Biomarkers of diabetic kidney disease

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

BACKGROUND

Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.

METHODS

The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy '. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package 'WGCNA' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the 'ClusterProfiler' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the 'ConsensusClusterPlus 'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients.

RESULTS

Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05).

CONCLUSIONS

Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

Interaction of circulating TGFβ regulatory miRNAs in different severity of diabetic kidney disease

AIMS

To explore the interaction of TGFβ regulatory microRNAs (miRNAs) with different severities of diabetic kidney disease (DKD).

METHODS

According to different UACR (30 and 300 mg/g), 436 subjects were included, and high glucose induced RMCs were cultured. Real-time PCR, ELISA, and automatic biochemical analysis were used to measure miRNAs, TGFβ1, and other biochemical indicators in serum and RMCs. Target genes of miRNA were predicted and visualised by bioinformatics.

RESULTS

HbA1c, TGFβ1, miR-217, and miR-224 in T2DM patients increased with UACR, while miR-192 and miR-216a decreased. Ln UACR was positively correlated with HbA1c, TGFβ1, miR-217, and miR-224, and negatively correlated with miR-192 and miR-216a. High glucose and TGFβ1 affected miRNAs and these miRNAs affected each other. The miRNA target genes mainly revolve around PTEN, PI3K/Akt, and MAPK signalling pathways.

CONCLUSION

TGFβ regulatory miRNAs and different severity of DKD have a potential interaction regulating fibrosis through PTEN, PI3K/Akt, and MAPK pathways.

Urinary podocyte markers in diabetic kidney disease

Podocytes are involved in maintaining kidney function and are a major focus of research on diabetic kidney disease (DKD). Urinary biomarkers derived from podocyte fragments and molecules have been proposed for the diagnosis and monitoring of DKD. Various methods have been used to detect intact podocytes and podocyte-derived microvesicles in urine, including centrifugation, visualization, and molecular quantification. Quantification of podocyte-specific protein targets and messenger RNA levels can be performed by Western blotting or enzyme-linked immunosorbent assay and quantitative polymerase chain reaction, respectively. At present, many of these techniques are expensive and labor-intensive, all limiting their widespread use in routine clinical tests. While the potential of urinary podocyte markers for monitoring and risk stratification of DKD has been explored, systematic studies and external validation are lacking in the current literature. Standardization and automation of laboratory methods should be a priority for future research, and the added value of these methods to routine clinical tests should be defined.

Biomarkers and signaling pathways of diabetic nephropathy and peripheral neuropathy: possible therapeutic intervention of rutin and quercetin

Diabetic nephropathy and peripheral neuropathy are the two main complications of chronic diabetes that contribute to high morbidity and mortality. These conditions are characterized by the dysregulation of multiple molecular signaling pathways and the presence of specific biomarkers such as inflammatory cytokines, indicators of oxidative stress, and components of the renin-angiotensin system. In this review, we systematically collected and collated the relevant information from MEDLINE, EMBASE, ELSEVIER, PUBMED, GOOGLE, WEB OF SCIENCE, and SCOPUS databases. This review was conceived with primary objective of revealing the functions of these biomarkers and signaling pathways in the initiation and progression of diabetic nephropathy and peripheral neuropathy. We also highlighted the potential therapeutic effectiveness of rutin and quercetin, two plant-derived flavonoids known for their antioxidant and anti-inflammatory properties. The findings of our study demonstrated that both flavonoids can regulate important disease-promoting systems, such as inflammation, oxidative stress, and dysregulation of the renin-angiotensin system. Importantly, rutin and quercetin have shown protective benefits against nephropathy and neuropathy in diabetic animal models, suggesting them as potential therapeutic agents. These findings provide a solid foundation for further comprehensive investigations and clinical trials to evaluate the potential of rutin and quercetin in the management of diabetic nephropathy and peripheral neuropathy. This may contribute to the development of more efficient and comprehensive treatment approaches for diabetes-associated complications.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

BACKGROUND

Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes.

OBJECTIVE

This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors.

METHODS

We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI.

RESULTS

Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR).

CONCLUSIONS

This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Investigating HMGB1 as a potential serum biomarker for early diabetic nephropathy monitoring by quantitative proteomics

Current diagnostic methods for diabetic nephropathy (DN) lack precision, especially in early stages and monitoring progression. This study aims to find potential biomarkers for DN progression and evaluate their accuracy. Using serum samples from healthy controls (NC), diabetic patients (DM), early-medium stage DN (DN-EM), and late-stage DN (DN-L), researchers employed quantitative proteomics and Mfuzz clustering analysis revealed 15 proteins showing increased expression during DN progression, hinting at their biomarker potential. Combining Mfuzz clustering with weighted gene co-expression network analysis (WGCNA) highlighted five candidates (HMGB1, CD44, FBLN1, PTPRG, and ADAMTSL4). HMGB1 emerged as a promising biomarker, closely correlated with renal function changes. Experimental validation supported HMGB1's upregulation under high glucose conditions, reinforcing its potential as an early detection biomarker for DN. This research advances DN understanding and identifies five potential biomarkers, notably HMGB1, as a promising early monitoring target. These findings set the stage for future clinical diagnostic applications in DN.

Urinary biomarkers associated with acute kidney injury in pediatric mechanical circulatory support patients

BACKGROUND

In patients requiring mechanical circulatory support (MCS), the incidence of acute kidney injury (AKI) is between 37 and 63%. In this study, we performed an exploratory analysis evaluating the relationship of multiple urine biomarkers with AKI development in pediatric MCS patients.

METHODS

This is a single center retrospective study in a pediatric cohort receiving MCS from August 2014 to November 2020. We measured 14 urine biomarkers of kidney injury on day 1 following MCS initiation and analyzed their association with development of AKI in the first 7 days of MCS initiation.

RESULTS

Sixty patients met inclusion criteria. Patients with AKI were more likely to be supported by venoarterial extracorporeal membrane oxygenation (65% vs. 8.3%, p < 0.001), compared to the no AKI group and less likely to have ventricular assist devices (10% vs. 50%, p < 0.001). There was a significant increase in the median urine albumin and urine osteoactivin in the AKI group, compared to the no AKI group (p = 0.020 and p = 0.018, respectively). When normalized to urine creatinine (UCr), an increased log osteoactivin/UCr was associated with higher odds of AKI development (OR: 2.05; 95% CI: 1.07, 4.44; p = 0.028), and higher log epidermal growth factor (EGF)/UCr (OR: 0.41; 95% CI: 0.15, 0.96) was associated with decreased odds of AKI.

CONCLUSIONS

Early increase in urine osteoactivin is associated with AKI development within 7 days of MCS initiation in pediatric patients. Contrary, an increased urine EGF is associated with kidney protection. A higher resolution version of the Graphical abstract is available as Supplementary information.

