Abstract 14727: Meteorin-like Glial Cell Differentiation Regulator is a Novel Protein Biomarker of Cardiovascular Outcomes in Patients With Diabetes and Vascular Disease: A TECOS Substudy
Bibliographic record
Abstract
Introduction: To date, there are limited data on the potential role of proteomic biomarkers to predict future cardiovascular (CV) events among patients with type 2 diabetes mellitus (DM). Hypothesis: Specific protein biomarkers will be predictive of major adverse CV events (MACE) and incident heart failure hospitalization (HFH) among patients with DM. Methods: Using the Olink aptamer-based platform, we performed proteomic profiling (>700 proteins) on 440 paired cases and matched controls from placebo-assigned participants in the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). Cases were defined as having met the primary composite outcome of MACE or incident HFH and matched to controls on baseline prevalent heart failure, coronary artery disease, BMI, hemoglobin A1C, creatinine, low-density lipoprotein cholesterol, fasting status and ejection fraction. Conditional logistic regression was used to determine the association between log-transformed relative protein expression and incident MACE or HFH. False-discovery-rate (FDR) was used to adjust for multiple comparisons. Results: We identified three specific proteins that were significantly associated with prevalent MACE or HFH: METRNL, Notch 3, and ROR1 (OR 2.1, 1.6, 1.7 and q-value 0.01, 0.02, and 0.05 respectively) (Figure 1). METRNL, in particular, performed similarly to the established biomarker NT-proBNP (Figure 1). When MACE and HFH were analyzed separately, METRNL, in particular, remained strongly associated with both outcomes (OR 2.0, p<0.001 and OR 2.7, p=0.05). Conclusions: Three novel protein biomarkers, in particular METRNL (a circulating adipokine that regulates insulin-sensitivity), may identify diabetic patients at high risk for subsequent HF and MACE. Additional studies are needed to replicate these findings and uncover the biologic mechanism linking adipokine signaling and heart failure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".