Healthy Eating Index is associated with certain markers of inflammation and insulin resistance but not with lipid profile in individuals at cardiometabolic risk
Bibliographic record
Abstract
Eating habits may influence inflammatory status and insulin resistance, both involved in the genesis of cardiometabolic diseases; an index of overall diet quality may be useful to identify risk for these diseases. We investigated whether the Healthy Eating Index (HEI-2005), adapted to Brazilian habits (B-HEI), was associated with markers of inflammation, insulin resistance and lipid profile in individuals at cardiometabolic risk. Two hundred and four prediabetic individuals (64.7% women) were enrolled in this cross-sectional study. Anthropometric measurements, 24-h dietary recalls used to calculate the B-HEI, and blood samples were collected. ANOVA was used for comparisons of clinical variables across the B-HEI tertiles and multiple linear regressions employed to test associations between clinical variables and B-HEI total score. Significant trends to decrease mean values of body mass index (BMI) (p = 0.03) and C-reactive protein concentrations (p = 0.02) across the tertiles of B-HEI, but not other biomarkers, were observed. Waist circumference, HOMA-IR and C-reactive protein were inversely associated with the B-HEI (p < 0.05), after adjusting for age, sex, BMI, and physical activity level. Also, a direct association of adiponectin concentrations with B-HEI was detected after adjustments (p = 0.001). Data from this study indicate that the B-HEI may be useful to identify the body adiposity-induced pro-inflammatory status and insulin resistance in individuals at cardiometabolic risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".