Early Diagnosis Of Chronic Kidney Disease Research Articles

#2945 Albuminuria – a neglected cause of referral to Nephrology

Abstract Background and Aims Albuminuria has been a recognized marker of cardiovascular risk for several years. Since 2012, the KDIGO guidelines have included this marker in the definition of chronic kidney disease (CKD), regardless of the Glomerular Filtration Rate (GFR). Despite this, a decade after, albuminuria is rarely researched as a marker of kidney disease, especially in Primary Health Care. We aim to assess the percentage of patients referred from the Health Centre to the Nephrology consultation of a district hospital, with an indication of the albuminuria value and also which percentage considered it the main cause of referral. Method Retrospective observational study of all patients referred from the Health Centre to the Nephrology consultation, from January 2022 to May 2023. A statistical analysis was carried out using Microsoft Excel 2016 (Microsoft ™). Results A total of 248 patients were referred and accepted for the nephrology consultation, 52.4% female, average age 74.8 years, with almost 80% (n = 198) of patients aged 65 or over and 41.5% (n = 103) aged over 80. The reasons for referral were: 53.6% due to an increase in creatinine levels; 21% due to imaging alterations; 19% due to a reduction in GFR and only 6.4% (16 patients) due to a urinary alteration, which included the presence of albuminuria. Only 26.2% (n = 65) of the patients referred had mentioned the value of albuminuria, and of these, around 73% (n = 49) already had an albuminuria value greater than or equal to 30 mg/dL; however, in only 14 of these patients (5.6% of all the patients referred) was albuminuria the main reason for the referral. Of the 49 patients with albuminuria: 44 patients (89.8%) had cardiovascular disease (around 98% with hypertension and around 28% with HF) and 34 patients (69.4%) had diabetes; 32 patients (65.3%) were medicated with ACE inhibitors/ARBs and only 13 patients (26.5%) were medicated with iSGLT2. The percentages of use of ACEIs/ARAs and iSGLT2 remain the same when analyzing the 14 patients who were referred for albuminuria. Conclusion Albuminuria remains an overlooked marker of CKD despite being an early marker and for which there are several interventions that seem to prevent the worsening and progression of CKD. It is imperative to train doctors, especially family doctors, in the early diagnosis of CKD, particularly in the presence of albuminuria and to implement measures to control it.

Early detection of chronic kidney disease using eurygasters optimization algorithm with ensemble deep learning approach

Chronic kidney disease (CKD) emerges as a global health problem with high morbidity and mortality rates, and it induces other diseases. Patients often fail to notice these diseases because there are no obvious symptoms during the earlier stage of CKD. Earlier diagnosis of CKD allows them to receive early medical intervention to improve the disease progression and outcome. Earlier stratification of CKD can affect the health care provided to patients through various options, such as kidney transportation, hemodialysis, or pharmacological care in milder cases. Recently, deep learning (DL) and machine learning (ML) approaches have gained significance in the domain of medical diagnoses due to their high prediction accuracy. DL and ML approaches can successfully aid physicians in achieving these goals due to their accurate and fast detection performance. The performance of the proposed technique mainly depends on selecting the suitable algorithms and appropriate features. Thus, the study introduces an Eurygasters Optimization Algorithm with an Ensemble DL CKD detection (EOAEDL-CKDD) method. The EOAEDL-CKDD method aims to detect and classify the presence of CKD using feature selection and hyperparameter tuning strategies. The EOAEDL-CKDD method applies a min-max scalar to convert the data into a uniform format to achieve this. The EOAEDL-CKDD technique exploits EOA to select features. This is followed by an ensemble of long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and bidirectional LSTM (BiLSTM) models that are used for the CKD detection process. Finally, a shuffled frog leap algorithm (SFLA) based hyperparameter selection process is carried out to choose the ensemble models' hyperparameter values optimally. The empirical assessment of the EOAEDL-CKDD method is tested on the benchmark CKD dataset. The experimental values highlighted that the EOAEDL-CKDD technique gains an optimal detection rate compared to existing models.

