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.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Alzheimer’s Disease (AD) and dementia present major and escalating public health concerns for the U.S., especially among ethnoculturally diverse (e.g., Latinx, non-Latinx Black [NLB]) populations who represent an increasing percentage of the older adult population in the US and bear greater AD burden compared to non-Latinx Whites (NLWs). Notably, neurocognition and functional status are highly correlated in those with AD. However, little has been done to understand these associations and validate functional measures across geographically diverse, multiethnic samples. The aims of this study were to characterize the neurocognition and functional status of a large, multiethnic sample and subsequently examine any associations between neurocognition and functional status among Latinx, NLB, and NLW older adults.

This cross-sectional, retrospective study utilized archival data drawn from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). ADNI is a national, longitudinal, multi-site, observational study aiming to measure the progression of AD (see https://adni-info.org). Study measures included the: 1) Alzheimer’s Disease Assessment Scale Cognitive subscale (ADAS-cog; 13-items), a global neurocognitive battery evaluating neurocognition in people with AD; 2) Functional Activities Questionnaire (FAQ; 10-item questionnaire) to assess functional status; 3) Geriatric Depression Scale (GDS; 15-item questionnaire) for depression; and 4) American National Adult Reading Test (ANART; 50-word test) for reading level. The sample included 1537 older adults who completed baseline visits for the ADNI study, 1333 of whom were NLW, 123 NLB, and 81 Latinx. The average age of the sample was 73 years, average 16 years of education, and 53% male. Compared to the NLW group, the NLB and Latinx groups were significantly younger and had a higher percentage of female participants. Compared to NLW and Latinx groups, the NLB group also had significantly fewer years of education and lower reading scores. Potential confounds (i.e., demographic variables, depression) were identified a priori based on the literature and subsequently analyzed for inclusion as covariates in the primary analyses. Analyses revealed variables were non-normally distributed, therefore Independent Samples Kruskal-Wallis tests and Spearman’s Correlations were computed to examine differences and correlations between ethnocultural groups.

After controlling for age and education, Latinx and NLB groups had significantly higher ADAS-cog and FAQ scores than the NLW group (Hs = 9.50-21.53, ps < .05). Spearman’s partial correlations controlling for age, education, gender, and depression revealed that higher ADAS-cog scores were associated with higher FAQ scores within Latinx (p=.49, p<.001), NLB (p=.66, p<.001), and NLW (p=.60, p<.001) groups.

Findings indicate that neurocognition is positively associated with functional status and support the ecological and external validity of the ADAS-cog and FAQ for use with NLB and Latinx older adults, in addition to previously established work with more homogenous samples. Study strengths include the overall sample size, geographic diversity, and standardization of research approaches. Study limitations include high education level and low comorbidity rates present in the sample, limiting the generalizability of the results, in addition to the unbalanced ethnocultural groups, further emphasizing the need for increased inclusion efforts of ethnoculturally diverse older adults into brain health research studies.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health is still being unravelled. It is important to identify which individuals are at greatest risk of worsening symptoms. This study aimed to examine changes in depression, anxiety and post-traumatic stress disorder (PTSD) symptoms using prospective and retrospective symptom change assessments, and to find and examine the effect of key risk factors.

Online questionnaires were administered to 34 465 individuals (aged 16 years or above) in April/May 2020 in the UK, recruited from existing cohorts or via social media. Around one-third (n = 12 718) of included participants had prior diagnoses of depression or anxiety and had completed pre-pandemic mental health assessments (between September 2018 and February 2020), allowing prospective investigation of symptom change.

Prospective symptom analyses showed small decreases in depression (PHQ-9: −0.43 points) and anxiety [generalised anxiety disorder scale – 7 items (GAD)-7: −0.33 points] and increases in PTSD (PCL-6: 0.22 points). Conversely, retrospective symptom analyses demonstrated significant large increases (PHQ-9: 2.40; GAD-7 = 1.97), with 55% reported worsening mental health since the beginning of the pandemic on a global change rating. Across both prospective and retrospective measures of symptom change, worsening depression, anxiety and PTSD symptoms were associated with prior mental health diagnoses, female gender, young age and unemployed/student status.

We highlight the effect of prior mental health diagnoses on worsening mental health during the pandemic and confirm previously reported sociodemographic risk factors. Discrepancies between prospective and retrospective measures of changes in mental health may be related to recall bias-related underestimation of prior symptom severity.

