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Record W4406925020 · doi:10.1016/j.ebiom.2025.105576

Genetic insights into psychotic major depressive disorder: bridging the mood-psychotic disorder spectrum

2025· article· en· W4406925020 on OpenAlexaff
Nguyen Thi Thuy Dung, Joeri Meijsen, Robert Sigström, Ralf Kuja‐Halkola, Ying Xiong, Arvid Harder, Kaarina Kowalec, Joëlle A. Pasman, Carolina Scarpa, Elin Hörbeck, Lina Jönsson, Sara Hägg, Niamh Mullins, Kevin S. O’Connell, Christina Dalman, Dorte Helenius, Richard Zetterberg, Henrik Larsson, Paul Lichtenstein, Ole A. Andreassen, Thomas Werge, Alfonso Buil, Mikael Landén, Patrick F. Sullivan, Yi Lu

Bibliographic record

VenueEBioMedicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of Manitoba
FundersUppsala Multidisciplinary Center for Advanced Computational ScienceEuropean Research CouncilSouth East Regional Health AuthorityNorges ForskningsrådNovo Nordisk FondenForskningsrådet om Hälsa, Arbetsliv och VälfärdNational Institutes of HealthNational Institute of Mental HealthVetenskapsrådetNovo NordiskStiftelsen för Strategisk ForskningHjärnfondenEuropean CommissionStiftelsen för Miljöstrategisk Forskning
KeywordsBridging (networking)Schizophrenia spectrumSpectrum disorderMajor depressive disorderPsychiatryMoodMedicinePsychologyClinical psychologyPsychosisComputer science

Abstract

fetched live from OpenAlex

Background Psychotic major depressive disorder (MDD), a subtype of MDD characterised by psychotic symptoms that occur exclusively during mood episode, is clinically significant yet underexplored genetically due to its rarity. This study comprehensively examines the genetic basis of psychotic MDD and elucidates its position within the mood-psychotic spectrum. Methods This population-based cohort study used Swedish and Danish registry data for over 5.1 M individuals born between 1958 and 1993/1996. Specialist-diagnosed psychotic MDD was defined using ICD-10 sub-codes of MDD, F32.2/F32.3. We estimated familial aggregation/coaggregation using generalised estimating equations, heritability and genetic correlations using structural equation modelling. We also analysed ∼30,000 genotyped MDD cases from the UK Biobank and a Swedish cohort to explore which polygenic risk score (PRS) may predispose individuals to psychotic MDD. Findings With over 10,000 psychotic MDD identified from the two nationwide patient registers, this study highlights the familial aggregation of psychotic MDD, co-aggregation with mood and psychotic disorders, and its stronger genetic correlation with schizophrenia compared to non-psychotic MDD. The familial risks increased with closer biological relatedness, suggesting genetic influence. Pedigree-heritability of psychotic MDD was 30.17% (95% CI 23.53–36.80%). While the genetic correlation between psychotic and non-psychotic MDD was high (0.82, 95% CI 0.73–0.92), the psychotic subgroup showed a higher genetic correlation with schizophrenia than non-psychotic MDD (0.67 vs 0.46, p-value 7.55∗10 −4 ). Within 30,000 genotyped MDD cases, individuals with psychotic MDD had higher mean PRS for schizophrenia and BD but a lower MDD PRS than non-psychotic MDD. PRS for BD type-I was associated with increased odds of psychotic MDD, while BD type-II PRS showed no significant association with psychotic MDD. Interpretation This study provides evidence for the genetic basis of psychotic MDD, underscoring its unique position bridging the spectrum of mood and psychotic disorders. These findings advance our understanding of the aetiology of psychotic MDD and contribute to the limited body of evidence on this phenotype by utilising large-scale population-based data. Funding European Research Council; US National Institutes of Mental Health; European Union Horizon 2020 Program; Swedish Research Council; Research Council of Norway; Swedish Foundation for Strategic Research; Hjärnfonden.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.262
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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