Enhancing kidney disease prediction with optimized forest and ECG signals data

To improve the early detection of Chronic Kidney Disease (CKD) utilizing electrocardiogram (ECG) data, this study explores the use of the Optimized Forest (Opt-Forest) model. Exploiting the possible relationship between kidney function and ECG data, we investigate Opt-Forest's performance in comparison to popular machine learning (ML) models. We evaluate Opt-Forest and find that it outperforms other options in CKD prediction based on many measures such as classification accuracy (CA), false positive rate (FPR), and true positive rate (TPR). In comparison to previous models, Opt-Forest has superior sensitivity and specificity, with a TPR of 0.787 and a low FPR of 0.174. With an accuracy of 78.68 %, a KS of 0.641, and a low RMSE of 0.174, Opt-Forest also demonstrates robustness in CKD prediction. This study demonstrates the potential of Opt-Forest to improve patient outcomes and medical diagnostics, as well as the usefulness of ECG data in enhancing early CKD diagnosis. Prospective research avenues to advance precision medicine in nephrology involve investigating deep learning methodologies and incorporating patient-specific data.

InspeCKD - Analysis of the use of diagnostics in patients at high risk for chronic kidney disease in German general practitioner (GP) practices

Despite the high prevalence of chronic kidney disease (CKD) in Germany, only a small proportion of patients are currently diagnosed with CKD. Patients with hypertension, diabetes mellitus, and/or cardiovascular disease have a significantly increased risk of developing CKD and rapid disease progression and should therefore be screened and monitored in accordance with the guidelines. The aim of this retrospective, cross-sectional study was to gain insights into appropriate diagnosis of patients at risk for CKD in German general practitioner practices. For the analysis of the use of CKD-relevant diagnostics, electronic patient records from German general practitioner practices were analyzed. Adults with hypertension and/or diabetes mellitus and/or cardiovascular disease with a documented observation period of at least one year were included in the study. Data from a total of 448,837 patients from 1244 general practitioner practices were analyzed. 75.8% of patients had hypertension, 35.1% had cardiovascular disease, and 32.4% had diabetes mellitus. During a mean observation period of 1.7 years, serum creatinine was assessed at least once in 45.5% of patients. A urine dipstick test for albuminuria was performed in 7.9% of patients and in 0.4% of patients, urine albumin-to-creatine ratio (UACR) was measured. Laboratory diagnostics were initiated a little more frequently in high-risk patients compared to the overall cohort. The study highlights that despite known risk factors, guideline compliant CKD screening is rarely performed in German general practitioner practices, which implicates the need to increase the awareness of early diagnosis of CKD in patients at risk.

Evaluation of Individual Variation of d-Amino Acids in Human Plasma by a Two-Dimensional LC-MS/MS System and Application to the Early Diagnosis of Chronic Kidney Disease.

For the discovery of sensitive biomarkers of kidney function focusing on chiral amino acids, a multiple heart-cutting two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS)/MS system has been designed/developed. As the target analytes, alanine (Ala), aspartic acid, glutamic acid (Glu), leucine (Leu), lysine, methionine, phenylalanine (Phe), proline (Pro), serine (Ser), and valine were selected considering the presence of their d-forms in mammals. The 2D LC-MS/MS system consisted of the nonenantioselective reversed-phase separation of the target amino acids, the separations of the d- and l-enantiomers, and detection using MS/MS. Using the method, the plasma chiral amino acids, precolumn derivatized with 4-fluoro-7-nitro-2,1,3-benzoxadiazole, were isolated from other intrinsic substances, then determined without losing sensitivity by the fully automated whole-peak volume transfer operation from first to second dimension. In all of the tested plasma samples obtained from five healthy individuals and 15 patients with chronic kidney disease (CKD), the target chiral amino acids were determined without interference. In healthy individuals, the levels of all the tested d-amino acids were regulated in the low ranges. In contrast, the % d values of Glu, Leu, and Phe significantly increased with the progress of kidney dysfunction, besides the previously reported values of d-Ala, Pro, and Ser. Concerning Phe, the significant increase of the % d values (p < 0.05) was reported for the first time even in the mild CKD group compared to those of the healthy group; d-Phe might be a more sensitive marker than the previously reported d-forms. These results demonstrated the potential of these d-forms as the sensitive biomarkers of kidney function for the early diagnosis of CKD.

