Diabetes, obesity & metabolism · 2026
A Cross-Nationally Validated Nomogram for Cardiometabolic Multimorbidity in Overweight/Obese Older Adults: CHARLS and HRS Cohorts.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Wei Yanhan, He Siyi, Chen Huizhen, Wang Yanzhong
Аннотация
AIMS: Middle-aged and older adults with overweight or obesity are at increased risk of cardiometabolic multimorbidity (CMM), yet validated prediction tools tailored to this population remain limited. This study aimed to develop and validate a multidimensional nomogram integrating metabolic, functional and psychological predictors.
MATERIALS AND METHODS: Using the China Health and Retirement Longitudinal Study (CHARLS) data (2011 and 2015), 3965 overweight/obese adults with a body mass index (BMI) ≥ 24.0 kg/m2 were randomly split into development (n = 2775) and validation (n = 1190) sets. Candidate predictors spanned socio-demographic, lifestyle, functional, psychological and biochemical factors. External validation included 8180 overweight/obese participants from the US Health and Retirement Study (HRS). Least absolute shrinkage and selection operator and multivariable logistic regression were applied to build a nomogram. Performance was assessed by area under the curve (AUC), calibration and decision curve analysis.
RESULTS: CMM prevalence was 3.91%. Eight variables were retained: residence, Activities of daily living (ADL) score, the Center for Epidemiologic Studies Depression Scale (CESD-10) score, haemoglobin A1c (HbA1c), hypertension, arthritis, dyslipidaemia and memory disorder. AUCs were 0.871 (95% CI: 0.838-0.905) (development), 0.817 (95% CI: 0.768-0.867) (validation) and 0.733 (95% CI: 0.714-0.751) in HRS external validation, with acceptable calibration in CHARLS and evidence of underestimation in HRS that improved after intercept recalibration, and clear net benefit. Positive predictive values were additionally reported to support clinical interpretation under the low CMM prevalence.
CONCLUSIONS: This nomogram integrating metabolic, functional and psychological predictors demonstrated strong discrimination in the CHARLS cohort and maintained adequate discriminative performance in a cross-national external validation using the US HRS cohort despite differences in population characteristics and measurement instruments, supporting its potential utility for early CMM risk stratification in overweight/obese older adults. The external HRS validation showed fair discrimination, suggesting that model recalibration may be necessary before broad cross-national implementation.
MATERIALS AND METHODS: Using the China Health and Retirement Longitudinal Study (CHARLS) data (2011 and 2015), 3965 overweight/obese adults with a body mass index (BMI) ≥ 24.0 kg/m2 were randomly split into development (n = 2775) and validation (n = 1190) sets. Candidate predictors spanned socio-demographic, lifestyle, functional, psychological and biochemical factors. External validation included 8180 overweight/obese participants from the US Health and Retirement Study (HRS). Least absolute shrinkage and selection operator and multivariable logistic regression were applied to build a nomogram. Performance was assessed by area under the curve (AUC), calibration and decision curve analysis.
RESULTS: CMM prevalence was 3.91%. Eight variables were retained: residence, Activities of daily living (ADL) score, the Center for Epidemiologic Studies Depression Scale (CESD-10) score, haemoglobin A1c (HbA1c), hypertension, arthritis, dyslipidaemia and memory disorder. AUCs were 0.871 (95% CI: 0.838-0.905) (development), 0.817 (95% CI: 0.768-0.867) (validation) and 0.733 (95% CI: 0.714-0.751) in HRS external validation, with acceptable calibration in CHARLS and evidence of underestimation in HRS that improved after intercept recalibration, and clear net benefit. Positive predictive values were additionally reported to support clinical interpretation under the low CMM prevalence.
CONCLUSIONS: This nomogram integrating metabolic, functional and psychological predictors demonstrated strong discrimination in the CHARLS cohort and maintained adequate discriminative performance in a cross-national external validation using the US HRS cohort despite differences in population characteristics and measurement instruments, supporting its potential utility for early CMM risk stratification in overweight/obese older adults. The external HRS validation showed fair discrimination, suggesting that model recalibration may be necessary before broad cross-national implementation.