Increased Proinsulin Levels and Decreased Acute Insulin Response Independently Predict the Incidence of Type 2 Diabetes in the Insulin Resistance Atherosclerosis Study
Bibliographic record
Abstract
Previous studies have indicated that beta-cell dysfunction predicts the development of diabetes, although it is unknown whether the use of combinations of insulin secretory measures further improves prediction. The Insulin Resistance Atherosclerosis Study is a prospective, multicenter, epidemiological study of the relationship between insulin sensitivity and the risk of diabetes and cardiovascular disease. At baseline, fasting concentrations of insulin, intact proinsulin (PI), and split PI were measured, and acute insulin response (AIR) was determined during a frequently sampled intravenous glucose tolerance test (FSIGTT). Subjects who were nondiabetic at baseline (n = 903) were reexamined after 5 years of follow-up; 148 had developed diabetes. In separate logistic regression models adjusted for age, sex, clinic, and ethnicity, 1 SD differences in measures of beta-cell dysfunction were associated with diabetes incidence (AIR: odds ratio [OR] 0.37, 95% CI 0.27-0.52; intact PI: OR 1.90, 95% CI 1.57-2.30; split PI: OR 1.94, 95% CI 1.63-2.31). After additional adjustment for BMI, impaired glucose tolerance, and insulin sensitivity, these measures continued to be significantly associated with risk of diabetes (all P < 0.0001). Furthermore, in models that included both PI and AIR, each was an independent predictor, and individuals who had combined low AIR and high PI experienced the highest diabetes risk. In conclusion, both low AIR and high PI independently predicted diabetes in a well-characterized multiethnic population. Although fasting PI is simpler to assess, determining AIR from an FSIGTT may further improve prediction. If pharmacological agents to prevent diabetes are proved to be efficacious in ongoing clinical trials, then it may be beneficial to perform FSIGTTs to identify better (for intensive intervention) prediabetic subjects who would ultimately require lifelong pharmacological therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".