Effects of unsaturated fatty acids (USFA) on human gut microbiome profile in a subset of canola oil multicenter intervention trial (COMIT)
Bibliographic record
Abstract
The effects of USFA treatments on gut microbiome profile were studied in 25 subjects (13 obese, 9 overweight, 3 normal) in a double‐blinded randomized crossover study consisting of five 30 d periods. Treatments included 60 g of canola oil (63% MUFA, 20% LA, 10% ALA), high oleic canola oil (72% MUFA, 15% LA, 2% ALA), high oleic canola/DHA oil (64% MUFA, 13% LA, 6% DHA), corn/safflower oil (18% MUFA, 69% LA) and flax/safflower oil (18% MUFA, 38% LA, 32% ALA). Stool samples were collected at the end of each period. DNA was extracted and amplified for pyrosequencing. Sequences were edited and taxonomically classified using mothur software and Silva database. Categorical data were analyzed using GLIMMIX of SAS and PLS‐DA of SIMCA. A total of 17 phyla and 195 genera were identified. The USFA treatments did not affect microbiome at the phylum level. However, obese participants had a higher proportion of Firmicutes to Bacteroidetes than overweight or normal groups ( P <0.05). At the genus level, three MUFA diets increased Clostridiales, Parabacteroides , Prevotella , and Turicibacter's population. Anaerostipes , Coprobacillus, and Faecalibacterium population were greater in high oleic canola oil compared to that in high oleic canola/DHA oil. Data suggest that obesity was associated with the decline in Bacteroidetes while USFA consumption only affected the microbiome at the genus level. Grant Funding Source : ARDI
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".