Gut microbiota and intestinal barrier function in subjects with cognitive impairments: a cross-sectional study
Bibliographic record
Abstract
Background Gut-brain axis might play an important role in cognitive impairments by various diseases including Alzheimer’s disease (AD). Objective To investigate the differences in gut microbial composition, intestinal barrier function, and systemic inflammation in patients with AD or mild cognitive impairment (MCI), and normal control (NC) cases. Methods A total of 118 subjects (45 AD, 38 MCI, and 35 NC) were recruited. Cognitive function was assessed using Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment Scale (MoCA). Functional ability was assessed using Activity of Daily Living Scale (ADL). The composition of gut microbiome was examined by 16S rRNA high-throughput sequencing. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict functional transfer of gut microbiota. Gut barrier dysfunction was evaluated by measuring the levels of diamine oxidase (DAO), D-lactic acid (DA), and endotoxin (ET). The serum high-sensitivity C-reactive protein (hs-CRP) level was used to indicate systemic inflammation. Results Compared with normal controls, patients with cognitive impairments (AD and MCI) had lower abundance of Dorea and higher levels of DAO, DA, and ET. Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that the pathways related to glycan biosynthesis and metabolism increased in MCI patients, while the ones related to membrane transport decreased. The abundance of Bacteroides and Faecalibacterium was negatively correlated with the content of ET, and positively correlated with the scores of MMSE and MoCA. The hs-CRP levels were similar among the three groups. A significant negative correlation was observed between the severity of gut barrier dysfunction and cognitive function. Conclusion Cognitive impairments might be associated with gut microbial dysbiosis and intestinal barrier dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".