Urinary podocyte stress marker as a prognostic indicator for diabetic kidney disease

BACKGROUND

Diabetic kidney diseases (DKD) is a the most common cause of end-stage kidney disease (ESKD) around the world. Previous studies suggest that urinary podocyte stress biomarker, e.g. podocin:nephrin mRNA ratio, is a surrogate marker of podocyte injury in non-diabetic kidney diseases.

METHOD

We studied 118 patients with biopsy-proved DKD and 13 non-diabetic controls. Their urinary mRNA levels of nephrin, podocin, and aquaporin-2 (AQP2) were quantified. Renal events, defined as death, dialysis, or 40% reduction in glomerular filtration rate, were determined at 12 months.

RESULTS

Urinary podocin:nephrin mRNA ratio of DKD was significantly higher than the control group (p = 0.0019), while urinary nephrin:AQP2 or podocin:AQP2 ratios were not different between groups. In DKD, urinary podocin:nephrin mRNA ratio correlated with the severity of tubulointerstitial fibrosis (r = 0.254, p = 0.006). and was associated with the renal event-free survival in 12 months (unadjusted hazard ratio [HR], 1.523; 95% confidence interval [CI] 1.157-2.006; p = 0.003). After adjusting for clinical and pathological factors, urinary podocin:nephrin mRNA ratio have a trend to predict renal event-free survival (adjusted HR, 1.327; 95%CI 0.980-1.797; p = 0.067), but the result did not reach statistical significance.

CONCLUSION

Urinary podocin:nephrin mRNA ratio has a marginal prognostic value in biopsy-proven DKD. Further validation is required for DKD patients without kidney biopsy.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study

Aims

This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

Methods

Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves.

Results

44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR.

Conclusion

Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.

Identification of stable reference genes for relative quantification of long RNA expression in urinary extracellular vesicles

Urinary extracellular vesicles (uEVs) are rich in valuable biomolecule information which are increasingly recognized as potential biomarkers for various diseases. uEV long RNAs are among the critical cargos capable of providing unique transcriptome information of the source cells. However, consensus regarding ideal reference genes for relative long RNAs quantification in uEVs is not available as of date. Here we explored stable reference genes through profiling the long RNA expression by RNA-seq following unsupervised analysis and validation studies. Candidate reference genes were identified using four algorithms: NormFinder, GeNorm, BestKeeper and the Delta Ct method, followed by validation. RNA profile showed uEVs contained abundant long RNAs information and the core transcriptome was related to cellular structures, especially ribosome which functions mainly as translation, protein and RNA binding molecules. Analysis of RNA-seq data identified RPL18A, RPL11, RPL27, RACK1, RPSA, RPL41, H1-2, RPL4, GAPDH, RPS27A as candidate reference genes. RT-qPCR validation revealed that RPL41, RPSA and RPL18A were reliable reference genes for long RNA quantification in uEVs from patients with diabetes mellitus (DM), diabetic nephropathy (DN), IgA nephropathy (IgAN) and prostate cancer (PCA). Interestingly, RPL41 also outperformed traditional reference genes in renal tissues of DN and IgAN, as well as in plasma EVs of several types of cancers. The stable reference genes identified in this study may facilitate development of uEVs as novel biomarkers and increase the accuracy and comparability of biomarker studies.

Urinary PART1 and PLA2R1 Could Potentially Serve as Diagnostic Markers for Diabetic Kidney Disease Patients

Background

Diabetic kidney disease (DKD) is a chronic renal disease which could eventually develop into renal failure. Though albuminuria and estimated glomerular filtration rate (eGFR) are helpful for the diagnosis of DKD, the lack of specific biomarkers reduces the efficiency of therapeutic interventions.

Methods

Based on bulk-seq of 56 urine samples collected at different time points (including 11 acquired from DKD patients and 11 from healthy controls), in corporation of scRNA-seq data of urine samples and snRNA-seq data of renal punctures from DKD patients (retrieved from NCBI GEO Omnibus), urine-kidney specific genes were identified by Multiple Biological Information methods.

Results

Forty urine-kidney specific genes/differentially expressed genes (DEGs) were identified to be highly related to kidney injury and proteinuria for the DKD patients. Most of these genes participate in regulating glucagon and apoptosis, among which, urinary PART1 (mainly derived from distal tubular cells) and PLA2R1 (podocyte cell surface marker) could be used together for the early diagnosis of DKD. Moreover, urinary PART1 was significantly associated with multiple clinical indicators, and remained stable over time in urine.

Conclusion

Urinary PART1 and PLA2R1 could be shed lights on the discovery and development of non-invasive diagnostic method for DKD, especially in early stages.

Potential application of Klotho as a prognostic biomarker for patients with diabetic kidney disease: a meta-analysis of clinical studies

Background

Diabetic kidney disease (DKD) is a serious diabetic complication and the performance of serum Klotho in DKD's prognostic evaluation is controversial.

Objective

To assess the association of serum Klotho with adverse kidney and non-kidney clinical outcomes in patients with DKD.

Design

Clinical studies regarding the relationship of serum Klotho with DKD were included. Study quality was assessed using the Newcastle-Ottawa scale. Subgroup and sensitive analyses were performed to search for the source of heterogeneity.

Data sources and methods

We comprehensively searched PubMed, Embase, Web of Science, and Cochrane library databases up to 27 September 2022. The associations of Klotho with albuminuria, such as the urinary albumin creatinine ratio (UACR), kidney outcomes such as persistent albuminuria, estimated glomerular filtration rate decline, and non-kidney outcomes such as diabetic retinopathy, cardiovascular morbidity, and mortality, were evaluated. The indicators, such as the correlation coefficient (r), odds ratio (OR), relative risk, and hazard ratio, were retrieved or calculated from the eligible studies.

Results

In all, 17 studies involving 5682 participants fulfilled the inclusion criteria and were included in this meta-analysis. There was no significant association of serum Klotho with UACR in DKD patients [summary r, -0.28 (-0.55, 0.04)] with high heterogeneity. By contrast, a strong association was observed regarding serum Klotho with kidney outcomes [pooled OR, 1.60 (1.15, 2.23)], non-kidney outcomes [pooled OR, 2.78 (2.11, 3.66)], or combined kidney and non-kidney outcomes [pooled OR, 1.96 (1.45, 2.65)] with moderate heterogeneity. Subgroup analysis indicated that age, study design, and the estimated glomerular filtration rate may be the sources of heterogeneity.

Conclusion

A decreased serum Klotho level is possibly associated with an increased risk of developing kidney and non-kidney clinical outcomes in DKD patients; thus, Klotho may be a possible biomarker to predict DKD clinical outcomes. Additional studies are needed to clarify and validate Klotho's prognostic value.

