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Assessment of a low skeletal muscle mass (SM) is important for diagnosis of ageing and disease-associated sarcopenia and is hindered by heterogeneous methods and terminologies that lead to differences in diagnostic criteria among studies and even among consensus definitions. The aim of this review was to analyze and summarize previously published cut-offs for SM applied in clinical and research settings and to facilitate comparison of results between studies. Multiple published reference values for discrepant parameters of SM were identified from 64 studies and the underlying methodological assumptions and limitations are compared including different concepts for normalization of SM for body size and fat mass (FM). Single computed tomography or magnetic resonance imaging images and appendicular lean soft tissue by dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are taken as a valid substitute of total SM because they show a high correlation with results from whole body imaging in cross-sectional and longitudinal analyses. However, the random error of these methods limits the applicability of these substitutes in the assessment of individual cases and together with the systematic error limits the accurate detection of changes in SM. Adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and may justify normalization of SM for FM. In conclusion, results for SM can only be compared with reference values using the same method, BIA- or DXA-device and an appropriate reference population. Limitations of proxies for total SM as well as normalization of SM for FM are important content-related issues that need to be considered in longitudinal studies, populations with obesity or older subjects.

According to the World Health Organization Expert Consultation, current body mass index (BMI) cut-offs should be retained as an international classification. However, there are ethnic differences in BMI-associated health risks that may be caused by differences in body fat or skeletal muscle mass and these may affect the interpretation of phase angle and bioelectrical impedance vector analysis (BIVA). Therefore, the aim of this study was to compare body composition measured by bioelectrical impedance analysis among 1048 German, 1026 Mexican, and 995 Japanese adults encompassing a wide range of ages and BMIs (18-78 years; BMI, 13.9-44.3 kg/m 2 ). Regression analyses between body composition parameters and BMI were used to predict ethnic-specific reference values at the standard BMI cut-offs of 18.5, 25, and 30 kg/m 2 . German men and women had a higher fat-free mass per fat mass compared with Mexicans. Normal-weight Japanese were similar to Mexicans but approached the German phenotype with increasing BMI. The skeletal muscle index (SMI, kg/m 2 ) was highest in Germans, whereas in BIVA, the Mexican group had the longest vector, and the Japanese group had the lowest phase angle and the highest extracellular/total body water ratio. Ethnic differences in regional partitioning of fat and muscle mass at the trunk and the extremities contribute to differences in BIVA and phase angle. In conclusion, not only the relationship between BMI and adiposity is ethnic specific; in addition, fat distribution, SMI, and muscle mass distribution vary at the same BMI. These results emphasize the need for ethnic-specific normal values in the diagnosis of obesity and sarcopenia.

BMI is widely used as a measure of weight status and disease risks; it defines overweight and obesity based on statistical criteria. BMI is a score; neither is it biologically sound nor does it reflect a suitable phenotype worthwhile to study. Because of its limited value, BMI cannot provide profound insight into obesity biology and its co-morbidity. Alternative assessments of weight status include detailed phenotyping by body composition analysis (BCA). However, predicting disease risks, fat mass, and fat-free mass as assessed by validated techniques (i.e., densitometry, dual energy X ray absorptiometry, and bioelectrical impedance analysis) does not exceed the value of BMI. Going beyond BMI and descriptive BCA, the concept of functional body composition (FBC) integrates body components into regulatory systems. FBC refers to the masses of body components, organs, and tissues as well as to their inter-relationships within the context of endocrine, metabolic and immune functions. FBC can be used to define specific phenotypes of obesity, e.g. the sarcopenic-obese patient. Well-characterized obesity phenotypes are a precondition for targeted research (e.g., on the genomics of obesity) and patient-centered care (e.g., adequate treatment of individual obese phenotypes such as the sarcopenic-obese patient). FBC contributes to a future definition of overweight and obesity based on physiological criteria rather than on body weight alone., (© 2016 The Author(s) Published by S. Karger GmbH, Freiburg.)

