Polygenic Risk and Progression to Bipolar or Psychotic Disorders Among Individuals Diagnosed With Unipolar Depression in Early Life

Association of healthy lifestyle factors and genetic liability with bipolar disorder: Findings from the UK Biobank

BACKGROUND

The interplay between genetic and lifestyle factors in the development of bipolar disorder (BD) remains unclear.

METHODS

A cohort study was carried out on 365,517 participants from the UK Biobank. Lifestyle scores, based on smoking, physical activity, diet, alcohol consumption, sedentary behavior, sleep duration, and social contact, were grouped as favorable (scores 6-7), intermediate (scores 4-5), or unfavorable (scores 0-3). The BD polygenic risk score (PRS) was also categorized into high, intermediate, and low-risk groups using PRS tertiles. Cox regression models determined hazard ratios (HRs) and 95 % confidence intervals (CIs) for BD.

RESULTS

During the 12.9-year follow-up, 529 individuals developed BD. Comparing those with favorable lifestyles to those with unfavorable participants, the HR of developing BD was 3.28 (95 % CI, 2.76-3.89). Similarly, individuals with a high PRS had a risk of 3.20 (95 % CI, 2.83-3.63) compared to those with a low PRS. Notably, individuals with both a high PRS and an unfavorable lifestyle had a significantly higher risk of BD (HR = 6.31, 95 % CI, 4.14-9.63) compared to those with a low PRS and a favorable lifestyle. Additionally, the interaction between PRS and lifestyle contributed an additional risk, with a relative excess risk of 1.74 (95 % CI, 0.40-3.07) and an attributable proportion due to the interaction of 0.37 (95 % CI, 0.16-0.58).

CONCLUSIONS

Our findings suggest that genetic liability for BD, measured as PRS, and lifestyle have an additive effect on the risk of developing BD. A favorable lifestyle was associated with a reduced risk of developing BD.

Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults

AIM

To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders.

METHODS

Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on).

RESULTS

Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings.

CONCLUSIONS

The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.

Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls

OBJECTIVE

There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls.

METHOD

Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components.

RESULTS

The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD.

CONCLUSION

BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

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.

Polygenic Risk Scores for Bipolar Disorder: Progress and Perspectives

Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD.

Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth

There is a gap in knowledge regarding the polygenic underpinnings of brain anomalies observed in youth bipolar disorder (BD). This study examined the association of a polygenic risk score for BD (BD-PRS) with grey matter structure and white matter integrity in youth with and without BD. 113 participants were included in the analyses, including 78 participants with both T1-weighted and diffusion-weighted MRI images, 32 participants with T1-weighted images only, and 3 participants with diffusion-weighted images only. BD-PRS was calculated using PRS-CS-auto and was based on independent adult genome-wide summary statistics. Vertex- and voxel-wise analyses examined the associations of BD-PRS with grey matter metrics (cortical volume [CV], cortical surface area [CSA], cortical thickness [CTh]) and fractional anisotropy [FA] in the combined sample, and separately in BD and HC. In the combined sample of participants with T1-weighted images (n = 110, 66 BD, 44 HC), higher BD-PRS was associated with smaller grey matter metrics in frontal and temporal regions. In within-group analyses, higher BD-PRS was associated with lower CTh of frontal, temporal, and fusiform gyrus in BD, and with lower CV and CSA of superior frontal gyrus in HC. In the combined sample of participants with diffusion-weighted images (n = 81, 49 BD, 32 HC), higher BD-PRS was associated with lower FA in widespread white matter regions. In summary, BD-PRS calculated based on adult genetic data was negatively associated with grey matter structure and FA in youth in regions implicated in BD, which may suggest neuroimaging markers of vulnerability to BD. Future longitudinal studies are needed to examine whether BD-PRS predicts neurodevelopmental changes in BD vs. HC and its interaction with course of illness and long-term medication use.

Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers

Background

Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents.

Materials and methods

A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software.

Results

In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future.

