Effects of <scp>SGLT2</scp> inhibitors across the spectrum of albuminuria in cardiovascular–kidney–metabolic conditions: A pooled analysis of randomised trials
Bibliographic record
Abstract
BACKGROUND: Albuminuria is associated with an increased risk of cardiovascular and kidney events. Sodium glucose co-transporter 2 inhibitors (SGLT2i) reduce albuminuria and improve kidney outcomes in patients with albuminuric chronic kidney disease (CKD). Patients with low- or without albuminuria have been underrepresented in randomised clinical trials (RCTs), and the effects of SGLT2i on cardiovascular and kidney outcomes across the full range of albuminuria require further investigation. AIMS: To study the effects of SGLT2i on kidney and cardiovascular outcomes across albuminuria levels in populations with different cardiovascular-kidney-metabolic (CKM) risk. METHODS: Individual-patient data pooled analysis of RCTs across the CKM spectrum. Outcomes were studied across urinary albumin-to-creatinine ratio (UACR) both as categorical and continuous variables using survival and mixed effects models. RESULTS: ) baseline UACR was 28 (8-240) mg/g: 13 669 (51.1%) had UACR <30 mg/g, 6904 (25.8%) UACR 30-300 mg/g, and 6177 (23.1%) UACR >300 mg/g. Compared to patients with lower UACR, those with higher UACR were younger, with a more frequent history of hypertension, diabetes, and obesity, and lower eGFR. UACR was linearly associated with kidney and cardiovascular outcomes as well as mortality. Compared to placebo, SGLT2i reduced the risk of kidney events, HF hospitalisations, atherothrombotic events, cardiovascular and all-cause mortality across the full UACR spectrum (Pinteraction >0.1 for all outcomes). Compared to placebo, SGLT2i reduced albuminuria levels by 13%, on average: gMratio 0.87, 95%CI 0.85-0.88, p < 0.001. CONCLUSIONS: Higher albuminuria was associated with an increased risk of cardiovascular and kidney outcomes. SGLT2i improved cardiovascular and kidney outcomes across the full range of albuminuria, including normo-albuminuria.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".