Purpose of Review: For the assessment of energy balance or monitoring of therapeutic interventions, there is a need for noninvasive and highly precise methods of body composition analysis that are able to accurately measure small changes in fat and fat-free mass (FFM)., Recent Findings: The use of quantitative magnetic resonance (QMR) for measurement of body composition has long been established in animal studies. There are, however, only a few human studies that examine the validity of this method. These studies have consistently shown a high precision of QMR and only a small underestimation of fat mass by QMR when compared with a 4-compartment model as a reference. An underestimation of fat mass by QMR is also supported by the comparison between measured energy balance (as a difference between energy intake and energy expenditure) and energy balance predicted from changes in fat mass and FFM. Fewer calories were lost and gained as fat mass compared with the value expected from measured energy balance., Summary: Current evidence in healthy humans has shown that QMR is a valid and precise method for noninvasive measurement of body composition. Contrary to standard reference methods, such as densitometry and dual X-ray absorptiometry, QMR results are independent of FFM hydration. However, despite a high accuracy and a low minimal detectable change, underestimation of fat mass by QMR is possible and limits the use of this method for quantification of energy balance.

Background: In humans, resting energy expenditure (REE) can be calculated from organ and tissue masses using constant specific organ metabolic rates. However, interspecies data suggest allometric relations between body mass and organ metabolic rate with higher specific metabolic rates in mammals with a smaller body mass., Objective: The objective was to compare the accuracy of REE prediction with the use of either constant or body mass-dependent specific organ metabolic rates., Design: Healthy subjects (79 women, 75 men) within the normal range of fat mass (FM) expected for a healthy body mass index and aged 18-78 y were stratified into tertiles of body mass. Fifty subjects were grouped as tertile 1 (<66.3 kg), 52 as tertile 2 (> or =66.3 to < or =77.2 kg), and 52 as tertile 3 (>77.2 kg). Magnetic resonance imaging was used to assess the volume of 4 internal organs (brain, heart, liver, and kidneys). REE was measured by indirect calorimetry (REE(m)) and compared with REE calculated from previously published constant (REE(c1)) and body mass-dependent organ metabolic rates (REE(c2))., Results: REE(m) increased significantly with weight tertile (tertile 1: 5536 +/- 529 kJ/d; tertile 2: 6389 +/- 672 kJ/d; tertile 3: 7467 +/- 745 kJ/d; P < 0.01). The deviation REE(m)-REE(c1) did not differ between weight tertiles (tertile 1: 66 +/- 382 kJ/d; tertile 2: 167 +/- 507 kJ/d; tertile 3: 86 +/- 480 kJ/d; NS) and showed no relation with body mass (r = -0.05, NS). By contrast, REE(m)-REE(c2) increased with increasing weight tertile (tertile 1: -45 +/- 369 kJ/d; tertile 2: 150 +/- 503 kJ/d; tertile 3: 193 +/- 482 kJ/d; P < 0.05) and correlated significantly with body mass (r = 0.16, P < 0.05)., Conclusion: Our data do not support a lower specific organ metabolic rate in humans with a larger body mass than in those with a smaller body mass.

Background: The use of bioelectrical impedance phase angle has been recommended as a prognostic tool in the clinical setting, but published reference data bases are discrepant and incomplete (eg, they do not consider body mass index [BMI], and data are lacking for children)., Methods: Phase angle reference values stratified by age, sex, and BMI were generated in a large German data base of 15,605 children and adolescents and 214,732 adults, and the determinants of phase angle values were assessed. The reference values were applied to 3 groups of patients and compared with previously published reference values from the United States and Switzerland., Results: Gender and age were the main determinants of phase angle in adults, with men and younger subjects having higher phase angles. In children and adolescents, age and BMI were the main determinants of phase angle. In normal and overweight adults, phase angle increased with increasing BMI, but there was an inverse association at a BMI >40 kg/m2. In cirrhosis, the prevalence of a low phase angle increased with the state of disease, whereas it was not different between patients with the metabolic syndrome and controls. There are considerable differences between phase angle reference values from different populations. These differences are not explained by age or BMI and may be due to differences between impedance analyzers., Conclusion: The determinants of phase angle differ between adults and children. In adults, the influence of BMI on phase angle depended on the BMI range. The prognostic value of phase angle may differ in different clinical settings. The use of population-specific and probably impedance-analyzer-specific reference values for phase angle is recommended.

