Characterization of the gut microbiome in Alzheimer disease and mild cognitive impairment among older adults in Uganda: A case–control study
Bibliographic record
Abstract
Alzheimer disease (AD) is associated with significant shifts in the gut microbiome and is characterized by reduced microbial diversity and changes in the abundance of specific taxa. These alterations can disrupt the gut-brain axis, leading to increased intestinal permeability ("leaky gut"), systemic inflammation, and oxidative stress. Such microbial changes are thought to contribute to neurodegenerative changes, as observed in AD and cognitive decline, thus emphasizing the role of the microbiome in aging-related neurological health. Our study in urban and rural population in Uganda recruited 104 participants aged 60 years and older, categorized into AD, mild cognitive impairment (MCI), and control groups based on Montreal Cognitive Assessment (MoCA) scores and ICD-11/DSM-V criteria. DNA was extracted from fecal samples using a QIAamp kit and polymerase chain reaction (PCR) products were sequenced using Nanopore. We used diversity indices, principal coordinate analysis (PCoA), permutational multivariate analysis of variance (PERMANOVA), and linear discriminant analysis effect size (LefSe) to identify significant microbial differences among groups. Gut microbiome diversity, as measured by the Chao1 and Shannon indices, was significantly reduced in patients with AD. The AD group had the lowest diversity compared to that of the control group (P < .05). PCoA showed distinct microbial shifts between patients with AD and controls, with MCI showing an intermediate profile. Genera such as Novosphingobium and Staphylococcus were more prevalent in the controls, whereas Hafnia-Obesumbacterium and Dickeya were more common in AD. Age-related changes included increases in Exiguobacterium and Carnobacterium and decreases in Acinetobacter and Klebsiella. Distinct microbial profiles were identified in the AD, MCI, and control groups, suggesting potential microbiome markers of cognitive impairment in the Ugandan population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".