Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

A new era in the science and care of kidney diseases

Notable progress in basic, translational and clinical nephrology research has been made over the past five decades. Nonetheless, many challenges remain, including obstacles to the early detection of kidney disease, disparities in access to care and variability in responses to existing and emerging therapies. Innovations in drug development, research technologies, tissue engineering and regenerative medicine have the potential to improve patient outcomes. Exciting prospects include the availability of new drugs to slow or halt the progression of chronic kidney disease, the development of bioartificial kidneys that mimic healthy kidney functions, and tissue engineering techniques that could enable transplantable kidneys to be created from the cells of the recipient, removing the risk of rejection. Cell and gene therapies have the potential to be applied for kidney tissue regeneration and repair. In addition, about 30% of kidney disease cases are monogenic and could potentially be treated using these genetic medicine approaches. Systemic diseases that involve the kidney, such as diabetes mellitus and hypertension, might also be amenable to these treatments. Continued investment, communication, collaboration and translation of innovations are crucial to realize their full potential. In addition, increasing sophistication in exploring large datasets, implementation science, and qualitative methodologies will improve the ability to deliver transformational kidney health strategies.

Association of Circulating Carbohydrate Antigen 19-9 Level with Type 2 Diabetic Kidney Disease in Chinese Adults: A Cross-Sectional Study

Objective

Very few and conflicting data are available regarding the correlation between circulating carbohydrate antigen 19-9 (CA19-9) levels and diabetic kidney disease (DKD) and its components including albuminuria and a low estimated glomerular filtration rate (eGFR). This study aimed to examine the association of circulating CA19-9 and DKD in Chinese patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 402 hospitalized T2DM patients between September 2017 and December 2021 were included in this cross-sectional study. There were 224 and 178 subjects in non-DKD and DKD groups, respectively. Serum CA19-9 was measured by chemiluminescence method, and its potential relationship with DKD was evaluated by multivariate logistic regression and correlation analyses, and receiver operating characteristic (ROC) curve analysis.

Results

T2DM patients with DKD had significantly higher serum CA19-9 levels than those without, and serum CA19-9 levels were positively related to urinary albumin-to-creatinine ratio and negatively to eGFR (P<0.01). Multivariate regression analysis revealed that serum CA 19-9 was an independent factor of DKD [odds ratio (OR), 1.018; 95% confidence interval (CI), 1.002-1.035; P<0.05]. Moreover, an increased progressively risk of DKD with an increase in serum CA19-9 quartiles was observed (P for trend <0.001), and T2DM patients in the highest serum CA19-9 quartile were associated with an increased likelihood of DKD when compared to those in the lowest quartile (OR: 2.936, 95% CI 1.129-7.633, P<0.05). Last, the analysis of ROC curves suggested that serum CA 19-9 at a cut of 25.09 U/mL resulted in the highest Youden index with sensitivity 43.8% and 75.4% specificity to predict the presence of DKD.

Conclusion

These results showed that high circulating CA19-9 was related to DKD and may serve as a useful biomarker of DKD in hospitalized Chinese T2DM patients.