Background: Bioelectrical impedance analysis (BIA) gives resistance (R) and reactance (Xc). R and Xc normalized for body height (H) can be plotted as a bivariate vector (H(2)/Xc versus H(2)/R). Vector BIA is useful for studying the determinants of BIA results., Objective: We investigated the effect of age on BIA results and its relevance to body-composition analysis in a large database of impedance vector distributions stratified by age, sex, and body mass index (BMI)., Design: Mean bivariate vector distribution patterns (95% confidence ellipses) were examined in a German population of 15605 children and adolescents and 213294 adults. Children and adolescents were divided into 3 age groups with up to 5 BMI categories. In adults, 5 BMI categories were stratified into 7 age groups., Results: Mean impedance vectors were shorter in children than in adults. The vector distribution pattern was influenced by sex, BMI, and age, with shorter vectors in females than in males and longer vectors with increasing BMI. Consistent with a decrease in body cell mass with increasing age, there was a downward slope in the mean vector with age as a result of a decrease in the H(2)/Xc vector component. By contrast, there was no age-dependent increase in the H(2)/R vector component. In women of the same BMI at different ages, H(2)/R and percentage fat mass tended to decrease with age., Conclusions: The lack of an age-dependent increase in the H(2)/R vector component renders conventional BIA unsuitable for an examination of the age-related increase in body fat mass. By contrast, the increase in the H(2)/Xc vector component with advancing age suggests the potential of BIA to depict the age-related decrease in body cell mass.

Background: Leptin has been considered a starvation hormone, but its role in malnourished patients is unknown., Objective: We aimed to characterize the role of leptin in metabolic adaptation in women with anorexia nervosa (AN)., Design: In a cross-sectional study, 57 women with AN [mean (+/-SD) body mass index (kg/m(2)) on admission: 15.2 +/- 1.5] were compared with 49 healthy, normal-weight women (mean body mass index: 22.3 +/- 2.3). Nineteen patients were reinvestigated during weight gain 43 and 84 d after baseline. We measured serum concentrations of leptin, soluble leptin receptor, insulin, ghrelin, and thyroid hormones [thyrotropin, triiodothyronine (T(3)), and thyroxine]; fat mass (FM) and fat-free mass (FFM); resting energy expenditure (REE); energy intake; and eating behavior., Results: Compared with values in the control women, leptin, T(3), REE, FM, and FFM were lower in the women with AN, but the leptin secretion rate was not significantly different. Leptin correlated with FM (r = 0.83, P < 0.001), T(3) (r = 0.68, P < 0.001), respiratory quotient (r = -0.47, P < 0.001), and REE (r = 0.58, P < 0.001). The association with REE weakened after adjustment for FFM and disappeared after further adjustment for T(3). Hunger and appetite had positive, whereas satiety and restraint had negative, associations with leptin. During weight gain (9.0 +/- 3.3 kg in 84 d), serum leptin and the leptin secretion rate increased. Changes in leptin secretion were associated with energy intake and REE. The initial changes in the leptin secretion rate (ie, the difference between baseline and 43 d) were negatively associated with changes in body weight from 43 to 84 d., Conclusions: Leptin contributes to metabolic adaptation in women with AN. The leptin response is associated with weight gain.

This study examined the effect of exogenous ketone bodies (KB) on oxygen consumption (V̇ o 2), carbon dioxide production (V̇ co 2), and glucose metabolism. The data were compared with the effects of endogenous ketonemia during both, a ketogenic diet or fasting. Eight healthy individuals [24.1 ± 2.5 yr, body mass index (BMI) 24.3 ± 3.1 kg/m2] participated in a crossover intervention study and were studied in a whole-room indirect calorimeter (WRIC) to assess macronutrient oxidation following four 24-h interventions: isocaloric controlled mixed diet (ISO), ISO supplemented with ketone salts (38.7 g of β-hydroxybutyrate/day, EXO), isocaloric ketogenic diet (KETO), and total fasting (FAST). A physical activity level of 1.65 was obtained. In addition to plasma KB, 24-h C-peptide and KB excretion rates in the urine and postprandial glucose and insulin levels were measured. Although 24-h KB excretion increased in response to KETO and FAST, there was a modest increase in response to EXO only (P < 0.05). When compared with ISO, V̇ o 2 significantly increased in KETO (P < 0.01) and EXO (P < 0.001), whereas there was no difference in FAST. V̇ co 2 increased in EXO but decreased in KETO (both P < 0.01) and FAST (P < 0.001), resulting in 24-h respiratory exchange ratios (RER) of 0.828 ± 0.024 (ISO) and 0.811 ± 0.024 (EXO) (P < 0.05). In response to EXO there were no differences in basal and postprandial glucose and insulin levels, as well as in insulin sensitivity. When compared with ISO, EXO, and KETO, FAST increased homeostatic model assessment β-cell function (HOMA-B) (all P < 0.05). In conclusion, at energy balance exogenous ketone salts decreased respiratory exchange ratio without affecting glucose tolerance. NEW & NOTEWORTHY: Our findings revealed that during isocaloric nutrition, additional exogenous ketone salts increased V̇ o 2 and V̇ co 2 while lowering the respiratory exchange ratio (RER). Ketone salts had no effect on postprandial glucose metabolism. [ABSTRACT FROM AUTHOR]

