The effect of probiotics on select cognitive domains in mild cognitive impairment and Alzheimer's disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are progressive neurodegenerative disorders, and probiotics may offer therapeutic benefits by modulating gut microbiota and reducing inflammation. Objective: This study systematically evaluated the impact of probiotics on cognitive function in MCI and AD through a meta-analysis of randomized controlled trials (RCTs). Methods: A systematic review and meta-analysis were performed following PRISMA 2020 guidelines. PubMed, Embase, EBSCO, and Cochrane databases were searched for RCTs (January 2000-January 2024) on probiotic interventions lasting 8-24 weeks. Cognitive outcomes included Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), language, naming, visual-spatial, memory, and attention. Data were analyzed using R with a random-effects model to calculate pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs). Risk of bias was rigorously assessed. Results: Out of 2000 articles, 500 full texts were screened, and 10 studies were included. The meta-analysis showed varied effect sizes: MMSE (SMD: 0.28, 95%CI -0.35-0.91, p = 0.38), MoCA (SMD: 0.51, 95%CI -0.49-1.52, p = 0.33), language (SMD: -0.12, 95% CI -0.54-0.29, p = 0.56), naming (SMD: 0.02, 95%CI -0.69-0.74, p = 0.95), visual-spatial (SMD: 0.38, 95%CI -0.13-0.88, p = 0.14), memory (SMD: 0.20, 95%CI -0.15-0.55, p = 0.26), and attention (SMD: -0.07, 95%CI -0.44-0.30, p = 0.71). Positive SMDs suggest cognitive improvement, while non-significant negative SMDs indicate trends toward decline, inclined by probiotic strains, duration, and participant characteristics. Conclusions: Probiotics did not significantly improve cognitive function in MCI and AD patients, with variability in effects across cognitive domains, suggesting the need for tailored interventions and future studies.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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".