Biomarkers For Diabetic Kidney Disease Research Articles

#2293 Cardiac autonomic neuropathy: an independent risk factor for renal decline in diabetic kidney disease

Abstract Background and Aims Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) and end stage kidney disease (ESKD) in the UK and worldwide. Due to its unpredictability and incurable nature, novel risk factors associated with incidence and progression of DKD need to be identified. One such factor is cardiac autonomic neuropathy (CAN). Many studies over last 30 years had demonstrated a significant relationship between CAN and incidence and progression of DKD using estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR) as end points. However, no prospective studies have looked into relationship between CAN diagnosed by gold standard cardiac autonomic reflex tests (CARTs) and DKD with different prognostic risks as per KDIGO classification. The aim of the study is to examine this relationship further. Method Data from 91 participants (type 1 and type 2 DM) with eGFR >30 ml/min/1.73 m2 who underwent assessment for CAN using CARTs as per O'Brien's and Ewing's criteria during baseline visits performed between 2007 and 2010 were collected. Additional participants (n = 113) were also recruited in 2021 as part of iBEAt study which is an EU-funded, multi-centre renal imaging study to identify novel biomarkers for DKD. Data analysis for 204 participants in total was performed for this study. CAN was defined as 2 or more abnormal CARTs out of 5 (Table 1). The level of prognostic risk was calculated based on eGFR (G1-G5) and UACR (A1-A3), following KDIGO risk table from 2012 CKD guidelines. eGFR was calculated by chronic kidney disease epidemiology collaboration (CKD-EPI) equation and UACR was assessed by early morning urine sample. Participants were categorised as follow: low risk, moderate risk and high risk. Due to limited numbers of participants with G3 in eGFR, we have combined ‘high risk’ and ‘very high risk’ into one group. CAN was divided into 2 groups—with and without CAN. Differences in proportions were examined using chi-square test. The association between baseline CAN and risk of renal decline was assessed using multinominal logistic regression adjusted for baseline age, HbA1c, ACEi/ARBs use, retinopathy status, total cholesterol and triglyceride. If significant differences were found, individual groups were compared by Bonferroni post hoc test. All analyses were performed in SPSS (version 29.0) and correlations were considered significant if p < 0.05. Results We analysed data of 204 participants (130 T1DM, 74 T2DM), 44.6% (n = 91) of them were women, mean (SD) age at baseline visit was 51 (15.7) years, eGFR 82.6 (13) ml/min/1.73 m2, UACR 5.2 (21.8) mg/mmol, duration of diabetes at baseline 19.7 (12.3) years and 36.8% had hypertension. Baseline eGFR and UACR were available for 198 and 193 participants respectively. Of these, 42.9% (85/198) had G1, 47.5 (94/198) had G2 and 9.6 (19/198) had G3. For UACR, 80.8% (156/193) had A1, 15.5% (30/193) had A2 and 3.6% (7/193) had A3. 53.4% (103/204) of participants had CAN and 46.6% (101/204) had no CAN. A higher proportion of participants was in low prognostic risk category 77.8% (154/198), followed by 13.1% (26/198) with moderate risk and 9.8% (18/198) with high risk for CKD progression. The risk of DKD progression was significantly associated with presence of CAN in multinominal logistic regression analysis (p = 0.020). The presence of CAN was closely related to low risk (95% CI: 1.44 to 22.04, p = 0.013) and moderately increased risk (95% CI: 1.03 to 24.7, p = 0.04). The participants with moderate and high risk had lower eGFR (p < 0.001) and higher UACR with increased risk (p < 0.001) (Table 2). This finding was independent of baseline age, HbA1C, retinopathy, ACEI/ARB use, type of diabetes, cholesterol and triglyceride levels at baseline as measured by logistic regression. Conclusion To our knowledge, this is one of the first studies to examine the relationship between presence of CAN in three stages of prognostic risk in DKD. We demonstrated that presence of CAN was an independent risk factor for decline in renal function in DKD. Further large prospective studies are required to confirm the findings of this study and examine the underlying mechanisms how CAN leads to greater risk of DKD.

