Anti-Dyslipidemic, Anti-Hypertensive, and Glycemic Control Effects of Akkermansia Muciniphila: Lesser-Known Benefits in Metabolic Syndrome – A Narrative Review
Bibliographic record
Abstract
According to data from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States between 2011 and 2018, the prevalence of Metabolic Syndrome (MetS) increased from 37.6% to 41.8%. Similarly, a Japanese study carried out between 2008 and 2015 reported a crude death rate of 1.6% for those with MetS. One new approach to managing MetS is lifestyle modification, in which probiotics play crucial roles. Beneficial microorganisms that are essential in maintaining human health are called Probiotics. As various animal and human studies indicate, a novel, promising probiotic species recently discovered is Akkermansia muciniphila ( A. muciniphila ). Several studies have highlighted the positive role of A. muciniphila in modulating the MetS. However, few reports have comprehensively discussed the effects of A. muciniphila on MetS-related dyslipidemia, hypertension, glycemic control-related insulin conditions, and chronic low-grade inflammation. Therefore, in this review, we aimed to discuss the up-to-date findings, aspects, and controversy about the role of A. muciniphila in the pathophysiology of MetS and its possible impact on MetS onset modulation, which could be beneficial in pinpointing the mechanisms of short-chain fatty acids-related glucagon-like peptide-1/peptide YY, dysbiosis process, thermogenesis, Toll-like receptors 5, hepatic lipids, angiotensin-converting enzyme inhibitory bioactive peptides, trimethylamine-N-oxide/hydrogen sulfide functions, and others for the management of MetS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".