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.

Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care.

To extract information about suicidality status from the admission notes in electronic health records (EHRs) using privacy-sensitive, locally hosted LLMs, specifically evaluating the efficacy of Llama-2 models.

We compared the performance of several variants of the open source LLM Llama-2 in extracting suicidality status from 100 psychiatric reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies.

A German fine-tuned Llama-2 model showed the highest accuracy (87.5%), sensitivity (83.0%) and specificity (91.8%) in identifying suicidality, with significant improvements in sensitivity and specificity across various prompt designs.

The study demonstrates the capability of LLMs, particularly Llama-2, in accurately extracting information on suicidality from psychiatric records while preserving data privacy. This suggests their application in surveillance systems for psychiatric emergencies and improving the clinical management of suicidality by improving systematic quality control and research.

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Traumatic brain injury (TBI) and concussion are associated with increased dementia risk. Accurate TBI/concussion exposure estimates are relatively unknown for less common neurodegenerative conditions like frontotemporal dementia (FTD). We evaluated lifetime TBI and concussion frequency in patients diagnosed with a range of FTD spectrum conditions and related prior head trauma to cavum septum pellucidum (CSP) characteristics observable on MRI.

We administered the Ohio State University TBI Identification and Boston University Head Impact Exposure Assessment to 108 patients (age 69.5 ± 8.0, 35% female, 93% white or unknown race) diagnosed at the UCSF Memory and Aging Center with one of the following FTD or related conditions: behavioral variant frontotemporal dementia (N=39), semantic variant primary progressive aphasia (N=16), nonfluent variant PPA (N=23), corticobasal syndrome (N=14), or progressive supranuclear palsy (N=16). Data were also obtained from 217 controls (“HC”; age 76.8 ± 8.0, 53% female, 91% white or unknown race). CSP characteristics were defined based on width or “grade” (0-1 vs. 2+) and length of anterior-posterior separation (millimeters). We first describe frequency of any and multiple (2+) prior TBI based on different but commonly used definitions: TBI with loss of consciousness (LOC), TBI with LOC or posttraumatic amnesia (LOC/PTA), TBI with LOC/PTA or other symptoms like dizziness, nausea, “seeing stars,” etc. (“concussion”). TBI/concussion frequency was then compared between FTD and HC using chi-square. Associations between TBI/concussion and CSP characteristics were analyzed with chi-square (CSP grade) and Mann-Whitney U tests (CSP length). We explored sex differences due to typically higher rates of TBI among males.

History of any TBI with LOC (FTD=20.0%, HC=19.2%), TBI with LOC/PTA (FTD:32.2%, HC=31.5%), and concussion (FTD: 50.0%, HC=44.3%) was common but not different between study groups (p’s>.4). In both FTD and HC, prior TBI/concussion was nominally more frequent in males but not significantly greater than females. Frequency of repeat TBI/concussion (2+) also did not differ significantly between FTD and HC (repeat TBI with LOC: 6.7% vs. 3.3%, TBI with LOC/PTA: 12.2% vs. 10.3%, concussion: 30.2% vs. 28.7%; p’s>.2). Prior TBI/concussion was not significantly related to CSP grade or length in the total sample or within the FTD or HC groups.

TBI/concussion rates depend heavily on the symptom definition used for classifying prior injury. Lifetime symptomatic TBI/concussion is common but has an unclear impact on risk for FTD-related diagnoses. Larger samples are needed to appropriately evaluate sex differences, to evaluate whether TBI/concussion rates differ between specific FTD phenotypes, and to understand the rates and effects of more extensive repetitive head trauma (symptomatic and asymptomatic) in patients with FTD.

To determine the effectiveness of active, upper-room, germicidal ultraviolet (GUV) devices in reducing bacterial contamination in patient rooms in air and on surfaces as a supplement to the central heating, ventilation, and air conditioning (HVAC) air handling unit (AHU) with MERV 14 filters and UV-C disinfection.

This study was conducted in an academic medical center, burn intensive care unit (BICU), for 4 months in 2022. Room occupancy was monitored and recorded. In total, 402 preinstallation and postinstallation bacterial air and non–high-touch surface samples were obtained from 10 BICU patient rooms. Airborne particle counts were measured in the rooms, and bacterial air samples were obtained from the patient-room supply air vents and outdoor air, before and after the intervention. After preintervention samples were obtained, an active, upper-room, GUV air disinfection system was deployed in each of the patient rooms in the BICU.

The average levels of airborne bacteria of 395 CFU/m3 before GUV device installation and 37 CFU/m3 after installation indicated an 89% overall decrease (P < .0001). Levels of surface-borne bacteria were associated with a 69% decrease (P < .0001) after GUV device installation. Outdoor levels of airborne bacteria averaged 341 CFU/m3 in March before installation and 676 CFU/m3 in June after installation, but this increase was not significant (P = .517).

Significant reductions in air and surface contamination occurred in all rooms and areas and were not associated with variations in outdoor air concentrations of bacteria. The significant decrease of surface bacteria is an unexpected benefit associated with in-room GUV air disinfection, which can potentially reduce overall bioburden.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

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.