Early Detection Of Chronic Kidney Disease Research Articles

O40 TELOMERE LENGTH AS A BIOMARKER FOR KIDNEY HEALTH: EVIDENCE FROM A MENDELIAN RANDOMISATION STUDY

Background and Objective: Chronic kidney disease (CKD) is a significant public health challenge, often progressing undetected until advanced stages. Early detection through biomarkers can enhance CKD management and improve patient outcomes. Previous studies suggested a link between telomeres and kidney health, but these results were inconsistent. This study aims to clarify the association between telomere length, kidney function and disease, and assess its causality using Mendelian randomisation (MR). Methods: Using the UK Biobank data, we examined the relationship between leukocyte telomere length (LTL) and kidney health. We performed multiple linear and logistic regression analyses using estimated glomerular filtration rate (eGFR) measures and reported kidney disease diagnoses, adjusting for age, sex, white blood cell count, body mass index, and hypertension. The causality of associations was tested using MR with established genetic instruments. Results: 338,143 participants were included in our analysis. The cohort had an average age of 56.52 years, with 54.41% being females. LTL was associated with eGFR from serum creatinine (eGFRcrea, beta = 0.001, p <0.01), serum cystatin C (eGFRcys, beta = 0.004, p <0.0001), and their combination (eGFRcreacys, beta = 0.003, p <0.0001). Additionally, longer LTL was associated with reduced odds of CKD (OR = 0.94 [95% CI 0.93-0.96], p <0.0001). Significant associations were also observed between LTL and acute renal failure (OR = 0.95 [95% CI 0.93-0.97], p <0.0001), and hypertensive kidney disease (OR = 0.91 [95% CI 0.86-0.96], p <0.001). MR analysis supported a causal effect of LTL on eGFRcrea (Effect size = 0.006, Bonferroni corrected p = 0.04). However, no causal effect was observed between LTL and CKD or other kidney diseases. Bi-directional MR analysis revealed that the relationship between LTL and kidney function is unidirectional. Conclusions: Longer LTL correlates with better kidney function and reduced kidney disease risk. MR findings suggest a potential causal relationship between LTL and kidney function, highlighting telomeres as promising biomarkers for early detection of CKD.

The impact of population ageing on the burden of chronic kidney disease.

The burden of chronic kidney disease (CKD) and its risk factors are projected to rise in parallel with the rapidly ageing global population. By 2050, the prevalence of CKD category G3-G5 may exceed 10% in some regions, resulting in substantial health and economic burdens that will disproportionately affect lower-income countries. The extent to which the CKD epidemic can be mitigated depends largely on the uptake of prevention efforts to address modifiable risk factors, the implementation of cost-effective screening programmes for early detection of CKD in high-risk individuals and widespread access and affordability of new-generation kidney-protective drugs to prevent the development and delay the progression of CKD. Older patients require a multidisciplinary integrated approach to manage their multimorbidity, polypharmacy, high rates of adverse outcomes, mental health, fatigue and other age-related symptoms. In those who progress to kidney failure, comprehensive conservative management should be offered as a viable option during the shared decision-making process to collaboratively determine a treatment approach that respects the values and wishes of the patient. Interventions that maintain or improve quality of life, including pain management and palliative care services when appropriate, should also be made available.

Knowledge, attitude and practices towards prevention and early detection of chronic kidney disease and associated factors in Ethiopia: A cross-sectional study.

