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生物标志物和精神障碍:使用随机森林算法的相关性分析

Joice M A Rodolpho¹, Krissia F Godoy¹, Bruna D L Fragelli²

¹Laboratory of Inflammation and Infectious Diseases, Federal University of São Carlos (UFSCar), São Carlos 13565-905, SP, Brazil.

|June 26, 2025

概括

像维生素D和炎症性细胞因子这样的生物标志物在预测抑郁和焦虑时显示出性别特异性的相关性. 机器学习确定了关键指标,突出了这些常见心理健康障碍的男女差异.

科学领域:

心理健康研究心理健康研究
生物标志物发现发现
机器学习在医学中的应用

背景情况:

抑郁和焦虑影响全球数以百万计的人,影响日常生活,并导致严重后果.
炎症性细胞因子 (IL-1β,IL-6,TNF),皮质醇和维生素D都与这些疾病的病理生理学有关.
现有研究表明,特定生物标志物与心理健康状况之间存在潜在联系,但性别特异性差异需要进一步调查.

研究的目的:

识别和评估各种生物标志物在预测抑郁和焦虑方面的重要性.
调查心理健康障碍生物标志物的预测相关性的潜在的性别特异性差异.
应用机器学习,特别是随机森林,进行可靠的生物标志物分析.

主要方法:

使用随机森林机器学习算法与交叉验证.
分析了一组生物标志物,包括炎症性细胞因子,皮质醇,维生素D和心脏标志物,对96名被诊断患有精神障碍的参与者 (50名女性,46名男性) 进行了分析.
评估了抑郁症和焦虑症生物标志物的预测重要性,检查了性别特定的模式.

主要成果:

对抑郁症和焦虑症都观察到生物标志物相关性的性别特异.
对于抑郁症,维生素D,C反应蛋白 (CRP) 和D-二次体在男性中具有最强的预测性;IL-6,CRP和维生素D在女性中具有重要意义.
对于焦虑,维生素D和肌球蛋白对男性很重要;IL-8和维生素D对女性很重要.

结论:

该研究成功地确定了抑郁症和焦虑症的关键生物标志物,具有显著的性别特异性变异.
维生素D已成为跨性别和疾病的重要生物标志物,强调其在心理健康方面的潜在作用.
随机森林方法在强大识别预测生物标志物方面被证明是有效的,为精神障碍的复杂病理生理提供了洞察力.

关键词:

在IL-6的研究中,IL-6被认为是IL-6的基因.这就是TNF TNF.焦虑是一种焦虑.生物标志物生物标志物皮质醇是一种皮质醇.细胞因子细胞因子抑郁抑郁症抑郁症抑郁症是一种机器学习是机器学习.随机的森林随机的森林维生素D是维生素D的重要组成部分.

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