Detailed Body Composition Analysis Research Articles

Automatic segmentation of large-scale CT image datasets for detailed body composition analysis

BackgroundBody composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.MethodsThe study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.ResultsThe Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909–0.996), UNET++ 0.981 (0.927–0.996), Ghost-UNET 0.961 (0.904–0.991), and Ghost-UNET++ 0.968 (0.910–0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.ConclusionFully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.

The relationship between body composition measured by dual-energy X-ray absorptiometry and maximal strength in classic powerlifting.

The purpose of this study was to conduct a detailed body composition analysis by Dual-Energy X-Ray Absorptiometry (DEXA scan) on classic powerlifters to understand better the relationship between various body composition variables and maximal strength. Fifteen drug-free classic powerlifters (9 males and 6 females) underwent a DEXA scan and absolute (kg) and relative (Wilks) maximal strength from all 3 powerlifting events (squat, bench and deadlift) was registered from the previous provincial championship. A 2 tailed Pearson correlation analysis was conducted in order to present the relationships between body composition variables and performance measures. The main outcome of the present study is that experience in resistance training (r=0.52 to 0.74), lean body tissue (r= 0.61 to 0.95), arms mass (r=0.57 to 0.86), legs mass (r=0.53 to 0.63), bone mineral content (BMC) (r=0.52 to 0.91) and bone mineral density (BMD) (r=0.53 to 91) measures were significantly correlated (P<0.05) with most absolute (kg) and relative (Wilks) maximal strength measures. The results of the present study could guide practitioners working with athletes aged between 22 and 35 years old practicing sports with higher risks of fractures (e.g., alpine skiing) to potentially utilize the squat, the bench press and the deadlift not only to build strength in order to increase sport performance, but also to increase BMC and BMD of their athletes trough heavy eccentric loading to help reduce the risks of fractures. Further studies should be directed towards interventions trying to reveal that some type of relationship does exist between BMC, BMD and powerlifting performance and in what direction.

Structural and functional body components in athletic health and performance phenotypes.

Advances in body composition assessment enable a detailed body composition analyses and the respective organization at different levels. Sports-related professionals are interested in understanding how and which body components are relevant for improving performance, prevent injury risk, and monitor athletic health. The aim of this review is to propose an integrative model that links performance, injury risk, and athletic health with body components, and to report their cross-sectional and longitudinal associations. Cross-sectional studies reveal that endurance athletes with higher fat mass (FM) show a longer race time, whereas a higher fat-free mass benefits power and strength-related tasks. Longitudinal studies indicated that increases in intracellular water, assessed through dilution techniques, were associated with power and strength improvements, independently of weight and lean-soft-tissue changes. There is evidence that athletes involved in weight-sensitive sports restrict energy intake, thus reducing energy availability (EA) and compromising bone health (Female Athlete Triad). To counteract the low EA and related negative energy balance, metabolic adaption (MA) occurs to promote energy conservation. Currently, reference values for body composition assessment using anthropometry and DXA are available for a few sports, according to sex. More research is needed to develop a functional body composition profile according to sports-specific requirements.

Age-Dependent Changes in Resting Energy Expenditure (REE): Insights from Detailed Body Composition Analysis in Normal and Overweight Healthy Caucasians.

Age-related changes in organ and tissue masses may add to changes in the relationship between resting energy expenditure (REE) and fat free mass (FFM) in normal and overweight healthy Caucasians. Secondary analysis using cross-sectional data of 714 healthy normal and overweight Caucasian subjects (age 18–83 years) with comprehensive information on FFM, organ and tissue masses (as assessed by magnetic resonance imaging (MRI)), body density (as assessed by Air Displacement Plethysmography (ADP)) and hydration (as assessed by deuterium dilution (D2O)) and REE (as assessed by indirect calorimetry). High metabolic rate organs (HMR) summarized brain, heart, liver and kidney masses. Ratios of HMR organs and muscle mass (MM) in relation to FFM were considered. REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. REE, FFM, specific metabolic rates, the REE-FFM relationship, HOMA, CRP, and thyroid hormone levels change with age. The age-related decrease in FFM explained 59.7% of decreases in REE. Mean residuals of the REE-FFM association were positive in young adults but became negative in older subjects. When compared to young adults, proportions of MM to FFM decreased with age, whereas contributions of liver and heart did not differ between age groups. HOMA, TSH and inflammation (plasma CRP-levels) explained 4.2%, 2.0% and 1.4% of the variance in the REE-FFM residuals, but age and plasma T3-levels had no effects. HMR to FFM and MM to FFM ratios together added 11.8% on to the variance of REE-FFM residuals. Differences between REE and REEc increased with age, suggesting age-related changes in specific metabolic rates of organs and tissues. This bias was partly explained by plasmaT3-levels. Age-related changes in REE are explained by (i) decreases in fat free mass; (ii) a decrease in the contributions of organ and muscle masses to FFM; and (iii) decreases in specific organ and tissue metabolic rates. Age-dependent changes in the REE-FFMassociation are explained by composition of FFM, inflammation and thyroid hormones.

