Excess Dietary Fructose Does Not Alter Gut Microbiota or Permeability in Humans: A Randomized Controlled Pilot Study
Bibliographic record
Abstract
ABSTRACT Background and Aims Non-alcoholic fatty liver disease (NAFLD) is an increasing cause of chronic liver disease that accompanies obesity and the metabolic syndrome. Excess fructose consumption can initiate or exacerbate NAFLD due in part to a consequence of impaired hepatic fructose metabolism. Pre-clinical data have emphasized that fructose-induced altered gut microbiome, increased gut permeability, and endotoxemia play an important role in NAFLD, but human studies are sparse. The present study aimed to explore the relevance of these pre-clinical studies to observations in humans. Methods We performed a classical double-blind metabolic unit study in 10 obese subjects (BMI 30-40 mg/kg/m 2 ) providing 75gms. of either fructose or glucose in their individual diets substituted isocalorically for complex carbohydrates in a cross-over study. Excess fructose intake was provided in the fructose arm of the study and totaled a mean of 22.7% of calories. Results Routine blood, uric acid, liver function and lipid measurements were unaffected by the fructose intervention. The fecal microbiome (including Akkermansia muciniphilia ), fecal metabolites, gut permeability, indices of endotoxemia, gut damage or inflammation and plasma metabolites were essentially unchanged by either intervention. Conclusions Although pre-clinical rodent studies have shown that excess fructose causes pronounced changes in the gut microbiome, metabolome, and permeability as well as endotoxemia, this did not occur in obese individuals fed fructose in amounts known to enhance NAFLD. Therapeutic efforts to improve NAFLD through changes in the gut microbiome and gut homeostasis may not be beneficial.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".