ACE and non-ACE pathways in the renal vascular response to RAS interruption in type 1 diabetes mellitus

ACE and non-ACE pathways in the renal vascular response to RAS interruption in type 1 diabetes mellitus.BackgroundThe enormous contribution of renin-angiotensin system (RAS) interruption with ACE (angiotensin-converting enzyme) inhibitors and angiotensin II receptor blockers (ARB) in the treatment of diabetic nephropathy has led to interest in the factors involved in angiotensin II (Ang II) generation. In normal subjects, RAS interruption using an ARB produced a 50% greater renal plasma flow (RPF) rise than with an ACE inhibitor, suggesting a substantial contribution of non-ACE pathways. Moreover, immunohistochemistry studies in kidneys of overtly proteinuric diabetic subjects showed up-regulation of chymase, an alternative Ang II-generating enzyme. Our aim was to determine the degree to which the non-ACE pathways contribute to RAS activation in type 1 diabetes mellitus (DM).MethodsType 1DM patients (N = 37, 14 M/23 F; age 31 ± 2 years; DM duration 16 ± 1.7 years; HbA1c 7.7.0 ± 0.3%) were studied on a high-salt diet. They received captopril 25mg po one day and candesartan 16mg po the next day. RPF and glomerular filtration rate (GFR) were measured before and up to 4 hours after drug administration.ResultsBoth captopril and candesartan induced a significant rise in RPF (baseline vs. peak <0.0001 for both), and the rise was concordant for the 2 drugs (r = 0.77,P < 0.001). However, the RPF responses were not significantly different between the 2 drugs (captopril 72 ± 11mL/min/1.73m2, candesartan 75 ± 12,P = 0.841).ConclusionIn predominantly normoalbuminuric, normotensive type 1 DM, activation of the intrarenal RAS reflects a mechanism involving primarily the classic ACE pathway

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

Diabetic Myonecrosis: A Diagnostic and Treatment Challenge in Longstanding Diabetes

Objective. Diabetes mellitus is associated with microvascular and macrovascular complications; the most commonly recognized ones include diabetic nephropathy, retinopathy, and neuropathy. Less well-known complications are equally important, as timely recognition and treatment are essential to decrease short- and long-term morbidity. Methods. Herein, we describe a case of a 41-year-old female with longstanding, uncontrolled type 2 diabetes, who presented with classical findings of diabetic myonecrosis. Results. Our patient underwent extensive laboratory and imaging studies prior to diagnosis due to its rarity and similarity in presentation with other commonly noted musculoskeletal conditions. We emphasize the clinical presentation, laboratory and imaging findings, treatment regimen, and prognosis associated with diabetic myonecrosis. Conclusion. Diabetic myonecrosis is a rare complication of longstanding, poorly controlled diabetes mellitus. The diagnosis requires a high index of suspicion in the right clinical setting: acute onset nontraumatic muscular pain with associated findings on clinical exam, laboratory studies, and imaging. While the short-term prognosis is good, the recurrence rate remains high and long-term prognosis is poor given underlying uncontrolled diabetes and associated sequelae