Combined proteomics and single cell RNA-sequencing analysis to identify biomarkers of disease diagnosis and disease exacerbation for systemic lupus erythematosus.

Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease for which there is no cure. Effective diagnosis and precise assessment of disease exacerbation remains a major challenge. Methods: We performed peripheral blood mononuclear cell (PBMC) proteomics of a discovery cohort, including patients with active SLE and inactive SLE, patients with rheumatoid arthritis (RA), and healthy controls (HC). Then, we performed a machine learning pipeline to identify biomarker combinations. The biomarker combinations were further validated using enzyme-linked immunosorbent assays (ELISAs) in another cohort. Single-cell RNA sequencing (scRNA-seq) data from active SLE, inactive SLE, and HC PBMC samples further elucidated the potential immune cellular sources of each of these PBMC biomarkers. Results: Screening of the PBMC proteome identified 1023, 168, and 124 proteins that were significantly different between SLE vs. HC, SLE vs. RA, and active SLE vs. inactive SLE, respectively. The machine learning pipeline identified two biomarker combinations that accurately distinguished patients with SLE from controls and discriminated between active and inactive SLE. The validated results of ELISAs for two biomarker combinations were in line with the discovery cohort results. Among them, the six-protein combination (IFIT3, MX1, TOMM40, STAT1, STAT2, and OAS3) exhibited good performance for SLE disease diagnosis, with AUC of 0.723 and 0.815 for distinguishing SLE from HC and RA, respectively. Nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1, and STAT2) showed a robust performance in assessing disease exacerbation (AUC=0.990). Further, the potential immune cellular sources of nine PBMC biomarkers, which had the consistent changes with the proteomics data, were elucidated by PBMC scRNAseq. Discussion: Unbiased proteomic quantification and experimental validation of PBMC samples from two cohorts of patients with SLE were identified as biomarker combinations for diagnosis and activity monitoring. Furthermore, the immune cell subtype origin of the biomarkers in the transcript expression level was determined using PBMC scRNAseq. These findings present valuable PBMC biomarkers associated with SLE and may reveal potential therapeutic targets.

Consistency Analysis of 3 Detection Systems for Measuring Serum C-Reactive Protein.

BACKGROUND C-reactive protein (CRP) is an important clinical indicator. There are many methods and instruments for CRP measurement, and therefore the consistency of CRP values measured between instruments needs to be evaluated. This study aimed to compare the consistency of 3 serum CRP detection systems using turbidimetry. MATERIAL AND METHODS The consistency of CRP measured by 3 instruments, the Mindray BC-5390, Mindray BC-6800, and Johnson Vitros5600, was evaluated, and the consistency of blood routine measurement between the BC-5390 and BC-6800 was also evaluated. Pearson correlation analysis was used to evaluate the correlation of different instrument's test results (R, correlation coefficient). The consistency of instruments was assessed by Passing-Bablok analysis and weighted Deming analysis. RESULTS CRP data and route blood test data from 847 patients were used for analysis. The results showed that there were differences in the CRP values measured by the Mindray BC5390, Mindray BC6800, and Johnson Vitros5600 (χ²=78.573, P<0.001). The CRP measurement results of the BC5390 analyzer were consistent with those of the BC6800 analyzer (R=0.994, P<0.001) and Vitros5600 analyzer (R=0.983, P<0.001). However, there was a constant deviation in the CRP values measured by the BC-6800 and Vitros5600 analyzer (R=0.994, P<0.001). In the measurement of routine blood laboratory tests, the BC5390 analyzer and BC6800 analyzer were found to be interchangeable. CONCLUSIONS This study analyzed the consistency of CRP detection by 3 instruments, the Mindray BC-5390, Mindray BC-6800, and Johnson Vitros5600, and may provide a reference for the selection of CRP detection instruments.

