P1019CANAGLIFLOZIN AND RISK OF SKIN AND SOFT TISSUE INFECTIONS IN PEOPLE WITH DIABETES MELLITUS AND KIDNEY DISEASE - A POST-HOC ANALYSIS OF THE CREDENCE TRIAL
Bibliographic record
Abstract
Abstract Background and Aims The skin’s hypertonic microenvironment has a hypothesized protective antimicrobial function that may be disrupted by SGLT2i. The association between sodium glucose cotransporter inhibitors (SGLT2i) and genital mycotic infections is well established, but it is not known if these agents increase the risk of other skin and soft tissue infections (SSTI). We aimed to describe SSTI in the CREDENCE trial, and determine whether canagliflozin affects the risk of skin and soft tissue infections (SSTIs) overall and in subgroups. Method We performed a post-hoc analysis of the CREDENCE trial that randomised people with type 2 diabetes and albuminuric stage 2 and 3 chronic kidney disease to either canagliflozin 100mg daily or placebo. Infections reported as adverse events were assessed by two blinded authors following predetermined criteria for SSTI with discrepancies resolved by consensus. We analysed the risks of SSTIs in the on-treatment population as the more conservative approach, with a sensitivity analysis conducted in the intention-to-treat population. Univariable time-to first-event regression models were assessed. Results Overall 373/4397 (8.5%) participants experienced 478 events comprising 252 bacterial skin infections (including 2 episodes of necrotising fasciitis), 94 fungal skin infections, 109 other skin infections and 23 soft tissue infections. Of these, 136/478 (28%) were serious. Drug was continued in 290/373 (78%) of first events, with similar frequency of subsequent events between groups (31/133 (23%) and 33/157 (21%) for those continuing canagliflozin and placebo respectively). In both cases of necrotising fasciitis, drug was withdrawn and the participants recovered.Canagliflozin did not increase the risk of SSTI (HR 0.85 [95% Confidence Interval (CI) 0.69-1.04] p=0.11) (Figure 1). Results were similar in the intention-to-treat population (HR 0.88 [95% CI 0.73-1.07] p=0.20), in analyses confined to serious SSTI (HR 0.83 [95% CI 0.58-1.21] p=0.33), and in the predefined subgroups. Conclusion Although other studies suggest that SGLT2i may reduce the sodium content of the skin, we found that canagliflozin does not increase the risk of skin and soft tissue infections, overall or in any subgroup, in people with type 2 diabetes mellitus and albuminuric chronic kidney disease.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".