Reference Values of Skeletal Muscle Mass for Korean Children and Adolescents Using Data from the Korean National Health and Nutrition Examination Survey 2009-2011.

BackgroundSkeletal muscle mass (SMM) plays a crucial role in systemic glucose metabolism.ObjectiveTo obtain reference data on absolute and relative values of SMM for Korean children and adolescents.MethodsCross-sectional results from 1919 children and adolescents (1024 boys) aged 10–18 years that underwent dual-energy X-ray absorptiometry (DXA) during the Korean National Health and Nutrition Examination Survey 2009–2011 were analyzed. SMMs were evaluated as follows; absolute SMM (appendicular skeletal muscle mass [ASM]) and relative SMMs, namely, height-adjusted skeletal muscle index (SMI; ASM/height2), %SMM (ASM/weight x 100), and skeletal muscle-to-body fat ratio (MFR; ASM/body fat mass).ResultsPercentile curves illustrated the developmental patterns of the SMMs of Korean children and adolescents. ASM and SMI increased with age in both genders, and increased from age 10 throughout adolescence in boys, whereas in girls, they increased until age 13 and then stabilized. In boys, %SMM and MFR were highest at age 15 and then slowly stabilized or decreased, whereas in girls, they peaked at age 10 to 11 and then decreased through adolescence. Cut-off values for low MFR were identified and a significant association was found between a low MFR and high risk of metabolic syndrome. However, this association was found to be dependent on gender and the level of BMI.ConclusionThis study provides reference values of absolute and relative SMM for Korean children and adolescents. Detailed body composition analyses including skeletal muscle and fat mass might provide improved measures of metabolic risk.

Detection of muscle wasting in patients with chronic heart failure using C-terminal agrin fragment: results from the Studies Investigating Co-morbidities Aggravating Heart Failure (SICA-HF).

Skeletal muscle wasting affects 20% of patients with chronic heart failure and has serious implications for their activities of daily living. Assessment of muscle wasting is technically challenging. C-terminal agrin-fragment (CAF), a breakdown product of the synaptically located protein agrin, has shown early promise as biomarker of muscle wasting. We sought to investigate the diagnostic properties of CAF in muscle wasting among patients with heart failure. We assessed serum CAF levels in 196 patients who participated in the Studies Investigating Co-morbidities Aggravating Heart Failure (SICA-HF). Muscle wasting was identified using dual-energy X-ray absorptiometry (DEXA) in 38 patients (19.4%). Patients with muscle wasting demonstrated higher CAF values than those without (125.1 ± 59.5 pmol/L vs. 103.8 ± 42.9 pmol/L, P = 0.01). Using receiver operating characteristics (ROC), we calculated the optimal CAF value to identify patients with muscle wasting as >87.5 pmol/L, which had a sensitivity of 78.9% and a specificity of 43.7%. The area under the ROC curve was 0.63 (95% confidence interval 0.56-0.70). Using simple regression, we found that serum CAF was associated with handgrip (R = - 0.17, P = 0.03) and quadriceps strength (R = - 0.31, P < 0.0001), peak oxygen consumption (R = - 0.5, P < 0.0001), 6-min walk distance (R = - 0.32, P < 0.0001), and gait speed (R = - 0.2, P = 0.001), as well as with parameters of kidney and liver function, iron metabolism and storage. CAF shows good sensitivity for the detection of skeletal muscle wasting in patients with heart failure. Its assessment may be useful to identify patients who should undergo additional testing, such as detailed body composition analysis. As no other biomarker is currently available, further investigation is warranted.

