Subtypes Of Bipolar Disorder Research Articles

Thyroid Function and Mood Disorders: A Mendelian Randomization Study.

BackgroundObservational studies suggest that even minor variations in thyroid function are associated with the risk of mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD). However, it is unknown whether these associations are causal or not. We used a Mendelian Randomization (MR) approach to investigate causal effects of minor variations in TSH and FT4 levels on MDD and BD risk.MethodsWe performed two-sample MR analyses using data from the largest publicly available genome-wide association studies on normal-range TSH (N=54,288) and FT4 (N=49,269) levels, MDD (170,756 cases, 329,443 controls) and BD (20,352 cases, 31,358 controls). Secondary MR analyses investigated the effects of TSH and FT4 levels on specific MDD and BD subtypes. Reverse MR was also performed to assess the effects of MDD and BD on TSH and FT4 levels.ResultsThere were no associations between genetically predicted TSH and FT4 levels and MDD risk, nor MDD subtypes and minor depressive symptoms. A one standard deviation increase in FT4 levels was nominally associated with an 11% decrease in the overall BD risk (OR=0.89, 95%CI=0.80-0.98, P=0.022) and a 13% decrease in the BD type 1 risk (OR=0.87, 95%CI=0.75-1.00, P=0.047). In the reverse direction, genetic predisposition to MDD and BD was not associated with TSH nor FT4 levels.ConclusionsVariations in normal-range TSH and FT4 levels have no effects on the risk of MDD and its subtypes, and neither on minor depressive symptoms. This indicates that depressive symptoms should not be attributed to minor variations in thyroid function. Borderline associations with BD and BD type 1 risks suggest that further clinical studies should investigate the effect of thyroid hormone treatment in BD.

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics, and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads, and prioritize genes for functional follow-up studies.

PFC mTOR signaling as a biological signature for cognitive deficits in bipolar disorder without psychosis

Vanderplow et al.1 report decreased PFC Akt-mTOR signaling in males with bipolar disorder (BD) without psychosis compared with those with psychosis, possibly related to cognitive deficits. Understanding how cognition differs between these BD subtypes clinically and biologically remains a challenge.

Pharmacogenomics of Lithium Response in Bipolar Disorder.

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

Affective and non-affective cognition in patients with bipolar disorder type I and type II in full or partial remission: Associations with familial risk

BackgroundThe upcoming conversion of the ICD-11 will subdivide patients with bipolar disorder (BD) into BD type I (BD-I) and BD type II (BD-II). This study aimed to investigate whether cognitive impairments could aid as objective cognitive biomarkers for recently diagnosed BD subtypes by comparing cognitive profiles between BD subtypes, their unaffected relatives (UR), and healthy controls (HC). MethodsThe sample included 76 patients with BD-I, 149 patients with BD-II, 28 UR of patients with BD-I (UR-I), 50 UR of patients with BD-II (UR-II) and 168 HC from the Bipolar Illness Onset study, who were assessed with an extensive non-affective and affective cognitive test battery. ResultsThe results showed no significant differences in affective or non-affective cognition between BD-I and BD-II. Compared to HC, patients with BD-I (but not BD-II) showed worse performance in verbal fluency (p = .01) and were slower at recognising fearful faces (p = .045), while patients with BD-II (but not BD-I) displayed generally poorer recognition of facial expressions (p = .02). Only UR-I showed lower performance on verbal fluency (p = .049) and aberrant affective cognition (ps≤.047) compared to HC. LimitationsThe potential confounding effects of medication were not explored. ConclusionsThe lack of significant differences in cognitive profiles between recently diagnosed BD-I and BD-II suggests that neither affective nor non-affective cognition are indicative of BD subtype.

Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families

The two major subtypes of bipolar disorder (BD), BD-I and BD-II, are distinguished based on the presence of manic or hypomanic episodes. Historically, BD-II was perceived as a less severe form of BD-I. Recent research has challenged this concept of a severity continuum. Studies in large samples of unrelated patients have described clinical and genetic differences between the subtypes. Besides an increased schizophrenia polygenic risk load in BD-I, these studies also observed an increased depression risk load in BD-II patients. The present study assessed whether such clinical and genetic differences are also found in BD patients from multiplex families, which exhibit reduced genetic and environmental heterogeneity. Comparing 252 BD-I and 75 BD-II patients from the Andalusian Bipolar Family (ABiF) study, the clinical course, symptoms during depressive and manic episodes, and psychiatric comorbidities were analyzed. Furthermore, polygenic risk scores (PRS) for BD, schizophrenia, and depression were assessed. BD-I patients not only suffered from more severe symptoms during manic episodes but also more frequently showed incapacity during depressive episodes. A higher BD PRS was significantly associated with suicidal ideation. Moreover, BD-I cases exhibited lower depression PRS. In line with a severity continuum from BD-II to BD-I, our results link BD-I to a more pronounced clinical presentation in both mania and depression and indicate that the polygenic risk load of BD predisposes to more severe disorder characteristics. Nevertheless, our results suggest that the genetic risk burden for depression also shapes disorder presentation and increases the likelihood of BD-II subtype development.

Causal associations of intelligence with schizophrenia and bipolar disorder: A Mendelian randomization analysis.

Intelligence is inversely associated with schizophrenia (SCZ) and bipolar disorder (BD); it remains unclear whether low intelligence is a cause or consequence. We investigated causal associations of intelligence with SCZ or BD risk and a shared risk between SCZ and BD and SCZ-specific risk. To estimate putative causal associations, we performed multi-single nucleotide polymorphism (SNP) Mendelian randomization (MR) using generalized summary-data-based MR (GSMR). Summary-level datasets from five GWASs (intelligence, SCZ vs. control [CON], BD vs. CON, SCZ+BD vs. CON, and SCZ vs. BD; sample sizes of up to 269,867) were utilized. A strong bidirectional association between risks for SCZ and BD was observed (odds ratio; ORSCZ→BD=1.47, p=2.89×10-41, ORBD→SCZ=1.44, p=1.85×10-52). Low intelligence was bidirectionally associated with a high risk for SCZ, with a stronger effect of intelligence on SCZ risk (ORlower intelligence → SCZ=1.62, p=3.23×10-14) than the reverse (ORSCZ→lower intelligence=1.06, p=3.70×10-23). Furthermore, low intelligence affected a shared risk between SCZ and BD (OR lower intelligence → SCZ+BD=1.23, p=3.41×10-5) and SCZ-specific risk (ORlower intelligence → SCZvsBD=1.64, p=9.72×10-10); the shared risk (ORSCZ+BD→lower intelligence=1.04, p=3.09×10-14) but not SCZ-specific risk (ORSCZvsBD→lower intelligence=1.00, p=0.88) weakly affected low intelligence. Conversely, there was no significant causal association between intelligence and BD risk (p>0.05). These findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and intelligence decline. Moreover, a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that patients with these genetic factors should be categorized as having a cognitive disorder SCZ or BD subtype.

Decreased cortical gyrification in patients with bipolar disorder.

An aberrant neural connectivity has been known to be associated with bipolar disorder (BD). Local gyrification may reflect the early neural development of cortical connectivity and has been studied as a possible endophenotype of psychiatric disorders. This study aimed to investigate differences in the local gyrification index (LGI) in each cortical region between patients with BD and healthy controls (HCs). LGI values, as measured using FreeSurfer software, were compared between 61 patients with BD and 183 HCs. The values were also compared between patients with BD type I and type II as a sub-group analysis. Furthermore, we evaluated whether there was a correlation between LGI values and illness duration or depressive symptom severity in patients with BD. Patients with BD showed significant hypogyria in various cortical regions, including the left inferior frontal gyrus (pars opercularis), precentral gyrus, postcentral gyrus, superior temporal cortex, insula, right entorhinal cortex, and both transverse temporal cortices, compared to HCs after the Bonferroni correction (p < 0.05/66, 0.000758). LGI was not associated with clinical factors such as illness duration, depressive symptom severity, and lithium treatment. No significant differences in cortical gyrification according to the BD subtype were found. BD appears to be characterized by a significant regionally localized hypogyria, in various cortical areas. This abnormality may be a structural and developmental endophenotype marking the risk for BD, and it might help to clarify the etiology of BD.

Sex Differences in the Longitudinal Course and Outcome of Bipolar Disorder in Youth.