Soluble tumor necrosis factor receptor type 1 is an alternative marker of urinary albumin-creatinine ratio and estimated glomerular filtration rate for predicting the decline of renal function in subjects with type 2 diabetes mellitus

BackgroundUrinary albumin-creatinine ratio (UACR) and estimated glomerular filtration rates (eGFR) help predict worsening diabetic kidney disease (DKD) but have their limitations. Soluble tumor necrosis factor receptor type 1 (sTNFR1) is a biomarker of DKD. The predictive abilities of sTNFR1 and UACR plus eGFR have not been compared. MethodsThis prospective cohort study included patients with type 2 diabetes (T2D) to identify the risk factors of worsening DKD. Renal events were defined as > 30 % loss in eGFR based on consecutive tests after 6 months. The associations of sTNFR1, UACR, and eGFR levels and the risks of renal events were tested using a Cox regression model and the area under the curve (AUC) was compared between sTNFR1 levels and UACR plus eGFR using receiver-operating characteristic (ROC) analysis. The accuracy of stratification was evaluated using Kaplan–Meier analysis. ResultsLevels of sTNFR1 and UACR were associated with risks of > 30 % decline in eGFR after adjusting for relevant factors. The association between sTNFR1 levels and renal outcomes was independent of UACR and eGFR at baseline. The AUC of sTNFR1 level was comparable with that of combined UACR and eGFR (0.73 vs. 0.71, respectively, p = 0.72) and the results persisted for quartile groups of sTNFR1 and risk categories of Kidney Disease: Improving Global Outcomes (KDIGO) (0.70 vs. 0.71, respectively, p = 0.84). Both stratifications by sTNFR1 levels and KDIGO were accurate. ConclusionsTNFR1 could be an alternative marker for identifying patients with diabetes at risk of declining renal function.

Multi-omics Analyses Identify AKR1A1 as a Biomarker for Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease. As many genes associate with DKD, multi-omics approaches were employed to narrow the list of functional genes, gene products and related pathways providing insights into the pathophysiological mechanisms of DKD. The Kidney Precision Medicine Project human kidney single-cell RNA-sequencing (scRNAseq) dataset and Mendeley Data on human kidney cortex biopsy proteomics were utilized. R package Seurat was used to analyze scRNAseq and subset proximal tubule cells. PathfindR was applied for pathway analysis in cell type-specific differentially expressed genes and R limma package was used to analyze differential protein expression in kidney cortex. A total of 790 differentially expressed genes were identified in proximal tubule cells, including 530 upregulated and 260 downregulated transcripts. Compared with differentially expressed proteins, 24 genes/proteins were in common. An integrated analysis combining protein quantitative trait loci (pQTL), GWAS hits (estimated glomerular filtration rate) and a plasma metabolomics analysis was performed using baseline metabolites predictive of DKD progression in our longitudinal Diabetes Heart Study samples. Aldo-keto reductase family 1 member A1 gene (AKR1A1) was revealed as a potential molecular hub for DKD cellular dysfunction in several cross-linked pathways featured by deficiency of this enzyme.

Screening candidate diagnostic biomarkers for diabetic kidney disease.

There are big differences in treatments and prognosis between diabetic kidney disease (DKD) and non-diabetic renal disease (NDRD). However, DKD patients couldn't be diagnosed early due to lack of special biomarkers. Urine is an ideal non-invasive sample for screening DKD biomarkers. This study aims to explore DKD special biomarkers by urinary proteomics. According to the result of renal biopsy, 142 type 2 diabetes mellitus (T2DM) patients were divided into 2 groups: DKD (n = 83) and NDRD (n = 59). Ten patients were selected from each group to define urinary protein profiles by label-free quantitative proteomics. The candidate proteins were further verifyied by parallel reaction monitoring (PRM) methods (n = 40). Proteins which perform the same trend both in PRM and proteomics were verified by enzyme-linked immunosorbent assays (ELISA) with expanding the sample size (n = 82). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of diagnostic biomarkers. We identified 417 peptides in urinary proteins showing significant difference between DKD and NDRD. PRM verification identified C7, SERPINA4, IGHG1, SEMG2, PGLS, GGT1, CDH2, CDH1 was consistent with the proteomic results and p < 0.05. Three potential biomarkers for DKD, C7, SERPINA4, and gGT1, were verified by ELISA. The combinatied SERPINA4/Ucr and gGT1/Ucr (AUC = 0.758, p = 0.001) displayed higher diagnostic efficiency than C7/Ucr (AUC = 0.632, p = 0.048), SERPINA4/Ucr (AUC = 0.661, p = 0.032), and gGT1/Ucr (AUC = 0.661, p = 0.029) respectively. The combined index SERPINA4/Ucr and gGT1/Ucr can be considered as candidate biomarkers for diabetic nephropathy after adjusting by urine creatinine.

Urinary exosomal microRNA-145-5p and microRNA-27a-3p act as noninvasive diagnostic biomarkers for diabetic kidney disease.