Early detection of chronic kidney disease (CKD) is important because it enables clinicians to initiate effective treatment, preventing loss of kidney function, and delaying or avoiding progression to kidney failure. This study was aimed to assess knowledge, attitude, and practices towards prevention and early detection of CKD and associated factors. Institution based cross-sectional survey was done at Adama Hospital Medical College, Ethiopia, between November 24/2021 and December 24/2021 among 190 hypertensive patients. Data were entered into EpiData version 4.2.0.0 and analyzed by Statistical Package for Social Sciences (SPSS) version 23. The level of good knowledge, positive attitude, and good practice was 40.5%, 53.7%, and 47.4%, respectively. Government employed (AOR = 3.30, 95%CI: 1.38, 7.90), having an average monthly income of ≥3000 ETB (61.43 US dollars) (AOR = 2.95, 95%CI: 1.31, 6.66), and having a duration of ≥4 years since diagnosis of hypertension (AOR = 2.37, 95%CI: 1.11, 5.06) were factors significantly associated with good knowledge. Government employed (AOR = 2.56, 95%CI: 1.12, 5.87), having duration of hypertension ≥4 years since diagnosis (AOR = 2.16, 95%CI: 1.07, 4.36) were factors significantly associated with positive attitude. Government employed (AOR = 4.16, 95%CI: 1.38, 12.58), having an average monthly income of ≥3000 ETB (61.43 US dollars) (AOR = 6.74, 95%CI: 2.93, 15.52), having good knowledge towards prevention and early detection of CKD (AOR = 2.57, 95%CI: 1.14, 5.80) were significantly associated with good practice. The level of good knowledge, positive attitude, and good practice towards was low. Educational programs on these issues are required to minimize the burdens.

On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence

Chronic Kidney Disease (CKD) is increasingly recognised as a major health concern due to its rising prevalence. The average survival period without functioning kidneys is typically limited to approximately 18 days, creating a significant need for kidney transplants and dialysis. Early detection of CKD is crucial, and machine learning methods have proven effective in diagnosing the condition, despite their often opaque decision-making processes. This study utilised explainable machine learning to predict CKD, thereby overcoming the 'black box' nature of traditional machine learning predictions. Of the six machine learning algorithms evaluated, the extreme gradient boost (XGB) demonstrated the highest accuracy. For interpretability, the study employed Shapley Additive Explanations (SHAP) and Partial Dependency Plots (PDP), which elucidate the rationale behind the predictions and support the decision-making process. Moreover, for the first time, a graphical user interface with explanations was developed to diagnose the likelihood of CKD. Given the critical nature and high stakes of CKD, the use of explainable machine learning can aid healthcare professionals in making accurate diagnoses and identifying root causes.

Detection of chronic kidney disease using binary whale optimization algorithm

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

#2343 Fabry disease screening in patients with chronic kidney disease and dialysis at a single medical center in Taiwan