The association of plasma NT-proBNP level and progression of diabetic kidney disease

AIMS

Diabetic kidney disease (DKD) is the most common cause of end-stage kidney disease (ESKD). The identification of risk factors involved in the progression of DKD to ESKD is expected to result in early detection and appropriate intervention and improve prognosis. This study aimed to explore whether plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) was associated with kidney outcomes in patients with type 2 diabetes mellitus (T2DM) and biopsy-proven DKD.

METHODS

Patients with biopsy-proven DKD who were followed up at West China Hospital over 12 months were enrolled. The kidney outcome was defined as progression to ESKD. The cutoff value of plasma NT-proBNP concentration was calculated by using receiver operating characteristic (ROC) curve analysis. The influence of NT-proBNP levels on kidney outcome in patients with DKD was assessed using Cox regression analysis.

RESULTS

A total of 30 (24.5%) patients reached ESKD during a median follow-up of 24.1 months. The baseline serum NT-proBNP level had a significant correlation with baseline proteinuria, kidney function, glomerular lesions, interstitial fibrosis tubular atrophy (IFTA), and arteriolar hyalinosis. Multivariate Cox regression analysis indicated that increased NT-proBNP level was significantly associated with a higher risk of progression to ESKD (HR 6.43; 95% CI (1.65-25.10, p = 0.007), and each 1 SD increase in LG (NT-proBNP) was also associated with a higher risk (HR 2.43; 95% CI 1.94-5.29, p = 0.047) of an adverse kidney outcome after adjusting for confounding factors.

CONCLUSIONS

A higher level of plasma NT-proBNP predicts kidney prognosis in patients with biopsy-proven DKD. This warrants further investigation into the potential mechanisms.

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.

Combined detection of urinary biomarkers noninvasively predicts extent of renal injury in patients with early diabetic kidney disease with kidney qi deficiency syndrome: A retrospective investigation

Incipient diagnosis and noninvasive forecasts using urinary biomarkers are important for preventing diabetic kidney disease (DKD) progression, but they are also controversial. Previous studies have shown a potential relationship between urinary tubular biomarkers (UTBs) and traditional Chinese medicine (TCM) syndrome in patients with DKD. Thus, we further evaluated the clinical significance of combined detection of urinary biomarkers in noninvasively predicting the extent of renal damage in patients with early DKD with kidney qi deficiency syndrome, and preliminarily explored the potential biological link between UTBs and TCM syndrome in DKD. We categorized 92 patients with Type 2 diabetes mellitus into three groups as follows: 20 patients with normoalbuminuria, 50 patients with microalbuminuria, and 22 patients with macroalbuminuria. We found that, in all groups, 24 hr urinary albumin (24hUAlb) and urinary albumin-to-creatinine ratio (UACR) showed stepwise and significant increases. Urinary cystatin C (UCysC), urinary N-acetyl-β-d-glucosaminidase (UNAG), and urinary retinol-binding protein (URBP) synchronously increased gradually, consistent with the degree of albuminuria in all groups. Moreover, 24hUAlb and UACR were positively correlated with UCysC, UNAG, and URBP, respectively. In 72 patients with Type 2 DKD with albuminuria, a positive correlation was observed between UNAG and URBP, UCysC was also positively correlated with UNAG and URBP, respectively. Additionally, TCM syndrome distributional characteristics in all patients were consistent with clinical manifestations of kidney qi deficiency syndrome. Therefore, the combined detection of UCysC, UNAG, URBP, and UAlb may be used as a practical clinical technique to noninvasively forecast the extent of renal injury in patients with early Type 2 DKD with kidney qi deficiency syndrome. UTBs may be one of the biological bases of the specific TCM syndromes in DKD.

Combination Therapy of RAS Inhibition and SGLT2 Inhibitors Decreases Levels of Endotrophin in Persons with Type 2 Diabetes

We investigated for the first time the effect of combination therapy of renin-angiotensin system inhibition (RASi) and sodium-glucose co-transporter-2 inhibitors (SGLT2is) on endotrophin (ETP), a pro-fibrotic signaling molecule reflecting collagen type VI formation, measured in the plasma of persons with type 2 diabetes (T2D). ETP was measured using the PRO-C6 ELISA in 294 individuals from the "Drug combinations for rewriting trajectories of renal pathologies in type 2 diabetes" (DC-ren) project. In the DC-ren study, kidney disease progression was defined as a >10% decline in the estimated glomerular filtration rate (eGFR) to an eGFR < 60 mL/min/1.73 m2. Among the investigated circulating markers, ETP was the most significant predictor of future eGFR. Combination therapy of RASi and SGLT2is led to a significant reduction in ETP levels compared to RASi monotherapy (p for slope difference = 0.002). Higher levels of baseline plasma ETP were associated with a significantly increased risk of kidney disease progression (p = 0.007). In conclusion, plasma ETP identified individuals at higher risk of kidney disease progression. The observed decreased levels of plasma ETP with combination therapy of RASi and SGLT2is in persons with T2D may reflect a reduced risk of kidney disease progression following treatment with SGLT2is.

The relationship between serum γ-glutamyltransferase (GGT) and diabetic nephropathy in patients with type 2 diabetes mellitus: a cross-sectional study

The relationship between serum γ-glutamyltransferase (GGT) and renal dysfunction is controversial. In this study, we examined the relationship of serum GGT to diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM). A total of 577 patients with T2DM were enrolled and their basic information and laboratory data were collected and analyzed. The prevalence of DN increased with the elevated serum GGT tertiles. The level of serum GGT in the DN group was higher than in the non-DN groups. Multivariate logistic analysis showed that high GGT was independent risks for DN (OR = 1.041, 95% CIs 1.023-1.059). And the OR of log-transformed serum GGT for DN was 6.190 (95% CIs 4.248-9.021). The OR of DN across increasing tertiles of serum GGT were 1.00, 3.288 (1.851-5.840), and 5.059 (2.620-9.769) (P for trend < 0.001). Stratified receiver operating characteristic (ROC) analysis by gender showed that the area under ROC curve (AUC) value for GGT was 0.781 (0.732-0.825, P < 0.05) in male and was 0.817 (0.761-0.864, P < 0.05) in female. Compared with female, GGT in male showed lower sensitivity (52.86% vs. 82.05%) and higher specificity (90.32% vs. 55.26%). And the AUC value for GGT was greater than creatinine (Cr) and estimated glomerular filtration rate (eGFR) in male and smaller than Cr and eGFR in female, respectively. In Conclusion, there was an independently positive relationship between serum GGT levels and DN, which suggested that elevated GGT was a potential indicator for risk of DN. There were gender differences in the predictive property of GGT for DN.

Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes

Diabetes mellitus is one of the most common causes of chronic kidney disease. Kidney involvement in patients with diabetes has a wide spectrum of clinical presentations ranging from asymptomatic to overt proteinuria and kidney failure. The development of kidney disease in diabetes is associated with structural changes in multiple kidney compartments, such as the vascular system and glomeruli. Glomerular alterations include thickening of the glomerular basement membrane, loss of podocytes, and segmental mesangiolysis, which may lead to microaneurysms and the development of pathognomonic Kimmelstiel-Wilson nodules. Beyond lesions directly related to diabetes, awareness of the possible coexistence of nondiabetic kidney disease in patients with diabetes is increasing. These nondiabetic lesions include focal segmental glomerulosclerosis, IgA nephropathy, and other primary or secondary renal disorders. Differential diagnosis of these conditions is crucial in guiding clinical management and therapeutic approaches. However, the relationship between diabetes and the kidney is bidirectional; thus, new-onset diabetes may also occur as a complication of the treatment in patients with renal diseases. Here, we review the complex and multifaceted correlation between diabetes and kidney diseases and discuss clinical presentation and course, differential diagnosis, and therapeutic oppor-tunities offered by novel drugs.

Diagnostic challenges of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal disease (ESRD). It has been evidenced that targeted interventions at an early stage of DKD can efficiently prevent or delay the progression of kidney failure and improve patient outcomes. Therefore, regular screening for DKD has become one of the fundamental principles of diabetes care. Long-established biomarkers such as serum-creatinine-based estimates of glomerular filtration rate and albuminuria are currently the cornerstone of diagnosis and risk stratification in routine clinical practice. However, their immanent biological limitations and analytical variations may influence the clinical interpretation of the results. Recently proposed new predictive equations without the variable of race, together with the evidence on better accuracy of combined serum creatinine and cystatin C equations, and both race- and sex-free cystatin C-based equation, have enabled an improvement in the detection of DKD, but also require the harmonization of the recommended laboratory tests, wider availability of cystatin C testing and specific approach in various populations. Considering the complex pathophysiology of DKD, particularly in type 2 diabetes, a panel of biomarkers is needed to classify patients in terms of the rate of disease progression and/or response to specific interventions. With a personalized approach to diagnosis and treatment, in the future, it will be possible to respond to DKD better and enable improved outcomes for numerous patients worldwide.

Urinary Fetuin-A Fragments Predict Progressive Estimated Glomerular Filtration Rate Decline in Two Independent Type 2 Diabetes Cohorts of Different Ethnicities

INTRODUCTION

There is a great clinical need for novel markers to predict kidney function decline in patients with type 2 diabetes. We explored the potential of posttranslationally modified fetuin-A fragments in urine (uPTM-FetA) as such a marker.

METHODS

We included patients with type 2 diabetes from two independent, nonoverlapping prospective cohort studies. A cut-off for uPTM-FetA, measured via ELISA method, was determined using the Youden index in the primary cohort of patients with type 2 diabetes from Taiwan. Kidney endpoint was defined as an estimated glomerular filtration rate (eGFR) decline ≥30% from baseline, reaching of an eGFR <15 mL/min/1.73 m2, or a need of renal replacement therapy. Prospective associations were assessed in Cox regression models. All analyses were replicated in a cohort of patients with type 2 diabetes from the Netherlands.

RESULTS

In total, 294 patients with type 2 diabetes (age 61 ± 10 years, 55% male, eGFR 88 ± 16 mL/min/1.73 m2) were included in the primary cohort. During a follow-up of median 4.6 years, 42 participants (14%) experienced the kidney endpoint. Using the defined cut-off, a high uPTM-FetA was associated with a higher risk of renal function decline (Plog-rank < 0.0001). This association was similar in subgroups depending on albuminuria. This association remained, independent of age, sex, baseline eGFR, albuminuria, HbA1c, and other potential confounders (HR: 9.94; 95% CI: 2.96-33.40; p < 0.001 in the final model). Analyses in the validation cohort (376 patients with type 2 diabetes, age 64 ± 11 years, 66% male, eGFR 76 ± 24 mL/min/1.73 m2) using the same cut-off yielded similar results.

CONCLUSION

uPTM-FetA was independently associated with kidney function decline in patients with type 2 diabetes validated in a 2-cohort study. The significant additive predictive power of this biomarker from conventional risk factors suggests its clinical use for renal function progression in patients with type 2 diabetes.

Comprehensive advancements in the prevention and treatment of diabetic nephropathy: A narrative review

Diabetic nephropathy (DN) is a common and severe complication of diabetes mellitus and is the leading cause of chronic kidney disease (CKD) worldwide. Despite current treatments, many individuals with DN progress to end-stage renal disease (ESRD), requiring dialysis or kidney transplantation. The advancement in our understanding of the pathogenesis of diabetic nephropathy has led to the development of new prevention and treatment strategies. We comprehensively reviewed the literature on advances in the prevention and treatment of DN. We searched PubMed, Scopus, and Web of Science databases for articles published between 2000 and 2023, using keywords such as "diabetic nephropathy," "prevention," "treatment," and "recent advances." The recent advances in the prevention and treatment of DN include novel approaches targeting inflammation and fibrosis, such as inhibitors of the nuclear factor kappa-B (NF-kB) pathway, inhibitors of the transforming growth factor-beta (TGF-beta) pathway, and anti-inflammatory cytokines. Other promising strategies include stem cell therapy, gene therapy, and artificial intelligence-based approaches, such as predictive models based on machine learning algorithms that can identify individuals at high risk of developing DN and guide personalized treatment strategies. Combination therapies targeting multiple disease pathways may also offer the most significant potential for improving outcomes for individuals with DN. Overall, the recent advances in the prevention and treatment of DN represent promising avenues for future research and clinical development. Novel therapies targeting inflammation and fibrosis, stem cell and gene therapies, and artificial intelligence-based approaches all show great potential for improving outcomes for individuals with DN.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults

Background

Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).

Methods

We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).

Results

ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.

Conclusions

Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.

Funding

This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Optic Nerve Head Capillary Network Quantified by Optical Coherence Tomography Angiography and Decline of Renal Function in Type 2 Diabetes: A Three-Year Prospective Study

PURPOSE

To assess the association of optic capillary perfusion with decline in the estimated glomerular filtration rate (eGFR) and to clarify its added value.

DESIGN

Prospective, observational cohort study.

METHODS

Patients with type 2 diabetes mellitus without diabetic retinopathy (non-DR) underwent standardized examinations annually during a 3-year follow-up period. The superficial capillary plexus (SCP), deep capillary plexus (DCP), and radial peripapillary plexus (RPC) of optic nerve head (ONH) were visualized using optical coherence tomography angiography (OCTA), and the perfusion density (PD) and vascular density were quantified for the whole image and circumpapillary regions of the ONH.  The lowest tercile of annual eGFR slope was defined as the rapidly progressive group, and the highest tercile was considered the stable group.

