Abstract
PurposeTo predict the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics.MethodsGut microbiota and clinical data from 180 subjects (120 for training set and 60 for validation) attending the West China Hospital (WCH) were collected between June 2018 and January 2021. Based on the gut microbiota and clinical data from 120 subjects (66 non-kidney stone individuals and 54 kidney stone patients), we evaluated eight machine learning methods to predict the occurrence of calcium oxalate kidney stones.ResultsWith fivefold cross-validation, the random forest method produced the best area under the curve (AUC) of 0.94. We further applied random forest to an independent validation dataset with 60 samples (34 non-kidney stone individuals and 26 kidney stone patients), which yielded an AUC of 0.88.ConclusionOur results demonstrated that clinical data combined with gut microbiota characteristics may help predict the occurrence of kidney stones.
Highlights
Nephrolithiasis is a common urological disease, with a constantly increasing prevalence in recent years
The gut microbiota is crucial in maintaining environmental homeostasis in the gut. 16S ribosomal RNA(rRNA) sequencing offers more possibilities to reveal the diversity of microbes, as several studies have shown significant differences in the gut microbiota between patients with and without kidney stones [4, 5]
Univariate association analyses revealed no significant differences in age, sex, BMI, propionic acid concentration, isobutyric acid concentration, isovaleric acid concentration, valeric acid concentration, hexanoic acid concentration, calcium concentration and uric acid concentration between non-kidney stone individuals (NS) and kidney stone (KS) (Table 1)
Summary
Nephrolithiasis is a common urological disease, with a constantly increasing prevalence in recent years. Calcium oxalate stones, which accounts for about 80% of kidney stone types, is the most common category of kidney stones [1]. 16S ribosomal RNA(rRNA) sequencing offers more possibilities to reveal the diversity of microbes, as several studies have shown significant differences in the gut microbiota between patients with and without kidney stones [4, 5]. Short-chain fatty acids (SFCA) are health-friendly metabolites, produced by the gut microbiota [6] and through different metabolic pathways [7, 8], that provide an ideal environment for the production of acetate, propionate and butyrate [9]. Huang [10] found that short-chain fatty acids have an inhibitory effect on the oxidative stress and inflammatory response of glomerular lineage membrane
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