Causal associations between fatty acid measures and schizophrenia – a two-sample Mendelian randomization study
Bibliographic record
Abstract
Abstract Objective Although studies suggest that erythrocyte concentrations of omega-3 and omega-6 fatty acids are lower in individuals with schizophrenia, evidence of beneficial effects of omega-3 fatty acid supplementation is limited. This study therefore aimed to determine whether omega-3 and omega-6 fatty acid levels are causally related to schizophrenia. Methods Causality was evaluated using the inverse variance weighted (IVW) 2-sample Mendelian randomization (MR) method using fatty acid levels and schizophrenia genome-wide association study results. Weighted median, weighted mode, and MR Egger regression methods were used as sensitivity analyses. To address the mechanism, analyses were performed using instruments within the FADS and ELOVL2 genes. Multivariable MR (MVMR) was used to estimate direct effects of omega-3 fatty acids on schizophrenia, independent of omega-6 fatty acids, lipoproteins and triglycerides. Results MR analyses indicated that long-chain omega-3 and omega-6 fatty acid levels were associated with lower risk of schizophrenia (docosahexaenoic acid [DHA] ORIVW: 0.83, 95% CI: 0.75-0.92). In contrast, short-chain fatty acids were associated with an increased risk of schizophrenia (alpha-linolenic acid ORIVW: 1.07, 95% CI: 0.98-1.18). Causal effects were consistent across sensitivity and FADS single-SNP analyses. MVMR indicated that the protective effect of DHA on schizophrenia persisted after conditioning on other lipids (ORIVW: 0.84, 95% CI: 0.71-1.01). Conclusions Results are consistent with protective effects of long-chain omega-3 and omega-6 fatty acids on schizophrenia suggesting that people with schizophrenia may have difficulty converting short-chain to long-chain PUFAs. Long-chain PUFA supplementation or diet enrichment, particularly in higher risk individuals, might help prevent onset of disorder.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".