Early Diagnosis Of Kidney Disease Research Articles

An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease.

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

Symmetry dimethylarginine (SDMA) assay for diagnosis of kidney disease in dogs and cats: a mini review of case reports in Indonesia between 2021-2023

Symmetry Dimethylarginine (SDMA) is an important biomarker for early diagnosis of kidney disease. Studies suggest that SDMA is a more sensitive indicator of kidney function compared to traditional markers such as blood urea nitrogen (BUN) and creatinine. Since 2015, the International Renal Interest Society (IRIS) has established SDMA as one of the indicators for diagnosis and determining the stage of chronic kidney disease (CKD) in dogs and cats. SDMA assay use in veterinary clinics in Indonesia has empirically become more common. The aim of this study was to identify scientific reports on SDMA assay use in the diagnosis of kidney disease in dogs and cats in Indonesia. We performed a literature search in the Garuda portal and Google Scholar using the following keywords: “SDMA” or “Symmetry Dimethylarginine”, “dogs”, “cats” and “Indonesia”. The search strategy utilized English and Bahasa Indonesia, and the results were screened for relevance to the topic, and limited to studies conducted in Indonesia. Seven scientific articles in national and international journals reported the use of SDMA assay in cases of kidney disease in dogs or cats in Indonesia, published between 2021-2023. It is expected that SDMA assay use will continue to increase in veterinary practice settings in the country, and it is therefore critical to have a proper understanding of the advantages and limitations of SDMA use for kidney disease diagnosis.

Proteomics of urinary small extracellular vesicles in early diagnosis of kidney diseases in children-expectations and limitations.

The primary function of the kidneys is to maintain systemic homeostasis (disruption of renal structure and function results in multilevel impairment of body function). Kidney diseases are characterized by a chronic, progressive course and may result in the development of chronic kidney disease (CKD). Evaluation of the composition of the proteome of urinary small extracellular vesicles (sEVs) as a so-called liquid biopsy is a promising new research direction. Knowing the composition of sEV could allow localization of cellular changes in specific sections of the nephron or the interstitial tissue before fixed changes, detectable only at an advanced stage of the disease, occur. Research is currently underway on the role of sEVs in the diagnosis and monitoring of many disease entities. Reports in the literature on the subject include: diabetic nephropathy, focal glomerulosclerosis in the course of glomerulopathies, renal fibrosis of various etiologies. Studies on pediatric patients are still few, involving piloting if small groups of patients without validation studies. Here, we review the literature addressing the use of sEV for diagnosis of the most common urinary disorders in children. We evaluate the clinical utility and define limitations of markers present in sEV as potential liquid biopsy.

DIAGNOSIS OF KIDNEY CYST, TUMOR AND STONE FROM CT SCAN IMAGESUSING FEATURE FUSION HYPERGRAPH CONVOLUTIONAL NEURAL NETWORK (F2HCN2)

The development of a computational tool to diagnose kidney disorders in their early stages is necessary due to the rise in chronic kidney diseases (CKDs) and the global shortage of nephrologists. The three common renal disorders covered in this study are kidney stones, cysts, and tumors. Early diagnosis of these diseases from the computed tomography (CT) images is a challenging task. Yet, present graph convolutional neural network (GCNN) approaches have the issue of overdependence on the adjacency matrix. Moreover, compared to deep convolutional neural network (CNN) models, a single modal feature results in low accuracy and robustness. In this paper, we proposed the feature fusion hypergraph CNN (F<sup>2</sup>HCN<sup>2</sup>) to accurately diagnose kidney diseases in the early stage based on CT scan images. The discriminative features of the images are extracted using DarkNet19 and residual features are extracted using ResNet50. The extracted features are classified using feature fusion hypergraph CNN. The proposed model is trained with 12,446 CT whole urogram and abdomen images. The hypergraph representation learning is performed to train the network with the fused features. Deep learning metrics including accuracy, F1 score (F1), recall, positive predictive value (PPV), receiver operating characteristic curve, and area under curve (AUC) are used to validate the proposed model. It outperforms compared to other state-of-the-art algorithms with accuracy of 99.71%. The proposed F<sup>2</sup>HCN<sup>2</sup> is a robust computer-aided tool for the early diagnosis of kidney diseases. It will assist the radiologist for better prognosis for kidney related abnormalities.

