Association between cancer prevalence and use of thiazolidinediones: results from the Vermont Diabetes Information System
Bibliographic record
Abstract
BACKGROUND: Peroxisome proliferator-activated receptors (PPARs) have emerged as important drug targets for diabetes. Drugs that activate PPARgamma, such as the thiazolidinediones (TZDs), are widely used for treatment of Type 2 diabetes mellitus. PPARgamma signaling could also play an anti-neoplastic role in several in vitro models, although conflicting results are reported from in vivo models. The effects of TZDs on cancer risk in humans needs to be resolved as these drugs are prescribed for long periods of time in patients with diabetes. METHODS: A total of 1003 subjects in community practice settings were interviewed at home at the time of enrolment into the Vermont Diabetes Information System, a clinical decision support program. Patients self-reported their personal and clinical characteristics, including any history of malignancy. Laboratory data were obtained directly from the clinical laboratory and current medications were obtained by direct observation of medication containers. We performed a cross-sectional analysis of the interviewed subjects to assess a possible association between cancer diagnosis and the use of TZDs. RESULTS: In a multivariate logistic regression model, a diagnosis of cancer was significantly associated with TZD use, even after correcting for potential confounders including other oral anti-diabetic agents (sulfonylureas and biguanides), age, glycosylated hemoglobin A1C, body mass index, cigarette smoking, high comorbidity, and number of prescription medications (odds ratio = 1.59, P = 0.04). This association was particularly strong among patients using rosiglitazone (OR = 1.89, P = 0.02), and among women (OR = 2.07, P = 0.01). CONCLUSION: These data suggest an association between TZD use and cancer in patients with diabetes. Further studies are required to determine if this association is causal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".