Despite substantial literature on sex differences in adults with bipolar disorder (BD), little is known about this topic in youth; this study examines sex differences in mood symptomatology and psychiatric comorbidity in prospectively followed youth with BD. A subsample of the Course and Outcome of Bipolar Youth study (N = 370; female n = 199, male n = 171) enrolled October 2000-July 2006 (age at intake = 7-17.11 years) who met DSM-IV criteria for bipolar I disorder (BD-I; n = 221), bipolar II disorder (BD-II; n = 26), or operationalized BD not otherwise specified (BD-NOS; n = 123) with ≥ 4 years follow-up was included. Analyses examined sex differences at intake and, prospectively, in mood symptomatology and psychiatric comorbidity for a mean ± SD follow-up of 10.5 ± 1.72 years. Females were older than males at intake (mean ± SD age = 13.33 ± 3.32 vs 12.04 ± 3.16 years; P = .0002) and at age at mood onset (9.33 ± 4.22 vs 7.53 ± 3.74 years; P < .0001). After adjustment for confounders, males spent more time with syndromal ADHD (Padjusted = .001) and females spent more time with syndromal anxiety (Padjusted = .02). There were trends toward males spending more time with substance use disorder and females having more non-suicidal self-injurious behavior (Padjusted = .07 and .09, respectively). There were no sex differences on outcome variables, including rate of or time to recovery and recurrence. Contrasting with adult literature, this study identified minimal sex differences in the course of youth with BD. Longer-term studies are needed to clarify if youth-onset BD remains a "sex neutral" subtype of BD or diverges according to sex in adulthood.

Gender Differences in Clinical Characteristics and Comorbidities in Bipolar Disorder: a Study from South India.

The clinical features and course of bipolar disorder differ between women and men; however, studies are limited in Indian population. The objective of this study was to identify gender differences in patients with bipolar disorder. This was a cross-sectional, hospital-based observational study conducted over a period of 25months. The sample consisted of 110 males and 90 females with a diagnosis of bipolar disorder according to ICD-10 Diagnostic Criteria for Research. Socio-demographic and clinical details were collected using semi-structured proforma. All patients were evaluated on Mini International Neuropsychiatric Interview Plus, Presumptive Stressful Life Events Scale and Clinical Global Impression. Sample consisted of 55% men and 45% women. The total number of episodes was similar between genders, however, the number of manic episodes (p = 0.004) was significantly more in males and the number of depressive (p = 0.003) and mixed episodes (p = 0.018) were significantly more in females. Majority of males had first episode of mania, whereas, first episode in females were mostly depressive (p < 0.001). Comorbid physical disorders were seen in 61.1% females and 40% males. Bipolar disorder subtype, episode types and number varied across gender. Co-morbid hypothyroidism, migraine, and obesity are seen more often in women and substance use was higher in men.

Metacognitions and repetitive negative thinking in bipolar disorder and healthy controls: A comparative study

BackgroundMetacognitive deficits and repetitive negative thinking are poorly explored in bipolar disorder (BD). The majority of the published studies concerned patients with bipolar depression, without differentiating among BD subtypes. The most common dysfunctional metacognitions, measured with the Metacognition Questionnaire-30 (MCQ-30), were Negative Beliefs about the Uncontrollability and Danger of Worry (NB), Cognitive Confidence (CC) and Beliefs about the Need to Control Thoughts (NC). Worry and rumination also seem to influence every phase of BD.This study aimed to investigate metacognitions and repetitive negative thinking in euthymic patients with BD. MethodUsing the MCQ-30, the Penn State Worry Questionnaire (PSWQ) and the Ruminative Responses Scale (RRS), we compared 57 BD-I and 48 BD-II patients with 78 healthy controls. ResultsBoth BD groups showed significantly higher NB, CC, NC and total MCQ-30 scores. ‘Positive Beliefs About Worry’ (PBW) showed a significantly higher score only in the BD-II group. Rumination scores were significantly higher in both patient samples. Worry did not show any significant differences between groups. LimitationsThe primary limitations are related to the size of the samples and the research design. ConclusionsOur findings suggested that metacognitive deficits and negative repetitive thinking were associated with euthymic BD. Rumination, NB, CC, and TC may represent trait-dependent features related to the inter-episodic phase of the disorder. A higher PBW score seemed to be a distinctive feature only for patients with euthymic BD-II. The results offer new perspectives in the psychotherapeutic treatment of these patients.

