Relative fat-free mass deficiency and left ventricular adaptation to obesity: The Strong Heart Study☆,☆☆
Introduction
Bone and muscle growth are influenced by gravity and physical activity stimulating mechanoreceptors regulating production of growth factors [1]. The word “sarcopenia” describes in general the process of age-related muscle loss and the associated frailty condition [2], [3]. Definition of sarcopenia requires studying body composition and is still controversial [3].
A condition of relative loss of fat-free mass, however, has been recently recognized in the presence of obesity and called “sarcopenic obesity” [4]. The phenotype of sarcopenic obesity strongly depends on the adopted definition. Since obese individuals have larger amount of both fat and lean mass, they usually have a “normal” absolute quantity of muscle mass, and therefore they do not appear to be sarcopenic, though their muscle mass might be relatively inadequate for their size [5]. Thus, higher body mass index (BMI) can mask sarcopenia [3]. In general, excess energy intake, physical inactivity, low-grade inflammation, insulin resistance and changes in hormonal milieu are thought to be the main characteristics of sarcopenic obesity [6]. Because sarcopenia is generally considered a characteristic that might increase risk of morbidity in obesity [7], it is of interest to focus on phenotypic characteristics that might be associated with cardiovacular (CV) risk. Because left ventricular (LV) mass is substantially influenced by fat free mass [8], sarcopenia in the obese individual might be thought to be associated with reduced amount of LV mass, which might be protective, as LV hypertrophy is the most potent marker of cardiovascular risk [9]. Specifically, at present, there is little characterization of the CV phenotype of sarcopenia in obese populations. Accordingly, this study has been conceived to assess whether reduced fat-free mass is associated with the magnitude of LV mass, in a population of obese men and women.
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Population
The Strong Heart Study (SHS) is a population-based cohort study of CV risk factors and disease in 4549 American Indians from 3 communities in Arizona, 7 in Southwestern Oklahoma and 3 in South and North Dakota, which has been extensively described [10], [11], [12]. For the purpose of the present analysis we analyzed participants to the 2nd exam, which included an echocardiogram (N = 3638, 89% of all living); we excluded participants who had history or signs of prevalent heart failure or coronary
Results
In the group of 1199 non-obese participants, 688 were women (57%), 437 hypertensive (37%) and 435 diabetic (36%). The average BMI was 26.09 ± 2.75 kg/m2, WHR was 0.94 ± 0.06 and FFM was 48.89 ± 9.63 kg. Table 1 displays the equation describing the variability of FFM and the multicollinearity diagnostic. The equation could explain 79% of the variance of FFM with a standard error of the estimate that was < 10% of the mean. Variance inflation factor was < 1.3 for all variables, demonstrating an optimal
Discussion
This is the first observational study evaluating LV adaptation to obesity in relation with relative FFM deficiency, in a large population-based cohort with high prevalence of obesity. Our attention has been especially focused on LV mass, which is considered the most potent (and correctible) marker of cardiovascular risk [9], [22], [23], recently shown to be a bioassay also for other harmful cardiovascular CV characteristics, including LV geometry and function [24].
Much debate is still ongoing
Acknowledgments
The authors wish to thank the Indian Health Service, the Strong Heart Study Participants, the Participating Tribal Communities and the Strong Heart Study Center Coordinators for their help in the realization of this project.
Views expressed in this paper are those of the authors and do not necessarily reflect those of the Indian Health Service or the Federal Government.
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This work has been supported by grants HL41642, HL41652, HL41654, HL65521 and M10RR0047-34 (GCRC) from the National Institutes of Health, Bethesda, MD.
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All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.