Associations between Polymorphisms in Genes Involved in Fatty Acid Metabolism and Dietary Fat Intakes
Bibliographic record
Abstract
<i>Background:</i> 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. <i>Methods:</i> 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 <i>PPAR</i>α Leu162Val, <i>PPAR</i>γ Pro12Ala, <i>PPAR</i>δ –87T>C, <i>PPARGC1</i>α Gly482Ser, <i>FASN </i>Val1483Ile and <i>SREBF1</i> c.*619C>G. Statistical analyses were executed with SAS statistical package. <i>Results:</i> Carriers of the Ala12 allele of <i>PPAR</i>γ Pro12Ala polymorphism had higher intakes of total fat (p = 0.04). For <i>FASN </i>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 <i>PPAR</i>α Leu162Val, <i>PPAR</i>δ –87T>C,<i> PPARGC1</i>α Gly482Ser and <i>SREBF1</i> c.*619C>G polymorphisms. <i>Conclusions:</i> Polymorphisms in <i>PPAR</i>γ and <i>FASN</i> seem to be associated with dietary fat intakes. Genetic variants are important to take into account when studying dietary intakes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".