Impact of Fecal Microbiota Transplantation on Obesity and Metabolic Syndrome—A Systematic Review
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) is a gut microbial-modulation strategy that has been investigated for the treatment of a variety of human diseases, including obesity-associated metabolic disorders. This study appraises current literature and provides an overview of the effectiveness and limitations of FMT as a potential therapeutic strategy for obesity and metabolic syndrome (MS). Five electronic databases and two gray literature sources were searched up to 10 December 2018. All interventional and observational studies that contained information on the relevant population (adult patients with obesity and MS), intervention (receiving allogeneic FMT) and outcomes (metabolic parameters) were eligible. From 1096 unique citations, three randomized placebo-controlled studies (76 patients with obesity and MS, body mass index = 34.8 ± 4.1 kg/m2, fasting plasma glucose = 5.8 ± 0.7 mmol/L) were included for review. Studies reported mixed results with regards to improvement in metabolic parameters. Two studies reported improved peripheral insulin sensitivity (rate of glucose disappearance, RD) at 6 weeks in patients receiving donor FMT versus patients receiving the placebo control. In addition, one study observed lower HbA1c levels in FMT patients at 6 weeks. No differences in fasting plasma glucose, hepatic insulin sensitivity, body mass index (BMI), or cholesterol markers were observed between two groups across all included studies. While promising, the influence of FMT on long-term clinical endpoints needs to be further explored. Future studies are also required to better understand the mechanisms through which changes in gut microbial ecology and engraftment of microbiota affect metabolic outcomes for patients with obesity and MS. In addition, further research is needed to better define the optimal fecal microbial preparation, dosing, and method of delivery.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".