Sex-Specific Models to Predict Insulin Secretion and Sensitivity in Subjects with Overweight and Obesity
Bibliographic record
Abstract
Sex-specific differences exist in insulin secretion (ISec) and sensitivity (IS) in humans. However, current fasting indices used to estimate them, such as HOMA and QUICKI, are not sex-specific. We aimed to develop sex-specific models to improve the prediction of ISec and IS by fasting measures in adults with overweight/obesity. A post hoc analysis was conducted on baseline data of two clinical trials completed between 2010 and 2020 (37 men and 61 postmenopausal women, 45–73 years, BMI > 25 kg/m2, without chronic disease). Glucose-induced insulin or C-peptide secretions and IS were measured using gold-standard Botnia-clamps, which is a 1 h intravenous glucose tolerance test followed by a 3 h hyperinsulinemic–euglycemic clamp. Stepwise regression analysis using anthropometric and fasting plasma glucose, insulin, and lipoprotein-related measures was used to predict ISec and IS. First-phase, second-phase and total glucose-induced ISec were predicted by a combination of fasting plasma insulin and apoB without or with plasma glucose, triglyceride, and waist circumference in women (R2 = 0.58–0.69), and by plasma insulin and glucose without or with BMI and cholesterol in men (R2 = 0.41–0.83). Plasma C-peptide, alone in men or followed by glucose in women, predicted C-peptide secretion. IS was predicted by plasma insulin and waist circumference, followed by HDL-C in women (R2 = 0.57) or by glucose in men (R2 = 0.67). The sex-specific models agreed with the Botnia-clamp measurements of ISec and IS more than with HOMA or QUICKI. Sex-specific models incorporating anthropometric and lipoprotein-related parameters allowed better prediction of ISec and IS in subjects with overweight or obesity than current indices that rely on glucose and insulin alone.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".