Title of project

Addressing variability in body composition and fat distribution to better assess the risk of diabetes and cardiovascular disease

Abstract

Background: Body mass index (BMI) is commonly used as an indicator of adiposity. However, BMI can be misleading in individual assessments as it fails to capture differences in body composition and fat distribution, which are crucial for evaluating the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). Furthermore, the applicability of BMI in assessing T2D and CVD risk varies between sexes due to inherent differences in body composition and fat distribution. Understanding the role of body composition and fat distribution at different BMI levels across sexes is essential for improving personalized T2D and CVD risk assessment and management.

Aim: Our study aims to unravel the intricacies of individual variations in body composition and fat distribution and their implications for T2D and CVD risk. Recognizing the limitations of BMI as a measure of adiposity, the study will identify distinct population subgroups characterized by unique body composition and fat distribution patterns. Furthermore, the study will develop polygenic scores for these subgroups and apply them to calibrate BMI values for more accurate T2D and CVD risk assessment.

Methods: Our study will analyze data from the UK Biobank and the Danish Diet, Cancer and Health cohort. The UK Biobank encompasses comprehensive body composition data obtained through DEXA scans and fat distribution measures from MRI imaging. The Danish Diet, Cancer, and Health cohort employed bioimpedance scans to assess body composition and waist and hip measurements for fat distribution. We will classify participants into subgroups based on these measures using cluster analysis. Associations between the identified subtypes and the incidence of T2D and CVD will be examined using Cox regression models. We will also identify genetic clusters based on BMI-associated genetic variants and develop corresponding polygenic scores. These scores will be used to genetically calibrate BMI values, which will then be reassessed for their T2D and CVD risk assessment performance. We will explore biological mechanisms underlying the genetic subclusters through gene enrichment analyses and by examining plasma proteomic and metabolomic profiles. All analyses will be stratified by sex to assess sex-specific effects.