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Objective: This study aimed to investigate the association between weight-adjusted waist index (WWI), a novel adiposity index, and kidney stone (KS). Methods: Data were obtained from the National Health and Nutrition Examination Survey 2007–2018. According to the history of KS, participants were divided into the non-stone group and the stone group. Weighted multivariable logistic regression analyses were used to evaluate the correlation between WWI and KS in unadjusted, partially adjusted, and all-adjusted models. A restricted cubic spline (RCS) analysis assessed the association between continuous WWI and KS risk and obtained the risk function inflection point. Then, subgroup analysis based on the risk function inflection point was conducted to dissect the association in specific subgroups. In addition, the above analyst methods were repeatedly performed in populations after propensity score matching (PSM). The receiver operating characteristic (ROC) curve was applied to compare the ability to predict KS occurrence among WWI, visceral adiposity index (VAI), and body mass index (BMI). Results: Weighted multivariable logistic regression analyses found a positive association between continuous WWI and KS risk in the all-adjusted model [odds ratio (OR) = 1.03; 95% confidence interval (CI), 1.02–1.04; P < 0.001]. In further analysis, the Q4 WWI group was linked to the highest KS risk when compared to the Q1–Q3 group (OR = 1.06; 95% CI, 1.05–1.08, P < 0.001). RCS analysis found a linear significant correlation between continuous WWI and KS risk, and the risk function inflection point is 11.08 cm/√kg. Subgroup analysis confirmed that WWI was associated with KS risk in different groups. After PSM, increased WWI was still related to a high risk of KS. Moreover, the ROC curve demonstrated that WWI has a higher predictive ability of KS occurrence than VAI and BMI (area under curve, 0.612 vs. 0.581 vs. 0.569). Conclusion: In the US adult population, elevated WWI value was associated with an increased risk of KS. Furthermore, WWI was a better predictor of KS occurrence than VAI and BMI. [ABSTRACT FROM AUTHOR]

Kidney Stone Disease (KSD) constitutes a multifaceted disorder, emerging from a confluence of environmental and genetic determinants, and is characterized by a high frequency of occurrence and recurrence. Our objective is to elucidate potential causative proteins and identify prospective pharmacological targets within the context of KSD. This investigation harnessed the unparalleled breadth of plasma protein and KSD pooled genome-wide association study (GWAS) data, sourced from the United Kingdom Biobank Pharma Proteomics Project (UKBPPP) and the FinnGen database version R10. Through Mendelian randomization analysis, proteins exhibiting a causal influence on KSD were pinpointed. Subsequent co-localization analyses affirmed the stability of these findings, while enrichment analyses evaluated their potential for pharmacological intervention. Culminating the study, a phenome-wide association study (PheWAS) was executed, encompassing all phenotypes (2408 phenotypes) catalogued in the FinnGen database version R10. Our MR analysis identified a significant association between elevated plasma levels of proteins FKBPL, ITIH3, and SERPINC1 and increased risk of KSD based on genetic predictors. Conversely, proteins CACYBP, DAG1, ITIH1, and SEMA6C showed a protective effect against KSD, documented with statistical significance (PFDR<0.05). Co-localization analysis confirmed these seven proteins share genetic variants with KSD, signaling a shared genetic basis (PPH3 + PPH4 > 0.8). Enrichment analysis revealed key pathways including hyaluronan metabolism, collagen-rich extracellular matrix, and serine-type endopeptidase inhibition. Additionally, our PheWAS connected the associated proteins with 356 distinct diseases (PFDR<0.05), highlighting intricate disease interrelations. In conclusion, our research elucidated a causal nexus between seven plasma proteins and KSD, enriching our grasp of prospective therapeutic targets. [ABSTRACT FROM AUTHOR]

Objective: To compare the Guy’s, S.T.O.N.E, and CROES nomograms for predicting stone-free status in patients who underwent percutaneous nephrolithotomy for renal stones. Materials and Methods: The data of 1114 patients who underwent percutaneous nephrolithotomy for renal calculi between 11/2008 and 08/2018 in our clinic were retrospectively reviewed. Various parameters evaluated by preoperative computed tomography and the scoring systems of the patients and postoperative stone-free status were compared. Results: Out of 1000 patients who met the study criteria. Gender, body mass index, and stone density were not statistically different between the group with residual stones and the stone-free group. However, stone size, number of renal accesses, duration of fluoroscopy usage, duration of operation, number of stones, and complication rate were significantly higher in the group with residual stones than in the stone-free group. A statistically significant correlation was found between the postoperative stone-free rate and scoring systems. The applicability and preoperative prediction ability of all three systems were evaluated by receiver operating characteristic analysis. The area under the curve (AUC) was detected in the Guy’s, CROES, and S.T.O.N.E scoring system (AUC: 0.642, 0.665, 0.592 respectively). Conclusion: In this study, where the perioperative and postoperative results of 1000 patients were evaluated, we found that all three scoring systems could predict the stone-free rate. “We believe that the use of these scoring systems before surgery can guide surgeons.” [ABSTRACT FROM AUTHOR]

