{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T02:56:58Z","timestamp":1781233018262,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2024YFC2510400"],"award-info":[{"award-number":["2024YFC2510400"]}]},{"name":"the Tongzhou District Science and Technology Program of Beijing Municipality","award":["2024YFC2510402"],"award-info":[{"award-number":["2024YFC2510402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Disease count alone does not show which multimorbidity combinations diversify or persist. We examined longitudinal changes in clinically recognisable multimorbidity profiles across three national ageing cohorts. Methods: Harmonised data from the China Health and Retirement Longitudinal Study (CHARLS), the English Longitudinal Study of Ageing (ELSA), and the U.S. Health and Retirement Study (HRS) were analysed. Eight physician-diagnosed chronic conditions were encoded as binary states, and wave-to-wave transitions (four windows in CHARLS and ELSA; five in HRS) were assessed within each cohort. State-level measures characterised accumulation, branching, persistence, and stabilisation sensitivity, supplemented by sensitivity analyses, BranchScore decomposition, prevalence-adjusted enrichment, and a disease-count-preserving permutation null model. Results: The analysis included 17,142 CHARLS, 10,272 ELSA, and 22,034 HRS participants, with baseline multimorbidity of 23.7%, 41.0%, and 58.5%, respectively. Transitions are concentrated around common profiles. One- and two-condition states (hypertension, diabetes, heart disease, chronic lung disease, psychiatric or emotional disorders) showed faster accumulation and greater branching; later persistent states were dominated by cardiometabolic-musculoskeletal combinations, particularly hypertension-heart disease-arthritis and hypertension-diabetes-arthritis. Targeted stabilisation produced modest perturbations exceeding random benchmarks. Count-preserving null models showed that early branching was largely structural, whereas selected lock-in states exceeded null expectations. Conclusions: Across three ageing cohorts, multimorbidity trajectories reflected disease composition as well as count. Because branching was strongly influenced by disease-count geometry, early branching states should be interpreted as structural cohort-level features of the cumulative framework rather than inherently predictive clinical entities. Selected cardiometabolic-musculoskeletal profiles were more persistent and may help frame integrated long-term management; patient-level prediction requires outcome-based studies.<\/jats:p>","DOI":"10.3390\/jcm15124542","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T01:52:08Z","timestamp":1781229128000},"page":"4542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimorbidity Trajectories Across Three National Ageing Cohorts: Early Branching States and Persistent Cardiometabolic-Musculoskeletal Profiles"],"prefix":"10.3390","volume":"15","author":[{"given":"Long","family":"Chen","sequence":"first","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"},{"name":"Arthritis Institute, Peking University, Beijing 100044, China"},{"name":"Peking University Health Science Center, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senyang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"},{"name":"Arthritis Institute, Peking University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqi","family":"Su","sequence":"additional","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"},{"name":"Arthritis Institute, Peking University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1830-9244","authenticated-orcid":false,"given":"Jianhao","family":"Lin","sequence":"additional","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6966-3134","authenticated-orcid":false,"given":"Dan","family":"Xing","sequence":"additional","affiliation":[{"name":"Arthritis Clinic & Research Center, Peking University People\u2019s Hospital, Peking University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1093\/epirev\/mxs009","article-title":"Multimorbidity in older adults","volume":"35","author":"Salive","year":"2013","journal-title":"Epidemiol. 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