Biomarkers For Diabetic Kidney Disease Research Articles

Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease

Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional "stress score" that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.

Novel Biomarkers of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a highly prevalent condition worldwide. It represents one of the most common complications arising from diabetes mellitus (DM) and is the leading cause of end-stage kidney disease (ESKD). Its development involves three fundamental components: the hemodynamic, metabolic, and inflammatory axes. Clinically, persistent albuminuria in association with a progressive decline in glomerular filtration rate (GFR) defines this disease. However, as these alterations are not specific to DKD, there is a need to discuss novel biomarkers arising from its pathogenesis which may aid in the diagnosis, follow-up, therapeutic response, and prognosis of the disease.

Research progress of omics biomarkers for diabetic kidney disease

糖尿病肾病（DKD）是糖尿病最常见的慢性并发症，晚期可出现终末期肾病，严重危害人类健康。因此，对DKD进行早期预测具有重要意义。随着组学研究技术的不断发展，基因组学、蛋白质组学、代谢组学等组学生物标志物在DKD早期预测中的潜在应用价值逐渐显现。本文对近年来DKD组学生物标志物的研究进展进行综述。.

Predictive value of ferroptosis-related biomarkers for diabetic kidney disease: a prospective observational study

AimsTo explore the predictive value of ferroptosis-related (FR) biomarkers for diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).MethodsThis prospective observational study enrolled patients with T2DM at the Second Hospital of Jilin University between December 2021 and March 2022. DKD was measured by the urinary albumin-to-creatinine ratio. Receiver operating characteristic curve (ROC) analysis was performed to assess the predictive value of ferroptosis-related biomarkers for DKD.The risk factors for massive proteinuria were performed by multivariable logistic regression analysis.ResultsFinally, 118 patients (53.0 ± 12.2 years, 76 males) were enrolled, 52 of them without DKD (had normal proteinuria), while 66 with DKD. (Forty-one had microproteinuria, and 25 had massive proteinuria.) FR biomarkers, including acyl-CoA synthase long chain family member 4 (ACSL4), malondialdehyde (MDA), and reactive oxygen species (ROS), were significantly higher in the massive proteinuria group than in the other groups, while glutathione peroxidase 4 (GPX4) was significantly lower (all P < 0.05). The area under the ROC of the combination of GPX4, ACSL4, MDA, and ROS for predicting DKD was 0.804 (P < 0.001). Additionally, multivariate logistic regression analysis showed that the course of disease and ferritin levels were independent risk factors for massive proteinuria, while high serum iron, transferrin, and GPX4 levels were independent protective factors for massive proteinuria in patients with T2DM (all P < 0.05).ConclusionsThe GPX4, ACSL4, MDA, and ROS combination might have a good predictive value for DKD. Additionally, the course of disease, ferritin levels, serum iron, transferrin, and GPX4 were independently associated with massive proteinuria.

Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network

Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network

Identification of Oxidative Stress-Related Biomarkers in Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease throughout the world. In kidney disease, oxidative stress has been linked to both antioxidant depletions and increased reactive oxygen species (ROS) production. Thus, the objective of this study was to identify biomarkers related to oxidative stress in DKD. The gene expression profile of the DKD was extracted from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the "limma" R package, and weighted gene coexpression network analysis (WGCNA) was used to find the gene modules that were most related to DKD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using "Org.Hs.eg.db" R package. The protein-protein interaction (PPI) network was constructed using the STRING database. The hub genes were identified by the Molecular Complex Detection (MCODE) plug-in of Cytoscape software. The diagnostic capacity of hub genes was verified using the receiver operating characteristic (ROC) curve. Correlations between diagnostic genes were analyzed using the "corrplot" package. In addition, the miRNA gene transcription factor (TF) network was used to explain the regulatory mechanism of hub genes in DKD. DEGs analysis and WGCNA-identified 160 key genes were identified in DKD patients. Among them, nine oxidative stress-related genes were identified as candidate hub genes for DKD. Using the PPI network, five hub genes, NR4A2, DUSP1, FOS, JUN, and PTGS2, were subsequently identified. All the hub genes were downregulated in DKD and had a high diagnostic value of DKD. The regulatory mechanism of hub genes was analyzed from the miRNA gene-TF network. Our study identified NR4A2, DUSP1, FOS, JUN, and PTGS2 as hub genes of DKD. These genes may serve as potential therapeutic targets for DKD patients.

