Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study
Bibliographic record
Abstract
Chronic skin ulcers, although rare, pose severe and debilitating challenges. The identification of causal metabolite biomarkers presents an opportunity to refine effective risk assessment strategies for this condition. In this study, we conducted a comprehensive Two-Sample Mendelian Randomization (TSMR) investigation to delineate the potential causal effects of plasma metabolites on chronic skin ulcer risk. Exposure data comprised 14,296 participants with 913 metabolites from INTERVAL/EPIC-Norfolk, and 8,299 participants with 1,091 metabolites and 309 ratios from the Canadian Longitudinal Study on Aging (CLSA). Outcome data came from the finngen_R9_L12_CHRONICULCEROFSKIN (1,840 cases, 353,088 controls) and UK Biobank Chronic ulcer of skin (495 cases, 455,853 controls) cohorts. Leveraging the inverse-variance weighted (IVW) method, alongside MR-Egger and MR-PRESSO sensitivity analyses, we evaluated metabolite associations with chronic skin ulcer risk. Further assessment involved a phenome-wide MR (Phe-MR) analysis to explore potential repercussions of targeting identified metabolites for intervention. Our study identified 12 distinct metabolites significantly associated with chronic skin ulcers, demonstrating consistent and replicable results. Notably, X-19,141 exhibited the highest reproducibility. These findings highlight novel plasma metabolites relevant to chronic skin ulcers, offering theoretical underpinnings for mechanistic research and clinical strategies in prevention and treatment.
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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 | 0.055 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".