DataSheet_1_Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms.docx

Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose (“e-nose”), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18–61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set’s variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.

为评估精神症状的潜在生物标志物，有必要构建一套非侵入性、易于使用且实用的生物过程数据集。先前的研究，尽管样本量相对有限，表明在高度控制的采样条件下，从人体呼出气体（呼出物）中提取的挥发性有机化合物，通常通过电子鼻（“e-nose”）进行测量，可能与身心健康相关。本研究采用了一种简化的数据收集方法，并试图在标准的研究环境下，在大型的跨诊断社区数据集中（N = 1207；女性746名；年龄18-61岁）对先前电子鼻与心理健康之间的联系进行复制和扩展。通过因子分析获得潜在的呼出物变量，并利用这些潜在变量，采用机器学习方法在独立的训练集和测试集中预测三种独立症状（抑郁、焦虑和物质使用障碍）。在调整年龄、性别、体重指数和吸烟状况后，由训练集产生的最佳拟合算法对测试集的方差解释不足0%。在每种情况下，标准误差均包含零线，表明模型对临床症状不具有预测性。尽管预测了一些样本方差，但研究结果无法推广到样本外数据。基于这些发现，我们得出结论，相较于先前报道的更少控制的环镜，通过电子鼻测量的呼出物无法提供对当前抑郁、焦虑或物质使用严重程度的临床有用评估。