Chesnut Megan, Harati Sahar, Paredes Pablo, Khan Yasser, Foudeh Amir, Kim Jayoung, Bao Zhenan, Williams Leanne M
Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Chronic Stress (Thousand Oaks). 2021 Apr 22;5:24705470211000338. doi: 10.1177/24705470211000338. eCollection 2021 Jan-Dec.
Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.
抑郁和焦虑会扰乱日常功能，其影响可能是长期且具有破坏性的，但目前尚无既定的生理指标可用于预测发病、诊断或指导治疗。在本综述中，我们将抑郁和焦虑概念化为对重复性压力的适应不良反应。我们概述了慢性压力在抑郁和焦虑中的作用，并综述了当前关于抑郁和焦虑客观压力指标的知识。我们重点关注了在抑郁和焦虑研究中得到充分研究且与临床情绪状态相关的皮质醇、心率变异性和皮肤电传导。我们针对PubMed进行了有针对性的搜索，优先选择将抑郁和焦虑与皮质醇、心率变异性和皮肤电传导联系起来的荟萃分析。一致的研究结果包括：抑郁和焦虑患者的心率变异性降低；抑郁患者的静息和相位性皮肤电传导降低；抑郁患者在一天中的不同时间及全天的皮质醇水平升高。然后，我们简要概述了特定类型抑郁和焦虑所特有的神经回路破坏情况。我们还进行了一项说明性分析，使用预测模型来确定压力标志物如何促成特定的症状亚组，以及神经回路如何对此预测有意义地补充。为此，我们实施了一个基于树的多类分类模型，将心率变异性的生理标志物作为预测因子，将包括正常情绪在内的四种症状亚型作为目标变量。我们在验证集上达到了40%的准确率。然后，我们将神经回路测量值添加到预测因子集中，以确定准确预测每种症状亚型的神经回路功能障碍和生理标志物的组合。达到54%的准确率表明这些神经生理预测因子与表征每种亚型的精神状态之间存在密切关系。进一步阐明生理标志物、神经回路功能障碍和由此产生的症状之间复杂关系的工作，将推动我们对抑郁和焦虑潜在病理生理途径的理解。