Conclusion

This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.

Relationship of Family Genetic Risk Score With Diagnostic Trajectory in a Swedish National Sample of Incident Cases of Major Depression, Bipolar Disorder, Other Nonaffective Psychosis, and Schizophrenia

Importance

Since its inception under Kraepelin in the modern era, diagnostic stability and familial/genetic risk have been among the most important psychiatric nosologic validators.

Objective

To assess the interrelationships of family genetic risk score (FGRS) with diagnostic stability or diagnostic change in major depression (MD), bipolar disorder (BD), other nonaffective psychosis (ONAP), and schizophrenia.

Design, Setting, and Participants

This longitudinal population-based cohort (N = 4 171 120) included individuals with incident cases of MD (n = 235 095), BD (n = 11 681), ONAP (n = 16 009), and schizophrenia (n = 6312) who had at least 1 further diagnosis of the 4 disorders during follow-up, as assessed from Swedish national medical registries, observed over a mean (SD) of 13.1 (5.9) years until a mean (SD) age of 48.4 (12.3) years. Data were collected from January 1973 to December 2018, and data were analyzed from August to September 2022.

Exposures

FGRS for MD, BD, ONAP, and schizophrenia, calculated from morbidity risks for disorders in first-degree through fifth-degree relatives, controlling for cohabitation effects.

Main Outcomes and Measures

Final diagnostic outcome of MD, BD, ONAP, or schizophrenia.

Results

Of 269 097 included individuals, 173 061 (64.3%) were female, and the mean (SD) age at first registration was 35.1 (11.9) years. Diagnostic stability was highest for MD (214 794 [91.4%]), followed by schizophrenia (4621 [73.2%]), BD (7428 [63.6%]), and ONAP (6738 [42.1%]). The second most common final diagnosis for each of these MD, schizophrenia, BD, and ONAP were BD (15 506 [6.6%]), ONAP (1110 [17.6%]), MD (2681 [23.0%]), and schizophrenia (4401 [27.5%]), respectively. A high FGRS for the incident diagnosis was consistently associated with diagnostic stability, while a high FGRS for the final diagnosis and a low FGRS for the incident diagnosis was associated with diagnostic change. In multivariate models, those in the upper 5% of genetic risk had an odds ratio (OR) of 1.75 or greater for the following diagnostic transition: for MD FGRS, ONAP to MD (OR, 1.91; 95% CI, 1.59-2.29) and schizophrenia to MD (OR, 2.45; 95% CI, 1.64-3.68); for BD FGRS, MD to BD (OR, 2.60; 95% CI, 2.47-2.73), ONAP to BD (OR, 2.16; 95% CI, 1.85-2.52), and schizophrenia to BD (OR, 2.20; 95% CI, 1.39-3.49); for ONAP FGRS, MD to ONAP (OR, 1.80; 95% CI, 1.62-2.02), MD to schizophrenia (OR, 1.95; 95% CI, 1.58-2.41), and BD to schizophrenia (OR, 1.89; 95% CI, 1.39-2.56); and for schizophrenia FGRS, MD to schizophrenia (OR, 1.80; 95% CI, 1.46-2.23), and BD to schizophrenia (OR, 1.75; 95% CI, 1.25-2.45). FGRS profiles for incident cases confirmed at final diagnosis were more homogenous than genetic profiles for those who changed diagnoses.

Conclusions and Relevance

In a large population-based longitudinal cohort, the genetic risk factors for MD, BD, ONAP, and schizophrenia were meaningfully and systematically associated with the diagnostic trajectories of these 4 disorders. Over time, clinical diagnosis and genetic risk profiles became increasingly consilient, thereby providing genetic validation of these diagnostic constructs. Diagnostically unstable incident cases were more genetically heterogeneous than those who were diagnostically stable over time.

Polygenic Risk and Episode Polarity Among Individuals With Bipolar Disorder

OBJECTIVE

The authors investigated associations between polygenic liabilities for bipolar disorder, major depression, and schizophrenia and episode polarity among individuals with bipolar disorder.

