Diagnosis Of Kidney Disease Research Articles

New potential biomarkers for early chronic kidney disease diagnosis in patients with different glucose tolerance status.

The purpose of the present study was to investigate the role of oxidative stress, platelet activation, and endocan levels in renal dysfunction in normal glucose tolerance (NGT) patients with 1-h plasma glucose values ≥155 mg/dl (NGT ≥ 155), compared to NGT < 155, impaired glucose tolerance (IGT), and type 2 diabetes mellitus (T2DM) newly diagnosed subjects. We enlisted 233 patients subjected to an oral glucose tolerance test (OGTT). The serum levels of platelet activation (glycoprotein VI and sP-selectin), oxidative stress biomarkers (8-isoprostane and Nox-2), and endocan were evaluated using an ELISA test. Among NGT < 155 patients and the T2DM group, there was a statistically significant increase in 8-isoprostane (p < 0.0001), Nox-2 (p < 0.0001), glycoprotein VI (p < 0.0001), and sP-selectin (p < 0.0001) serum levels. Higher serum endocan levels were found with the worsening of metabolic profile (p < 0.0001); specifically, NGT ≥ 155 patients presented higher serum endocan values when compared to NGT < 155 patients (p < 0.0001). From the multivariate linear regression analysis, 1-h glucose resulted in the major predictor of estimated glomerular filtration rate (e-GFR) justifying 23.6% of its variation (p < 0.0001); 8-isoprostane and Nox-2 added respectively another 6.0% (p < 0.0001) and 3.2% (p = 0.001). Our study confirmed the link between 1-h post-load glucose ≥155 mg/dl during OGTT and the possible increased risk for chronic kidney disease (CKD) in newly diagnosed patients. The novelty is that we demonstrated a progressive increase in oxidative stress, platelet activation, and serum endocan levels with the worsening of metabolic profile, which becomes evident early during the progression of CKD.

Renal function, sex and age influence purines and pyrimidines in urine and could lead to diagnostic misinterpretation

Glomerular filtration rate (GFR) is commonly used in clinical practice for the diagnosis and follow-up of chronic kidney disease. Screening for inborn errors of metabolism (IEM) is based on analysis of biomarkers in urine, reported by their ratio to urinary creatinine (crn). Impaired renal function may complicate the interpretation of several biomarkers used for screening of IEM. Our goal was to investigate the influence of kidney function, in terms of measured GFR (mGFR) on purines and pyrimidines in urine, in addition to the relationship to sex, age, pH and ketosis. Children (n = 96) with chronic kidney disease (CKD), in different CKD stages, were included. Urine samples were obtained prior to the injection of iohexol. Serum samples at 7 time-points were used to calculate mGFR based on iohexol plasma clearance. The association with sex, age, ketosis and pH was examined in samples of the laboratory production from 2015 to 2021 (n = 8192). Age was a highly significant covariate for all markers. GFR correlated positively to several purines and pyrimidines; the ratios hypoxanthine/crn, xanthine/crn and urate/crn (p = 2.0 × 10−14, < 3 × 10−15 and 7.2 × 10−4, respectively), and the ratios orotic acid/crn, uracil/crn, and carbamyl-β-alanine/crn (p = 0.03, 1.4 × 10−6 and 0.003, respectively). The values of urate/crn, xanthine/crn, uracil/crn, and carbamyl-β-alanine/crn were higher in females above 16 years of age. Ketosis and pH influenced some markers. In conclusion, decreased renal function interferes with the excretion of urinary purines and pyrimidines, and this could change decision limits substantially, e.g. result in false negative results in Lesch-Nyhan syndrome. SynopsisGFR influences purines and pyrimidines in urine.Clinical Trial Registration: ClinicalTrials.gov, Identifier NCT01092260, https://clinicaltrials.gov/ct2/show/NCT01092260?term=tondel&rank=2

Applying Ayurvedic Principles for Diagnosis and Treatment of Chronic Kidney Diseases: An Integrative Approach

Applying Ayurvedic Principles for Diagnosis and Treatment of Chronic Kidney Diseases: An Integrative Approach

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients' quality of life and prolong survival time. In this study, we explore the intelligibility of machine-learning and deep-learning models on end-stage renal disease (ESRD) prediction, based on readily-accessible clinical and laboratory features of patients suffering from CKD. Eight machine learning models were used to predict whether a patient suffering from CKD would progress to ESRD within three years based on demographics, clinical,and comorbidity information. LASSO, random forest, and XGBoost were used to identify the most significant markers. In addition, we introduced four advanced attribution methods to the deep learning model to enhance model intelligibility. The deep learning model achieved an AUC-ROC of 0.8991, which was significantly higher than that of baseline models. The interpretation generated by deep learning with attribution methods, random forest, and XGBoost was consistent with clinical knowledge, whereas LASSO-based interpretation was inconsistent. Hematuria, proteinuria, potassium, urine albumin to creatinine ratio were positively associated with the progression of CKD, while eGFR and urine creatinine were negatively associated. In conclusion, deep learning with attribution algorithms could identify intelligible features of CKD progression. Our model identified a number of critical, but under-reported features, which may be novel markers for CKD progression. This study provides physicians with solid data-driven evidence for using machine learning for CKD clinical management and treatment.

