Urinary extracellular vesicles provide a novel source for valuable biomarkers for kidney and urogenital diseases: Current isolation protocols include laborious, sequential centrifugation steps which hampers their widespread research and clinical use. Furthermore, large individual urine sample volumes or sizable target cohorts are to be processed (e.g. for biobanking), the storage capacity is an additional problem. Thus, alternative methods are necessary to overcome such limitations. We have developed a practical vesicle isolation technique to yield easily manageable sample volumes in an exceptionally cost efficient way to facilitate their full utilization in less privileged environments and maximize the benefit of biobanking. Urinary vesicles were isolated by hydrostatic dialysis with minimal interference of soluble proteins or vesicle loss. Large volumes of urine were concentrated up to 1/100 of original volume and the dialysis step allowed equalization of urine physico-chemical characteristics. Vesicle fractions were found suitable to any applications, including RNA analysis. In the yield, our hydrostatic filtration dialysis system outperforms the conventional ultracentrifugation-based methods and the labour intensive and potentially hazardous step of ultracentrifugations are eliminated. Likewise, the need for trained laboratory personnel and heavy initial investment is avoided. Thus, our method qualifies as a method for laboratories working with urinary vesicles and biobanking.

Diabetic nephropathy (DN) is one of the major complications of diabetes mellitus (DM), leads to chronic kidney disease (CKD), and, ultimately, is the main cause for end-stage kidney disease (ESKD). Beyond urinary albumin, no reliable biomarkers are available for accurate early diagnostics. Urinary extracellular vesicles (UEVs) have recently emerged as an interesting source of diagnostic and prognostic disease biomarkers. Here we used a protease and respective protease inhibitor array to profile urines of type 1 diabetes patients at different stages of kidney involvement. Urine samples were divided into groups based on the level of albuminuria and UEVs isolated by hydrostatic dialysis and screened for relative changes of 34 different proteases and 32 protease inhibitors, respectively. Interestingly, myeloblastin and its natural inhibitor elafin showed an increase in the normo- and microalbuminuric groups. Similarly, a characteristic pattern was observed in the array of protease inhibitors, with a marked increase of cystatin B, natural inhibitor of cathepsins L, H, and B as well as of neutrophil gelatinase-associated Lipocalin (NGAL) in the normoalbuminuric group. This study shows for the first time the distinctive alterations in comprehensive protease profiles of UEVs in diabetic nephropathy and uncovers intriguing mechanistic, prognostic, and diagnostic features of kidney damage in diabetes.

The prevalence of prehypertension has increased in China, and prehypertension frequently progress to hypertension over a short time period; both have become public health problems. Therefore, this study was conducted to determine the relationship between the Visceral Adiposity Index (VAI) and blood pressure (BP) in China. A cross-sectional epidemiological survey was conducted in China using a stratified random cluster sampling method. Sex-specific VAI quartile cut-off points were used as follows: 0.88, 1.41, 2.45 in males and 0.85, 1.33, 2.22 in females. Prehypertension and hypertension were each defined according to The Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) guidelines. A multivariate logistic analysis was conducted to analyze the relationship among VAI, prehypertension and hypertension. The ORs for prehypertension and hypertension in the upper quartiles of the VAI were 1.514 (1.074-2.133), P=0.018 and 1.660 (1.084-2.542), P=0.020, in males, after adjusting for age, education, smoking habits, alcohol consumption, physical activity, serum creatinine, fasting glucose, and plasma insulin. Following further adjustments for the above confounders, chronic kidney disease, and diabetes, the ORs for prehypertension and hypertension in the upper quartile of the VAI were 1.660 1.533 (1.086-2.165), P=0.015, and 1.743 (1.133-2.680), P=0.011, in males. The ORs for prehypertension and hypertension in the upper quartile of the VAI were 1.691 (1.223-2.338), P=0.001, and 1.682 (1.162-2.435), P=0.006, in females, after adjusting for age, education, smoking habits, alcohol consumption, physical activity, serum creatinine, fasting glucose, and plasma insulin. Following further adjustments for the above confounders, chronic kidney disease, and diabetes, the ORs for prehypertension and hypertension in the upper quartile of the VAI were 1.688 (1.220-2.334), P=0.002, and 1.657 (1.141-2.406), P=0.008, in females. A higher VAI was positively associated with both prehypertension and hypertension in both males and females. It is both essential and urgent that clinicians take steps to control and prevent visceral adiposity.

