Efficacy and safety of probiotic supplements on cognitive function: a systematic review and meta-analysis of randomized clinical trials
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aimed to evaluate the efficacy and safety of probiotic supplementation on cognitive function in individuals over 18 years of age. METHODS: Randomized clinical trials (RCTs) assessing the impact of probiotics on cognitive function were included. Searches were conducted across four medical databases from inception to August 2024. The outcomes were cognitive function measured by Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Cognition Categorical Fluency Test (CFT), and adverse events. Data were extracted and analyzed using a random-effects model, with results reported as mean differences (MD) and relative risks (RR) with 95% confidence intervals (CI). To evaluate statistical heterogeneity, the I² statistic and the tau squared value (τ²) were used. The risk of bias was assessed using the RoB 2.0 tool, and the certainty of evidence was evaluated with GRADE. RESULTS: A total of 34 RCTs involving 2,390 participants were included in the meta-analysis. Limited evidence suggests a possible improvement in cognitive function from probiotics use at 12 weeks (MD 4.23, 95% CI 2.77 to 5.68; certainty of evidence (CoE) was low, I² = 0%) for MMSE and cognitive function (MD 1.21; 95% CI 0.06 to 2.36; certainty of evidence (CoE) was low; I² = 35%) for MoCA; however, due to the very low certainty found, the evidence is very uncertain. On the other hand, probiotic supplementation can improve cognitive performance, as measured by CFT (MD 3.94, 95% CI: 3.20 to 4.69, low certainty of evidence; I² = 0%). Probiotics did not reduce the risk of any adverse event (RR 0.91, 95% CI 0.65 to 1.27; Certainty of evidence (CoE) was Very Low). CONCLUSIONS: Our study found that probiotics improved cognitive function, especially after 12 weeks of supplementation, using the MoCA test. However, although probiotics show potential benefits, the current evidence remains highly uncertain, warranting further rigorous trials.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.001 | 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".