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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.

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Title: Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study.
Authors: Li, Enguang1 (AUTHOR), Ai, Fangzhu2 (AUTHOR), Tang, Ping3 (AUTHOR) lhyytp@163.com, Wen, Hongjuan1 (AUTHOR) wenhongjuan2004@163.com, Guo, Botang3 (AUTHOR) hmugbt@hrbmu.edu.cn
Source: Inquiry (00469580). 4/6/2026, Vol. 63, p1-19. 19p.
Subject Terms: *Quality of life, *Machine learning, *Algorithms, *Educational attainment, *Mental depression, *Evaluation, Prevention of mental depression, Mental depression risk factors, Risk assessment, Cross-sectional method, Leukocytes, Random forest algorithms, Prediction models, Body mass index, Research funding, Mathematics, T-test (Statistics), Receiver operating characteristic curves, Sex distribution, Lymphocyte count, Probability theory, Interviewing, Questionnaires, Research evaluation, Logistic regression analysis, Blood urea nitrogen, Chi-squared test, Mann Whitney U Test, Surveys, Support vector machines, Osteoarthritis, Calibration, Data analysis software, Accuracy, Sarcopenia, Sleep disorders, Regression analysis, Sensitivity & specificity (Statistics), Disease complications, Middle age, Old age
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Inquiry (00469580) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1177/00469580261436992
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 19
        StartPage: 1
    Subjects:
      – SubjectFull: Quality of life
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Educational attainment
        Type: general
      – SubjectFull: Mental depression
        Type: general
      – SubjectFull: Evaluation
        Type: general
      – SubjectFull: Prevention of mental depression
        Type: general
      – SubjectFull: Mental depression risk factors
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Cross-sectional method
        Type: general
      – SubjectFull: Leukocytes
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Body mass index
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Receiver operating characteristic curves
        Type: general
      – SubjectFull: Sex distribution
        Type: general
      – SubjectFull: Lymphocyte count
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      – SubjectFull: Probability theory
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      – SubjectFull: Interviewing
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        Type: general
      – SubjectFull: Research evaluation
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      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Blood urea nitrogen
        Type: general
      – SubjectFull: Chi-squared test
        Type: general
      – SubjectFull: Mann Whitney U Test
        Type: general
      – SubjectFull: Surveys
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Osteoarthritis
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Sarcopenia
        Type: general
      – SubjectFull: Sleep disorders
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
      – SubjectFull: Disease complications
        Type: general
      – SubjectFull: Middle age
        Type: general
      – SubjectFull: Old age
        Type: general
    Titles:
      – TitleFull: Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study.
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            – D: 06
              M: 04
              Text: 4/6/2026
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              Y: 2026
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