Genetic Architecture and Risk of Childhood Maltreatment Across 5 Psychiatric Diagnoses
Notice bibliographique
Résumé
Importance: Childhood maltreatment (CM) is associated with psychiatric disorders. The underlying mechanisms are complex and involve genetics. Objective: To investigate the polygenic architecture of CM-exposed individuals across psychiatric conditions and if genetics modulates absolute CM risk in the presence of high-impact risk factors such as parental psychiatric diagnoses. Design, Setting, and Participants: The population-based case-cohort iPSYCH was used to analyze 13 polygenic scores (PGS) in CM-exposed individuals across 5 psychiatric International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnoses benchmarked against controls. Individuals were stratified into PGS quantiles, and absolute CM risk was calculated using Cox regression. Sex-specific analyses were also performed. Data were analyzed from June 2022 to December 2024. Exposures: PGS of phenotypes of psychiatric disorders, CM, educational attainment, and substance use. Main Outcomes and Measures: PGSs were generated using summary statistics from genome-wide association studies of phenotypes representing psychiatric disorders, CM, educational attainment, and substance use and tested for their association with CM across psychiatric disorders. Results: This study included 102 856 individuals (mean [SD] age, 22.6 [7.1] years; 54 918 male [53.4%]) 8 to 35 years old. A total of 2179 CM-exposed individuals were analyzed across individuals with attention-deficit/hyperactivity disorder (ADHD; n = 22 674), autism (n = 18 941), schizophrenia (n = 6103), bipolar disorder (n = 3061), depression (n = 28 896), and controls (n = 34 689). PGSs for ADHD and educational attainment were associated with CM across all psychiatric diagnoses. The absolute CM risk was increased in the highest PGS groups, eg, for ADHD, the absolute CM risk was 5.6% in the highest ADHD-PGS quartile whereas it was only 3.3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile 4 vs quantile 1 = 1.81; 95% CI, 1.47-2.22). CM risk was more than twice as high for children with parents with a psychiatric diagnosis (5.7%) than for children with parents without a psychiatric diagnosis (2.5%), but even in the presence of this risk factor, individuals could still be stratified into risk groups based on their genetics. No genetic differences between CM-exposed males and females were observed, but there were striking sex differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile and 2.0% for males. Conclusions and Relevance: Results of this case-control study suggest that individuals with high ADHD-PRS and/or low educational attainment-PRS had an associated elevated risk of CM. Extra attention should be given to individuals at high risk for CM across all 5 psychiatric diagnoses, ie, females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».