Objective: This study's objective was to develop models predicting the relative reduction in skeletal muscle (SM) mass during periods of voluntary calorie restriction (CR) and to validate model predictions in longitudinally monitored samples. Methods: The model development group included healthy nonexercising adults (n = 897) who had whole‐body SM mass measured with magnetic resonance imaging. Model predictions of relative SM changes with CR were evaluated in two longitudinal studies, one 12 to 14 weeks in duration (n = 74) and the other 12 months in duration (n = 26). Results: A series of SM prediction models were developed in a sample of 415 males and 482 females. Model‐predicted changes in SM mass relative to changes in body weight (i.e., ΔSM/Δbody weight) with a representative model were (mean ± SE) 0.26 ± 0.013 in males and 0.14 ± 0.007 in females (sex difference, p < 0.001). The actual mean proportions of weight loss as SM in the longitudinal studies were 0.23 ± 0.02/0.20 ± 0.06 in males and 0.10 ± 0.02/0.17 ± 0.03 in females, similar to model‐predicted values. Conclusions: Nonelderly males and females with overweight and obesity experience respective reductions in SM mass with voluntary CR in the absence of a structured exercise program of about 2 to 2.5 kg and 1 to 1.5 kg per 10‐kg weight loss, respectively. These estimates are predicted to be influenced by interactions between age and body mass index in males, a hypothesis that needs future testing. [ABSTRACT FROM AUTHOR]

Background: Programing of body composition during intrauterine growth may contribute to the higher risk for cardio-metabolic disease in individuals born small or large for gestational age (SGA, LGA). Compensations of intrauterine growth by catch-up or catch-down postnatal growth may lead to adverse consequences like a thin-fat phenotype. Methods: The impact of (i) birth weight as well as (ii) the interaction between birth weight and catch-up or catch-down growth during the first 2 years of life on fat-free mass index (FFMI) and fat mass index (FMI) in 3,204 5–7-year-old children were investigated using Hattori's body composition chart. Body composition results were compared to appropriate for gestational age (AGA) birth weight with the same body mass index (BMI). Results: In total, 299 children at age 5–7 years were categorized as SGA, 2,583 as AGA, and 322 as LGA. When compared to AGA-children, BMI at 5–7 years of age was higher in LGA-children (15.5 vs. 16.2 kg/m2; p < 0.001) but not different in SGA-children. Compared to AGA with the same BMI, LGA was associated with higher FMI and a lower FFMI in 5–7-year-old girls. This phenotype was also seen for both sexes with catch-down growth during the first 2 years of life whereas catch-up growth prevented the higher FMI and lower FFMI per BMI. By contrast, SGA was associated with a higher FFMI and lower FMI in 5–7-year-old boys compared to AGA boys with the same BMI. This phenotype was also seen with catch-down growth in both genders whereas catch-up growth in girls led to more gain in FMI per BMI. Conclusion: LGA with a compensatory catch-down postnatal growth may be a risk factor for the development of disproportionate gain in fat over lean mass whereas SGA with a catch-down postnatal growth seems to favor the subsequent accretion of lean over fat mass. A higher propensity of lean mass accretion during postnatal growth in boys compared to girls explains sex differences in these phenotypes. [ABSTRACT FROM AUTHOR]

Introduction: Body composition assessment is superior to the use of body mass index (BMI) to characterize the nutritional status in pediatric populations. For data interpretation, suitable reference data are needed; hence, we aimed to generate age-dependent and sex-specific body composition reference data in a larger population of children and adolescents in Germany. Methods: This is a cross-sectional study on a representative group of 15,392 5- to 17-year-old children and adolescents. Body composition was assessed by bioelectrical impedance analysis using a population-specific algorithm validated against air displacement plethysmography. Age- and sex-specific percentiles for BMI, fat mass index (FMI), fat-free mass index (FFMI), and a "load-capacity model" (characterized by the ratios of fat mass [FM]/ fatt-free mass [FFM] and FM/FFM2) were modeled using the LMS method. Results: BMI, FMI, FFMI, FM/FFM, and FM/FFM2 curves showed similar shapes between boys and girls with steady increases in BMI, FMI, and FFMI, while FM/FFM2-centiles decreased during early childhood and adolescence. Sex differences were observed in FMI and FM/FFM percentiles with increases in FMI up to age 9 years followed by a steady decrease in FM/FFM during and after puberty with a fast-growing FFMI up to age 17 in boys. The prevalence of low FFM relative to FM reached more than 60% in overweight children and adolescents. Conclusion: These pediatric body composition reference data enable physicians and public health scientists to monitor body composition during growth and development and to interpret individual data. The data point out to an early risk of sarcopenia in overweight children and adolescents. [ABSTRACT FROM AUTHOR]

