Oral Aloe Vera Supplementations’ Effects on Skin Wrinkles, Hydration, Elasticity, Transepidermal Water Loss, and Collagen Score: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
This systematic literature review and meta-analysis evaluated the efficacy of oral aloe vera supplementation on skin aging parameters including wrinkles, hydration, elasticity, transepidermal water loss (TEWL), and collagen score. We searched MEDLINE, EMBASE, AMED, PubMed, CINAHL, CENTRAL, and trial registries up to July 2024 for randomized controlled trials. Four RCTs (n=284 participants) met the inclusion criteria. Compared with placebo, aloe vera supplements had little to no effect on skin hydration after 4 weeks (SMD, 0.61; 95% CI [-0.55, 1.77]), 8 weeks (MD, 0.58; 95% CI [-0.70, 1.87]) or 12 weeks (SMD 0.69; 95% CI [-0.82, 2.19]). Similarly, TEWL, elasticity, and collagen scores did not differ significantly from placebo. The certainty of the evidence for all assessed outcomes is low due to concerns regarding imprecision, and inconsistency. A marginal reduction in wrinkle width was observed in adults ≥40 years, but this was based on a single study. Heterogeneity in formulations (19–40 µg sterols), small sample sizes, and short trial durations (≤12 weeks) limited the robustness of conclusions. While preclinical data suggest Aloe vera sterols may stimulate collagen synthesis, clinical evidence remains inconclusive. Current evidence does not support oral Aloe vera as an effective intervention for skin aging, though subgroup-specific effects warrant further investigation.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".