Visceral adiposity index (VAI) was closely associated with metabolic syndrome, however almost no research focused on VAI and hyperuricemia, therefore, this study was conducted to determine the relationship of VAI and hyperuricemia free of metabolic syndrome and estimate the power of VAI as predictor for hyperuricemia. A cross-sectional research coming from a health check-up program was conducted. All participants were divided into four groups according to VAI quartiles. A multivariate logistic analysis was used to analyze the relationship between the quartiles and hyperuricemia. A receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of predictions for hyperuricemia. VAI was independent risk factor of hyperuricemia. The ORs of which in the upper quartile were 3.077 (95%CI 1.78-5.293), P = 0.000, in model 1, after adjusting for age, systolic blood pressure, diastolic blood pressure, heart rate, fast plasma glucose, serum creatinine, triglyceride, total cholesterol, high density lipoprotein cholesterol, and low density lipoprotein cholesterol; and 3.041 (95CI 1.767-5.233), P = 0.000, in model 2, after adjusting for the above plus physical activity, diet, smoking habits, alcohol consumption, hypertension and diabetes history. The area under the ROC curve (AUC) value of VAI was 0.618 (95%CI 0.572-0.665), P = 0.000; it was higher than WC, which was 0.556 (95%CI 0.508-0.604), P = 0.024, for hyperuricemia. VAI was associated with hyperuricemia among individuals free of metabolic syndrome, and also a powerful indicator.

Aim To study the relationship of visceral adiposity index (VAI) and prediabetes and the power of which in predicting of prediabetes. Methods A cross-sectional epidemiological survey was conducted. All participants were divided into four groups: VAI and WC (both are normal), VAI↑ and WC (postcritical VAI and normal WC), VAI and WC↑ (Normal VAI and postcritical WC), and VAI↑ and WC↑ (postcritical VAI and postcritical WC). A multivariate logistic analysis was used to analyze the relationship between the four groups and prediabetes, and diabetes. A receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of predictions for prediabetes and diabetes. Results Both VAI and WC were independent risk factors of Prediabetes. The ORs for Prediabetes in the VAI↑&WC group were 1.641 (95%CI 1.146–2.349), P=0.007, in males, in Model 2. The ORs for Prediabetes in the VAI&WC↑ group were 1.454 (95%CI 1.055–2.005), P=0.022, in males, in Model 2. The ORs for Prediabetes in the VAI↑&WC group were 2.305 (95%CI 1.623–3.273), P=0.000, in females, in Model 2. The ORs for Prediabetes in the VAI&WC↑ group were 1.997 (95%CI 1.529–2.608), P=0.000, in females, in Model 2. The AUC value of VAI were 0.601 (95%CI 0.568–0.634), P=0.000, in prediabetes of men; which were 0.645 (95%CI 0.618–0.672), P=0.000, in prediabetes women. WC had the highest AUC value of 0.605 (95%CI 0.571–0.638), P=0.000, in prediabetes of men, also had the highest of AUC value of 0.673 (95%CI 0.648–0.697), P=0.000, in prediabetes of women. Conclusion VAI was positively associated with prediabetes, and also a usefulindicator of prediabetes. ^* These authors contributed equally to this project

Background: The prevalence of chronic kidney disease (CKD) and prediabetes has increased in China, and at different rates in different locations. Therefore a community-based screening research was conducted in order to determine the prevalence of CKD and prediabetes, and to analyze associated risk factors of CKD and prediabetes in a city of Southern China. Methods: Atotal of 7801 community residents aged 18 year and older from 6 communities were screened by a stratified random cluster sampling method. An estimated glomerular filtration rate (eGFR), albuminuria, fasting plasma glucose (FPG), and homeostatic model assessment of insulin resistance (HOMA-IR) were measured. Age-standardized prevalence was calculated by the direct method with the use of data on the population distribution in China in 2006. Multivariate logistic analysis was used to analyze the risk factors of CKD and prediabetes, and association of insulin resistance (IR) with CKD and prediabetes was analyzed. Results: The age-standardized prevalence of CKD was 12.5%, eGFR 30 mg/g was 10.3%. The age-standardized prevalence of prediabetes was 12.1%. Logistic regression suggests that IR was a common independent risk factor of CKD and prediabetes. Further analysis show that HOMA-IR was increased with the aggravation of kidney injury and FPG. Conclusion: CKD and prediabetes have become a major public health problem in Zhuhai, Southern China; insulin resistance may be an important risk factor.