The Relationship between Healthy Eating Index and Lipid Profile in Healthy Individuals: A Systematic Review
Bibliographic record
Abstract
Abstract Background: The Healthy Eating Index (HEI) and Alternate Healthy Eating Index (AHEI) are instruments developed by competing American research teams, aiming to assess the level of adherence to a dietary pattern, claimed to prevent chronic illness conditions such as dyslipidemia. This systematic review evaluated cross-sectional studies examining the association between HEI/AHEI score and the lipid profile in healthy participants. Methods: The systematic review was Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant, and a search process was conducted through Scopus, Web of Knowledge, Google Scholar, Cochrane, PubMed, and ScienceDirect up to November 2022. Studies assessing the relationship between HEI/AHEI and lipid profile (low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG)) were eligible for inclusion. The statistical differences in outcomes, anthropometric indices, and demographic data were extracted from the selected studies. Also, the quality assessment of studies was performed using the Newcastle–Ottawa scale. Results: The systematic search presented 17 cross-sectional studies. Most of the studies revealed a significant correlation between HEI score and lipid profile (LDL-C, HDL-C, TG, and TC) ( P < 0.05), while a few of them indicated a significant relationship between AHEI score and these factors. Overall, the elevation of HEI/AHEI score was associated with the improvement in lipid profile ( P < 0.05), though this association was more obvious for HEI compared with AHEI. Conclusions: Overall, the results of the study indicated that an improved lipid profile in healthy individuals is associated with a higher score in either HEI or AHEI. Further research in the future is required to confirm the claim.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".