Associations of schizophrenia with arrhythmic disorders and electrocardiogram traits: an in-depth genetic exploration of population samples
Bibliographic record
Abstract
Abstract Background An important contributor to the decreased life expectancy of individuals with schizophrenia is sudden cardiac death. While arrhythmic disorders play an important role in this, the nature of the relation between schizophrenia and arrhythmia is not fully understood. Methods We leveraged summary-level data of large-scale genome-wide association studies of schizophrenia (53,386 cases 77,258 controls), arrhythmic disorders (atrial fibrillation, 55,114 cases 482,295 controls; Brugada syndrome, 2,820 cases 10,001 controls) and electrocardiogram traits (heart rate (variability), PR interval, QT interval, JT interval, and QRS duration, n=46,952-293,051). First, we examined shared genetic liability by assessing global and local genetic correlations and conducting functional annotation. Next, we explored bidirectional causal relations between schizophrenia and arrhythmic disorders and electrocardiogram traits using Mendelian randomization. Outcomes There was no evidence for global genetic correlations, except between schizophrenia and Brugada (r g =0·14, p= 4·0E-04). In contrast, strong positive and negative local genetic correlations between schizophrenia and all cardiac traits were found across the genome. In the strongest associated regions, genes related to immune system and viral response mechanisms were overrepresented. Mendelian randomization indicated a causal, increasing effect of liability to schizophrenia on Brugada syndrome (OR=1·15, p= 0·009) and heart rate during activity (beta=0·25, p= 0·015). Interpretation While there was little evidence for global genetic correlations, specific genomic regions and biological pathways important for both schizophrenia and arrhythmic disorders and electrocardiogram traits emerged. The putative causal effect of liability to schizophrenia on Brugada warrants increased cardiac monitoring and potentially early medical intervention in patients with schizophrenia. Funding European Research Council Starting Grant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".