The Diagnostic Performance of a Clinical Diagnosis of Diabetic Kidney Disease.

Diabetic kidney disease (DKD), a common cause of CKD and kidney failure, is usually diagnosed clinically. However, there is little evidence comparing the performance of a clinical diagnosis to biopsy-proven diagnosis. Diagnostic performance of a clinical diagnosis was determined in a group of patients with diabetes and chronic kidney disease who underwent kidney biopsy after an initial clinical diagnosis. A data analysis of 54 patients who were part of a study cohort for a prospective analysis of cardiovascular and kidney outcomes and who had undergone kidney biopsy after an initial clinical diagnosis of DKD or non-DKD (NDKD) at enrolment was used. We determined the sensitivity, specificity, and positive and negative predictive values of a clinical diagnosis of DKD. A total of 37 of 43 patients clinically diagnosed with DKD also had biopsy-proven DKD, whilst only 1 of 11 patients who had clinically diagnosed NDKD had biopsy-proven DKD. Sensitivity was 97.4%, specificity was 62.5%, positive predictive value 86%, and negative predictive value 90.9%. Comparable values were obtained when analysis was restricted to those with primary rather than secondary diagnosis of DKD or when restricted to those with only DKD found at biopsy. A clinical diagnosis of DKD has high sensitivity and is unlikely to overlook cases but may lead to overdiagnosis.

Decreased levels of perfluoroalkyl substances in patients receiving hemodialysis treatment

Perfluoroalkyl substances (PFAS) have been reported to be harmful to multiple organs in the human body. Based on a previous study suggesting that hemodialysis (HD) may be a means of eliminating PFAS from the human body, we aimed to compare the serum PFAS concentrations of patients undergoing regular HD, patients with chronic kidney disease (CKD) and controls. Additionally, we also investigated the correlation between PFAS and biochemical data, as well as concurrent comorbidities. We recruited 301 participants who had been on maintenance dialysis for >90 days, 20 participants with stage 5 non-dialysis CKD, and 55 control participants who did not have a diagnosis of kidney disease, with a mean creatinine level of 0.77 mg/dl. Eight different PFAS, namely perfluorooctanoic acid (PFOA), total and linear perfluorooctanesulfonic acid (PFOS), perfluoroheptanoic acid (PFHpA), perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUnDA), were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Spearman correlation and multivariable linear regression with 5 % false discovery rate were used to evaluate the relationships between PFAS and clinical parameters in HD patients and controls. Circulating concentrations of seven PFAS, including total and linear PFOS (T-PFOS and L-PFOS) PFDA, PFNA, PFHxS, PFOA, and PFUnDA, were significantly lower in the HD group compared to the CKD and control group. For the interplay between biochemical data and PFAS, all of the studied PFAS were positively correlated with aspartate aminotransferase, alanine aminotransferase, glucose, blood urea nitrogen, ferritin, and vitamin D in the controls, while in HD patients, the PFAS were all positively correlated with albumin, uric acid, iron, and vitamin D. These findings may offer valuable insights for future studies seeking to eliminate PFAS.

Design and methodology of the PRIMETIME 1 cohort study: PRecIsion MEdicine based on kidney TIssue Molecular interrogation in diabetic nEphropathy.

Clinical features of diabetic kidney disease alone cannot differentiate between the histopathology that defines diabetic nephropathy (DN) and non-diabetic nephropathy (NDN). A kidney biopsy is necessary to make the definitive diagnosis of DN. However, there is no consensus on when to perform a kidney biopsy in individuals with diabetes and kidney disease. Furthermore, the implications of NDN versus DN for management, morbidity and kidney prognosis are unclear. To address the gap in knowledge, we aimed to create a national retrospective cohort of people with diabetes and a performed kidney biopsy. Adults diagnosed with diabetes in Denmark between 1996 and 2020 who had a kidney biopsy performed were included. The cohort was established by linking a nationwide diabetes registry with the Danish Pathology Registry. Data from 11 national registries and databases were compiled. The type of kidney disease was classified using a three-step analysis of Systematized Nomenclature of Medicine codes reported in relation to the histopathological examinations of kidney tissue. The final cohort and classification of kidney disease was as follows: out of 485 989 individuals with diabetes 2586 were included, 2259 of whom had type 2 diabetes. We were able to classify 599 (26.5%) with DN, 703 (31.1%) with NDN and 165 (7.3%) with mixed disease in individuals with type 2 diabetes. In individuals with type 1 diabetes, 132 (40.4%) had DN, 73 (22.3%) NDN and 39 (11.9%) mixed disease. The remaining could not be classified or had normal histology. The overall median (Q1-Q3) follow-up time was 3.8 (1.6-7.2) years. This cohort is a novel platform based on high-quality registry data for important longitudinal studies of the impact of kidney disease diagnosis on prognosis. With regular updates of data from the Danish registries, the presented follow-up will increase over time and is only limited by emigration or death.

