Increased prevalence of kidney cysts in individuals carrying heterozygous COL4A3 or COL4A4 pathogenic variants.

BACKGROUND: Clinical variability among individuals with heterozygous pathogenic/likely pathogenic (P/LP) variants in the COL4A3/COL4A4 genes (also called autosomal dominant Alport syndrome or COL4A3/COL4A4-related disorder) is huge; many individuals are asymptomatic or show microhematuria, while others may develop proteinuria and chronic kidney disease (CKD). The prevalence of simple kidney cysts (KC) in the general population varies according to age, and patients with advanced CKD are prone to have them. A possible association between heterozygous COL4A3, COL4A4 and COL4A5 P/LP variants and KC has been described in small cohorts. The presence of KC in a multicenter cohort of individuals with heterozygous P/LP variants in the COL4A3/COL4A4 genes is assessed in this study. METHODS: We evaluated the presence of KC by ultrasound in 157 individuals with P/LP variants in COL4A3 (40.7%) or COL4A4 (53.5%) without kidney replacement therapy. The association between presence of KC and age, proteinuria, estimated glomerular filtration rate (eGFR) and causative gene was analyzed. Prevalence of KC was compared with historical case series in the general population. RESULTS: Half of the individuals with P/LP variants in COL4A3/COL4A4 showed KC, which is a significantly higher percentage than in the general population. Only 3.8% (6/157) had cystic nephromegaly. Age and eGFR showed an association with the presence of KC (P < .001). No association was found between KC and proteinuria, sex or causative gene. CONCLUSIONS: Individuals with COL4A3/COL4A4 P/LP variants are prone to develop KC more frequently than the general population, and their presence is related to age and to eGFR. Neither proteinuria, sex nor the causative gene influences the presence of KC in these individuals.

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.

Assessment of Tacrolimus Neurotoxicity Measured by Retinal OCT.

BACKGROUND: Neurotoxicity secondary to anticalcineurinics is a prevalent side effect in transplant recipients. Optical coherence tomography (OCT) scans the central nervous system by direct access to the retina. OCT assesses central nervous system involvement by measuring the thickness of the retinal layers, especially the ganglion cell layer (GCL). The retinal scan divides the scanned area into affected and unaffected segments, which can be quantified for each eye. The aim of this study was to determine retinal GCL thickness by means of OCT, analyzing the proportion of affected segments in individuals exposed to tacrolimus compared with a control group. MATERIALS AND METHODS: We evaluated 20 renal transplant recipients exposed to tacrolimus for ≥6 months. The number of affected segments in the GCL of the retina was quantified by OCT. The control group was drawn from the general population attending routine ophthalmologic checkups. RESULTS: Patients exposed to tacrolimus had a pathologic examination in 50% of cases compared with 20% in the control group (P < .044). Furthermore, among patients with an exposure time to tacrolimus >5 years, the examination was pathologic in 80% (P < .005). Linear regression analysis showed the presence of GCL segments with decreased thickness to be associated with the duration of exposure to tacrolimus (P = .036) and the time in dialysis before kidney transplant (P = .030). CONCLUSION: Although this is a preliminary study, OCT scanning could serve to detect the neurotoxic effect of tacrolimus on the retinal GCL and central nervous system in renal transplant recipients.