Abstract Background and Aims Chronic Kidney Disease (CKD) poses a significant global health challenge, with diverse contributing factors impacting its progression. Among these, Fabry Disease, a rare lysosomal storage disorder, has emerged as a potential complicating factor. Despite its relevance, the prevalence of Fabry Disease among CKD patients, particularly in Taiwan, remains insufficiently explored. Recognizing and understanding the occurrence of Fabry Disease in CKD populations are crucial for comprehensive patient care and may have implications for treatment strategies. Additionally, investigating the relationship between kidney function and Plasma α-Gal A activity is essential. α-Gal A, the enzyme deficient in Fabry Disease, may experience altered activity levels in the presence of compromised kidney function. Clarifying this connection is pivotal for gaining insights into the intricate interplay between Fabry Disease and CKD. This study aims to achieve three key objectives (1) Determine Fabry Disease Prevalence among CKD Patients in Taiwan.(2) Investigate the Impact of Kidney Function on Plasma α-Gal A Activity.(3) Characterize Gene Mutation Patterns and Demographic Data of Fabry Patients in CKD Population in Taiwan: Method Fabry Disease assessment involved a two-stage process, tailored to the patient's gender. Males underwent a dry blood test measuring α-galactosidase activity; samples below 15% of the adult average were deemed positive. Females were screened based on lyso-Gb3 levels in whole blood; a result exceeding 0.8 ng/ml was considered positive. Positive screenings prompted genomic DNA extraction, and the GLA gene's seven exons and intron 4 segment ([GRCh38/Hg38] chrX:101,399,610-101,399,874) were PCR-amplified. Sanger sequencing identified pathogenic variants. This gender-specific, multi-step approach ensures precision. Defined thresholds and advanced genomic techniques enhance diagnostic accuracy. The method provides a robust means of identifying Fabry Disease, supporting early intervention and personalized treatment. Results Study Recruitment: Initially, 2000 subjects enrolled, but 155 withdrew, yielding a final cohort of 1845 patients for analysis. Screening and Referral: Among 1602 subjects, 26 males (1.6%) were referred for DBS LysoGB3 check-up due to low serum α-Gal A activity. In a cohort of 243 females, 6 individuals (2.4%) underwent GLA genetic sequencing due to elevated lyso-GB3 levels. Clinical Presentation - Male Case: A male with acute stroke symptoms and CKD stage 3b (eGFR 34.5 mL/min/1.73 m²) showed significantly low DBS α-Gal A activity (0.48 μmol/hr) and elevated Plasma LysoGB3 (28.08 ng/ml).GLA genetic sequencing revealed a specific deletion (c.1072-1074delGAG, p. (Glu358 del)) in exon 7, leading to a Fabry disease diagnosis, classifying it as a specific type. The family pedigrees of this male patient are visually represented in Fig. 1, illustrating the genetic relationships within the family. Clinical Presentation - Female Case: A female on hemodialysis for four years, cause unknown, exhibited a DBS LysoGB3 level of 0.5 ng/ml. GLA genetic sequencing identified a nucleotide variation (c.640-801G>A) in intron 4, establishing a diagnosis of Fabry disease with the cardiac variant (301500). Fig. 2 demonstrates the relationship between serum DBS GLA activity and different stages of CKD. Our analysis reveals that in CKD stages 1, 2, 3 (3a and 3b), and 4, the serum GLA activity is significantly higher compared to stage 5. Conclusion The prevalence of Fabry Disease in our cohort was 0.1% (2 out of 1845 individuals), featuring one female with the cardiac variant and one male with the class type. Our investigation unveiled a significant rise in serum GLA activity in CKD stages 1–4 compared to stage 5, suggesting potential implications for early CKD detection. Moreover, serum α-Gal A activity displayed a notable correlation with age, BUN, and eGFR across CKD stages 1–5, questioning its status as a biomarker for advanced CKD and aging in CKD patients. This prompts consideration for a large-scale study to validate this hypothesis.

#894 Dynamic multi-omics and mechanistic modelling approach provides deeper insight into kidney fibrosis progression

Abstract Background and Aims Chronic kidney disease (CKD) affects more than 10% of the world's population and causes millions of deaths annually. Organ fibrosis is the main driver of CKD, leading to excessive accumulation of extracellular matrix (ECM). The presence of myofibroblasts correlates with disease progression. The pro-fibrotic responses of these cells, such as ECM production, are driven by the master regulator TGF-β. Despite progress in understanding the disease, there are no treatments available, and current diagnostic indicators neither facilitate early detection of CKD nor correlate with actual renal damage [1–4] Using human kidney-derived PDGFRβ+ mesenchymal cells we aim to provide time-resolved mechanistic insight into CKD progression. Method Our goal is to understand and model how these cells contribute to fibrosis by studying their kinetic response to TGF-β. Therefore, we use multiple omics measurements: transcriptomics, secretomics and phospho-/proteomics coupled with quantitative imaging of the ECM. For each time point, we quantified differential changes in omics and estimated the activities of transcription factors, kinases and phosphatases [5]. Furthermore, we used causal reasoning to draw mechanistic relationships between omics layers within and across time points. Results For validation, we focused on a set of transcription factors that also showed importance in the network context that considers several data modalities. Using knock-down experiments, we show the relevance of these transcription factors for the ECM deposition as well as the fibrotic response to TGF-β. Conclusion Taken together, by applying mechanistic modeling and the integration of multiple datasets, we are contributing to a better understanding of the factors and mechanisms driving kidney fibrosis. Furthermore, we will publish this dataset and thus provide a valuable resource for researchers in the field of CKD.

Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.

Using the Nephrology Referral Form in Italian primary care to improve the care pathway of patients with anemia or other complications related to chronic kidney disease: survey findings

Introduction: Anemia is a potentially reversible condition in early chronic kidney disease (CKD) that requires timely intervention. General practitioners (GPs) play a crucial role in recognizing CKD. A new Nephrology Referral Form (NRF) was developed and tested in the Italian setting. Methods: This mixed-methods survey, conducted between 2021 and 2022, introduced the NRF through focus group discussions involving a scientific committee. The NRF was tested in a 6-month trial involving 24 GPs each from Lazio and Puglia regions. GPs provided feedback on the use of the NRF in clinical practice through a questionnaire sent via Microsoft Form. The data were analyzed descriptively. Results: After 6 months, 41.67% of the GPs were using the NRF at least once a week. Diabetes mellitus and hypertension were common triggers for NRF assessments. GPs overwhelmingly agreed (96%) on the NRF’s utility in identifying CKD cases, with 92% citing its effectiveness in diagnosing well-defined cases. The NRF facilitated specialist referrals, with 83% of GPs reporting increased referrals compared to the prior 6 months. Feedback underscored the NRF’s positive impact, suggesting improvements such as additional referral centers, regional/national networks, enhanced GP training, and increased collaboration. Conclusion: GPs regularly used the NRF to identify and diagnose cases of CKD, streamlining the referral process and increasing referrals to specialists. Feedback emphasized the NRF’s positive impact and highlighted its potential as a valuable tool for enhancing early CKD detection, interventions, and fostering multidisciplinary management in primary care for better patient outcomes.

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

IJCM_385A: Establishment of a CKD Screening and treatment program at a secondary care hospital in rural Karnataka - A case study

Background: With the rapidly increasing incidence of kidney disease due to the rise in non- communicable diseases like diabetes and hypertension in India, a felt need for easily accessible Dialysis unit was found during the community assessment. Chronic Kidney Disease (CKD) education and screening program was also proposed to be established along with this to improve awareness and early detection. This would help in health system strengthening and prevent patients from travelling to far away locations for the dialysis sessions. Objective: 1. To conduct CKD education and screening camps in rural areas of Karkala taluk Methodology: The project was implemented for a duration of 12 months starting from July 2021. Camps were held along with help from local clubs & Rotary Club. The following blood tests were carried out: Glycosylated Hemoglobin, Random Blood Sugar (RBS) or Fasting Blood Sugar (FBS), Serum Creatinine and Lipid Profile. These tests were done free of cost for all camp attendees. If the parameters were found to be abnormal, they were referred to Dr TMA Pai Rotary Hospital, Karkala for confirmation of the diagnosis and further management. Along with screening, Health education sessions were also be carried out to create awareness regarding diabetes and Chronic Kidney Disease among the rural population. The Dialysis unit was set up with 3 dialysis machines with funding from Rotary International under the Global Grant scheme. Dialysis was provided at subsidized to Below Poverty Line (BPL) patients. Results: A total of 10 CKD screening & awareness camps were held during the project period. A total of 588 patients were screened. 132 were referred for further management. 8 patients were referred for Dialysis. Average of 170 dialysis sessions are carried out per months. 805 participants attended the training sessions. Conclusion: Similar set ups in rural areas will help in early detection of CKD and make dialysis more affordable and accessible.