Precisely Constructing Renal-Clearable and LAP-Activatable Ratiometric Molecular Probes for Early Diagnosis of Acute and Chronic Kidney Injury Via Optimizing Asymmetric DPP Dyes.

Fluorescence analysis is an increasingly important contributor to the early diagnosis of kidney diseases. To achieve precise visualization of the kidneys and early diagnosis of related diseases, an asymmetric pyrrolopyrrolidone (DPP) dye platform with C-aromatic substituents and N-lipophilic/hydrophilic modification was constructed. Based on these, we developed the renal-clearable, water-soluble, and kidney injury biomarker leucine aminopeptidase (LAP) activated ratiometric fluorescent probe DPP-S-L. In the mouse model of cisplatin-induced acute kidney injury and during the development of type 2 diabetes to diabetic kidney disease, we visualized for the first time the upregulation of LAP in the kidney and urine by dual-channel ratiometric fluorescence signal and diagnosed the kidney injury earlier and more sensitively than blood/urine enzyme detection and tissue analysis. This study showcases an excellent asymmetric DPP dye platform and renal-clearable ratiometric fluorescent probe design strategy that is extended to determination and visualization of other biomarkers for early disease diagnosis.

Digital twin of renal system with CT-radiography for the early diagnosis of chronic kidney diseases

Kidney failure, also called as end-stage renal disease is the final stage of chronic kidney disease(CKD). Kidney failure cannot be revered and is the life-threatening if left untreated. Hence there is a demand for the early and automatic detection of CKD from radiology images based on deep learning techniques. This work focuses on the diagnosis of the predominant CKDs such as kidney stone, cyst and tumor from CT scan images. CT scan abdomen and urogram images are used to construct the digital-twin for the diagnosis of CKD in the robust manner. The proposed work is based on the dataset which contains 12,446 unique CT scan images, within it in which the cyst contains 3,709, normal 5,077, stone 1,377, and tumor 2,283. Deep features are extracted from these images and generated hyper edges from these features. These features are used to construct hypergraph representing the renal images. These hypergraphs are used in hypergraph convolutional neural network for representational learning. Finally, the model is validated by hold-out dataset. Deep learning metrics including precision, recall, accuracy, and FI score are used to validate the proposed model. It outperforms compared to other state of art algorithms with a superior validation accuracy of 99.71. The proposed model is a robust digital-twin for the early diagnosis of kidney diseases. It will assist the nephrologist for the better prognosis of kidney related abnormalities.

StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images.

Kidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidney stones in a medical decision support system is of paramount importance. Therefore, in this study, we propose "StoneNet", a lightweight and high-performance model for the detection of kidney stones based on MobileNet using depthwise separable convolution. The proposed model includes a combination of global average pooling (GAP), batch normalization, dropout layer, and dense layers. Our study shows that using GAP instead of flattening layers greatly improves the robustness of the model by significantly reducing the parameters. The developed model is benchmarked against four pre-trained models as well as the state-of-the-art heavy model. The results show that the proposed model can achieve the highest accuracy of 97.98%, and only requires training and testing time of 996.88s and 14.62s. Several parameters, such as different batch sizes and optimizers, were considered to validate the proposed model. The proposed model is computationally faster and provides optimal performance than other considered models. Experiments on a large kidney dataset of 1799 CT images show that StoneNet has superior performance in terms of higher accuracy and lower complexity. The proposed model can assist the radiologist in faster diagnosis of kidney stones and has great potential for deployment in real-time applications.

Evaluation of renal markers in systemic autoimmune diseases.

