Long‐term weight maintenance and risk of cardiometabolic multimorbidity and its subtypes in middle‐aged and older adults: A pooled landmark analysis in China and the UK
Pooled result · Incident CMMResult — go to the resultHazard ratio 0.762 95% CI 0.669–0.868Authors' conclusion
Higher TTR-BMI is an independent predictor of lower CMM risk in middle-aged and older adults, offering superior prognostic value for metabolic multimorbidity patterns. These findings highlight the clinical importance of prioritising long-term weight maintenance within a target range, rather than single time-point assessments, as a key strategy for delaying the onset of cardiometabolic disease.
Study context
- Population
- Adults aged ≥45 years from China Health and Retirement Longitudinal Study (CHARLS) (China) and English Longitudinal Study of Ageing (ELSA) (UK)
- Participants
- 9,167 adults2 prospective cohorts
- Comparison
- Time in target range for body mass index (BMI): always in range vs never in range
- Outcome
- Incident cardiometabolic multimorbidity (CMM)
Methodological detailslandmark analysis, multivariable Cox proportional hazards models
- Landmark analysis
- Multivariable Cox proportional hazards models
Pooled result
Incident CMM
Hazard ratio
0.762
95% CI 0.669–0.868
Always in rangevs Never in range
HR 0.762 (95% CI 0.669–0.868)
As reported in the abstract
- Each 1-SD increase in time in target range for BMI (TTR-BMI), HR
- 0.896 (95% CI 0.845-0.950)
Further pooled results
Cardiometabolic multimorbidity (CMM) subtype: co-occurrence of diabetes and dyslipidaemia
- HR per TTR-BMI (95% CI 0.59-0.98); inverse association most pronounced for metabolic-dominant phenotypes
- 0.76
Subgroups
- Associations robust across cohorts and stronger in men and individuals aged ≥60 years
Original publication
Long‐term weight maintenance and risk of cardiometabolic multimorbidity and its subtypes in middle‐aged and older adults: A pooled landmark analysis in China and the UK. Diabetes Obes Metab. Published 28 January 2026. doi:10. 1111/ dom. 70519
Composed by AI from the published abstract and checked against it. How Briefs are made →