The association of alcohol intake with kidney stone disease (KSD) is not clear based on current clinical evidence. We examined the National Health and Nutrition Examination Survey (NHANES) 2007–2018 and used logistic regression analyses to determine the independent association between alcohol intake and prevalent KSD. In total, 29,684 participants were eligible for the final analysis, including 2840 prevalent stone formers (SFs). The mean alcohol intake was 37.0 ± 2.4 g/day among SFs compared to 42.7 ± 0.9 among non-SFs (p = 0.04). Beer [odds ratio (OR) = 0.76, 95% CI: 0.61–0.94, p = 0.01] and wine (OR = 0.75, 95% CI: 0.59–0.96, p = 0.03) intakes were strongly associated with lower odds of prevalent KSD, while liquor intake had no association. Furthermore, the effects of beer and wine intakes on stone formation were dose-dependent. The OR for comparing participants drinking 1–14 g/day of beer to non-drinkers was 1.41 (95%CI: 0.97–2.05, p = 0.07), that of >14–≤28 g/day of beer to non-drinkers was 0.65 (95% CI: 0.42–1.00, p = 0.05), that of >28–≤56 g/day of beer to non-drinkers was 0.60 (95% CI: 0.39–0.93, p = 0.02), and that of >56 g/day of beer to non-drinkers was 0.34 (95% CI: 0.20–0.57, p < 0.001). Interestingly, the effect of wine intake was only significant among participants drinking moderate amounts (>14–28 g/day), with an OR of 0.54 (95% CI: 0.36–0.81, p = 0.003) compared to non-drinkers, but this effect was lost when comparing low-level (1–14 g/day) and heavy (>28 g/day) wine drinkers to non-drinkers. These effects were consistent in spline models. This study suggests that both moderate to heavy beer intake and moderate wine intake are associated with a reduced risk of KSD. Future prospective studies are needed to clarify the causal relationship. [ABSTRACT FROM AUTHOR]

Calcium-containing stones represent the most common form of kidney calculi, frequently linked to idiopathic hypercalciuria, though their precise pathogenesis remains elusive. This research aimed to elucidate the molecular mechanisms involved by employing urinary exosomal microRNAs as proxies for renal tissue analysis. Elevated miR-148b-5p levels were observed in exosomes derived from patients with kidney stones. Systemic administration of miR-148b-5p in rat models resulted in heightened urinary calcium excretion, whereas its inhibition reduced stone formation. RNA immunoprecipitation combined with deep sequencing identified miR-148b-5p as a suppressor of calcitonin receptor (Calcr) expression, thereby promoting urinary calcium excretion and stone formation. Mice deficient in Calcr in distal epithelial cells demonstrated elevated urinary calcium excretion and renal calcification. Mechanistically, miR-148b-5p regulated Calcr through the circRNA-83536/miR-24-3p signaling pathway. Human kidney tissue samples corroborated these results. In summary, miR-148b-5p regulates the formation of calcium-containing kidney stones via the circRNA-83536/miR-24-3p/Calcr axis, presenting a potential target for novel therapeutic interventions to prevent calcium nephrolithiasis. [ABSTRACT FROM AUTHOR]

Genetic variability persists across diverse populations, and it may impact the characterization of heritable diseases in different ancestral groups. Cystinosis is a metabolic disease caused by pathogenic variants in the CTNS gene causing the cellular accumulation of cystine. We attempted to assess the currently poorly characterized prevalence of cystinosis by employing a population genetics methodology. However, we encountered a significant challenge due to genetic variations across different populations, and the consideration of potential disparities in access to healthcare made our results inconclusive. Pathogenic CTNS variants were identified in a representative global population cohort using The Human Gene Mutation Database (HGMD) and the 1000 Genomes (1 KG) database. The c.124G>A (p.Val42Ile) variant was reported to be pathogenic based on an observation in the white population presenting with atypical phenotypes, but it would be reclassified as benign in the African ancestral group if applying the ACMG allele frequency guideline due to its high allele frequency specifically in this population. Inclusion or exclusion of this c.124G>A (p.Val42Ile) variant results in a significant change in estimated disease prevalence, which can impact the diagnosis and treatment of affected patients with a broad range of phenotypic presentations. This observation led us to postulate that pathogenic manifestations of the disease may be underdiagnosed due to variable expressivity and systemic inequities in access to care, specifically in the African subpopulation. We call for a more cautious and inclusive approach to achieve more equitable care across diverse populations. [ABSTRACT FROM AUTHOR]