Metabolomics study reveals blood biomarkers for early diagnosis of chronic kidney disease and IgA nephropathy: A retrospective cross-sectional study

Background and aimsChronic kidney disease (CKD) causes low quality of life and alarming morbidity and mortality. The crucial to retard CKD progression is to diagnose early for timely treatment. IgA nephropathy (IgAN) is a typical CKD and the most common glomerulonephritis. Both CKD and IgAN lack accurate and sensitive blood biomarkers for early diagnosis. Here we report the potential of plasma biomarkers for early diagnosis of CKD and IgAN. Materials and methodsPlasma levels of metabolites derived from tryptophan were quantified with an LC-MS/MS-based metabolomics for two cohorts. Based on the predictive probability of each metabolite, multivariate models including logistic regression and random forest were used to establish the early diagnostic biomarkers for CKD and IgAN. ResultsThe plasma melatonin diagnosed early CKD (stages Ⅰ−Ⅱ) with an accuracy exceeding 95%, and a panel of melatonin and tryptophan achieved a remarkable 100% accuracy in diagnosing early CKD. Furthermore, indole-3-lactic acid had an excellent ability to distinguish IgAN among CKD patients. Based on the CKD screening and IgAN diagnosis primarily contributed by melatonin and indole-3-lactic acid, early IgAN could be diagnosed with an accuracy of over 85%. ConclusionsThis study provides promising plasma biomarkers for early diagnosis of CKD and IgAN.

The role of the albumin-to-creatinine ratio in the diagnosis of diabetic kidney disease (literature review and own findings)

Based on our own practical experience and data from scientific literature, we can assert that measuring the ratio of albumin to creatinine and calculating the glomerular filtration rate are important tools for the timely diagnosis of chronic kidney disease (CKD), monitoring treatment effectiveness, and determining the dynamics of a patient’s renal condition. Early diagnosis of CKD allows us to take necessary measures to slow down or even halt the progression of the disease. By timely administering nephroprotective drugs, such as SGLT-2 inhibitors, we can improve the prognosis and quality of life for patients, which holds medical, social, and economic significance.

Classification of chronic kidney disease based on machine learning techniques

&lt;span&gt;In recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD). Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health dataset information into consideration, employing nine distinct ML algorithms; random forest (RF), naïve bayes (NB), support vector machine (SVM), decision tree (DT), logistic regression (LR), extreme gradient boosting (XGB), adaptive boosting (ADB), k-nearest neighbors (KNN), and neural network (NN). Machine learning algorithms had been utilized and conducted in four experiments, then they were compared using five performance measures; F1-score, precision, accuracy, recall and run time are used to evaluate the performance. Results had shown that AdaBoost (ADA) outperformed other techniques with achieved accuracy of 99.17%.&lt;/span&gt;

Early detection of chronic kidney disease using recursive feature elimination and cross-validated XGBoost model

