Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

Potassium sodium hydrogen citrate intervention on gut microbiota and clinical features in uric acid stone patients

The high recurrence rate of renal uric acid stone (UAS) poses a significant challenge for urologists, and potassium sodium hydrogen citrate (PSHC) has been proven to be an effective oral dissolution drug. However, no studies have investigated the impact of PSHC on gut microbiota and its metabolites during stone dissolution therapy. We prospectively recruited 37 UAS patients and 40 healthy subjects, of which 12 patients completed a 3-month pharmacological intervention. Fasting vein blood was extracted and mid-stream urine was retained for biochemical testing. Fecal samples were collected for 16S ribosomal RNA (rRNA) gene sequencing and short chain fatty acids (SCFAs) content determination. UAS patients exhibited comorbidities such as obesity, hypertension, gout, and dyslipidemia. The richness and diversity of the gut microbiota were significantly decreased in UAS patients, Bacteroides and Fusobacterium were dominant genera while Subdoligranulum and Bifidobacterium were poorly enriched. After PSHC intervention, there was a significant reduction in stone size accompanied by decreased serum uric acid and increased urinary pH levels. The abundance of pathogenic bacterium Fusobacterium was significantly downregulated following the intervention, whereas there was an upregulation observed in SCFA-producing bacteria Lachnoclostridium and Parasutterella, leading to a significant elevation in butyric acid content. Functions related to fatty acid synthesis and amino acid metabolism within the microbiota showed upregulation following PSHC intervention. The correlation analysis revealed a positive association between stone pathogenic bacteria abundance and clinical factors for stone formation, while a negative correlation with SCFAs contents. Our preliminary study revealed that alterations in gut microbiota and metabolites were the crucial physiological adaptation to PSHC intervention. Targeted regulation of microbiota and SCFA holds promise for enhancing drug therapy efficacy and preventing stone recurrence. KEY POINTS: • Bacteroides and Fusobacterium were identified as dominant genera for UAS patients • After PSHC intervention, Fusobacterium decreased and butyric acid content increased • The microbiota increased capacity for fatty acid synthesis after PSHC intervention.

Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations

PURPOSES

Our aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics.

METHODS

We developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m2) using gender, age, BMI, gout, diabetes mellitus, estimated glomerular filtration rate, bacteriuria, urine pH, urine red blood cell counts, and urine specific gravity. The data were collected from hospitals in Kaohsiung, Taiwan between 2012 and 2021.

RESULTS

Of the 2928 subjects we enrolled, 1148 (39.21%) had clinically significant nephrolithiasis and 1780 (60.79%) did not. The testing dataset consisted of data collected from 574 subjects, 235 (40.94%) with clinically significant nephrolithiasis and 339 (59.06%) without. One model had a testing area under curve of 0.965 (95% CI, 0.9506-0.9794), a sensitivity of 0.860 (95% CI, 0.8152-0.9040), a specificity of 0.947 (95% CI, 0.9230-0.9708), a positive predictive value of 0.918 (95% CI, 0.8820-0.9544), and negative predictive value of 0.907 (95% CI, 0.8756-0.9371).

CONCLUSION

This ML-based model was found able to effectively distinguish the overweight and obese subjects with clinically significant nephrolithiasis from those without. We believe that such a model can serve as an easily accessible and reliable screening tool for nephrolithiasis in overweight and obesity populations and make possible early intervention such as lifestyle modifications and medication for prevention stone complications.

The role of the general practictioner in the management of urinary calculi

BACKGROUND

The prevalence of kidney stones tends to increase worldwide due to dietary and climate changes. Disease management involves a high consumption of healthcare system resources which can be reduced with primary prevention measures and prophylaxis of recurrences. In this field, collaboration between general practitioners (GPs) and hospitals is crucial.

METHODS

a panel composed of general practitioners and academic and hospital clinicians expert in the treatment of urinary stones met with the aim of identifying the activities that require the participation of the GP in the management process of the kidney stone patient.

RESULTS

Collaboration between GP and hospital was found crucial in the treatment of renal colic and its infectious complications, expulsive treatment of ureteral stones, chemolysis of uric acid stones, long-term follow-up after active treatment of urinary stones, prevention of recurrence and primary prevention in the general population.

CONCLUSIONS

The role of the GP is crucial in the management and prevention of urinary stones. Community hospitals which are normally led by GPs in liaison with consultants and other health professional can have a role in assisting multidisciplinary working as extended primary care.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.