METHODS

The sample consisted of 2,705 individuals diagnosed with bipolar disorder at Danish psychiatric hospitals between January 1995 and March 2017. DNA was obtained from dried blood spots collected at birth as part of routine screening. Polygenic risk scores (PRSs) for bipolar disorder, major depression, and schizophrenia were generated using a meta-PRS method combining internally and externally trained components. Associations between PRS and polarity at first episode, polarity at any episode, and number of episodes with a given polarity were evaluated for each disorder-specific PRS using logistic and negative binominal regressions adjusted for the other two PRSs, age, sex, genotype platform, and five ancestral principal components.

RESULTS

PRS for bipolar disorder was positively associated with any manic episodes (odds ratio=1.23, 95% CI=1.09-1.38). PRS for depression was positively associated with any depressive (odds ratio=1.11, 95% CI=1.01-1.23) and mixed (odds ratio=1.15, 95% CI=1.03-1.28) episodes and negatively associated with any manic episodes (odds ratio=0.76, 95% CI=0.69-0.84). PRS for schizophrenia was positively associated with any manic episodes (odds ratio=1.13, 95% CI=1.01-1.27), but only when psychotic symptoms were present (odds ratio for psychotic mania: 1.27, 95% CI=1.05-1.54; odds ratio for nonpsychotic mania: 1.06, 95% CI=0.93-1.20). These patterns were similar for first-episode polarity and for the number of episodes within each pole.

CONCLUSIONS

PRSs for bipolar disorder, major depression, and schizophrenia are associated with episode polarity and psychotic symptoms in a congruent manner among individuals with bipolar disorder.

Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent psychiatric condition characterised by a heterogeneous clinical presentation and an estimated twin-based heritability of ~40-50 %. Different clinical MDD subtypes might partly reflect distinctive underlying genetics. This study aims to investigate if polygenic risk scores (PRSs) for different psychiatric disorders, personality traits, and substance use-related traits may be associated with different clinical subtypes of MDD (i.e., MDD with melancholic or psychotic features), higher symptom severity, or different clusters of depressive symptoms (i.e., sadness symptoms, typical neurovegetative symptoms, detachment symptoms, and negative thoughts).

METHODS

The target sample included 1149 patients with MDD, recruited by the European Group for the Study of Resistant Depression. PRSs for 25 psychiatric disorders and traits were computed based on the most recent publicly available summary statistics of the largest genome-wide association studies. PRSs were then used as predictors in regression models, adjusting for age, sex, population stratification, and recruitment sites.

RESULTS

Patients with MDD having higher PRS for MDD and loneliness were more likely to exhibit melancholic features of MDD (p = 0.0009 and p = 0.005, respectively). Moreover, patients with higher PRS for alcohol intake and post-traumatic stress disorder were more likely to experience greater typical neurovegetative symptoms (p = 0.0012 and p = 0.0045, respectively).

LIMITATIONS

The proportion of phenotypic variance explained by the PRSs was limited.

CONCLUSIONS

This study suggests that melancholic features and typical neurovegetative symptoms of MDD may show distinctive underlying genetics. Our findings provide a new contribution to the understanding of the genetic heterogeneity of MDD.

Polygenic liability, stressful life events and risk for secondary-treated depression in early life: a nationwide register-based case-cohort study

BACKGROUND

In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark.

METHODS

Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions.

RESULTS

Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (β = -0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15.

CONCLUSIONS

These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals.

