Role of oral microbiota in Alzheimer’s disease: A systematic review of clinical studies
Bibliographic record
Abstract
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world, causing dementia among the elderly. Oral microbiome may be associated with AD. This systematic review summarizes the current role of the oral microbiome in the etiology and diagnosis of AD. Articles included were sourced primarily from electronic databases including PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library from January 2011 to August 2022 and in OpenGrey and Google Scholar for grey literature. Relevant studies were selected using a two-stage approach involving the screening of titles and abstracts and full-text evaluation by two authors. Risk of bias was also performed using the Newcastle Ottawa scale (NOS) before qualitative synthesis. 18 studies out of 1079 citations were included in this review. The median NOS rating (IQR) of the reviewed studies was 8 (7.25 – 9). Most studies suggested that there was an association between oral microbiome and AD. Some claimed that oral microbiome might be the risk factor of AD using disparate approaches. Others also detected antibodies to oral microorganisms among AD patients and observed a significantly different alpha diversity among patients with AD than controls. Although limited by the number of studies, this review found that a change in the oral microbiome may be indicative of AD severity. Oral microbiome may be associated with Alzheimer’s disease. Some microbial species may be risk factors or aid diagnosis for AD, however more research is still needed to establish their role in AD etiology and noninvasive diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".