The association of clot lysis time with total obesity is partly independent from the association of PAI-1 with central obesity in African adults

IntroductionPreliminary evidence indicates that the association of fibrinolytic potential, measured as clot lysis time (CLT), with body composition may differ from that of plasminogen activator inhibitor type-1 (PAI-1). We therefore investigated the association between fibrinolytic markers (plasminogen activator inhibitor type-1 activity (PAI-1act) and CLT) and body composition using detailed body composition analyses. Materials and methodsData from 1288 Africans were cross-sectionally analyzed. Body composition analysis included BMI, waist circumference (WC); waist to height ratio (WHtR), skinfolds and body fat percentage measured with air-displacement plethysmography and bioelectrical impedance analysis. ResultsPAI-1act and CLT were significantly higher in women than in men, despite adjustment for differences in body composition. PAI-1act and CLT showed similar linear positive relationships with body composition (BMI, WC, WHtR, skinfolds) in men. In women CLT also showed a linear relationship with body composition, while PAI-1act levels plateaued at higher BMI and did not differ across skinfold categories. PAI-1act showed stronger correlations with body composition markers in men than it did in women, while no sex differences existed for CLT. PAI-1act associated more strongly with central obesity, while CLT associated with total body fat. ConclusionsObserved differences may be related to differences in adipose tissue type, distribution and sequence of accumulation between sexes. PAI-1act is strongly influenced by accumulation of visceral adipose tissue, whereas CLT is associated with obesity independent of type and sequence of body fat accumulation in this African adult study population.

Advances in the understanding of specific metabolic rates of major organs and tissues in humans

To present recent evidence on organ and tissue metabolic rates in humans to explain the variance in resting energy expenditure (REE). In humans, present knowledge on specific metabolic activities (i.e. ki-values) refers to seven organs and tissues - brain, heart, liver, kidneys, skeletal muscle, adipose tissue and residual mass - with ki-values of 240, 440, 200, 440, 13, 4.5 and 12 kcal/kg/day, provided by Elia in 1992. Detailed body composition data, as derived from whole body MRI together with measurements of whole body REE, were used to validate ki-values in nonobese, healthy and middle-aged adults. There is no sex difference, but minor, that is 2 and 3% deviations are found for age above 55 years and obesity, respectively. By contrast, in adolescents, differences of about 100 kcal/day or 7.3% of measured REE were observed. There is first evidence for changes in ki-values with either weight loss or weight regain after weight loss. Altogether these data suggest that in adolescence and at age above 55 years, in the obese and with weight change, organ and tissue masses differ in cellularity and/or their specific metabolic rates. Presently, direct assessment of specific organ and tissue metabolic rates in humans by either NMR spectroscopy or PET, together with detailed body composition analysis, has not been performed systematically. We need to become more skilled in methods and models used for detailed body composition analysis together with detailed assessment of energy expenditure in humans.

Adaptive thermogenesis with weight loss in humans

Adaptive thermogenesis (AT) with weight loss refers to underfeeding-associated fall in resting and non-resting energy expenditure (REE, non-REE); this is independent of body weight and body composition. In humans, the existence of AT was inconsistently shown and its clinical significance has been questioned. Discrepant findings are mainly due to different definitions of AT, the use of various and nonstandardized study protocols, and the limits of accuracy of methods to assess energy expenditure. With controlled underfeeding, AT takes more than 2 wk to develop. AT accounts to an average of 0.5 MJ (or 120 kcal) with a considerable between subject variance. Low-sympathetic nervous system activity, 3,5,3'-tri-iodothyronine (T3) and leptin are likely to add to AT; however, the kinetic changes of their plasma levels with underfeeding differ from the time course of AT and controlled intervention studies substituting and titrating these hormones are rare in humans. AT in response to underfeeding is independent of thermogenesis in response to either diet or cold. Although fat-free mass (FFM) and, thus, liver, and skeletal muscle are considered as major sites of AT, cold-induced nonshivering thermogenesis relates to the metabolism of brown adipose tissue (BAT). In humans, diet-induced thermogenesis is related to postprandial substrate metabolism of FFM with a questionable role of BAT. Obviously, the REE component of AT differs from and its non-REE component with respect to organ contribution as well as mechanisms. Thus, AT cannot be considered as unique. AT should be characterized based on individual components of daily energy expenditure, detailed body composition analyses, and mathematical modeling. The biological basis of AT as well as the influences of age, sex, obesity, stress, and inflammation remain to be established in humans.