RESULTS

A total of 906 patients were included for 3-mm × 3-mm OCTA analysis. After adjusting for other confounders, each 1% decrease in baseline whole en face PD in SCP and RPC was associated with accelerated rates of decline in eGFR by -0.53 mL/min/1.73/m2 per year (95% confidence interval [CI] -0.17 to -0.90; P = .004) and -0.60 mL/min/1.73/m2 per year (95% CI 0.28-0.91), respectively. Adding both whole-image PD in SCP and whole-image PD in RPC to the conventional model increased the area under the curve from 0.696 (95% CI 0.654-0.737) to 0.725 (95% CI 0.685-0.765; P = .031). Another cohort of 400 eligible patients with 6-mm × 6 mm OCTA imaging validated the significant associations between ONH perfusion and rate of eGFR decline (P < .05).

CONCLUSIONS

Reduced capillary perfusion of ONH in patients with type 2 diabetes mellitus is associated with a greater eGFR decline, and it has additional predictive value for detecting an early stage and progression.

Potential role of microRNA-503 in Icariin-mediated prevention of high glucose-induced endoplasmic reticulum stress

BACKGROUND

Dysregulated microRNA (miRNA) is crucial in the progression of diabetic nephropathy (DN).

AIM

To investigate the potential molecular mechanism of Icariin (ICA) in regulating endoplasmic reticulum (ER) stress-mediated apoptosis in high glucose (HG)-induced primary rat kidney cells (PRKs), with emphasis on the role of miR-503 and sirtuin 4 (SIRT4) in this process.

METHODS

Single intraperitoneal injection of streptozotocin (65 mg/kg) in Sprague-Dawley rats induce DN in the in vivo hyperglycemic model. Glucose-treated PRKs were used as an in vitro HG model. An immunofluorescence assay identified isolated PRKs. Cell Counting Kit-8 and flow cytometry analyzed the effect of ICA treatment on cell viability and apoptosis, respectively. Real-time quantitative polymerase chain reaction and western blot analyzed the levels of ER stress-related proteins. Dual luciferase analysis of miR-503 binding to downstream SIRT4 was performed.

RESULTS

ICA treatment alleviated the upregulated miR-503 expression in vivo (DN) and in vitro (HG). Mechanistically, ICA reduced HG-induced miR-503 overexpression, thereby counteracting its function in downregulating SIRT4 levels. ICA regulated the miR-503/SIRT4 axis and subsequent ER stress to alleviate HG-induced PRKs injury.

CONCLUSION

ICA reduced HG-mediated inhibition of cell viability, promotion of apoptosis, and ER stress in PRKs. These effects involved regulation of the miR-503/SIRT4 axis. These findings indicate the potential of ICA to treat DN, and implicate miR-503 as a viable target for therapeutic interventions in DN.

Urinary growth differentiation factor 15 predicts renal function decline in diabetic kidney disease

Sensitive biomarkers can enhance the diagnosis, prognosis, and surveillance of chronic kidney disease (CKD), such as diabetic kidney disease (DKD). Plasma growth differentiation factor 15 (GDF15) levels are a novel biomarker for mitochondria-associated diseases; however, it may not be a useful indicator for CKD as its levels increase with declining renal function. This study explores urinary GDF15's potential as a marker for CKD. The plasma and urinary GDF15 as well as 15 uremic toxins were measured in 103 patients with CKD. The relationship between the urinary GDF15-creatinine ratio and the uremic toxins and other clinical characteristics was investigated. Urinary GDF15-creatinine ratios were less related to renal function and uremic toxin levels compared to plasma GDF15. Additionally, the ratios were significantly higher in patients with CKD patients with diabetes (p = 0.0012) and reduced with statin treatment. In a different retrospective DKD cohort study (U-CARE, n = 342), multiple and logistic regression analyses revealed that the baseline urinary GDF15-creatinine ratios predicted a decline in estimated glomerular filtration rate (eGFR) over 2 years. Compared to the plasma GDF15 level, the urinary GDF15-creatinine ratio is less dependent on renal function and sensitively fluctuates with diabetes and statin treatment. It may serve as a good prognostic marker for renal function decline in patients with DKD similar to the urine albumin-creatinine ratio.

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Background

Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.

Methods

The patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.

Results

During a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.

Conclusion

Our results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

Gypenosides suppress fibrosis of the renal NRK-49F cells by targeting miR-378a-5p through the PI3K/AKT signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

The incidence of renal fibrosis caused by chronic kidney disease is increasing year by year. Preventing the activation and conversion of kidney-intrinsic fibroblasts to a myofibroblast phenotype is an important target for blocking the development of renal interstitial fibrosis. Our team established a stable renal interstitial fibrosis cell model in the early stage, and the screening results showed that GPs has good anti-fibrosis potential. At this stage, only a few literatures have reported its anti-fibrosis effect, and the mechanism of action is still unclear.

AIM OF THE STUDY

The massive synthesis and secretion of extracellular-matrix (ECM) components by activated fibroblasts in the kidneys causes irreversible renal interstitial fibrosis. Gypenosides (GPs) have been shown to decelerate this process, in which micro RNAs (miRNAs) play an important regulatory role. This study aimed to evaluate the mechanism underlying the suppressive effect of GPs on renal fibrosis.

MATERIALS AND METHODS

This study used TGF-β1-stimulated NRK-49F renal cells as an in-vitro model of renal interstitial fibrosis. First, the concentration range of GPs that significantly affects the cytoactive was determined. Then, the anti-fibrotic effects of various concentrations of GPs in the in-vitro model were assessed via immunofluorescence, western blotting, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Non-coding-RNA sequencing combined with bioinformatics was used to predict the mechanistic basis of the anti-fibrotic effect of GPs, and qRT-PCR was used to verify the sequencing results and bioinformatic predictions. The identified relationships of the anti-fibrotic effect of GPs with miR-378a-5p and the PI3K/AKT signaling were evaluated using a miR-NC mimic and the PI3K inhibitor LY294002 as controls, respectively.

RESULTS

TGF-β1 stimulation up-regulated α-SMA, COL1, and COL3 in NRK-49F cells, and this effect was suppressed by GPs. Additionally, TGF-β1 stimulation significantly changed the expression levels of 151 miRNAs, and GPs significantly suppressed the effect of TGF-β1 on the levels of 18 of these miRNAs. Among them, miR-3588 and miR-378a-5p were down-regulated, and miR-135b-5p and miR-3068-5p were up-regulated upon TGF-β1 induction. Of these miRNAs, miR-378a-5p was predicted to target the mRNAs of numerous proteins mainly enriched in the PI3K/AKT signaling pathway. The miRNA transfection experiments with the miR-NC mimic and PI3K inhibitor as controls showed that miR-378a-5p overexpression could suppress the TGF-β1-induced up-regulation of α-SMA, COL1, PI3K, and AKT, including the phosphorylated form (p-AKT).

