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The top-down DSM/ICD categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodeling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterized by increased ROI, a more severe phenome, and more disabilities Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviors, as well as the phenome of mood disorders. Here we provide rules to compute bottom-up RADAR scores for MDMD, ROI, lifetime (LT) and current suicidal SI and SA, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.

BackgroundEarly life trauma (ELT) may drive mood disorder phenomenology, nitro-oxidative pathways and impairments in semantic memory. There are no data regarding the impact of ELT on affective phenomenology and whether these pathways are mediated by staging or lowered lipid-associated antioxidant defences.MethodsThis study examined healthy controls (n=54) and patients with affective disorders including major depression, bipolar disorder and anxiety disorders (n=118). ELT was assessed using the Child Trauma Questionnaire. In addition, we measured affective phenomenology and assayed advanced oxidation protein products; malondialdehyde, paraoxonase 1 (CMPAase) activity, high-sensitivity C-reactive protein (hsCRP), and high-density lipoprotein (HDL) cholesterol.ResultsELT was associated into with increased risk for mood and comorbid anxiety disorders and a more severe phenomenology, including staging characteristics, depression and anxiety severity, suicidal behaviours, type of treatments, disabilities, body mass index, smoking behaviour and hsCRP, as well as lowered health-related quality of life, antioxidant defences and semantic memory. The number of mood episodes and CMPAase/HDL-cholesterol levels could be reliably combined into a new vulnerability staging-biomarker index, which mediates in part the effects of ELT on affective phenomenology and oxidative stress. Moreover, the effects of female sex on mood disorders and affective phenomenology are mediated by ELT.DiscussionThe cumulative effects of different ELT drive many aspects of affective phenomenology either directly or indirectly through effects of staging and/or lipid–associated antioxidant defences. The results show that children, especially girls, with ELT are at great risk to develop mood disorders and more severe phenotypes of affective disorders.