Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis

Blood Calcium, Genetic Risk, and Risk of Incident Kidney Stone: A Population-Based Cohort Study

OBJECTIVE

To investigate the association between blood calcium concentration and incident kidney stone as well as to assess the role played by genetic susceptibility.

METHODS

We performed a population-based cohort study based on participants from the UK Biobank. A multivariable Cox proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% CIs of incident kidney stone for blood calcium level and polygenic risk score (PRS). In addition, the potential interaction was explored. The study was conducted from January 28, 2023, through June 4, 2023.

RESULTS

During the follow-up of 423,301 participants with a total of 5,490,332 person-years (median follow-up of 13.4 years), 4502 cases of kidney stone were recorded. Compared with the low blood calcium concentration group (first tertile), individuals in the high (third tertile) and moderate (second tertile) concentration groups had higher risks of kidney stone with HRs of 1.24 (95% CI, 1.15 to 1.33) and 1.11 (1.04 to 1.20), respectively. The PRS for kidney stone contained 40 independent single-nucleotide polymorphisms and was used to assign individuals to 3 groups according to the quintile. Participants with high (Q5) and moderate (Q2 to Q4) genetic risks had increased risks of kidney stone compared with low (Q1) genetic risk with HRs of 1.70 (1.53 to 1.89) and 1.31 (1.20 to 1.44), respectively. There was a joint cumulative risk of incident kidney stone between blood calcium concentration and genetic susceptibility.

CONCLUSIONS

Blood calcium concentration and PRS are significantly associated with incident kidney stone risk. Excessive blood calcium concentration might bring additional stone risk in populations at high genetic risk. A nonlinear correlation between blood calcium concentration and kidney stone risk was indicated.

Rare variants in the sodium-dependent phosphate transporter gene SLC34A3 explain missing heritability of urinary stone disease

Urinary stone disease (USD) is a major health burden affecting over 10% of the United Kingdom population. While stone disease is associated with lifestyle, genetic factors also strongly contribute. Common genetic variants at multiple loci from genome-wide association studies account for 5% of the estimated 45% heritability of the disorder. Here, we investigated the extent to which rare genetic variation contributes to the unexplained heritability of USD. Among participants of the United Kingdom 100,000-genome project, 374 unrelated individuals were identified and assigned diagnostic codes indicative of USD. Whole genome gene-based rare variant testing and polygenic risk scoring against a control population of 24,930 ancestry-matched controls was performed. We observed (and replicated in an independent dataset) exome-wide significant enrichment of monoallelic rare, predicted damaging variants in the SLC34A3 gene for a sodium-dependent phosphate transporter that were present in 5% cases compared with 1.6% of controls. This gene was previously associated with autosomal recessive disease. The effect on USD risk of having a qualifying SLC34A3 variant was greater than that of a standard deviation increase in polygenic risk derived from GWAS. Addition of the rare qualifying variants in SLC34A3 to a linear model including polygenic score increased the liability-adjusted heritability from 5.1% to 14.2% in the discovery cohort. We conclude that rare variants in SLC34A3 represent an important genetic risk factor for USD, with effect size intermediate between the fully penetrant rare variants linked with Mendelian disorders and common variants associated with USD. Thus, our findings explain some of the heritability unexplained by prior common variant genome-wide association studies.

Past, present and future of genomics for kidney stone disease

PURPOSE OF REVIEW

To summarize the latest findings and developments in genomics for kidney stone disease (KSD) that help to understand hereditary pathomechanisms, identify high risk stone formers, provide early treatment and prevent recurrent kidney stone formation.

RECENT FINDINGS

Several gene loci associated to KSD have presently been discovered in large Genome-wide association studies. Monogenic causes are rare, but are thought to have higher penetrance, while polygenic causes are more frequent with less penetrance. Although there is a great effort identifying genetic causes of KSD, targeted therapies are scarce.

SUMMARY

There have been great advancements in genetic research in identifying genetic variants associated with KSD. Identifying these variants and understanding the underlying pathophysiology will not only provide individual risk assessment but open the way for new treatment targets and preventive care strategies.

The genetics of kidney stone disease and nephrocalcinosis

Kidney stones (also known as urinary stones or nephrolithiasis) are highly prevalent, affecting approximately 10% of adults worldwide, and the incidence of stone disease is increasing. Kidney stone formation results from an imbalance of inhibitors and promoters of crystallization, and calcium-containing calculi account for over 80% of stones. In most patients, the underlying aetiology is thought to be multifactorial, with environmental, dietary, hormonal and genetic components. The advent of high-throughput sequencing techniques has enabled a monogenic cause of kidney stones to be identified in up to 30% of children and 10% of adults who form stones, with ~35 different genes implicated. In addition, genome-wide association studies have implicated a series of genes involved in renal tubular handling of lithogenic substrates and of inhibitors of crystallization in stone disease in the general population. Such findings will likely lead to the identification of additional treatment targets involving underlying enzymatic or protein defects, including but not limited to those that alter urinary biochemistry.

Whole Exome Sequencing in a Population With Severe Congenital Anomalies of Kidney and Urinary Tract

Fetal and neonatal interventions (e.g., amnioinfusions, amniotic shunting, and infant dialysis) have increased survival of infants with severe Congenital Anomalies of the Kidney and Urinary Tract (CAKUT), however, outcomes vary dramatically. Our aim was to perform Whole Exome Sequencing (WES) in a unique severe CAKUT population with the goal to identify new variants that will enhance prediction of postnatal outcomes. We performed trio WES on five infants with severe CAKUT (undergoing fetal interventions and/or those who initiated renal replacement therapy (RRT) within 1 month of life) and their parents as well as three singletons. We identified three potential candidate gene variants (NSUN7, MTMR3, CEP162) and validated two variants in known CAKUT genes (GATA3 and FRAS1) showing strong enrichment in this severe phenotype population. Based on our small pilot study of a unique severe CAKUT population, WES appears to be a potential tool to help predict the course of infants with severe CAKUT prenatally.

Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure

Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.

Dhavileswarapu AVL, Naik N, Karimi H, Hegde P, Rai BP, Somani BK. Big Data Analytics in urology: the story so far and the road ahead

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.