Summary: Traditional diagnosis and understanding of the pathophysiology of obesity are based on excessive fat storage due to a chronically positive energy balance characterized by body mass index (BMI). Quantitative and qualitative analysis of lean and adipose tissue compartments by body composition analysis reveals that characterization of obesity as "overfat" does not facilitate a comprehensive understanding of obesity‐associated health risk. Instead of being related to fat mass, body composition characteristics underlying BMI‐associated prognosis may depend (i) on accelerated growth by a gain in lean mass or fat‐free mass (FFM) in children with early BMI rebound or adolescents with early puberty; (ii) on a low muscle mass in aging, associated chronic disease, or severe illness; and (iii) on impaired adipose tissue expandability with respect to cardiometabolic risk. It is therefore time to call the adipocentric paradigm of obesity into question and to avoid the use of BMI and body fat percentage. By contrast, obesity should be seen in face of a limited FFM/muscle mass together with a limited capacity of fat storage. [ABSTRACT FROM AUTHOR]

It seems reasonable that overweight and obesity should be defined based on body composition rather than indirect indices like BMI or waist circumference. The use of conventional parameters like fat mass or visceral fat is however of similar and limited value for disease risk prediction at the population level and does not contribute much beyond the use of simple BMI or waist circumference. This conundrum may be partly explained by using complex phenotypes (e.g., Metabolic Syndrome or whole body insulin resistance) rather than more disease-specific outcomes like liver- and muscle insulin resistance. In addition, there are multifactorial causes of similar body composition phenotypes that may add to explain the variance in metabolic consequences of these phenotypes. An intriguing hypothesis is that fat mass represents the metabolic load that interacts with fat-free mass that stands for metabolic capacity to determine disease risk. This concept has important implications for assessment of healthy growth and development and when it is challenged with weight gain in adults. Integration of body composition information at the whole body, organ-tissue and cellular level is not required to improve the diagnosis of obesity but facilitates a better understanding of the etiology of obesity-associated metabolic complications. [ABSTRACT FROM AUTHOR]

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis. [ABSTRACT FROM AUTHOR]

Background: This study examined sex-specific differences in physical health-related quality of life (HRQoL) across subgroups of metabolic health and obesity. We specifically asked whether (1) obesity is related to lower HRQoL independent of metabolic health status and potential confounders, and (2) whether associations are similar in men and women.Methods: We used cross-sectional data from the German Health Interview and Examination Survey 2008-11. Physical HRQoL was measured using the Short Form-36 version 2 physical component summary (PCS) score. Based on harmonized ATPIII criteria for the definition of the metabolic health and a body mass index ≥ 30 kg/m2 to define obesity, individuals were classified as metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). Sex-specific analyses including multivariable linear regression analyses were based on PCS as the dependent variable, metabolic health and obesity category as the independent variable with three categories and MHNO as the reference, and age, education, lifestyle and comorbidities as confounders.Results: This study included 6860 participants (3298 men, 3562 women). Compared to MHNO, all other metabolic health and obesity categories had significantly lower PCS in both sexes. As reflected by the beta coefficients [95% confidence interval] from bivariable linear regression models, a significant inverse association with PCS was strongest for MUO (men: -7.0 [-8.2; -5.8]; women: -9.0 [-10.2; -7.9]), intermediate for MUNO (men: -4.2 [-5.3; -3.1]; women: -5.6 [-6.8; -4.4]) and least pronounced for MHO (men: -2.2 [-3.6; -0.8]; women -3.9 [-5.4; -2.5]). Differences in relation to MHNO remained statistically significant for all groups after adjusting for confounders, but decreased in particular for MUNO (men:-1.3 [-2.3; -0.3]; women: -1.5 [-2.7; -0.3].Conclusions: Obesity was significantly related to lower physical HRQoL, independent of metabolic health status. Potential confounders including age, educational status, health-related behaviors, and comorbidities explained parts of the inverse relationship. Associations were evident in both sexes and consistently more pronounced among women than men. [ABSTRACT FROM AUTHOR]

