Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol – paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

当前对情绪障碍的诊断未经交叉验证。当前论文的目的是解释机器学习技术如何用于 a) 构建一个模型，该模型将风险/恢复力 (R/R)、不良结果通路 (AOP)、分期和情绪障碍的表型结合起来，以及b) 公开基于这些特征集的新类别。本研究使用 67 名健康对照者和 105 名情绪障碍患者的数据进行。R/R 比作为对氧磷酶 1 (PON1) 基因、PON1 酶活性和生命早期创伤 (ELT) 的组合进行评估，预测高密度脂蛋白胆固醇 - 对氧磷酶 1 复合物 (HDL-PON1)，反应性氧和氮物种 (RONS)、硝基氧化应激毒性 (NOSTOX)、分期（抑郁症和轻躁狂发作次数以及自杀企图），和现象（汉密尔顿抑郁和焦虑评分和临床总体印象；当前的自杀意念；生活质量和残疾测量）评分。偏最小二乘路径分析表明，44.2% 的表型变异由 ELT、RONS/NOSTOX 和分期分数解释。对所有这些特征集进行的聚类分析发现了两个不同的患者群，即 69.5% 的患者被分配到高 R/R、RONS/NOSTOX、分期和表型评分的类别，而 30.5% 的患者被分配到分期增加的类别和现象分数。这种分类跨越了双相（BP1/BP2）和重度抑郁症分类，并且比后一种分类更具特色。