Associations between Polymorphisms in Genes Involved in Fatty Acid Metabolism and Dietary Fat Intakes
Bibliographic record
Abstract
BACKGROUND: Obesity prevalence is growing in our population. Twin studies have estimated the heritability of dietary intakes to about 30%. The objective of this study was to verify whether polymorphisms in genes involved in fatty acid metabolism are associated with dietary fat intakes. METHODS: Seven hundred participants were recruited. A validated food frequency questionnaire was used to assess dietary intakes. PCR-RFLP and TAQMAN methodology were used to genotype PPARα Leu162Val, PPARγ Pro12Ala, PPARδ -87T>C, PPARGC1α Gly482Ser, FASN Val1483Ile and SREBF1 c.*619C>G. Statistical analyses were executed with SAS statistical package. RESULTS: Carriers of the Ala12 allele of PPARγ Pro12Ala polymorphism had higher intakes of total fat (p = 0.04). For FASN Val1483Ile polymorphism, significant gene-sex interaction effects were found for total fat and saturated fat intakes (p = 0.02 and p = 0.002, respectively). No significant difference in fat intakes was observed for PPARα Leu162Val, PPARδ -87T>C, PPARGC1α Gly482Ser and SREBF1 c.*619C>G polymorphisms. CONCLUSIONS: Polymorphisms in PPARγ and FASN seem to be associated with dietary fat intakes. Genetic variants are important to take into account when studying dietary intakes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".