Alteration of Gut Microbiota in Alzheimer’s Disease and Their Relation to the Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Dysbiosis of gut microbiota has been reported to be enrolled in the pathogenesis of Alzheimer's disease (AD). However, there is a lack of relevant studies on this topic in Egyptian patients with AD. OBJECTIVE: To investigate different species of gut microbiota in Egyptian patients with AD and correlate microbiota bacterial abundance with clinical data. METHODS: The study included 25 patients with AD and 25 healthy volunteers as age and sex-matched controls. Clinical data was taken for each patient, including medical history and examination; Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were assessed for each participant. Bacterial DNA was extracted from stool, and abundance quantified via qPCR using 16S rRNA group-specific primers. RESULTS: Akkermansia, Enterobacteria, Bacteroidetes, Bacillus cereus, Prevotella, and Clostridium cluster IV were more abundant in the AD group than in the control group, although there was significantly less abundance of Bifidobacterium spp., Firmicutes, and Actinobacteria in patients with AD than in controls, whereas no such significance was found for lactic acid bacteria between both groups. Lactic acid bacteria and Prevotella abundance was negatively correlated with cognitive impairment (p = 0.03 with MMSE, and p = 0.03 with MoCA). Prevotella abundance was positively correlated with age of onset and duration of illness and negatively correlated with smoking and coronary heart disease (p = 0.007, p = 0.03, p = 0.035, and p = 0.047, respectively). CONCLUSION: The current work highlighted a significant relationship between AD and gut microbiota dysbiosis. A higher abundance of Prevotella species and lactic acid bacteria was correlated with cognition.
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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) | 0.000 | 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 | 0.000 |
| 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".