, KCNQ5_rs2796052, CDKAL1_rs9356744, BDNF_rs6265, FTO_rs1421085, MC4R_rs17782313 and GIPR_rs1444988703 adjusting for covariates excluding LBM. However, the best model with 5 single nucleotide polymorphisms (SNP) contained COL3A1_rs3106801, ADAMTS3_rs13105983, KCNQ5_rs2796044, ZFHX3_rs9938769 and MIR17HG_rs7318578 from GMDR when including LBM in covariates. These genetic variants were specific for AO risk. The expression quantitative trait locus (eQTL) analysis showed that the SNP effects were associated with the corresponding gene expression. The high PRS with the 5 SNP model was positively associated with an increased risk of AO of 1.639 (1.463-1.836). The PRS interacted with the age of menarche, energy intake and physical activity. In conclusion, adults with a high PRS, particularly those experiencing early menarche, may particularly benefit from not exceeding energy requirements and from regular physical exercise in order to prevent abdominal obesity. This finding can be applied to personalised nutrition advice to prevent abdominal obesity.

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