Characterisation of age and polarity at onset in bipolar disorder

Editorial: Combining Genetic and Clinical Predictors of Bipolar Disorder: Towards Improving Diagnostic Precision in Youth

Bipolar disorder (BD) is a complex, heterogeneous illness, with 60% to 85% of its variance attributed to genetic factors.1 Adolescence marks the first peak period of risk for the onset of BD, with the initial (hypo)manic episode often preceded by childhood psychopathology, including anxiety and sleep disorders, as well as internalizing symptoms.2 Given the non-specific nature of childhood antecedents, combined with the prominence of depressive episodes in the early illness course, accurate diagnosis is often delayed by 8 to 10 years from onset.3 Yet, the early course of BD in youth is already associated with significant morbidity and mortality. Therefore, more accurate and timely diagnosis is a priority. One way forward could be to combine biomarkers with clinical variables to help validate diagnoses, improve individual risk prediction and treatment, and advance discovery research into pathogenesis.

Genetic overlap between schizophrenia spectrum disorders and Alzheimer's disease: Current evidence and future directions - An integrative review

Schizophrenia and Alzheimer's disease (AD) are distinct neurodegenerative disorders characterized by progressive cognitive deficits and structural alterations in the brain. Schizophrenia typically emerges in adolescence or early adulthood with symptoms such as hallucinations, delusions, and cognitive impairments, whereas AD primarily affects elderly individuals, causing progressive memory loss, cognitive decline, and behavioral changes. Delusional disorder, which often emerges later in life, shares some features with schizophrenia and is considered a schizophrenia spectrum disorder. Patients with schizophrenia or delusional disorder, particularly women and those aged 65 years or older, have an increased risk of developing AD later in life. In contrast, approximately 30 % of AD patients exhibit psychotic symptoms, which accelerate cognitive decline and worsen health outcomes. This integrative review explored the genetic overlap between schizophrenia spectrum disorders and AD to identify potential shared genetic factors. The genetic correlations between schizophrenia and AD were weak but positive (rg=0.03-0.10). Polygenic risk scores (PRSs) for schizophrenia and AD indicate some genetic predisposition, although findings are inconsistent among studies; e.g., PRS-schizophrenia or PRS-AD were associated with the risk of developing psychosis in patients with AD. A higher PRS for various developmental and psychiatric disorders was correlated with an earlier age at onset of schizophrenia. Research gaps include the need for studies on the impacts of PRS-AD on the risk of schizophrenia, genetic correlations between later-onset delusional disorder and AD, and genetic relationships between AD and late-onset schizophrenia (LOS) with a greater risk of progressing to AD. Further investigation into these genetic overlaps is crucial to enhance prevention, treatment, and prognosis for affected patients.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population

BACKGROUND

Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data.

METHODS

The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions.

RESULTS

The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes.

CONCLUSIONS

Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

Inflammatory and oxidative stress biomarkers in children and adolescents with bipolar disorder - A systematic review and meta-analysis

Evidence suggests a role for low-grade inflammation and oxidative stress in the pathophysiology of bipolar disorder. We conducted a systematic review and meta-analysis of peripheral markers of inflammation and oxidative stress in children and adolescents under 20 years of age with bipolar disorder. We searched PubMed, Embase and psycINFO and performed random effects meta-analysis calculating standardized mean differences (SMD) of marker levels between patients with bipolar disorder and healthy control individuals. Ten studies comprising a total of 418 patients with bipolar disorder and 3017 healthy control individuals were included. The levels of C-Reactive Protein were higher in patients with bipolar disorder compared with healthy individuals (SMD 0.53; 95 %CI: 0.33-0.74; I2 = 0 %). For other biomarkers there were no statistically significant differences between groups. Findings were limited by a low number of studies and participants and methodological issues in the included studies. More and larger studies using rigorous methodology are needed to establish the role of inflammation and oxidative stress in children and adolescents with bipolar disorder.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder

AIM

We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK.

METHODS

We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used.

RESULTS

SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified.

LIMITATIONS

The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information.

CONCLUSION

Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

Occurrence of postpartum manic or mixed episodes in women with bipolar I disorder: A systematic review and meta-analysis

OBJECTIVE

Accurate information on the frequency and prevalence of manic or mixed episodes is important for therapeutic, prognostic, and safety concerns. We aimed to estimate the risk of relapse of manic and mixed episodes after delivery in women with bipolar I disorder or schizoaffective disorder-bipolar type.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search in PubMed, PsycINFO, Embase, and Cochrane databases was carried out on November 17, 2022, using the terms ((bipolar disorder) OR (manic depressive illness)) AND (mania)) AND (postpartum)) AND (recurrence)) AND (relapse). The search was updated on March 29, 2023. Case studies and qualitative analyses were excluded. Twelve studies reporting on 3595 deliveries in 2183 women were included in the quantitative analysis.