Evaluation of renal markers in systemic autoimmune diseases.

Renal involvement is a common occurrence in subjects with systemic autoimmune diseases. The renal manifestation and its severity depend on the underlying condition and may reversely complicate the clinical course of autoimmune diseases. Renal function markers have been widely used in the assessment of normal functioning of kidneys including glomerular filtration rate and concentrating and diluting capacity of the kidney. An increase or decrease in the values of these markers may indicate kidney dysfunction. In this study, a number of critical renal markers were examined in seropositive autoimmune diseases including systemic lupus erythematosus (SLE), connective tissue disorder (CTD), and rheumatoid arthritis (RA). The data from three cohorts of subjects enrolled in renal markers and autoimmune antibody testing between January 2015 to August 2019 were retrospectively studied. The prevalence of renal markers that were out of the reference range and their average levels in female and male subgroups across SLE, CTD, and RA cohorts were compared and analyzed. The levels of renal markers are significantly affected by the presence of autoantibodies, in particular eGFR, cystatin C, and albumin. Autoantibodies were also more frequent in subjects with severe renal function damage. Close follow-up of both renal markers and autoantibodies may potentially assist in the early diagnosis of kidney diseases and improve the survival and life expectancy of autoimmune patients.

A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure.

Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing. We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing. For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use. We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.

Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

437-P: Establishing a Simple Decision-Making Model for the Differential Diagnosis of Diabetes Kidney Disease and Nondiabetes Kidney Disease in T2D Patients through Machine Learning

Objective: To study the 2-DM patients with renal biopsy, analyze the risk factors of DKD and establish a simple decision model to differentiate DKD and NDKD. Methods: 99 2-DM patients were admitted to hospital in Beijing from December 1, 2018 to December 31, 2021. All of the patients accepted kidney biopsy and biochemical test, fundus photography and OCTA. According to the pathological reports, 99 cases were divided into DKD group and NDKD group. The difference indicators of the two groups were compared. The risk factors of DKD were analyzed by logistic regression. A simple and convenient method to distinguish DKD and NDKD was established through the decision tree model. Results: This study was a cross-sectional study. Compared with DKD and NDKD groups, the ratio of male/female in DKD group was higher than that in NDKD group (P=0.033), DR prevalence rate was significantly higher than that in NDKD group (P&amp;lt;0.001), course DM, FBG, HBAIC, SBP, TC and EGFR were also higher than those in NDKD group, and HGB decreased. Binary logistic regression analysis was conducted to determine whether DKD was combined. After adjusting for the above indicators, age, DR, course of DM, HBA1C and HGB were still independently related to DKD. A decision tree model was established with the training set: the course DM, DR, and HBA1C were risk factors of DKD. The accuracy of prediction classification is 75%, the sensitivity was 46.2%, and the specificity was 88.9%. Conclusion: If the patients with type 2 diabetes kidney injury have poor blood glucose control, no DR but anemia, a renal puncture should be considered for definite diagnosis of NDKD. Disclosure L.Zhang: None. Funding Beijing Science &amp; Technology Development of Traditional Chinese Medicine (JJ-2020-25)

Reasoning for obligatory preclinical diagnostics of feline chronic kidney disease

