Effects of Peroxisome Proliferator-Activated Receptors, Dietary Fat Intakes and Gene–Diet Interactions on Peak Particle Diameters of Low-Density Lipoproteins
Bibliographic record
Abstract
UNLABELLED: The risk of cardiovascular diseases (CVDs) is modulated by gene-diet interactions. The objective of this study was to examine whether gene-diet interactions affect peak particle diameters (PPD) of low-density lipoprotein (LDL). METHODS: The study included 674 participants. A food frequency questionnaire was administered to obtain dietary information. LDL-PPD was determined by non-denaturing 2-16% polyacrylamide gradient gel electrophoresis. Peroxisome proliferator-activated receptor (PPAR) gene polymorphisms PPARα L162V (rs1800206), PPARγ P12A (rs1801282) and PPARδ -87T→C (rs2016520) were determined by PCR-RFLP. RESULTS: Among carriers of thePPARα L162V polymorphism, gene-diet interaction effects on LDL-PPD were observed with saturated fat (p=0.0005) and total dietary fat (p=0.006). Among PPARα V162 carriers, subjects with higher saturated fat intakes had smaller LDL-PPD than those with lower intakes (254.23±2.74 vs. 256.21±2.61 Å, respectively, p=0.007). Among subjects homozygous for the PPARα L162 allele, those with higher saturated fat intakes had larger LDL-PPD than those with lower saturated fat intakes (255.86±2.66 vs. 255.05±2.65 Å, respectively, p=0.01). Gene-diet interactions were also found for PPARγ P12A polymorphism with saturated fat intake (p=0.04) and for PPARδ -87T→C with the polyunsaturated/saturated fat ratio (p=0.0013). CONCLUSIONS: These results stress that dietary factors should be included in studies determining the effect of different polymorphisms on CVD risk factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".