Bipolar disorder in youths is associated with metabolic dysregulation, impaired inhibitory control and limbic network connectivity changes.

To investigate whether limbic network connectivity is associated with appetite hormones and inhibitory control in youths with bipolar disorder.

The current study included 68 youths with bipolar disorder and 57 age- and gender-matched controls. They completed clinical assessments, the go/no-go (GNG) task, fasting appetite hormone (leptin, adiponectin, insulin and ghrelin) tests and resting-state functional magnetic resonance imaging. Region of interest (ROI)-to-ROI functional connectivity was analysed across 17 limbic ROIs. Cluster-level inferences were derived by using threshold-free cluster enhancement with family-wise error correction (p < 0.05). Furthermore, associations with appetite hormones and GNG task parameters were investigated.

Compared with the control individuals, youths with bipolar disorder exhibited elevated leptin (p = 0.005) and insulin levels (p < 0.001), poorer performance on the GNG task and hyperconnectivity within the limbic network, identifying a significant cluster comprising the right anterior parahippocampal gyrus (aPHG)–right thalamus and right aPHG–hypothalamus edges (cluster-level family-wise error-corrected p = 0.045). No significant correlations were observed between limbic hyperconnectivity, appetite hormone levels and GNG task performance.

Current cross-sectional cohort indicates that youths with bipolar disorder exhibit concurrent alterations in appetite hormones, inhibitory control and limbic connectivity. These findings provide a characterisation of youth bipolar disorder across multiple indices, although longitudinal studies are necessary to understand how these features relate to long-term clinical trajectories.

Bipolar disorder (BD) is marked by recurrent mood episodes and functional impairment. Growing evidence implicates mitochondrial dysfunction, oxidative stress, and metabolic disturbances as important to its pathophysiology, affecting neuronal signalling, synaptic plasticity, and inflammatory pathways.

This review synthesises current evidence linking energetic metabolism to BD, integrates neuroimaging and peripheral biomarker findings, and discusses emerging therapeutic strategies targeting mitochondrial and metabolic dysfunction.

A systematic search following PRISMA guidelines was conducted across PubMed, MEDLINE, EMBASE, Scopus, and Cochrane Library using ‘bipolar disorder’, ‘energetic metabolism’, and ‘mitochondria’. Inclusion criteria comprised peer-reviewed human studies, original research, and meta-analyses; paediatric, non-English, dietary, genetic, or comorbidity-focused studies were excluded. Additional manual searches targeted biomarker-specific studies.

Evidence consistently shows impaired oxidative phosphorylation, reduced ATP and phosphocreatine, altered NAD+/NADH ratios, and increased oxidative and inflammatory markers in BD. Neuroimaging demonstrates reduced PCr/ATP ratios, elevated lactate, and decreased TCA cycle intermediates, indicating cerebral energy deficits. Peripheral biomarkers: including decreased ETC complex activity, increased MDA and 8-OHdG, altered purinergic enzymes, and elevated mtDNA reflect systemic bioenergetic stress. Emerging interventions targeting mitochondrial cofactors (creatine, CoQ10, carnitine, NAC), metabolic strategies (ketogenic diet), and immunomodulatory or purinergic pathway modulators show promise.

Emerging evidence suggests that systemic bioenergetic dysregulation may contribute to the pathophysiology of BD. Integrating mitochondrial, metabolic, and inflammatory biomarkers with neuroimaging may enable biologically informed patient stratification and precision therapeutics aimed at restoring energetic homeostasis. Large, standardised multicenter studies are required to translate these findings into clinical practice.

Childhood maltreatment (CM) has been linked to poor emotional intelligence (EI) and psychosocial functioning (PSF) in patients with bipolar disorder (BD). However, no study has investigated whether EI mediates the relationship between CM and PSF in patients with BD. Here, we aimed to assess the association of CM with EI and PSF in euthymic patients with BD compared with healthy controls (HCs).

We assessed 124 euthymic participants with a BD diagnosis and 124 HCs by means of the Mayer–Salovey–Caruso Emotional Intelligence Test, the Functioning Assessment Short Test and the Childhood Trauma Questionnaire. Linear regression models were constructed using CM categorized as no-CM, low/moderate-CM and high-CM. We subsequently assessed whether EI mediates the relationship between CM and PSF.

Compared with HCs, patients with BD reported higher rates of CM and lower rates of PSF and EI. In the BD group, subjects who exhibited low/moderate CM obtained lower levels of PSF and EI than those with no-CM and high-CM levels. The regression models demonstrated that, compared with the no-CM category, the low/moderate CM category was inversely associated with EI strategic area scores in patients with BD. However, EI did not mediate the relationship between CM and PSF.