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

Novel Chemiluminescence Immunoassay (CLIA) for the Sensitive Determination of Kidney Injury Molecule-1 in Human Urine

Kidney injury molecule 1 (KIM-1) is markedly upregulated in the proximal tubule after injury and is maladaptive when chronically expressed. A practical sensitive chemiluminescence immunoassay combined with magnetic bead separation for the determination of KIM-1 in human urine has been developed. Based on a sandwich-type immunoassay, the analyte is captured by the monoclonal antibodies (mAb) coated in magnetic beads and those labeled with the alkaline phosphatase. The changes of KIM-1 concentration levels were monitored by the CLIA for the early diagnosis of kidney disease. The established immunoassay exhibited a wide linear range from 0.5 to 81 ng/mL with a detection limit of 0.17 ng/mL. The recoveries, specificity, and interference results of the developed method were satisfactory. The whole analysis time was 30 min. The developed method was for the detection of kidney disease in patients. This work is a novel chemiluminescence immunoassay with excellent performance for the early diagnosis of kidney diseases.

The Influence of the Severity of Early Chronic Kidney Disease on Oxidative Stress in Patients with and without Type 2 Diabetes Mellitus.

Early Chronic Kidney Disease (CKD) is a condition that tends to progress to End-Stage Kidney Disease (ESKD). Early diagnosis of kidney disease in the early stages can reduce complications. Alterations in renal function represent a complication of diabetes mellitus (DM). The mechanisms underlying the progression of CKD in diabetes could be associated with oxidative and inflammatory processes. This study aimed to evaluate the state of inflammation and oxidative stress (OS) on the progression of CKD in the early stages in patients with and without type 2 diabetes mellitus (T2DM). An analytical cross-sectional study was carried out in patients with CKD in early stages (1, 2, 3) with and without T2DM. The ELISA method determined the expression of pro-inflammatory cytokines IL-6 and TNF-α as well as lipoperoxides (LPO), nitric oxide (NO), and superoxide dismutase activity (SOD). Colorimetric methods determined glutathione peroxidase (GPx) and total antioxidant capacity (TAC). Patients with CKD and T2DM had significantly decreased antioxidant defenses for SOD (p < 0.01), GPx (p < 0.01), and TAC (p < 0.01) compared to patients without T2DM. Consequently, patients with T2DM had higher concentrations of oxidant markers, NO (p < 0.01), inflammation markers, IL-6 (p < 0.01), and TNF-α than patients without T2DM. CKD stages were not related to oxidative, antioxidant, and inflammatory marker outcomes in T2DM patients. Patients without T2DM presented an increase in SOD (p = 0.04) and a decrease in NO (p < 0.01) when the stage of CKD increased. In conclusion, patients with T2DM present higher levels of oxidative and inflammatory markers accompanied by a decrease in antioxidant defense. However, these oxidative status markers were associated with CKD stage progression in patients without T2DM. Thus, NO and SOD markers could help detect the early stages of CKD in patients who have not yet developed metabolic comorbidities such as T2DM.

Establishment of a reference interval for urinary albumin-to-creatinine ratio in dogs.

Albuminuria is an important marker of renal damage and can precede proteinuria; thus, it can be a useful analyte in the early diagnosis of kidney diseases. Albuminuria has also been found in dogs with hypertension, inflammatory, infectious, and neoplastic diseases. The aim of this study was to establish a reference interval (RI) for albuminuria in dogs. One hundred sixty-four clinically healthy dogs were enrolled in the study. Urinary albumin was determined by the immunoturbidimetric method, and albumin excretion was expressed as the urinary albumin-to-creatinine (UAC) ratio. The RI for UAC was established. After exclusions, 124 dogs from 32 breeds remained. The median UAC of the study population was 3.0mg/g (range: 0-48). The RI was defined as 0-19 mg/g (with a 90% CI for the upper limit of 13-28 mg/g). No significant difference was found between male and female dogs or between different age and body weight groups. The results of Sighthounds (n=30) and Beagle dogs (n=23) did not differ from the other breeds. The canine RI of UAC is similar but somewhat narrower than the human RI.

