Distinct Serum and Fecal Metabolite Profiles Linking With Gut Microbiome in Older Adults With Frailty
Bibliographic record
Abstract
Frailty is a critical aging-related syndrome but the underlying metabolic mechanism remains poorly understood. The aim of this study was to identify novel biomarkers and reveal potential mechanisms of frailty based on the integrated analysis of metabolome and gut microbiome. In this study, twenty subjects consisted of five middle-aged adults and fifteen older adults, of which fifteen older subjects were divided into three groups: non-frail, pre-frail, and frail, with five subjects in each group. The presence of frailty, pre-frailty, or non-frailty was established according to the physical frailty phenotype (PFP). We applied non-targeted metabolomics to serum and feces samples and used 16S rDNA gene sequencing to detect the fecal microbiome. The associations between metabolites and gut microbiota were analyzed by the Spearman’s correlation analysis. Serum metabolic shifts in frailty mainly included fatty acids and derivatives, carbohydrates, and monosaccharides. Most of the metabolites belonging to these classes increased in the serum of frail older adults. Propylparaben was found to gradually decrease in non-frail, pre-frail, and frail older adults. Distinct changes in fecal metabolite profiles and gut microbiota were also found among middle-aged adults, non-frail and frail older subjects. The relative abundance of Faecalibacteriu , Roseburia , and Fusicatenibacter decreased while the abundance of Parabacteroides and Bacteroides increased in frailty. The above altered microbes were associated with the changed serum metabolites in frailty, which included dodecanedioic acid, D-ribose, D-(-)-mannitol, creatine and indole, and their related fecal metabolites. The changed microbiome and related metabolites may be used as the biomarkers of frailty and is worthy of further mechanistic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".