CONCLUSION

GPs inhibit the PI3K/AKT signaling by up-regulating miR-378a-5p in TGF-β1-stimulated NRK-49F cells and thereby reduce their massive secretion of ECM components. Given that this in-vitro model of renal interstitial fibrosis closely mimics the in-vivo pathogenesis, our results most likely apply to the in-vivo conditions.

Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus

Background

Diabetic kidney disease (DKD), a common microvascular complication of diabetes mellitus (DM), is always asymptomatic until it develops to the advanced stage. Thus, we aim to develop a nomogram prediction model for progression to DKD in newly diagnosed type 2 diabetes mellitus (T2DM).

Methods

This was a single-center analysis of prospective data collected from 521 newly diagnosed patients with T2DM. All related clinical records were incorporated, including the triglyceride-glucose index (TyG index). The least absolute shrinkage and selection operator (LASSO) was used to build a prediction model. In addition, discrimination, calibration, and clinical practicality of the nomogram were evaluated.

Results

In this study, 156 participants were incorporated as the validation set, while the remaining 365 were incorporated into the training set. The predictive factors included in the individualized nomogram prediction model included 5 variables. The area under the curve (AUC) for the prediction model was 0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. The model performed exceptionally well in terms of predictive accuracy and clinical applicability, according to calibration curves and decision curve analysis.

Conclusion

The predictive nomogram for the risk of DKD in newly diagnosed T2DM patients had outstanding discrimination and calibration, which could help in clinical practice.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study

Objective

Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Methods

Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings.

Results

DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C).

Conclusion

A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

Huobahuagen tablet improves renal function in diabetic kidney disease: a real-world retrospective cohort study

Objective

We aimed to explore the value of Huobahuagen tablet (HBT) in improving decreased renal function for patients with diabetic kidney disease (DKD) over time.

Methods

This was a single-center, retrospective, real-world study on eligible 122 DKD patients who continued to use HBT + Huangkui capsule (HKC) therapy or HKC therapy without interruption or alteration in Jiangsu Province Hospital of Chinese Medicine from July 2016 to March 2022. The primary observation outcomes included estimated glomerular filtration rate (eGFR) at baseline and 1-, 3-, 6-, 9-, and 12-month follow-up visits and changes in eGFR from baseline (ΔeGFR). Propensity score (PS) and inverse probability treatment weighting (IPTW) were used to control for confounders.

Results

eGFR was significantly higher in the HBT + HKC group than in the HKC alone group at the 6-, 9-, and 12-month follow-up visits (p = 0.0448, 0.0002, and 0.0037, respectively), indicating the superiority of HBT + HKC over HBT alone. Furthermore, the ΔeGFR of the HBT + HKC group was significantly higher than that of the HKC alone group at the 6- and 12-month follow-up visits (p = 0.0369 and 0.0267, respectively). In the DKD G4 patients, eGFR was higher in the HBT + HKC group at the 1-, 3-, 6-, 9-, and 12-month follow-up visits compared with baseline, with statistically significant differences at the 1-, 3-, and 6- month follow-up visits (p = 0.0256, 0.0069, and 0.0252, respectively). The fluctuations in ΔeGFR ranged from 2.54 ± 4.34 to 5.01 ± 5.55 ml/min/1.73 m². Change in the urinary albumin/creatinine ratio from baseline did not exhibit a significant difference between the two groups at any of the follow-up visits (p > 0.05 for all). Adverse event incidence was low in both groups.

Conclusion

The findings of this study based on real-world clinical practice indicate that HBT + HKC therapy exhibited better efficacy in improving and protecting renal function with a favorable safety profile than HKC therapy alone. However, further large-scale prospective randomized controlled trials are warranted to confirm these results.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Osteopontin as a Biomarker in Chronic Kidney Disease

Osteopontin (OPN) is a ubiquitously expressed protein with a wide range of physiological functions, including roles in bone mineralization, immune regulation, and wound healing. OPN has been implicated in the pathogenesis of several forms of chronic kidney disease (CKD) where it promotes inflammation and fibrosis and regulates calcium and phosphate metabolism. OPN expression is increased in the kidneys, blood, and urine of patients with CKD, particularly in those with diabetic kidney disease and glomerulonephritis. The full-length OPN protein is cleaved by various proteases, including thrombin, matrix metalloproteinase (MMP)-3, MMP-7, cathepsin-D, and plasmin, producing N-terminal OPN (ntOPN), which may have more detrimental effects in CKD. Studies suggest that OPN may serve as a biomarker in CKD, and while more research is needed to fully evaluate and validate OPN and ntOPN as CKD biomarkers, the available evidence suggests that they are promising candidates for further investigation. Targeting OPN may be a potential treatment strategy. Several studies show that inhibition of OPN expression or activity can attenuate kidney injury and improve kidney function. In addition to its effects on kidney function, OPN has been linked to cardiovascular disease, which is a major cause of morbidity and mortality in patients with CKD.

Sodium Magnetic Resonance Imaging Shows Impairment of the Counter-current Multiplication System in Diabetic Mice Kidney

Key Points

23Na MRI allows us to noninvasively assess sodium distribution. We propose the utility of 23Na MRI for evaluating functional changes in diabetic kidney disease and not as a marker reflecting structural damage. 23Na MRI may be an early marker for structures beyond the glomeruli, enabling prompt intervention with novel efficacious tubule-targeting therapies.

Background

Sodium magnetic resonance imaging can noninvasively assess sodium distribution, specifically sodium concentration in the countercurrent multiplication system in the kidney, which forms a sodium concentration gradient from the cortex to the medulla, enabling efficient water reabsorption. This study aimed to investigate whether sodium magnetic resonance imaging can detect changes in sodium concentrations under normal conditions in mice and in disease models, such as a mouse model with diabetes mellitus.

Methods

We performed sodium and proton nuclear magnetic resonance imaging using a 9.4-T vertical standard-bore superconducting magnet.

Results

A condition of deep anesthesia, with widened breath intervals, or furosemide administration in 6-week-old C57BL/6JJcl mice showed a decrease in both tissue sodium concentrations in the medulla and sodium concentration gradients from the cortex to the medulla. Furthermore, sodium magnetic resonance imaging revealed reductions in the sodium concentration in the medulla and in the gradient from the cortex to the medulla in BKS.Cg-Leprdb+/+ Leprdb/Jcl mice at very early type 2 diabetes mellitus stages compared with corresponding control BKS.Cg-m+/m+/Jcl mice.

