From Clinical to Benchside: <scp><i>Lacticaseibacillus</i></scp> and <scp><i>Faecalibacterium</i></scp> Are Positively Associated With Muscle Health and Alleviate Age‐Related Muscle Disorder
Bibliographic record
Abstract
ABSTRACT Sarcopenia is an age‐related muscle disorder that increases risks of adverse clinical outcomes, but its treatments are still limited. Gut microbiota is potentially associated with sarcopenia, and its role is still unclear. To investigate the role of gut microbiota in sarcopenia, we first compared gut microbiota and metabolites composition in old participants with or without sarcopenia. Fecal microbiota transplantation (FMT) from human donors to antibiotic‐treated recipient mice was then performed. Specific probiotics and their mechanisms to treat aged mice were identified. Old people with sarcopenia had different microbial composition and metabolites, including Paraprevotella , Lachnospira , short‐chain fatty acids, and purine. After FMT, mice receiving microbes from people with sarcopenia displayed lower muscle mass and strength compared with those receiving microbes from non‐sarcopenic donors. Lacticaseibacillus rhamnosus (LR) and Faecalibacterium prausnitzii (FP) were positively related to muscle health of old people, and enhanced muscle mass and function of aged mice. Transcriptomics showed that genes related to tricarboxylic acid cycle (TCA) were enriched after treatments. Metabolic analysis showed increased substrates of TCA cycle in both LR and FP supernatants. Muscle mitochondria density, ATP content, NAD + /NADH, mitochondrial dynamics and biogenesis proteins, as well as colon tight junction proteins of aged mice were improved by both probiotics. LR and the combination of two probiotics also benefit intestinal immune health by reducing CD8 + IFNγ + T cells. Gut microbiota dysbiosis is a pathogenesis of sarcopenia, and muscle‐related probiotics could alleviate age‐related muscle disorders mainly through mitochondria improvement. Further clinical translation is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".