Evaluation of existing and candidate measures of obesity for detection of insulin resistance and cardiometabolic risk among Asian Indians: Development of two clinically useful models

BackgroundThe optimal measure of obesity continues to be debated. The objective of this study was to evaluate existing and candidate measures of obesity for detecting the presence of cardiometabolic risk and insulin resistance among Asian Indians. MethodsAnthropometry, detailed body composition analysis, blood pressure, lipids, fasting blood glucose and fasting serum insulin were measured in a cross-sectional study involving 507 subjects from North India. ResultsIn females, all indices of obesity, except waist-to-hip ratio (WHR) and total body fat (TBF) to waist circumference (WC) ratio and in men, all indices of obesity including fat mass index and WHR, were significantly associated with insulin resistance and cardiovascular risk factors (p<0.05). Using stepwise logistic regression, two models were developed excluding WC and WHR, respectively. In model 1, subscapular skinfold thickness, WHR and age in males, and waist circumference to square of height ratio (WS2R) and age in females; and in model 2, subscapular skinfold thickness, WS2R and visceral adiposity index in males, and TBF to WC ratio, WS2R and age in females showed strong and significant association with the presence of cardiovascular risk factors or insulin resistance. ConclusionsThe clinical models for measurement of obesity developed by us would help in detecting cardiometabolic risk in Asian Indians.

Contribution of individual organ mass loss to weight loss–associated decline in resting energy expenditure

Background:Weight loss leads to reduced resting energy expenditure (REE) independent of fat-free mass (FFM) and fat mass (FM) loss, but the effect of changes in FFM composition is unclear.Objective:We hypothesized that a decrease in REE adjusted for FFM with weight loss would be partly explained by a disproportionate loss in the high metabolic activity component of FFM.Design:Forty-five overweight and obese women [body mass index (in kg/m2): 28.7–46.8] aged 22–46 y followed a low-calorie diet for 12.7 ± 2.2 wk. Body composition was measured by magnetic resonance imaging, dual-energy X-ray absorptiometry, and a 4-compartment model. REE measured by indirect calorimetry (REEm) was compared with REE calculated from detailed body-composition analysis (REEc) by using specific organ metabolic rates (ie, organ REE/mass).Results:Weight loss was 9.5 ± 3.4 kg (8.0 ± 2.9 kg FM and 1.5 ± 3.1 kg FFM). Decreases in REE (8%), free triiodothyronine concentrations (8%), muscle (3%), heart (5%), liver (4%), and kidney mass (6%) were observed (all P< 0.05). Relative loss in organ mass was significantly higher (P< 0.01) than was the change in low metabolically active FFM components (muscle, bone, and residual mass). After weight loss, REEm – REEc decreased from 0.24 ± 0.58 to 0.01 ± 0.44 MJ/d (P= 0.01) and correlated with the decrease in free triiodothyronine concentrations (r= 0.33, P< 0.05). Women with high adaptive thermogenesis (defined as REEm – REEc < 0.17 MJ/d) had less weight loss and conserved FFM, liver, and kidney mass.Conclusions:After weight loss, almost 50% of the decrease in REEm was explained by losses in FFM and FM. The variability in REEm explained by body composition increased to 60% by also considering the weight of individual organs.

Functional body composition: insights into the regulation of energy metabolism and some clinical applications

The application of advanced methods and techniques and their continuous development enable detailed body composition analyses (BCAs) and modeling of body composition at different levels (e.g., at atomic, molecular, organ-tissue and whole body level). Functional body composition integrates body components into regulatory systems (e.g., on energy balance). Regulation of body weight is closely linked to the mass and function of individual body components. Fat mass is part of the energy intake regulatory feedback system. In addition, fat-free mass (FFM) and fat mass are both determinants of resting energy expenditure (REE). Up to 80% of the variance in energy intake and energy expenditure is explained by body composition. A deviation from normal associations between body components and function suggests a metabolic disequilibrium (e.g., in the REE-FFM relationship or in the plasma leptin-fat mass association) that may occur in response to weight changes and diseases. The concept of functional body composition adds to a more sophisticated view on nutritional status and diseases, as well as to a characterization of biomedical traits that will provide functional evidence relating genetic variants.

