Nutritional correlates of dietary glycaemic index: new aspects from a population perspective
Bibliographic record
Abstract
The role of dietary glycaemic index (GI) and glycaemic load (GL) in disease aetiology is of increasing interest. However, nutritional factors related to dietary GI and GL are not well understood from a population perspective. We aimed to investigate the relation ship between GI and GL and dietary intake at the food and nutrient level. Study subjects were 1071 non-diabetic adults from the Insulin Resistance Atherosclerosis Study, Exam I, 1992-4. Usual dietary intake was assessed with a 114-item modified Block food frequency questionnaire. Published GI values were assigned to food line items. Correlation and regression analyses were conducted. Intake of white bread, beer, meats and fries/fried potatoes was positively associated with average GI, as was fat, starch and alcohol intake (before and after energy adjustment). Intake of fruits and low-fat milk was inversely associated with GI, as were intakes of mono- and disaccharides, and fibre. GL was positively correlated with carbohydrate foods and inversely with non-carbohydrate foods. Gender-specific regression models identified eight food groups explaining 63% (men) and 55% (women) total GI variation after adjusting for demographics; 70% of variation in GL was explained by eleven (men) and nine (women) food groups, respectively. Although the GI of a food is an indicator of the ability of carbohydrates to raise blood glucose, dietary GI, unlike GL, appears to reflect more dimensions of diet than just carbohydrates, such as the combination of foods consumed. This may have implications for the interpretation of dietary GI in epidemiologic studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".