Multiscale neural signatures of major depressive, anxiety, and stress-related disorders

The extent of shared and distinct neural mechanisms underlying major depressive disorder (MDD), anxiety, and stress-related disorders is still unclear. We compared the neural signatures of these disorders in 5,405 UK Biobank patients and 21,727 healthy controls. We found the greatest case–control differences in resting-state functional connectivity and cortical thickness in MDD, followed by anxiety and stress-related disorders. Neural signatures for MDD and anxiety disorders were highly concordant, whereas stress-related disorders showed a distinct pattern. Controlling for cross-disorder genetic risk somewhat decreased the similarity between functional neural signatures of stress-related disorders and both MDD and anxiety disorders. Among cases and healthy controls, reduced within-network and increased between-network frontoparietal and default mode connectivity were associated with poorer cognitive performance (processing speed, attention, associative learning, and fluid intelligence). These results provide evidence for distinct neural circuit function impairments in MDD and anxiety disorders compared to stress disorders, yet cognitive impairment appears unrelated to diagnosis and varies with circuit function.

Genetics of age-at-onset in major depression

Major depression (MD) is a complex, heterogeneous neuropsychiatric disorder. An early age at onset of major depression (AAO-MD) has been associated with more severe illness, psychosis, and suicidality. However, not much is known about what contributes to individual variation in this important clinical characteristic. This study sought to investigate the genetic components underlying AAO-MD. To investigate the genetics of AAO-MD, we conducted a genome-wide association meta-analysis of AAO-MD based on self-reported age of symptoms onset and self-reported age at first diagnosis from the UK Biobank cohort (total N = 94,154). We examined the genetic relationship between AAO-MD and five other psychiatric disorders. Polygenic risk scores were derived to examine their association with five psychiatric outcomes and AAO-MD in independent sub-samples. We found a small but significant SNP-heritability (~6%) for the AAO-MD phenotype. No SNP or gene reached SNP or gene-level significance. We found evidence that AAO-MD has genetic overlap with MD risk ([Formula: see text] = -0.49). Similarly, we found shared genetic risks between AAO-MD and autism-spectrum disorder, schizophrenia, bipolar disorder, and anorexia nervosa ([Formula: see text] range: -0.3 to -0.5). Polygenic risk scores for AAO-MD were associated with MD, schizophrenia, and bipolar disorder, and AAO-MD was in turn associated with polygenic risk scores derived from these disorders. Overall, our results indicate that AAO-MD is heritable, and there is an inverse genetic relationship between AAO-MD and both major depression and other psychiatric disorders, meaning that SNPs associated with earlier age at onset tend to increase the risk for psychiatric disorders. These findings suggest that the genetics of AAO-MD contribute to the shared genetic architecture observed between psychiatric disorders.

Accounting for age of onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here, we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), which jointly accounts for age of onset and sex as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data and to mortality in the UK Biobank and found 20 genome-wide significant associations with LT-FH++, compared to ten for LT-FH and eight for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

Role of Polygenic Risk Score in the Familial Transmission of Bipolar Disorder in Youth

IMPORTANCE

Establishing genetic contributions to the transmission of bipolar disorder (BD) from parents to offspring may inform the risk of developing this disorder and further serve to validate BD in youth.

OBJECTIVE

To evaluate the specific association of BD polygenic risk scores (PRSs) on the familial transmission and validity of pediatric BD.

DESIGN, SETTING, AND PARTICIPANTS

This community-based case-control longitudinal study (Pittsburgh Biological Offspring Study) included parents with BD I/II and their offspring and parents without BD (healthy or non-BD psychopathology) and their offspring. Participants were recruited between March 2001 and May 2007, and analysis took place from December 2020 to September 2021.

EXPOSURES

PRSs for BD, major depressive disorder, schizophrenia, and attention-deficit/hyperactivity disorder.

MAIN OUTCOMES AND MEASURES

Participants were prospectively evaluated using standardized interviews blind to parental diagnosis. DNA was extracted from saliva and genotyped. PRSs were constructed based on independent large-scale genome-wide association studies.

