Cardiac and Inflammatory Biomarkers Are Associated with Worsening Renal Outcomes in Patients with Type 2 Diabetes Mellitus: Observations from SAVOR-TIMI 53
Bibliographic record
Abstract
Abstract BACKGROUND Cardiac and renal diseases commonly occur with bidirectional interactions. We hypothesized that cardiac and inflammatory biomarkers may assist in identification of patients with type 2 diabetes mellitus (T2DM) at high risk of worsening renal function. METHODS In this exploratory analysis from SAVOR-TIMI 53, concentrations of high-sensitivity cardiac troponin T (hs-TnT), N-terminal pro–B-type natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP) were measured in baseline serum samples of 12310 patients. The primary end point for this analysis was a ≥40% decrease in estimated glomerular filtration rate (eGFR) at end of treatment (EOT) at a median of 2.1 years. The relationships between biomarkers and the end point were modeled using adjusted logistic and Cox regression. RESULTS After multivariable adjustment including baseline renal function, each biomarker was independently associated with an increased risk of ≥40% decrease in eGFR at EOT [Quartile (Q) Q4 vs Q1: hs-TnT adjusted odds ratio (OR), 5.63 (3.49–9.10); NT-proBNP adjusted OR, 3.53 (2.29–5.45); hs-CRP adjusted OR, 1.84 (95% CI, 1.27–2.68); all P values ≤0.001]. Furthermore, each biomarker was independently associated with higher risk of worsening of urinary albumin-to-creatinine ratio (UACR) category (all P values ≤0.002). Sensitivity analyses in patients without heart failure and eGFR >60 mL/min provided similar results. In an adjusted multimarker model, hs-TnT and NT-proBNP remained significantly associated with both renal outcomes (all P values <0.01). CONCLUSIONS hs-TnT, NT-proBNP, and hs-CRP were each associated with worsening of renal function [reduction in eGFR (≥40%) and deterioration in UACR class] in high-risk patients with T2DM. Patients with high cardiac or inflammatory biomarkers should be treated not only for their risk of cardiovascular outcomes but also followed for renal deterioration.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".