A Cross-Nationally Validated Nomogram for Cardiometabolic Multimorbidity in Overweight/Obese Older Adults: CHARLS and
Yanhan Wei1, Siyi He2, Huizhen Chen2,3
1Institute of Health Informatics, University College London, London, UK.
Insights
A new nomogram accurately predicts cardiometabolic multimorbidity (CMM) in overweight/obese older adults using metabolic, functional, and psychological factors. This tool shows strong performance in Chinese and US populations, aiding early risk stratification.
Area of Science:
- Gerontology
- Metabolic Health
- Epidemiology
Background:
- Overweight and obesity increase cardiometabolic multimorbidity (CMM) risk in older adults.
- Limited validated prediction tools exist for CMM in this demographic.
Purpose of the Study:
- Develop and validate a multidimensional nomogram for CMM risk prediction.
- Integrate metabolic, functional, and psychological predictors.
Main Methods:
- Utilized China Health and Retirement Longitudinal Study (CHARLS) data (n=3965) for development and validation.
- Employed Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression.
- Externally validated the nomogram using US Health and Retirement Study (HRS) data (n=8180).
Main Results:
- Identified eight key predictors: residence, ADL score, CESD-10 score, HbA1c, hypertension, arthritis, dyslipidemia, and memory disorder.
- Achieved strong discrimination (AUC 0.871 development, 0.817 validation, 0.733 external HRS).
- Demonstrated acceptable calibration with improved performance after recalibration in the HRS cohort.
Conclusions:
- The developed nomogram effectively predicts CMM risk in overweight/obese older adults.
- Shows strong discriminative performance in Chinese and adequate performance in US cohorts.
- Supports potential utility for early CMM risk stratification, with recalibration recommended for cross-national use.
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.
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