RESULTS

A total of 156 parents with BD I/II and 180 parents without BD (mean [SD] age, 39.6 [7.9] years; 241 female [72%]) as well as 251 offspring of parents with BD and 158 offspring of parents without BD (mean [SD] age, 10.4 [4.7] years; 213 female [52%]) of European ancestry were analyzed. Participants were assessed a mean of 6.7 times during a mean (SD) of 13 (3.4) years of follow-up (84% retention). More offspring of parents with BD developed BD (58 [23.1%] vs 8 [5.1%]; P < .001) and depression (126 [50.2%] vs 52 [32.9%]; P < .001) compared with offspring of parents without BD. BD PRS was higher in both parents and offspring with BD than parents and offspring without BD (parents: odds ratio, 1.50; 95% CI, 1.19-1.89; P < .001; explained 4.8% of the phenotypic variance vs offspring: hazard ratio, 1.34; 95% CI, 1.03-1.7; P = .02; explained 5.0% of the phenotypic variance). BD PRS did not differ across BD subtypes. In a model combining parental and offspring BD PRS, the parental BD PRS association with offspring BD was fully mediated by offspring BD PRS (hazard ratio, 1.40; 95% CI, 1.05-1.86; P = .02). Parental BD had a stronger direct association than parental or offspring BD PRS with offspring BD risk (hazard ratio, 5.21; 95% CI, 1.86-14.62; P = .002), explaining 30% of the variance. Parental and offspring BD PRS explained 6% of the BD onset variance beyond parental diagnosis. There were no significant between-group differences in PRSs for major depressive disorder, schizophrenia, and attention-deficit/hyperactivity disorder in parents or offspring and they were not significantly associated with BD onset.

CONCLUSIONS AND RELEVANCE

The findings of this study add to the extant clinical validation of BD in youth. Parental BD and offspring BD PRS independently associated with the risk of BD in offspring. Although this is promising, the association of BD PRS was relatively small and cannot be used alone to determine BD risk until further developments occur.

Seasonality in Major Depressive Disorder: Effect of Sex and Age

BACKGROUND

Aside from the concept of seasonal affective disorder, the evidence for a seasonal pattern (SP) of major depressive disorder (MDD) is controversial. Furthermore, the effect of sex and age is still unclear.

METHODS

This is a nationwide, registry-based study assessing all inpatient admissions in mental health hospitals due to MDD episodes according to ICD-10 (moderate (F32/33.1), severe (F32/33.2) and severe with psychotic features (F32/33.3)) in Austria across 14 years. Calculations were based on deviations from expected monthly admissions.

RESULTS

The sample comprised 231,824 hospitalisations (36.8% men) for MDD. A significant SP (p=0.001) in moderate and severe depressive episodes in both women and men with decreased admission rates in the summer months and December was detected. In psychotic depression a significant SP was only evidenced in women (p = 0.002, men: p = 0.291). Patients older than 55 years had a reduced SP compared to those being younger.

LIMITATIONS

Only anonymised admission data of inpatient treatments were available. Hospitalization rates cannot fully be equated to the occurrence of MDD.

CONCLUSIONS

The current study indicates a seasonal variation in MDD symptoms that may go beyond seasonal affective disorder. Knowledge about the predictability of depressive symptoms in patients should encourage preventive strategies.

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders

BACKGROUND

The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years.

METHODS

PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.

