Galectin-3 and Kidney Function in Type 2 Diabetes Treated with Dapagliflozin: Analysis from DECLARE-TIMI 58
Bibliographic record
Abstract
BACKGROUND: Galectin-3 (Gal-3) is a circulating biomarker of fibrosis, with higher levels being associated with an increased risk of progression of heart failure and kidney disease. Patients with type 2 diabetes mellitus (T2DM) are at increased risk of both. METHODS: DECLARE-TIMI 58 was a randomized, placebo-controlled trial of dapagliflozin in patients with T2DM with or at high risk for atherosclerotic cardiovascular disease and creatinine clearance ≥60 mL/min. In a nested biomarker substudy, Gal-3 was measured at baseline and in adjusted analyses associated with the prespecified kidney-specific composite endpoint [Kidney-EP; sustained ≥40% decrease in estimated glomerular filtration rate (eGFR) to <60 mL/min, new end-stage kidney disease or adjudicated kidney-related death]. RESULTS: Among 14 530 pts, median Gal-3 was 14.9 ng/mL [interquartile range (IQR), 11.9, 18.4]. Gal-3 was weakly associated with urine albumin creatinine ratio (r = 0.098, P < 0.0001) and eGFR (r = -0.27, P < 0.001) at baseline and independently associated with the Kidney-EP:adj hazard ratio (HR) 1.15 [95% confidence interval (CI) 1.03, 1.28] per 1-SD log (Gal-3), P = 0.013. Dapagliflozin significantly reduced the relative risk of the Kidney-EP across quartiles of baseline Gal-3 [overall HR 0.45 (95% CI 0.23, 0.85), P < 0.0001; P interaction = 0.87]. A greater risk difference was observed with dapagliflozin in patients with higher Gal-3, in whom a higher absolute risk at baseline was observed [absolute risk reduction (ARR) Q4 1.9 (95% CI 0.6, 3.2) vs. Q1 0.6% (-0.1, 1.3), ARR P trend 0.048]. CONCLUSIONS: Plasma Gal-3 is independently associated with the progression of kidney dysfunction in patients with T2DM and normal kidney function. There was a gradient of greater absolute benefit for reducing kidney disease progression in patients treated with dapagliflozin and with higher Gal-3 concentrations at baseline, in whom a higher absolute risk was observed. REGISTRATION: clinicaltrials.gov (NCT01730534).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".