Steroid Sex Hormones, Sex Hormone–Binding Globulin, and Diabetes Incidence in the Diabetes Prevention Program
Bibliographic record
Abstract
CONTEXT: Steroid sex hormones and SHBG may modify metabolism and diabetes risk, with implications for sex-specific diabetes risk and effects of prevention interventions. OBJECTIVE: This study aimed to evaluate the relationships of steroid sex hormones, SHBG and SHBG single-nucleotide polymorphisms (SNPs) with diabetes risk factors and with progression to diabetes in the Diabetes Prevention Program (DPP). DESIGN AND SETTING: This was a secondary analysis of a multicenter randomized clinical trial involving 27 U.S. academic institutions. PARTICIPANTS: The study included 2898 DPP participants: 969 men, 948 premenopausal women not taking exogenous sex hormones, 550 postmenopausal women not taking exogenous sex hormones, and 431 postmenopausal women taking exogenous sex hormones. INTERVENTIONS: Participants were randomized to receive intensive lifestyle intervention, metformin, or placebo. MAIN OUTCOMES: Associations of steroid sex hormones, SHBG, and SHBG SNPs with glycemia and diabetes risk factors, and with incident diabetes over median 3.0 years (maximum, 5.0 y). RESULTS: T and DHT were inversely associated with fasting glucose in men, and estrone sulfate was directly associated with 2-hour post-challenge glucose in men and premenopausal women. SHBG was associated with fasting glucose in premenopausal women not taking exogenous sex hormones, and in postmenopausal women taking exogenous sex hormones, but not in the other groups. Diabetes incidence was directly associated with estrone and estradiol and inversely with T in men; the association with T was lost after adjustment for waist circumference. Sex steroids were not associated with diabetes outcomes in women. SHBG and SHBG SNPs did not predict incident diabetes in the DPP population. CONCLUSIONS: Estrogens and T predicted diabetes risk in men but not in women. SHBG and its polymorphisms did not predict risk in men or women. Diabetes risk is more potently determined by obesity and glycemia than by sex hormones.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".