Malnutrition and hypermetabolism in patients with liver cirrhosis

Clinical signs of protein-energy malnutrition (PEM) are frequently seen in cirrhotic patients and may be obvious at all clinical stages of liver disease and even in clinically stable situations (1). Malnutrition in cirrhotic patients is readily understood as a consequence of metabolic disturbances in combination with low spontaneous dietary intake (1). Because PEM is associated with clinical complications of cirrhosis, such as ascites, and because it increases mortality with and without liver transplantation (2, 3), its early identification and characterization are mandatory. However, PEM is frequently underdiagnosed in clinical practice. This is explained in part by the fact that water retention increases body weight and thus masks losses of body mass. There are also methodologic limitations: for example, standard 2-component models used for assessment of body composition cannot be used with confidence in cirrhotic patients because of great variability in the density and hydration of fat-free mass [FFM (4)]. In this issue of the Journal, Peng et al (5) addressed body composition as well as the metabolic and functional consequences of malnutrition in cirrhotic patients. This cross-sectional study is exceptional, because the authors have taken into account essential procedural issues relevant to establishing nutritional status and metabolism. The strengths of the study include a large population of 268 well-characterized, clinically stable patients and detailed measurements of body composition, muscle function, and resting energy expenditure (REE) obtained by using state-of-the-art technologies, including neutron activation and a 6-compartment model of body composition. Functional and metabolic data can thus be adjusted for tissue hydration to allow comparisons between individual subjects. Whereas the prevalence of overhydration varied between 40% and 84%, Peng et al found significant protein depletion in 51% of the patient group. Protein depletion was more likely in men than in women, for an unexplained effect of sex. The severity of PEM increased with progression of liver disease and was associated with reduced muscle strength. In addition, hypermetabolism that was independent of clinical data as well as of PEM was found in 15% of the patients. The limits of cross-sectional data became evident, because energy and protein intake also showed no association with PEM. Thus, further longitudinal studies are needed. The finding of significant protein depletion is in line with previous data (4, 6) suggesting that neutron activation and multicomponent models have high accuracy without loss of precision in their assessment of PEM in the cirrhotic patient. Because these methods are cumbersome and not available in clinical practice, simpler nutritional assessment schemes are required for clinical use. A global assessment scheme combining body mass index and midupper arm circumference with dietary intake data provided a reproducible and valid method for assessing nutritional status in patients with cirrhosis (7). However, only detailed body composition analysis, as was performed by Peng et al, identifies early and specific deficits that can provide a suitable basis for targeted interventions. The finding of a high prevalence of hypermetabolism extends our present knowledge on the variance of REE in cirrhosis. The prevalence of hypermetabolism was lower in the study of Peng et al than in 2 other comprehensive studies in this area (8, 9). These differences are explained in part by the use of different definitions of hypermetabolism. Peng et al (5) used predicted REE on the basis of FFM corrected for hydration, whereas others used standard body weight–based formulae such as Harris-Benedict or uncorrected FFM values, or both. Approximately 50% of the variance in REE in clinically stable cirrhosis patients was explained by FFM, irrespective of the method used to assess FFM and of a suitable correction for hydration (5, 8, 9). The data suggest that FFM is the major determinant of REE in cirrhosis. Although hypermetabolism is a frequent feature of cirrhosis, its cause is still unclear. Peng et al (5) found no association of hypermetabolism with sex, etiology, severity of disease, protein depletion, or the presence of ascites or tumor. There is indirect evidence that increased sympathetic nervous system activity, possibly due to disturbancies in liver circulation resulting in a hyperdynamic status, explains 25% of hypermetabolism in liver cirrhosis (8). More detailed body-composition analyses may provide further clues. One may speculate that the composition of FFM—ie, the relation between muscle and organ mass—varies in cirrhosis. The greater loss of muscle mass than of nonmuscle FFM results in a relative increase in the mass of “high-metabolic-rate” organs such as heart, brain, and kidneys, which increases the REE per kg FFM (10). This idea is supported by the finding that hypermetabolic patients tended to weigh less (5) and to suffer more frequently from malnutrition (8) than did normometabolic patients. The fact that functional liver mass is also reduced in cirrhosis adds to interindividual variances in metabolic data.

