Genetic risk scores link body fat distribution with specific cardiometabolic profiles

Mathilde Svendstrup, Camilla H Sandholt, Ehm Astrid Andersson Galijatovic, Allan Linneberg, Torben Jørgensen, Thorkild I A Sørensen, Oluf Pedersen, Niels Grarup, Henrik Vestergaard, Torben Hansen

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalObesity
Volume24
Issue number8
Pages (from-to)1778-1785
Number of pages8
ISSN1930-7381
DOIs
Publication statusPublished - 2016
Externally publishedYes

Fingerprint

Dive into the research topics of 'Genetic risk scores link body fat distribution with specific cardiometabolic profiles'. Together they form a unique fingerprint.

Cite this