Comparative Analysis of the Therapeutic Potential of Probiotics in Treating Psoriasis, Acne, and Atopic Dermatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Probiotics have recently garnered attention for their ability to manage skin conditions, as they support the immune system, the gut-skin connection, and lower inflammation. This systematic review and meta-analysis sought to determine the effectiveness of oral probiotic supplementation to improve clinical outcomes in dermatological disorders such as psoriasis, acne, and atopic dermatitis. Methods: This PRISMA 2020 systematic review and meta-analysis study assessed the effect of probiotics on different skin conditions. The search for relevant literature was performed till May 2025. The included studies were randomized controlled trials, or observational or retrospective studies. The extraction of data was carried out independently, and the risk of bias was evaluated using the Cochrane and Newcastle-Ottawa tools. RevMan 5.4.1 was used to determine meta-analyses based on inverse variance and the random-effects model. The I2 statistic was used to determine the heterogeneity. Results: Twelve studies were identified as being included. Two studies examining the benefits of probiotics in alleviating psoriasis symptoms showed a significant difference in PASI scores in a meta-analysis (SMD = -2.17; I2 = 71%). There were two articles on acne that showed a considerably high probability of clinical improvement (OR = 3.06; I2 = 0%). The two trials on atopic dermatitis demonstrated a positive odds ratio of SCORAD reduction (OR=3.72; 95% CI: 1.72 to 8.05). Confirmation of effect using subgroup and sensitivity analysis was confirmed. Discussion: Clinical severity scores are also significantly decreased with oral supplementation of probiotics in the case of psoriasis, acne, and atopic dermatitis. A key limitation of this review is the small number of studies available for each condition, limiting the generalizability of the findings. A series of further large-scale, standardized trials is required to confirm long-term efficacy.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.056 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".