The Role of Plasma Metabolites in Mediating the Effect of Gut Microbiota on Obstructive Sleep Apnea: A Two-Step, Two-Sample Mendelian Randomization Study
Bibliographic record
Abstract
Background: Recent research has increasingly underscored a significant correlation between gut microbiota and obstructive sleep apnea (OSA). Probiotics have emerged as promising adjunctive interventions for OSA. Metabolites and their related biochemical pathways have emerged as important contributors to the development of OSA. This study aimed to estimate the causal association between gut microbiota and OSA and to quantify the mediating effects of metabolites. Methods: We employed two-step, two-sample Mendelian randomization techniques, utilizing single nucleotide polymorphisms as genetic instruments for exposures and mediators. Summary statistics were obtained from genome-wide association studies of gut microbiota (the Dutch Microbiome Project, n=7,738), plasma metabolites (the Canadian Longitudinal Study on Aging cohort, n=8,299), and OSA (FinnGen database, n=410,385). To ensure the robustness of our findings, sensitivity analyses and heterogeneity tests were systematically conducted. Results: In the Dutch Microbiome Project, species Parabacteroides merdae, genus Faecalibacterium, species Faecalibacterium prausnitzii and species Bifidobacterium longum demonstrated a potential protective association with OSA. We included the top 10 metabolites with potential biological significance as candidate mediators. Among them, only 2-hydroxypalmitate was associated with a reduced risk of OSA. 2-hydroxypalmitate partially mediated the association between species Parabacteroides merdae and OSA, with a mediation proportion of 20.53%. Conclusion: The study highlighted the protective effect of species Parabacteroides merdae against OSA. It also revealed the mediating role of 2-hydroxypalmitate in the relationship between species Parabacteroides merdae and OSA.
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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 | 0.058 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".