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Genetic risk scores link body fat distribution with specific cardiometabolic profiles
Author(s) -
Svendstrup Mathilde,
Sandholt Camilla H.,
Andersson Galijatovic Ehm Astrid,
Linneberg Allan,
Jørgensen Torben,
Sørensen Thorkild I.A.,
Pedersen Oluf,
Grarup Niels,
Hansen Torben,
Vestergaard Henrik
Publication year - 2016
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.21473
Subject(s) - waist , medicine , body mass index , anthropometry , single nucleotide polymorphism , diabetes mellitus , proportional hazards model , type 2 diabetes , waist–hip ratio , triglyceride , prospective cohort study , endocrinology , cholesterol , biology , genotype , genetics , gene
Objective Forty‐nine known single nucleotide polymorphisms (SNPs) associating with body mass index (BMI)‐adjusted waist‐hip‐ratio (WHR) (WHRadjBMI) were recently suggested to cluster into three groups with different associations to cardiometabolic traits. Genetic risk scores of the clusters on the risk of incident diabetes and associations with detailed cardiometabolic phenotypes were tested. Methods In a prospective study of 6,121 Inter99 individuals, the risk of incident diabetes using Cox proportional hazards regression was evaluated. Using linear regession, the associations between genetic risk scores and anthropometry and blood samples at fasting and during an oral glucose tolerance test were tested. Analyses were adjusted for age, sex, and BMI. Results Cluster 1 associated with an increased risk of diabetes (HR = 1.05, P = 2.74 × 10 − 4 ) and with a poor metabolic profile, including fasting serum triglyceride (β = 0.98% mmol/L, P = 3.33 × 10 − 8 ) and Matsuda index (β = −0.74%, P = 1.29 × 10 − 4 ). No similar associations for Clusters 2 and 3 were found. The three clusters showed different patterns of association with waist circumference, hip circumference, and height. Conclusions Our results suggest that the 49 WHRadjBMI‐associated SNPs affect metabolic health differently depending on the cluster of SNPs. The clusters further associate differently with anthropometric measures.