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Purpose: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks. Methods: DeepRetina uses the improved Xception65 to extract and learn the characteristics of retinal layers. The Xception65-extracted feature maps are inputted to an atrous spatial pyramid pooling module to obtain multiscale feature information. This information is then recovered to capture clearer retinal layer boundaries in the encoder-decoder module, thus completing retinal layer auto-segmentation of the retinal optical coherence tomography (OCT) images. Results: We validated this method using a retinal OCT image database containing 280 volumes (40 B-scans per volume) to demonstrate its effectiveness. The results showed that the method exhibits excellent performance in terms of the mean intersection over union and sensitivity (Se), which are as high as 90.41 and 92.15%, respectively. The intersection over union and Se values of the nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, outer limiting membrane, photoreceptor inner segment, photoreceptor outer segment, and pigment epithelium layer were found to be above 88%. Conclusions: DeepRetina can automate the segmentation of retinal layers and has great potential for the early diagnosis of fundus retinal diseases. In addition, our approach will provide a segmentation model framework for other types of tissues and cells in clinical practice. Translational Relevance: Automating the segmentation of retinal layers can help effectively diagnose and monitor clinical retinal diseases. In addition, it requires only a small amount of manual segmentation, significantly improving work efficiency.

FecalNet: Automated detection of visible components in human feces using deep learning.

PURPOSE: To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks. METHODS: FecalNet uses the ResNet152 residual network to extract and learn the characteristics of visible components in fecal microscopic images, acquire feature maps in combination with the feature pyramid network, apply the full convolutional network to classify and locate the fecal components, and implement the improved focal loss function to reoptimize the classification results. This allowed the complete automation of the detection and identification of the visible components in feces. RESULTS: We validated this method using a fecal database of 1,122 patients. The results indicated a mean average precision (mAP) of 92.16% and an average recall (AR) of 93.56%. The average precision (AP) and AR of erythrocyte, leukocyte, intestinal mucosal epithelial cells, hookworm eggs, ascarid eggs, and whipworm eggs were 92.82% and 93.38%, 93.99% and 96.11%, 90.71% and 92.41%, 89.95% and 93.88%, 96.90% and 91.21%, and 88.61% and 94.37%, respectively. The average times required by the GPU and the CPU to analyze a fecal microscopic image are approximately 0.14 and 1.02 s, respectively. CONCLUSION: FecalNet can automate the detection and identification of visible components in feces. It also provides a detection and identification framework for detecting several other types of cells in clinical practice.

[Artificial intelligence empowers laboratory medicine in Industry 4.0].

Since 2017, China, the United States, and the European Union have successively issued national-level artificial intelligence (AI) strategic development plans, and the human history is about to witness the 4th industrial revolution with the theme of "intelligence". In the field of medical testing, the explosive growth of AI theories and technologies also provide a new direction for the development of medical testing theory, methods and applications. We review the evolution of AI and the recent progress in three major elements of AI, namely algorithms, data and computing power, and elaborate on the combined innovation of "AI + testing" in light of the key application dimensions of medical testing. The major applications include specimen collection robots, sample dilution robots and sample transfer robots involved in the processing of test specimens; test item mining such as tumor markers and pharmacogenomics; cytomorphology, laboratory medicine data processing, auxiliary diagnostic models, and internet-based medical tests. With the advent of the era of Industry 4.0, AI technology will promote the development of medical testing from automation to a highly intelligent stage.

Inspection of visible components in urine based on deep learning.

PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy. In this study, a method is proposed for the analysis of urinary particles based on deep learning. METHODS: We used seven cellular components (i.e., erythrocytes, leukocytes, epithelial, low-transitional epithelium, casts, crystal, and squamous epithelial cells) in the microscopic imaging of urine as the detection targets. After the extraction of features using Resnet50, feature maps of different sizes are obtained in the last few layers of the feature pyramid net (FPN). The feature maps are then input into the classification subnetwork and regression subnetwork for classification and localization respectively, and detection results are obtained. First, we introduce the basic model (RetinaNet) to detect the cellular components in urinary particles, and the features of the objects can then be extracted more effectively by replacing different basic networks. Lastly, the effects of different weight initialization methods and different anchor scales on the performance of the model are investigated. RESULTS: We obtained the optimal network structure based on the adjustment of the loss functional parameters, thereby achieving the best results in the test set of urinary particles. The experimental data yielded an accuracy of 88.65% with a processing time of only 0.2 s for each image on a GeForce GTX 1080 graphics processing unit (GPU). Our results demonstrate that this method cannot only achieve the speed of the first-stage target detector, but also the accuracy of the two-stage target algorithm in the analysis of urinary particles. CONCLUSION: This study developed new automated analysis urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of urinary particles. Moreover, our approach will be useful for the detection of other cells in the clinical setting.