Low/moderate-CM was associated with the worst EI scores in BD. However, we did not observe any mediating effect of EI on the relationship between CM and PSF. Our results emphasize the importance of assessing CM in mental health and suggest that targeted therapies should be administered to affected patients.

Bipolar disorder (BD) is characterized by mood fluctuations and is prone to misdiagnosis as major depressive disorder (MDD). Quantifying the relationship between symptom fluctuations and conversion risk is essential for early identification.

In a national prospective cohort of patients with MDD (enrolled from May 2022 to February 2025), depressive and manic symptoms were assessed using clinician-rated and self-report instruments. Two measures were calculated: the time in target range (TTR), the proportion of numbers of evaluations with scores below predefined clinical cutoffs; and Statistical Process Control (SPC) stability, evaluated with a person-specific approach defining each individual’s normal fluctuation range via their 25th–75th percentiles. The outcome was the incidence of manic or hypomanic episodes that met the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria. Cox regression models were used to evaluate the associations of the TTR and SPC with the conversion to BD.

Among the 2,367 eligible patients with MDD (mean [SD] age, 29.88 [9.79] years; 792 [33.46%] male), 69 (2.92%) converted to BD in the 52-week follow-up. During weeks 0-8, higher TTR of the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16) (cutoff set at ≤10) and the 33-item Hypomania Checklist (HCL-33) (cutoffs set at ≤7 and ≤15) were significantly associated with a lower risk of conversion between Weeks 8 and 52. Additionally, a greater SPC-evaluated stability of the HCL-33 scores was significantly associated with lower conversion risk over the same period.

While further validation in independent samples is still needed, continuous monitoring of hypomanic symptoms, especially evaluated with the HCL-33, may be of clinical significance for the early identification of conversion to BD.

Major psychiatric disorders (MPDs) and premenstrual disorders (PMDs) are often comorbid, with indications of shared genetic architecture. However, the specific genetic variants underlying this comorbidity remain elusive. The aim of this study was to identify genomic loci shared between MPDs and PMDs, and to assess the functional characteristics of genes mapped to these loci.

We used genome-wide association data for symptoms of PMDs (S-PMD, n = 67 068) from the Norwegian Mother, Father and Child Cohort Study (MoBa), and for MPDs (schizophrenia, n = 130 644; bipolar disorder, n = 827 335; major depression, n = 1 918 432) from the Psychiatric Genomic Consortium. Shared genetic loci were identified with the conjunctional false discovery rate (conjFDR). Genes mapped to the identified loci were included in functional and pharmacological gene-set enrichment analyses (GSEAs).

We identified 20 loci shared between S-PMD and the MPDs, including 19 novel loci for S-PMD (conjFDR<0.05). GSEA revealed genes involved in calcium signaling and neuronal projection pathways. The drug enrichment analysis identified 36 drugs, including riluzole and mianserin, significantly associated with the identified molecular pathways.

We identified 20 genetic loci shared between the S-PMD and MPDs, implicating calcium transmission in the pathogenesis of PMDs. Together with the drug enrichment results, this provides a biological basis for repurposing existing drugs in the treatment of PMD.

Mental and physical disorders are leading contributors to global disability. However, estimates of their health-related burden often overlook the pervasive comorbidity among conditions. This study aimed to quantify the independent and combined effects of these disorders on self-rated health in nationally representative Chinese adults, explicitly accounting for comorbidity.

Data were drawn from the China Mental Health Survey (CMHS), a cross-sectional nationally representative survey. Mental disorders were assessed using the Composite International Diagnostic Interview 3.0, and physical disorders were measured through structured self-report. Health-related burden was assessed using a 0–100 visual analogue scale (VAS) of self-rated health over the past 30 days. Nested regression models examined disorder-specific and multimorbidity effects, followed by interaction-based counterfactual simulations to estimate disorder-specific burden net of comorbidity at individual and population levels.

Among 27,505 respondents included in multivariate analyses, comorbidity was highly prevalent. In crude models, all 20 disorders were associated with lower VAS scores, with the largest decrements observed for mental disorders such as generalized anxiety disorder (GAD), bipolar disorder and major depressive disorder (MDD). After accounting for disorder type and comorbidity structure, many associations were attenuated and the independent effect of disorder count became non-significant. Counterfactual simulations showed the largest individual-level burdens for GAD (–14.71), cancer (–9.02), bipolar disorder (–8.59) and MDD (–8.53). At the population level, insomnia symptoms (–1.56), arthritis (–0.81) and cardiovascular disorders (–0.64) contributed the greatest burden.

Accounting for comorbidity substantially alters estimates of disorder-specific health burden. Mental disorders impose the greatest impairment at the individual level, whereas prevalent physical conditions dominate population burden.