RESULTS

The overall pooled estimate of postpartum relapse risk was 39% (95% CI = 29, 49; Q(11) = 211.08, p < 0.001; I2 = 96.31%). Among those who had a relapse, the pooled estimate of risk for manic and mixed episodes was 38% (95% CI = 28, 50; Q(11) = 101.17, p < 0.001; I2 = 91.06%). Using data from the nine studies that reported the percentage of medication use during pregnancy, we estimated a meta-regression model with the percent medication use as a continuous explanatory variable. The estimated prevalence of relapse was 58.1% (95% CI, 9.6 to 39.3 to 76.8) for studies with no medication use and 25.9% (95% CI, 10.5-41.3) for studies with 100% medication use. The difference between the two prevalence estimates was statistically significant, z = -2.099, p = 0.0359.

CONCLUSIONS

Our findings suggest an overall pooled estimate of postpartum relapse risk of 39%, while the pooled estimate of risk for manic and mixed episodes was 38%. These findings highlight the need to educate patients with bipolar I disorder, and their healthcare professionals about the high risk of relapse of manic or mixed episodes after delivery.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS

Background

Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).

Aims

Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis.

Methods

Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses.

Results

While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD.

Conclusions

We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

What Came First, Mania or Depression? Polarity at Onset in Bipolar I and II: Temperament and Clinical Course

(1) Background: Bipolar disorder (BD) is divided into type I (BD-I) and type II (BD-II). Polarity at onset (PO) is a proposal to specify the clinical course of BD, based on the type of the first episode at disorder onset-depressive (D-PO) or manic (M-PO). At the same time, affective temperaments represent preexisting variants of the spectrum of affective disorders. Our objectives were to investigate the hypothesis that temperament may exert an influence on PO, and that this factor can serve as an indicator of the forthcoming course of the disorder, carrying significant therapeutic implications. (2) Methods: We included 191 patients with BD and examined clinical variables and temperament; the latter was assessed using the short version of the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto-questionnaire (TEMPS-A-39-SV). We tested the associations between these variables and PO using standard univariate/bivariate methods followed by multivariate logistic regression models. (3) Results: 52.9% of the sample had D-PO and 47.1% had M-PO. D-PO and M-PO patients scored higher for dysthymic and hyperthymic temperaments, respectively (p < 0.001). Also, they differed in BD subtypes, age at first affective episode, illness duration, number of depressive episodes, seasonality, suicide risk, substance use, lithium, and benzodiazepine use (p < 0.05). Only BD-II and age at first depressive episode were predictors of D-PO, whereas BD-I, age at first manic/hypomanic episode, and hyperthymic temperament were predictors of M-PO (p < 0.01). (4) Conclusions: Our findings point to the importance of carefully assessing temperament and PO in patients with BD, to better predict the clinical course and tailor therapeutic interventions to individual patients' needs.

Antecedents to first episode psychosis and mania: Comparing the initial prodromes of schizophrenia and bipolar disorder in a retrospective population cohort

OBJECTIVE

We aim to compare the psychiatric antecedents of schizophrenia (SZ) and bipolar disorder (BD).

METHODS

Using the Rochester Epidemiology Project, we searched for residents of Olmsted County that had a diagnosis of SZ or BD. We confirmed each case using DSM-5 criteria and obtained the psychiatric antecedents.

RESULTS

We identified 205 cases with first episode psychosis or mania (SZ = 131; BD = 74). The mean age at first visit for mental health reasons was 12.3 ± 6.3 years for SZ and 13.9 ± 5.6 years for BD. The duration of the initial prodrome (time from first mental health visit to first episode) was similar for both groups (SZ 8.3 ± 6.2 years vs BD 7.3 ± 5.9 years). We found that SZ and BD have overlapping antecedents, but SZ was more common in males and in foreign born and had more learning deficits before the first episode. BD was more common in white population and had higher rates of depressive and adjustment disorders prior to first episode. BD also had more affective symptoms, nightmares, and panic attacks before the first episode. Both groups had similarly high rates of substance use (SZ 74 % vs BD 74.3 %), prescription of antidepressants (SZ 46.6 % vs BD 55.4 %) and stimulants (SZ 30.5 % vs BD 22.9 %).

