Causal associations between 1400 circulating metabolites and spontaneous abortion: A bidirectional 2-sample Mendelian randomization study
Bibliographic record
Abstract
Spontaneous abortion (SA) affects approximately 10% to 15% of clinically recognized pregnancies. The potential causal role of metabolic disturbances in SA remains poorly defined. We performed a bidirectional 2-sample Mendelian randomization (MR) analysis to evaluate causal associations between 1400 plasma metabolites and SA. Genetic instruments for metabolites were obtained from a genome-wide association study of 8299 individuals from the Canadian longitudinal study on aging. Summary statistics for SA were derived from the FinnGen R12 dataset, including 23,167 cases and 1,99,279 controls. Primary MR analyses used the inverse variance weighted method, with MR-Egger, weighted median, Mendelian Randomization Pleiotropy RESidual Sum and Outlier, and leave-one-out analyses conducted to assess robustness. Thirty-six circulating metabolites showed significant causal associations with SA. Key findings included protective associations for phenylacetylglutamine (odds ratio [OR], 0.89; 95% confidence interval [CI], 0.83-0.96; P = .0019) and risk-enhancing effects for the glycerol-to-carnitine ratio (OR, 1.07; 95% CI: 1.03-1.12; P = .0021), 1-methyl-5-imidazoleacetate (OR, 1.09; 95% CI: 1.03-1.16; P = .0040), and the caffeine-to-theophylline ratio (OR, 1.08; 95% CI: 1.03-1.15; P = .0041). Sensitivity analyses showed no evidence of horizontal pleiotropy or heterogeneity. Reverse MR identified 5 metabolites potentially influenced by genetic predisposition to SA, though forward causal effects were more prominent. This study provides genetic evidence supporting a causal role of specific circulating metabolites in the pathophysiology of SA. These findings offer novel mechanistic insights into early pregnancy loss and highlight potential biomarkers for reproductive risk stratification and targeted intervention.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".