Objectives: The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of stone events is also a significant limitation for clinical trials, where many patients must be enrolled to obtain sufficient stone events for analysis. In this study, we sought to use machine learning methods to identify a novel algorithm to predict stone recurrence. Subjects/Patients and Methods: Patients enrolled in the Registry for Stones of the Kidney and Ureter (ReSKU), a registry of nephrolithiasis patients collected between 2015–2020, with at least one prospectively collected 24-hour urine test (Litholink 24-hour urine test; Labcorp) were included in the training set. A validation set was obtained from chart review of stone patients not enrolled in ReSKU with 24-hour urine data. Stone events were defined as either an office visit where a patient reports symptomatic passage of stones or a surgical procedure for stone removal. Seven prediction classification methods were evaluated. Predictive analyses and receiver operator characteristics (ROC) curve generation were performed in R. Results: A training set of 423 kidney stone patients with stone event data and 24-hour urine samples were trained using the prediction classification methods. The highest performing prediction model was a Logistic Regression with ElasticNet machine learning model (area under curve [AUC] = 0.65). Restricting analysis to high confidence predictions significantly improved model accuracy (AUC = 0.82). The prediction model was validated on a validation set of 172 stone patients with stone event data and 24-hour urine samples. Prediction accuracy in the validation set demonstrated moderate discriminative ability (AUC = 0.64). Repeat modeling was performed with four of the highest scoring features, and ROC analyses demonstrated minimal loss in accuracy (AUC = 0.63). Conclusion: Machine-learning models based on 24-hour urine data can predict stone recurrences with a moderate degree of accuracy. [ABSTRACT FROM AUTHOR]

Objective To investigate the impact of kidney stone physical characteristics obtained through CT three-dimensional reconstruction on the efficiency of flexible ureteroscopic lithotripsy (FURL). Methods A total of 86 patients with kidney stones who met the inclusion criteria were selected from West China Airport Hospital of Sichuan University between July 2021 and December 2022. The physical characteristics of the kidney stones, including number (units), length (mm), density (Hu), volume (cm³), and mass (Hu*cm³), were measured using CT three-dimensional reconstruction technology before surgery. All patients underwent FURL. Post-FURL, the patients were divided into a stone clearance group and a stone residual group based on the stone clearance status. Multifactorial binary logistic regression analysis was used to identify the influencing factors of the stone clearance rate after FURL. Receiver operating characteristic (ROC) curves were plotted to analyze the predictive value of each stone characteristic for stone clearance after FURL. The correlation between each stone characteristic and operation time was analyzed using the Spearman correlation coefficient. Results There were 67 cases in the stone clearance group and 19 cases in the stone residual group, with a stone clearance rate of 77.9%. Multivariate analysis showed that stone length, density, volume, and mass were independent risk factors affecting the stone clearance rate after FURL (P＜0.05). ROC curves showed that the area under the curve (AUC) for stone length, density, volume, and mass were 0.891, 0.674, 0.919, and 0.957, respectively, with stone mass having a significantly higher predictive efficiency for stone clearance than the other characteristics. The Spearman correlation coefficient indicated that stone length, density, volume, and mass were positively correlated with operation time (r=0.521, 0.226, 0.573, 0.611, P＜0.05), with stone mass showing the highest correlation with operation time. Conclusion Stone length, density, volume, and mass obtained through CT three-dimensional reconstruction are all risk factors affecting the treatment of kidney stones by FURL. Among these, stone mass, as an indicator of stone burden, can better predict the lithotripsy efficiency of FURL in treating kidney stones. [ABSTRACT FROM AUTHOR]

