Identification of Novel Biomarkers and Pathways for Coronary Artery Calcification in Non-Diabetic Patients on Hemodialysis Using Metabolomic Profiling
Bibliographic record
Abstract
Background: A better understanding of pathophysiology involving coronary artery calcification (CAC) in hemodialysis (HD) patients will help to develop new therapies. We sought to identify the differences in metabolomics profiles between HD patients with and without CAC. Methods: This is a case-control study nested within a cohort of 568 incident HD patients from the Predictors of Arrhythmic and Cardiovascular Risk in ESRD (PACE) study. Cases were non-diabetics with a CAC score >100 (n=51), and controls were nondiabetics with a CAC score of 0 (n=48). We measured 452 serum metabolites in each participant using liquid chromatography-mass spectrometry. Metabolites and pathway scores were compared using Mann-Whitney U tests, partial least squares-discriminant analyses, and pathway enrichment analyses. Multiple logistic regression was used to examine the associations of key metabolites and pathways with CAC. Results: Cases had a median CAC score of 466 (IQR 246-981). Compared to controls, cases were older (64±13 vs. 42±12 years) and were less likely to be African American (51% vs. 94%). We identified three metabolites in bile acid synthesis (chenodeoxycholic, deoxycholic, and glycolithocholic acids) and one metabolic pathway (arginine/proline metabolism) that were associated with CAC. After adjusting for demographics, higher levels of chenodeoxycholic, deoxycholic, and glycolithocholic acids were associated with higher odds of having CAC. Comparing the third with the first tertile of each bile acid, the adjusted OR (95% CI) was 6.34 (1.12-36.06), 6.73 (1.20-37.82), and 8.53 (1.50-48.49), respectively. Using the first principal component (PC1) score, arginine/proline metabolism was associated with CAC after adjusting for demographics [OR: 1.83 (95% CI: 1.06-3.15) per 1 unit higher in PC1 score], and the association remained significant after additional adjustments for statin use. Conclusions: Among HD patients without diabetes mellitus, chenodeoxycholic, deoxycholic, and glycolithocholic acids may be potential biomarkers for CAC, and arginine/proline metabolism may emerge as a new pathway in the pathogenesis of CAC and could be a potential treatment target. Funding: NIDDK Support, Other NIH Support - National Center for Advancing Translational Science, Private Foundation Support
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".