Genome-wide association studies in East Asian populations have identified the gene variant, rs12415800, of the Sirtuin 1 gene (SIRT1) as a major depressive disorder (MDD) risk locus with the A allele conferring increased risk. Accumulating evidence suggests that SIRT1-related genetic effects extend beyond MDD.

To investigate the association between rs12415800 and bipolar disorder, with additional analysis of MDD.

We analysed data from the Taiwan Biobank linked to the National Health Insurance Research Database, including 145 780 participants of Han Chinese ancestry. Clinically diagnosed bipolar disorder and MDD were identified using standardised ICD-9-CM and ICD-10-CM codes. Multivariable logistic regression models were applied with the GG genotype as the reference, adjusting for age and sex. Stratified analyses were conducted by age group and sex.

Both the rs12415800 AG and AA genotypes were associated with an increased risk of bipolar disorder. Compared with GG genotype carriers, AA genotype carriers had a significantly higher risk of bipolar disorder overall (adjusted odds ratio (aOR) = 1.20, 95% CI = 1.05–1.37), particularly among participants aged ≥50 years (aOR = 1.38, 95% CI = 1.14–1.68). The overall and age-stratified associations remained materially similar after excluding participants with schizophrenia or schizo-affective disorder, although sex-specific findings were inconsistent.

Our findings suggest that genetic variation near SIRT1, particularly the rs12415800 risk A allele, may be associated with bipolar disorder susceptibility, especially among older individuals. Given prior evidence linking this allele to reduced SIRT1 mRNA expression, further investigation of SIRT1-related pathways in the pathophysiology of bipolar disorder is warranted.

Maladaptive eating behaviors in mood disorders are diverse and clinically relevant but have only been characterized into phenotypes based on theoretical evidence. The objective of the current study is to assess the prevalence of maladaptive eating behavior phenotypes (EBP) in mood disorders based on empirical evidence.

An online survey package including five questionnaires was administered to assess the prevalence and proportion of maladaptive eating behaviors in a mood disorder population (n = 100). Principal component analysis (PCA) and mixed measures analysis of variance (mANOVA) were conducted to characterize maladaptive EBPs.

Numerous maladaptive eating behaviors were identified in MDD and BD, including poor dietary quality, emotional eating, binge eating, and night eating behaviors. Differences between diagnostic groups are discussed. Three EBPs were characterized according to the PCA: (1) food impulsivity; (2) emotional eating; and (3) mindfulness in eating, with a significant influence of age and medication on the characterizations according to the mANOVA.

Maladaptive eating behaviors occur in mood disorders and can be characterized into distinct EBPS. These findings should be interpreted as preliminary and hypothesis-generating given the sample size, absence of a healthy comparator group, and potential confounds. As psychiatry moves toward a more comprehensive, personalized approach to diagnosis and treatment, EBPs require prospective validation in larger, controlled investigations to determine clinical relevance.

Cerebral perfusion abnormalities may contribute to the pathophysiology of psychosis spectrum disorders (PSD), including schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychotic features (BP), yet large-scale investigations remain limited.

Pseudo-continuous arterial spin labeling scans were acquired from 2412 SZ, 2278 SAD, 158 BP, 401 neurotypical controls (NC) and processed using ExploreASL and FreeSurfer to quantify resting-state cerebral blood flow (CBF) across total gray matter and 60 brain regions. General linear regression compared CBF between NC and diagnostic groups or biologically informed biotypes derived from neurophysiological clustering. Within patient groups, Spearman correlations assessed associations between cognitive/clinical measures and CBF. Analyses were corrected for multiple comparisons.

Compared to NC, patient groups showed significantly lower total gray matter CBF (PSD: Cohen’s d=-0.124, p-adjusted=0.037; BP: d=-0.259, p-adjusted=0.004; the most severe biotype, Biotype 1: d=-0.222, p-adjusted=0.006). BP and Biotype 1 exhibited significant hypoperfusion in 25 regions spanning bilateral temporal, occipital, inferior frontal cortices, and subcortical structures (BP d= -0.21 to -0.33; Biotype 1 d=-0.19 to -0.26). No significant CBF-cognition associations were observed. Within PSD, higher total gray matter CBF correlated with greater psychotic symptom severity, while right superior temporal sulcus hyperperfusion was associated with greater psychotic, manic, and depressive symptoms.

Distinct perfusion patterns exist in PSD, with widespread hypoperfusion in BP and Biotype 1 and hyperperfusion associated with greater symptom severity, supporting perfusion as a potential biomarker to stratify PSD subtypes.

Identifying how affect and daily behaviors influence each other is central to understanding mood disorder development. This study investigated how negative affect (NA), positive affect (PA), physical activity, and smartphone use influence one another in daily life, and whether familial risk or mood disorder history shapes these relationships.

