Prognostic effects of multimorbidity clusters on health outcomes in adults: A systematic review and meta-analysis

Abstract

<p>Background: Multimorbidity is an important global health concern. We evaluated the prognostic impacts of multimorbidity clusters on health outcomes in adults. Methods: This study was registered in PROSPERO (CRD42024528148), and no funding was received. Eight databases (PubMed, EMBASE, Cochrane Library, Web of Science, PsycINFO, CINAHL, Wan Fang, and CNKI) were searched for longitudinal studies reporting the prognostic impacts of multimorbidity clusters. Methodological quality was assessed using Newcastle–Ottawa Scale. Data analysis incorporated narrative synthesis, random-effects meta-analysis, subgroup analysis, meta-regression, sensitivity analysis, and Egger's test. Results: Forty articles identifying 12 multimorbidity clusters were included. Cardiometabolic multimorbidity (adjusted hazard ratio [HR]: 1.97, 95 % confidence interval [CI]: 1.76–2.21; adjusted odds ratio [OR]: 1.44, 95 % CI: 1.16–1.80) had strong prognostic impact on all-cause mortality, followed by cardiopulmonary (adjusted HR: 1.70, 95 % CI: 1.38–2.09), and digestive multimorbidity (adjusted HR: 1.46, 95 % CI: 1.11–1.93). It also predicted circulatory (adjusted HR: 3.41, 95 % CI: 2.27–5.12) and cancer mortality (adjusted HR: 1.32, 95 % CI: 1.04–1.67), activities of daily living disability (adjusted OR: 1.76, 95 % CI: 1.57–1.99), and depression (adjusted OR: 1.53, 95 % CI: 1.27–1.85). Multi-system multimorbidity predicted all-cause mortality (adjusted OR: 1.41, 95 % CI: 1.12–1.77) and activities of daily living disability (adjusted OR: 2.04, 95 % CI: 1.36–3.05). Cardiometabolic multimorbidity predicted a higher risk of all-cause mortality when identified using a pre-determined method. Conclusion: Multimorbidity clusters strongly impact activities of daily living, depression, and mortality, with cardiometabolic multimorbidity warranting particular attention. However, due to methodological limitations, heterogeneity, Asian-dominant samples, and language bias, these results should be interpreted with caution.</p>