Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long‑term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk.

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.

Emerging trends in gene and bipolar disorder research: a bibliometric analysis and network visualisation

This study aims to use a bibliometric technique to evaluate the scientific output of gene and bipolar disorder research. The search query related to gene and bipolar disorder from the Scopus database identified 1848 documents from 1951 to 2020. The growth in the publications increased since early 1990, peaked in 2011, and started to decline thereafter. High occurrence in author keywords suggests that some research topics, such as "polymorphism", "linkage" and "association study" have waned over time, whereas others, such as "DNA methylation," "circadian rhythm," "" and "meta-analysis," are now the emerging trends in gene and bipolar disorder research. The USA was the country with the highest production followed by the UK, Canada, Italy and Germany. The leading institutions were Cardiff University in the UK, the National Institute of Mental Health (NIMH) in the USA, King's College London in the UK and the University of California, San Diego in the USA. The leading journals publishing gene and bipolar literature were the American Journal of Medical Genetics Neuropsychiatric Genetics, Molecular Psychiatry and Psychiatric Genetics. The top authors in the number of publications were Craddock N, Serretti A and Rietschel M. According to the co-authorship network analysis of authors, the majority of the authors in the same clusters were closely linked together and originated from the same or neighbouring country. The findings of this study may be useful in identifying emerging topics for future research and promoting research collaboration in the field of genetic studies related to bipolar disorder.

The Genetic Side of the Mood: A Scientometric Review of the Genetic Basis of Mood Disorders

Mood disorders are highly heritable psychiatric disorders. Over the years, many genetic polymorphisms have been identified to pose a higher risk for the development of mood disorders. To overview the literature on the genetics of mood disorders, a scientometric analysis was performed on a sample of 5342 documents downloaded from Scopus. The most active countries and the most impactful documents in the field were identified. Furthermore, a total of 13 main thematic clusters emerged in the literature. From the qualitative inspection of clusters, it emerged that the research interest moved from a monogenic to a polygenic risk framework. Researchers have moved from the study of single genes in the early 1990s to conducting genome-wide association studies around 2015. In this way, genetic overlaps between mood disorders and other psychiatric conditions emerged too. Furthermore, around the 2010s, the interaction between genes and environmental factors emerged as pivotal in understanding the risk for mood disorders. The inspection of thematic clusters provides a valuable insight into the past and recent trends of research in the genetics of mood disorders and sheds light onto future lines of research.

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

BACKGROUND

Bipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion.

METHOD

By following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods.

RESULTS

It was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability.

CONCLUSION

The risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.