Conclusions

The kidneys of BKS.Cg-Leprdb+/+ Leprdb/Jcl mice aged 6 weeks showed impairments in the countercurrent multiplication system. We propose the utility of 23Na MRI for evaluating functional changes in diabetic kidney disease and not as a marker that reflects structural damage. Thus, 23Na MRI may be a potentially very early marker for structures beyond the glomerulus; this may prompt intervention with novel efficacious tubule-targeting therapies.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

SH3YL1 Protein Predicts Renal Outcomes in Patients with Type 2 Diabetes

NADPH oxidase (NOX)-derived oxidative stress is an important factor in renal progression, with NOX4 being the predominant NOX in the kidney. Recently, Src homology 3 (SH3) domain-containing YSC84-like 1 (SH3YL1) was reported to be a regulator of NOX4. In this study, we tested whether the SH3YL1 protein could predict 3-year renal outcomes in patients with type 2 diabetes. A total of 131 patients with type 2 diabetes were enrolled in this study. Renal events were defined as a 15% decline in the estimated glomerular filtration rate (eGFR) from the baseline, the initiation of renal replacement therapy, or death during the 3 years. The levels of the urinary SH3YL1-to-creatinine ratio (USCR) were significantly different among the five stages of chronic kidney disease (CKD) and the three groups, based on albuminuria levels. The USCR levels showed a significant negative correlation with eGFR and a positive correlation with the urinary albumin-to-creatinine ratio (UACR). Plasma SH3YL1 levels were significantly correlated with UACR. The highest tertile group of USCR and plasma SH3YL1 had a significantly lower probability of renal event-free survival. Furthermore, the highest tertile group of USCR showed a significant association with the incidence of renal events after full adjustment: adjusted hazard ratio (4.636: 95% confidence interval, 1.416-15.181, p = 0.011). This study suggests that SH3YL1 is a new diagnostic biomarker for renal outcomes in patients with type 2 diabetes.

Abelmoschus Manihot ameliorates the levels of circulating metabolites in diabetic nephropathy by modulating gut microbiota in non-obese diabetes mice

Huangkui capsule (HKC), a traditional Chinese medicine, has been used for medication of kidney diseases, including diabetic nephropathy (DN). The current study aimed to evaluate the effects of HKC in the modulation of gut microbiota and the amelioration of metabolite levels by using non-obese diabetes (NOD) mice with DN. The microbiota from three parts of intestines (duodenum, ileum and colon) in NOD mice with and without HKC treatment were analysed using 16S rDNA sequencing techniques. Untargeted metabolomics in plasma of NOD mice were analysed with liquid mass spectrometry. Results showed that HKC administration ameliorated DN in NOD mice and the flora in duodenum were more sensitive to HKC intervention, while the flora in colon had more effects on metabolism. The bacterial genera such as Faecalitalea and Muribaculum significantly increased and negatively correlated with most of the altered metabolites after HKC treatment, while Phyllobacterium, Weissella and Akkermansia showed an opposite trend. The plasma metabolites, mainly including amino acids and fatty acids such as methionine sulfoxide, BCAAs and cis-7-Hexadecenoic acid, exhibited a distinct return to normal after HKC treatment. The current study thereby provides experimental evidence suggesting that HKC may modulate gut microbiota and subsequently ameliorate the metabolite levels in DN.

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.

The Role of MicroRNA in the Pathogenesis of Diabetic Nephropathy

Diabetic nephropathy is one of the most common and severe complications of diabetes mellitus, affecting one in every five patients suffering from diabetes. Despite extensive research, the exact pathogenesis of diabetic nephropathy is still unclear. Several factors and pathways are known to be involved in the development of the disease, such as reactive oxygen species or the activation of the renin–angiotensin–aldosterone system. The expression of those proteins might be extensively regulated by microRNA. Recent research suggests that in diabetic nephropathy patients, the profile of miRNA is significantly changed. In this review, we focus on the actions of miRNA in various pathways involved in the pathogenesis of diabetic nephropathy and the clinical usage of miRNAs as biomarkers and therapeutic targets.

Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease

Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.

Circulating thrombospondin-2 level for identifying individuals with rapidly declining kidney function trajectory in type 2 diabetes: a prospective study of the Hong Kong West Diabetes Registry

BACKGROUND

Thrombospondin-2 (TSP2) is a matricellular protein with tissue expression induced by hyperglycaemia. TSP2 has been implicated in non-diabetic renal injury in preclinical studies and high circulating levels were associated with worse kidney function in cross-sectional clinical studies. Therefore, we investigated the prospective associations of circulating TSP2 level with kidney function decline and the trajectories of estimated glomerular filtration rate (eGFR) in type 2 diabetes.

METHODS

Baseline serum TSP2 level was measured in 5471 patients with type 2 diabetes to evaluate its association with incident eGFR decline, defined as ≥ 40% sustained eGFR decline, using multivariable Cox regression analysis. Among participants with relatively preserved kidney function (Baseline eGFR ≥ 60 ml/min/1.73m2), joint latent class modelling was employed to identify three different eGFR trajectories. Their associations with baseline serum TSP2 was evaluated using multinomial logistic regression analysis. The predictive performance of serum TSP2 level was examined using time-dependent c-statistics and calibration statistics.

RESULTS

Over a median follow-up of 8.8 years, 1083 patients (19.8%) developed eGFR decline. Baseline serum TSP2 level was independently associated with incident eGFR decline (HR 1.21, 95%CI 1.07-1.37, P = 0.002). With internal validation, incorporating serum TSP2 to a model of clinical risk factors including albuminuria led to significant improvement in c-statistics from 83.9 to 84.4 (P < 0.001). Among patients with eGFR ≥ 60 ml/min/1.73m2, baseline serum TSP2 level was independently associated with a rapidly declining eGFR trajectory (HR 1.63, 95%CI 1.26-2.10, P < 0.001).

CONCLUSION

Serum TSP2 level was independently associated with incident eGFR decline, particularly a rapidly declining trajectory, in type 2 diabetes.

A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

The growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.

RESULTS

Our simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from confounding, and identified independent prognostic markers of DKD severity. Furthermore, TSP-based methods achieved competitive classification accuracy in DKD to LASSO and random forests, while providing more parsimonious models.

CONCLUSIONS

We extended TSP-based methods to account for covariates, via a simple, easy to implement residualizing process. Our covariate-adjusted TSP method identified metabolite features, uncorrelated from clinical covariates, that discriminate DKD severity stage based on the relative ordering between two features, and thus provide insights into future studies on the order reversals in early vs advanced disease states.