Novel Biochemical Insights in the Cerebrospinal Fluid of Patients with Neurosyphilis Based on a Metabonomics Study.

Neurosyphilis is a chronic central nervous system infectious disease caused by Treponema pallidum. Our aim was to study the metabolic profiling in the cerebrospinal fluid of neurosyphilis patients and identify specific potential biomarkers. Fifteen cerebrospinal fluid samples from neurosyphilis patients and 14 non-neurosyphilis samples were analyzed by liquid chromatography-mass spectrometer (LC-MS). The LC-MS data were preprocessed by supervised pattern recognition to obtain diagnostic models. Both orthogonal projections to a latent structures discriminant analysis (OPLS-DA) and a t test were used to obtain specific metabolites for neurosyphilis. LC-MS data showed that the metabolites in cerebrospinal fluid (CSF) from neurosyphilis are different from the non-neurosyphilis group. The OPLS-DA model parameters R2Y and Q2Y are both more than 0.7 and indicated a satisfactory diagnostic performance. Bilirubin, L-histidine, prostaglandin E2, alpha-kamlolenic acid, and butyryl-L-carnitine and palmitoyl-L-carnitine were identified as novel potential biomarkers for neurosyphilis. The metabolic study of CSF may provide a new way to explore the pathogenesis of neurosyphilis.

Potential biomarkers for antidiastole of tuberculous and malignant pleural effusion by proteome analysis.

AIM: To investigate novel potential biomarkers for antidiastole of tuberculous pleural effusion (TPE) from malignant pleural effusion (MPE). MATERIALS & METHODS: iTRAQTM-coupled LC-MS/MS were applied to analyze the proteome of TPE and MPE samples. The candidate proteins were verified by enzyme-linked immunosorbent assay. RESULTS: A total of 432 differential proteins were identified. Enzyme-linked immunosorbent assay revealed significantly higher levels of fibronectin (FN) and cathepsin G (CTSG) in MPE than in TPE, but lower levels of leukotriene-A4 hydrolase (LTA4H). The receiver operator characteristic values were 0.285 for FN, 0.64 for LTA4H, 0.337 for CTSG and 0.793 for a combination of these candidate markers. CONCLUSION: FN, LTA4H and CTSG were identified as potential biomarkers to differentiate TPE from MPE and their combination exhibited higher diagnostic capacity.

Serum SAA1 and APOE are novel indicators for human cytomegalovirus infection.

Human cytomegalovirus (HCMV) infection is a global concern and highly infectious. HCMV-infected individuals are often carriers with damaged immunity. However few diagnostic indicators block HMCV control and prevention. Thus, we measured 21 serum proteins related to HCMV infection using iTRAQ-labeling based quantitative proteomic approaches and SAA1 and APOE were confirmed as candidate serum indicators for identification of HMCV infection according to ROC curve analysis and that co-occurrence of SAA1 and APOE are better markers than individual proteins.

Metabonomics screening of serum identifies pyroglutamate as a diagnostic biomarker for nonalcoholic steatohepatitis.

OBJECTIVE: A key step in managing non-alcoholic fatty liver disease (NAFLD) is to differentiate nonalcoholic steatohepatitis (NASH) from simple steatosis (SS). METHOD: Serum samples were collected from three groups: NASH patients (N=21), SS patients (N=38) and healthy controls (N=31). High performance liquid chromatography-mass spectrometry (HPLC-MS) was used to analyse the metabolic profile of the serum samples. The acquired data were processed by multivariate principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) to identify novel metabolites. The potential biomarkers were quantitatively determined and their diagnostic power was further validated. RESULTS: A total of 56 metabolites were capable of distinguishing NASH from SS samples based on the OPLS-DA model. Pyroglutamate was found to be the most promising factor in distinguishing the NASH from SS groups. With an optimal cut-off value of 4.82mmol/L, the sensitivity and specificity of the diagnosis of NASH were 72% and 85%, respectively. The area under the receiver operating characteristic (AUROC) of the pyroglutamate levels of NASH versus SS patients was more than those of tumor necrosis factor-α, adiponectin and interleukin-8. CONCLUSION: These data suggest that pyroglutamate may be a new and useful biomarker for the diagnosis of NASH.

Flexible and Highly Photosensitive Electrolyte-Gated Organic Transistors with Ionogel/Silver Nanowire Membranes.

Flexible and low-voltage photosensors with high near-infrared (NIR) sensitivity are critical for realization of interacting humans with robots and environments by thermal imaging or night vision techniques. In this work, we for the first time develop an easy and cost-effective process to fabricate flexible and ultrathin electrolyte-gated organic phototransistors (EGOPTs) with high transparent nanocomposite membranes of high-conductivity silver nanowire (AgNW) networks and large-capacitance iontronic films. A high responsivity of 1.5 × 103 A·W1-, high sensitivity of 7.5 × 105, and 3 dB bandwidth of ∼100 Hz can be achieved at very low operational voltages. Experimental studies in temporal photoresponse characteristics reveal the device has a shorter photoresponse time at lower light intensity since strong interactions between photoexcited hole carriers and anions induce extra long-lived trap states. The devices, benefiting from fast and air-stable operations, provide the possibility of the organic photosensors for constructing cost-effective and smart optoelectronic systems in the future.

Liver tissue metabolic profiling and pathways of non-alcoholic steatohepatitis in rats.

AIM: The mechanisms of non-alcoholic steatohepatitis (NASH) in hepatocytes are unknown. Our aim is to study the tissue metabolic profiling and pathways of NASH. METHODS: We built rat models for simple steatosis and NASH and analyzed the liver extract using a liquid chromatograph-mass spectrometer. The acquired data were processed by multivariate principal component analysis and partial least squares discriminant analysis (PLS-DA) to obtain metabolic profiling. Orthogonal projections to latent structures DA was used to obtain metabolites capable of distinguishing NASH and steatosis. The total differences in the metabolites between groups were analyzed to determine their metabolic pathways. RESULTS: Principal component analysis showed that the metabolic profiles of NASH and steatosis are different. The PLS-DA modeling revealed a clear separation between two groups with parameters R2 Y and Q2 Y all greater than 0.7. The orthogonal projections to latent structures DA model identified 171 metabolites capable of distinguishing NASH from steatosis. The identified metabolites are involved in fatty acid metabolism, tryptophan metabolism, the urea cycle, and the citric acid cycle in hepatocytes. CONCLUSIONS: These metabolic profiles and pathways in rat hepatocytes will offer useful information when studying metabolic disorders in patients with NASH.

Method Comparison and Bias Estimation of Blood Urea Nitrogen (BUN), Creatinine (Cr), and Uric Acid (UA) Measurements between Two Analytical Methods.

