Genomics yields biological and phenotypic insights into bipolar disorder.

Bipolar disorder is a leading contributor to the global burden of disease. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.

Machine learning-based prediction of illness course in major depression: The relevance of risk factors.

Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.

Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).

[Long-term courses in schizophrenia : A review of current results and developments].

Despite classical conceptions of schizophrenia as a progressive illness with a high chronification, current long-term follow-up studies show a higher proportion of remission, possibly also a higher proportion of recovery than previously assumed. The heterogeneity of clinical courses is also reflected in different trajectories of cognitive and biological (e.g.