Effect of Prebiotic Fiber‐Induced Changes in Gut Microbiota on Adiposity in Obese and Overweight Children
Bibliographic record
Abstract
Childhood obesity has dramatically increased. Unfortunately, excess weight tends to persist into adulthood therefore therapies which target childhood obesity are essential in halting the obesity epidemic. Our objective was to determine if prebiotic fiber intake reduces body fat, pro‐inflammatory cytokines and insulin levels in overweight and obese children and to examine if this is due to a shift in gut microbiota composition. Overweight and obese children (蠅85 th BMI percentile) aged 7‐12y (n=39) were randomized to consume 8g/day of prebiotic fiber (1:1 inulin:oligofructose) or equicaloric placebo for 16 weeks. Body fat (measured by dual‐energy X‐ray absorptiometry), pro‐inflammatory cytokines and insulin (quantified from fasted blood serum), and gut microbiota (quantified from stool) were measured at baseline and week 16. Statistical significance was determined using non‐parametric Mann‐Whitney U‐Test at p蠄0.05. The first cohort (n=13) have completed the study with the final n=26 to finish December 2014. There was a trend for prebiotic fiber to reduce trunk body fat compared to placebo (‐1.42% vs +0.37%; p=0.15). Waist circumference (‐2.22 vs +0.03 cm), interferon gamma (‐1.4 vs +2.1 pg/mL) and fasted insulin levels (‐53.6 vs +155.3 pg/mL) were reduced with prebiotic and increased with placebo but not significantly. Bifidobacteria abundance significantly increased with prebiotic (p=0.03). Prebiotic fiber is a potential non‐invasive treatment option to reduce body fat in obese and overweight children by gut microbiota modulation. Funded by BMO Financial Group/Alberta Children's Hospital Research Institute and Canadian Institutes of Health Research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".