DISCUSSIONS

We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

Polygenic risk scores across the extended psychosis spectrum

As early detection of symptoms in the subclinical to clinical psychosis spectrum may improve health outcomes, knowing the probabilistic susceptibility of developing a disorder could guide mitigation measures and clinical intervention. In this context, polygenic risk scores (PRSs) quantifying the additive effects of multiple common genetic variants hold the potential to predict complex diseases and index severity gradients. PRSs for schizophrenia (SZ) and bipolar disorder (BD) were computed using Bayesian regression and continuous shrinkage priors based on the latest SZ and BD genome-wide association studies (Psychiatric Genomics Consortium, third release). Eight well-phenotyped groups (n = 1580; 56% males) were assessed: control (n = 305), lower (n = 117) and higher (n = 113) schizotypy (both groups of healthy individuals), at-risk for psychosis (n = 120), BD type-I (n = 359), BD type-II (n = 96), schizoaffective disorder (n = 86), and SZ groups (n = 384). PRS differences were investigated for binary traits and the quantitative Positive and Negative Syndrome Scale. Both BD-PRS and SZ-PRS significantly differentiated controls from at-risk and clinical groups (Nagelkerke's pseudo-R2: 1.3-7.7%), except for BD type-II for SZ-PRS. Out of 28 pairwise comparisons for SZ-PRS and BD-PRS, 9 and 12, respectively, reached the Bonferroni-corrected significance. BD-PRS differed between control and at-risk groups, but not between at-risk and BD type-I groups. There was no difference between controls and schizotypy. SZ-PRSs, but not BD-PRSs, were positively associated with transdiagnostic symptomology. Overall, PRSs support the continuum model across the psychosis spectrum at the genomic level with possible irregularities for schizotypy. The at-risk state demands heightened clinical attention and research addressing symptom course specifiers. Continued efforts are needed to refine the diagnostic and prognostic accuracy of PRSs in mental healthcare.

Associations between patterns in comorbid diagnostic trajectories of individuals with schizophrenia and etiological factors

Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders.

Genetic contributions to bipolar disorder: current status and future directions

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

Polygenic Liability and Recurrence of Depression in Patients With First-Onset Depression Treated in Hospital-Based Settings

This cohort study examines the association of polygenic risk score for major depression with risk of recurrence in individuals diagnosed with unipolar depression in hospital-based settings and estimates the absolute risk of recurrence based on polygenic risk.

Family Genetic Risk Scores and the Genetic Architecture of Major Affective and Psychotic Disorders in a Swedish National Sample

Importance

Family and genetic approaches have traditionally been used to evaluate our diagnostic concepts. Using a novel method, the family genetic risk score (FGRS), can we validate the genetic architecture of major affective and psychotic disorders in a national Swedish sample?

Objective

To determine whether FGRSs, calculated for the entire Swedish population, can elucidate the genetic relationship between major affective and psychotic disorders and clarify the association of genetic risk with important clinical features of disease.

Design, Setting, and Participants

This cohort study included the native Swedish population born from January 1, 1950, through December 31, 1995, and followed up through December 31, 2017. Data were collected from Swedish population-based primary care, specialist, and hospital registers, including age at first registration for a psychiatric diagnosis and number of registrations for major depression, bipolar disorder, and schizophrenia. Data were analyzed from October 15, 2020, to February 2, 2021.

Exposures

FGRSs for major depression, bipolar disorder, and schizophrenia calculated from morbidity risks for disorders in first- through fifth-degree relatives, controlling for cohabitation.

Main Outcomes and Measures

Diagnoses of major depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other nonaffective psychoses (ONAPs), age at registration, and number of registrations for major depression, bipolar disorder, and schizophrenia. Diagnostic conversion of major depression to bipolar disorder and ONAPs to schizophrenia was assessed by Cox proportional hazards regression models.

Results

The cohort included 4 129 002 individuals (51.4% male) with a mean (SD) age at follow-up of 45.5 (13.4) years. Mean FGRSs for major depression, bipolar disorder, and schizophrenia produced distinct patterns for major depression, bipolar disorder, schizophrenia, schizoaffective disorder, and ONAPs with large separations between disorders. In major depression, bipolar disorder, and schizophrenia, high FGRSs were associated with early age at onset and high rates of recurrence: a high mean FGRS for bipolar disorder was associated with early age at onset (younger than 25 years, 0.11; 95% CI, 0.11-0.12) and higher recurrence (8 or more registrations, 0.11; 95% CI, 0.11-0.12) in major depression. The schizophrenia FGRS was separately associated with psychotic and nonpsychotic forms of major depression (0.10; 95% CI, 0.06-0.14 vs 0.03; 95% CI, 0.02-0.03) and bipolar disorder (0.22; 95% CI, 0.16-0.28 vs 0.11; 95% CI, 0.09-0.12). The bipolar disorder and schizophrenia FGRSs were associated with conversion from major depression to bipolar disorder (eg, hazard ratio, 1.70 [95% CI, 1.63-1.78] for high vs low bipolar FGRS) and ONAP to schizophrenia (eg, hazard ratio, 1.38 [95% CI, 1.27-1.51] for high vs low schizophrenia FGRS).