BACKGROUND: A comparison of any two methods is of great importance in a clinical laboratory. In this study, our aim is to compare the assay results of blood urea nitrogen (BUN), creatinine (Cr), and uric acid (UA) obtained through two distinct methods and then assess the analytical agreement of the two methods. METHODS: A test method (Vitros5600 system) measuring BUN, Cr, and UA analytes was compared with a reference method (Hitachi7600 system). The Clinical and Laboratory Standards Institute (CLSI) document EP9-A2 guidelines were followed to evaluate the method comparison and bias using 40 patient samples. RESULTS: A high correlation between the two methods was found for all of the samples (R2 > 0.990). The regression parameters were BUN (R2 = 0.9996, slope = 1.025, intercept = 0.1156), Cr (R2 = 0.9993, slope = 0.9993, intercept = 4.661), and UA (R2 = 0.9971, slope = 1.011, intercept = 1.311). Compared with the Hitachi7600 reference method, the Vitros5600 test method showed that the 95% confidence interval for the predicted bias at medical decision levels was less than the acceptable error. More importantly, Bland-Altman plots indicated that a minimal positive bias (mean ± SD) was observed: BUN (0.352 ± 0.289 mmol/L), Cr (2.702 ± 7.683 µmol/L), UA (5.398 ± 7.086 µmol/L). CONCLUSIONS: The Vitros5600 and Hitachi7600 systems have good correlation and bias for detecting BUN, Cr, and UA analytes. The two systems have a high method agreement.

Tripterygium glycosides inhibit inflammatory mediators in the rat synovial RSC-364 cell line stimulated with interleukin-1ß.

Tripterygium glycosides (TG) are extracted from a traditional Chinese medicinal herb. Using the compound, progress has been made in the treatment of rheumatoid arthritis (RA), but the underlying mechanism of its action is poorly understood. The purpose of the present study was to investigate the role of TG in preventing inflammatory arthritis. An inflammatory cell model was established in the rat synovial RSC-364 cell line via induction with interleukin (IL)-1ß. The expression of IL-32 and matrix metalloproteinases (MMP-1 and MMP-9) was determined using an enzyme-linked immunosorbent assay. Compared with the control group (without IL-1ß), IL-1ß in the treatment group induced the expression of IL-32, MMP-1 and MMP-9 in RSC-364 cells. When a different dose of TG was added to RSC-364 cells stimulated with IL-1ß, TG decreased the expression levels of IL-32, MMP-1 and MMP-9 in a dose-dependent manner. These results indicated that TG suppressed the inflammation response in RSC-364 cells. Taken together, these findings may contribute to a better understanding of the role of TG in the anti-inflammatory therapeutics for RA.

Quantitative Proteomic Analysis of Peripheral Blood Mononuclear Cells in Ankylosing Spondylitis by iTRAQ.

This study was designed to identify and quantify the different proteins expression levels in ankylosing spondylitis (AS) and to explore the pathogenesis of AS. We performed isobaric tags for relative and absolute quantitation (iTRAQ) coupled with multiple chromatographic fractionation and tandem mass spectrometry to detect the proteins profiling in peripheral blood mononuclear cells (PBMCs) from AS patients and healthy controls. Mascot software and the International Protein Index and the Gene Ontology (GO) database were used to conduct the bioinformatics analysis. The differentially expressed proteins were validated by enzyme-linked immunosorbent assay (ELISA). A total of 1,232 proteins were identified by iTRAQ, of which 183 showed differential expression and 18 differentially expressed proteins were acute phase reactants. Upon mapping of the differentially expressed proteins to GO database, we found four differentially expressed proteins involved in the biological process of cell killing, including up-regulated cathepsin G (CTSG), neutrophil defensin3 (DEFA3), protein tyrosine phosphatase receptor type C (PTPRC), and down-regulated peroxiredoxin-1(PRDX1),which were consistent with the verified results of ELISA. Our proteomic analyses suggested that the proteins involved in the biological process of cell killing might play an important role in the pathogenesis of AS.

Quantitative proteomic analysis of Down syndrome in the umbilical cord blood using iTRAQ.

Down sydrome (DS) is a relatively frequent chromosomal disorder, which has no safe and effective method of prenatal diagnosis to date. The present study was designed to identify DS biomarkers. We quantified the changes in the umbilical cord blood protein levels between DS-affected and healthy (control) pregnant females using isobaric tags for relative and absolute quantification (iTRAQ) and Gene Ontology (GO) analysis. A total of 505 proteins were identified, and of these, five proteins showed significantly different concentrations between the DS and the control group. These proteins may thus be relevant to DS and constitute potential DS biomarkers.