CONCLUSIONS

The psychiatric antecedents of SZ and BD usually start during adolescence, overlap, and present in unspecific ways. The initial prodromes are more alike than distinct. Further studies are encouraged to continue looking for specific factors that distinguish the antecedents of these two disorders.

Predicting diagnostic conversion from major depressive disorder to bipolar disorder: an EHR based study from Colombia

Bipolar Disorder (BD) is a severe and chronic disorder characterized by recurrent episodes of depression, mania, and/or hypomania. Most BD patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study leverages electronic health record (EHR) data from the Clínica San Juan de Dios Manizales in Colombia to identify features predictive of the transition from Major Depressive Disorder (MDD) to BD. Analyzing EHR data from 13,607 patients diagnosed with MDD over 15 years, we identified 1,610 cases of conversion to BD. Using a multivariate Cox regression model, we identified severity of the initial MDD episode, the presence of psychosis and hospitalization at first episode, family history of mood or psychotic disorders, female gender to be predictive of the conversion to BD. Additionally, we observed associations with medication classes (prescriptions of mood stabilizers, antipsychotics, and antidepressants) and clinical features (delusions, suicide attempt, suicidal ideation, use of marijuana and alcohol use/abuse) derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within five years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%. Our study confirms many previously identified risk factors identified through registry-based studies (such as female gender and psychotic depression at the index MDD episode), and identifies novel ones (specifically, suicidal ideation and suicide attempt extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion as well as underscore its potential for the identification of novel risk factors and improving early diagnosis.

Alcohol use disorders in patients with bipolar disorder: a systematic review and meta-analysis

Alcohol consumption has a key role in more than 200 diseases and health injuries, being an important factor for social and public health costs. Studies with clinical populations show an association between alcohol use disorders (AUD) and bipolar disorder. In this meta-analysis we included studies, reports, or summaries identified in Google Scholar, Lilacs, Medline, and MedCaribe that reported original data published up to 31 January 2023. We included cross-sectional and longitudinal observational studies that investigated the prevalence of AUD in patients with bipolar disorder. We calculated the prevalence rates and conducted a meta-analysis using a random effects model. The meta-analysis included 20 unique studies conducted in 12 countries, with a total sample of 32,886 individuals with bipolar disorder, comprising 17,923 women and 13,963 men, all aged 18 years or older. The prevalence of AUD in individuals with bipolar disorder was found to be 29.12%, while the prevalence of Alcohol Dependence (AD) was 15.87% and the prevalence of Alcohol Abuse (AA) was 18.74%. The high prevalence of AUD individuals with bipolar disorder is important because it highlights the need for targeted interventions to prevent and address comorbid conditions, which may improve treatment outcomes, reduce harm, and promote public health.

The genetic basis of onset age in schizophrenia: evidence and models

Schizophrenia is a heritable neurocognitive disorder affecting about 1% of the population, and usually has an onset age at around 21-25 in males and 25-30 in females. Recent advances in genetics have helped to identify many common and rare variants for the liability to schizophrenia. Earlier evidence appeared to suggest that younger onset age is associated with higher genetic liability to schizophrenia. Clinical longitudinal research also found that early and very-early onset schizophrenia are associated with poor clinical, neurocognitive, and functional profiles. A recent study reported a heritability of 0.33 for schizophrenia onset age, but the genetic basis of this trait in schizophrenia remains elusive. In the pre-Genome-Wide Association Study (GWAS) era, genetic loci found to be associated with onset age were seldom replicated. In the post-Genome-Wide Association Study era, new conceptual frameworks are needed to clarify the role of onset age in genetic research in schizophrenia, and to identify its genetic basis. In this review, we first discussed the potential of onset age as a characterizing/subtyping feature for psychosis, and as an important phenotypic dimension of schizophrenia. Second, we reviewed the methods, samples, findings and limitations of previous genetic research on onset age in schizophrenia. Third, we discussed a potential conceptual framework for studying the genetic basis of onset age, as well as the concepts of susceptibility, modifier, and "mixed" genes. Fourth, we discussed the limitations of this review. Lastly, we discussed the potential clinical implications for genetic research of onset age of schizophrenia, and how future research can unveil the potential mechanisms for this trait.

Hormonal and inflammatory signatures of different mood episodes in bipolar disorder: a large-scale clinical study

BACKGROUND

Bipolar disorder (BD) is characterized by intensive mood fluctuations. While hormones imbalance plays important role in the mood swings, it is unknown whether peripheral hormones profiles could differentiate the manic and depressive mood episodes in BD. In this study, we investigated the changes of various hormones and inflammatory markers across distinct mood episodes of BD in a large clinical study to provide mood episode-specific peripheral biomarkers for BD.