The use of antidepressant medications for Bipolar I and II disorders

Given that the patterns and clinical correlates related to antidepressant drugs (ADs) prescription for Bipolar Disorder (BD) remain generally unclear, this study aimed to compare socio-demographic and clinical features of BD patients treated vs. not treated with ADs. The sample consists of 287 currently euthymic bipolar patients. Among participants (mean age=51.9±15.02), 157 54.7% were receiving ADs. Based on the main findings, subjects given ADs were older and more frequently retired than those without receiving ADs. Moreover, patients given ADs were more likely to have had a first major depressive episode. Lifetime substance abuse/dependence history was less frequently reported among patients given ADs. Furthermore, ADs given patients had a higher number of affective episodes, and longer duration of their illness. Additionally, subjects treated with ADs reported higher hopelessness levels, and lower positive reinterpretations than those who were not treated with ADs. Factors associated with ADs-use by multivariate modeling were reduced personal autonomy (OR=.070), and hopelessness levels (OR=1.391). These results may help clinicians to better understand the clinical correlates of BD subtypes and improve their differential management. Additional studies are needed to replicate these findings, and facilitate the differential trajectories of BD patients based on socio-demographic/clinical profiles.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Neural Subtypes of Euthymic Bipolar I Disorder Characterized by Emotion Regulation Circuitry

BackgroundCurrent diagnostic strategy for bipolar disorders relies on symptomological classification. Yet, responses to both pharmacological and psychotherapeutic treatments vary widely, suggesting that underlying neuropathological differences are not well defined by current nosology. Classifying patients with bipolar disorder based on emotion regulation network (ERN) activation may account for some of the heterogeneity within the disorder. MethodsEuthymic participants diagnosed with bipolar I disorder (n = 86) and healthy subjects (n = 80) underwent functional magnetic resonance imaging scans while engaged in emotional reappraisal of negative stimuli. After determining average regional activations in key network regions, we applied agglomerative hierarchical clustering to identify subtypes of bipolar disorder. Next, we examined relations among neural subtypes, demographic variables, and mood symptoms. ResultsAnalyses revealed two primary neural subtypes of euthymic bipolar I disorder participants. The first subtype, ERN cluster 1, was characterized by increased amygdala activation and slightly increased ventrolateral prefrontal and subgenual cingulate activation, whereas ERN cluster 2 was defined by decreased amygdala activation with wider-spread prefrontal activation. Cluster 1 was associated with a higher number of hospitalizations for depression (odds ratio = 1.30, 95% confidence interval = 1.02–1.64) and later onset of manic episodes (odds ratio = 1.06, 95% confidence interval = 1.00–21.13) than cluster 2. ERN clusters of healthy subjects differed from bipolar disorder clusters and were defined by differential activation of the prefrontal cortex. ERN clusters of healthy subjects, which differed from bipolar disorder clusters, were defined by differential activation of the prefrontal cortex. ConclusionsEmotion regulation circuitry can distinguish neurobiological subtypes of bipolar disorder in the euthymic state. These subtypes, which are differentially associated with indices of illness severity and subsyndromal affective symptoms, may help to inform relapse risk and more personalized treatment approaches.

Similarities and Differences Across Bipolar Disorder Subtypes Among Adolescents.

Objectives: To compare demographic, clinical, and familial characteristics across bipolar disorder (BD) subtypes in adolescents. Methods: A total of 168 participants, 13 to 19 years of age, with BD-I (n = 41), BD-II (n = 68), or operationalized BD-not otherwise specified (NOS) (n = 59) were recruited from a tertiary subspecialty clinic at an academic health sciences center. Diagnoses were determined using the semistructured K-SADS-PL (Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version) interview. Omnibus analyses were followed up with post hoc pairwise comparisons. Results: After controlling for age, race, and living with both natural parents, BD-I was associated with greater functional impairment, increased rates of psychiatric hospitalization, psychosis, and lifetime exposure to second-generation antipsychotics and lithium, less self-injurious behavior, less anxiety disorders, and less severe worst lifetime depression and lower levels of emotional dysregulation and lability compared with both BD-II and BD-NOS. Lifetime most severe manic symptoms were highest in BD-I, lowest in BD-NOS, with BD-II intermediate. Lifetime exposure to psychosocial treatment followed the opposite pattern: lowest in BD-I, highest in BD-NOS, with BD-II intermediate. Variables for which there were no significant between-group differences included suicidal ideation, suicide attempts, comorbidities other than anxiety, or family history of BD. Conclusion: Among observed differences, most distinguish BD-I from other subtypes, whereas few variables differed between BD-II and BD-NOS. Different BD subtypes share important similarities in multiple clinical and familial characteristics, including family history of BD. Present findings support and extend knowledge regarding the course and outcome of bipolar youth study operationalized definition of BD-NOS. Further research is warranted to evaluate intermediate phenotypes and treatment strategies that address these subtype-related differences.

