Single Nucleotide Polymorphisms in FADS2, CETP and LPL and the Associations with Blood Lipids and Fatty Acids Differ in a Sex‐Specific Manner
Bibliographic record
Abstract
Objective Blood lipid and fatty acid profiles are strongly influenced by diet and genes. The current investigation examined if common single nucleotide polymorphisms (SNPs) in genes linked to fatty acid metabolism are associated with blood lipids and fatty acids in a sex‐specific manner. Methods Fasted serum samples were used to analyze blood lipids and red blood cells (RBCs) were used to measure fatty acid profiles in 94 adults (19–30 yrs). A panel of 23 SNPs in 17 genes linked to fatty acid metabolism were examined by linear regression for genotype‐sex interactions. Results HDL‐c levels were higher in women than men, while the total cholesterol/HDL‐c ratio showed the opposite relationship (p<0.01). Numerous sex‐specific differences in RBC fatty acids, as well as various desaturase/elongase activity estimates, were also observed (p<0.01). The following SNPs showed statistically significant genotype‐sex interactions: 1) rs328 in LPL with HDL‐c levels (p interaction =0.0478), 2) rs1800775 in CETP with the total cholesterol/HDL‐c ratio (p interaction =0.0329), and 3) rs174575 in FADS2 with estimated desaturase activity (p interaction =0.048). Further, rs174537 in FADS1 and rs3211956 in CD36 were significantly associated with RBC arachidonic acid and docosahexaenoic acid levels, respectively, but not in a sex‐specific manner. Conclusion Our findings suggest that studying genetic variants may help elucidate sex‐specific risks in cardiometabolic complications. Consequently, this information can be used to guide the development of sex‐specific dietary recommendations to prevent and/or mitigate cardiometabolic risk. Support or Funding Information Supported by OMAFRA.
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 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 | 0.000 | 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) | 0.002 | 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".