The effects of hydrogen-rich water on gut microbiota and related health outcomes: A systematic review
Bibliographic record
Abstract
Hydrogen-rich water (HRW) has emerged as a promising therapeutic intervention due to its antioxidant and anti-inflammatory properties. Recent studies suggest that the ingestion of HRW may alter gut microbiota composition, potentially influencing various health outcomes such as metabolic, inflammatory, and neurological conditions. However, no comprehensive synthesis of the evidence exists. This systematic review aims to evaluate and synthesize the available literature on the effects of HRW on gut microbiota composition and its associated health outcomes. We conducted a systematic search of electronic databases, including PubMed, Scopus, Google Scholar, Cochrane Library, ProQuest, Web of Science, and Gray Literature. We included studies of human or animal populations exposed to HRW, focusing on randomized controlled trials, cohort studies, case-control studies, and relevant in vivo / in vitro studies. Two independent reviewers carried out data extraction, and they assessed the risk of bias using appropriate tools for each study design. We will synthesize the findings narratively to identify the impact of HRW on gut microbiota diversity and health-related outcomes such as metabolic, inflammatory, and immune system markers. This review aims to provide a comprehensive understanding of HRW's effects on gut microbiota and its broader health implications, highlighting current evidence gaps and suggesting directions for future research.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".