Mediation of Regional Cerebral Blood Flow in the Relationship between Specific Gut Microbiota and Cognition in Vascular Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Gut microbiota could affect the onset and development of vascular cognitive impairment (VCI) through modulating metabolic and immune pathways. However, the vascular mechanisms involved remain unclear. OBJECTIVE: To investigate the gut microbiota associated with VCI and examine the mediating effects of regional cerebral blood flow (CBF) to explore potential therapeutic targets for VCI. METHODS: This prospective study enrolled patients with VCI (n = 16) and healthy controls (n = 18) from the Chinese Imaging, Biomarkers, and Lifestyle study between January 1 and June 30, 2022. The gut microbiota composition and diversity were determined by 16 S ribosomal RNA gene sequencing. The association between gut microbiota and Montreal Cognitive Assessment (MoCA) scores was determined using Spearman's correlation analysis. Regional CBF was calculated using pseudo-continuous arterial spin labeling. The mediating effects of regional CBF on the relationship between specific gut microbiota and cognition in VCI were investigated using mediation analysis. RESULTS: Compared to healthy controls, patients with VCI had significantly greater abundance of Bifidobacterium, Veillonella, R uminococcus gnavus , Fusobacterium, and Erysipelatoclostridium and smaller abundance of Collinsella. The abundance of Ruminococcus gnavus was negatively associated with MoCA scores in patients with VCI, with the CBF in the left hypothalamus, right hypothalamus, and left amygdala accounting for 63.96%, 48.22%, and 36.51%, respectively, of this association after adjusting for confounders. CONCLUSIONS: Ruminococcus gnavus is associated with cognition in VCI, which is strongly mediated by CBF in the bilateral hypothalamus and left amygdala. These findings highlight the potential regulatory roles of nutrition and metabolism-related areas of the brain in VCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".