Secretory sphingomyelinase is upregulated in chronic heart failure: a second messenger system of immune activation relates to body composition, muscular functional capacity, and peripheral blood flow

Sphingomyelinases (SMase) are key regulating enzymes of the intracellular and paracrine ceramide second messenger system that mediates immune response to inflammatory cytokines and oxidative stress. Vascular endothelial cells are a rich and regulatable source of secretory SMase (S-SMase). Chronic heart failure (CHF) is a state of endothelial dysfunction and latent immune activation. The significance of S-SMase has not been studied in CHF in detail. The aim of the present study is to characterize S-SMase activity in patients with CHF in relation to disease severity and to pathophysiological characteristics such as immune activation, vasodilator capacity, and skeletal muscle function and body composition. S-SMase activity was assessed by a fluorimetric method in 112 patients with CHF (age, 63 +/- 11 years; NYHA class I/II/III/IV, 9/48/46/9; LVEF, 30 +/- 15%; peak VO(2), 18.6 +/- 6.7 mL/kg/min) and in two control groups (healthy, n = 13 and hypertensive controls, n = 11). S-SMase activity was similar in both control groups (healthy, 150 +/- 121 pmol/mL h; hypertensive, 157 +/- 134 pmol/mL h) but was increased by >90% in CHF patients (299 +/- 283 pmol/mL h; P = 0.004). S-SMase elevation was not different between ischaemic and non-ischaemic CHF and increased stepwise with NYHA class (I, 206 +/- 202; II, 284 +/- 242; III, 306 +/- 212; IV, 440 +/- 665 pmol/mL h; P = 0.003). S-SMase correlated with peak VO(2) (R = -0.33, P = 0.0007) and with cytokine activation [tumour necrosis factor-alpha (TNF-alpha) R = 0.22, P = 0.02; sTNF-R1 R = 0.39, P < 0.0001]. S-SMase further correlated with reduced skeletal (quadriceps) muscle strength (R = -0.46, P < 0.0001) as well as impaired peripheral vasodilator capacity (R = -0.34, P = 0.02). In detailed body composition analysis (DEXA scan), S-SMase activity was highest in patients with cardiac cachexia (405 +/- 357 vs. non-cachectic patients: 233 +/- 202 pmol/mL h; P = 0.0007) and related to reduced lean tissue parameters but not to fat tissue parameters. In Cox proportional hazard analysis, elevated SMase related to impaired survival, independent of age, NYHA class, and mean BP (hazard ratio 2.92; 95% confidence interval 1.035-8.24; P = 0.04). S-SMase is upregulated in CHF, independent of aetiology. The association of S-SMase with clinical status, tissue amount, functional capacity of skeletal muscle tissue, and vasodilator capacity suggests that S-SMase-mediated signalling may contribute to regulatory processes of CHF pathophysiology.

The Adipocyte as an Important Target Cell for Trypanosoma cruzi Infection

Adipose tissue plays an active role in normal metabolic homeostasis as well as in the development of human disease. Beyond its obvious role as a depot for triglycerides, adipose tissue controls energy expenditure through secretion of several factors. Little attention has been given to the role of adipocytes in the pathogenesis of Chagas disease and the associated metabolic alterations. Our previous studies have indicated that hyperglycemia significantly increases parasitemia and mortality in mice infected with Trypanosoma cruzi. We determined the consequences of adipocyte infection in vitro and in vivo. Cultured 3T3-L1 adipocytes can be infected with high efficiency. Electron micrographs of infected cells revealed a large number of intracellular parasites that cluster around lipid droplets. Furthermore, infected adipocytes exhibited changes in expression levels of a number of different adipocyte-specific or adipocyte-enriched proteins. The adipocyte is therefore an important target cell during acute Chagas disease. Infection of adipocytes by T. cruzi profoundly influences the pattern of adipokines. During chronic infection, adipocytes may represent an important long-term reservoir for parasites from which relapse of infection can occur. We have demonstrated that acute infection has a unique metabolic profile with a high degree of local inflammation in adipose tissue, hypoadiponectinemia, hypoglycemia, and hypoinsulinemia but with relatively normal glucose disposal during an oral glucose tolerance test.

