Polymorphisms in the stearoyl-CoA desaturase gene modify blood glucose response to dietary oils varying in MUFA content in adults with obesity
Bibliographic record
Abstract
Diets varying in SFA and MUFA content can impact glycaemic control; however, whether underlying differences in genetic make-up can influence blood glucose responses to these dietary fatty acids is unknown. We examined the impact of dietary oils varying in SFA/MUFA content on changes in blood glucose levels (primary outcome) and whether these changes were modified by variants in the stearoyl-CoA desaturase (SCD) gene (secondary outcome). Obese men and women participating in the randomised, crossover, isoenergetic, controlled-feeding Canola Oil Multicenter Intervention Trial II consumed three dietary oils for 6 weeks, with washout periods of ˜6 weeks between each treatment. Diets studied included a high SFA/low MUFA Control oil (36·6 % SFA/28·2 % MUFA), a conventional canola oil (6·2 % SFA/63·1 % MUFA) and a high-oleic acid canola oil (5·8 % SFA/74·7 % MUFA). No differences in fasting blood glucose were observed following the consumption of the dietary oils. However, when stratified by SCD genotypes, significant SNP-by-treatment interactions on blood glucose response were found with additive models for rs1502593 (P = 0·01), rs3071 (P = 0·02) and rs522951 (P = 0·03). The interaction for rs3071 remained significant (P = 0·005) when analysed with a recessive model, where individuals carrying the CC genotype showed an increase (0·14 (sem 0·09) mmol/l) in blood glucose levels with the Control oil diet, but reductions in blood glucose with both MUFA oil diets. Individuals carrying the AA and AC genotypes experienced reductions in blood glucose in response to all three oils. These findings identify a potential new target for personalised nutrition approaches aimed at improving glycaemic control.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".