Dietary Fiber Supplementation Normalizes Serum Metabolites of Adults with Overweight/Obesity in a 12‐Week Randomized Control Trial
Bibliographic record
Abstract
Obesity predisposes to the development of several metabolic insults such as glucose intolerance, dyslipidemia and hypertension. It is also associated with alterations in gut microbiota and their metabolites in humans. A growing body of evidence demonstrates the importance of dietary fiber in preventing and managing several metabolic diseases. The current study aimed to provide mechanistic insight into the potential beneficial effects of yellow pea fiber in treating obesity‐related metabolic dysfunctions. A total of 53 overweight and obese adults were randomly assigned to a pea fiber (PF, n=29) or control (CO, n=24) group for 12 weeks. The PF group received wafers containing 5g/serving of yellow pea fiber thrice a day, while the CO group received an isocaloric amount of control wafer with no fiber. Serum and fecal samples were collected at baseline and at the end of the study. We employed 1 H‐NMR spectroscopy and LC‐MS to analyse serum metabolites; and GC‐MS and HPLC‐DAD to analyse fecal short chain fatty acids (SCFAs) and bile acids (BAs) respectively. Serum metabolomics analysis revealed impairments in acetylcarnitine, hexose, and certain glycerophsopholipids, SCFAs and amino acids concentrations in CO group which were not observed in the PF group compared to their baseline values. On the other hand, there was no significant change in fecal SCFAs and BAs in CO group. However, we found a significant increase in fecal acetate ( P =0.039) and significant decreases in fecal isovalerate ( P =0.015), cholic acid ( P =0.011), deoxycholic acid ( P =0.014) and total BAs ( P =0.042) content in the PF group compared to baseline. A pathway analysis indicated potential defects in the synthesis and degradation of ketone bodies, aminoacyl‐tRNA biosynthesis, glycerophospholipid metabolism, butanoate metabolism and glycine, serine and threonine metabolism in CO group which could be prevented by the addition of yellow pea fiber in the diet. Thus, the current study provided crucial mechanistic insight for the role of yellow pea fiber in the management of obesity and related diseases Support or Funding Information Alberta Innovates‐Health Solutions, Alberta Innovates‐Bio Solutions, Alberta Pulse Growers Commission; Eyes High Postdoctoral Fellowship
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".