Cross-Sectional and Longitudinal Changes of Glucose Effectiveness in Relation to Glucose Tolerance
Bibliographic record
Abstract
OBJECTIVE: Glucose effectiveness (S(G)), the capacity of glucose to enhance its own disposition, is an independent predictor of future diabetes. However, there are data on cross-sectional and longitudinal changes of S(G) and its components, basal insulin effect on S(G) (BIE) and S(G) at zero insulin (GEZI), but the natural course of S(G) has not been described in a large population. RESEARCH DESIGN AND METHODS: S(G) was measured at baseline in 1,265 participants (aged 40-69 years) and at the 5-year examination in 827 participants in the Insulin Resistance Atherosclerosis Study (IRAS) using the frequently sampled intravenous glucose tolerance test. None of these participants were treated with glucose-lowering agents. RESULTS: In cross-sectional analyses, S(G), BIE, and GEZI deteriorated with worsening of glucose tolerance (P < 0.001 for all three associations). In longitudinal analyses among subjects with normal glucose tolerance (NGT) at baseline, S(G), BIE, and GEZI declined in those who progressed to impaired glucose tolerance (IGT) or diabetes (P < 0.001 for all three measures). More modest longitudinal changes were demonstrated in individuals with IGT. The transition back to NGT (as opposed to no change) compared with the transition to diabetes was statistically significant for S(G) (P = 0.049) and BIE (P = 0.042) and was not a statistically significant trend for GEZI (P = 0.332). In individuals with diabetes, only BIE had a significant decline (P = 0.003). CONCLUSIONS: S(G), BIE, and GEZI decline in subjects whose glycemic status worsens. S(G) and GEZI deteriorate more in the initial stages of the disease process.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".