Determining the concentration of procalcitonin using a magnetic particles-based chemiluminescence assay for the clinical diagnosis of sepsis.

Our objective is to develop an assay based on magnetic particles (MPs) to determine the concentration of procalcitonin (PCT) using a chemiluminescence immunoassay (CLIA). Fluorescein isothiocyanate (FITC) and N-(aminobutyl)-N-(ethylisoluminol) (ABEI) were used to label two different anti-procalcitonin (PCT) monoclonal antibodies. The labeled antibodies, the PCT antigen, and the anti-FITC antibody-coated MPs formed a double-sandwiched immunocomplex. The measured relative light units (RLUs) of ABEI in the substrate solution were directly proportional to the amount of PCT present in the samples. The proposed method was linear to 600 ng/mL with a detection limit of 0.03 ng/mL. The coefficient of variation (CV) was <5% and <6% for the intra- and inter-assay precision, respectively. The average recoveries were between 95 and 107%. The linearity-dilution effect gave a linear correlation coefficient of 0.9912. This proposed assay provided an alternative method to quantitatively measure PCT in serum for the diagnosis of sepsis.

Continuous detection of muscle aspect ratio using keypoint tracking in ultrasonography.

Muscle aspect ratio of cross-sectional area is one of the most widely used parameters for quantifying muscle function in both diagnosis and rehabilitation assessment. Ultrasound imaging has been frequently used to noninvasively study the characteristics of human muscles as a reliable method. However, the aspect ratio measurement is traditionally conducted by the manual digitization of reference points; thus, it is subjective, time-consuming, and prone to errors. In this paper, a novel method is proposed to continuously detect the muscle aspect ratio. Two keypoint pairs are manually digitized on the lateral and longitudinal borders at the first frame, and automatically tracked by an optical flow technique at the subsequent frames. The muscle aspect ratio is thereby obtained based on the estimated muscle width and thickness. Six ultrasound sequences from different subjects are used to evaluate this method, and the overall coefficient of multiple correlation of the results between manual and proposed methods is 0.97 ± 0.02. The linear regression shows that a good linear correlation between the results of the two methods is obtained (R(2) = 0.974), with difference -0.01 ± 0.16. The method proposed here provides an accurate, high repeatable, and efficient approach for estimating muscle aspect ratio during human motion, thus justifying its application in biological sciences.

Development of a rapid and high-performance chemiluminescence immunoassay based on magnetic particles for protein S100B in human serum.

Protein S100B is a clinically useful non-invasive biomarker for brain cell damage. A rapid chemiluminescence immunoassay (CLIA) for S100B in human serum has been developed. Fluorescein isothiocyanate (FITC) and N-(aminobutyl)-N-(ethylisoluminol) (ABEI) are used to label two different monoclonal antibodies of anti-S100B. Protein S100B in serum combines with labeled antibodies and can form a sandwiched immunoreaction. A simplified separation procedure based on the use of magnetic particles (MPs) that were coated with anti-FITC antibody is performed to remove the unwanted materials. After adding the substrate solution, the relative light unit (RLU) of ABEI is measured and is found to be directly proportional to the concentration of S100B in serum. The relevant variables involved in the CLIA signals are optimized and the parameters of the proposed method are evaluated. The results demonstrate that the method is linear to 25 ng/mL S100B with a detection limit of 0.02 ng/mL. The coefficient of variation (CV) is < 5% and < 6% for intra- and interassay precision, respectively. The average recoveries are between 97 and 107%. The linearity-dilution effect produces a linear correlation coefficient of 0.9988. Compared with the commercial kit, the proposed method shows a correlation of 0.9897. The proposed method displays acceptable performance for quantification of S100B and is appropriate for use in clinical diagnosis.

A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy.

BACKGROUND: Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology. METHODS: A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile. RESULTS: The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%. CONCLUSION: The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.