Genetic Variation in <i>PPARG</i> Encoding Peroxisome Proliferator-Activated Receptor γ Associated With Carotid Atherosclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Peroxisome proliferator-activated receptor gamma is a crucial molecule in atherogenesis because it is associated with metabolic risk factors such as obesity and diabetes and also plays a key role in subcellular metabolism of arterial wall macrophage foam cells. Genetic variation in PPARG has been associated with metabolic and cardiovascular end points. METHODS: We investigated the relationship between 2 common PPARG polymorphisms, namely P12A and c.1431C>T, and carotid atherosclerosis in a sample of 161 Canadian aboriginal people. Dependent variables were carotid intima media thickness (IMT), assessed using B-mode ultrasonography, and total carotid plaque volume (TPV), assessed using 3D ultrasound. RESULTS: Using multivariate analysis, we found that subjects with > or =1 PPARG A12 allele had less carotid IMT than others (0.72+/-0.03 versus 0.80+/-0.02 mm; P=0.0045), with no between-genotype difference in TPV. In contrast, subjects with the PPARG c.1431T allele had greater TPV than others (124+/-18.4 versus 65.1+/-23.7 mm3; P=0.0079), with no between-genotype difference in IMT. CONCLUSIONS: The findings show an association between PPARG genotypes and carotid arterial phenotypes, and further reflect the prevailing view that the PPARG A12 allele protects against deleterious phenotypes. Also, whereas IMT and TPV are somewhat correlated with each other, they might also represent distinct traits with discrete determinants representing different stages of atherogenesis.
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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".