Associations between Dietary Patterns and LDL Peak Particle Diameter: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: Dietary patterns are used to evaluate the effects of overall nutritional habits on health status, and low-density lipoprotein-peak particle diameter (LDL-PPD) has been recognized as an emerging risk factor for cardiovascular disease (CVD). The aim of this study is to verify whether an association exists between dietary patterns and LDL-PPD. METHODS: A total of 635 participants aged between 18 and 55 years were included in this cross-sectional study. Nutritional information was collected with a validated food frequency questionnaire. To establish dietary patterns, factor analysis was performed, which led to characterization of the Western and Prudent dietary patterns. Nondenaturing 2%-16% polyacrylamide gradient gel electrophoresis was used to characterize LDL-PPD. RESULTS: The Western pattern was characterized by high consumption of food such as refined grains, French fries, and red meats, and the Prudent pattern by nonhydrogenated fat, vegetables, eggs, and fish. For the Western profile, a negative correlation was found between score value and LDL-PPD before (r = -0.082, p = 0.039) and after adjustment for age (r = -0.080, p = 0.043). A negative correlation between scores for the Prudent profile and the LDL-PPD adjusted for age, sex, plasma triglycerides, and energy was observed (r = -0.12204, p = 0.0021). After division by tertiles and adjustment for the confounding effects of age, sex, plasma triglyceride levels, and energy, a significant difference (p = 0.0015) in LDL-PPD was noted between the highest tertile (255.21 ± 3.61 Å) and the lowest tertile (255.79 ± 3.68 Å) of the Prudent pattern. CONCLUSIONS: Dietary patterns, such as the Western and the Prudent, are associated with LDL-PPD. Dietary patterns can be used to assess the effects of nutritional habits on health status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".