Subtle morpho-functional kidney changes in type-2 diabetes mellitus patients: A duplex ultrasound assessment.

Objectives:Diabetes mellitus (DM) is a common endocrine disease with serious effects on multiple organs including the kidneys. This study aimed to investigate the subtle effects of type 2 DM (T2DM) on the kidneys.Methods:This was a prospective case-control study conducted in the Radiology Department of University of Science and Technology Hospital (USTH) campus, Sana’a, Republic of Yemen, from 1 January 2020 to 31 November 2020. The renal length (RL), renal width (RW), resistive index (RI), and pulsatility index (PI) were prospectively measured in patients with T2DM and healthy controls. The results were compared using the independent samples t-test. Comparisons were likewise performed between patients with controlled DM and patients with uncontrolled DM.Results:A total of hundred individuals, 50 diabetic patients and 50 controls, were enrolled in this study. Their mean age was 54 ± 7.88 years (range: 40–75 years). The RL, RI, and PI of both kidneys were significantly higher in T2DM than in the control group. Moreover, the RL, RI, PI and creatinine were slightly higher in patients with uncontrolled than in those with controlled DM.Conclusion:T2DM has significant accentuating effects on the RL, RI and PI associated with low effective renal plasma flow, even before acute kidney injury or chronic kidney disease diagnosis, which may be attenuated by careful regulation of DM. Ultrasound Doppler is a highly valuable imaging modality for evaluating the subtle effects of T2DM on kidney dimensions and blood flow. The RI can be implemented as a tool for the early diagnosis of kidney disease and contribute to slowing the disease progression and preventing renal failure.

A Bright, Renal‐Clearable NIR‐II Brush Macromolecular Probe with Long Blood Circulation Time for Kidney Disease Bioimaging

AbstractEarly detection of kidney disease is of vital importance due to its current prevalence worldwide. Fluorescence imaging, especially in the second near‐infrared window (NIR‐II) has been regarded as a promising technique for the early diagnosis of kidney disease due to the superior resolution and sensitivity. However, the reported NIR‐II organic renal‐clearable probes are hampered by their low brightness (ϵmaxΦf&gt;1000 nm&lt;10 M−1 cm−1) and limited blood circulation time (t1/2&lt;2 h), which impede the targeted imaging performance. Herein, we develop the aza‐boron‐dipyrromethene (aza‐BODIPY) brush macromolecular probes (Fudan BDIPY Probes (FBP 912)) with high brightness (ϵmaxΦf&gt;1000 nm≈60 M−1 cm−1), which is about 10‐fold higher than that of previously reported NIR‐II renal‐clearable organic probes. FBP 912 exhibits an average diameter of ≈4 nm and high renal clearance efficiency (≈65 % excretion through the kidney within 12 h), showing superior performance for non‐invasively diagnosis of renal ischemia‐reperfusion injury (RIR) earlier than clinical serum‐based protocols. Additionally, the high molecular weight polymer brush enables FBP 912 with prolonged circulation time (t1/2≈6.1 h) and higher brightness than traditional PEGylated renal‐clearable control fluorophores (t1/2&lt;2 h), facilitating for 4T1 tumor passive targeted imaging and renal cell carcinoma active targeted imaging with higher signal‐to‐noise ratio and extended retention time.

A Bright, Renal-Clearable NIR-II Brush Macromolecular Probe with Long Blood Circulation Time for Kidney Disease Bioimaging.