Pediatric Diabetic Nephropathy: Novel Insights from microRNAs

Diabetic nephropathy (DN) represents the most common microvascular complication in patients with diabetes. This progressive kidney disease has been recognized as the major cause of end-stage renal disease with higher morbidity and mortality. However, its tangled pathophysiology is still not fully known. Due to the serious health burden of DN, novel potential biomarkers have been proposed to improve early identification of the disease. In this complex landscape, several lines of evidence supported a critical role of microRNAs (miRNAs) in regulating posttranscriptional levels of protein-coding genes involved in DN pathophysiology. Indeed, intriguing data showed that deregulation of certain miRNAs (e.g., miRNAs 21, -25, -92, -210, -126, -216, and -377) were pathogenically linked to the onset and the progression of DN, suggesting not only a role as early biomarkers but also as potential therapeutic targets. To date, these regulatory biomolecules represent the most promising diagnostic and therapeutic options for DN in adult patients, while similar pediatric evidence is still limited. More, findings from these elegant studies, although promising, need to be deeper investigated in larger validation studies. In an attempt to provide a comprehensive pediatric overview in the field, we aimed to summarize the most recent evidence on the emerging role of miRNAs in pediatric DN pathophysiology.

Urinary Extracellular Vesicles in Chronic Kidney Disease: From Bench to Bedside?

Extracellular vesicles are a diverse group of particles that include exosomes, microvesicles, and apoptotic bodies and are defined by size, composition, site of origin, and density. They incorporate various bioactive molecules from their cell of origin during formation, such as soluble proteins, membrane receptors, nucleic acids (mRNAs and miRNAs), and lipids, which can then be transferred to target cells. Extracellular vesicles/exosomes have been extensively studied as a critical factor in pathophysiological processes of human diseases. Urinary extracellular vesicles could be a promising liquid biopsy for determining the pattern and/or severity of kidney histologic injury. The signature of urinary extracellular vesicles may pave the way for noninvasive methods to supplement existing testing methods for diagnosing kidney diseases. We discuss the potential role of urinary extracellular vesicles in various chronic kidney diseases in this review, highlighting open questions and discussing the potential for future research.

Integrative analyses of biomarkers and pathways for diabetic nephropathy

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

Transcriptome Analysis Identifies Oxidative Stress Injury Biomarkers for Diabetic Nephropathy

BACKGROUND

The early diagnosis of diabetic nephropathy (DN) is essential for improving the prognosis and effectively manage patients affected with this disease. The standard biomarkers, including albuminuria and glomerular filtration rate, are not very precise. New molecular biomarkers are needed to more accurately identify DN and better predict disease progression. Characteristic DN biomarkers can be identified using transcriptomic analysis.

AIM OF THE STUDY

To evaluate the transcriptomic profile of controls (CTRLs, n = 15), patients with prediabetes (PREDM, n = 15), patients with type-2 diabetes mellitus (DM2, n = 15), and patients with DN (n = 15) by microarray analysis to find new biomarkers. RT-PCR was then used to confirm gene biomarkers specific for DN.

MATERIALS AND METHODS

Blood samples were used to isolate RNA for microarray expression analysis. 26,803 unique gene sequences and 30,606 LncRNA sequences were evaluated-Selected gene biomarkers for DN were validated using qPCR assays. Sensitivity, specificity, and area under the curve (AUC) were calculated as measures of diagnostic accuracy.

RESULTS

The DN transcriptome was composed of 300 induced genes, compared to CTRLs, PREDM, and DM-2 groups. RT-qPCR assays validated that METLL22, PFKL, CCNB1 and CASP2 genes were induced in the DN group compared to CTRLs, PREDM, and DM-2 groups. The ROC analysis for these four genes showed 0.9719, 0.8853, 0.8533 and 0.7748 AUC values, respectively.

CONCLUSION

Among induced genes in the DN group, we found that CASP2, PFKL and CCNB1 may potentially be used as biomarkers to diagnose DN. Of these, METLL22 had the highest AUC score, at 0.9719.

Traditional Chinese medicine body constitution predicts new-onset diabetic albuminuria in patients with type 2 diabetes: Taichung diabetic body constitution prospective cohort study

This prospective cohort study explored whether body constitution (BC) independently predicts new-onset albuminuria in persons with type 2 diabetes mellitus (T2DM) enrolled in the diabetes care management program (DCMP) of a medical center, providing evidence of integrating traditional Chinese medicine into DCMP for improving care quality. Persons with T2DM (n = 426) originally without albuminuria enrolled in DCMP were recruited in 2010 and were then followed up to 2015 for detecting new-onset albuminuria. The participants received urinalysis and blood test annually. Albuminuria was determined by an elevated urinary albumin/creatinine ratio (≥ 30 µg/mg), and poor glucose control was defined as Glycosylated hemoglobin above or equal to 7%. BC type (Yin deficiency, Yang deficiency, and phlegm stasis) was assessed using a well-validated body constitution questionnaire at baseline. Risk factors for albuminuria (sociodemographic factors, diabetes history, lifestyle behaviors, lipid profile, blood pressure, and kidney function) were also recorded. Hazard ratios (HR) of albuminuria for BC were estimated using multivariate Cox proportional hazards model. During the 4-year follow-up period, albuminuria occurred in 30.5% of participants (n = 130). The HR indicated that Yin deficiency was significantly associated with an increased risk of new-onset albuminuria in persons with T2DM and good glucose control after adjustment for other risk factors (HR = 2.09; 95% confidence interval = 1.05-4.17, P = .04), but not in those with poor glucose control. In persons with T2DM and poor glucose control, phlegm stasis was also significantly associated with a higher risk of albuminuria (2.26; 1.03-4.94, P = .04) after multivariate adjustment, but not in those with good glucose control. In addition to already-known risk factors, BC is an independent and significant factor associated with new-onset albuminuria in persons with T2DM. Our results imply Yin deficiency and phlegm stasis interacting with glucose control status may affect new-onset albuminuria in persons with T2DM.

Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study

In view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) models were applied to evaluate the relationship between serum Mb and DKD. Serum Mb-mediated renal function impairment induced by MetS components was verified by causal mediation effect analysis. The area under the receiver operating characteristic curve of the DKD machine learning models incorporating serum Mb and MetS components reached 0.85. Feature importance analysis and SHAP showed that serum Mb and MetS components were important features. Further RCS models of DKD showed that the odds ratio was greater than 1 when serum Mb was > 80. Serum Mb showed a significant indirect effect in renal function impairment when using MetS components such as HOMA-IR, HGI and HDL-C/TC as a reason. Moderately elevated serum Mb is associated with the risk of DKD. Serum Mb may mediate MetS component-caused renal function impairment.