Diseases of the urinary system of various etiologies are common in all species of animals and over time, they can lead to the development of chronic kidney disease (CKD) as the progressive process in which renal function gradually decreases over several years. In modern veterinary medicine, there is a problem of a steeply increase in cases of chronic kidney disease in cats. Pathology occurs most often in aged animals, but recent studies have registered cases of chronic renal dysfunction in young ones as well. In chronic kidney disease the kidneys do not work well for a long time, causing a number of changes in the body. Unfortunately, early symptoms are often missed by pet owners and the disease is not noticed for a long time. In most cases, the diagnosis of chronic kidney disease is established only when effective treatment is no longer possible or ineffective and treatment is able to maintain the homeostasis of the body only to a certain extent and for a short time. Undoubtedly, this approach to the diagnosis and treatment of kidney disease requires changes. In view of the fact that the duration of the preclinical stage of CKD can reach several years, the goals of our study were to show the benefits of early diagnosis of chronic kidney disease in cats and the introduction of a new concept of systematic body screening of clinically healthy animals and animals at risk. Also, the goals were to delay the moment of the kidneys compensatory reserve exhaustion and onset of clinical symptoms, which require constant symptomatic and replacement therapy. Reducing the percentage of early mortality of patients and the maximum possible increase in the duration of the preclinical stage of renal pathology were also aimed at. Efforts were also aimed at reducing the percentage of early mortality of patients and the maximum possible increase in the duration of the preclinical stage of renal pathology. Since nephroprotective therapy at the preclinical stage is highly effective in most clinical cases and allows to significantly slow down the progression of any chronic nephropathy, including CKD, there is a need for the formation of innovative diagnostic approaches at the early stages of the disease. It is proposed to conduct a cumulative assessment of the anamnesis in animals from risk groups, monitoring the level of creatinine in the blood serum, complete clinical analysis of urine, ultrasound examination of the kidneys, measurement of protein concentration, since these indicators have not only prognostic value and determine the tactics of treatment, but are also important factors pathogenesis of CKD. Since each parameter will not provide a complete state of the disease, since none of them is 100 % specific, an integrated approach to the early diagnosis of chronic kidney disease is needed for. It is necessary to take into account all the methods listed above, as well as monitoring the condition of the body of animals that have contracted any viral or bacterial infection, or have acute kidney damage, diabetes, hypertension, or metabolic disorders.

Learning from deep learning and pathomics

The suffix -omics has been applied to a variety of approaches with the overarching aim to identify, describe, and quantify processes that contribute to the form and function of cells and tissues. Historically, histopathologic approaches have relied on assessment of lesions to describe a pattern of injury and when possible, give a specific etiologic diagnosis. Broad pathologic entities have been split into smaller groups to provide more individualized diagnostic, prognostic, and etiologic information as a step towards precision medicine. More recently, pathomics has emerged as a complement to traditional histopathology, applying deep learning algorithms to digital histopathology images for detailed quantitative mining of visible or subvisual features that correlate with specific diagnoses, disease trajectories, or shared injury patterns. In a recent publication in Nature Communications, Hölscher and colleagues describe this exciting new approach of pathomics, with the potential to provide insights into kidney disease diagnosis, prognosis, and mechanisms ( 1 Hölscher D.L. Bouteldja N. Joodaki M. et al. Next-Generation Morphometry for pathomics-data mining in histopathology. Nat Commun. 2023; 14: 470 Crossref PubMed Scopus (1) Google Scholar ).

The role of tubulointerstitial markers in differential diagnosis and prognosis in patients with type 2 diabetes and biopsy proven diabetic kidney disease

ObjectiveTo evaluate the potential application of tubularinterstitial biomarkers in the differential diagnosis of diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD), as well as investigate key clinical and pathological parameters to help improve the stratification of patients according to end-stage renal disease risk. Methods132 type 2 diabetic patients with chronic kidney disease were enrolled. Patients were categorized into 2 groups according to the renal biopsy results: DKD (n = 61) and NDKD (n = 71).The independent factors of the occurrence of DKD and the diagnostic implications of tubular biomarkers were explored by logistic regression and receiver-operating characteristic curve analysis. Furthermore, predictors were analyzed by least absolute shrinkage and selection operator regression, and constructed a new model for predicting the unfavorable renal outcomes through Cox proportional hazard regression analysis. ResultsSerum neutrophil gelatinase-associated lipocalin (sNGAL) (OR = 1.007; 95%CI = [1.003, 1.012], p = 0.001) was identified as an independent risk factor for the occurrence of DKD in diabetic patients with CKD. Tubular biomarkers including sNGAL, N-acetyl-β-D-glucosaminidase and β2 microglobulin (β2-MG) could complement albuminuria for DKD detection (AUC = 0.926, specificity = 90.14%, sensitivity = 80.33%).Moreover, among of the 47 variables, 4 predictors such as sNGAL, interstitial fibrosis and tubular atrophy (IFTA)score, β2-MG and estimated glomerular filtration rate were selected to construct a new model for predicting the unfavorable renal outcomes through regression analysis. sNGAL (HR = 1.004; 95%CI = [1.001, 1.007], p = 0.013), IFTA score of 2 (HR = 4.283; 95%CI = [1.086, 16.881], p = 0.038), and IFTA score of 3 (HR = 6.855; 95%CI = [1.766, 26.610], p = 0.005) were considered to be independent risk factors for unfavorable renal outcomes. ConclusionsTubulointerstitial injury in DKD is independently associated with renal function decline and routinely detected tubular biomarkers are able to enhance the level of non-invasive diagnosis of DKD beyond traditional factors.

