Peptiduria: a potential early predictor of diabetic kidney disease.

BACKGROUND: To protect the kidney effectively with medication in type 2 diabetics, it is crucial to identify such at-risk patients early for treatment. We investigated whether peptiduria precedes proteinuria (the earliest urinary marker in our model), and thereby serve as an early predictor of diabetic nephropathy. METHODS: A longitudinal study was performed in a rat model of diabetic nephropathy. Peptides, defined as degradation products of proteins of < 13 kD size, were quantified by a previously validated method using a combination of Lowry and Biorad protein assays. Peptides in urine were also confirmed by chromatographically separating low molecular weight fractions from urine and quantifying albumin fragments in these fractions by enzyme immunoassay. Also, the mechanism of peptiduria was addressed by measuring acid phosphatase, a marker of lysosomal activity, in urine and on kidney sections (histochemically). RESULTS: In rats with diabetic nephropathy, proteinuria occurred after 12 weeks of diabetes, while peptiduria occurred as early as 2 weeks after diabetes. Peptiduria was confirmed by showing that the chromatographically separated low molecular weight fractions of urine containing albumin fragments is in proportion to the level of peptiduria. The time course of peptiduria paralleled the increase in urinary acid phosphatase suggesting that the mechanism of early peptiduria could be due to upregulation of lysosomal enzyme activity in the tubules. CONCLUSIONS: Our results showing that peptiduria precedes proteinuria in diabetic nephropathy provide a compelling rationale to perform a prospective human clinical trial to investigate whether peptiduria can serve as an early predictor of diabetic nephropathy.

In Diabetic Kidney Disease Urinary Exosomes Better Represent Kidney Specific Protein Alterations Than Whole Urine.

BACKGROUND: Predicting or diagnosing underlying kidney disease by analyzing whole urine remains the mainstay of nephrology practice. However, whole urine is a poor compartment to assess many structural changes in the kidney because whole urine contains only a few proteins derived from the kidney itself. Urinary exosomes, on the other hand, which are derived from the kidney, contain proteins secreted by the kidney. We experimentally tested the hypothesis that 'urinary exosomes more faithfully represent changes in the kidney tissue than whole urine'. A direct comparison between whole urine and urine exosomal levels of two chosen kidney disease markers, gelatinase and ceruloplasmin, was carried out on diabetic kidney disease patients. METHODS: Urinary exosomes were separated from whole urine by sequential centrifugation including ultra-centrifugation. Gelatinase activity was measured using fluorosceinated gelatin as the substrate, and ceruloplasmin was measured by sandwich ELISA. A few kidney specimens from patients biopsied for atypical features were histochemically stained for validation of the biochemical results. RESULTS: We found that changes in both, gelatinase (decreased activity) and ceruloplasmin (increased levels), in the urinary exosomes of diabetic kidney patients were in agreement with the alterations of these two proteins in the kidney tissue. In contrast, the levels of these two proteins in whole urine were highly variable and did not correlate with levels in the diabetic kidney tissue. CONCLUSION: In conclusion, these results confirmed our hypothesis that protein markers in urinary exosomes better reflected the underlying protein changes in the kidney than in whole urine samples.

Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19.

OBJECTIVES: There exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation. METHODS: We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images acquired from 528 hospitalised patients with COVID-19. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for the purpose of validation. RESULTS: We found that our deep learning model predicted the need for mechanical ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately 3 days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24%-13.25%. CONCLUSIONS: Our deep learning model accurately predicted the need for mechanical ventilation early during hospitalisation of patients with COVID-19. Until effective preventive or treatment measures become widely available for patients with COVID-19, prognostic stratification as provided by our model is likely to be highly valuable.

Decreased urinary peptide excretion in patients with renal disease.

BACKGROUND: Normal urine contains low-molecular-weight peptides or protein fragments that have been poorly studied, primarily because of the technical difficulty of measuring peptides in the presence of proteins. We studied these substances in healthy subjects and patients with renal disease and varying degrees of proteinuria to understand the factors that determine their excretion. METHODS: We estimated these substances as the difference between results using the Lowry method (which detects both proteins and peptides) and those obtained using the dye-binding Bradford (Biorad) method (Biorad Laboratories Inc, Hercules, CA; which detects only proteins). RESULTS: We validated this 2-assay approach to measure peptide levels by showing that such proteins as immunoglobulin G, albumin, and lysozyme were measured equally by the Lowry and Biorad methods, whereas degraded proteins were recognized by the Lowry method only, but not by the Biorad method. We found that healthy subjects excreted less than 200 mg of protein, but 3 to 4 g of peptides/g creatinine; thus, peptides constituted approximately 95% of total protein material excreted in urine. Patients with renal disease and proteinuria had a progressive decrease in peptide excretion, ranging from 3 to 0 g/g creatinine. Twenty-five percent of nephrotic patients (18 of 72 patients) excreted very small amounts of peptides in urine (0% to 10% of total protein material). CONCLUSION: We found that healthy persons excrete substantial amounts of peptides in urine, and this excretion decreases in the presence of proteinuric renal disease. It is possible that these peptides in urine arise from the tubular degradation of filtered proteins and exocytosis of protein fragments toward the urinary side, a process that becomes increasingly impaired as proteinuria increases.