Bibliographic record
Abstract
Gut microbes and their metabolic products (often referred to as postbiotics) have become the zeitgeist of health researchers and the public alike. Perhaps this is due to the advent of modern sequencing technologies that now allow non-microbiologists (including the general population) to map out the members of the microbiome in short order and at a low cost. Despite our fascination with how changes in microbes (often at the phyla level) correlate with diseases from Alzheimer to type 2 diabetes mellitus, we are still lagging in understanding the mechanistic nature of how these tiny organisms are driving health outcomes. In the recent mini review from Drs William Massey and Mark Brown (1), a summary of gut microbial postbiotic mechanisms is presented. Gut hormones. The first thing that comes to mind might be enteroendocrine cells that line the gut epithelium and secrete their peptide hormone products like glucagon-like peptide-1 or ghrelin. Instead, the authors in this mini review frame the microbes as the hormone-producing cells. And, indeed, gut microbes secrete a variety of postbiotics, which can enter circulation and find their target receptors ultimately influencing metabolism. The authors explore the production, signaling, and physiological effects of short chain fatty acids (SCFA), secondary bile acids, amino acid metabolites, and a variety of amines. While it is important to recognize this mini review cannot provide an exhaustive list, the authors seek to provide the key elements needed to build confidence in postbiotics as effectors of metabolism. These include the concentrations found in circulation, the known cellular receptors, and the physiological effects. For some bacterial products like SCFAs, mechanistic details are well established for both the production and for how increased levels of distinct SCFAs can reverse metabolic complications. Indeed, SCFAs produced in the lumen of the gut can stimulate neighboring glucagon-like peptide-1 (GLP-1)-secreting cells through free fatty acid receptor signaling (2). And, of course, GLP-1 action is major piece of the diabetes treatment puzzle (3). The finding that gut endocrine cells (L cells for GLP-1) act as the intermediary between microbial postbiotics and physiological effects is not surprising. Not only do they share the same physical environment, enteroendocrine hormones primarily regulate aspects related to metabolism (glucose regulation, appetite, gut proliferation). Nevertheless, this mini review highlights many scenarios where the postbiotic leaves the gut epithelium, travels in circulation, and signals in its target organ. A key element captured in this review is that not all microbial postbiotics are beneficial in metabolic regulation. This is the case for microbial trimethylamine. As the authors report, this postbiotic travels to the liver where it is oxidized to trimethylamine-N-oxide (TMAO), and elevated TMAO has been associated with cardiovascular complications, type 2 diabetes mellitus, and, in a recent meta-analysis, obesity (4). While mechanisms for these interactions are sparse, a recent study indicated direct interaction of TMAO with kinases in the unfolded protein response (5). Despite the expanding knowledge of how the gut microbiome can influence metabolic health and disease and the small probiotic trials (and now meta-analysis of these trials) demonstrating benefits in glucose regulation (6), treatments are not widely embraced. This may be due to issues with how metagenomic studies are conducted and conflicting effects of microbes and their metabolites in the literature (7). Another treatment barrier is that changing the gut microbiome (through probiotics or prebiotics) would rely heavily on a dietary intervention, and long-term success with dietary-based treatments of metabolic diseases is poor (8). Given this, the field of postbiotics and their direct delivery may hold the most promise in treatment of metabolic diseases. Disclosures: JG has nothing to disclose. Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.044 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".