Relationship Between Insulin Sensitivity and β-Cell Secretion in Nondiabetic Subjects with Rheumatoid Arthritis
Bibliographic record
Abstract
Objective. In nondiabetic healthy individuals, insulin secretion and sensitivity are linked by a negative feedback loop characterized by a hyperbolic function. We aimed to study the association of traditional insulin resistance (IR) factors with insulin secretion and sensitivity, and to determine whether the hyperbolic equilibrium of this relation is preserved in patients with rheumatoid arthritis (RA). Methods. This was a cross-sectional study encompassing 361 nondiabetic individuals: 151 with RA and 210 controls. Insulin, C-peptide, and IR indices by homeostatic model (HOMA2) were assessed. A multivariable analysis was performed to evaluate the differences in the correlation of traditional IR-related factors with glucose homeostasis molecules, as well as IR indices between patients and controls. Nonlinear regression analysis was used to assess the hyperbolic relation of insulin sensitivity and secretion. Results. HOMA2-IR indices were higher in patients with RA than controls. Hepatic insulin extraction, as assessed by the insulin:C-peptide molar ratio, was lower in patients with RA after multivariable analysis (0.08 ± 0.02 vs 0.14 ± 0.07, p < 0.001). Traditional IR-related factors showed significantly lower adjusted correlation coefficients with IR indices in patients with RA. The association between insulin sensitivity and secretion showed a different hyperbolic relation in patients with RA: the variability explained by the curve was lower in RA (nonlinear r2= 0.845 vs r2= 0.928, p = 0.001) and β coefficients (−0.74, 95% CI −0.77 to −0.70 vs −1.09, 95% CI −1.17 to −1.02, ng/ml, p < 0.001) were different in RA. Conclusion. The traditional factors associated with IR in healthy individuals are less related to IR in patients with RA. Insulin sensitivity and secretion yield a different hyperbolic equilibrium in RA.
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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) | 0.000 | 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".