Anxiety disorders are highly prevalent with an early age of onset. Understanding the aetiology of disorder emergence and recovery is important for establishing preventative measures and optimising treatment. Experimental approaches can serve as a useful model for disorder and recovery relevant processes. One such model is fear conditioning. We conducted a remote fear conditioning paradigm in monozygotic and dizygotic twins to determine the degree and extent of overlap between genetic and environmental influences on fear acquisition and extinction.

In total, 1937 twins aged 22–25 years, including 538 complete pairs from the Twins Early Development Study took part in a fear conditioning experiment delivered remotely via the Fear Learning and Anxiety Response (FLARe) smartphone app. In the fear acquisition phase, participants were exposed to two neutral shape stimuli, one of which was repeatedly paired with a loud aversive noise, while the other was never paired with anything aversive. In the extinction phase, the shapes were repeatedly presented again, this time without the aversive noise. Outcomes were participant ratings of how much they expected the aversive noise to occur when they saw either shape, throughout each phase.

Twin analyses indicated a significant contribution of genetic effects to the initial acquisition and consolidation of fear, and the extinction of fear (15, 30 and 15%, respectively) with the remainder of variance due to the non-shared environment. Multivariate analyses revealed that the development of fear and fear extinction show moderate genetic overlap (genetic correlations 0.4–0.5).

Fear acquisition and extinction are heritable, and share some, but not all of the same genetic influences.

Depression and obesity are highly prevalent major public health problems that frequently co-occur. Shared aetiological factors have been found between depression and obesity. The role of the fat mass and obesity associated (FTO) gene in body mass index (BMI) and obesity has been confirmed in many independent studies. Recently, we reported the first study implicating FTO in the association between depression and obesity.

We aimed to confirm these findings by investigating the FTO rs9939609 polymorphism in a meta-analysis of 13,701 individuals.

The sample consists of 6,902 depressed cases and 6,799 controls from five studies (Radiant, PsyCoLaus, GSK, MARS and NESDA/NTR). Common inclusion criteria were information available on a lifetime DSM-IV diagnosis of major depressive disorder (MDD), BMI and genotype data. Linear regression models for quantitative traits assuming an additive genetic model were performed to test for association and interaction between rs9939609, BMI and depression. Fixed and random-effects meta-analyses were performed.

Fixed-effects meta-analyses support a significant association between rs9939609 polymorphism and BMI (whole-sample: ß=0.07, p=1.29×10-12, depressive-cases: ß=0.12, p=6.92×10-12). No association was found in controls (ß=0.02, p=0.15). Meta-analyses further support a significant interaction between FTO, BMI and depression (fixed-effects: ß=0.13, p=3.087×10-7; random-effects: ß=0.12, p=0.027), wherein depressed carriers of the risk allele have an additional increase of 2.2% in BMI.

This meta-analysis demonstrates a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. Depression-related alterations in key biological processes may interact with the rs9939609 FTO risk allele to increase obesity risk.

Longitudinal cognitive change before and after acetyl cholinesterase inhibitor (AChEI) treatment initiation in Alzheimer's disease has never been described previously in a representative clinical population.

To model longitudinal changes in cognitive function for before and after AChEI prescription.

To further investigate differences in response by cognitive function at treatment initiation.

A retrospective longitudinal analysis was carried out of all 1843 patients from the South London and Maudsley NHS Foundation Trust (a large mental health provider to a catchment population of approximately 1.2 m) who were prescribed AChEIs between 2003–10 and had a minimum of one MMSE score within 1 year before treatment initiation and one MMSE score within 3 years after this. Manually extracted MMSE scores were analyzed over this period using three-piece linear mixed models.

Rates of MMSE change were −1.9 (95% CI −2.3,−1.4) in the year before treatment initiation, +1.3 (0.9,1.7) in the 6 months after treatment initiation, and −2.4 (−2.6,−2.3) from 6 months to 3 years. The difference between pre-treatment and 6-month-post-treatment slopes was −0.6 (−1.8,0.6) at baseline (treatment initiation) MMSE of 25 or over, +2.7 (1.7,3.7) at MMSE 21–24, and +4.6 (3.6,5.7) at MMSE 10–20.

In this naturalistic sample, a clear cognitive response to AChEI treatment was observed over the first six months followed by an unchanged decline. Response was substantially higher for patients with lower MMSE scores at treatment initiation.