METHODS

A total of 8332 BD patients (n = 2679 depressive episode; n = 5653 manic episode) were included. All patients were in acute state of mood episodes and need hospitalization. A panel of blood tests were performed for levels of sex hormones (serum levels of testosterone, estradiol, and progesterone), stress hormones (adrenocorticotropic hormone and cortisol), and an inflammation marker (C-reactive protein, CRP). A receiver operating characteristic (ROC) curve was used to analyze the discriminatory potential of the biomarkers for mood episodes.

RESULTS

In overall comparison between mood episodes, the BD patients expressed higher levels of testosterone, estradiol, progesterone, and CRP (P < 0.001) and lower adrenocorticotropic hormone (ACTH) level (P < 0.001) during manic episode. The episode-specific changes of testosterone, ACTH, and CRP levels remained between the two groups (P < 0.001) after correction for the confounding factors including age, sex, BMI, occupation, marital status, tobacco use, alcohol consumption, psychotic symptoms, and age at onset. Furthermore, we found a sex- and age-specific impact of combined biomarkers in mood episodes in male BD patients aged ≥ 45 years (AUC = 0.70, 95% CI, 0.634-0.747), not in females.

CONCLUSIONS

While both hormone and inflammatory change is independently associated with mood episodes, we found that the combination of sex hormones, stress hormones and CRP could be more effective to differentiate the manic and depressive episode. The biological signatures of mood episodes in BD patients may be sex- and age-specific. Our findings not only provide mood episode-related biological markers, but also better support for targeted intervention in BD treatments.

Predominant polarity as a neurobiological specifier in bipolar disorder: Evidence from a multimodal neuroimaging study

BACKGROUND

While predominant (PP) and onset polarity (OP) have considerable clinical and treatment implications in bipolar disorder (BD), the neurobiological underpinnings of PP and OP from a radiological perspective remain largely unknown. The main objective of this study is to investigate the neuroanatomical profile of polarity subphenotypes (PP and OP) in euthymic BD patients, using a standardized multimodal neuroimaging protocol to evaluate regional gray matter (GM) volumes, cortical thickness, as well as white matter (WM) integrity of major projection, commissural and association tracts.

METHODS

Forty-two euthymic BD patients stratified for PP and OP and 42 healthy controls (HC) were included in this computational neuroimaging study to comprehensively characterize gray and white matter alterations. Univariate analyses of covariance (ANCOVAs) were conducted with Bonferroni corrections for each MRI modality and Cohen's d effect sizes were calculated for group comparisons.

RESULTS

Phenotype-associated cortical thickness abnormalities and volumetric alterations were identified, but no WM changes ascertained. Specifically, we found a main effect of OP on GM volume of left middle frontal gyrus and of OP and PP (either or both) on cortical thickness of various regions previously implicated in BD, i.e. inferior frontal gyrus-pars opercularis (left) and pars orbitalis (bilateral), left lateral orbitofrontal gyrus, bilateral medial segment of the superior frontal gyrus, left planum polare, right anterior cingulate gyrus, left anterior and posterior insula, bilateral frontal operculum (both OP and PP); left anterior and posterior orbitofrontal gyrus, left transverse temporal gyrus, right posterior insula (only OP); and right medial frontal cortex (only PP). Based on the magnitude of differences on pairwise comparisons, we found a large effect of OP on cortical thickness in a single region (left anterior orbitofrontal gyrus) (OP-M > OP-D), while PP subgroups showed large or medium effect size differences in cortical thickness (PP-M > PP-D) in a wider array of regions (right medial frontal cortex, left frontal operculum, left inferior frontal gyrus-pars opercularis, bilateral medial segment of the superior frontal gyrus). For most regions, PP-D patients showed the greatest decreases in cortical thickness compared to HC while PP-M showed the smallest, with PP-U showing an "unspecified" pattern mostly lying in-between PP-D and PP-M.

