Investigating Causal Links between Metabolite Profiles and Ulcerative Colitis: A Bidirectional Mendelian Randomization Study
Bibliographic record
Abstract
Abstract Background: While metabolic biomarkers are known to play a significant role in the development of ulcerative colitis (UC), the exact causal relationships between them remain uncertain and warrant further investigations. Here we report a bidirectional two-sample Mendelian randomization (MR) study to evaluate causal relationships between 503 blood metabolites and UC. Methods: We used genome-wide association study (GWAS) data on blood metabolite levels from two separate studies on European individuals ( n = 8299 and 24,925). In addition, for UC, we utilized GWAS data from the same ancestry, including 417,932 participants, comprising 5371 UC cases and 412,561 controls. We employed the inverse variance weighted method for our discovery stage of MR analyses. Then, we used other methods, including MR-Egger, weighted median, weighted mode, simple mode, MR-pleiotropy residual sum and outlier, heterogeneity, and pleiotropy tests for sensitivity analyses to further validate our findings and assess the robustness of our results. Results: Our study suggests that total lipids in small high-density lipoprotein levels (S.HDL.L) are marginal significant positive associated with the development of UC (odds ratio = 1.167, 95% confidence interval: 0.998–1.364, P = 0.051). In addition, UC did not have a statistically significant effect on the metabolites. Conclusions: Total lipids in S.HDL.L may offer a potential trend as valuable circulating metabolic biomarkers for the screening and prevention of UC in clinical practice. In addition, they could serve as potential candidate molecules for elucidating the mechanisms underlying UC and for identifying suitable drug targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".