Metabolic pathway and genetically causal links of 1,400 circulating metabolites on the risk of intracranial aneurysms and aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
BACKGROUND: The rupture of intracranial aneurysms (IAs) leads to aneurysmal subarachnoid hemorrhage (aSAH), which is associated with significant disability and mortality rates. This study aims to identify metabolic markers causally linked to the occurrence of IAs and aSAH through Mendelian randomization (MR), thereby offering novel predictive and therapeutic targets. METHODS: We conducted a genome-wide association study (GWAS) on IAs and aSAH, analyzing 1,400 metabolomic indices from the Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8,299). Subsequently, we employed two-sample Mendelian randomization to ascertain potential causal relationships between each metabolite and the conditions IAs and aSAH by various MR methodologies, including MR Egger, Weighted median, Inverse variance weighted (IVW), MR-PRESSO, Simple mode, and Weighted mode. The heterogeneity of instrumental variables was assessed using Cochran's Q statistics, and metabolic pathway analyses were performed via the Metaconflict 5.0 platform. RESULTS: Our analysis found that 87 metabolites/metabolic ratios were associated with IAs, and 85 metabolites/metabolic ratios were associated with aSAH. After false discovery rate (FDR) correction and sensitivity analyses, nine metabolites/metabolic ratios were significantly causally associated with aSAH. Conversely, while 87 metabolites and their ratios initially showed potential causal links with IA, none demonstrated significant causal associations post-FDR correction. The study also pinpointed eight significant metabolic pathways implicated in both IAs and aSAH. CONCLUSION: This study found that nine circulating metabolites and their ratios with significant causal associations to aSAH, while no metabolites and their ratios were causally linked to IAs. These results suggest possible mechanisms and predictive molecular targets for IAs and aSAH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".