#5147 PREDICTING NON-DIABETIC KIDNEY DISEASE IN PATIENTS WITH TYPE 2 DIABETES MELLITUS: EXTERNAL VALIDATION OF FOUR PREDICTIVE MODELS

Abstract Background and Aims Several prediction models have been published for estimating the probability of non-diabetic kidney disease in kidney biopsies performed in patients with diabetes mellitus. External validation and comparison of these models is essential prior to their clinical application. The aim of this study was to validate and compare the predictive capacity of four previously described clinical models as a tool to predict non-diabetic kidney disease (NDKD) in diabetic patients. Method We retrospectively reviewed kidney biopsies performed in patients with type 2 diabetes mellitus between January 1999 and December 2022 in our centre. The probability of presence of NDKD was calculated by using the clinical and laboratory parameters at the time of the decision to perform a biopsy, according to 4 models; Surinrat's model, Liu's model, Li's model and Garcia-Martin's model (see Table 1). Overall model fit was assessed, calibration curves were plotted and discrimination for each model was assessed by using the receiver operating characteristic (ROC) curve in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. Results The study included 280 patients, 68.9% were males and the mean age was 65.4 ± 11.9 years. 172 patients (61.4%) had a diagnosis of non-diabetic kidney disease. On calculating the risk of NDKD, Surinrat's, Li's and Garcia-Martin's models predicted 33.6%, 64.8% and 53.4% of NDKD when using the “probable NDKD” threshold, and 60.7%, 81% and 90.7% respectively when including the “grey zones”. Fleiss’ Kappa test showed a fair agreement between the four models when using the “probable NDKD” threshold (k = 0.23, 95% CI 0.13-0.33, p&amp;lt;0.001) and when including the “grey zones” (k = 0.39, 95% CI 0.29-0.49, p&amp;lt;0.001). On using the “probable NDKD” threshold, the overall accuracy for Surinrat's, Liu's, Li's and Garcia-Martin's models was 50.7%, 62.7%, 63.6% and 65.3% respectively, while on including the “grey zones” the accuracy slightly increased in the first to models to 54.3% and 66.4% respectively while decreasing in Garcia-Martin's model to 64.2%. Area under the ROC curve (AUC) was higher in Garcia-Martin's model (AUC 0.66, 95% CI 0.58-0.74, p&amp;lt;0.001) compared to Lin's model (AUC 0.59, 95% CI 0.50-0.67, p = 0.044), while Surinrat's and Liu's models did not discriminate (AUC 0.55, 95% CI 0.47-0.64, p = 0.214 and AUC 0.58, 95% CI 0.49-0.63, p = 0.084). Conclusion There is a fair agreement between the four models for predicting non-diabetic kidney disease in type 2 diabetic patients. Of the models studied, Garcia-Martin's model most accurately predicted non-diabetic kidney disease in type 2 diabetes patients, although discrimination remained poor. These models may not be suitable for guiding clinicians on indicating a kidney biopsy in diabetic patients in our population.

#4410 NOVEL DOMINANT ALG5 VARIANT IN TWO UNRELATED FAMILIES WITH LATE-ONSET ADPKD AND ATYPICAL TUBULOINTERSTITIAL CHANGES

Abstract Background and Aims Autosomal dominant polycystic kidney disease (ADPKD) is a monogenic disorder renowned for its clinical and genetic heterogeneity. Recently, variants in the ALG5 gene have been described as a cause of atypical ADPKD and interstitial fibrosis through the disruption of polycystin 1 maturation and trafficking via underglycosylation. In this report, we investigated genetic cause of the disease in two unrelated Irish families displaying late-onset ADPKD phenotype with atypical tubulointerstitial changes. Method Routine clinical and radiological evaluations were carried out. Available kidney biopsy specimens were reassessed. Genetic testing was performed on probands and all relatives with cystic kidneys. First, a custom-targeted gene panel of 227 genes associated with kidney disease was used, but the causal variant was not identified. Then reading frame-changing variants in variable number tandem repeats region of MUC1 were excluded by single-molecule real-time sequencing. Whole exome sequencing revealed a variant in the ALG5 gene, which is not included in the initial custom gene panel. By targeted Sanger sequencing, segregation of the variant with the disease phenotype was confirmed in both investigated families. Multiple bioinformatic tools were employed to predict the effect on protein structure and functions. Results The clinical diagnosis was consistent in most of the 20 affected individuals with non-enlarged cystic kidneys and few or no liver cysts. All but 1 individual underwent radiological assessment; 7 had kidney and liver cysts, 7 had only kidney cysts, and 5 had no kidney cysts at ages 68.6±14.4, 55.1±15.8, 46.8±15.6 years, respectively. Polycystic liver phenotype (&amp;gt;20 cysts) was present in two individuals. Biopsy-proven extensive kidney interstitial fibrosis and cystic tubular dilation were evident in four affected individuals with available kidney samples. Of the 20 genetically-defined individuals, 4 had end-stage kidney failure at a mean age of 70.25±3.1 years. Ten individuals were CKD stage 3 or greater, while 6 were CKD stage 4 or lower at ages 43.2±12.6 and 63.1±7.5 years (P value 0.004). A novel heterozygous missense variant in the ALG5 gene (NM_013338.5, c.235C&amp;gt;T, p.Arg79Trp) was identified in affected members from investigated families. Genetic screening of 20 affected and 10 unaffected individuals from both pedigrees revealed segregation of the variant with the disease phenotype. The novel ALG5 variant was classified as likely pathogenic as it was absent in the Genome Aggregation Database (gnomAD), is located in a conserved region and is predicted to be deleterious on protein stability. Conclusion This study expands the clinical and genetic spectrum of the identified range of ADPKD, considering the pathogenic ALG5 variants. Establishing a precise diagnosis of atypical cystic and interstitial kidney disease is crucial, with essential implications including follow-up, genetic counselling, prognostication, and therapeutic interventions.