Common and distinct global functional connectivity density alterations in patients with bipolar disorder with and without auditory verbal hallucination during major depressive episodes.

Although an increasing number of studies has explored the neural mechanisms of auditory verbal hallucination (AVH) using many modalities, including neuroimaging, neurotransmitters, and electroencephalography, the etiology of AVH remains unclear. In this study, we investigated the neuroimaging characteristics of AVH in patients with bipolar disorder (BD) experiencing depressive episodes with and without AVH. For this study, we recruited 80 patients with BD and depressive status (40 with and 40 without AVH), and 40 healthy individuals. Their global functional connectivity density (gFCD) was screened by functional magnetic resonance imaging. Differences in gFCD among the three groups were tested using voxel-wise one-way analysis of covariance. Patients in both BD groups demonstrated increased gFCD in the central parietal lobe, insular lobe, and middle cingulate cortex, and decreased gFCD in the posterior parietal cortex, lateral prefrontal cortex, and occipital lobe (all bilateral). We defined these alterations as the common aberrant gFCD pattern for BD with and without AVH. Compared with the other two groups, patients in the BD with AVH group demonstrated increased gFCD in the Broca and Wernicke regions, and decreased gFCD in the hippocampus (all bilateral). We defined these alterations as the distinct aberrant gFCD pattern for BD with AVH. To our knowledge, this report is the first to date to describe gFCD alterations in patients with BD with and without AVH. Our findings suggest that disturbances in brain activity and information communication capacity in patients with BD and AVH are located mainly in the left frontoparietal network, control network, and memory circuit. However, these observations were made only in patients with BD during depressive episodes, and without consideration of many factors, such as the treatment mode, symptom relapse, and BD subtype. Hence, the conclusions of this study merely provide clues for further study, and do not fully represent brain alterations in patients with BD and AVH. Further large-sample cohort studies are needed to clarify and expand on these findings.

Differential core pharmacotherapy in bipolar I versus bipolar II disorder and European versus American patients not in a syndromal episode.

Assess bipolar disorder subtype and treatment location effects on bipolar disorder core pharmacotherapy. Outpatients not in a syndromal episode referred to the University of Milan and Stanford University Bipolar Disorder Clinics were assessed with SCID for the fourth Edition of the Diagnostic and Statistical Manual of Mood Disorders, and the Systematic Treatment Enhancement Program for Bipolar Disorder Affective Disorders Evaluation, respectively. Prevalence and clinical correlates of antidepressant, antipsychotic, and mood stabilizer use, in aggregate and individually, were compared in bipolar I (BDI) versus II (BDII) patients in Milan/Stanford and in Milan versus Stanford patients, stratified by subtype. Milan/Stanford pooled BDI versus BDII patients significantly more often took antipsychotic (69.8 versus 44.8%), mood stabilizers (68.6 versus 57.7%), and valproate (40.1 versus 17.5%), and less often took antidepressants (23.1 versus 55.6%) and lamotrigine (9.9 versus 25.2%). Milan versus Stanford patients (stratified by bipolar disorder subtype) significantly more often took antipsychotic (BDI and BDII), antidepressants (BDII), and valproate (BDII), and less often took lamotrigine (BDI). Research regarding bipolar disorder core pharmacotherapy relationships with bipolar subtype and treatment location is warranted to enhance clinical management.

A Systematic Review of Studies Reporting Data-Driven Cognitive Subtypes across the Psychosis Spectrum

