Precision medicine in diabetic nephropathy and chronic kidney disease

Tubular biomarkers in proteinuric kidney disease: histology correlation and kidney prognosis of tubular biomarkers

Background

Proteinuria is not only a biomarker of chronic kidney disease (CKD) but also a driver of CKD progression. The aim of this study was to evaluate serum and urinary tubular biomarkers in patients with biopsied proteinuric kidney disease and to correlate them with histology and kidney outcomes.

Methods

A single-center retrospective study was conducted on a cohort of 156 patients from January 2016 to December 2021. The following urinary and serum biomarkers were analyzed on the day of kidney biopsy: beta 2 microglobulin (β2-mcg), alpha 1 microglobulin (α1-mcg), neutrophil gelatinase-associated lipocalin (NGAL), urinary kidney injury molecule-1 (uKIM-1), monocyte chemoattractant protein-1 (MCP-1), urinary Dickkopf-3 (uDKK3), uromodulin (urinary uUMOD), serum kidney injury molecule-1 (sKIM-1) and serum uromodulin (sUMOD). A composite outcome of kidney progression or death was recorded during a median follow-up period of 26 months.

Results

Multivariate regression analysis identified sUMOD (β-0.357, P < .001) and uDKK3 (β 0.483, P < .001) as independent predictors of interstitial fibrosis, adjusted for age, estimated glomerular filtration rate (eGFR) and log proteinuria. Elevated levels of MCP-1 [odds ratio 15.61, 95% confidence interval (CI) 3.52-69.20] were associated with a higher risk of cortical interstitial inflammation >10% adjusted for eGFR, log proteinuria and microhematuria. Upper tertiles of uDKK3 were associated with greater eGFR decline during follow-up. Although not a predictor of the composite outcome, doubling of uDKK3 was a predictor of kidney events (hazard ratio 2.26, 95% CI 1.04-4.94) after adjustment for interstitial fibrosis, eGFR and proteinuria.

Conclusions

Tubular markers may have prognostic value in proteinuric kidney disease, correlating with specific histologic parameters and identifying cases at higher risk of CKD progression.

Novel insights into transfer RNA-derived small RNA (tsRNA) in cardio-metabolic diseases

Cardio-metabolic diseases, including a cluster of metabolic disorders and their secondary affections on cardiovascular physiology, are gradually brought to the forefront by researchers due to their high prevalence and mortality, as well as an unidentified pathogenesis. tRNA-derived small RNAs (tsRNAs), cleaved by several specific enzymes and once considered as some "metabolic junks" in the past, have been proved to possess numerous functions in human bodies. More interestingly, such a potential also seems to influence the progression of cardio-metabolic diseases to some extent. In this review, the biogenesis, classification and mechanisms of tsRNAs will be discussed based on some latest studies, and their relations with several cardio-metabolic diseases will be highlighted in sequence. Lastly, some future prospects, such as their clinical applications as biomarkers and therapeutic targets will also be mentioned, in order to provide researchers with a comprehensive understanding of the research status of tsRNAs as well as its association with cardio-metabolic diseases, thus presenting as a beacon to indicate directions for the next stage of study.

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease

Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.

Precision Nephrology in Patients with Diabetes and Chronic Kidney Disease

Diabetes is the leading cause of kidney failure and specifically, diabetic kidney disease (DKD) occurs in up to 30% of all diabetic patients. Kidney disease attributed to diabetes is a major contributor to the global burden of the disease in terms of clinical and socio-economic impact, not only because of the risk of progression to End-Stage Kidney Disease (ESKD), but also because of the associated increase in cardiovascular (CV) risk. Despite the introduction of novel treatments that allow us to reduce the risk of future outcomes, a striking residual cardiorenal risk has been reported. This risk is explained by both the heterogeneity of DKD and the individual variability in response to nephroprotective treatments. Strategies that have been proposed to improve DKD patient care are to develop novel biomarkers that classify with greater accuracy patients with respect to their future risk (prognostic) and biomarkers that are able to predict the response to nephroprotective treatment (predictive). In this review, we summarize the principal prognostic biomarkers of type 1 and type 2 diabetes and the novel markers that help clinicians to individualize treatments and the basis of the characteristics that predict an optimal response.

Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease

This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithms on patients with diabetic kidney disease (DKD). 44 patients diagnosed with DKD were randomly divided into two groups: group A (no nursing intervention) and group B (comprehensive nursing). In the same period, 32 healthy volunteers were selected as the control group. Ultrasonographic images based on the $K$ non-local-means (KNL-Means) filtering algorithm were used to perform imaging examinations in healthy people and DKD patients before and after care. The results suggested that compared with those of the SAE reconstruction algorithm and KAVD reconstruction algorithm, the PSNR value of artificial bee colony algorithm reconstruction of image was higher and the MSE value was lower. The resistant index (RI) of DKD patients in group B after nursing was $0.63\pm 0.06$ , apparently distinct from the RI of the healthy people (controls) in the same group ( $0.58\pm 0.06$ ) and the RI of DKD patients in group A ( $0.68\pm 0.07$ ) ( $P<0.05$ ). The incidence rate of complications in DKD patients in group B was apparently inferior to that in group A. After comprehensive nursing intervention (CNI), the scores of all dimensions of quality of life (QoL) in DKD patients in group B were obviously superior versus those in DKD patients in group A. It suggests that implementation of nursing intervention for DKD patients can effectively help patients improve and control the level of renal function, while ultrasound images based on intelligent algorithm can dynamically detect the changes in the level of renal function in patients, which has the value of clinical promotion.