The Age-Related Decline in Resting Energy Expenditure in Humans Is Due to the Loss of Fat-Free Mass and to Alterations in Its Metabolically Active Components

There is conflicting evidence as to whether the age-related decline in resting energy expenditure (REE) can be attributed to i) absolute changes in fat-free mass (FFM), ii) alterations in the composition of FFM or iii) decreasing organ metabolic rates. This study directly addressed the first and second hypotheses by quantification of metabolically active components of FFM assuming constant tissue respiration rates to calculate REE (REEc). REE was measured (REEm) in 26 young (13 females, 13 males, age 22-31 y) and 26 elderly subjects (15 females, 11 males, age 60-82 y) by indirect calorimetry and detailed body composition analysis was obtained using bioelectrical impedance analysis (BIA), dual energy X-ray absorptiometry (DXA), and MRI. Specific organ metabolic rates were taken from the literature. REEm adjusted for differences in FFM was lower in older subjects than in younger control subjects (5.43 +/- 0.61 MJ/d compared with 6.37 +/- 0.48 MJ/d; P < 0.001). Skeletal muscle mass plus liver mass accounted for 86% and 48% of the variance in REE in young and elderly subjects, respectively. The difference between REEm and REEc was 0.03 +/- 0.40 MJ/d and -0.36 +/- 0.70 MJ/d in young and elderly subjects, respectively. In the elderly 58% of the difference in variance was attributed to heart mass. REEm - REEc was -1.40 +/- 0.44 MJ/d in subjects with hypertensive cardiac hypertrophy, i.e., heart mass > 500 g, suggesting a decrease in heart metabolic rate with increasing heart mass. Excluding five elderly subjects with cardiac hypertrophy resulted in agreement between REEm and REEc in the elderly (-0.10 +/- 0.48 MJ/d). We concluded that the age-related decline in REE is attributed to a reduction in FFM as well as in proportional changes in its metabolically active components. There is no evidence for a decreasing organ metabolic rate in healthy aging.

Metabolically active components of fat-free mass and resting energy expenditure in humans: recent lessons from imaging technologies.

Imaging technologies, i.e. magnetic resonance imaging (MRI), computer tomography (CT) and dual-energy X-ray absorptiometry (DEXA), are precise and accurate techniques used to study lean body mass and adipose tissue distribution. CT and MRI can also be used to assess metabolically active components of fat-free mass (FFM). (Throughout this article, metabolic activity is defined with respect to oxidative metabolism.) To date a total of 116 in vivo measurements of organ masses (OM), in combination with the measurement of resting energy expenditure (REE), have been reported. These data suggest that MRI- or CT-derived OM explains part (approximately 5-10%) of the interindividual variance in REE. The data also suggest that REE can be reconstructed from detailed body composition analysis. Calculating REE from the sum of individual OM multiplied by a constant organ tissue-respiration rate showed a high correlation between calculated and measured REE, with only small and non-significant differences of 83-96 kJ d-1. In addition to CT- and MRI-derived OM, data are available of 244 obese and non-obese subjects regarding the association between regional components of lean body mass (LBM, assessed by DEXA) and REE. These results suggest that measurement of LBM distribution also provides the opportunity to adjust for the non-linearity of REE on body mass. Assessment of metabolically active components of FFM or LBM may also add to our understanding of malnutrition-, obesity- and disease states-related variance in REE. There is need for (1) standardization of imaging technology in body composition research; (2) reference data on detailed body composition, also including more recent autopsy data; (3) reducing the number of assumptions in model-based predictions; and (4) a combination of imaging technologies with in vivo measurements of individual OM respiration.

Metabolically active components of fat free mass and resting energy expenditure in nonobese adults.

Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFM(BIA) (r = 0.92), muscle mass(DEXA) (r = 0.89), and sum of internal organs(MRI) (r = 0.90). In a multiple stepwise regression analysis, FFM(BIA) alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organs(MRI) into the model increased the r(2) to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscle(DEXA) and liver mass(MRI) significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.

Detailed Body Composition Analysis on Female Rats Subjected to a Program of Swimming

Detailed Body Composition Analysis on Female Rats Subjected to a Program of Swimming