Cystic fibrosis‐related diabetes develops from a combination of insulin secretion defects and insulin resistance
Bibliographic record
Abstract
AIM: The relative contributions of insulin secretory defects and possible additional contribution of insulin resistance for the development of cystic fibrosis (CF)-related diabetes (CFRD) are poorly understood. We aimed to (a) determine which indices of insulin resistance predict progression to CFRD, and (b) to model the relative contributions of insulin secretory function and insulin resistance to predict the risk of CFRD. MATERIALS AND METHODS: Three hundred and three individuals living with CF underwent a 2-h oral glucose tolerance test with blood sampling every 30 min at 12-24-month intervals until they developed CFRD or until the end of follow-up (up to 15 years). Indices of insulin resistance (e.g. Stumvoll) and insulin secretion were calculated from oral glucose tolerance test glucose and insulin measurements. CFRD-free survival was assessed by survival analysis. RESULTS: Estimated insulin resistance showed associations with glucose homeostasis and risk of progression to CFRD. The CFRD-free survival was significantly different between quartiles of insulin resistance (p < 0.0001). When patients were subdivided according to both insulin resistance and insulin secretion (insulinogenic index), CFRD-free survival was significantly lower in those with combined lowest insulin secretion and highest insulin resistance (Stumvoll) indices (hazard ratio: 11.2; p < 0.0001). There was no significant difference when the same analysis was performed for the nine other indices. CONCLUSIONS: Insulin resistance is correlated with glucose homeostasis and the risk of progression to CFRD. Patients combining low insulin secretion and high insulin resistance had the greatest odds of developing CFRD over a 15-year period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".