#4044 FOCAL SEGMENTAL GLOMERULOSCLEROSIS DUE TO A3243G POINT MUTATION IN THE MTDNA CODING FOR TRNALEU(UUR)

Abstract Background and Aims Mitochondrial Diseases represent a heterogeneous set of maternally inherited diseases that arise by mutations either of the mitochondrial DNA (mtDNA) or in genes of nuclear DNA (nDNA) linked to the cell mitochondrial cross-talk. Many mutations in mtDNA that code for different genes are linked to a wide spectrum of kidney manifestations ranging from focal segmental glomerulosclerosis (FSGS), tubule-interstitial disease (TIN), nephrotic syndrome (NS), and proximal or distal tubulopathy. Renal manifestations of mtDNA disorders are poorly recognized in clinical practice and often misdiagnosed for other conditions like FSGS, Alport Syndrome, steroid-resistant nephrotic syndrome. The guanine for adenine point mutation (A–&amp;gt;G) at position 3243 in the mtDNA has emerged as the most common mutation found in patient with FSGS often accompained by diabetes mellitus and deafness. Focal Segmental Glomerulosclerosis, (FSGS) is the prevalent histologic finding in patients with mtDNA point mutations. Epidemiologic studies have shown that about 1% of diabetic populations in Europe and Japan have this mutation. Method A 34-year-old man presented for evaluation with recently diagnosed Type II diabetes mellitus and serum creatinine of 3.9 mg/dL with nephrotic range proteinuria (3.2 gr/die). His family history included Type II diabetes mellitus, chronic kidney disease, coronary artery disease, and hypertension. The patient medical history included hypertension, dyslipidemia, and sensorineural hearing loss which had initially developed during adolescence. Serum creatinine was reportedly elevated years before this presentation, and urinalysis showed mild proteinuria with no microhematuria, white cells, or casts. Results On evaluation his blood pressure was 150/90 mmHg, presented mild peripheral leg edema. Laboratory results showed serum creatinine 3.4 mg/dl (eGFR 23 ml/min), HbA1c of 7.2%, albumin 2.8 gr/dL. Urinalysis showed protein (3+), glucose (+1) and no hematuria. Urinary protein-creatinine ratio was 3 mg/g. Serum C3 and C4, immunoglobulins, liver enzymes were normal; autoimmunity tested negative (ANCA, Anti-PLA2r, Anti nuclear antobodies). A kidney biopsy was performed. Light microscopy revealed glomeruli with segmental obliteration of glomerular capillaries by collagenous sclerosis (Fig. 1). Both the Jones methenamine silver stain and Masson's trichrome stain (Fig. 1B, C) showed segmental collagen deposition and sclerosis of glomerular capillaries. Tubular atrophy and fibrosis were also present. Immunofluorescence microscopy was negative. A diagnosis of not otherwise specified focal segmental glomerulosclerosis (NOS FSGS) was made. Interestingly electron microscopy showed mitochondrial abnormalities in the tubular cells. In particular, the cytoplasm of a tubular cell appears to be whiffed by mitochondria of enlarged size and altered shape (Fig. 2). Mithocondria appeared dysmorphic with extensive reduction of mitochondrial cristae (Fig. 2 arrows). The patient was screened for mtDNA mutations and guanine for adenine substitution (A–&amp;gt;G) at position 3243 was detected. The patient received a diagnosis of chronic kidney disease with histological features of FSGS due to an A3243G point mutation in the mtDNA with associated diabetes mellitus and sensorineural deafness. Conclusion This case underscores the role of electron microscopy in identifying mithocondrial related nephropathies. Even more important is to evaluate mtDNA mutations in cases with familial FSGS, sensorineural hearing loss, cardiomyopathy and diabetes.