Chronic kidney disease (CKD) is listed among the top 20 leading causes of death worldwide, highlighting the urgent need for effective methods of early disease prediction for chronic conditions. This paper presents a novel strategy for the early diagnosis of CKD, leveraging machine learning (ML) methods to support researchers in their pursuit of preventative approaches. The study incorporates data from 400 patients, analyzing a comprehensive set of 25 attributes. The missing values in the dataset handled by mean and mode statistics were employed for numerical and nominal dimensions respectively. The recursive feature elimination (RFE) with cross-validation (CV) technique was utilized to identify the most crucial attributes. The resulting predictive model achieved positive outcomes in terms of accuracy, precision, recall, and [Formula: see text]-measure across all implemented classification algorithms (random forest (RF), Naive Bayes (NB), decision tree (DT), multi-layer neural network (MLNN), partial decision tree algorithm (PART), repeated incremental pruning to produce error reduction (RIPPER), [Formula: see text] Star and extreme gradient boosting (XGBoost)). Notably, the XGBoost classification algorithm demonstrated an impressive accuracy of up to 99.5%. The utilization of predictive models is indispensable for healthcare professionals, as they play a pivotal role in enabling early detection of CKD and mitigating the risk of kidney failure occurrence. This research offers valuable insights into the development of reliable methods for the early identification of CKD, providing a promising avenue for enhancing patient outcomes and reducing the burden of this prevalent chronic condition.

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.

Health Inequalities in kiDney Disease, mEeting the urgent Need to identify early disease in high-risk communities: a feasibility study of home albuminuria testing in people with high Blood Pressure (HIDDEN-BP).

Chronic kidney disease (CKD) is prevalent in the UK, associated with significant mortality and morbidity, and disproportionately affects minority ethnic groups. The most common causes for CKD are diabetes and hypertension. An estimated glomerular function (eGFR) blood test and an albumin creatinine ratio (uACR) urine test can be used to assess the level of CKD and predict the risk of adverse outcomes such as cardiovascular disease, end stage renal failure, and death. However, UK National CKD audit data suggest that only 30% of those with hypertension have a recorded uACR. Our quality improvement project working with community stakeholders and Healthy.io digital enterprise will evaluate the feasibility of uACR self-testing in the community. To promote uACR testing using smartphone home self-testing kits (Heathy.io) to improve the early diagnosis of CKD in individuals with hypertension. Individuals with hypertension in three GP practices in ethnically diverse areas in London will be invited to use a home self-testing uACR kit. Healthy.io (providers of kit) will complete patient enrolment and make one attempt to contact non-responders. Non-responders will be offered peer support to assist engagement. Qualitative data will be sought regarding the acceptability of the test for both clinicians and patients. We will recruit three practices in South London and mail 200 digital urine testing kits. Uptake and results will be presented. Improving equity in uACR testing through quality improvement tools can deliver a sustainable project for improving patient renal care. Leveraging innovative methods provides the potential for effective, equitable, and efficient services.

Performance Evaluation of Various Machine Learning Algorithms in Prediction of Chronic kidney disease (CKD)

With a high rate of morbidity and mortality as well as the ability to spread other diseases, chronic kidney disease (CKD) is a major worldwide health concern. Patients sometimes overlook the disease in the early stages of CKD since there are no evident symptoms. Early diagnosis of CKD enables patients to receive effective treatment in time to slow the disease's progression. Due to their quick and precise detection capabilities, machine learning models can help therapists accomplish this goal efficiently. In this research, we suggest a machine learning approach to CKD diagnosis. The website KAGGLE provided the CKD data set, which has a significant number of missing values.. The mean value is used to fill in the blanks; for object data types (strings), we utilized the most frequent object (string) to replace the missing values. Since patients may overlook particular measurements for a variety of reasons, missing values are typically observed in real-world medical scenarios. Four machine learning algorithms—Logistic Regression, SVM, Random Forest Classifier, and Decision Tree Classifier—were applied to create models after successfully completing the incomplete data set. Random Forest has the highest accuracy of these machine learning models.

