Dent's Disease: A Cause of Monogenic Kidney Stones and Nephrocalcinosis.

Kidney stones are becoming increasingly common, affecting up to 10% of adults. A small percentage are of monogenic origin, such as Dent's disease (DD). DD is a syndrome that causes low-molecular-weight proteinuria, hypercalciuria, nephrolithiasis, and nephrocalcinosis. It is X-linked, and most patients have mutations in the CLCN5 gene. We performed a review of the literature and evaluated the case series (n = 6) of a single center in Spain, reviewing the natural evolution of kidney stones, clinical implications, laboratory analyses, radiological development, and treatment. All patients had a genetically confirmed diagnosis, with the CLCN5 mutation being the most frequent (66%). All patients had proteinuria and albuminuria, while only two and three presented hypercalciuria and phosphate abnormalities, respectively. Only one patient did not develop lithiasis, with most (60%) requiring extracorporeal shock wave lithotripsy or surgery during follow-up. Most of the patients are under nephrological follow-up, and two have either received a renal transplant or are awaiting one. The management of these patients is similar to that with lithiasis of non-monogenic origin, with the difference that early genetic diagnosis can help avoid unnecessary treatments, genetic counseling can be provided, and some monogenic kidney stones may benefit from targeted treatments.

Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

Autosomal dominant polycystic kidney disease in young adults.

Background: The clinical manifestations of autosomal dominant polycystic kidney disease (ADPKD) usually appear in adulthood, however pediatric series report a high morbidity. The objective of the study was to analyze the clinical characteristics of ADPKD in young adults. Methods: Family history, hypertension, albuminuria, estimated glomerular filtration rate (eGFR) and imaging tests were examined in 346 young adults (18-30 years old) out of 2521 patients in the Spanish ADPKD registry (REPQRAD). A literature review searched for reports on hypertension in series with more than 50 young (age <30 years) ADPKD patients. Results: The mean age of this young adult cohort was 25.24 (SD 3.72) years. The mean age at diagnosis of hypertension was 21.15 (SD 4.62) years, while in the overall REPQRAD population was aged 37.6 years. The prevalence of hypertension was 28.03% and increased with age (18-24 years, 16.8%; 25-30 years, 36.8%). Although prevalence was lower in women than in men, the age at onset of hypertension (21 years) was similar in both sexes. Mean eGFR was 108 (SD 21) mL/min/1.73 m2, 38.0% had liver cysts and 3.45% of those studied had intracranial aneurysms. In multivariate analyses, hematuria episodes and kidney length were independent predictors of hypertension (area under the curve 0.75). The prevalence of hypertension in 22 pediatric cohorts was 20%-40%, but no literature reports on hypertension in young ADPKD adults were found. Conclusions: Young adults present non-negligible ADPKD-related morbidity. This supports the need for a thorough assessment of young adults at risk of ADPKD that allows early diagnosis and treatment of hypertension.