Risk factors and urinary biomarkers of non-albuminuric and albuminuric chronic kidney disease in patients with type 2 diabetes

Urinary Markers of Tubular Injury and Renal Fibrosis in Patients with Type 2 Diabetes and Different Phenotypes of Chronic Kidney Disease

This study assessed the urinary excretion of markers and mediators of tubular injury and renal fibrosis in patients with type 2 diabetes (T2D) and non-albuminuric and albuminuric patterns of chronic kidney disease (CKD). One hundred and forty patients with long-term T2D and different patterns of CKD and twenty non-diabetic individuals were included. Urinary retinol-binding protein 4 (RBP-4), glutathione-S-transferase α₁ and π (GST-α₁ and GST-π), transforming growth factor β (TGF-β), type I and type IV collagen (Col1 and Col4), bone morphogenic protein 7 (BMP-7), and hepatocyte growth factor (HGF) were assessed by ELISA. Patients with T2D demonstrated increased urinary excretion of RBP-4, GST-π, Col4, BMP-7, and HGF (all p < 0.05 vs. control). The excretion of RBP-4, GST-π, Col1, and Col4 was increased in patients with elevated albumin-to-creatinine ratio (UACR; all p < 0.05 vs. control), while BMP-7 and HGF were increased innormoalbuminuric patients also (p < 0.05). Urinary RBP-4, GST-α₁, Col1, Col4, and HGF correlated positively with UACR; meanwhile, no correlations with glomerular filtration rate were found. The results demonstrate that elevated urinary excretions of the markers of tubular injury (RBP-4, GST-π) and renal fibrosis (Col1, Col4), as well as HGF, an antifibrotic regulator, are associated with the albuminuric pattern of CKD in subjects with T2D.

Novel plasma protein biomarkers from critically ill sepsis patients

BACKGROUND

Despite the high morbidity and mortality associated with sepsis, the relationship between the plasma proteome and clinical outcome is poorly understood. In this study, we used targeted plasma proteomics to identify novel biomarkers of sepsis in critically ill patients.

METHODS

Blood was obtained from 15 critically ill patients with suspected/confirmed sepsis (Sepsis-3.0 criteria) on intensive care unit (ICU) Day-1 and Day-3, as well as age- and sex-matched 15 healthy control subjects. A total of 1161 plasma proteins were measured with proximal extension assays. Promising sepsis biomarkers were narrowed with machine learning and then correlated with relevant clinical and laboratory variables.

RESULTS

The median age for critically ill sepsis patients was 56 (IQR 51-61) years. The median MODS and SOFA values were 7 (IQR 5.0-8.0) and 7 (IQR 5.0-9.0) on ICU Day-1, and 4 (IQR 3.5-7.0) and 6 (IQR 3.5-7.0) on ICU Day-3, respectively. Targeted proteomics, together with feature selection, identified the leading proteins that distinguished sepsis patients from healthy control subjects with ≥ 90% classification accuracy; 25 proteins on ICU Day-1 and 26 proteins on ICU Day-3 (6 proteins overlapped both ICU days; PRTN3, UPAR, GDF8, NTRK3, WFDC2 and CXCL13). Only 7 of the leading proteins changed significantly between ICU Day-1 and Day-3 (IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5, and ITGAV; P < 0.01). Significant correlations were observed between a variety of patient clinical/laboratory variables and the expression of 15 proteins on ICU Day-1 and 14 proteins on ICU Day-3 (P < 0.05).

CONCLUSIONS

Targeted proteomics with feature selection identified proteins altered in critically ill sepsis patients relative to healthy control subjects. Correlations between protein expression and clinical/laboratory variables were identified, each providing pathophysiological insight. Our exploratory data provide a rationale for further hypothesis-driven sepsis research.

Non-albuminuric Diabetic Kidney Disease Phenotype: Beyond Albuminuria

Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease worldwide. Its pathogenic mechanism is complex, and it can affect the entire structures of the kidneys such as the glomerulus, tubules and interstitium. Currently, the urinary albumin excretion rate and the estimated glomerular filtration rate are widely accepted as diagnostic criteria. However, some studies have reported a different or non-classical clinical course of DKD, with some patients showing declined kidney function with normal levels of albuminuria, known as the 'non-albuminuric DKD' phenotype. The pathogenesis of this phenotype remains unclear, but some clinical and pathological features have been postulated. This review explores the evidence regarding this topic.

Is type 2 diabetes mellitus another intercellular junction-related disorder?

Type 2 diabetes mellitus (T2D) is nowadays a worldwide epidemic and has become a major challenge for health systems around the world. It is a multifactorial disorder, characterized by a chronic state of hyperglycemia caused by defects in the production as well as in the peripheral action of insulin. This minireview highlights the experimental and clinical evidence that supports the novel idea that intercellular junctions (IJs)-mediated cell-cell contacts play a role in the pathogenesis of T2D. It focuses on IJs repercussion for endocrine pancreas, intestinal barrier, and kidney dysfunctions that contribute to the onset and evolution of this metabolic disorder.

Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

Trajectories of kidney function in diabetes: a clinicopathological update

Diabetic nephropathy has been traditionally diagnosed based on persistently high albuminuria and a subsequent decline in glomerular filtration rate (GFR), which is widely recognized as the classical phenotype of diabetic kidney disease (DKD). Several studies have emphasized that trajectories of kidney function in patients with diabetes (specifically, changes in GFR and albuminuria over time) can differ from this classical DKD phenotype. Three alternative DKD phenotypes have been reported to date and are characterized by albuminuria regression, a rapid decline in GFR, or non-proteinuric or non-albuminuric DKD. Although kidney biopsies are not typically required for the diagnosis of DKD, a few studies of biopsy samples from patients with DKD have demonstrated that changes in kidney function associate with specific histopathological findings in diabetes. In addition, various clinical and biochemical parameters are related to trajectories of GFR and albuminuria. Collectively, pathological and clinical characteristics can be used to predict trajectories of GFR and albuminuria in diabetes. Furthermore, cohort studies have suggested that the risks of kidney and cardiovascular outcomes might vary among different phenotypes of DKD. A broader understanding of the clinical course of DKD is therefore crucial to improve risk stratification and enable early interventions that prevent adverse outcomes.

Empagliflozin alleviates podocytopathy and enhances glomerular nephrin expression in db/db diabetic mice

BACKGROUND

Modern guidelines recommend sodium-glucose cotransporter-2 (SGLT2) inhibitors as the preferred antihyperglycemic agents for patients with type 2 diabetes and chronic kidney disease. However, the mechanisms underlying the renal protective effect of SGLT2 inhibitors are not fully understood.

AIM

To estimate the effect of the SGLT2 inhibitor, empagliflozin (EMPA), on the structure of podocytes and nephrin expression in glomeruli in db/db diabetic mice.

METHODS

We treated 8-wk-old male db/db mice with EMPA (10 mg/kg/d) or vehicle for 8 wk. Age-matched male db/+ mice were included as non-diabetic controls. Parameters of body composition, glycemic and lipid control, and plasma concentrations of leptin, insulin and glucagon were assessed. We evaluated renal hypertrophy as kidney weight adjusted to lean mass, renal function as plasma levels of creatinine, and albuminuria as the urinary albumin-to-creatinine ratio (UACR). Renal structures were studied by light and transmission electron microscopy with a focus on mesangial volume and podocyte structure, respectively. Glomerular nephrin and transforming growth factor beta (TGF-β) were assessed by immunohistochemistry.

RESULTS

Severe obesity and hyperglycemia developed in db/db mice prior to the start of the experiment; increased plasma concentrations of fructosamine, glycated albumin, cholesterol, leptin, and insulin, and elevated UACR were detected. Mesangial expansion, glomerular basement membrane thickening, and increased area of TGF-β staining in glomeruli were revealed in vehicle-treated mice. Podocytopathy was manifested by effacement of foot processes; nephrin-positive areas in glomeruli were reduced. EMPA decreased the levels of glucose, fructosamine and glycated albumin, UACR, kidney hypertrophy, mesangial expansion, glomerular basement membrane thickening, and glomerular TGF-β staining, alleviated podocytopathy and restored glomerular staining of nephrin.

CONCLUSION

These data indicate that EMPA attenuates podocytopathy in experimental diabetic kidney disease. The anti-albuminuric effect of EMPA could be attributed to mitigation of podocyte injury and enhancement of nephrin expression.

Multiplex Bead Array Assay of a Panel of Circulating Cytokines and Growth Factors in Patients with Albuminuric and Non-AlbuminuricDiabetic Kidney Disease

A panel of cytokines and growth factors, mediating low-grade inflammation and fibrosis, was assessed in patients with type 2 diabetes (T2D) and different patterns of chronic kidney disease (CKD). Patients with long-term T2D (N = 130) were classified into four groups: no signs of CKD; estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m² without albuminuria; albuminuria and eGFR ≥60 mL/min/1.73 m²; albuminuria and eGFR <60 mL/min/1.73 m². Thirty healthy subjects were acted as control. Twenty-seven cytokines and growth factors were assessed in serum by multiplex bead array assay. Serum hs-CRP, urinary nephrin, podocine, and WFDC2 were measured by ELISA. Patients with T2D showed elevated IL-1Ra, IL-6, IL-17A, G-CSF, IP-10, MIP-1α, and bFGF levels; concentrations of IL-4, IL-12, IL-15, INF-γ, and VEGF were decreased. IL-6, IL-17A, G-CSF, MIP-1α, and bFGF correlated negatively with eGFR; IL-10 and VEGF demonstrated negative associations with WFDC2; no relationships with podocyte markers were found. Adjusted IL-17A and MIP-1α were predictors of non-albuminuric CKD, IL-13 predicted albuminuria with preserved renal function, meanwhile, IL-6 and hsCRP were predictors of albuminuria with eGFR decline. Therefore, albuminuric and non-albuminuric CKD in T2D patients are associated with different pro-inflammatory shifts in the panel of circulating cytokines.

Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.