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Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study.

Sarcopenia is associated with an elevated burden of depressive symptoms, yet screening tools may have limited accuracy and generalizability in this population. We developed and validated an interpretable machine-learning model to predict depressive symptoms risk among middle-aged and older adults wi...

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Publicado en:Inquiry (00469580) Vol. 63; pp. 1 - 20
Autores principales: Li, Enguang, Ai, Fangzhu, Tang, Ping, Wen, Hongjuan, Guo, Botang
Formato: research tables/charts Journal Article
Publicado: Sage Publications Inc. 4/6/2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 4/6/2026
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study.
      aug:
        au:
          Li, Enguang
          Ai, Fangzhu
          Tang, Ping
          Wen, Hongjuan
          Guo, Botang
        affil: College of Management, Changchun University of Chinese Medicine, Jilin, People's Republic of China
      sug:
        subj:
          Sarcopenia Complications
          Risk Assessment
          Prediction Models
          Depression Risk Factors
          Depression Risk Factors
          Human
          Middle Age
          Aged
          Machine Learning Algorithms
          Surveys
          Cross Sectional Studies
          Calibration
          Educational Status
          Sleep Disorders Complications
          Sex Factors
          Leukocytes Analysis
          Male
          Female
          Blood Urea Nitrogen Evaluation
          Osteoarthritis Complications
          Body Mass Index Evaluation
          Lymphocyte Count Evaluation
          Probability
          Interviews
          Questionnaires
          Funding Source
          Depression Prevention and Control
          Depression Symptoms
          Quality of Life
          Support Vector Machine
          Mathematics
          Algorithms
          Random Forest
          Regression
          Data Analysis Software
          Chi Square Test
          T-Tests
          Mann-Whitney U Test
          ROC Curve
          Validity
          Sensitivity and Specificity
          Precision
          Calibration Evaluation
          Logistic Regression
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
          Female
      ab: Sarcopenia is associated with an elevated burden of depressive symptoms, yet screening tools may have limited accuracy and generalizability in this population. We developed and validated an interpretable machine-learning model to predict depressive symptoms risk among middle-aged and older adults with sarcopenia using National Health and Nutrition Examination Survey (NHANES) 2007-2020 data. In this cross-sectional study, we included 913 participants with sarcopenia aged ≥45 years from NHANES 2007-2020. Candidate predictors were selected using Boruta followed by least absolute shrinkage and selection operator (LASSO). Multiple machine-learning models were developed and internally validated for discrimination, calibration, and clinical utility. Shapley Additive exPlanations (SHAP) were used to support interpretability. Reporting followed the TRIPOD+AI guidance. Nine predictors were retained after Boruta–LASSO selection. In the validation set, the logistic regression model showed the best overall performance (AUC 0.794; Brier score 0.065). SHAP analysis highlighted key contributors including education level, sleep disorder, sex, poverty-income ratio, blood urea nitrogen, osteoarthritis, white blood cell count, absolute lymphocyte count, and body mass index. The final model was presented as a clinically usable nomogram for individualized depressive symptoms risk estimation. We developed a validated, interpretable machine-learning model for predicting depressive symptoms risk in middle-aged and older adults with sarcopenia using NHANES data. The nomogram may facilitate rapid risk stratification and targeted interventions to support risk stratification and targeted supportive care addressing both physical and mental health needs.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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