The delineation of cognitive subtypes of schizophrenia and bipolar disorder may offer a means of determining shared genetic markers and neuropathology among individuals with these conditions. We systematically reviewed the evidence from published studies reporting the use of data-driven (i.e., unsupervised) clustering methods to delineate cognitive subtypes among adults diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder. We reviewed 24 studies in total, contributing data to 13 analyses of schizophrenia spectrum patients, 8 analyses of bipolar disorder, and 5 analyses of mixed samples of schizophrenia and bipolar disorder participants. Studies of bipolar disorder most consistently revealed a 3-cluster solution, comprising a subgroup with 'near-normal' (cognitively spared) cognition and two other subgroups demonstrating graded deficits across cognitive domains. In contrast, there was no clear consensus regarding the number of cognitive subtypes among studies of cognitive subtypes in schizophrenia, while four of the five studies of mixed diagnostic groups reported a 4-cluster solution. Common to all cluster solutions was a severe cognitive deficit subtype with cognitive impairments of moderate to large effect size relative to healthy controls. Our review highlights several key factors (e.g., symptom profile, sample size, statistical procedures, and cognitive domains examined) that may influence the results of data-driven clustering methods, and which were largely inconsistent across the studies reviewed. This synthesis of findings suggests caution should be exercised when interpreting the utility of particular cognitive subtypes for biological investigation, and demonstrates much heterogeneity among studies using unsupervised clustering approaches to cognitive subtyping within and across the psychosis spectrum.

Comparison of Genetic Liability for Sleep Traits Among Individuals With Bipolar Disorder I or II and Control Participants

Insomnia, hypersomnia, and an evening chronotype are common in individuals with bipolar disorder (BD), but whether this reflects shared genetic liability is unclear. Stratifying by BD subtypes could elucidate this association and inform sleep and BD research. To assess whether polygenic risk scores (PRSs) for sleep traits are associated with BD subtypes I and II. This case-control study was conducted in the United Kingdom and Sweden with participants with BD and control participants. Multinomial regression was used to assess whether PRSs for insomnia, daytime sleepiness, sleep duration, and chronotype are associated with BD subtypes compared with control participants. Affected individuals were recruited from the Bipolar Disorder Research Network. Control participants were recruited from the 1958 British Birth Cohort and the UK Blood Service. Analyses were repeated in an independent Swedish sample from August 2018 to July 2019. All participants were of European ancestry. Standardized PRSs derived using alleles from genome-wide association studies of insomnia, sleep duration, daytime sleepiness, and chronotype. These were adjusted for the first 10 population principal components, genotyping platforms, and sex. Association of PRSs with BD subtypes, determined by semistructured psychiatric interview and case notes. The main analysis included 4672 participants with BD (3132 female participants [67.0%]; 3404 with BD-I [72.9%]) and 5714 control participants (2812 female participants [49.2%]). Insomnia PRS was associated with increased risk of BD-II (relative risk [RR], 1.14 [95% CI, 1.07-1.21]; P = 8.26 × 10-5) but not BD-I (RR, 0.98 [95% CI, 0.94-1.03]; P = .409) relative to control participants. Sleep-duration PRS was associated with BD-I (RR, 1.10 [95% CI, 1.06-1.15]; P = 1.13 × 10-5) but not BD-II (RR, 0.99 [95% CI, 0.93-1.06]; P = .818). Associations between (1) insomnia PRS and BD-II and (2) sleep-duration PRS and BD-I were replicated in the Swedish sample of 4366 individuals with BD (2697 female participants [61.8%]; 2627 with BD-I [60.2%]) and 6091 control participants (3767 female participants [61.8%]). Chronotype and daytime-sleepiness PRS were not associated with BD subtypes. Per this analysis, BD subtypes differ in genetic liability to insomnia and hypersomnia, providing further evidence that the distinction between BD-I and BD-II has genetic validity. This distinction will be crucial in selecting participants for future research on the role of sleep disturbance in BD.

Toxoplasmosis in a Cohort of Italian Patients With Bipolar and Psychotic Disorders: How Infection May Affect Clinical Features?

This study investigated the seroprevalence of Toxoplasma gondii in a cohort of 101 Italian inpatients affected by mood or schizophrenia-spectrum disorders and compared clinical features between seronegative and seropositive subjects. Patients diagnosed according to DSM-5 criteria underwent clinical assessments and blood collection to test parasite-specific IgG/IgM serum levels. Twenty-eight patients (27.7%) had IgG anti-T. gondii, and none had IgM antibodies. We found higher prevalence rate in patients aged 40 years or older, as compared with younger. No significant association was detected between T. gondii and a specific diagnostic category; however, bipolar disorder (BD)-II showed the highest positivity rate (40.9%). The seropositive status was significantly associated with a lower presence of psychotic symptoms, higher number of total episodes of predominant excitatory polarity, longer illness duration, and lower severity of current episode, particularly anxiety, depressive, and withdrawal/retardation symptoms. These preliminary results seem to point out an association between chronic toxoplasmosis and a specific subtype of BD.