Evidence mapPaperPMID 41928335Full record

ArticleDiabetology & metabolic syndrome2026

Frailty predicts the onset and dynamic progression of cardio-renal-metabolic multimorbidity: a multistate modeling analysis from the UK Biobank.

Lulu Liu, Xuhou Ji, Guangzan Yu, Yunlong Wang, Hua He

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Article in Diabetology & metabolic syndrome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Lulu LiuCardiac Division of Emergency Intensive Care Unit, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
Xuhou JiCardiac Division of Emergency Intensive Care Unit, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
Guangzan YuCardiac Division of Emergency Intensive Care Unit, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
Yunlong WangArrhythmia Center, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
Hua HeCardiac Division of Emergency Intensive Care Unit, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China. hehua430@163.com.

Funding

Natural Science Foundation of Beijing Municipality 7202037
6 · The paper itself

Abstract

backgroundFrailty has increasingly been recognized as an important contributor to cardio-renal-metabolic (CRM) diseases. However, most previous studies have focused on isolated outcomes or cross-sectional disease status, lacking a dynamic perspective on disease progression. This study aimed to evaluate the predictive value of frailty for CRM onset and its multistage progression using multistate modeling.

methodsIn this prospective cohort study of 85,003 UK Biobank participants free of CRM diseases at baseline, frailty was categorized as robust, prefrail, and frail. Associations with CRM incidence and progression were examined using Cox proportional hazards and multistate models. The predictive performance of frailty was further assessed using the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

resultsOver a mean follow-up of 12.0 years, 39.6% of participants were robust, 51.9% were prefrail, and 8.5% were frail. Frailty was independently associated with higher risks of CRM multimorbidity in both incidence and progression analyses. Compared with the robust group, the prefrail group had 28.2%, 36.5%, and 38.0% higher risks of first, double, and triple CRM outcomes, respectively (all p < 0.01), while the frail group had corresponding increases of 81.2%, 118.2%, and 171.1% (all p < 0.001). In multistate models, prefrail participants had 27.5% and 9.1% higher risks of transitioning from a healthy state to the first and from the first to double CRM diseases, respectively (both p < 0.05), whereas the frail group showed respective increases of 78.9% and 32.0% (both p < 0.001). Frailty demonstrated modest discriminative ability for CRM outcomes and provided statistically significant and meaningful improvements in NRI and IDI.

conclusionFrailty was independently associated with both the onset and progression of CRM diseases. Incorporating frailty assessment into risk stratification and preventive frameworks may enhance early identification and mitigation of CRM multimorbidity. Further large-scale prospective studies are warranted to validate these associations and clarify their clinical and public health implications.

Indexed as

Cardio-renal-metabolic multimorbidityFrailtyMulti-state modelUK Biobank

Identifiers

PMID41928335
PMCPMC13173895

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