How do patients experience chronic kidney disease? A mixed-methods study among patients in Sweden

Abstract Background Advanced chronic kidney disease (CKD) severely impacts patients’ lives. Nevertheless, little is known about patients’ own experiences of living with CKD in Sweden. The objective of this study was to describe the patient’s perspective on CKD—including diagnosis, treatment journey, and healthcare interactions—and how CKD impacts patients’ health-related quality of life (HRQoL) and work capacity. Subject and methods A mixed-methods approach was used that incorporated (i) a quantitative online survey with study-specific questions and assessments of HRQoL and work impairment among 178 patients with CKD in Sweden, and (ii) 40 qualitative interviews to gain an in-depth understanding of the patient experience. Results HRQoL and work capacity were notably impacted by CKD and deteriorated with advancing disease stage. Patients experienced numerous symptoms long before diagnosis, but still found diagnosis unexpected. Fatigue was the most frequently reported symptom impacting all areas of patients’ lives. In the interviews, patients reported that they worried what their future life would be like, a dread of dialysis and organ transplantation, and how they implemented lifestyle changes to mitigate progression. In primary healthcare, patients struggled to have their symptoms taken seriously. Once referred to kidney specialists, patients were satisfied with healthcare. Patients wished they had been diagnosed sooner in their disease journey. Conclusion These findings highlight an unmet need from the patient’s perspective in primary healthcare, driven primarily by lack of early CKD diagnosis. In Sweden, there is a need for increased education on CKD in primary care, and a need for increased availability of specialized kidney care.

Computational Analysis Reveals Distinctive Interaction of miRNAs with Target Genes in the Pathogenesis of Chronic Kidney Disease

The regulation of genes is crucial for maintaining a healthy intracellular environment, and any dysregulation of gene expression leads to several pathological complications. It is known that many diseases, including kidney diseases, are regulated by miRNAs. However, the data on the use of miRNAs as biomarkers for the diagnosis and treatment of chronic kidney disease (CKD) are not conclusive. The purpose of this study was to elucidate the potential of miRNAs as an efficient biomarker for the detection and treatment of CKD at its early stages. Gene expression profiling data were acquired from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) were identified. miRNAs directly associated with CKD were obtained from an extensive literature search. Network illustration of miRNAs and their projected target differentially expressed genes (tDEGs) was accomplished, followed by functional enrichment analysis. hsa-miR-1-3p, hsa-miR-206, hsa-miR-494 and hsa-miR-577 exhibited a strong association with CKD through the regulation of genes involved in signal transduction, cell proliferation, the regulation of transcription and apoptotic process. All these miRNAs have shown significant contributions to the inflammatory response and the processes which eventually lead to the pathogenesis of CKD. The in silico approach used in this research represents a comprehensive analysis of identified miRNAs and their target genes for the identification of molecular markers of disease processes. The outcomes of the study recommend further efforts for developing miRNA biomarkers set for the early diagnosis of CKD.

Untargeted Metabolomics by Ultra-High-Performance Liquid Chromatography Coupled with Electrospray Ionization-Quadrupole-Time of Flight-Mass Spectrometry Analysis Identifies a Specific Metabolomic Profile in Patients with Early Chronic Kidney Disease

Chronic kidney disease (CKD) has emerged as one of the most progressive diseases with increased mortality and morbidity. Metabolomics offers new insights into CKD pathogenesis and the discovery of new biomarkers for the early diagnosis of CKD. The aim of this cross-sectional study was to assess metabolomic profiling of serum and urine samples obtained from CKD patients. Untargeted metabolomics followed by multivariate and univariate analysis of blood and urine samples from 88 patients with CKD, staged by estimated glomerular filtration rate (eGFR), and 20 healthy control subjects was performed using ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry. Serum levels of Oleoyl glycine, alpha-lipoic acid, Propylthiouracil, and L-cysteine correlated directly with eGFR. Negative correlations were observed between serum 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinyl glycine, Propenoylcarnitine, Uridine, and All-trans retinoic acid levels and eGFR. In urine samples, the majority of molecules were increased in patients with advanced CKD as compared with early CKD patients and controls. Amino acids, antioxidants, uremic toxins, acylcarnitines, and tryptophane metabolites were found in all CKD stages. Their dual variations in serum and urine may explain their impact on both glomerular and tubular structures, even in the early stages of CKD. Patients with CKD display a specific metabolomic profile. Since this paper represents a pilot study, future research is needed to confirm our findings that metabolites can serve as indicators of early CKD.

Regional Differences in Medical Costs of Chronic Kidney Disease in the South Korean Population: Marginalized Two-Part Model

There are regional gaps in the access to medical services for patients with chronic kidney disease (CKD), and it is necessary to reduce those gaps, including the gaps involving medical costs. This study aimed to analyze regional differences in the medical costs associated with CKD in the South Korean population. This longitudinal cohort study included participants randomly sampled from the National Health Insurance Service-National Sample Cohort of South Korea. To select those who were newly diagnosed with CKD, we excluded those who were diagnosed in 2002-2003 and 2018-2019. A total of 5903 patients with CKD were finally included. We used a marginalized two-part longitudinal model to assess total medical costs. Our cohort included 4775 (59.9%) men and 3191 (40.1%) women. Of these, 971 (12.2%) and 6995 (87.8%) lived in medically vulnerable and nonvulnerable regions, respectively. The postdiagnosis costs showed a significant difference between the regions (estimate: -0.0152, 95% confidence limit: -0.0171 to -0.0133). The difference in medical expenses between the vulnerable and nonvulnerable regions showed an increase each year after the diagnosis. Patients with CKD living in medically vulnerable regions are likely to have higher postdiagnostic medical expenses compared to those living in regions that are not medically vulnerable. Efforts to improve early diagnosis of CKD are needed. Relevant policies should be drafted to decrease the medical costs of patients with CKD disease living in medically deprived areas.

Data-Driven Early Diagnosis of Chronic Kidney Disease: Development and Evaluation of an Explainable AI Model

Chronic Kidney Disease (CKD) is currently experiencing a growing worldwide incidence and can lead to premature mortality if diagnosed late, resulting in rising costs to healthcare systems. Artificial Intelligence (AI) and Machine Learning (ML) offer the possibility of an early diagnosis of CKD that could revert further kidney damage. However, clinicians may be hesitant to adopt AI models if the reasoning behind the predictions is not understandable. Since eXplainable AI (XAI) addresses the clinicians’ requirement of understanding AI models’ output, this work presents the development and evaluation of an explainable CKD prediction model that provides information about how different patient’s clinical features contribute to CKD early diagnosis. The model was developed using an optimization framework that balances classification accuracy and explainability. The main contribution of the paper lies in an explainable data-driven approach to offer quantitative information about the contribution of certain clinical features in the early diagnosis of CKD. As a result, the optimal explainable prediction model implements an extreme gradient boosting classifier using 3 features (hemoglobin, specific gravity, and hypertension) with an accuracy of 99.2% (standard deviation 0.8) and 97.5% with a 5-fold cross-validation and with new unseen data respectively. In addition, an explainability analysis shows that hemoglobin is the most relevant feature that influences the prediction, followed by specific gravity and hypertension. This small number of features selected results in a reduced cost of the early diagnosis of CKD implying a promising solution for developing countries.

PS-BPC02-3: EVALUATION OF VASCULAR FUNCTION FOR PREDICTION OF RENAL PROGNOSIS IN JAPANESE PATIENTS WITH LIFESTYLE-RELATED DISEASES.

