METHODS: To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).
RESULTS: Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.
CONCLUSIONS: The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.
METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.
RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.
CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.
METHODS: We included 23,288 patients with incident stroke admitted between 2005 and 2017 and 68,675 matched nonstroke controls. Information on mental disorders was obtained from medical claims data within the 3 years before the stroke incidence. Cox proportional hazards models considering death as a competing risk event were constructed to estimate the hazard ratio of AP incidence by the end of 2018 associated with stroke and selected mental disorders.
RESULTS: After ≤14 years of follow-up, AP incidence was higher in the patients with stroke than in the controls (11.30/1000 vs. 1.51/1000 person-years), representing a covariate-adjusted subdistribution hazard ratio (sHR) of 3.64, with no significant sex difference. The sHR significantly decreased with increasing age in both sexes. Stratified analyses indicated schizophrenia but not depression or bipolar affective disorder increased the risk of AP in the patients with stroke.
CONCLUSION: Compared with their corresponding counterparts, the patients with schizophrenia only, stroke only, and both stroke and schizophrenia had a significantly higher sHR of 4.01, 5.16, and 8.01, respectively. The risk of AP was higher in younger stroke patients than those older than 60 years. Moreover, schizophrenia was found to increase the risk of AP in patients with stroke.
METHODS: 6,305 college students (39.3% men; 60.7% women) from six Chinese provincial-level jurisdictions completed a paper-and-pencil survey with Psychological Strain Scales (PSS-40) and Depression, Anxiety, and Stress Scales-21 (DASS-21), both validated in Chinese populations.
RESULTS: Both PSS-40 and DASS-21 have high internal consistency reliabilities, and are highly correlated with each other. Hence, Chinese college students with greater psychological strains (value, aspiration, deprivation, or coping) have greater depression, anxiety, and stress. These results still held after controlling for relevant socio-demographic variables in the multiple regression models.
LIMITATIONS: This was a cross-sectional study, and the sample only included several provinces in mainland China, not a representative sample of all of them.
CONCLUSIONS: Mood disorders and psychopathologies are linked to suicidal thoughts and behaviors. The results of this study extend the Strain Theory of Suicide from explaining the risk factors of suicidality to mood disorders and psychopathologies. Hence, these findings can inform prevention measures among college students, and possibly the general population.