Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

Background: The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods: This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results: All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion: The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care.The research in Spain was funded by grants from the Spanish Ministry of Health (grant FIS references: PI04/1980, PI0/41771, PI04/2450, and PI06/1442), Andalusian Council of Health (grant references: 05/403, 06/278 and 08/0194), and the Spanish Ministry of Education and Science (grant reference SAF 2006/07192). The Malaga sample, as part of the predictD-International study, was also funded by a grant from The European Commission (reference QL4-CT2002-00683)

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

Depressive symptoms in later life: differential impact of social support and motivational processes on depression in individuals with and without cognitive impairment

This study investigates the role of a motivational process based on a composite of four subcomponents (self-efficacy, decision regulation, activation regulation and motivation regulation), as a mediator of the relationship between social support and depression assessed with the Geriatric Depression Scale in cognitively impaired and unimpaired individuals. Participants were 229 adults with a mean age of 74 years (range: 52–94 years). The sample comprised 64 participants diagnosed with mild cognitive impairment (MCI), 47 participants diagnosed with early-stage Alzheimer’s disease (AD), and a group of 118 participants without any cognitive impairment. In this cross-sectional study, bivariate correlations and linear regression models were used to assess the association between the predictor variables and depression. Linear regression models were controlled for age, gender, education, cognitive status, cognitive impairment and activities. In the total sample, social support (β = −0.15, p < 0.05) and motivational processes (β = −0.41, p < 0.001) were significantly associated with depression; the impact of social support was mediated by motivational processes. While motivational processes were associated with depression in all three groups (no impairment: β = −0.61, p < 0.001; MCI: β = −0.28, p < 0.05; early AD: β = −0.30, p < 0.06), social support lost significance (no impairment: β = −0.36, p < 0.001; MCI: β = 0.07, p = 0.59; early AD: β = −0.08, p = 0.62). Based on these findings, it can be argued that the impact of social support on depressive symptoms is attenuated by cerebral deterioration in cognitively impaired individuals, while motivational processes remain relevant

Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies

Data and code availability: • Summary statistics are available from Figshare through the following link https://pgc.unc.edu/for-researchers/download-results/ (https://doi.org/10.6084/m9.figshare.27061255). These data are publicly available as of the date of publication. • Individual data are made available following an approved application to the PGC Data Access Committee (https://pgc.unc.edu/for-researchers/data-access-committee/). These data are available as of the date of publication. • Available summary statistics, including 23andMe data, require an approved application to 23andMe here: https://research.23andme.com/dataset-access/. These data are available as of the date of publication. • Summary statistics for the Genetic Association Information Network (GAIN), NeuroGenetics Research Consortium (NGRC), Gene Environment Association Studies Initiative (GENEVA, Melanoma Study), and other studies are available from The Database of Genotypes and Phenotypes (dbGaP: https://dbgap.ncbi.nlm.nih.gov/). These data are available as of the date of publication. Instructions on how to access dbGap data are available here: https://www.ncbi.nlm.nih.gov/gap/docs/submissionguide/. • Additional deposited reference dataset availability is here: Haplotype Reference Consortium (European Genome-Phenome Archive, https://ega-archive.org), GTEx v8 (GTEx Portal, https://gtexportal.org), Human Brain Cell Atlas (CELL×GENE Discover, https://cellxgene.cziscience.com/), eQTLGen (https://www.eqtlgen.org), MetaBrain (https://www.metabrain.nl), Brain pQTL (AD Knowledge Portal, https://adknowledgeportal.synapse.org), and SynGO (https://syngoportal.org/). • Additional quality control information, gene-based association summary statistics in fastBAT (including figures), Hi-C, genetic correlation results, full drug target enrichment findings, single-cell enrichment figures, and PGS plots are also available for download from Figshare through the following link: https://pgc.unc.edu/for-researchers/download-results/ (https://doi.org/10.6084/m9.figshare.27089614). See STAR Methods for a key resources table. These data are publicly available as of the date of publication. • Project code is available from https://github.com/psychiatric-genomics-consortium/mdd-wave3-meta.STAR★Methods are available online at: https://www.cell.com/cell/fulltext/S0092-8674(24)01415-6?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867424014156%3Fshowall%3Dtrue#sec-9 .Supplemental information is available online at: https://www.cell.com/cell/fulltext/S0092-8674(24)01415-6?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867424014156%3Fshowall%3Dtrue#app-1 .Consortia members are listed online at: https://www.sciencedirect.com/science/article/pii/S0092867424014156#sec5 .In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award no. 1IK2BX005058 and I01CX001849. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. Major funding for the PGC is from the US National Institutes of Health (MH124873 and MH124871). Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org/) hosted by SURFsara. The iPSYCH team acknowledges funding from the Lundbeck Foundation (grants R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and the GenomeDK HPC facility. This research has been conducted using the UK Biobank Resource (application 4844) and data from dbGaP (accession phs000021, phs000196, and phs000187) and including data from the Molecular Genetics of Schizophrenia Collaboration (Pablo Gejman, Northwestern University), the NINDS CIDR:NGRC Parkinson’s Disease Study, and the SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation (supported by the FNIH GAIN study, CA093459, CA097007, ES011740, and CA133996). Individual study funding and other acknowledgments are provided in the supplementary study information (Methods S1). This paper represents independent research partly funded by the NIHR Maudsley Biomedical Research Centre and Maudsley NHS Foundation Trust and King’s College London, and the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The current work was also supported by the Wellcome Trust (220857/Z/20/Z) and the European Union under the Horizon 2020 research and innovation programme (no. 847776 and 948561)