Examining the Role of Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists on the Human Gut Microbiome: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Introduction Extant literature has highlighted the role of the gut microbiome on various health conditions, including mood disorders and neurodegenerative diseases. Notwithstanding, the effects of glucagon-like peptide-1 (GLP-1) and GLP-1 receptor agonists (GLP-1RAs) on the gut microbiome have been inadequately investigated. Changes in the gut microbiome are characterized by changes in microbial abundance, microbial genomes, and microbial diversity. Herein, we conducted a comprehensive synthesis of the role of GLP-1 and GLP-1RAs on the gut microbiome. Methods Relevant articles were retrieved from OVID (MedLine, Embase, AMED, PsycInfo, JBI EBP Database), PubMed, and Web of Science from the database inception to August 19, 2024. Primary research evaluating the role of GLP-1 and GLP-1RAs on the gut microbiome were included for analysis. Results GLP-1 is associated with changes in microbial abundance, including, but not limited to Akkermansia, Sutterella, Bifidobacterium. GLP-1 was not associated with changes in microbial gene count. Additionally, dulaglutide was positively correlated to the relative abundance of Bacteroides. Similarly, liraglutide was associated with varied changes in microbial diversity, community richness. Additionally, liraglutide was associated with varied changes in microbial abundance, including, but not limited to Akkermansia, Sutterella, Bacteroides, Ruminococcus, and Actinomyces. Discussion Both GLP-1 and GLP-1RAs are associated with overlapping and discrete changes in the gut microbiome. Included studies predominantly involved persons with diabetes and were limited by sample sizes. Future research should be directed to examining how GLP-1 and GLP-1RA mediated changes in the gut microbiome may subserve potential therapeutic effects and health conditions in a more diverse population.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".