Current update and future directions on gut microbiome and nephrolithiasis
Bibliographic record
Abstract
The incidence of nephrolithiasis is increasing worldwide. Understanding how gut microbiome influences oxalate homeostasis has the potential to offer new strategies to prevent nephrolithiasis. The literature was reviewed to gather the evidence on the association between gut microbiome, hyperoxaluria and nephrolithiasis, and to identify the therapeutic interventions focused on the gut microbiome that could decrease hyperoxaluria and prevent nephrolithiasis. Gut microbiome is constituted by a plethora of microbiota including Oxalobacter formigenes (Oxf) and lactobacilli. Oxf can degrade dietary oxalate and induce enteral oxalate secretion. Animal studies suggested an association between oral Oxf supplementation and a decrease in hyperoxaluria. However, human studies have showed inconsistent results. Oral supplementation of lactobacilli did not show benefit in decreasing the hyperoxaluria. Antibiotic exposure, by affecting the gut microbiome, has been associated with an increase in nephrolithiasis. In vivo studies suggest fecal transplantation as a potential treatment option for reducing nephrolithiasis, but needs further evaluation in clinical studies. The current evidence suggests an association between gut microbiome and nephrolithiasis. However, the strategies focused on modulating gut microbiome for decreasing hyperoxaluria and preventing nephrolithiasis need further research. Judicious use of antibiotics in those predisposed to nephrolithiasis offers a preventative strategy for decreasing nephrolithiasis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".