Diabetic kidney disease (DKD), characterized by increased urinary microalbumin levels and decreased renal function, is the primary cause of end-stage renal disease. Its pathological mechanisms are complicated and multifactorial; Therefore, sensitive and specific biomarkers are needed. Urinary exosome originate from diverse renal cells in nephron segments and partially mirror the pathological changes in the kidney. The microRNAs (miRNAs) in urinary exosome are remarkably stable and highly tissue-specific for the kidney. To determine if urinary exosomal miRNAs from diabetic patients can serve as noninvasive biomarkers for early DKD diagnosis. Type 2 diabetic mellitus (T2DM) patients were recruited from the Second Hospital of Hebei Medical University and were divided into two groups: DM, diabetic patients without albuminuria [urinary albumin to creatinine ratio (UACR) < 30 mg/g] and DKD, diabetic patients with albuminuria (UACR ≥ 30 mg/g). Healthy subjects were the normal control (NC) group. Urinary exosomal miR-145-5p, miR-27a-3p, and miR-29c-3p, were detected using real-time quantitative polymerase chain reaction. The correlation between exosomal miRNAs and the clinical indexes was evaluated. The diagnostic values of exosomal miR-145-5p and miR-27a-3p in DKD were determined using receiver operating characteristic (ROC) analysis. Biological functions of miR-145-5p were investigated by performing Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Urinary exosomal expression of miR-145-5p and miR-27a-3p was more upregulated in the DKD group than in the DM group (miR-145-5p: 4.54 ± 1.45 vs 1.95 ± 0.93, P < 0.001; miR-27a-3p: 2.33 ± 0.79 vs 1.71 ± 0.76, P < 0.05) and the NC group (miR-145-5p: 4.54 ± 1.45 vs 1.55 ± 0.83, P < 0.001; miR-27a-3p: 2.33 ± 0.79 vs 1.10 ± 0.51, P < 0.001). The exosomal miR-145-5p and miR-27a-3p positively correlated with albuminuria and serum creatinine and negatively correlated with the estimated glomerular filtration rate. miR-27a-3p was also closely related to blood glucose, glycosylated hemoglobin A1c, and low-density lipoprotein cholesterol. ROC analysis revealed that miR-145-5p had a better area under the curve of 0.88 [95% confidence interval (CI): 0.784-0.985, P < 0.0001] in diagnosing DKD than miR-27a-3p with 0.71 (95%CI: 0.547-0.871, P = 0.0239). Bioinformatics analysis revealed that the target genes of miR-145-5p were located in the actin filament, cytoskeleton, and extracellular exosome and were involved in the pathological processes of DKD, including apoptosis, inflammation, and fibrosis. Urinary exosomal miR-145-5p and miR-27a-3p may serve as novel noninvasive diagnostic biomarkers or promising therapeutic targets for DKD.

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Association between continuous glucose monitoring-derived glycemic control indices and urinary biomarkers of diabetic kidney disease: Hyogo Diabetes Hypoglycemia Cognition Complications study

AimsGlomerular damage and proximal tubular damage play an important role in the pathogenesis of diabetic kidney disease. This study aimed to investigate the relationship between the urinary markers of proximal tubular injury, including urinary liver-type fatty acid-binding protein-to-creatinine ratio (uL-FABP/Cr) and urinary N-acetyl-β-d-glucosaminidase-to-creatinine ratio (uNAG/Cr), and glycemic control status.MethodsThis cross-sectional study included 245 and 39 patients with type 2 diabetes mellitus (T2DM) and non-T2DM (NDM), respectively. The participants of this study were fitted with retrospective CGM, and glycemic control indices, such as time in range (TIR) and glycemia risk index (GRI), were calculated.ResultsThe results were presented as medians (interquartile ranges). The uL-FABP/Cr was significantly higher in the microalbuminuria than in the normo-albuminuria group [4.2 (2.7–7.1) and 2.2 (1.4–3.4) μg/gCr, respectively, P < 0.001], while the uNAG/Cr in the normo-albuminuria group [6.3 (4.5–10.1) U/gCr] was significantly higher than that in the NDM group [5.3 (3.8–6.3) U/gCr, P = 0.048] but significantly lower than that in the microalbuminuria group [9.2 (6.4–11.1) U/gCr, P = 0.004]. The multivariate logistic regression analysis indicated that CGM-derived TIR was significantly associated with the urinary albumin-to-creatinine ratio [uAlb/Cr, odds ratio (OR) 0.985, 95% confidence interval (CI) 0.971–0.998, P = 0.029] and uNAG/Cr (OR 0.973, 95% CI 0.957–0.989, P = 0.001) independent of renal function. GRI was similarly associated with uAlb/Cr and uNAG/Cr.ConclusionThe findings of this study indicated that uNAG/Cr was elevated before albuminuria development and was associated with CGM-derived TIR and GRI.

