Recommendations for the Evaluation of Left Ventricular Diastolic Function by Echocardiography

A

Relationships among HIV infection, metabolic risk factors, and left ventricular structure and function

Our objective was to determine if the presence of metabolic complications (MC) conveyed an additional risk for left ventricular (LV) dysfunction in people with HIV. HIV(+) and HIV(−) men and women were categorized into four groups: (1) HIV(+) with MC (43±7 years, n=64), (2) HIV(+) without MC (42±7 years, n=59), (3) HIV(−) with MC (44±8 years, n=37), or (4) HIV(−) controls without MC (42±8 years, n=41). All participants underwent two-dimensional (2-D), Doppler, and tissue Doppler echocardiography. Overall, the prevalence of systolic dysfunction (15 vs. 4%, p=0.02) and LV hypertrophy (9 vs. 1%, p=0.03) was greater in HIV(+) than in HIV(−) participants. Participants with MC had a greater prevalence of LV hypertrophy (10% vs. 1%). Early mitral annular velocity during diastole was significantly (p<0.005) lower in groups with MC (HIV(+)/MC(+): 11.6±2.3, HIV(−)/MC(+): 12.0±2.3 vs. HIV(+)/MC(−): 12.4±2.3, HIV(−)/MC(−): 13.1±2.4 cm/s) and tended to be lower in groups with HIV (p=0.10). However, there was no interaction effect of HIV and MC for any systolic or diastolic variable. Regardless of HIV status, participants with MC had reduced LV diastolic function. Although both the presence of MC and HIV infection were associated with lower diastolic function, there was no additive negative effect of HIV on diastolic function beyond the effect of MC. Also, HIV was independently associated with lower systolic function. Clinical monitoring of LV function in individuals with metabolic risk factors, regardless of HIV status, is warranted

Effects of human immunodeficiency virus and metabolic complications on myocardial nutrient metabolism, blood flow, and oxygen consumption: a cross-sectional analysis

<p>Abstract</p> <p>Background</p> <p>In the general population, peripheral metabolic complications (MC) increase the risk for left ventricular dysfunction. Human immunodeficiency virus infection (HIV) and combination anti-retroviral therapy (cART) are associated with MC, left ventricular dysfunction, and a higher incidence of cardiovascular events than the general population. We examined whether myocardial nutrient metabolism and left ventricular dysfunction are related to one another and worse in HIV infected men treated with cART vs. HIV-negative men with or without MC.</p> <p>Methods</p> <p>Prospective, cross-sectional study of myocardial glucose and fatty acid metabolism and left ventricular function in HIV+ and HIV-negative men with and without MC. Myocardial glucose utilization (GLUT), and fatty acid oxidation and utilization rates were quantified using ¹¹C-glucose and ¹¹C-palmitate and myocardial positron emission tomography (PET) imaging in four groups of men: 23 HIV+ men with MC+ (HIV+/MC+, 42 ± 6 yrs), 15 HIV+ men without MC (HIV+/MC-, 41 ± 6 yrs), 9 HIV-negative men with MC (HIV-/MC+, 33 ± 5 yrs), and 22 HIV-negative men without MC (HIV-/MC-, 25 ± 6 yrs). Left ventricular function parameters were quantified using echocardiography.</p> <p>Results</p> <p>Myocardial glucose utilization was similar among groups, however when normalized to fasting plasma insulin concentration (GLUT/INS) was lower (p < 0.01) in men with metabolic complications (HIV+: 9.2 ± 6.2 vs. HIV-: 10.4 ± 8.1 nmol/g/min/μU/mL) than men without metabolic complications (HIV+: 45.0 ± 33.3 vs. HIV-: 60.3 ± 53.0 nmol/g/min/μU/mL). Lower GLUT/INS was associated with lower myocardial relaxation velocity during early diastole (r = 0.39, p < 0.001).</p> <p>Conclusion</p> <p>Men with metabolic complications, irrespective of HIV infection, had lower basal myocardial glucose utilization rates per unit insulin that were related to left ventricular diastolic impairments, indicating that well-controlled HIV infection is not an independent risk factor for blunted myocardial glucose utilization per unit of insulin.</p> <p>Trial Registration</p> <p>NIH Clinical Trials <a href="http://clinicaltrials.gov/ct2/show/NCT00656851">NCT00656851</a></p

Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function

<p>Abstract</p> <p>Background</p> <p>Abnormalities in myocardial metabolism and/or regulatory genes have been implicated in left ventricular systolic dysfunction. However, the extent to which these modulate left ventricular diastolic function (LVDF) is uncertain.</p> <p>Methods</p> <p>Independent component analysis was applied to extract latent LVDF traits from 14 measured echocardiography-derived endophenotypes of LVDF in 403 Caucasians. Genetic association was assessed between measured and latent LVDF traits and 64 single nucleotide polymorphisms (SNPs) in three peroxisome proliferator-activated receptor <it>(PPAR)</it>-complex genes involved in the transcriptional regulation of fatty acid metabolism.</p> <p>Results</p> <p>By linear regression analysis, 7 SNPs (4 in <it>PPARA</it>, 2 in <it>PPARGC1A</it>, 1 in <it>PPARG</it>) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes. Frequency distribution of <it>P </it>values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF. Ridge regression was applied to investigate within-gene and gene-gene interactions. In the within-gene analysis, there were five significant pair-wise interactions in <it>PPARGC1A </it>and none in <it>PPARA </it>or <it>PPARG</it>. In the gene-gene analysis, significant interactions were found between rs4253655 in <it>PPARA </it>and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in <it>PPARGC1A</it>, and between rs1151996 in <it>PPARG </it>and rs4697046 in <it>PPARGC1A </it>(p = 0.01).</p> <p>Conclusions</p> <p>Myocardial metabolism <it>PPAR</it>-complex genes, including within and between genes interactions, may play an important role modulating left ventricular diastolic function.</p

Recommendations for the design of laboratory studies on non-target arthropods for risk assessment of genetically engineered plants

This paper provides recommendations on experimental design for early-tier laboratory studies used in risk assessments to evaluate potential adverse impacts of arthropod-resistant genetically engineered (GE) plants on non-target arthropods (NTAs). While we rely heavily on the currently used proteins from Bacillus thuringiensis (Bt) in this discussion, the concepts apply to other arthropod-active proteins. A risk may exist if the newly acquired trait of the GE plant has adverse effects on NTAs when they are exposed to the arthropod-active protein. Typically, the risk assessment follows a tiered approach that starts with laboratory studies under worst-case exposure conditions; such studies have a high ability to detect adverse effects on non-target species. Clear guidance on how such data are produced in laboratory studies assists the product developers and risk assessors. The studies should be reproducible and test clearly defined risk hypotheses. These properties contribute to the robustness of, and confidence in, environmental risk assessments for GE plants. Data from NTA studies, collected during the analysis phase of an environmental risk assessment, are critical to the outcome of the assessment and ultimately the decision taken by regulatory authorities on the release of a GE plant. Confidence in the results of early-tier laboratory studies is a precondition for the acceptance of data across regulatory jurisdictions and should encourage agencies to share useful information and thus avoid redundant testing

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery