Diagnosis Of Kidney Disease Research Articles

Diagnosis and Management of Polycystic Kidney Disease in a Commercially Insured Population

Diagnosis and Management of Polycystic Kidney Disease in a Commercially Insured Population

Why Am I on Dialysis? Exploring the Gap between Patient Perspectives and Clinical Diagnosis of Kidney Disease in California's Central Valley

Why Am I on Dialysis? Exploring the Gap between Patient Perspectives and Clinical Diagnosis of Kidney Disease in California's Central Valley

Clinical Validation of a Mobile-Based ACR (Urine Albumin-to-Creatinine Ratio) Test Kit: Comparative Analysis with Benchtop Devices

Background: Albuminuria, a marker for diabetes complications and kidney disease, is typically tested in hospitals, limiting patient monitoring. The mobile-based ACR (urine albumin-to-creatinine ratio) test, QSCheck-UISACR, was developed for easy detection of albuminuria. This study aimed to validate its clinical efficacy compared to a conventional benchtop device (Cybow R-50S) for early kidney disease diagnosis. Methods: Conducted in three hospitals with 303 kidney disease patients, urine samples were tested using both the mobile ACR kit and the benchtop device. Parameters measured included microalbumin, creatinine, and their ratio (albumin-to-creatinine ratio). Exact Agreement and Within One Block Agreement were calculated to compare methods.Results: Data collected from three university hospitals were aggregated and analyzed, showing that the microalbumin Exact Agreement was 73.3%, with a Within One Block Agreement of 96.0%. For creatinine, the Exact Agreement was 75.6%, and the Within One Block Agreement was 95.7%. Based on these two test results, the albumin-to-creatinine ratio analysis demonstrated an Exact Agreement of 78.5% and a Within One Block Agreement of 98.0%. These findings highlight the robust performance of the tests across the evaluated biomarkers.Conclusions: The mobile-based ACR test kit demonstrated high accuracy and comparability to the benchtop device, suggesting its potential as a reliable tool for early kidney disease screening in primary care settings, enhancing patient accessibility and reducing healthcare costs.

Intensity grading of kidney biopsy direct immunofluorescence IgG image via semantically enhanced feature network

The intensity of direct immunofluorescence IgG imaging for kidney biopsy is an important reference index for the diagnosis of common chronic kidney diseases (CKD) such as membranous nephropathy (MN). Although this index is widely used in diagnosing membranous nephropathy, the intensity levels are currently judged only by the subjective perception of fluorescence luminance in pathological images through physicians’ naked eyes. Therefore, consistent intensity level classification cannot be achieved. To address this issue, this paper proposes a Preprocessing Semantically Enhanced Feature image classification method. The segmentation network is used to segment the original kidney biopsy direct immunofluorescence IgG images to isolate the diseased glomeruli. These segmented glomeruli images are then compared with the original glomeruli images to standardize the kidney biopsy direct immunofluorescence images. Simultaneously, considering the characteristics of small inter-class differences and large intra-class differences in fine-grained images, a semantically enhanced feature image classification model is employed to automatically classify the intensity of the standardized images. The method was evaluated on a constructed real clinical dataset, and the experimental results demonstrated that the proposed method achieved high classification accuracy on this dataset. The source code is made available on https://github.com/SnowRain510/PSEF-NET

Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy

BackgroundKidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.MethodsWe included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).ResultsOverall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75–0.99) and 0.74 (95% CI 0.52–0.93) for total parenchymal and cortical ROI features, respectively.ConclusionSignificant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.

Optical chemical gas sensor based on spectral autocorrelation: A method for online detection of nitric oxide and ammonia in exhaled breath

The detection of nitric oxide (NO) and ammonia (NH3) in exhaled breath (EB) plays a crucial role in non-invasive diagnosis of airway inflammatory and kidney diseases. Here we report an optical chemical gas sensor for NO and NH3 based on spectral autocorrelation. In our sensor system, the target gas is removed through a chemical reaction with a sensing material and the spectrum of the target gas is extracted from the spectra obtained before and after the reaction by autocorrelation. Our method focuses on the ability of the material to eliminate the target gas without the need for detecting signals of the reaction, greatly relaxing the requirements on material properties. Fundamentally different from optical sensors that obtain spectra by spectral decoupling, our sensor relies on autocorrelation operation to acquire the spectrum of the target gas, thereby overcoming spectra overlap. Lab-based results show that the sensor enables detection of NO (2.28–451.82 ppb) and NH3 (11.52–12,725.42 ppb) with mean absolute errors (MAEs) of 0.79 ppb and 6.13 ppb and measurement accuracies of 0.8 % and 1.4 %, respectively. The EB experiment proves that the sensor can detect exhaled NO and NH3 and distinguish EB between healthy subjects and simulated patients.

Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection

This paper introduces a new privacy-preserving transfer learning framework for the classification of kidney diseases. In the proposed framework, transfer learning is employed for feature extraction, and differential privacy is used to obtain noisy gradients. A variety of CNN architectures, including Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19 are utilized to evaluate the proposed framework. Analysis of a large dataset of 12,400 labeled kidney CT images shows that transfer learning architectures based on the proposed framework achieve excellent accuracy ratios in privacy-preserving classification. These results demonstrate the effectiveness of the proposed framework in enabling transfer learning models to classify kidney diseases while ensuring privacy. The MobileNet architecture stands out for its exceptional performance, with an impressive accuracy of 99.83% in privacy-preserving classification. Considering the findings of this study, it is evident that the proposed framework is appropriate for the early and private diagnosis of kidney diseases and promotes the achievement of promising results in this field.

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet’s robust feature extraction capabilities with ConvNeXt’s advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study’s methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Advancements in Machine Learning and Deep Learning for Early Diagnosis of Chronic Kidney Diseases: A Comprehensive Review

Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has been a burgeoning interest in leveraging machine learning (ML) and deep learning (DL) techniques to enhance the early diagnosis of CKD. This comprehensive review explores the advancements in ML and DL models applied to CKD diagnosis, focusing on their ability to integrate diverse data sources including clinical biomarkers, imaging modalities, and patient demographics. Key ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and neural network architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are examined in the context of their performance in predicting CKD progression, classifying disease stages, and identifying at-risk populations. Furthermore, the review discusses challenges such as data quality, model interpretability, and integration into clinical practice, alongside emerging trends in explainable AI, transfer learning, federated learning, and integration with electronic health records (EHRs). By synthesizing findings from recent literature, this paper aims to provide insights into current methodologies, identify gaps for future research, and underscore the transformative potential of ML and DL in revolutionizing early CKD diagnosis and management..

Novel Biomarkers for Kidney Disease: New Frontiers in Early Diagnosis and Monitoring

Kidney diseases pose a significant threat to global public health, with early diagnosis and accurate monitoring being crucial for improving patient outcomes. However, traditional kidney disease markers such as serum creatinine and blood urea nitrogen have limitations in sensitivity and specificity. In recent years, rapid advancements in molecular biology and proteomics have led to the discovery of a series of novel biomarkers that are gradually being applied in clinical practice. This review examines novel kidney disease biomarkers, including cystatin C (Cys-C), neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule-1 (KIM-1). These markers not only reflect kidney injury earlier and more accurately but may also provide important bases for kidney disease classification, severity assessment, and prognosis prediction. Additionally, this paper explores the potential of multi-marker combined detection strategies in improving diagnostic accuracy. Although these novel markers show immense potential, they still face challenges such as standardization and cost-effectiveness before widespread clinical application. Future research should focus on optimizing detection methods, establishing clinical decision thresholds, and evaluating the value of these markers in personalized medicine, thereby paving new paths for precise diagnosis and treatment of kidney diseases.

Diagnosis of Chronic Kidney Disease Within a Comprehensive Artificial Intelligence-Driven Healthcare System

Diagnosis of Chronic Kidney Disease Within a Comprehensive Artificial Intelligence-Driven Healthcare System

O61 HYPERTENSION IN COLOMBIA: ETHNIC GROUP COMPARISONS IN CHARACTERIZATION AND SURVIVAL

Background and objective: Research has shown that ethnic minorities may be disproportionately affected by arterial hypertension. The aim of this study is to compare the overall survival (OS) HTN patients within the Colombian health system, by their ethnic group. Methods: Design: Longitudinal retrospective cohort study. Population: Adults diagnosed with HTN from June 1, 2014, to July 30, 2015, from the National Registry of Chronic Kidney Disease (NRCKD). The follow-up period was until July 30, 2023. Analysis: The probability of global survival from the date of diagnosis was estimated using Kaplan-Meier analysis. An unadjusted Royston Parmar flexible regression model was fitted. Right-censoring was defined as abandonment or completion of follow-up without death occurring. Results: 5,634,812 cases were identified from june 1st 2022 to july 30th 2023, and 299,161 incident cases were included in the survival analysis. 95.29% had no ethnic group, 1.20% were indigenous, 3.47% were Afro-descendant and 0.03% were Gypsy (ROMA). ROMA group had a higher proportion of residents in Bogotá, D.C. compared to other groups (13.96% vs ∼3%). Afro-descendant group had a higher proportion of obesity (30.61% vs. 27.86%) and diagnosis of Chronic Kidney Disease (27.31% vs. 15.69%) compared to cases with no ethnic group, while ROMA group had a higher occurrence of DM cases (23.46% vs. 21.66%). 9 years OS was higher in ROMA group (OS = 0.85; CI 95% 0.71 – 0.93) compared to indigenous (OS = 0.83; CI 95% 0.81 – 0.84) and afro-descendants (OS = 0.83; CI 95% 0.83 – 0.84). The probability of all-cause mortality was approximately 20% higher among afro-descendants and indigenous people (HR = 1.20; 95% CI 1.14 – 1.25 and HR = 1.22; 95% CI 1.12 – 1.32, respectively) compared to the non-ethnic group. In contrast, the ROMA did not present differences (p = 0.712). Conclusions: In the Colombian health system, differences in OS among afro-descendant and indigenous groups are observed compared to non-ethnic population.

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 &lt;0.01), serum cystatin C (eGFRcys, beta = 0.004, p &lt;0.0001), and their combination (eGFRcreacys, beta = 0.003, p &lt;0.0001). Additionally, longer LTL was associated with reduced odds of CKD (OR = 0.94 [95% CI 0.93-0.96], p &lt;0.0001). Significant associations were also observed between LTL and acute renal failure (OR = 0.95 [95% CI 0.93-0.97], p &lt;0.0001), and hypertensive kidney disease (OR = 0.91 [95% CI 0.86-0.96], p &lt;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.

T1 Mapping of the Abdomen, From the AJR "How We Do It" Special Series.

By exploiting different tissues' characteristic T1 relaxation times, T1-weighted images help distinguish normal and abnormal tissues, aiding assessment of diffuse and local pathologies. However, such images do not provide quantitative T1 values. Advances in abdominal MRI techniques have enabled measurement of abdominal organs' T1 relaxation times, which can be used to create color-coded quantitative maps. T1 mapping is sensitive to tissue microenvironments including inflammation and fibrosis and has received substantial interest for noninvasive imaging of abdominal organ pathology. In particular, quantitative mapping provides a powerful tool for evaluation of diffuse disease by making apparent changes in T1 occurring across organs that may otherwise be difficult to identify. Quantitative measurement also facilitates sensitive monitoring of longitudinal T1 changes. Increased T1 in liver helps to predict parenchymal fibro-inflammation, in pancreas is associated with reduced exocrine function from chronic or autoimmune pancreatitis, and in kidney is associated with impaired renal function and aids diagnosis of chronic kidney disease. In this review, we describe the acquisition, postprocessing, and analysis of T1 maps in the abdomen and explore applications in liver, spleen, pancreas, and kidney. We highlight practical aspects of implementation and standardization, technical pitfalls and confounding factors, and areas of likely greatest clinical impact.

Diagnostic performance of an albuminuria point-of-care test in screening for chronic kidney disease among young people living with HIV in Uganda: a cross-sectional study

ObjectivesThe main aim was to determine the diagnostic performance of an albuminuria point-of-care test (POC) for diagnosis of chronic kidney disease among young people living with HIV (YPLHIV) in Uganda.DesignWe...

Is hyperammonemia helpful in detecting syndromic tubulopathies with early extrarenal manifestations? A case report of Lowe’s syndrome

BackgroundGenerally, it is not well known that Lowe’s syndrome may coexist with hyperammonemia and hipocarnitynemia. The importance of hyperammonemia in the diagnosis of kidney diseases is not completely understood.Case presentationWe present the history of a 13-year-old boy, admitted to the hospital due to proteinuria. In the past, the boy was diagnosed with binocular cataracts in infancy. Then he went through neurological diagnostic tests which diagnosed muscular hypotonia and psychomotor retardation but no inherited errors of metabolism were found. Proteinuria has been observed since the age of 2. Ultrasound imaging at the age of 5 showed the presence of a shading deposit in the kidney. At the age of 13, the boy was referred to the Pediatric Nephrology Ward. The laboratory tests revealed: a reduction of glomerular filtration rate, metabolic acidosis, proteinuria, hypercalciuria, increased activity of AST (SGOT), CK, LDH, hyperammonemia, and decreased concentration of total carnitine in blood serum. Based on the clinical presentation, Lowe’s syndrome was diagnosed. The genetic testing revealed an OCRL gene hemizygous mutation.ConclusionLowe’s syndrome is an example of a disease in which clinical symptoms—although occurring early and in high intensity—may not raise the suspicion of tubulopathy for a long time if they are not analyzed in a complex manner. There is a necessity to educate healthcare practitioners from other fields about the extrarenal symptoms of genetically determined tubulopathies.l-carnitine deficiency may be a symptom of proximal tubulopathy, including Lowe’s syndrome. l-carnitine deficiency leads to disturbances in the efficiency of the urea cycle, which results in hyperammonemia. Hyperammonemia is not only a symptom of inborn errors of metabolism and liver failure, but it may also lead to the diagnosis of tubulopathy.Since carnitine supplementation could have the desired beneficial effect on the patient’s general condition, it is postulated to conduct further studies on larger groups of patients with Lowe’s syndrome.

Barriers to early diagnosis of chronic kidney disease and use of sodium-glucose cotransporter-2 inhibitors for renal protection: A comprehensive review and call to action.

Chronic kidney disease (CKD) affects approximately 13% of people globally, including 20%-48% with type 2 diabetes (T2D), resulting in significant morbidity, mortality, and healthcare costs. There is an urgent need to increase early screening and intervention for CKD. We are experts in diabetology and nephrology in Central Europe and Israel. Herein, we review evidence supporting the use of sodium-glucose cotransporter-2 (SGLT2) inhibitors for kidney protection and discuss barriers to early CKD diagnosis and treatment, including in our respective countries. SGLT2 inhibitors exert cardiorenal protective effects, demonstrated in the renal outcomes trials (EMPA-KIDNEY, DAPA-CKD, CREDENCE) of empagliflozin, dapagliflozin, and canagliflozin in patients with CKD. EMPA-KIDNEY demonstrated cardiorenal efficacy across the broadest renal range, regardless of T2D status. Renoprotective evidence also comes from large real-world studies. International guidelines recommend first-line SGLT2 inhibitors for patients with T2D and estimated glomerular filtration rate (eGFR) ≥20 mL/min/1.73 m2, and that glucagon-like peptide-1 receptor agonists may also be administered if required for additional glucose control. Although these guidelines recommend at least annual eGFR and urine albumin-to-creatinine ratio screening for patients with T2D, observational studies suggest that only half are screened. Diagnosis is hampered by asymptomatic early CKD and under-recognition among patients with T2D and clinicians, including limited knowledge/use of guidelines and resources. Based on our experience and on the literature, we recommend robust screening programmes, potentially with albuminuria self-testing, and SGLT2 inhibitor reimbursement at general practitioner (GP) and specialist levels. High-tech tools (artificial intelligence, smartphone apps, etc.) are providing exciting opportunities to identify high-risk individuals, self-screen, detect abnormalities in images, and assist with prescribing and treatment adherence. Better education is also needed, alongside provision of concise guidelines, enabling GPs to identify who would benefit from early initiation of renoprotective therapy; although, regardless of current renal function, cardiorenal protection is provided by SGLT2 inhibitor therapy.

Sex Disparity in the In-Hospital Outcomes of Patients with Kidney Disease Admitted for Myocardial Infarction: Insights from a Large National Database.

There is limited evidence as to the effect of sex on the outcomes of patients admitted for ST-elevation myocardial infarction (STEMI) who have a concomitant diagnosis of chronic kidney disease (CKD) and end-stage renal disease (ESRD). We aimed to determine if there are differences in the outcomes between males and females in these patient populations. Data were obtained from the National Inpatient Sample database and patients were selected using the International Classification of Diseases, Ninth and Tenth Revision (ICD-9 and -10) codes. Hospitalizations for patients with CKD who had STEMI from 2012 to 2020 were included. The primary outcome of interest was in-hospital mortality. Secondary outcomes evaluated included ischemic stroke, major bleeding complications, pressor requirement, permanent pacemaker implantation, percutaneous coronary intervention, coronary artery bypass grafting, surgery, pericardiocentesis, mechanical circulatory support, and mechanical ventilation. A total of 1,283,255 STEMI patients without CKD, 158,715 STEMI patients with CKD, and 22,690 STEMI patients with ESRD were identified and analyzed. Among patients with STEMI and CKD, females demonstrated higher in-hospital mortality compared to male counterparts (16.7% vs. 12.7%, aOR = 1.13, 95% CI: 1.05-1.21, p &lt; 0.01). While there was no sex difference in the in-hospital mortality among STEMI patients with ESRD, female patients in this group were less likely to receive coronary artery bypass grafting and mechanical circulatory support. Increased in-hospital mortality rates were shown for females admitted for STEMI with CKD. Among patients with ESRD who had STEMI, females were less likely to receive coronary artery bypass grafting and mechanical circulatory support. Further research needs to be conducted to better explain this said difference in outcomes.

Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.

This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images. According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians. When diagnosing CKD or non-CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1-G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1-G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4-G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4-G5 was 0.838 and 0.963, respectively (all p > .05). Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD.

Copper-loaded ZIF-8-based immunosensor for early diagnosis of chronic kidney disease by detecting neutrophil gelatinase-associated lipocalin (NGAL) biomarker

Metal-organic frameworks (MOFs) is a promising contender among the nanomaterials for biosensors due to their special properties, including high surface area, tunable porosity, and adaptable functionalization competences. Zeolitic imidazolate frameworks (ZIFs), a subclass of MOFs, have drawn precise attention for their excellent chemical stability and ease of synthesis. This study, for the first time, concentrates on the preparation and characterization of a Cu-loaded ZIF-8-based immunosensor for the detection of neutrophil gelatinase-associated lipocalin (NGAL), a crucial biomarker for chronic kidney disease (CKD). Loading copper (Cu) into the ZIF-8 framework improves its electrochemical properties, conductivity, and surface area, leading it an ideal platform for antibody immobilization and NGAL detection. The immunosensor utilized Staphylococcal Protein A (SPA) IgG binding protein for antibody immobilization, providing an alternative to traditional crosslinking chemistry, enhancing biosensor functionality. Structural analysis, morphological assessment, elemental mapping, and stoichiometric analysis confirmed the successful synthesis of Cu-loaded ZIF-8 nanocomposite. Assessment of the electrochemical performance of the immunosensor demonstrated sufficiently low limit of detection (80 pg mL−1) over a wide range of 0.1 ng mL−1 to 1000 ng mL−1 of NGAL along with superior selectivity, reproducibility, stability, and clinical feasibility. This advancement contributes to the rising knowledge on MOF-based biosensors and holds promise for future applications in disease diagnosis and healthcare monitoring.