Purpose: This ex vivo study aimed to compare a newly developed dual-source photon-counting CT (PCCT) with a 3rd generation dual-source dual-energy CT (DECT) for the detection and measurement (stone lengths and volumetrics) of urinary stones. Methods: 143 urinary stones with a known geometry were physically measured and defined as reference values. Next, urinary stones were placed in an anthropomorphic abdomen-model and were scanned with DECT and PCCT. Images were read by two experienced examiners and automatically evaluated using a specific software. Results: DECT and PCCT showed a high sensitivity for manual stone detection of 97.9% and 94.4%, and for automatic detection of 93.0% and 87.4%, respectively. Compared to that uric acid and xanthine stones were recognized slightly worse by DECT and PCCT with manual stone detection (93.3% and 82.2%), and with automatic detection (77.8% and 60.0%). All other stone entities were completely recognized. By comparing the maximum diameter of the reference value and DECT, Pearson-correlation was 0.96 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurement, and for PCCT it was 0.94 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurements. DECT and PCCT can also reliably determine volume manually and automatically with a Pearson-correlation of 0.99 (p < 0.001), respectively. Conclusion: Both CTs showed comparable results in stone detection, length measurement and volumetry compared to the reference values. Automatic measurement tends to underestimate the maximum diameter. DECT proved to be slightly superior in the recognition of xanthine and uric acid stones. [ABSTRACT FROM AUTHOR]

Thiazide and thiazide‐like diuretics (thiazides) belong to the most frequently prescribed drugs worldwide. By virtue of their natriuretic and vasodilating properties, thiazides effectively lower blood pressure and prevent adverse cardiovascular outcomes. In addition, through their unique characteristic of reducing urine calcium, thiazides are also widely employed for the prevention of kidney stone recurrence and reduction of bone fracture risk. Since their introduction into clinical medicine in the early 1960s, thiazides have been recognized for their association with metabolic side effects, particularly impaired glucose tolerance, and new‐onset diabetes mellitus. Numerous hypotheses have been advanced to explain thiazide‐induced glucose intolerance, yet underlying mechanisms remain poorly defined. Regrettably, the lack of understanding and unpredictability of these side effects has prompted numerous physicians to refrain from prescribing these effective, inexpensive, and widely accessible drugs. In this review, we outline the pharmacology and mechanism of action of thiazides, highlight recent advances in the understanding of thiazide‐induced glucose intolerance, and provide an up‐to‐date discussion on the role of thiazides in kidney stone prevention. [ABSTRACT FROM AUTHOR]

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support. [ABSTRACT FROM AUTHOR]

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Background: The prevalence of kidney stones reduces the patients' quality of life and imposes high costs on patients and health care system. Extracorporeal shock wave lithotripsy is a safe procedure to treat urinary stones. Appropriate strategies such as self-care programs can effectively reduce the prevalence and recurrence of this disease, and increase quality of life, save costs, and prevent diseases by increasing patient participation. The aim of this study is to design and implement a web-based self-care application for patients with kidney stones undergoing extracorporeal shock wave lithotripsy. Methods: This paper is an applied study and was performed in Sabalan Hospital in Ardabil affiliated to the Social Security Organization of Iran in three stages including needs assessment, design and evaluation. For needs assessment and application design, a questionnaire was designed by searching library resources. The questionnaire was completed by clinical specialists and specialists in health information management and information technology. Then, based on the approved information elements and capabilities, a web-based self-care application was designed. The usability of the designed program was evaluated with the participation of 20 users. Results: The results of the survey showed that the experts confirmed all the identified items and components, and considered them necessary. Users rated the usability of the self-care program at a good level with an average score of 7.9 out of 9. Conclusion: The usability ofthe self-care application was satisfactory to users in various areas of overall application performance, screen, terminology and information, learning and general impressions. [ABSTRACT FROM AUTHOR]

Objective: To compare the Guy’s, S.T.O.N.E, and CROES nomograms for predicting stone-free status in patients who underwent percutaneous nephrolithotomy for renal stones. Materials and Methods: The data of 1114 patients who underwent percutaneous nephrolithotomy for renal calculi between 11/2008 and 08/2018 in our clinic were retrospectively reviewed. Various parameters evaluated by preoperative computed tomography and the scoring systems of the patients and postoperative stone-free status were compared. Results: Out of 1000 patients who met the study criteria. Gender, body mass index, and stone density were not statistically different between the group with residual stones and the stone-free group. However, stone size, number of renal accesses, duration of fluoroscopy usage, duration of operation, number of stones, and complication rate were significantly higher in the group with residual stones than in the stone-free group. A statistically significant correlation was found between the postoperative stone-free rate and scoring systems. The applicability and preoperative prediction ability of all three systems were evaluated by receiver operating characteristic analysis. The area under the curve (AUC) was detected in the Guy’s, CROES, and S.T.O.N.E scoring system (AUC: 0.642, 0.665, 0.592 respectively). Conclusion: In this study, where the perioperative and postoperative results of 1000 patients were evaluated, we found that all three scoring systems could predict the stone-free rate. “We believe that the use of these scoring systems before surgery can guide surgeons.”

BackgroundArsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. ObjectiveTo investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. MethodsA questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. ResultsWe found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P