Integrated analysis of gut microbiota, fecal and serum metabolites in type 2 diabetes mellitus with peripheral neuropathy
Bibliographic record
Abstract
PURPOSE: Diabetic peripheral neuropathy (DPN) is one of the most common complications of type 2 diabetes mellitus (T2DM). In recent years, it has been reported that the progression of DPN is associated with altered gut microbiota and serum metabolites. However, the alterations of the gut microbiota and interaction with metabolites are not well understood in DPN patients. Therefore, we compared the gut microbiota and fecal and serum metabolic profiles of DPN and comprehensively analyzed the potential mechanisms of DPN. METHODS: H-NMR)-based metabolomics technique. The profile of gut microbiota was determined by 16S rRNA sequencing. Liquid chromatography-mass spectrometry (LC-MS) was used to detect fecal metabolites. The changes of gut microbiota, fecal and serum metabolites were compared and correlation analysis was conducted among them and clinical indicators, such as visual analogue scale (VAS) score, Toronto Clinical Scoring System (TCSS) score and electromyography indicators. RESULTS: Bacteria at the genus level in 8 and the species level in 13, and 28 fecal metabolites, and 5 serum metabolites were significantly altered in DPN patients. In particular, genus Faecalibacterium, species Collinsella_aerofaciens, fecal glycocholic acid and serum formate were significantly reduced in DPN, while genus Megamonas, fecal maleamic acid and serum uric acid (UA) were enriched. These changes are related to bile acid metabolism, amino acid metabolism, and mitochondrial dysfunction, as well as significantly correlated with VAS score, TCSS score and electromyography indicators. CONCLUSION: This study discovered the abnormal gut microbiota in DPN patients, with alterations in fecal and serum metabolism. It is speculated that the gut microbiota may lead to metabolic imbalance, accelerating the DPN progression. Multi-omics analysis was used to identify the possible mechanism of the gut microbiota-metabolism-mitochondrial axis in the progression of DPN, which may provide a potential therapeutic target for the diagnosis and treatment of DPN.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".