The impact of educational attainment, intelligence and intellectual disability on schizophrenia: a Swedish population-based register and genetic study
Bibliographic record
Abstract
Schizophrenia (SCZ) is highly heterogenous and no subtypes characterizing treatment response or longitudinal course well. Cognitive impairment is a core clinical feature of SCZ and a determinant of poorer outcome. Genetic overlap between SCZ and cognitive traits is complex, with limited studies of comprehensive epidemiological and genomic evidence. To examine the relation between SCZ and three cognitive traits, educational attainment (EDU), premorbid cognitive ability, and intellectual disability (ID), we used two Swedish samples: a national cohort (14,230 SCZ cases and 3,816,264 controls) and a subsample with comprehensive genetic data (4992 cases and 6009 controls). Population-based analyses confirmed worse cognition as a risk factor for SCZ, and the pedigree and SNP-based genetic correlations were comparable. In the genotyped cases, those with high EDU and premorbid cognitive ability tended to have higher polygenetic risk scores (PRS) of EDU and intelligence and fewer rare exonic variants. Finally, by applying an empirical clustering method, we dissected SCZ cases into four replicable subgroups characterized by EDU and ID. In particular, the subgroup with higher EDU in the national cohort had fewer adverse outcomes including long hospitalization and death. In the genotyped subsample, this subgroup had higher PRS of EDU and no excess of rare genetic burdens than controls. In conclusion, we found extensive evidence of a robust relation between cognitive traits and SCZ, underscoring the importance of cognition in dissecting the heterogeneity of SCZ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".