Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes.

ABSTRACT:

Background

The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes.

Methods

Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. T

SUBMITTER: Yacaman Mendez D 

PROVIDER: S-EPMC9578256 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature