Whole grain intake and its cross-sectional association with obesity, insulin resistance, inflammation, diabetes and subclinical CVD: The MESA Study
Bibliographic record
Abstract
We examined the relationship between whole grain intake and obesity, insulin resistance, inflammation, diabetes and subclinical CVD using baseline data from the Multi-Ethnic Study of Atherosclerosis. Whole grain intake was measured by a 127-item FFQ in 5496 men and women free of CHD and previously known diabetes. Mean whole grain intake was 0.5 (sd 0.5) servings per d; biochemical measures reflect fasting levels. After adjustment for demographic and health behaviour variables, mean differences for the highest quintile of whole grain intake minus the lowest quintile of intake were 0.6 kg/m2 for BMI, 0.36 mg/l for C-reactive protein, 0.82 micromol/l for homocysteine, 0.15 mU/l*mmol/l for homeostasis model assessment (HOMA), 0.48 mU/l for serum insulin, 2.0 mg/dl for glucose and 5.7 % for prevalence of newly diagnosed impaired fasting glucose (glucose >or= 100 mg/dl or diabetes medication). These differences represent 11-13 % of a standard deviation of BMI, HOMA, glucose and impaired fasting glucose, but 23 %, 52 % and 80 % of a standard deviation of homocysteine, C-reactive protein and insulin, respectively. An inverse association between whole grains and urine albumin excretion was suggested but retained statistical significance after adjustment only in Chinese and Hispanic participants. No associations were observed between whole grain intake and two subclinical disease measures: carotid intima-media thickness and coronary artery calcification. Concordant with previous research, whole grain intake was inversely associated with obesity, insulin resistance, inflammation and elevated fasting glucose or newly diagnosed diabetes. Counter to hypothesis, however, whole grain intake was unrelated to subclinical CVD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".