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BMC Medicine
Published in: BMC Medicine 1/2022

Open Access 01-12-2022 | Public Health | Research article

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

Authors: Diego Yacamán Méndez, Minhao Zhou, Ylva Trolle Lagerros, Donaji V. Gómez Velasco, Per Tynelius, Hrafnhildur Gudjonsdottir, Antonio Ponce de Leon, Katarina Eeg-Olofsson, Claes-Göran Östenson, Boel Brynedal, Carlos A. Aguilar Salinas, David Ebbevi, Anton Lager

Published in: BMC Medicine | Issue 1/2022

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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. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes.

Results

Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years.

Conclusions

Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.
Appendix
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Metadata
Title
Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes
Authors
Diego Yacamán Méndez
Minhao Zhou
Ylva Trolle Lagerros
Donaji V. Gómez Velasco
Per Tynelius
Hrafnhildur Gudjonsdottir
Antonio Ponce de Leon
Katarina Eeg-Olofsson
Claes-Göran Östenson
Boel Brynedal
Carlos A. Aguilar Salinas
David Ebbevi
Anton Lager
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-022-02551-6

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