Abstract Background Bipolar disorder (BD) is a chronic and debilitating psychiatric illness characterized by recurrent episodes of mania and depression. Despite its high heritability, the underlying molecular mechanisms remain incompletely understood. Gene expression studies, particularly those focusing on total messenger RNA (mRNA), offer a promising avenue for identifying biomarkers and understanding disease pathophysiology. Among available treatments, lithium remains a first-line mood stabilizer with proven efficacy in reducing recurrence and suicide risk in BD patients. However, response to lithium is highly variable, and predictive biomarkers for treatment outcomes are lacking. Investigating total mRNA expression profiles in blood samples from BD patients characterized for lithium response may provide valuable insights into disease mechanisms and treatment response. Aims & Objectives To identify gene expression markers of disease risk and of response to lithium treatment in bipolar disorder. Method RNA sequencing was performed in a sample of 90 Caucasian patients with a diagnosis of BD type I or BD type II according to DSM-5 and 59 non-psychiatric controls with no personal or familial history of psychiatric disorders. Participants were recruited at the Unit of Clinical Pharmacology and the Unit of Clinical Psychiatry of the University of Cagliari and University Hospital Agency of Cagliari, and at the Psychiatric Hospital “Villa Santa Chiara”, Verona (Italy). For a subgroup of patients (n = 58) response to long-term lithium treatment was characterized with the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder scale (Alda scale). Total RNA was extracted from fasting peripheral venous blood samples. Library preparation and bulk RNA sequencing was performed using the Illumina Stranded Total RNA Prep, and paired-end sequencing was performed on a NextSeq 2000 platform (Illumina). After quality control, raw data were processed with the rnaseq nf-core pipeline, alignment with the reference genome (GRCh38) was performed with STAR, while gene expression levels were estimated with RSEM. Identification of differentially expressed genes (DEG) between patients and controls and responders and non-responders to lithium, adjusting for age and sex, was conducted with DESeq2. Results were adjusted for multiple testing based on false discovery rate (FDR) and an adjusted p-value < 0.05 was considered significant. Results We identified 37 DEGs between patients with BD and controls with an adjusted p-value < 0.05, of which 19 were upregulated and 18 downregulated in patients. DEG significant after multiple testing correction are reported in Table 1. DEG were enriched for the protein folding chaperone molecular function GO term (enrichment ratio: 28.79, p = 0.0002, FDR = 0.041, DNAJB1, HSP90AA1 and HSPH1). No DEG was significantly associated with lithium response after multiple testing correction. However, GO analyses on the nominally significant genes showed a significant enrichment for toll-like receptor binding molecular function GO term. Discussion & Conclusions Our study suggests that BD patients present significant differences in gene expression patterns compared to healthy controls. Pathway analyses suggest that protein misfolding and endoplasmic reticulum alterations could be implicated in the pathophysiology of BD, while response to lithium might be related to modulation of inflammatory response through toll-like receptor biding.
A. Squassina, M. Manchia, C. Chillotti et al.· International Journal of Neu...· 0 citations
Major depressive disorder (MDD) accounts for a substantial share of global disability-adjusted life years and remains inadequately treated despite pharmacological advances. Growing evidence implicates endocrine-disrupting chemicals (EDCs)—particularly phthalates and bisphenols—as environmental contributors to the onset of these conditions. This narrative review examines evidence from PubMed, Scopus, and Web of Science (January 2000–March 2025) on the relationship between early-life exposure to these compounds and the development of mood disorders, with emphasis on molecular and neurobiological mechanisms. Phthalates such as di(2-ethylhexyl) phthalate (DEHP), and bisphenols such as bisphenol A (BPA), are detected ubiquitously in human urine, blood, placenta, and umbilical cord blood. Key mechanisms identified include Nrf2/HO-1-driven oxidative stress and neuronal apoptosis, disruption of calcium signalling and synaptic plasticity via CREB phosphorylation deficits, epigenetic suppression of brain-derived neurotrophic factor (BDNF) via promoter hypermethylation, NF-κB/NLRP3/IL-1β neuroinflammatory cascades, interference with thyroid hormone bioavailability through transthyretin competition, and PPAR-mediated disruption of brain lipid metabolism. Prenatal and early-life exposure has been associated with ADHD, cognitive impairment, autism spectrum disorder, and elevated risk of depressive and anxiety phenotypes in epidemiological cohorts. Psychological vulnerability factors—perceived stress, deficient emotion regulation, and adverse childhood experiences—likely amplify this biological susceptibility through HPA axis sensitisation. Methodological limitations of current evidence, including reliance on single-spot urine samples and residual confounding, are critically appraised. Future research priorities include longitudinal biomonitoring cohorts, brain organoid mechanistic models, and integration of validated psychiatric assessments into environmental health study designs.
M. Lastretti, Andrea Faa, Monica Piras et al.· Environments· 0 citations
Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity, beyond subtypes.
Tracey van der Veen, M. Tesfaye, J. M. K. Yang et al.· Research Square· 0 citations
Background: Artificial intelligence (AI)-driven conversational systems are increasingly capable of simulating empathy, adapting to individual users, and fostering emotional bonds that blur the boundary between tool and companion. This scoping review maps the extent and nature of published evidence regarding psychological mechanisms underlying emotional reliance on AI chatbots and associated mental health implications. Methods: Conducted in accordance with PRISMA-ScR guidelines, we systematically searched PubMed/MEDLINE, PsycINFO, Web of Science, Scopus, and IEEE Xplore from database inception to March 2026. Two independent reviewers screened records and extracted data using the PCC (Population, Concept, Context) framework. Thematic synthesis was performed to map evidence across psychological, clinical, and developmental domains. Results: Of 1847 records identified, 46 studies met inclusion criteria. Key themes included: (1) the ELIZA effect as a foundational mechanism of human–AI attachment, with documented cases of severe dependency including fatal outcomes; (2) anthropomorphization and artificial intimacy fostered by adaptive, personalized AI design; (3) proposal of Generative AI Dependency (GAID) as a conceptual framework mapping onto behavioral addiction components, pending empirical validation; (4) particular vulnerability of adolescents and lonely individuals to exclusive affective bonds with AI; and (5) potential erosion of human relational capacities, empathy development, and tolerance for interpersonal complexity. Significant gaps were identified in longitudinal research, validated screening tools, and intervention protocols. Conclusions: Emotional reliance on AI represents an emerging clinical phenomenon with addiction-like features requiring specific diagnostic frameworks, evidence-based interventions, and ethical design guidelines. Future research should prioritize longitudinal studies examining developmental impacts and neurobiological investigations of AI-mediated reinforcement mechanisms.
M. Lastretti, M. Manchia, Matteo Fraschini et al.· Psychiatry International· 0 citations
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