Integrating social and cardiometabolic risk factors in predicting cardiometabolic multimorbidity: a Bayesian model development and internal validation in Swedish middle-aged adults.

Authors

Kanya Anindya, Juan Merlo, Lars Lind, Tomas Jernberg, Lars Weinehall, Maria Rosvall, Marcus Bendtsen, Nawi Ng

Published in

Scientific reports. Volume 16. Issue 1. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

Cardiometabolic diseases often cluster as multimorbidity, yet current risk prediction models typically focus on a single disease and rarely incorporate social determinants. This study aims to develop a Bayesian risk prediction model for cardiometabolic multimorbidity in middle-aged Swedish adults. This prospective cohort included 11,964 adults without prior cardiometabolic disease from the Swedish CArdioPulmonary bioImage Study (SCAPIS; enrolment 2013-2016). Cardiometabolic disease was defined as incident type 2 diabetes (T2D), ischaemic heart disease/heart failure, or stroke. Regularised Bayesian logistic regression models were developed to predict any cardiometabolic disease (≥ 1 condition) and cardiometabolic multimorbidity (≥ 2 conditions) within 2-5 years. Twenty-two social and cardiometabolic risk predictors were considered. During follow-up, 6.0% of participants developed cardiometabolic disease(s) (5.7% with a single cardiometabolic disease and 0.3% with multimorbidity). The prediction model showed fair discrimination for any cardiometabolic disease (AUC 0.76, 95% CI 0.74-0.78) and good discrimination for multimorbidity (AUC 0.89, 95% CI 0.86-0.93). The highest predicted risks were observed among older foreign-born males with hypertension, low HDL-C, high waist circumference, and current/ex-smoker, with predicted risks of 34.7% (95% CrI 30.3%-39.5%) for any cardiometabolic disease and 4.4% (95% CrI 2.0%-7.9%) for multimorbidity. These findings suggest that social determinants and uncertainty estimates may provide useful information for cardiometabolic disease prediction, though further validation is needed before use in clinical practice.

PMID:
42642546
Bibliographic data and abstract were imported from PubMed on 26 Aug 2026.

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