#4869 ULTRASOUND IMAGING PATTERNS OF KIDNEY DISEASE IN PATIENTS WITH METABOLIC SYNDROME

Abstract Background and Aims Meeting definition of metabolic syndrome (MetS) requires detection of central obesity (increasing waist circumference, WC) plus any two of the other four factors (hyperglycemia, dyslipidemia, cardiovascular disease, hypertension) [1]. Up to date, kidney evaluation is not fully reflected in MetS evaluation. Multiparameter ultrasound (US) can provide accurate information on every MetS sign [2] and for specific detecting type of chronic kidney disease (CKD) [3]. The Aim was to study added diagnostic value of ultrasound diagnosis of chronic kidney disease in patients with MetS for precise diagnosis and patients stratification. Method We included 48 overweight subjects patients (27-75 years; 25 women) with signs of MetS, BMI &amp;gt; 30, waist circumference (WC) &amp;gt; 110. The control group included 50 healthy individuals (26 women), mean age 43 ± 7 years without clinical, laboratory signs of MetS, nephropathy and liver pathology. All patients underwent general clinical, lab tests; abdominal US including precise multiparameter US of kidneys as in [3] measuring shear wave elastography (SWE), Doppler in parenchyma and vessels. We determined fat accumulations measuring VF and subcutaneous (SF) fat thickness (cm) and areas (cm2) on US. Results We detected hypertension (in 62%); hypercholesterolemia (in 45%); hyperuricemia (in 28%); hyperglycemia (in 32%); nephropathy (in 37%), portal hypertension (in 32%) in obese individuals (vs controls p &amp;lt; 0.05). In all patients we found increasing of visceral fat and various abnormalities in kidneys on US, we distinguished patterns in according to constituents of MetS by its definition s follows: Diabetic nephropathy included mild decresing or normal kidneys size, thinning parenchyma, low differentiation, clear smooth margings, Doppler showed mild decreasing velocities, increasing of RI in segmental arteries over 0.7 (Figure 1). Ischemic pattern associated with advanced atherosclerosis included severe decreasing of size, thinned, hyperechoic parenchyma; decreasing velocities under 25 cm/s., severe increasing of RI over 0.8. Hypertension associated nephropathy: mild changes in structure, normal velocities, increased RI (Figure 2). Hypertension correlated with levels of RI. Nephropathy in hyperuricemia and gout: signs as in [3] included thinning, increasing echogenicity of kidney parenchyma (P &amp;lt; 0.05), detection of fibrotic changes and small hyperechoic inclusions, hilly margins, anaechoic strips under the capsule, RI increasing in segmental arteries over 0.7. “Circular pyramyds pattern” included hyoerechoic circles around pyramids (white pyramid sign, hyperdense renal pyramids sign) with normal size, normal / mild increase of RI. SWE showed increased parenchyma stiffness to 10 ± 1.7 kPa (6–17 kPa) versus 4.2 ± 1.2 kPa (P &amp;lt; .05), showed differences among patterns, highest values were in nephropathy in gout and ischemia. Conclusion Multiparameter ultrasound is effective for detecting and distinguishing patterns of kidney disease in MetS provides additional value for differentiation and modification treatment and targeted prevention. Kidney involvement should be considered in management of MetS.

#4427 USING REDUCED RANK REGRESSION TO INVESTIGATE THE RELATIONSHIP BETWEEN DIETARY PATTERNS AND ADULT KIDNEY FUNCTION IN THE GENERAL POPULATION