Early detection of kidney disease is of vital importance due to its current prevalence worldwide. Fluorescence imaging, especially in the second near-infrared window (NIR-II) has been regarded as a promising technique for the early diagnosis of kidney disease due to the superior resolution and sensitivity. However, the reported NIR-II organic renal-clearable probes are hampered by their low brightness (ϵmax Φf>1000 nm <10 M-1 cm-1 ) and limited blood circulation time (t1/2 <2 h), which impede the targeted imaging performance. Herein, we develop the aza-boron-dipyrromethene (aza-BODIPY) brush macromolecular probes (Fudan BDIPY Probes (FBP 912)) with high brightness (ϵmax Φf>1000 nm ≈60 M-1 cm-1 ), which is about 10-fold higher than that of previously reported NIR-II renal-clearable organic probes. FBP 912 exhibits an average diameter of ≈4 nm and high renal clearance efficiency (≈65 % excretion through the kidney within 12 h), showing superior performance for non-invasively diagnosis of renal ischemia-reperfusion injury (RIR) earlier than clinical serum-based protocols. Additionally, the high molecular weight polymer brush enables FBP 912 with prolonged circulation time (t1/2 ≈6.1 h) and higher brightness than traditional PEGylated renal-clearable control fluorophores (t1/2 <2 h), facilitating for 4T1 tumor passive targeted imaging and renal cell carcinoma active targeted imaging with higher signal-to-noise ratio and extended retention time.

Evaluation and replacement of renal function: history, advancement, and challenges

<p indent=0mm>The kidneys are important for the excretion of metabolic and toxic waste from the body, the production of erythropoietin, 1,25-OH-vitamin D, and renin, and the maintenance of the body’s water, salt, and acid-base balance. Various primary or secondary kidney diseases can result in a decreased number of nephrons, resulting in the retention of metabolites in the body, abnormal renal endocrine functions, water, electrolyte, and acid-base balance disorders, and even in the progression to end-stage kidney disease (ESKD) with an irreversible decline in renal function. Complete renal function evaluation includes evaluation of the glomerular function, renal tubular function, and endocrine function, which can be used in the early diagnosis of kidney disease, the determination of the stage of kidney diseases, and monitoring of treatment responses. Renal replacement therapy is essential for maintaining life and improving the quality of life in patients with ESKD. This article will briefly review the history and current status of renal function evaluation and replacement therapy.

Machine Learning-Based Rapid Diagnostic-Test Reader for Albuminuria Using Smartphone

Albuminuria is an excellent marker for early diagnosis of kidney and cardiovascular disease. Urine dipsticks that are widely used for rapid screening of albumin, lack sensitivity and specificity in lower concentrations (<300 mg/dL) which is clinically very significant for early diagnosis and often provide qualitative or semi-quantitative results. Precise quantification of lower concentrations is based on urinary analyzers that are not portable and cannot be used in point-of-care (PoC) settings. Here, the feasibility of an accessory free analytical device has been demonstrated using a smartphone. Amalgamation of a smartphone with a dipstick enables rapid and inexpensive diagnosis. It estimates not only the standard five concentrations used in dipstick method, but ten different concentrations. This enables accurate detection and quantification of albumin at lower concentration, clinically significant in the early diagnosis of kidney disease. In order to mitigate ambient light conditions and shadow of the smartphone, images of strips were taken in a smartphone camera with “Flash ON” mode. Machine learning algorithms were used to classify ten different albumin concentrations, corresponding to normal, micro albuminuria and macro albuminuria conditions. The study was performed under varying illumination conditions using multiple smartphones. Random Forest algorithm yields an accuracy of 92% in constant illumination and variable smartphone conditions. In variable smartphone and illumination condition, it yields 82% accuracy on the test data. The detection limit of the proposed method is 7.8125 mg / dL.

Room temperature ammonia gas sensor based on ionic conductive biomass hydrogels

Sensitive detection of ammonia (NH3) in exhaled human breath, may offer useful information for early diagnosis of kidney disease. Traditional chemical NH3 sensors usually suffer from ambient humidity. In this work, NH3 sensor operating under high humidity at 25 ℃ with enhanced response and selectivity has been achieved based on biomass hydrogel poly-l-glutamic acid and l-glutamic acid (PGA/GA). The response of the optimum PGA/GA sensor to 50 ppm NH3 at 80% relative humidity (RH) reaches 8.4, and the sensor can detect 0.5 ppm NH3. The special ionic conductive NH3 mechanism of the PGA/GA sensor was intensively studied by complex impedance plots and a quartz crystal microbalance. The high response, low detection limit, high stability and the non-toxic characteristics of biomaterials suggest PGA/GA sensors are promising for exhaled breath analyzer.

A Practical Non-Enzymatic Urea Sensor Based on Nico(OH)2 nanorods

Urea is an essential biomarker for the diagnosis of healthy kidneys because urea, a protein metabolite, is formed by the function of kidneys and is excreted into the urine. The urea level in human blood is between 10.2 and 49.8 mg/dL (equivalent to 3mM-7mM) [1]. When the concentration of urea in serum increases up to 720–900 mg/dL, kidney dialysis is required [2]. Since the early diagnosis of kidney disease is a useful way to stop or delay kidney dysfunction [3], selective and sensitive urea sensing through economic methods is very important.For urea detection, various methods such as infrared spectrometry [4], gas chromatography [5], and calorimetry [6] have been proposed as analytical methods. Another technique is the electrochemical process, which is most effective in the economic aspect [7]. Typical electrochemical urea sensing is based on the use of urease as an enzyme. Enzyme-based urea sensing, however, has limited practical applications due to its limited working conditions depending on the temperature, pH, and humidity. Non-enzymatic urea sensors with nickel-based catalysts have been mainly used due to their high stability, biocompatibility, and nontoxicity. However, the issues such as low electrical conductivity, a narrow range of urea reactions, and a high limit of detection require the investigation of new catalysts in non-enzymatic techniques for urea sensor application. The nanostructured Ni(OH)2 has a relatively high electrical conductivity [8]. Also, the bimetallic types of catalysts help to improve the issue of low electrical conductivity and sluggish charge transfer processes [7].Within, we studied a form of nickel hydroxide with cobalt (NiCo(OH)2) based on the nonenzymatic electrochemical sensor by hydrothermal method. The morphology and the structure of the NiCo(OH)2 catalysts were examined using transmission electron microscopy, scanning electron microscopy, and X-ray diffraction. The NiCo(OH)2 catalysts showed excellent electrochemical performance with competent linear sensitivity and selectivity to urea oxidation. Comprehensive knowledge of the performances regarding NiCo(OH)2 as a catalyst in non-enzyme based urea sensor will be given.

Hayvanlarda Böbrek Hastalıklarının Erken Teşhisinde Biyobelirteç Olarak Proteomiklerin Önemi

Hayvanlarda Böbrek Hastalıklarının Erken Teşhisinde Biyobelirteç Olarak Proteomiklerin Önemi

Comparative physiology of glomerular filtration rate by plasma clearance of exogenous creatinine and exo-iohexol in six different avian species

Early diagnosis of kidney diseases in avian species is limited. Endogenous markers currently used in avian practice are not sensitive enough to identify early kidney failure. Consequently, alternative markers should be evaluated. To be able to evaluate these alternative markers, an accurate marker to estimate the GFR should be validated. This study determined the GFR, measured as clearance of exogenous creatinine and exo-iohexol, in six different bird species, i.e. broiler chickens, laying chickens, turkeys, Muscovy ducks, pigeons and African grey parrots (4♀/4♂). To be able to compare the six bird species, normalization to bodyweight (BW) of the GFR was performed, after a good correlation between BW and kidney weight was demonstrated (R² = 0.9836). Clearance of exo-iohexol normalized to BW (mL/min/kg) was determined in all bird species, i.e. 3.09 in broiler chickens; 2.57 in laying chickens; 1.94 in turkeys; 1.29 in pigeons; 2.60 in ducks and 1.11 in parrots. However, these results differed significantly with the clearance of exogenous creatinine: 8.41 in broiler chickens; 9.33 in laying chickens; 5.62 in turkeys; 14.97 in pigeons; 17.59 in ducks and 25.56 in parrots 25.56. Iohexol is preferred to measure the GFR, since it is not prone to tubular reabsorption nor secretion.