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

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

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

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

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients 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 BPD and, importantly, BPD-D from MDD.

Major Depressive Disorder (MDD) is one of the most common mental illnesses worldwide and is strongly associated with suicidality. Commonly used treatments for MDD with suicidality include crisis intervention, oral antidepressants (although risk of suicidal behavior is high among non-responders and during the first 10-14 days of the treatment) benzodiazepines and lithium. Although several interventions addressing suicidality exist, only few studies have characterized in detail patients with MDD and suicidality, including treatment, clinical course and outcomes. Patient Characteristics, Validity of Clinical Diagnoses and Outcomes Associated with Suicidality in Inpatients with Symptoms of Depression (OASIS-D)-study is an investigator-initiated trial funded by Janssen-Cilag GmbH.

For population 1 out of 3 OASIS-D populations, to assess the sub-population of patients with suicidality and its correlates in hospitalized individuals with MDD.

The ongoing OASIS-D study consecutively examines hospitalized patients at 8 German psychiatric university hospitals treated as part of routine clinical care. A sub-group of patients with persistent suicidality after >48 hours post-hospitalization are assessed in detail and a sub-group of those are followed for 6 months to assess course and treatment of suicidality associated with MDD. The present analysis focuses on a preplanned interim analysis of the overall hospitalized population with MDD.

Of 2,049 inpatients (age=42.5±15.9 years, females=53.2%), 68.0% had severe MDD without psychosis and 21.2% had moderately severe MDD, with 16.7% having treatment-resistant MDD. Most inpatients referred themselves (49.4%), followed by referrals by outpatient care providers (14.6%), inpatient care providers (9.0%), family/friends (8.5%), and ambulance (6.8%). Of these admissions, 43.1% represented a psychiatric emergency, with suicidality being the reason in 35.9%. Altogether, 72.4% had at least current passive suicidal ideation (SI, lifetime=87.2%), including passive SI (25.1%), active SI without plan (15.5%), active SI with plan (14.2%), and active SI with plan+intent (14.1%), while 11.5% had attempted suicide ≤2 weeks before admission (lifetime=28.7%). Drug-induced mental and behavioral disorders (19.6%) were the most frequent comorbid disorders, followed by personality disorders (8.2%). Upon admission, 64.5% were receiving psychiatric medications, including antidepressants (46.7%), second-generation antipsychotics (23.0%), anxiolytics (11.4%) antiepileptics (6.0%), and lithium (2.8%). Altogether, 9.8% reported nonadherence to medications within 6 months of admission.

In adults admitted for MDD, suicidality was common, representing a psychiatric emergency in 35.9% of patients. Usual-care treatments and outcomes of suicidality in hospitalized adults with MDD require further study.

None Declared

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements.

A synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.

How do we limit global warming to 1.5 °C and why is it crucial? See highlights of latest climate science.