ARTICLE | doi:10.20944/preprints202009.0610.v1
Subject: Medicine & Pharmacology, Allergology Keywords: mood disorders; major depression; inflammation; neuro-immune; oxidative stress; nitrosative stress; biomarkers
Online: 25 September 2020 (11:48:43 CEST)
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.
ARTICLE | doi:10.20944/preprints202005.0258.v1
Subject: Medicine & Pharmacology, Psychiatry & Mental Health Studies Keywords: major depression; oxidative and nitrosative stress; antioxidants; inflammation; neuro-immune; biomarkers
Online: 15 May 2020 (16:52:52 CEST)
Background: Hypertension, atherogenicity and insulin resistance are major risk factors of cardiovascular disorder (CVD), which shows a strong comorbidity with major depression (MDD) and bipolar disorder (BD). Activated oxidative and nitrosative stress (O&NS), inflammatory pathways, and increased atherogenicity are shared pathways underpinning CVD and mood disorders. Methods: The current study examined the effects of lipid hydroperoxides (LOOH), superoxide dismutase (SOD), nitric oxide metabolites (NOx), advanced oxidation protein products (AOPP), and malondialdehyde (MDA) on systolic (SBP) and diastolic (DBP) blood pressure in 96 mood disordered patients and 60 healthy controls. Results: A large part of the variance in SBP (31.6%) was explained by the regression on a z unit-weighted composite score (based on LOOH, AOPP, SOD, NOx) reflecting nitro-oxidative stress toxicity (NOSTOX), coupled with highly sensitive C-reactive protein, body weight and use of antihypertensives. Increased DBP was best predicted (23.8%) by body mass index and NOSTOX. The most important O&NS biomarkers predicting an increased SBP were in descending order of significance: LOOH, AOPP and SOD. Higher levels of the atherogenic index of plasma, HOMA2 insulin resistance index and basal thyroid-stimulating hormone also contributed to increased SBP independently from NOSTOX. Although there were no significant changes in SBP/DBP in mood disorders, the associations between NOSTOX and blood pressure were significant in patients with mood disorders but not in healthy controls. Conclusions: Activated O&NS pathways including increased lipid peroxidation and protein oxidation, which indicates hypochlorous stress, are the most important predictors of an increased BP, especially in patients with mood disorders.
ARTICLE | doi:10.20944/preprints201812.0092.v1
Subject: Medicine & Pharmacology, Psychiatry & Mental Health Studies Keywords: staging, affective disorders, major depression, bipolar disorder, oxidative, neuro-immune
Online: 7 December 2018 (13:56:04 CET)
Although, staging models gained momentum to stage define affective disorders, no attempts were made to construct mathematical staging models using clinical and biomarker data in patients with major depression and bipolar disorder.The aims of this study were to use clinical and biomarker data to construct statistically-derived staging models, which are associated with early lifetime traumata (ELTs), affective phenomenology and biomarkers.In the current study, 172 subjects participated, 105 with affective disorders (both bipolar and unipolar) and 67 controls. Staging scores were computed by extracting latent vectors (LVs) from clinical data including ELTs, recurring flare ups and suicidal behaviors, outcome data such as disabilities and health-related quality of life (HR-QoL), and paraoxonase (PON)1 actvities and nitro-oxidative stress biomarkers.Recurrence of episodes and suicidal behaviors could reliably be combined into a LV with adequate composite reliability (the “recurrence LV”), which was associated with female sex, the combined effects of multiple ELTs, disabilities, HR-QoL and impairments in cognitive tests. All those factors could be combined into a reliable “ELT-staging LV” which was significantly associated with nitro-oxidative stress biomarkers. A reliable LV could be extracted from serum PON1 activities, recurrent flare ups, disabilities and HR-QoL.Our ELT-staging index scores the severity of a relevant affective dimension, shared by both major depression and bipolar disorder, namely the trajectory from ELTs, a relapsing course and suicidal behaviors to progressive disabilities. Patients were classified into three stages, namely an early stage; a relapse-regression stage; and a suicidal-regression stage. Lowered lipid-associated antioxidant defenses may be a drug target to prevent the transition from the early to the later regression stages.