Conclusions and Relevance

In this Swedish cohort study, the FGRSs for major depression, bipolar disorder, and schizophrenia for the Swedish population clearly separated major affective and psychotic disorders from each other in a larger and more representative patient sample than previously possible. These findings provide possible validation, from a genetic perspective, for these major diagnostic categories. These results replicated and extended prior observations on more limited samples of the association of FGRS with age at onset, recurrence, psychotic subtypes, and diagnostic conversions.

Early expressions of psychopathology and risk associated with trans-diagnostic transition to mood and psychotic disorders in adolescents and young adults

OBJECTIVES

The heterogeneity and comorbidity of major mental disorders presenting in adolescents and young adults has fostered calls for trans-diagnostic research. This study examines early expressions of psychopathology and risk and trans-diagnostic caseness in a community cohort of twins and non-twin siblings.

METHODS

Using data from the Brisbane Longitudinal Twin Study, we estimated median number of self-rated psychiatric symptoms, prevalence of subthreshold syndromes, family history of mood and/or psychotic disorders, and likelihood of subsequent trans-diagnostic caseness (individuals meeting diagnostic criteria for mood and/or psychotic syndromes). Next, we used cross-validated Chi-Square Automatic Interaction Detector (CHAID) analyses to identify the nature and relative importance of individual self-rated symptoms that predicted trans-diagnostic caseness. We examined the positive and negative predictive values (PPV; NPV) and accuracy of all classifications (Area under the Curve and 95% confidence intervals: AUC; 95% CI).

RESULTS

Of 1815 participants (Female 1050, 58%; mean age 26.40), more than one in four met caseness criteria for a mood and/or psychotic disorder. Examination of individual factors indicated that the AUC was highest for subthreshold syndromes, followed by family history then self-rated psychiatric symptoms, and that NPV always exceeded PPV for caseness. In contrast, the CHAID analysis (adjusted for age, sex, twin status) generated a classification tree comprising six trans-diagnostic symptoms. Whilst the contribution of two symptoms (need for sleep; physical activity) to the model was more difficult to interpret, CHAID analysis indicated that four self-rated symptoms (sadness; feeling overwhelmed; impaired concentration; paranoia) offered the best discrimination between cases and non-cases. These four symptoms showed different associations with family history status.

CONCLUSIONS

The findings need replication in independent cohorts. However, the use of CHAID might provide a means of identifying specific subsets of trans-diagnostic symptoms representing clinical phenotypes that predict transition to caseness in individuals at risk of onset of major mental disorders.

Schizophrenia polygenic risk scores in youth mental health: preliminary associations with diagnosis, clinical stage and functioning

BACKGROUND

The schizophrenia polygenic risk score (SCZ-PRS) is an emerging tool in psychiatry.

AIMS

We aimed to evaluate the utility of SCZ-PRS in a young, transdiagnostic, clinical cohort.

METHOD

SCZ-PRSs were calculated for young people who presented to early-intervention youth mental health clinics, including 158 patients of European ancestry, 113 of whom had longitudinal outcome data. We examined associations between SCZ-PRS and diagnosis, clinical stage and functioning at initial assessment, and new-onset psychotic disorder, clinical stage transition and functional course over time in contact with services.

