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 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) | 0.000 | 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) | 0.000 | 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".