Dietary Glycemic Index and Glycemic Load, Carbohydrate and Fiber Intake, and Measures of Insulin Sensitivity, Secretion, and Adiposity in the Insulin Resistance Atherosclerosis Study
Bibliographic record
Abstract
OBJECTIVE: We studied the association of digestible carbohydrates, fiber intake, glycemic index, and glycemic load with insulin sensitivity (S(I)), fasting insulin, acute insulin response (AIR), disposition index, BMI, and waist circumference. RESEARCH DESIGN AND METHODS: Data on 979 adults with normal (67%) and impaired (33%) glucose tolerance from the Insulin Resistance Atherosclerosis Study (1992-1994) were analyzed. Usual dietary intake was assessed via a 114-item interviewer-administered food frequency questionnaire from which nutrient intakes were estimated. Published glycemic index values were assigned to food items and average dietary glycemic index and glycemic load calculated per subject. S(I) and AIR were determined by frequently sampled intravenous glucose tolerance test. Disposition index was calculated by multiplying S(I) with AIR. Multiple linear regression modeling was employed. RESULTS: No association was observed between glycemic index and S(I), fasting insulin, AIR, disposition index, BMI, or waist circumference after adjustment for demographic characteristics or family history of diabetes, energy expenditure, and smoking. Associations observed for digestible carbohydrates and glycemic load, respectively, with S(I), insulin secretion, and adiposity (adjusted for demographics and main confounders) were entirely explained by energy intake. In contrast, fiber was associated positively with S(I) and disposition index and inversely with fasting insulin, BMI, and waist circumference but not with AIR. CONCLUSION: Carbohydrates as reflected in glycemic index and glycemic load may not be related to measures of insulin sensitivity, insulin secretion, and adiposity. Fiber intake may not only have beneficial effects on insulin sensitivity and adiposity, but also on pancreatic functionality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".