RESULTS

Compared with a control group, patients had elevated PRSs for schizophrenia, bipolar disorder and depression, but not for any non-psychiatric phenotype (for example cardiovascular disease). Higher SCZ-PRSs were elevated in participants with psychotic, bipolar, depressive, anxiety and other disorders. At initial assessment, overall SCZ-PRSs were associated with psychotic disorder (odds ratio (OR) per s.d. increase in SCZ-PRS was 1.68, 95% CI 1.08-2.59, P = 0.020), but not assignment as clinical stage 2+ (i.e. discrete, persistent or recurrent disorder) (OR = 0.90, 95% CI 0.64-1.26, P = 0.53) or functioning (R = 0.03, P = 0.76). Longitudinally, overall SCZ-PRSs were not significantly associated with new-onset psychotic disorder (OR = 0.84, 95% CI 0.34-2.03, P = 0.69), clinical stage transition (OR = 1.02, 95% CI 0.70-1.48, P = 0.92) or persistent functional impairment (OR = 0.84, 95% CI 0.52-1.38, P = 0.50).

CONCLUSIONS

In this preliminary study, SCZ-PRSs were associated with psychotic disorder at initial assessment in a young, transdiagnostic, clinical cohort accessing early-intervention services. Larger clinical studies are needed to further evaluate the clinical utility of SCZ-PRSs, especially among individuals with high SCZ-PRS burden.

Brief Research Report: A Pilot Study of Cognitive Behavioral Regulation Therapy (CBT-REG) for Young People at High Risk of Early Transition to Bipolar Disorders

Attempts to increase early identification of individuals in the early stages of bipolar disorders (i.e., individuals at high risk of bipolar disorders and/or experiencing a subthreshold syndrome with bipolar symptoms) have highlighted the need to develop high benefit-low risk interventions. We suggest that any new psychological therapy should (i) be acceptable to young people seeking help for the first time, (ii) be applicable to "at risk" conditions and sub-syndromal states and (iii) consider pluripotent factors that may be linked to illness progression not only for bipolar disorders specifically but also for other potential disease trajectories. However, evidence indicates that current interventions for youth with emerging mood disorders mainly represent approaches abbreviated from "disorder-specific" therapies used with older adults and are primarily offered to first episode cases of bipolar disorders who are also receiving psychotropic medication. This brief report discusses empirical findings used to construct core targets for therapeutic interventions that might reduce or delay transition to full-threshold bipolar disorders. We describe an intervention that includes strategies for problem-solving, reducing sleep-wake cycle disturbances, self-management of rumination and that addresses the needs of individuals with "sub-threshold" presentations who are probably at risk of developing a bipolar or other major mental disorders. Outcome data from a case series of 14 youth indicates that the intervention appears to demonstrate a relatively high benefit-to-risk ratio, promising levels of engagement with the therapy modules, and the therapy appears to be acceptable to a wide range of help-seeking youth with early expressions of bipolar psychopathology.

The Role of Gut Microbiota in the High-Risk Construct of Severe Mental Disorders: A Mini Review

Severe mental disorders (SMD) are highly prevalent psychiatric conditions exerting an enormous toll on society. Therefore, prevention of SMD has received enormous attention in the last two decades. Preventative approaches are based on the knowledge and detailed characterization of the developmental stages of SMD and on risk prediction. One relevant biological component, so far neglected in high risk research, is microbiota. The human microbiota consists in the ensemble of microbes, including viruses, bacteria, and eukaryotes, that inhabit several ecological niches of the organism. Due to its demonstrated role in modulating illness and health, as well in influencing behavior, much interest has focused on the characterization of the microbiota inhabiting the gut. Several studies in animal models have shown the early modifications in the gut microbiota might impact on neurodevelopment and the onset of deficits in social behavior corresponding to distinct neurosignaling alterations. However, despite this evidence, only one study investigated the effect of altered microbiome and risk of developing mental disorders in humans, showing that individuals at risk for SMD had significantly different global microbiome composition than healthy controls. We then offer a developmental perspective and provided mechanistic insights on how changes in the microbiota could influence the risk of SMD. We suggest that the analysis of microbiota should be included in the comprehensive assessment generally performed in populations at high risk for SMD as it can inform predictive models and ultimately preventative strategies.