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Purpose <p>There is a global increase of cardiovascular disease and diabetes (Cardio-Metabolic diseases: CMD). Suffering from depression or anxiety disorders increases the probability of developing CMD. In this study we tested a wide array of predictors for the onset of CMD with Machine Learning (ML), evaluating whether adding detailed psychiatric or biological variables increases predictive performance.</p> Methods <p>We analysed data from the Netherlands Study of Depression and Anxiety, a longitudinal cohort study (<i>N</i> = 2071), using 368 predictors covering 4 domains (demographic, lifestyle &amp; somatic, psychiatric, and biological markers). CMD onset (24% incidence) over a 9-year follow-up was defined using self-reported stroke, heart disease, diabetes with high fasting glucose levels and (antithrombotic, cardiovascular, or diabetes) medication use (ATC codes C01DA, C01-C05A-B, C07-C09A-B, C01DB, B01, A10A-X). Using different ML methods (Logistic regression, Support vector machine, Random forest, and XGBoost) we tested the predictive performance of single domains and domain combinations.</p> Results <p>The classifiers performed similarly, therefore the simplest classifier (Logistic regression) was selected. The Area Under the Receiver Operator Characteristic Curve (AUC-ROC) achieved by singe domains ranged from 0.569 to 0.649. The combination of demographics, lifestyle &amp; somatic indicators and psychiatric variables performed best (AUC-ROC = 0.669), but did not significantly outperform demographics. Age and hypertension contributed most to prediction; detailed psychiatric variables added relatively little.</p> Conclusion <p>In this longitudinal study, ML classifiers were not able to accurately predict 9-year CMD onset in a sample enriched of subjects with psychopathology. Detailed psychiatric/biological information did not substantially increase predictive performance.</p>

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Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月18日

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  • Arja O. Rydin,
  • George Aalbers,
  • Wessel A. van Eeden,
  • Femke Lamers,
  • Yuri Milaneschi,
  • Brenda W. J. H. Penninx

摘要

Purpose

There is a global increase of cardiovascular disease and diabetes (Cardio-Metabolic diseases: CMD). Suffering from depression or anxiety disorders increases the probability of developing CMD. In this study we tested a wide array of predictors for the onset of CMD with Machine Learning (ML), evaluating whether adding detailed psychiatric or biological variables increases predictive performance.

Methods

We analysed data from the Netherlands Study of Depression and Anxiety, a longitudinal cohort study (N = 2071), using 368 predictors covering 4 domains (demographic, lifestyle & somatic, psychiatric, and biological markers). CMD onset (24% incidence) over a 9-year follow-up was defined using self-reported stroke, heart disease, diabetes with high fasting glucose levels and (antithrombotic, cardiovascular, or diabetes) medication use (ATC codes C01DA, C01-C05A-B, C07-C09A-B, C01DB, B01, A10A-X). Using different ML methods (Logistic regression, Support vector machine, Random forest, and XGBoost) we tested the predictive performance of single domains and domain combinations.

Results

The classifiers performed similarly, therefore the simplest classifier (Logistic regression) was selected. The Area Under the Receiver Operator Characteristic Curve (AUC-ROC) achieved by singe domains ranged from 0.569 to 0.649. The combination of demographics, lifestyle & somatic indicators and psychiatric variables performed best (AUC-ROC = 0.669), but did not significantly outperform demographics. Age and hypertension contributed most to prediction; detailed psychiatric variables added relatively little.

Conclusion

In this longitudinal study, ML classifiers were not able to accurately predict 9-year CMD onset in a sample enriched of subjects with psychopathology. Detailed psychiatric/biological information did not substantially increase predictive performance.