Cardiometabolic risk factors are influenced by <i><scp>S</scp>tearoyl‐<scp>C</scp>o<scp>A D</scp>esaturase</i> (<i><scp>SCD</scp></i>) −<i>1</i> gene polymorphisms and <i>n</i>‐3 polyunsaturated fatty acid supplementation
Bibliographic record
Abstract
SCOPE: To determine if single nucleotide polymorphisms (SNPs) in stearoyl-CoA desaturase (SCD)-1 gene that encodes a key enzyme for fatty acid metabolism are associated with the response of cardiometabolic risk factors to n-3 PUFA supplementation. METHODS AND RESULTS: Two hundred and ten subjects completed a 2-week run-in period followed by 6-week supplementation with 5 g of fish oil (1.9-2.2 g eicosapentaenoic acid and 1.1 g docosahexaenoic acid). Risk factors were measured pre and post n-3 supplementation. Fatty acid composition of plasma phospholipids was analyzed by GC and the desaturase indices SCD16 (16:1n-7/16:0) and SCD18 (18:1n-9/18:0) were calculated. Genotyping of eight SNPs of the SCD1 gene was performed. N-3 PUFA supplementation decreased plasma triglycerides, as well as SCD16 and SCD18 indices, but increased fasting plasma glucose concentrations. SNPs in SCD1-modified cardiometabolic risk factors pre and post n-3 PUFA supplementation: triglyceride (rs508384, p = 0.0086), IL6 (rs3071, p = 0.0485), C-reactive protein (rs3829160, p = 0.0489), and SCD18 indices (rs2234970, p = 0.0337). A significant interaction effect between the SNP and n-3 PUFA supplementation was also observed for fasting plasma glucose levels (rs508384, p = 0.0262). CONCLUSION: These results suggest that cardiometabolic risk factors are modulated by genetic variations in the SCD1 gene alone or in combination with n-3 PUFA supplementation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".