In a 14-day experience sampling protocol, we measured NA and PA five times a day along with passive sensing of step count and screen-on time. We examined the effect of familial high risk and history of mood disorders on within-person affect–behavior dynamics using Dynamic Structural Equation Modeling.

The sample included offspring of parents with bipolar disorder (n = 82) and control participants (total n = 128, Mage = 37.8, 57% female), of which 32 had a history of mood disorders (recurrent: n = 3, single: n = 29). Results showed a credible bidirectional relationship between step count and PA (step count → PA: β = 0.06; 95% credibility interval [CrI], 0.02–0.09; PA → step count: β = .07; 95% CrI, 0.03–0.11), while NA exhibited no credible associations. Higher PA predicted decreased screen-on time (β = −.05; 95% CrI, −0.08 to −0.02), while increased screen-on time was associated with subsequent elevated NA (β = .03; 95% CrI, 0.01–0.06). Familial risk status and recurrent mood disorders did not alter these bidirectional effects, but were associated with lower PA and higher NA on average (all p < 0.001).

Findings highlight PA as a key driver in the interplay between behavior and affect. Supporting PA through increased activity may help improve daily emotional experiences, even in those with elevated mood disorder risk.

Cognitive deficits are one of the core features of older-age bipolar disorder (OABD). Psychotropic medications may influence cognition, especially in older populations, where anticholinergic effects are relevant. This cross-sectional study aimed to investigate the potential impact of anticholinergic load on cognitive performance in individuals with OABD.

One hundred and twenty-one euthymic or partially remitted OABD patients were included. Cognitive performance was assessed using a neuropsychological battery and summarized into attention, processing speed, working memory, visual and verbal memory, and executive functions. Anticholinergic load was assessed using the CRIDECO Anticholinergic Load Scale (CALS), and pharmacological burden was quantified using the defined daily dose (DDD). Associations were examined using hierarchical multivariable linear regression models, considering also relevant clinical covariates.

The interaction between CALS and DDD was associated with lower visual and verbal memory scores (p = 0.04). Age was inversely associated with cognitive performance across all domains (all p < 0.01). A higher number of manic episodes was associated with poorer attention, visual and verbal memory, and processing speed (all p < 0.01). Lifetime psychotic symptoms were associated with worse working memory (p = 0.02), and higher diazepam-equivalent doses were associated with worse visual and verbal memory performance (p = 0.02).

Although anticholinergic medications may contribute to cognitive burden, their impact appears dose-related and limited compared with the broader influence of illness-related factors. In OABD patients, prioritizing the management of illness-related characteristics alongside careful pharmacological management may therefore be crucial for preserving cognitive functioning.

Bipolar disorder is an episodic mental illness, often requiring complex pharmacological management. Lithium is a first-line treatment, while antidepressant monotherapy can precipitate mania symptoms in some patients.

To (a) describe patterns of antidepressant, antipsychotic and mood stabiliser prescribing in the 12-month periods before and after first-recorded bipolar diagnosis; (b) describe treatment trajectories in the year post-diagnosis; and (c) investigate associations between sociodemographic and treatment-related characteristics of (i) antidepressant monotherapy and (ii) lithium prescription post-diagnosis.

A longitudinal cohort study of patients with a first-recorded diagnosis of bipolar disorder between 2000 and 2022 derived from Clinical Practice Research Datalink. Prescriptions for antidepressants, antipsychotics and mood stabilisers were examined in the 12 months before and after diagnosis. Logistic regression models tested associations between sociodemographic and treatment-related characteristics and prescribing outcomes. A web application (https://dop-mhds.shinyapps.io/bpd-prescribing-uk-shiny/) was developed to visualise treatment trajectories over the year following diagnosis.

We included 40 965 patients with a first-recorded diagnosis of bipolar disorder between 2000 and 2022 (median age at diagnosis 43 years, 60.9% female, 11.1% ethnic minority). Of these, 32 460 (79.2%) were prescribed a relevant medication in the year prior to diagnosis compared with 37 208 (90.8%) in the year after. Antidepressant monotherapy was the most common pre-diagnosis treatment (48.4%), but was also prescribed to 28.9% during at least 1 month in the year after diagnosis. Antipsychotic monotherapy was the most common post-diagnosis treatment (36.0%). Lithium was prescribed to 18.5% within 1 year of diagnosis, with 4.0% receiving it as first-line monotherapy. Almost half (46.0%) switched medication at least once within 1 year. Males, ethnic minorities and patients residing in the most deprived areas had lower odds of receiving either lithium or antidepressant monotherapy.

Prescribing patterns for bipolar disorder in the UK are highly variable and often outside of guideline recommendations, with persistent use of antidepressant monotherapy and potential under-utilisation of lithium.