Clinical application of RUBCN/SESN2 mediated inhibition of autophagy as biomarkers of diabetic kidney disease

BackgroundDeregulated autophagy in diabetes has been a field of many experimental studies recently. Impaired autophagy in diabetic kidneys orchestrates every step of diabetic nephropathy (DN) pathogenesis. This study aimed to evaluate three autophagy regulators; RUBCN, mTOR, and SESN2 as clinically applicable indicators of DN progression and as early predictors of DN.MethodsThis retrospective study included 120 participants in 4 groups; G1: diabetic patients without albuminuria, G2: diabetic patients with microalbuminuria, G3: diabetic patients with macroalbuminuria and G4: healthy controls. RUBCN and SESN2 genes expression were tested by RT-qPCR. RUBCN, mTOR, and SESN2 serum proteins were quantitated by ELISA.ResultsRUBCN mRNA was over-expressed in diabetic patients relative to controls with the highest level found in G3 followed by G2 then G1; (9.04 ± 0.64, 5.18 ± 0.73, 1.94 ± 0.41 respectively. P < 0.001). SESN2 mRNA expression was at its lowest level in G3 followed by G2 then G1 (0.1 ± 0.06, 0.48 ± 0.11, 0.78 ± 0.13 respectively. P < 0.001). Similar parallel reduction in serum SENS2 was observed. Serum RUBCN and mTOR were significantly elevated in diabetic patients compared to controls, with the increase parallel to albuminuria degree. RUBCN expression, serum RUBCN and mTOR strongly correlated with albuminuria (r = 0.912, 0.925 and 0.867 respectively). SESN2 expression and serum level negatively correlated with albuminuria (r = − 0.897 and -0.828 respectively); (All p < 0.001). Regression analysis showed that serum RUBCN, mTOR, RUBCN and SESN2 mRNAs could successfully predict DN.ConclusionsThe study proves the overexpression of RUBCN and mTOR in DN and the down-expression of SESN2. The three markers can be clinically used to predict DN and to monitor disease progression.

Diagnostic significance of hsa_circ_0000146 and hsa_circ_0000072 biomarkers for Diabetic Kidney Disease in patients with type 2 diabetes mellitus.

Diabetic Kidney Disease (DKD) is a significant challenge in healthcare. However, there are currently no reliable biomarkers for renal impairment diagnosis, prognosis, or staging in DKD patients. CircRNAs and microRNAs have emerged as noninvasive and efficient biomarkers. We explored Cannabinoid receptor 1 (CNR1), C reactive protein (CRP), hsa_circ_ 0000146 and 0000072, and hsa-miR-21 and 495 as diagnostic biomarkers in DKD. The serum concentrations of CRP and CNR1 were measured using ELISA. Rt-qPCR was used to evaluate the expression levels of CNR1, circRNAs, and miRNAs in 55 controls, 55 type 2 diabetes mellitus patients, and 55 DKD patients. Their diagnostic value was determined by their ROC curve. KEGG pathway was used to predict the functional mechanism of the circRNA's target genes. DKD patients exhibited a significant increase in CRP and CNR1 levels and the expression of miR-21 and 495. The expression levels of circ_0000146 and 0000072 decreased in DKD patients. ROC analysis revealed that circRNAs and miRNAs alone or CNR1 and CRP have significant diagnostic potential. The functional prediction results showed the involvement of hsa_circ_0000146 and 0000072 in various pathways that regulate DKD. Therefore, the examined circRNAs and miRNAs may represent a novel noninvasive biomarker for diagnosing and staging DKD.

Novel biomarkers for diabetic kidney disease.

Although diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in patients with diabetes mellitus; its prevalence has failed to decline over the past 30 years. To identify those at high risk of developing DKD and disease progression at an early stage, extensive research has been ongoing in the search for prognostic and surrogate endpoint biomarkers for DKD. Although biomarkers are not used routinely in clinical practice or prospective clinical trials, many biomarkers have been developed to improve the early identification and prognostication of patients with DKD. Novel biomarkers that capture one specific mechanism of the DKD disease process have been developed, and studies have evaluated the prognostic value of assay-based biomarkers either in small sets or in combinations involving multiple biomarkers. More recently, several studies have assessed the prognostic value of omics- based biomarkers that include proteomics, metabolomics, and transcriptomics. This review will first describe the biomarkers used in current practice and their limitations, and then summarize the current status of novel biomarkers for DKD with respect to assay- based protein biomarkers, proteomics, metabolomics, and transcriptomics.

Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics

Diabetic kidney disease is the main cause of end-stage renal disease worldwide. The prediction of the clinical course of patients with diabetic kidney disease remains difficult, despite the identification of potential biomarkers; therefore, novel biomarkers are needed to predict the progression of the disease. We conducted non-targeted metabolomics using plasma and urine of patients with diabetic kidney disease whose estimated glomerular filtration rate was between 30 and 60 mL/min/1.73 m2. We analyzed how the estimated glomerular filtration rate changed over time (up to 30 months) to detect rapid decliners of kidney function. Conventional logistic analysis suggested that only one metabolite, urinary 1-methylpyridin-1-ium (NMP), was a promising biomarker. We then applied a deep learning method to identify potential biomarkers and physiological parameters to predict the progression of diabetic kidney disease in an explainable manner. We narrowed down 3388 variables to 50 using the deep learning method and conducted two regression models, piecewise linear and handcrafted linear regression, both of which examined the utility of biomarker combinations. Our analysis, based on the deep learning method, identified systolic blood pressure and urinary albumin-to-creatinine ratio, six identified metabolites, and three unidentified metabolites including urinary NMP, as potential biomarkers. This research suggests that the machine learning method can detect potential biomarkers that could otherwise escape identification using the conventional statistical method.

Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice.

BackgroundDiabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD.Materials and MethodsTwo datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software “Limma”, differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (—logFC— > 1, p-value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD.ResultsOur RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism–cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated).ConclusionOur study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease.

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor‐1 and ‐2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule‐1, beta‐2‐microglobulin and neutrophil gelatinase‐associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Association Between Serum Carnosinase Concentration and Activity and Renal Function Impairment in a Type-2 Diabetes Cohort.

Introduction: Genetic studies have identified associations of carnosinase 1 (CN1) polymorphisms with diabetic kidney disease (DKD). However, CN1 levels and activities have not been assessed as diagnostic or prognostic markers of DKD in cohorts of patients with type 2 diabetes (T2D). Methods: We established high-throughput, automated CN1 activity and concentration assays using robotic systems. Using these methods, we determined baseline serum CN1 levels and activity in a T2D cohort with 970 patients with no or only mild renal impairment. The patients were followed for a mean of 1.2 years. Baseline serum CN1 concentration and activity were assessed as predictors of renal function impairment and incident albuminuria during follow up. Results: CN1 concentration was significantly associated with age, gender and estimated glomerular filtration rate (eGFR) at baseline. CN1 activity was significantly associated with glycated hemoglobin A1c (HbA1c) and eGFR. Serum CN1 at baseline was associated with eGFR decline and predicted renal function impairment and incident albuminuria during the follow-up. Discussion: Baseline serum CN1 levels were associated with presence and progression of renal function decline in a cohort of T2D patients. Confirmation in larger cohorts with longer follow-up observation periods will be required to fully establish CN1 as a biomarker of DKD.

Study of Lipoprotein-Associated Phospholipase A2 as a Potential Biomarker for Diabetic Kidney Disease in Type 2 Diabetes

Study of Lipoprotein-Associated Phospholipase A2 as a Potential Biomarker for Diabetic Kidney Disease in Type 2 Diabetes

407-P: Skin Autofluorescence Is Associated with Tubular Injury Represented by Urinary Excretion of Liver-Type Fatty Acid–Binding Protein in People with Diabetes

Background: Advanced glycation end product (AGE) accumulation is thought to be an independent predictor for cardiovascular disease in people with diabetes and renal failure. Since it has remained unclear whether AGE has a clinical impact on diabetic kidney disease (DKD) , the aim of this study was to determine the association between AGE accumulation, measured as skin autofluorescence (AF) , and the progression of DKD. Methods: Skin AF was measured by AGE reader® and spot urine biomarkers, including albumin and liver-type fatty acid-binding protein (L-FABP) , were determined as the urine albumin-to-creatinine ratio (uACR) and the urine L-FABP-to-creatinine ratio (uL-FABPCR) in 300 Japanese people with diabetes (173 males and 127 females, mean age, 67.8 ± 10.7 years) . The relationships between skin AF and those urine biomarkers of DKD were statistically evaluated. Results: Simple regression analysis showed that skin AF was associated with uACR (p&amp;lt;0.05) , log-transformed uACR (p&amp;lt;0.01) and uL-FABPCR (p&amp;lt;0.001) . On the other hand, multivariate regression analysis including clinical confounding factors showed that skin AF independently contributed to the increment of uL-FABPCR (P &amp;lt;0.05) but not to that of uACR (P=0.399) or log-transformed uACR (P=0.315) . Conclusion: Skin AF was associated with urinary L-FABP but not with albuminuria in people with diabetes. The results suggest that skin AF can serve as a novel predictive factor for the development of diabetic tubular injury. Disclosure H.Yamagami: None. T.Yuasa: None. S.Yoshida: None. A.Kuroda: None. M.Matsuhisa: Research Support; Nissui , Sysmex Corp., Speaker's Bureau; Astellas Pharma Inc., Eli Lilly and Company, Merck Sharp &amp; Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk, Sanofi, Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company Limited. I.Endo: None. K.Aihara: None. M.Abe: None. S.Yasui: None. M.Hosoki: None. T.Hori: None. T.Hara: None. Y.Mitsui: None. K.Kurahashi: None. S.Nakamura: None. T.Otoda: None.

Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease.

Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

Renal and Inflammatory Proteins as Biomarkers of Diabetic Kidney Disease and Lupus Nephritis.

Current methods for differentiation of kidney disease types are unspecific and may be invasive. Thus, there is a need for development of new biomarkers of kidney disorders that are specific and less invasive. In this study, we analyzed serum samples of diabetic kidney disease (DKD) and lupus nephritis (LN) patients to identify biomarkers of these two disorders. Serum samples were analyzed by Simple Plex assays. We calculated the area under the curve (AUC) as well as receiver operating characteristics (ROC) to obtain the sensitivity and specificity and other biomarker-related variables of apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin- (IL-) 18, Lipocalin-2/NGAL, epidermal growth factor (EGF), u-Plasminogen Activator (uPA), and C-reactive protein (CRP) as potential biomarkers. Protein levels of ASC, IL-18, EGF, and Lipocalin-2/NGAL were higher in DKD and LN patients when compared to controls, whereas only uPA was elevated in DKD patients and CRP in LN patients. As determined by the AUC, of the six analytes studied, EGF (AUC = 0.9935), Lipocalin-2/NGAL (0.9554), ASC (0.7666), and uPA (0.7522) are reliable biomarkers of DKD, whereas EGF (1.000), Lipocalin-2/NGAL (0.9412), uPA (0.7443), and IL-18 (0.7384) are more reliable for LN. The biomarkers analyzed can differentiate between healthy and affected individuals. However, there was no difference between the levels of these biomarkers in DKD vs LN. Thus, although these biomarkers cannot be used to categorize patients between DKD and LN, they are useful as biomarkers of renal pathology.

POS-196 Metabolomic analysis with machine learning identified biomarkers to predict rapid decliner in diabetic kidney disease

Diabetic kidney disease still remains an important health care problem. Early identification of rapid decliners of diabetic kidney disease helps early interventions on the target population as well as efficient execution of clinical trials. Recent advancement in a comprehensive measurement of metabolites and machine learning analysis enables exploratory research, although the method of interpreting the results has not been determined. In this research, we performed comprehensive mass spectrometry of metabolites and explainable machine learning analysis to explore potential biomarkers of diabetic kidney disease.

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease.

Urine tricarboxylic acid (TCA) cycle organic anions (OAs) are elevated in diabetes and may be biomarkers for diabetic kidney disease (DKD) progression. We assessed associations of 10 urine TCA cycle OAs with estimated glomerular filtration rate (eGFR) and eGFR slope. This study is ancillary to the Simultaneous Risk Factor Control Using Telehealth to SlOw Progression of Diabetic Kidney Disease (STOP-DKD) Trial-a randomized trial of pharmacist-led medication and behavior management in 281 patients with early to moderate DKD at Duke from 2014 to 2015. We used linear mixed models to assess associations of urine TCA cycle OAs with outcomes and modelled TCA cycle OAs as: (1) the average of z-scores for each OA; and (2) principal component (PC) scores derived by principal component analysis (PCA). Untargeted urine metabolomics were added for additional discovery. Among 132 participants with 24h urine samples (50% men; 58% Black; mean age 64years [SD 9]; mean eGFR 74ml/min/1.73m2 [SD 21] and medianurine albumin-to-creatinine [UACR] 20mg/g [IQR 8-95]), PCA identified 3 OA metabolite PCs. Malate, fumarate, pyruvate, α-ketoglutarate, lactate, succinate and citrate/isocitrate loaded positively on PC1; methylsuccinate, ethylmalonate and succinate loaded positively on PC2; and methylmalonate, ethylmalonate and citrate/isocitrate loaded negatively on PC3. Over a median follow-up of 1.8years (IQR, 1.2 to 2.2), higher average OA z-score was strongly associated with higher eGFR after covariate adjustment (p = 0.01), but not with eGFR slope (p = 0.9). Higher PC3, but not other PCs, was associated with lower eGFR (p < 0.001). Conditional random forests and smooth clipped absolute deviation models confirmed methylmalonate, citrate/isocitrate, and ethylmalonate, and added lactate as top ranked metabolites in models of baseline eGFR (R-squared 0.32 and 0.33, respectively). Untargeted urine metabolites confirmed association of urine TCA cycle OAs with kidney function. Thus, lower urine TCA cycle OAs, most notably lower methylmalonate, ethylmalonate and citrate/isocitrate, are potential indicators of kidney impairment in early stage DKD.