Methylglyoxal Adducts Are Prognostic Biomarkers for Diabetic Kidney Disease in Patients With Type 1 Diabetes.

Over 30% of patients with type 1 diabetes develop diabetic kidney disease (DKD), significantly increasing mortality risk. The Diabetes Control and Complications Trial (DCCT) and follow-up study Epidemiology of Diabetes Interventions and Complications (EDIC) established that glycemic control measured by HbA1c predicts DKD risk. However, the continued high incidence of DKD reinforces the urgent need for additional biomarkers to supplement HbA1c. Here, we assessed biomarkers induced by methylglyoxal (MG), a metabolic by-product that forms covalent adducts on DNA, RNA, and protein called MG-adducts. Urinary MG-adducts were measured from patients with type 1 diabetes enrolled in DCCT/EDIC who did (cases, n=90) or did not (controls, n=117) develop DKD. Univariate and multivariable analysis revealed that measurements of MG-adducts independently predict DKD, even before established DKD biomarkers such as glomerular filtration rate and albumin excretion rate. Elevated levels of MG-adducts bestowed the greatest risk of developing DKD in a multivariable model that included HbA1c and other clinical covariates. Our work establishes a novel class of biomarkers to predict DKD risk and suggests that inclusion of MG-adducts may be a valuable tool to improve existing predictors of complications like DKD prior to overt disease to aid in identifying at-risk individuals and personalized risk management.

Quantitative profiling of carboxylic compounds by gas chromatography-mass spectrometry for revealing biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD), a common microvascular complication of diabetes, currently lacks specific diagnostic indicators and therapeutic targets, resulting in miss of early intervention. To profile metabolic conditions in complex and precious biological samples and screen potential biomarkers for DKD diagnosis and prognosis, a rapid, convenient and reliable quantification method for carboxyl compounds by gas chromatography-mass spectrometry (GC–MS) was established with isobutyl chloroformate derivatization. The derivatives were extracted with hexane, injected into GC–MS and quantified with selected ion monitoring mode. This method showed excellent linearity(R2 > 0.99), good recoveries (81.1%-115.5%), good repeatability (RSD < 20%) and sensitivity (LODs: 0.20–499.90 pg, LOQs: 2.00–1007.00 pg). Among the 37 carboxyl compounds analyzed, 12 metabolites in short-chain fatty acids (SCFAs) metabolism pathway and amino acid metabolism pathway were linked with DKD development and among them, 6 metabolites were associated with both development and prognosis of DKD in mice. In conclusion, a reliable, convenient and sensitive method based on isobutyl chloroformate derivatization and GC–MS analysis is established and successfully applied to quantify 37 carboxyl compounds in biological samples of mice and 12 potential biomarkers for DKD development and prognosis are screened.

Distal Tubule Urinary Biomarkers in Diabetic Kidney Disease: Results from the VA NEPHRON-D Trial

Distal Tubule Urinary Biomarkers in Diabetic Kidney Disease: Results from the VA NEPHRON-D Trial

Analysis of potential biomarkers for diabetic kidney disease based on single-cell RNA-sequencing integrated with a single-cell sequencing assay for transposase-accessible chromatin.

Diabetic kidney disease (DKD) is a renal microvascular disease caused by hyperglycemia that involves metabolic remodeling, oxidative stress, inflammation, and other factors. The mechanism is complex and not fully unraveled. We performed an integrated single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) and single-cell RNA-sequencing (scRNA-seq) analyses of kidneys from db/db and db/m mice to identify differential open chromatin regions and gene expression, particularly in genes related to proximal tubular reabsorption and secretion. We identified 9,776 differentially expressed genes (DEGs) and 884 cell type-specific transcription factors (TFs) across 15 cell types. Glucose and lipid transporters, and TFs related to the circadian rhythm in the proximal tubules had significantly higher expression in db/db mice than in db/m mice (P<0.01). Crosstalk between podocytes and tubular cells in the proximal tubules was enhanced, and renal inflammation, oxidative stress, and fibrosis pathways were activated in db/db mice. Western blotting and immunohistochemical staining results showed that Wfdc2 expression in the urine and kidneys of DKD patients was higher than that in non-diabetic kidney disease (NDKD) controls. The revealed landscape of chromatin accessibility and transcriptional profiles in db/db mice provide insights into the pathological mechanism of DKD.

