The relationship between visceral adiposity and left ventricular diastolic function: results from the Baltimore Longitudinal Study of Aging.

BACKGROUND AND AIMS: It is unclear whether subcutaneous and visceral fat are differentially correlated to the decline in left ventricular (LV) diastolic function with aging. This study sought to examine the hypothesis that age-related changes in the regional fat distribution account for changes in LV diastolic function and to explore potential mediators of this association. METHODS AND RESULTS: In this cross-sectional study, we evaluated 843 participants of the Baltimore Longitudinal Study of Aging with echocardiogram, dual-energy X-ray absorptiometry (DEXA), abdominal computed tomography (CT) and blood tests performed at the same visit. LV diastolic function was assessed by parameters of LV relaxation (E/A ratio, Em and Em/Am ratio) and LV filling pressures (E/Em ratio). Total body fat was computed by DEXA, while visceral and subcutaneous fat were determined from abdominal CT. In multivariate models adjusted for demographics, cardiovascular risk factors, antihypertensive medications, physical activity and LV mass, both visceral and subcutaneous fat were associated with LV diastolic dysfunction. When both measures of adiposity were simultaneously included in the same model, only visceral fat was significantly associated with LV diastolic dysfunction. Triglycerides and sex-hormone binding globulin, but not adiponectin and leptin, were found to be significant mediators of the relationship between visceral fat and LV diastolic function, explaining 28-47% of the association. Bootstrapping analyses confirmed the significance of these findings. CONCLUSIONS: Increased visceral adiposity is associated with LV diastolic dysfunction, possibly through a metabolic pathway involving blood lipids and ectopic fat accumulation rather than adipokines.

Neural network approach for image chromatic adaptation for skin color detection.

The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting--adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to human skin color. The comparison of our method with Gray World, White Patch and NN on White Patch methods for skin color stabilization is presented. The skin regions in the NN stabilized images are successfully detected using a computationally inexpensive thresholding operation. We also present results on detecting skin regions on a data set of test images. The results are promising and suggest a new approach for adapting human skin color using neural networks.

Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization.

The identification and characterization of regional body tissues is essential to understand changes that occur with aging and age-related metabolic diseases such as diabetes and obesity and how these diseases affect trajectories of health and functional status. Imaging technologies are frequently used to derive volumetric, area, and density measurements of different tissues. Despite the significance and direct applicability of automated tissue quantification and characterization techniques, these topics have remained relatively underexplored in the medical image analysis literature. We present a method for identification and characterization of muscle and adipose tissue in the midthigh region using MRI. We propose an image-based muscle quality prediction technique that estimates tissue-specific probability density models and their eigenstructures in the joint domain of water- and fat-suppressed voxel signal intensities along with volumetric and intensity-based tissue characteristics computed during the quantification stage. We evaluated the predictive capability of our approach against reference biomechanical muscle quality (MQ) measurements using statistical tests and classification performance experiments. The reference standard for MQ is defined as the ratio of muscle strength to muscle mass. The results show promise for the development of noninvasive image-based MQ descriptors.