Diet-Induced Alteration of Microbiota and Development of Obesity, Nonalcoholic Fatty Liver Disease, and Diabetes: Study Protocol of a Prospective Study
Bibliographic record
Abstract
BACKGROUND: Development of obesity and obesity-related diseases, such as type 2 diabetes mellitus and nonalcoholic fatty liver disease (NAFLD), is associated with altered gut microbiota composition. The aim of this study is to investigate associations among dietary compounds, intestinal cell function, and gut microbiota composition. We hypothesize that dietary lipid intake is associated with Paneth cell and goblet cell properties that affect gut microbiota composition. OBJECTIVE: The primary objective of this study is to determine whether a difference in dietary intake is associated with a difference in intestinal mucin-2 expression and gut microbiota composition. METHODS: This is a single-center prospective study, including 1 obese group undergoing laparoscopic Roux-en-y gastric bypass and 2 lean control groups undergoing either laparoscopic cholecystectomy or upper gastrointestinal endoscopy (n=228). During laparoscopy, biopsies will be taken of visceral fat (omentum majus), liver, muscle tissue of the abdominal wall, and subcutaneous fat. In the obese group, a small segment of the jejunum will be collected for analysis, which will be compared with an endoscopically derived jejunal biopsy from the upper gastrointestinal endoscopy control group. Stool samples for microbiota profiling will be collected at baseline and 1 year after surgery. Primary outcomes are fecal microbiota composition and mucus characteristics. Secondary outcomes include Paneth cell phenotype, body weight, diet composition, glucose tolerance, resolution of comorbidities, and weight loss 1 year after surgery. RESULTS: This trial is currently open for recruitment. The anticipated completion date is December 2019. CONCLUSIONS: The Diet-Induced Alteration of Microbiota and Development of Obesity, NAFLD, and Diabetes study will improve insight into the pathophysiology of obesity and its associated metabolic disorders. Better understanding of weight loss failure and weight regain following bariatric surgery might also behold new therapeutic opportunities for obesity and obesity-related comorbidities. TRIAL REGISTRATION: Netherlands Trial Register NTR5660; https://www.trialregister.nl/trial/5540 (Archived by WebCite at http://www.webcitation.org/78l7jOZre). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11553.
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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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".