Beta blocker use in subjects with type 2 diabetes mellitus and systolic heart failure does not worsen glycaemic control

<p>Abstract</p> <p>Background</p> <p>The prognostic benefits of beta-blockers (BB) in patients with systolic heart failure (SHF) are known but despite this, in patients with diabetes they are underutilized. The aim of this study was to assess the effect of beta-blockers (BB) on glycaemic control in patients with Type 2 Diabetes (T2DM) and systolic heart failure (SHF) stratified to beta-1 selective (Bisoprolol) vs. nonselective BB (Carvedilol).</p> <p>Methods</p> <p>This observational, cohort study was conducted in patients with T2DM and SHF attending an Australian tertiary teaching hospital's heart failure services. The primary endpoint was glycaemic control measured by glycosylated haemoglobin (HbA1c) at initiation and top dose of BB. Secondary endpoints included microalbuminuria, changes in lipid profile and estimated glomerular filtration rate (eGFR).</p> <p>Results</p> <p>125 patients were assessed. Both groups were well matched for gender, NYHA class and use of guideline validated heart failure and diabetic medications. The mean treatment duration was 1.9 ± 1.1 years with carvedilol and 1.4 ± 1.0 years with bisoprolol (<it>p </it>= ns). The carvedilol group achieved a reduction in HbA1c (7.8 ± 0.21% to 7.3 ± 0.17%, <it>p </it>= 0.02) whereas the bisoprolol group showed no change in HbA1c (7.0 ± 0.20% to 6.9 ± 0.23%, <it>p </it>= 0.92). There was no significant difference in the change in HbA1c from baseline to peak BB dose in the carvedilol group compared to the bisoprolol group. There was a similar deterioration in eGFR, but no significant changes in lipid profile or microalbuminuria in both groups (<it>p </it>= ns).</p> <p>Conclusion</p> <p>BB use did not worsen glycaemic control, lipid profile or albuminuria status in subjects with SHF and T2DM. Carvedilol significantly improved glycemic control in subjects with SHF and T2DM and this improvement was non significantly better than that obtained with bisoprolol. BB's should not be withheld from patients with T2DM and SHF.</p

Association between intrarenal arterial resistance and diastolic dysfunction in type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>In comparison to the well established changes in compliance that occur at the large vessel level in diabetes, much less is known about the changes in compliance of the cardiovascular system at the end-organ level. The aim of this study was therefore to examine whether there was a correlation between resistance of the intrarenal arteries of the kidney and compliance of the left ventricle, as estimated by measurements of diastolic function, in subjects with type 2 diabetes.</p> <p>Methods</p> <p>We studied 167 unselected clinic patients with type 2 diabetes with a kidney duplex scan to estimate intrarenal vascular resistance, i.e. the resistance index (RI = peak systolic velocity-minimum diastolic velocity/peak systolic velocity) and a transthoracic echocardiogram (TTE) employing tissue doppler studies to document diastolic and systolic ventricular function.</p> <p>Results</p> <p>Renal RI was significantly higher in subjects with diastolic dysfunction (0.72 ± 0.05) when compared with those who had a normal TTE examination (0.66 ± 0.06, p < 0.01). Renal RI values were correlated with markers of diastolic dysfunction including the E/Vp ratio (r = 0.41, p < 0.001), left atrial area (r = 0.36, p < 0.001), the E/A ratio (r = 0.36, p < 0.001) and the E/E' ratio (r = 0.31, p < 0.001). These associations were independent of systolic function, hypertension, the presence and severity of chronic kidney disease, the use of renin-angiotensin inhibitors and other potentially confounding variables.</p> <p>Conclusion</p> <p>Increasing vascular resistance of the intrarenal arteries was associated with markers of diastolic dysfunction in subjects with type 2 diabetes. These findings are consistent with the hypothesis that vascular and cardiac stiffening in diabetes are manifestations of common pathophysiological mechanisms.</p

Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization

A major source of uncertainty in both climate projections and seasonal forecasting of sea ice is inadequate representation of surface–atmosphere exchange processes. The observations needed to improve understanding and reduce uncertainty in surface exchange parameterizations are challenging to make and rare. Here we present a large dataset of ship-based measurements of surface momentum exchange (surface drag) in the vicinity of sea ice from the Arctic Clouds in Summer Experiment (ACSE) in July–October 2014, and the Arctic Ocean 2016 experiment (AO2016) in August–September 2016. The combined dataset provides an extensive record of momentum flux over a wide range of surface conditions spanning the late summer melt and early autumn freeze-up periods, and a wide range of atmospheric stabilities. Surface exchange coefficients are estimated from in situ eddy covariance measurements. The local sea-ice fraction is determined via automated processing of imagery from ship-mounted cameras. The surface drag coefficient, CD10n, peaks at local ice fractions of 0.6–0.8, consistent with both recent aircraft-based observations and theory. Two state-of-the-art parameterizations have been tuned to our observations, with both providing excellent fits to the measurements

Experimental and Human Evidence for Lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the Development of Cardiac Hypertrophy and heart failure

Background-Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin-2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants. Methods and Results-We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS, LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single-nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis-eQTL for LCN2 expression. Conclusions-Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure.Peer reviewe

A Finite Population Model of Molecular Evolution: Theory and Computation

This article is concerned with the evolution of haploid organisms that reproduce asexually. In a seminal piece of work, Eigen and coauthors proposed the quasispecies model in an attempt to understand such an evolutionary process. Their work has impacted antiviral treatment and vaccine design strategies. Yet, predictions of the quasispecies model are at best viewed as a guideline, primarily because it assumes an infinite population size, whereas realistic population sizes can be quite small. In this paper we consider a population genetics-based model aimed at understanding the evolution of such organisms with finite population sizes and present a rigorous study of the convergence and computational issues that arise therein. Our first result is structural and shows that, at any time during the evolution, as the population size tends to infinity, the distribution of genomes predicted by our model converges to that predicted by the quasispecies model. This justifies the continued use of the quasispecies model to derive guidelines for intervention. While the stationary state in the quasispecies model is readily obtained, due to the explosion of the state space in our model, exact computations are prohibitive. Our second set of results are computational in nature and address this issue. We derive conditions on the parameters of evolution under which our stochastic model mixes rapidly. Further, for a class of widely used fitness landscapes we give a fast deterministic algorithm which computes the stationary distribution of our model. These computational tools are expected to serve as a framework for the modeling of strategies for the deployment of mutagenic drugs. © Copyright 2012, Mary Ann Liebert, Inc. 2012

A finite population model of mlecular evolution: theory and computation

This article is concerned with the evolution of haploid organisms that reproduce asexually. In a seminal piece of work, Eigen and coauthors proposed the quasispecies model in an attempt to understand such an evolutionary process. Their work has impacted antiviral treatment and vaccine design strategies. Yet, predictions of the quasispecies model are at best viewed as a guideline, primarily because it assumes an infinite population size, whereas realistic population sizes can be quite small. In this paper we consider a population genetics-based model aimed at understanding the evolution of such organisms with finite population sizes and present a rigorous study of the convergence and computational issues that arise therein. Our first result is structural and shows that, at any time during the evolution, as the population size tends to infinity, the distribution of genomes predicted by our model converges to that predicted by the quasispecies model. This justifies the continued use of the quasispecies model to derive guidelines for intervention. While the stationary state in the quasispecies model is readily obtained, due to the explosion of the state space in our model, exact computations are prohibitive. Our second set of results are computational in nature and address this issue. We derive conditions on the parameters of evolution under which our stochastic model mixes rapidly. Further, for a class of widely used fitness landscapes we give a fast deterministic algorithm which computes the stationary distribution of our model. These computational tools are expected to serve as a framework for the modeling of strategies for the deployment of mutagenic drugs