Risk of dipeptidyl peptidase‐4 (DPP‐4) inhibitors on site‐specific cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
The long-term impact of dipeptidyl peptidase-4 (DPP-4) inhibition is unknown, and there are concerns about the influence of DPP-4 inhibition on carcinogenesis of the pancreas and thyroid. As DPP-4 is a rather unselective enzyme present in many tissues, we focused on all specific cancer types. PubMed and EMBASE were searched between January 2005 and April 2017 to identify studies comparing DPP-4 inhibitors with either placebo or active drugs on cancer risk. Studies were included if they reported on at least one specific cancer outcome and had a follow-up of at least 1 year after start of drug use. Methodological quality of the studies was assessed by the Cochrane Collaboration's tool and the Newcastle-Ottawa Scale. Twenty-five studies met the inclusion criteria (12 randomized controlled trials and 13 observational studies). Sample sizes of the DPP-4 inhibitor groups ranged from 29 to 8212 patients for randomized controlled trials and from 2422 to 71 137 patients for observational studies. Mean age ranged from 51 to 76 years, and mean follow-up was 1.5 years. None of the pooled (sensitivity) analyses, except the observational studies studying breast cancer (hazard ratio [95% CI]: 0.76 [0.60-0.96]), showed evidence for an association between DPP-4 inhibitors and site-specific cancer. Also for pancreatic and thyroid cancer, no statistically significant risk was found. Based on the current literature, it is not possible to conclude whether DPP-4 inhibitors were associated with an increased risk of site-specific cancer. Future studies should address the methodological limitations and follow patients for a longer period to determine the long-term cancer risk of DPP-4 inhibitors.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.052 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".