Abstract
Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples. Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability. Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance. Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD.
Highlights
The deployment of bioinformatic evaluations in psychiatry would revolutionize the ability to diagnose, treat, and prevent major depressive disorder (MDD)
Our experiments effectively demonstrated that the combination of genes and voice could further improve their ability to identify MDD, experimental results based on a more general population are needed before clinical application
With the p-value threshold at 0.005, the combined biomarker improved the AUC by 9.09% compared to genes only and 6.73% compared to voice only
Summary
The deployment of bioinformatic evaluations in psychiatry would revolutionize the ability to diagnose, treat, and prevent major depressive disorder (MDD). Only approximately half of the population suffering from MDD is currently identified and treated The difficulty in identifying MDD is one of the key barriers to the effective utilization of current medications. Diagnosis remains based on clinical interviews and mental status examination (Regier et al, 2013); screening instruments are hindered by poor specificity and sensitivity, and there are no reliable biomarkers. The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples
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