A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation
ABSTRACT Background Despite depression's significant public health impact, efficient and accessible screening tools utilizing routine clinical indicators remain limited. This study aimed to develop and validate a practical depression risk prediction model based on commonly available biochemical...
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Wiley
2025-07-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.70674 |
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| author | Lin Lin Liqun Zhang Jingdong Zhang Dapeng Ding |
| author_facet | Lin Lin Liqun Zhang Jingdong Zhang Dapeng Ding |
| author_sort | Lin Lin |
| collection | DOAJ |
| description | ABSTRACT Background Despite depression's significant public health impact, efficient and accessible screening tools utilizing routine clinical indicators remain limited. This study aimed to develop and validate a practical depression risk prediction model based on commonly available biochemical markers, facilitating widespread early screening and timely intervention in general clinical settings. Methods We formulated a model for depression, scrutinizing an assortment of biochemical indicators and their bidirectional interrelationships with depression, employing data derived from the National Health and Nutrition Examination Survey (NHANES) and leveraging the Mendelian randomization (MR) approach, a method that utilizes genetic variants as instrumental proxies to ascertain causal nexus between risk determinants and diseases. Results Using NHANES data (training cohort: n = 27,327; validation cohort: n = 4383), we developed two prediction models through LASSO and multivariate logistic regression. Both models demonstrated comparable performance in terms of discrimination (ROC curves), calibration (slope and Hosmer‐Lemeshow test), Brier score, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Given the similar performance metrics and more parsimonious nature, Model 2, with 14 variables, was selected as the final model. MR analysis revealed bidirectional relationships between biomarkers and depression. Higher body mass index level was associated with increased depression risk (odds ratio [OR]: 1.061, p = 0.008). Depression itself showed significant associations with increased ALP (OR: 1.048, p = 0.010), decreased BUN (OR: 0.966, p = 0.032), and TB (OR: 0.963, p = 0.044) levels. Conclusions Model 2, selected for its predictive accuracy and streamlined complexity, presents a pragmatic instrument for large‐scale population screenings, facilitating timely intervention and therapeutic strategies. |
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| id | doaj-art-d0ade5042b5d4c60b4a1f251ae0fbb5d |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| series | Brain and Behavior |
| spelling | doaj-art-d0ade5042b5d4c60b4a1f251ae0fbb5d2025-08-20T03:09:00ZengWileyBrain and Behavior2162-32792025-07-01157n/an/a10.1002/brb3.70674A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization ValidationLin Lin0Liqun Zhang1Jingdong Zhang2Dapeng Ding3Department of Clinical Laboratory MedicineFirst Affiliated Hospital of Dalian Medical University, Zhongshan Road, Xigang District Dalian Liaoning Province ChinaSchool of Biomedical Engineering, Faculty of MedicineDalian University of Technology, No. 2 Linggong Road, Ganjingzi District Dalian Liaoning Province ChinaSchool of Biomedical Engineering, Faculty of MedicineDalian University of Technology, No. 2 Linggong Road, Ganjingzi District Dalian Liaoning Province ChinaDepartment of Clinical Laboratory MedicineFirst Affiliated Hospital of Dalian Medical University, Zhongshan Road, Xigang District Dalian Liaoning Province ChinaABSTRACT Background Despite depression's significant public health impact, efficient and accessible screening tools utilizing routine clinical indicators remain limited. This study aimed to develop and validate a practical depression risk prediction model based on commonly available biochemical markers, facilitating widespread early screening and timely intervention in general clinical settings. Methods We formulated a model for depression, scrutinizing an assortment of biochemical indicators and their bidirectional interrelationships with depression, employing data derived from the National Health and Nutrition Examination Survey (NHANES) and leveraging the Mendelian randomization (MR) approach, a method that utilizes genetic variants as instrumental proxies to ascertain causal nexus between risk determinants and diseases. Results Using NHANES data (training cohort: n = 27,327; validation cohort: n = 4383), we developed two prediction models through LASSO and multivariate logistic regression. Both models demonstrated comparable performance in terms of discrimination (ROC curves), calibration (slope and Hosmer‐Lemeshow test), Brier score, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Given the similar performance metrics and more parsimonious nature, Model 2, with 14 variables, was selected as the final model. MR analysis revealed bidirectional relationships between biomarkers and depression. Higher body mass index level was associated with increased depression risk (odds ratio [OR]: 1.061, p = 0.008). Depression itself showed significant associations with increased ALP (OR: 1.048, p = 0.010), decreased BUN (OR: 0.966, p = 0.032), and TB (OR: 0.963, p = 0.044) levels. Conclusions Model 2, selected for its predictive accuracy and streamlined complexity, presents a pragmatic instrument for large‐scale population screenings, facilitating timely intervention and therapeutic strategies.https://doi.org/10.1002/brb3.70674biochemical markersdepressionMendelian randomization (MR)national health and nutrition examination survey (NHANES)predictive modeling |
| spellingShingle | Lin Lin Liqun Zhang Jingdong Zhang Dapeng Ding A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation Brain and Behavior biochemical markers depression Mendelian randomization (MR) national health and nutrition examination survey (NHANES) predictive modeling |
| title | A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation |
| title_full | A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation |
| title_fullStr | A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation |
| title_full_unstemmed | A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation |
| title_short | A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation |
| title_sort | novel depression risk prediction model using nhanes data with mendelian randomization validation |
| topic | biochemical markers depression Mendelian randomization (MR) national health and nutrition examination survey (NHANES) predictive modeling |
| url | https://doi.org/10.1002/brb3.70674 |
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