Urine myo-inositol as a novel prognostic biomarker for diabetic kidney disease: a targeted metabolomics study using nuclear magnetic resonance.

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients. We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity. During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction. Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.

Elevated serum trimethylamine oxide levels as potential biomarker for diabetic kidney disease

Diabetic kidney disease (DKD) has become a major cause of chronic kidney disease. However, early diagnosis of DKD is challenging. Trimethylamine oxide (TMAO) is an intestinal microbial metabolite which might be associated with diabetes complications. The aim of this study was to investigate the correlation between TMAO and DKD. A cross-sectional study was conducted. 108 T2DM patients and 33 healthy subjects were enrolled in this study. Multiple logistic regression analysis and area under receiver operating characteristic curve (AUROC) were performed to evaluate the correlation between serum TMAO and DKD. Serum TMAO levels were significantly higher in DKD patients than healthy control group and the NDKD (T2DM without combined DKD) group (P < 0.05). TMAO levels were negatively correlated with eGFR and positively correlated with urea nitrogen, ACR and DKD (P < 0.05). Logistic regression analysis indicated that serum TMAO was one of the independent risk factors for DKD patients (P < 0.05). In the diagnostic model, the AUROC of TMAO for the diagnosis of DKD was 0.691. Elevated levels of serum TMAO levels were positively associated with the risk of DKD in T2DM patients, which might be a potential biomarker for DKD.

In-depth urinary and exosome proteome profiling analysis identifies novel biomarkers for diabetic kidney disease.

Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM). Monitoring the early diagnostic period and disease progression plays a crucial role in treating DKD. In this study, to comprehensively elucidate the molecular characteristics of urinary proteins and urinary exosome proteins in type 2 DKD, we performed large-scale urinary proteomics (n=144) and urinary exosome proteomics (n=44) analyses on T2DM patients with albuminuria in varying degrees. The dynamics analysis of the urinary and exosome proteomes in our study provides a valuable resource for discovering potential urinary biomarkers in patients with DKD. A series of potential biomarkers, such as SERPINA1 and transferrin (TF), were detected and validated to be used for DKD diagnosis or disease monitoring. The results of our study comprehensively elucidated the changes in the urinary proteome and revealed several potential biomarkers reflecting the progression of DKD, which provide a reference for DKD biomarker screening.

12-OR: Urine Lactate Is a Disease Biomarker for Diabetic Kidney Disease

As lactate (LA) elevation is associated with mitochondrial dysfunction, we examined the role of LA in diabetic kidney disease (DKD) progression. Urine LA (ULA) was measured in patients with type 2 diabetes (T2D) in three independent cohort studies (HUNT3, SMART2D, and CRIC). ULA and plasma LA (PLA) were also measured in patients with type 1 diabetes (T1D) following eu- or hyperglycemic clamp, in mouse kidney sections, and sodium-glucose cotransporter-2 (SGLT2)-deficient mice. Mitochondrial functional changes in human kidney proximal tubule (HK2) cells were assayed in response to exogenous LA. Subjects in HUNT3 with DKD (n=39) had elevated ULA levels compared to healthy controls (n=53, p=0.023) (Fig. 1). In subjects with T2D from SMART2D (n=230) and CRIC (n=889), the third tertile of ULA/creatinine ratio was associated with a greater estimated glomerular filtration rate (eGFR) decline [-1.63 mL/min/1.73m2/year, p=0.007, and -0.63 mL/min/1.73m2, p=0.042], relative to the first tertile. Acute hyperglycemia stimulated PLA (p&amp;lt;0.0001) and ULA (p=0.001) in patients with T1D (n=39), and in kidney sections incubated with high glucose. Compared to wild-type, SGLT2-deficient mice had lower ULA levels. LA above 2.5 mM inhibited oxygen consumption rate in HK2 cells. Elevated ULA/creatinine ratio inhibits mitochondrial function in diabetes and identifies subjects at risk of rapid renal decline independent of chronic glycemic control. Disclosure L.Kugathasan: None. L.Natarajan: None. S.Lim: None. S.Waikar: None. Y.M.Osman: None. J.He: None. V.Vallon: Research Support; NIH - National Institutes of Health, Gilead Sciences, Inc., Merck Sharp &amp; Dohme Corp., Novo Nordisk, Maze Therapeutics, Boehringer-Ingelheim, Speaker's Bureau; AstraZeneca. S.Hallan: None. D.Cherney: Other Relationship; Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon and Novo-Nordisk, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring and Novo-Nordisk. K.Sharma: Advisory Panel; Reata Pharmaceuticals, Inc., Otsuka America Pharmaceutical, Inc. Cric study investigators: n/a. M.Darshi: Employee; Janssen Research &amp; Development, LLC. V.Srinivasan sridhar: Other Relationship; Merck &amp; Co., Inc. V.Drel: None. J.Tumova: None. J.Liu: None. J.Wang: None. B.A.Perkins: Advisory Panel; Dexcom, Inc., Insulet Corporation, Novo Nordisk, Sanofi, Vertex Pharmaceuticals Incorporated, Other Relationship; Abbott, Medtronic, Sanofi, Research Support; Novo Nordisk, Bank of Montreal (BMO). Y.Lytvyn: None.

