Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study
Bibliographic record
Abstract
AIMS: Evidence is limited as to who benefit the most from sodium-glucose cotransporter 2 inhibitors (SGLT2i), especially among people without elevated cardiovascular disease (CVD) risk. To address this knowledge gap, we investigated the heterogeneity in the effect of SGLT2i across CVD risk profiles. METHODS AND RESULTS: Using a target trial emulation framework, we compared SGLT2i vs. dipeptidyl peptidase 4 inhibitors (DPP4i) in a nationwide insurer-based database of working-age Japanese citizens in 2015-23. The primary outcome was a composite of all-cause death, myocardial infarction, stroke, or heart failure over 3 years. Machine learning causal forest was applied to assess heterogeneity by predicting individual-level risk reduction in primary outcomes by SGLT2i and its correlation with CVD risk score. Overall, among 150 830 individuals included in this study (mean age, 54 years; female, 13.3%), SGLT2i was associated with decreased risk of primary outcomes {3-year risk difference, +0.38 [95% confidence interval (CI): 0.16-0.61] percentage points}. The causal forest model revealed heterogeneity in the effectiveness of SGLT2i, with estimated benefit correlating weakly with CVD risk score (r = 0.287, P < 0.001). In particular, among 107 425 individuals with low CVD risk, 97 757 (91.0%) were predicted to benefit from SGLT2i. This subpopulation was characterized as individuals with higher blood pressure, body mass index, and fasting plasma glucose levels even with low CVD risk score. CONCLUSION: The cardioprotective effect of SGLT2i was heterogeneous and more strongly predicted by individual patient characteristics than by overall CVD risk score, highlighting the importance of considering its benefit beyond the conventional risk stratification approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".