Alterations in gut microbiota and plasma metabolites: a multi-omics study of mild cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
Introduction Emerging evidence suggests that gut microbiota and plasma metabolites may be associated with the onset and progression of Parkinson’s disease (PD). The interplay between gut microbiota and plasma metabolites in influencing the progression of cognitive impairment in PD is yet to be fully understood and requires further exploration. Our objective was to investigate the roles of gut microbiota and plasma metabolites in PD cognitive impairment. Methods We initially recruited 100 individuals with PD and 50 healthy controls (HCs). After excluding participants based on education level and cognitive screening criteria, the final cohort comprised 38 PD patients and 40 HCs. We examined fecal and plasma specimens from these participants. Cognitive function was assessed via the Montreal Cognitive Assessment (MoCA). Gut microbiota was analyzed through 16S rRNA sequencing, and plasma metabolites were evaluated via Liquid Chromatography–Mass Spectrometry (LC-MS). Using Spearman correlation to analyze the association between gut microbiota and plasma metabolites. Results PD patients with mild cognitive impairment (PD-MCI) exhibited distinct microbial and metabolic profiles compared to PD patients with normal cognition (PD-NC). Consistent with both the Gut Microbiota Health Index (GMHI) and Gut Microbiota Health Index (MDI), PD-MCI patients exhibited significant gut microbial dysbiosis. Multi-algorithm differential abundance analysis identified g__Eggerthella as a core depleted genus in PD-MCI, consistently validated across both LEfSe and MaAsLin2 analyses. Additional microbial alterations included depletion of Short-Chain Fatty Acids (SCFA)-producing genera ( g__Blautia , g__Lachnoclostridium , g__Erysipelatoclostridium , g__norank_f__norank_o__Oscillospirales, g__Megasphaera, and g__Lactococcus ) and enrichment of g__Senegalimassilia in PD-MCI. Metabolite analysis revealed that phenylalanine metabolism (including phenylacetylglutamine, 2-hydroxycinnamic acid, N-acetyl-L-phenylalanine, and phenylacetylglycine) and PPAR signaling pathways (including 8-hydroxy-5Z,9E,11Z,14Z-eicosatetraenoic acid) were downregulated in the PD-MCI group, while choline metabolism in cancer (including PC(18:1(11Z)/18:3(6Z,9Z,12Z)) and LysoPC(18:3(6Z,9Z,12Z)/0:0)) was upregulated. Notably, phenylacetylglutamine demonstrated robust diagnostic potential (AUC = 0.8222), emerging as a promising biomarker for PD-MCI. Correlation analysis revealed significant associations between key microbial taxa (particularly g__Eggerthella and SCFA-producing genera) and metabolites (phenylacetylglutamine, and uridine 2′,3′-cyclic phosphate), suggesting their interactive role in PD cognitive impairment through gut-brain axis mechanisms. Conclusion Our multi-omics study revealed distinct gut microbiota and metabolite alterations in PD patients with cognitive impairment, highlighting gut-brain axis dysfunction. Key microbial and metabolic markers demonstrated diagnostic potential, providing new insights into the pathophysiology of PD-related cognitive decline and potential targets for future therapeutic strategies.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".