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• W4387476775 abstract "Methods for predicting major depressive disorder (MDD) represent important tools for identifying individuals at increased risk for the disorder. In individuals who have not been directly screened for MDD using structured interviews, predictive approaches which utilize existing medical record data may be particularly useful for identifying patients who warrant such screenings. This research has been conducted using the UK Biobank Resource (UKB, ID30782). In this study, we investigate the utility of blood-based biomarkers, including 303 markers from blood chemistry and NMR assays, to predict various MDD-related phenotypes in the UKB. Using the CIDI-SF assessed in ∼⅓ of the UKB participants, we identified strictly-defined MDD cases based on self-reported symptoms and associated impairment: 45,341 probable (with ≥4 symptoms) and 39,524 definite cases (with ≥5 symptoms), along with 70,631 controls. Additionally, we defined three broad depression phenotypes: help-seeking (173,153 cases, 324,107 controls), cardinal depression symptoms (31,835 cases, 89,520 controls), and depression diagnosis (either self-reported or ICD-10 codes in electronic medical records: 60,517 cases, 441,963 controls). We apply 3 broad classes of machine learning (ML) algorithms, elastic nets, gradient boosting machines (GBM), and MBOOST, to predict these phenotypes, as well as CIDI-SF symptom count, solely using blood-based biomarkers. To assess prediction performance, we compare adjusted R-squared (r2 adj) values for covariate-only models using age and sex to predict the outcomes against the ML models utilizing blood-based measures in addition to covariates. We also estimate the genetic correlation with measured MDD phenotypes, as well as the Psychiatric Genomics Consortium (PGC) MDD. Furthermore, to demonstrate the broad utility of such a blood-based biomarker prediction approach, as well as generate data to serve as benchmark comparisons, we apply the same predictive approach to BMI, body fat percentage, height, and alcohol consumption, defined as drinks per week (DPW). Preliminary results show that predicted DPW were moderately well-predicted, with the best performing ML algorithm, GBM, producing adjusted R-squared of 0.354. However, MDD symptom count was not well predicted by blood-based biomarkers with the GBM prediction producing adjusted R-squared of 0.002. Given this performance, we expanded the framework to include all available UKB phenotypes as predictors in the MAGIC-LASSO approach and predicted MDD symptom counts with a phenotypic correlation of 0.69 between observed and measured symptom count. Genetic correlation between the predicted scores were 0.88 and 0.9 with measured scores and PGC MDD, respectively. These results support extending the MAGIC-LASSO approach to prediction via boosting algorithms and applying this approach to additional UKB MDD phenotypes. These results demonstrate blood-based biomarkers and ML based models can be useful in predicting some outcomes, such as DPW, but not MDD in the current implementation. Expanding the prediction space to include all available phenotypes results in improved prediction for MDD phenotypes. In persons where strictly-defined MDD is not directly assessed, such predictions may provide a useful screener to identify individuals for follow up clinical assessments and expand sample sizes for statistical genetic analyses." @default.
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• W4387476775 date "2023-10-01" @default.
• W4387476775 modified "2023-10-16" @default.
• W4387476775 title "T27. BLOOD-BASED BIOMARKERS TO PREDICT MAJOR DEPRESSIVE DISORDER PHENOTYPES: A MACHINE LEARNING APPROACH" @default.
• W4387476775 doi "https://doi.org/10.1016/j.euroneuro.2023.08.314" @default.
• W4387476775 hasPublicationYear "2023" @default.
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