Background: Since chronic kidney disease (CKD) is associated with the development of serious cardiovascular diseases such as stroke, coronary artery disease and heart failure, prevention and early diagnosis of CKD are important clinical issues. Many risk factors including diabetes, hypertension, dyslipidemia, obesity, and cigarette smoking are known to be involved in the development of CKD; however, there is no available biomarker that can predict the progression of CKD at an early stage. Although it has been reported that glomerular endothelial injury causes development of CKD, it remains unclear whether evaluation of vascular endothelial function can predict renal prognosis. Therefore, in the present study, we examined the association between vascular endothelial function and longitudinal eGFR change in patients with lifestyle-related diseases. Methods: We retrospectively evaluated 341 Japanese patients (176 males and 165 females; mean age, 63.87 ± 12.02 years) with lifestyle-related diseases in whom baseline vascular function was evaluated by flow-mediated dilation (FMD) and brachial-ankle pulse wave velocity (baPWV) and in whom 24-month longitudinal eGFR values were recorded after vascular testing at Tokushima University Hospital and Anan Medical Center, Tokushima Japan. The association between values of FMD and eGFR slope and the association between values of baPWV and eGFR slope were statistically analyzed. Statistical significance was set at p &lt; 0.05. Results: Single regression analysis showed that value of FMD was positively associated with eGFR slope in females (p &lt; 0.001) and non-smoking males (p = 0.033) but not in smoking males (p = 0.133). Multiple regression analysis including confounding factors demonstrated that the value of FMD remained a contributor against annual eGFR decline in females (p = 0.002) and non-smoking males (p = 0.046) but not in smoking males (p = 0.266). On the other hand, the value of baPWV had no significant association with eGFR slope regardless of sex and smoking. Conclusions: In patients with lifestyle-related diseases, FMD value can be used for prediction of renal prognosis represented by the eGFR slope in females and non-smoking males. Thus, early comprehensive intervention for improving vascular endothelial function might lead to a better renal prognosis in female and non-smoking male patients with lifestyle-related diseases.

Chronic kidney disease prediction based on machine learning algorithms

Chronic kidney disease (CKD) is a dangerous ailment that can last a person’s entire life and is caused by either kidney malignancy or decreased kidney functioning. It is feasible to halt or slow the progression of this chronic disease to an end-stage wherein dialysis or surgical intervention is the only method to preserve a patient’s life. Earlier detection and appropriate therapy can increase the likelihood of this happening. Throughout this research, the potential of several different machine learning approaches for providing an early diagnosis of CKD has been investigated. There has been a significant amount of research conducted on this topic. Nevertheless, we are bolstering our approach by making use of predictive modeling. Therefore, in our approach, we investigate the link that exists between data factors as well as the characteristics of the target class. We are capable of constructing a collection of prediction models with the help of machine learning and predictive analytics, thanks to the better measures of attributes that can be introduced using predictive modeling. This study starts with 25 variables in addition to the class property, but by the end, it has narrowed the list down to 30% of those parameters as the best subset to identify CKD. Twelve different machine learning-based classifiers have been tested in a supervised learning environment. Within the confines of a supervised learning environment, a total of 12 different machine learning-based classifiers have indeed been examined, with the greatest performance indicators being an accuracy of 0.983, a precision of 0.98, a recall of 0.98, and an F1-score of 0.98 for the XgBoost classifier. The way the research was done leads to the conclusion that recent improvements in machine learning, along with the help of predictive modeling, make for an interesting way to find new solutions that can then be used to test the accuracy of prediction in the field of kidney disease and beyond.

Serum Transforming Growth Factor-Beta 1 and Creatinine for Early Diagnosis of CKD of Unknown or Uncertain Etiology Phenotypes

The global burden of kidney diseases has disproportionally increased over the last 2 decades.1 Although diabetes and hypertension are leading causes of chronic kidney disease (CKD), environmental risk factors contribute to a significant disease burden in hot spots of Mesoamerican countries, such as India, and Sri Lanka.2,3 CKD associated with environmental risk factors, CKD of uncertain or unknown etiology (CKDu), is prevalent among farming communities living in these at-risk areas. Recently, a subcategory of patients has been reported from these at-risk areas for CKDu in Nicaragua and Sri Lanka.