Sodium–glucose cotransporter 2 (SGLT2) inhibitors and risk of chronic kidney disease–mineral and bone disorders in patients with type 2 diabetes mellitus and stage 1–3 chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: In patients with type 2 diabetes mellitus and chronic kidney disease (CKD), sodium-glucose cotransporter 2 (SGLT2) inhibitors improve renal outcomes, but may transiently affect biochemical markers of CKD-mineral and bone disorders (CKD-MBD). We sought to evaluate the long-term risk of CKD-MBD associated with use of SGLT2 inhibitors in this patient population. METHODS: We conducted a retrospective cohort study, employing a target trial emulation framework and using electronic medical records of patients from 9 hospitals in Taiwan (2016-2023). We included adults with type 2 diabetes mellitus and stage 1-3 CKD who had newly started either an SGLT2 inhibitor or, as a comparison group, a glucagon-like peptide-1 receptor agonist (GLP-1 RA). The primary outcome was a composite of incident biochemical abnormalities (serum phosphate > 1.5 mmol/L, serum calcium < 2.1 mmol/L, serum intact parathyroid hormone [iPTH] > 6.9 pmol/L, or serum 25-hydroxyvitamin D < 49.9 nmol/L). RESULTS: = 1459) with a median follow-up of 3.3 years. Compared with GLP-1 RAs, SGLT2 inhibitors were associated with a lower cumulative incidence of the composite primary outcome (hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.79-0.86), hyperphosphatemia (HR 0.83, 95% CI 0.76-0.91), hypocalcemia (HR 0.82, 95% CI 0.78-0.86), high serum iPTH levels (HR 0.66, 95% CI 0.57-0.78), and low serum 25-hydroxyvitamin D levels (HR 0.65, 95% CI 0.47-0.90). INTERPRETATION: Use of SGLT2 inhibitors was associated with a lower incidence of biochemical abnormalities related to CKD-MBD than GLP-1 RAs. These agents may be considered to reduce risk of CKD-MBD in patients with type 2 diabetes mellitus and stage 1-3 CKD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".