Genetic association between FADS and ELOVL polymorphisms and the circulating levels of EPA/DHA in humans: a scoping review
Bibliographic record
Abstract
BACKGROUND: Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) are two omega-3 fatty acids that can be synthesized out of their precursor alpha-linolenic acid (ALA). FADS and ELOVL genes encode the desaturase and elongase enzymes required for EPA and DHA synthesis from ALA; however, single nucleotide polymorphisms (SNPs) in FADS and ELOVL genes could modify the levels of EPA and DHA synthesized from ALA although there is no consensus in this area. This review aims to investigate EPA and DHA circulating levels in human blood and their association with FADS or ELOVL. METHODS: PubMed, Cochrane, and Scopus databases were used to identify research articles. They were subsequently reviewed by two independent investigators. RESULTS: Initially, 353 papers were identified. After removing duplicates and articles not meeting inclusion criteria, 98 full text papers were screened. Finally, this review included 40 studies investigating FADS and/or ELOVL polymorphisms. A total of 47 different SNPs in FADS genes were reported. FADS1 rs174537, rs174547, rs174556 and rs174561 were the most studied SNPs, with minor allele carriers having lower levels of EPA and DHA. SNPs in the FADS genes were in high linkage disequilibrium. SNPs in FADS were correlated with levels of EPA and DHA. No conclusion could be drawn with the ELOVL polymorphisms since the number of studies was too low. CONCLUSION: Specific SNPs in FADS gene, such as rs174537, have strong associations with circulating levels of EPA and DHA. Continued investigation regarding the impact of genetic variants related to EPA and DHA synthesis is warranted.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".