The PPAR‐gamma P12A polymorphism modulates the relationship between dietary fat intake and components of the metabolic syndrome: results from the Québec Family Study
Bibliographic record
Abstract
The metabolic syndrome is a complex disorder characterized by an atherogenic dyslipidemia resulting from the interaction between genetic and nutritional factors. The objective of this study was to examine in a cohort of 720 adults participating in the Québec Family Study (QFS) whether dietary fat interacts with the P12A polymorphism in the gene encoding the peroxisome proliferator-activated receptor-gamma (PPAR-gamma), a nuclear factor that regulates lipid and glucose homeostasis. Carriers of the A12 allele had a higher body mass index (BMI), waist circumference, fat mass as well as subcutaneous adipose tissue and visceral adipose tissue (VAT) areas both assessed by computed tomography than P12/P12 homozygotes. Total fat and saturated fat intakes estimated from a 3-day food record were significantly correlated with several components of the metabolic syndrome in P12/P12 homozygotes. None of these expected associations were observed among carriers of the A12 allele. Furthermore, in a model including the PPAR-gamma P12A polymorphism, fat intake, age and gender, PPAR-gamma P12A and its interaction with fat intake were associated with BMI and waist circumference. Similar results were obtained when saturated fat intake replaced total fat intake into the model. When the two genotype groups were further classified into quartiles of total fat or saturated fat intake and their characteristics compared, an increase in fat intake was associated with an increase in waist circumference in P12/P12 homozygotes but not in A12 carriers. There was no difference in the waist circumference in carriers of the A12 allele whether the fat or the saturated fat intake was high or low. These results suggest that the PPAR-gamma P12A polymorphism can modulate the association between dietary fat intake and components of the metabolic syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".