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揭示嗓音音高与重度抑郁症之间的关联:一项多中心基因研究。

Unraveling the associations between voice pitch and major depressive disorder: a multisite genetic study.

作者信息

Di Yazheng, Rahmani Elior, Mefford Joel, Wang Jinhan, Ravi Vijay, Gorla Aditya, Alwan Abeer, Kendler Kenneth S, Zhu Tingshao, Flint Jonathan

机构信息

CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China.

Department of Psychology, University of Chinese Academy of Sciences, 100049, Beijing, China.

出版信息

Mol Psychiatry. 2025 Jun;30(6):2686-2695. doi: 10.1038/s41380-024-02877-y. Epub 2024 Dec 31.

DOI:10.1038/s41380-024-02877-y
PMID:39741179
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12092242/
Abstract

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression. Sixteen features were replicated in an independent cohort, with absolute association coefficients (beta values) from the combined analysis ranging from 0.24 to 1.07, indicating moderate to large effects. The statistical significance of these associations remained robust, with P values ranging from 7.2 × 10 to 6.8 × 10. Eleven features were significantly associated with current depressive symptoms. Using genotype data, we found that this association was driven in part by a genetic correlation with MDD. Significant voice features, reflecting a slower pitch change and a lower pitch, achieved an AUC-ROC of 0.90 (sensitivity of 0.85 and specificity of 0.81) in MDD classification. Our results return vocal features to a more central position in clinical and research work on MDD.

摘要

由于缺乏明确的生物标志物,重度抑郁症(MDD)常常未被诊断出来。我们试图识别MDD的语音生物标志物,并将指示MDD易感性的生物标志物与反映当前抑郁症状的生物标志物区分开来。我们采用两阶段荟萃分析设计以消除混杂因素,在一项针对复发性抑郁症中国女性的多中心病例对照队列研究中,测试了代表音高的特征与MDD之间的关联。16个特征在一个独立队列中得到重复验证,联合分析得出的绝对关联系数(β值)在0.24至1.07之间,表明效应为中度至高度。这些关联的统计学显著性依然很强,P值范围为7.2×10至6.8×10。11个特征与当前抑郁症状显著相关。利用基因型数据,我们发现这种关联部分是由与MDD的遗传相关性驱动的。反映音高变化较慢和音高较低的显著语音特征在MDD分类中实现了0.90的曲线下面积(AUC-ROC)(灵敏度为0.85,特异性为0.81)。我们的研究结果使语音特征在MDD的临床和研究工作中重新占据更核心的地位。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4c3/12092242/a6f690bbc1fa/41380_2024_2877_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4c3/12092242/a6f690bbc1fa/41380_2024_2877_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4c3/12092242/a6f690bbc1fa/41380_2024_2877_Fig1_HTML.jpg

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Speech Commun. 2024 Sep;163. doi: 10.1016/j.specom.2024.103106. Epub 2024 Jul 18.
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A systematic review on automated clinical depression diagnosis.一项关于自动化临床抑郁症诊断的系统评价。
Npj Ment Health Res. 2023 Nov 20;2(1):20. doi: 10.1038/s44184-023-00040-z.
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The genetic basis of major depressive disorder.重度抑郁症的遗传基础。
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Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach.通过机器学习方法从视听行为指标中识别精神分裂症和抑郁症中的精神症状表现。
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Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders.结合多基因风险评分和语音特征来检测重度抑郁症
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