Effect of Long-Acting Insulin Analogs on the Risk of Cancer: A Systematic Review of Observational Studies
Bibliographic record
Abstract
OBJECTIVE: Observational studies examining the association between long-acting insulin analogs and cancer incidence have produced inconsistent results. We conducted a systematic review of these studies, focusing on their methodological strengths and weaknesses. RESEARCH DESIGN AND METHODS: We systematically searched MEDLINE and EMBASE from 2000 to 2014 to identify all observational studies evaluating the relationship between the long-acting insulin analogs and the risk of any and site-specific cancers (breast, colorectal, prostate). We included cohort and case-control studies published in English on insulin glargine and detemir and any cancer incidence among patients with type 1 or 2 diabetes. The methodological assessment involved the inclusion of prevalent users, inclusion of lag periods, time-related biases, and duration of follow-up between insulin initiation and cancer incidence. RESULTS: A total of 16 cohort and 3 case-control studies met our inclusion criteria. All studies evaluated insulin glargine, and four studies also examined insulin detemir. Follow-up ranged from 0.9 to 7.0 years. Thirteen of 15 studies reported no association between insulin glargine and detemir and any cancer. Four of 13 studies reported an increased risk of breast cancer with insulin glargine. In the quality assessment, 7 studies included prevalent users, 11 did not consider a lag period, 6 had time-related biases, and 16 had short (<5 years) follow-up. CONCLUSIONS: The observational studies examining the risk of cancer associated with long-acting insulin analogs have important methodological shortcomings that limit the conclusions that can be drawn. Thus, uncertainty remains, particularly for breast cancer risk.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".