Abstract Background and Aims Chronic kidney disease (CKD) is a severe public health burden, characterized by a gradual loss of kidney function over time. Diet is a modifiable lifestyle-related risk factor for CKD. However, there is uncertainty about which specific dietary patterns (DPs) are more beneficial or detrimental in CKD prevention. We aimed at deriving DPs using an hybrid approach that combines a priori knowledge and data-driven methods to identify dietary factors that may affect kidney function in healthy and diseased subjects. Method We analysed data of 8686 adults participating in the population-based Cooperative Health Research In South Tyrol (CHRIS) study. Kidney function was multiply assessed by the estimated glomerular filtration rate (eGFR), based on serum creatinine, using the 2021 CKD-EPI equation, the urinary albumin-to-creatinine ratio (UACR), and a kidney disease questionnaire. Participants were split between those free of diagnosed kidney disease, hypertension or diabetes (Group1, n = 6133) and those diagnosed with any of the three conditions (Group2, n = 2553). Diet was assessed through the self-administered and validated GA2LEN food frequency questionnaire (FFQ). The individual consumption of each food group was converted into portions per week and adjusted for total energy intake. DPs were estimated using reduced rank regression (RRR), based on four FFQ-derived nutrient mediators (total daily dietary protein; potassium; sodium; and phosphorus intake) selected based on known effects on kidney health. Generalized cross-validation identified an optimal number of 3 DPs. Factor Loading (FL)-based scores, either as continuous or stratified into sex-stratified tertiles (T1-T2-T3), were included in multiple-adjusted linear regression models for eGFR and log(UACR). Results Group1 participants (53.4% females) were younger and presented better kidney health (median, Mdn age 39.7 years; Mdn eGFR 101.8 ml/min/1.73m2, interquartile range, IQR 91.5-112.1; Mdn UACR 5.2 mg/g, IQR 3.5-8.8) than Group2 participants (Mdn age 57.2 years; Mdn eGFR 90.8 ml/min/1.73 m2, IQR 80.4-100.3; Mdn UACR 6.5 mg/g, IQR 4.1-12). The identified DPs were (Figure 1): DP1, reflecting greater consumption of all nutrients; DP2, reflecting increased potassium and phosphorus intake and lower sodium and protein intake; and DP3, reflecting increased intake of protein and phosphorus and lower intake of potassium and sodium. The 3 DPs presented stable FLs across groups. In Group1, DP1 was negatively associated with eGFR (larger effect size in males) both as linear score and in the 3rd tertile (Figure 2); DP2 was positively associated with eGFR in males and with UACR both at low and high levels of the score, with heterogeneous effects between males and females; DP3 was positively associated with UACR overall in the 3rd tertile. In Group 2, we observed protective effects of diet on eGFR, especially at lower DP1 and DP3 levels, and at high levels of DP2 (Figure 2). Similar to Group1, also in Group2 DP2 was positively associated with UACR both at low and high level of the score, but not in males. In its 3rd tertile, DP3 was negatively associated with UACR in females. Conclusion Using RRR proved to be a valid approach to integrate a priori knowledge about nutrients in the estimation of kidney function-oriented DPs. Our results showed heterogeneous effects of the DPs across kidney outcomes, possibly reflecting specificity to kidney function or damage. In individuals affected by any kidney disease, hypertension or diabetes, the effects of DPs on eGFR reflected possible benefits of specific diets, suggesting that disease-specific dietary interventions can be a fundamental and effective approach for disease control.

#3503 IMPACT OF ADOPTING THE NEW CKD-EPI 2021 EQUATION ON EGFR AND CKD DETECTION IN A MULTI-ETHNIC DUTCH POPULATION: THE HELIUS STUDY

Abstract Background and Aims The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is the current standard method for estimating GFR. There has been debate whether or not to exclude the race/ethnicity coefficient from this equation. Recently, the National Kidney Foundation and American Society of Nephrology Task Force on Reassessing Race in Diagnosing Kidney Disease recommended a new 2021 CKD-EPI race-free creatinine-based equation. The impact of this equation has not thoroughly been evaluated in the European population. We explored the impact of adopting this formula on both eGFR and estimates of CKD prevalence in a multi-ethnic Dutch population. Method We cross-sectionally analysed baseline data of 21,617 participants (mean age 44yr, 42% male) of the multi-ethnic HELIUS cohort study (Amsterdam, the Netherlands). Three groups were distinguished: participants of African Surinamese (4,151), Ghanaian (2,339) and other, non-African (15,127) background. eGFR was calculated using the 2009 and 2021 CKD-EPI equation. Multiple regression analyses were performed to determine differences in eGFR with additional adjustments for age, sex and traditional cardiovascular and renal risk factors. CKD prevalence (i.e., eGFR (&amp;lt;60mL/min/1.73m2) and/or ACR (≥3mg/mmol) was calculated. In each ethnic group three approaches to improve CKD case detection for both equations were compared by targeting participants with traditional renal risk factors (i.e., diabetes mellitus, hypertension or cardiovascular disease), by subsequently adding an age criterium (&amp;gt;40, &amp;gt;50, or &amp;gt;60 yrs), and low socio-economic status (none or elementary schooling). For each approach, c-statistics for CKD probability were compared. Results Differences between groups varied according to the equation used. Compared to participants with non-African backgrounds (mean eGFR 102 mL/min/1.73m2), age- and sex-adjusted differences (p&amp;lt;0.001) in the mean eGFR (SE) were 4.6±0.2 and -8.9±0.2 mL/min/1.73m2,in participants with African Surinamese, and 3.2±0.3 and -10.4±0.3 mL/min/1.73m2 in participants with Ghanaian background for the 2009 and 2021 CKD-EPI equations, respectively. Further adjustments did not change this. However, CKD prevalences were similar for both equations: 10.6% vs 10.8% (p = 0.30) in the whole cohort, 10.9 vs. 11.6% (p = 0.33) among African Surinamese and 12.0 vs 12.6% (p = 0.21) among Ghanaians. CKD case detection did not differ between the screening approaches nor between both equations. Also, c-statistics for CKD probabilities were not influenced by either equation. Conclusion In our cohort, adoption of the 2021 CKD-EPI equation leads to lower eGFR in participants of African Surinamese and Ghanaian background. However, only small not statistically significant differences in CKD prevalences, overall and in high-risk groups, were found. Our study indicates that discontinuation of the race-coefficient may have little impact on CKD detection in a multi-ethnic Dutch population.