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Objective <p>To develop and validate a nomogram prediction model for predicting the risk of cardiometabolic multimorbidity(CMM) in older adults, providing a basis for early risk stratification and clinical intervention.</p> Methods <p>Data from seven survey cycles (2005–2018) of the NHANES database were integrated, incorporating 6,338 older adults aged ≥ 65 years who met the inclusion criteria. LASSO regression was employed to screen characteristic variables associated with CMM, followed by multivariate logistic regression analysis to identify independent risk factors and construct the model. The model’s performance was evaluated using the receiver operating characteristic (ROC) curve (AUC) for discrimination, Spiegelhalter’s z-test for calibration, and Decision Curve Analysis (DCA) for clinical utility.</p> Results <p>LASSO regression identified 10 characteristic variables, all of which were confirmed as independent predictors via multivariate logistic regression: college education or above (OR = 0.731, 95% CI: 0.611–0.873, P = 0.001), waist circumference (OR = 1.026, 95% CI: 1.021–1.030, P &lt; 0.001), poverty-income ratio &gt; 3 (OR = 0.750, 95% CI: 0.622–0.904, P = 0.003), Patient Health Questionnaire-9 (PHQ-9) score (OR = 1.057, 95% CI: 1.041–1.073, P &lt; 0.001), high-density lipoprotein cholesterol (OR = 0.682, 95% CI: 0.585–0.796, P &lt; 0.001), hemoglobin (OR = 0.884, 95% CI: 0.847–0.922, P &lt; 0.001), red blood cell distribution width (OR = 1.052, 95% CI: 1.005–1.102, P = 0.030), blood urea nitrogen (OR = 1.104, 95% CI: 1.079–1.130, P &lt; 0.001), globulin (OR = 1.026, 95% CI: 1.014–1.038, P &lt; 0.001), and total cholesterol (OR = 0.644, 95% CI: 0.608–0.681, P &lt; 0.001). The AUC of the nomogram was 0.735 (95% CI: 0.723–0.748), and internal 10-fold cross-validation yielded a consistent AUC of 0.736 (95% CI: 0.721–0.748). Spiegelhalter’s z-test (z=-0.399, P = 0.655) indicated good calibration. DCA showed a positive net benefit when the threshold probability for CMM was between 15% and 77%. </p> Conclusion <p>The nomogram demonstrates satisfactory predictive performance CMM risk in older adults and serves as a valuable quantitative tool for individualized prevention and control.</p>

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Development and validation of a nomogram to predict the risk of cardiometabolic multimorbidity in older adults: data from the NHANES database

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  • 发表日期：

    2026年3月31日

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  • Jiao-yu Cao,
  • Xiao-juan Zhou,
  • Li-xiang Zhang

摘要

Objective

To develop and validate a nomogram prediction model for predicting the risk of cardiometabolic multimorbidity(CMM) in older adults, providing a basis for early risk stratification and clinical intervention.

Methods

Data from seven survey cycles (2005–2018) of the NHANES database were integrated, incorporating 6,338 older adults aged ≥ 65 years who met the inclusion criteria. LASSO regression was employed to screen characteristic variables associated with CMM, followed by multivariate logistic regression analysis to identify independent risk factors and construct the model. The model’s performance was evaluated using the receiver operating characteristic (ROC) curve (AUC) for discrimination, Spiegelhalter’s z-test for calibration, and Decision Curve Analysis (DCA) for clinical utility.

Results

LASSO regression identified 10 characteristic variables, all of which were confirmed as independent predictors via multivariate logistic regression: college education or above (OR = 0.731, 95% CI: 0.611–0.873, P = 0.001), waist circumference (OR = 1.026, 95% CI: 1.021–1.030, P < 0.001), poverty-income ratio > 3 (OR = 0.750, 95% CI: 0.622–0.904, P = 0.003), Patient Health Questionnaire-9 (PHQ-9) score (OR = 1.057, 95% CI: 1.041–1.073, P < 0.001), high-density lipoprotein cholesterol (OR = 0.682, 95% CI: 0.585–0.796, P < 0.001), hemoglobin (OR = 0.884, 95% CI: 0.847–0.922, P < 0.001), red blood cell distribution width (OR = 1.052, 95% CI: 1.005–1.102, P = 0.030), blood urea nitrogen (OR = 1.104, 95% CI: 1.079–1.130, P < 0.001), globulin (OR = 1.026, 95% CI: 1.014–1.038, P < 0.001), and total cholesterol (OR = 0.644, 95% CI: 0.608–0.681, P < 0.001). The AUC of the nomogram was 0.735 (95% CI: 0.723–0.748), and internal 10-fold cross-validation yielded a consistent AUC of 0.736 (95% CI: 0.721–0.748). Spiegelhalter’s z-test (z=-0.399, P = 0.655) indicated good calibration. DCA showed a positive net benefit when the threshold probability for CMM was between 15% and 77%.

Conclusion

The nomogram demonstrates satisfactory predictive performance CMM risk in older adults and serves as a valuable quantitative tool for individualized prevention and control.