The Triple Alliance: Microbiome, Mitochondria, and Metabolites in the Context of Age-Related Cognitive Decline and Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive, age-related neurodegenerative disorder that affects a large proportion of the older population. It currently lacks effective treatments, placing a heavy burden on patients, families, health care systems, and society. This is mainly due to our limited comprehension of the pathophysiology of AD progression, as well as the lack of effective drug targets and intervention timing to address the underlying pathology. AD is a multifactorial condition, and emerging evidence suggests that abnormalities in the gut microbiota play a significant role as environmental and multifaceted contributors to AD, although the exact mechanisms are yet to be fully explored. Changes in the composition of microbiota influence host neuronal health through their metabolites. These metabolites regulate intestinal epithelia, blood-brain barrier permeability, and neuroinflammation by affecting mitochondrial function. The decline in the proportion of beneficial microbes and their essential metabolites during aging and AD is directly linked to poor mitochondrial function, although the specific mechanisms remain unclear. In this review, we discuss recent developments in understanding the impact of the microbiome and its metabolites on various cell types, their influence on the integrity of the gut and blood-brain barriers, systemic and brain inflammation, and cell-specific effects in AD pathology. This information is expected to pave the way for a new understanding of the interactions between microbiota and mitochondria in AD, providing a foundation for the development of novel treatments for AD.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".