Potential relationship of the gut microbiome with testosterone level in men: a systematic review
Bibliographic record
Abstract
The gut microbiome influences the metabolism and health of several organs beyond the gut and has recently gained considerable attention. The gut plays a central role in food digestion, absorption, nutrition, and general health. Hence, the gut microbiome has become a research subject for almost all health conditions. Imbalance or dysbiosis in the gut microbiome can cause different health issues in men, including reproductive and sexual health issues associated with testosterone levels. Several studies have investigated the relationship between the gut microbiome and testosterone levels. In this systematic review, we aimed to examine the relationship between the gut microbiome and testosterone levels in men. Literature searches were conducted by scanning PubMed, ProQuest, EBSCO, Taylor & Francis Online, Wiley Online, Springer Link, Web of Science, Google Scholar, and Science Direct databases for relevant keywords following the preferred reporting items for the systematic review guidelines. This review included cross-sectional, case-control, retrospective, and prospective cohort studies. Quality assessment was conducted using the Newcastle-Ottawa Scale. We found a significant positive correlation between the gut microbiome and testosterone levels in men. Several microbes play substantial roles in testosterone production. Mechanisms have been proposed as factors that contribute to testosterone levels, namely the hypothalamus-pituitary-gonad axis modulation, androgen metabolism, and intestinal homeostasis, by balancing the bone morphogenic protein (BMP) and the Wnt diverse microbiome. Ruminococcus showed a stronger correlation with testosterone levels than other microorganisms. The gut microbiome has complex correlations with testosterone metabolism. However, the microbiome with the most significant influence on testosterone levels cannot be easily identified and requires further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".