Hemodynamic load regulates cardiac remodeling. In contrast to pressure overload (increased afterload), hearts subjected to volume overload (VO; preload) undergo a distinct pattern of eccentric remodeling, chamber dilation, and decreased extracellular matrix content. Critical profibrotic roles of cardiac fibroblasts (CFs) in postinfarct remodeling and in response to pressure overload have been well established. Little is known about the CF phenotype in response to VO. The present study characterized the phenotype of primary cultures of CFs isolated from hearts subjected to 4 wk of VO induced by an aortocaval fistula. Compared with CFs isolated from sham hearts, VO CFs displayed a “hypofibrotic” phenotype, characterized by a ~50% decrease in the profibrotic phenotypic markers α-smooth muscle actin, connective tissue growth factor, and collagen type I, despite increased levels of profibrotic transforming growth factor-β1 and an intact canonical transforming growth factor-β signaling pathway. Actin filament dynamics were characterized, which regulate the CF phenotype in response to biomechanical signals. Actin polymerization was determined by the relative amounts of G-actin monomers versus F-actin. Compared with sham CFs, VO CFs displayed ~78% less F-actin and an increased G-actin-to-F-actin ratio (G/F ratio). In sham CFs, treatment with the Rho kinase inhibitor Y-27632 to increase the G/F ratio resulted in recapitulation of the hypofibrotic CF phenotype observed in VO CFs. Conversely, treatment of VO CFs with jasplakinolide to decrease the G/F ratio restored a more profibrotic response (>2.5-fold increase in α-smooth muscle actin, connective tissue growth factor, and collagen type I). NEW & NOTEWORTHY The present study is the first to describe a “hypofibrotic” phenotype of cardiac fibroblasts isolated from a volume overload model. Our results suggest that biomechanical regulation of actin microfilament stability and assembly is a critical mediator of cardiac fibroblast phenotypic modulation.

The coronary microcirculation (CM) plays a critical role in the regulation of blood flow and nutrient exchange to support the viability of the heart. In many disease states, the CM becomes structurally and functionally impaired, and transthoracic Doppler echocardiography can be used as a non-invasive surrogate to assess CM disease. Analysis of Doppler echocardiography is prone to user bias and can be laborious, especially if additional parameters are collected. We hypothesized that we could develop a MATLAB algorithm to automatically analyze clinically-relevant and non-traditional parameters from murine PW Doppler coronary flow patterns that would reduce intra- and inter-operator bias, and analysis time. Our results show a significant reduction in intra- and inter-observer variability as well as a 30 fold decrease in analysis time with the automated program vs. manual analysis. Finally, we demonstrated good agreement between automated and manual analysis for clinically-relevant parameters under baseline and hyperemic conditions. Resulting coronary flow velocity reserve calculations were also found to be in good agreement. We present a MATLAB algorithm that is user friendly and robust in defining and measuring Doppler coronary flow pattern parameters for more efficient and potentially more insightful analysis assessed via Doppler echocardiography.

Type 2 diabetes mellitus (T2DM) is suggested to cause an “early vascular aging” phenomenon that is associated with vascular dysfunction, remodeling, and adverse alterations in vascular stiffness. Given that both T2DM and aging are prominent risk factors for cardiovascular disease, the aim of this study was to test the hypothesis that coronary resistance microvessel (CRM) remodeling and impairments in flow occur in the compound setting of T2DM and aging. Normal heterozygous Db/db controls and homozygous db/db mice were aged to 16 (young) or 36 (aged) weeks for all experiments and passive pressure myography and echocardiography were used to assess vascular mechanics, and structure. CRM wall thickness was significantly increased at each pressure in aged control mice compared to young control mice (9.4 ± 0.6 vs. 6.8 ± 0.2 μm, respectively, p < 0.001); however, there were no significant differences in CRM wall thickness of aged db/db mice vs. young db/db mice. Aged control mice had a higher medial CSA compared to young control mice (3847 ± 303 vs. 2715 ± 170 μm2, p < 0.01); however, there were no significant differences in medial CSA of aged db/db mice vs. young db/db mice. Elastic modulus was lower in aged control CRMs vs. young control CRMs (3.5x106± 0.7 × 106 vs. 8.7 × 106± 0.6 × 106, p < 0.0001). Elastic modulus remained the same in young db/db mice vs. aged db/db mice. These data show that the diabetic CRMs undergo adverse remodeling at an early age, similar to normal aged CRMs, that persists toward senescence, and it further suggests that diabetic CRMs are subject to an early aging phenomenon.

IntroductionType 2 diabetic (T2DM) coronary resistance microvessels (CRMs) show early inward hypertrophic remodeling and reduced wall stiffness, which are associated with decreased blood flow (CBF), coronary flow reserve, and disrupted flow patterns. These data may suggest that coronary microvascular disease (CMD) underlies the early pathophysiology of T2DM. Furthermore, distinct correlations have been identified between the Doppler coronary flow pattern in both normal and T2DM mice, potentially allowing for a predictive relationship between coronary flow patterns and CMD. Currently, CMD is extremely difficult to diagnose due to lack of non‐invasive methods. The goal of this study was to develop an applied predictive model for CMD non‐invasively in early T2DM using CBF combined with functional and structural measurements of the heart.MethodsCBF of the left main coronary artery, aortic flow, E & A wave, left ventricular structural dimensions, and aortic diameter were recorded using high frequency, high resolution non‐invasive Doppler echocardiography (Vevo2100, Visual Sonics, Toronto, Canada) in 37 normal heterozygous Db/db mice and 36 T2DM homozygous db/db mice. Coronary flow patterns were analyzed using a Matlab program developed in our laboratory to identify 13 distinct parameters. All other flow patterns and structural and functional features were manually measured in the Vevo2100 software resulting in 13 additional parameters. All the parameters were feature engineered, and a factor analysis model was created and tested using various machine learning algorithms.ResultsPhysiological data were subjected to a variety of machine learning models, and the “glmnet algorithm” in R software (generalized linear regression via penalized maximum likelihood) best predicted CMD with a cross validation accuracy of 80.19% and test dataset accuracy to date of 84.84%.ConclusionAn applied predictive model for CMD in T2DM was developed using 73 mice, which shows a promising ability to accurately predict CMD in T2DM. Further studies will be undertaken to improve the predictive accuracy of the model to be suitable for clinical diagnostics and/or decision support.Support or Funding InformationSupported by the National Institutes of Health (R00 HL‐116769 to AJT) and Nationwide Children's Hospital (to AJT and CWB).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.