Polycystic Ovary Syndrome, Combined Oral Contraceptives, and the Risk of Dysglycemia: A Population-Based Cohort Study With a Nested Pharmacoepidemiological Case-Control Study
Bibliographic record
Abstract
OBJECTIVE: Irregular menstrual cycles are associated with increased cardiovascular mortality. Polycystic ovary syndrome (PCOS) is characterized by androgen excess and irregular menses; androgens are drivers of increased metabolic risk in women with PCOS. Combined oral contraceptive pills (COCPs) are used in PCOS both for cycle regulation and to reduce the biologically active androgen fraction. We examined COCP use and risk of dysglycemia (prediabetes and type 2 diabetes) in women with PCOS. RESEARCH DESIGN AND METHODS: Using a large U.K. primary care database (The Health Improvement Network [THIN]; 3.7 million patients from 787 practices), we carried out a retrospective population-based cohort study to determine dysglycemia risk (64,051 women with PCOS and 123,545 matched control subjects), as well as a nested pharmacoepidemiological case-control study to investigate COCP use in relation to dysglycemia risk (2,407 women with PCOS with [case subjects] and without [control subjects] a diagnosis of dysglycemia during follow-up). Cox models were used to estimate the unadjusted and adjusted hazard ratio, and conditional logistic regression was used to obtain adjusted odds ratios (aORs). RESULTS: < 0.001; adjustment for age, social deprivation, BMI, ethnicity, and smoking), with increased rates of dysglycemia in all BMI subgroups. Women with PCOS and COCP use had a reduced dysglycemia risk (aOR 0.72, 95% CI 0.59-0.87). CONCLUSIONS: In this study, limited by its retrospective nature and the use of routinely collected electronic general practice record data, which does not allow for exclusion of the impact of prescription-by-indication bias, women with PCOS exposed to COCPs had a reduced risk of dysglycemia across all BMI subgroups. Future prospective studies should be considered for further understanding of these observations and potential causality.
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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) | 0.001 | 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".