The association of sodium‐glucose cotransporter 2 inhibitors with cancer: An overview of quantitative systematic reviews
Bibliographic record
Abstract
AIMS: To summarize reported cancer events associated with SGLT-2 inhibitors used in patients with type 2 diabetes mellitus, as well as assess the quality of included reviews. MATERIALS AND METHODS: In May 2019, we searched PubMed, Embase and the Cochrane Library for quantitative systematic reviews assessing the safety of SGLT-2 inhibitors. Data were abstracted using a standardized form, and methodological quality was assessed using the AMSTAR 2 tool. Main outcome measures included total cancer events and specific cancers such as breast cancer, bladder cancer, gastrointestinal cancer, prostate cancer, respiratory cancer, renal cancer and skin cancer. Pooled treatment effects from included reviews were summarized for SGLT-2 inhibitors as a class and for individual SGLT-2 inhibitors commonly used worldwide (canagliflozin, dapagliflozin and empagliflozin). RESULTS: We screened 1248 unique citations, of which eight quantitative systematic reviews meta-analysed results from studies reporting the association between an SGLT-2 inhibitor and any cancer. Only one review was rated as high quality according to AMSTAR 2 assessment. In total, data from 170 cancer-related point estimates (PE) were reported. As a class, SGLT-2 inhibitors were not associated with an increased risk of any cancer event versus placebo and active comparators. Most point estimates (7/143) were nonsignificant for individual cancers except for two associations. Empagliflozin was associated with an increased risk of bladder cancer versus placebo and active comparators in two reviews, while canagliflozin appeared protective for gastrointestinal cancer versus placebo and active comparators in one review. CONCLUSIONS: It appears that SGLT-2 inhibitors are not associated with an increased risk of total cancer or specific cancers in patients with type 2 diabetes. However, higher quality evidence is needed to derive confident conclusions.
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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 | 0.034 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".