Carbohydrate nutrition, glycaemic load, and plasma lipids: the Insulin Resistance Atherosclerosis Study
Bibliographic record
Abstract
AIMS: We evaluated the relationship of carbohydrate nutrition and selected food groups with lipids using data from the Insulin Resistance Atherosclerosis Study (IRAS Exam I, 1992-1994). METHODS AND RESULTS: A total of 1026 middle-aged adults with normal or impaired glucose tolerance had complete data on fasting lipids and usual dietary intake from an interviewer-administered, validated food frequency questionnaire. Published glycaemic index (GI) values were assigned to food items and average dietary GI and glycaemic load (GL) were calculated per participant. Intake of carbohydrates differed by gender, men consuming more absolute digestible carbohydrates with higher GI and GL than women. In multivariate models adjusting for energy intake, in men, GL and carbohydrates were associated positively with total and LDL cholesterol, and inversely with HDL. In women, associations were limited to triglycerides. We estimated that a 100 g higher intake in GL or carbohydrates was associated with a 7-8 mg/dL higher total or LDL cholesterol level in men, and a 13-17 mg/dL higher triglyceride level in women. In the combined sample, GL and carbohydrates were consistently associated with all lipid levels and GI was inversely associated with HDL cholesterol. CONCLUSION: Our findings underscore the importance of carbohydrate nutrition for plasma lipids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".