The association between family history and genomic burden with schizophrenia mortality: a Swedish population-based register and genetic sample study
Bibliographic record
Abstract
Individuals with schizophrenia (SCZ) have a 2-3-fold higher risk of mortality than the general population. Heritability of mortality in psychiatric disorders has been proposed; however, few have investigated SCZ family history and genetic variation, with all-cause and specific causes of death. We aimed to identify correlates of SCZ mortality using genetic epidemiological and genetic modelling in two samples: a Swedish national population sample and a genotyped subsample. In the Swedish national population sample followed from the first SCZ treatment contact until emigration, death or end of the follow-up, we investigated a standardised measure of SCZ family history. In a subgroup with comprehensive genetic data, we investigated the impact of common and rare genetic variation. Cox proportional hazards regression was used to estimate the association between various factors and mortality (all and specific causes). A total of 13727 SCZ cases fulfilled criteria for the population-based analyses (1268 deaths, 9.2%). The genomic subset contained 4991 cases (1353 deaths, 27.1%). Somatic mutations associated with clonal hematopoiesis with unknown drivers were associated with all-cause mortality (HR 1.77, 95% CI: 1.26-2.49). No other heritable measures were associated with all-cause mortality nor with any specific causes of death. Future studies in larger, comparable cohorts are warranted to further understand the association between hereditary measures and mortality in SCZ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".