#3802 ROLE OF NEUTROPHIL EXTRACELLULAR TRAPS IN DIABETIC KIDNEY DISEASE: DATA FROM INTEGRATION OF BULK RNA AND SINGLE-NUCLEUS RNA SEQUENCING ANALYSES

Abstract Background and Aims Neutrophil extracellular traps (NETs) is an important immune response against infections that is implicated in various immune-mediated conditions. Growing evidence shows that inflammation has key pathogenic contributions in diabetic kidney disease (DKD) but the role of NETs remains unclear. Method We evaluated the differentially expressed genes (DEGs) of NETs in human DKD using the bulk RNA sequencing datasets of kidney biopsy from DKD patients (GSE30528 and GSE30529). The candidate genes were further selected based on the machine learning algorithms (LASSO and SVM-RFE). The single-nucleus RNA sequencing (GSE195460 and GSE131882) and DKD bulk RNA sequencing (GSE30122) were used to validate the results. Receiver Operating Characteristics (ROC) curve were constructed to assess the diagnostic performance of the identified genes for DKD. Candidate drugs were further screened from the DSigDB database. Results Three characteristic genes (ITGAM, ITGB2 and TLR7) were selected using machine learning approach, which were all upregulated in human DKD glomerular and tubulointerstitial portion compared to healthy controls (all P &amp;lt;0.05). Single-cell analysis further demonstrated that ITGAM, ITGB2 and TLR7 were mainly overexpressed in leucocytes. ITGAM, ITGB2 and TLR7 expression in tubulointerstitial compartment also showed good diagnostic performance for DKD (ROC AUC 0.983, 0.975 and 0.958 respectively). Candidate drugs that target ITGAM, ITGB2 or TLR7 genes in DKD include ropivacaine, lidocaine and sulfasalazine. Conclusion ITGAM, ITGB2 and TLR7 may serve as biomarkers for DKD and drugs that target these genes may have therapeutic potential for DKD.

An Overview of Major Clinical Predictive Factors and Prognostic Biomarkers of Diabetic Kidney Disease in Children and Adolescents

The onset and progression of diabetic nephropathy represent a significant issue in diabetic management as it is the primary microvascular consequence of both type 1 and type 2 diabetes mellitus and a significant contributor to end stage renal disease-related mortality. Major risk factors for nephropathy in children and adolescents include hyperglycemia, HbA1c, diabetes duration, gender, blood pressure, dyslipidemia, uric acid, family history and genetic factors, smoking, puberty, and obesity. Metabolism and hemodynamic variables interact on a complex basis during disease development. A series of intricate molecular events take place in response to hyperglycemia, which disturbs the body's metabolic environment and causes glomerular enlargement, tubular inflammation, mesangial expansion, oxidative stress, and renal fibrosis. Fortunately, the main approaches for preventing the beginning and reducing the progression of diabetic kidney damage are rigorous glucose control and antihypertensive medications. As children and adolescents with T1D are at risk for developing early diabetic nephropathy, patients with T1D must have the best metabolic control, an early diagnosis, and timely treatment of dyslipidemia and hypertension. Prospects for better diabetic nephropathy outcomes are improving as novel approaches are developed. Novel biomarkers are increasingly proven to be more reliable instruments than the traditional microalbuminuria, which can forecast the development of disease. In addition to glomerular and tubular indicators, inflammation and oxidative stress markers have proven to be reliable diagnostic aids. In order to emphasize the methods being employed lately to enhance therapeutic approaches in diabetic nephropathy, certain emerging critical biomarkers are illustrated in this review.

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。.