Untargeted metabolomic profiling of serum from client-owned cats with early and late-stage chronic kidney disease

Evaluation of the metabolome could discover novel biomarkers of disease. To date, characterization of the serum metabolome of client-owned cats with chronic kidney disease (CKD), which shares numerous pathophysiological similarities to human CKD, has not been reported. CKD is a leading cause of feline morbidity and mortality, which can be lessened with early detection and appropriate treatment. Consequently, there is an urgent need for early-CKD biomarkers. The goal of this cross-sectional, prospective study was to characterize the global, non-targeted serum metabolome of cats with early versus late-stage CKD compared to healthy cats. Analysis revealed distinct separation of the serum metabolome between healthy cats, early-stage and late-stage CKD. Differentially abundant lipid and amino acid metabolites were the primary contributors to these differences and included metabolites central to the metabolism of fatty acids, essential amino acids and uremic toxins. Correlation of multiple lipid and amino acid metabolites with clinical metadata important to CKD monitoring and patient treatment (e.g. creatinine, muscle condition score) further illustrates the relevance of exploring these metabolite classes further for their capacity to serve as biomarkers of early CKD detection in both feline and human populations.

Plasma MCP-1 and TGF-β1 Levels are Associated with Kidney Injury in Children with Congenital Anomalies of the Kidney and Urinary Tract.

Congenital anomalies of the kidney and urinary tract (CAKUT) are primarily causal for end-stage renal disease and have significant implications for long-term survival. A total of 39 healthy controls and 94 children with chronic kidney disease (CKD) were enrolled (3-12 years old as children, 13-18 years old as adolescents), who were divided into CAKUT and Non-CAKUT according to the etiology of CKD. CKD group was further classified according to estimating glomerular filtration rate (eGFR). Circulating levels of inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemokine-1 (MCP-1), and transforming growth factor-β1 (TGF-β1) were analyzed. The relationship between these inflammatory markers with eGFR and the kidney injury parameter (urine protein) was investigated to assess their potential as early markers of disease progression. All circulating levels of these inflammatory cytokines were increased in CKD patients (including CAKUT and Non-CAKUT) compared with healthy subjects. The circulating levels of MCP-1 and TGF-β1 were increased in CAKUT adolescents compared with CAKUT children. In CAKUT children, levels of MCP-1 and TGF-β1 increased as CKD progressed, and MCP-1 and TGF-β1 were negatively and significantly correlated with eGFR and positively with urine protein. MCP-1 and TGF-β1 may contribute to the early detection of CKD and disease stage/progression in CAKUT children.

Role of Estimated Glomerular Filtration Rate in Clinical Research: The Never-Ending Matter.

Chronic kidney disease (CKD) burden is crucial both on a global scale and at individual patient level, affecting morbidity and mortality directly and through its effect on both cardiovascular damage and CKD progression to end-stage-kidney-disease (ESKD). Unfortunately, the awareness of CKD is poor, with few CKD patients conscious of the severity of their health status. The principal biomarker of kidney function is estimated glomerular filtration rate (eGFR). We searched the literature and present a review article with the aim of summarizing the role of eGFR in clinical research. In particular, we report the eGFR role as a prognostic, enrichment and endpoint biomarker and its role in the early detection of CKD. eGFR has a major role as a biomarker in clinical research. As a prognostic marker, eGFR reduction is associated with cardiovascular events, ESKD and mortality. As an enrichment biomarker, eGFR values are pivotal for selecting patients to be included in randomized and observational studies; it helps to test a pre-defined drug in early CKD or in more advanced CKD allowing also to avoid screening failures and to shorten the duration of clinical trials. Moreover, eGFR decline (expressed as a percentage of reduction from baseline or continuous slope) can be considered a good endpoint in clinic trials overcoming delays whilst waiting for hard endpoints to develop. eGFR is a strong clinical measure for both observational and intervention studies. It is also helpful in screening the general population for kidney disease and, in particular, to increase awareness of CKD.

Evaluating the potential of retinal photography in chronic kidney disease detection: a review.

Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

A SHAP and LIME based Explainable AI Solution for Predicting Chronic Kidney Diseases

Chronic Kidney Disease (CKD) presents a major global health issue, contributing to renal failure, cardiovascular problems, and elevated mortality rates. This research focuses on creating an effective machine learning (ML) model for CKD prediction, utilizing 25 features that represent different health indicators. We implemented three main algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), and Decision Tree, along with extensive preprocessing, feature selection, and hyperparameter optimization. Based on accuracy, the models were evaluated, along with the confusion matrix, and ROC curves. Furthermore, we employed SHAP (SHapley Additive exPlanations) for model interpretability, offering insights via summary plots, waterfall plots, force plots, and dependence plots. Our results indicate high prediction accuracy, with a 10% increase in performance with the Decision Tree model achieving near-perfect performance, highlighting its potential for early CKD detection and contributing to timely medical interventions.

Emerging Preventive Strategies in Chronic Kidney Disease: Recent Evidence and Gaps in Knowledge.

Chronic kidney disease (CKD) is increasingly prevalent worldwide and is associated with increased cardiovascular risk. New therapeutic options to slow CKD progression and reduce cardiovascular morbidity and mortality have recently emerged. This review highlights recent evidence and gaps in knowledge in emerging CKD preventive strategies. EMPA-Kidney trial found that empagliflozin, a sodium-glucose co-transporter 2 inhibitor (SGLT2i) led to 28% lower risk of progression of kidney disease or death from cardiovascular causes, compared to placebo. This reinforced the previous findings from DAPA-CKD and CREDENCE trials and led to inclusion of SGLT2i as the cornerstone of CKD preventive therapy in both diabetic and non-diabetic CKD. Finerenone, a selective nonsteroidal mineralocorticoid receptor antagonist, slowed diabetic kidney disease progression by 23% compared to placebo in a pool analysis of FIDELIO-DKD and FIGARO-DKD trials. Non-pharmacological interventions, including low protein diet, and early CKD detection and risk stratification strategies based on novel biomarkers have also gained momentum. Ongoing efforts to explore the wealth of molecular mechanisms in CKD, added to integrative omics modeling are well posed to lead to novel therapeutic targets in kidney care. While breakthrough pharmacological interventions continue to improve outcomes in CKD, the heterogeneity of kidney diseases warrants additional investigation. Further research into specific kidney disease mechanisms will facilitate the identification of patient populations most likely to benefit from targeted interventions.

Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.

Kidney urinary biomarkers in patients with branched-chain amino acid and cobalamin metabolism defects.

There is a clinical need for early detection of chronic kidney disease (CKD) in patients with organic acidurias. We measured kidney markers in a longitudinal study over 5 years in 40 patients with methylmalonic aciduria (Mut0 ), propionic aciduria (PA), cobalamin A (CblA), and cobalamin C (CblC) deficiencies. Neutrophil gelatinase-associated lipocalin (NGAL), calprotectin (CLP), kidney injury molecule-1 (KIM-1), dickkopf-3 (DKK-3), albumin and beta-2-microglobulin (B2MG) in urine, as well as cystatin C (CysC) in serum were quantified. In Mut0 patients, mean concentrations of B2MG, KIM-1, and DKK-3 were elevated compared with healthy controls, all markers indicative of proximal tubule damage. In PA patients, mean B2MG, albumin, and CLP were elevated, indicating signs of proximal tubule and glomerulus damage and inflammation. In CblC patients, mean B2MG, NGAL, and CLP were increased, and considered as markers for proximal and distal tubule damage and inflammation. B2MG, was elevated in all three diseases, and correlated with DKK-3 in Mut0 /CblA and with eGFR(CysC) and KIM-1 in PA patients, respectively. None of the markers were elevated in CblA patients. Significant deterioration of kidney function, as determined by steady increase in CysC concentrations was noted in seven patients within the observation period. None of the investigated biomarker profiles showed a clear increase or added value for early detection. In conclusion, we identified disease-specific biomarker profiles for inflammation, tubular, and proximal damage in the urine of Mut0 , PA, and CblC patients. Whether these biomarkers can be used for early detection of CKD requires further investigation, as significant kidney function deterioration was observed in only a few patients.