Oligofructose decreases serum lipopolysaccharide and plasminogen activator inhibitor‐1 in adults with overweight/obesity
Bibliographic record
Abstract
Objective To determine the effect of prebiotic supplementation on metabolic endotoxemia and systemic inflammation in adults with overweight and obesity. Methods Samples from a previously conducted randomized, double‐blind, placebo‐controlled trial were used for analysis. Participants were randomized to 21 g of oligofructose (n = 20; BMI 30.4 kg/m2) or a maltodextrin placebo (n = 17; BMI 29.5 kg/m2) for 12 weeks. A total of 37 participants had samples available for the current analysis. Resistin, adiponectin, plasminogen activator inhibitor‐1 (PAI‐1), interleukin‐6 (IL‐6), tumor necrosis factor‐α (TNF‐α), and macrophage chemoattractant protein‐1 (MCP‐1) were quantified using MILLIPLEX® assays. Lipopolysaccharide (LPS) was measured using PyroGene™ Recombinant Factor C Assay. Results Plasma LPS concentrations were reduced by 40% in the oligofructose group over 12 weeks compared to a 48% increase in the placebo group (P = 0.04). PAI‐1, a risk factor for thrombosis, was reduced to a greater extent in the oligofructose group (−17.3 ± 2.6 ng/ml) compared to the placebo group (−9.7 ± 1.8 ng/ml; P = 0.03). Oligofructose did not affect IL‐6, TNF‐α, MCP‐1, adiponectin, or resistin. Conclusions Oligofructose reduces metabolic endotoxemia and PAI‐1. Incorporating prebiotics into the diet through supplements or functional foods may help mitigate some markers of obesity‐associated inflammation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".