CONCLUSIONS

Our multimodal imaging findings suggest specific polarity BD subgroups with compromised cortical thickness; we recorded a greater impact of PP on brain structure compared to OP, which provides additional evidence that PP can be considered as a neurobiological specifier in BD.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

Growing up unequal? Socioeconomic disparities in mental disorders throughout childhood in Finland

Problems in mental health and socioeconomic health inequalities during childhood and adolescence are receiving important scientific and political attention. This in mind, we study how current family income and parental education are associated with psychiatric disorders among children in a well-developed welfare state, Finland. To gain a deeper understanding of how these disparities develop through early life course, we study the differences between genders, age groups, types of mental disorders, and also take into account the role of parental mental disorders. We exploit high-quality Finnish register data containing the whole population aged 4-17 with information on their families and parents. Our results of linear probability models show that lower parental education is consistently associated with higher probability of mental disorders throughout childhood, although some gender and disorder-specific differences are also identified. Interestingly, household income is related to mental health in more complex ways, having both negative and positive associations with psychiatric disorders. Inequalities are stronger among boys than girls, and the strongest associations are found among boys aged 7-12 and girls aged 13-17. Parental mental disorders increase the risk of children's psychiatric disorders but do not explain socioeconomic disparities. Considering the negative effects of mental problems on socioeconomic outcomes, inequalities in childhood mental health can be expected to reinforce other social inequalities in later life and should therefore be a focus of interventions.

Association between MTHFR (677C>T and 1298A>C) polymorphisms and psychiatric disorder: A meta-analysis

Recent studies showed that genetic polymorphism of 5,10-methylenetetrahydrofolate reductase (MTHFR) is related to attention-deficit hyperactivity disorder (ADHD), bipolar disorder (BD) and schizophrenia (SCZ). However, no consistent conclusion has been determined. This meta-analysis aims to interrogate the relationship between MTHFR gene polymorphisms (677C>T and 1298A>C) and the occurrence of ADHD, BD and SCZ. We retrieved case-control studies that met the inclusion criteria from the PubMed database. Associations between MTHFR polymorphisms (677C>T and 1298A>C) and ADHD, BD and SCZ were measured by means of odds ratios (ORs) using a random effects model and 95% confidence intervals (CIs). Additionally, sensitivity analysis and publication bias were performed. After inclusion criteria were met, a total of five studies with ADHD including 434 cases and 670 controls, 18 studies with BD including 4167 cases and 5901 controls and 44 studies with SCZ including 16,098 cases and 19913 controls were finally included in our meta-analysis. Overall, our meta-analytical results provided evidence that the MTHFR 677C>T was associated with occurrence of BD and SCZ, while the 1298A>C polymorphism was related to ADHD and BD, and additionally the sensitivity analysis indicated these results were stable and reliable. This may provide useful information for relevant studies on the etiology of psychiatric disorders.

Genetic risk for psychiatric illness is associated with the number of hospitalizations of bipolar disorder patients

OBJECTIVES

Bipolar disorder (BD) has a highly heterogeneous clinical course that is characterized by relapses and increased health care utilization in a significant fraction of patients. A thorough understanding of factors influencing illness course is essential for predicting disorder severity and developing targeted therapies.

METHODS

We performed polygenic score analyses in four cohorts (N = 954) to test whether the genetic risk for BD, schizophrenia, or major depression is associated with a severe course of BD. We analyzed BD patients with a minimum illness duration of five years. The severity of the disease course was assessed by using the number of hospitalizations in a mental health facility and a composite measure of longitudinal illness severity (OPCRIT item 90).

RESULTS

Our analyses showed that higher polygenic scores for BD (β = 0.11, SE = 0.03, p = 1.17 × 10-3) and schizophrenia (β = 0.09, SE = 0.03, p = 4.24 × 10-3), but not for major depression, were associated with more hospitalizations. None of the investigated polygenic scores was associated with the composite measure of longitudinal illness severity (OPCRIT item 90).

LIMITATIONS

We could not account for non-genetic influences on disease course. Our clinical sample contained more severe cases.

CONCLUSIONS

This study demonstrates that the genetic risk burden for psychiatric illness is associated with increased health care utilization, a proxy for disease severity, in BD patients. The findings are in line with previous observations made for patients diagnosed with schizophrenia or major depression. Therefore, in the future psychiatric disorder polygenic scores might become helpful for stratifying patients with high risk of a chronic manifestation and predicting disease course.

Investigating the phenotypic and genetic associations between personality traits and suicidal behavior across major mental health diagnoses

Personality traits influence risk for suicidal behavior. We examined phenotype- and genotype-level associations between the Big Five personality traits and suicidal ideation and attempt in major depressive, bipolar and schizoaffective disorder, and schizophrenia patients (N = 3012) using fixed- and random-effects inverse variance-weighted meta-analyses. Suicidal ideations were more likely to be reported by patients with higher neuroticism and lower extraversion phenotypic scores, but showed no significant association with polygenic load for these personality traits. Our findings provide new insights into the association between personality and suicidal behavior across mental illnesses and suggest that the genetic component of personality traits is unlikely to have strong causal effects on suicidal behavior.