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.