The role of probiotics in the improvement of cognitive performance of older adults: a meta-analysis
Bibliographic record
Abstract
With our increasing lifespan comes an increasing prevalence of age-related neurological diseases, which are often difficult to treat. The gut-brain axis may provide opportunities for cognitive health improvement through gut microbiota-targeting interventions, such as probiotics. The aim of this meta-analysis is to determine the clinical potential of probiotics for the amelioration of cognitive functioning in older adults. Systematic searches were executed in PubMed, Scopus, and Web of Science to retrieve published records of randomised controlled trials (RCTs). Records were assessed to fit the criteria of focusing on probiotic supplementation with cognitive functioning as the main outcome. After screening and assessment of 56 identified records, 20 RCTs were included for analysis. Reported means and standard deviations of cognitive test scores were used to calculate standardised mean differences (SMD) with a random effects model. Where applicable, blood concentrations of pro-inflammatory cytokines were taken as a secondary outcome. Based on the calculated SMDs it appears, overall, that supplementation of probiotics tends to have positive effects on both cognitive performance and reduction of inflammatory markers in older adults, albeit not significant (SMD [95%CI] = 0.19 [-0.13, 0.52] for cognitive performance, and SMD [95%CI] = -0.44 [-0.94, 0.06] for inflammation). The set of RCTs studied here is characterised by high heterogeneity, preventing the determination of a true overall effect size.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.048 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".