Background: Meal skipping has become an increasing trend of the modern lifestyle that may lead to obesity and type 2 diabetes. Objective: We investigated whether the timing of meal skipping impacts these risks by affecting circadian regulation of energy balance, glucose metabolism, and postprandial inflammatory responses. Design: In a randomized controlled crossover trial, 17 participants [body mass index (in kg/m²): 23.7 ± 4.6] underwent 3 isocaloric 24-h interventions (55%, 30%, and 15% carbohydrate, fat, and protein, respectively): a breakfast skipping day (BSD) and a dinner skipping day (DSD) separated by a conventional 3-meal-structure day (control). Energy and macronutrient balance was measured in a respiration chamber. Postprandial glucose, insulin, and inflammatory responses in leukocytes as well as 24-h glycemia and insulin secretion were analyzed. Results: When compared with the 3-meal control, 24-h energy expenditure was higher on both skipping days (BSD: +41 kcal/d; DSD: +91 kcal/d; both P < 0.01), whereas fat oxidation increased on the BSD only (+16 g/d; P < 0.001). Spontaneous physical activity, 24-h glycemia, and 24-h insulin secretion did not differ between intervention days. The postprandial homeostasis model assessment index (+54%) and glucose concentrations after lunch (+46%) were, however, higher on the BSD than on the DSD (both P < 0.05). Concomitantly, a longer fasting period with breakfast skipping also increased the inflammatory potential of peripheral blood cells after lunch. Conclusions: Compared with 3 meals/d, meal skipping increased energy expenditure. In contrast, higher postprandial insulin concentrations and increased fat oxidation with breakfast skipping suggest the development of metabolic inflexibility in response to prolonged fasting that may in the long term lead to low-grade inflammation and impaired glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT02635139. [ABSTRACT FROM AUTHOR]

Objective: Overfeeding with a high-fat and/or high-carbohydrate (CHO) diet is known to increase plasma concentrations of endotoxin (lipopolysaccharide [LPS]) that may lead to metabolic disturbances like insulin resistance. The impact of CHO quality (i.e., the glycemic index [GI]) independent of fat intake on metabolic endotoxemia remains unclear. In the present study, the effects of changes in energy balance and GI on plasma endotoxin were studied.Methods: Fifteen healthy young men overconsumed diets containing 65% CHO and 20% fat for 1 week (OF; +50% of energy requirement) followed by 3 weeks of caloric restriction (CR; -50% of energy requirement) and were then randomized to 2 weeks hypercaloric refeeding (RF, +50% of energy requirement) with either a low- or high-GI (40 vs 74) diet.Results: During OF, subjects gained 1.9 ± 0.7 kg body weight (+0.6 ± 0.8% fat mass) followed by a weight loss of 6.1 ± 0.8 kg (-2.0 ± 0.6% fat mass) and weight regain of 4.0 ± 0.6 kg (0.9 ± 0.8% fat mass). Fasting insulin and homeostasis model assessment-insulin resistance (HOMAIR) increased with OF and RF and decreased with CR, MatsudaISI decreased by 37% after RF (all p < 0.05). Endotoxin significantly increased by 30.8% with OF and by 24.7% with RF (both p < 0.05), whereas CR normalized endotoxin levels. No difference in endotoxin levels was observed between refeeding a hypercaloric high- or low-GI diet. Changes in endotoxin levels with RF were not related to changes in insulin sensitivity.Conclusion: A hypercaloric diet (OF and RF) increased plasma endotoxin irrespective of GI, whereas a negative energy balance did not reduce endotoxemia. Impaired insulin sensitivity with hypercaloric refeeding on a high-GI diet was not explained by metabolic endotoxemia. [ABSTRACT FROM AUTHOR]

Objective: The study examined potential changes in the proportion of metabolic health according to body size categories over time and across strata of sex and age, varying definitions of metabolic health. Methods: We analysed data from national health interview and examination surveys 1997–99 and 2008–11 for adults aged 18–79 years (GNHIES98: N = 6,565; DEGS1: 6,860). Metabolic health as defined by ATPIII criteria was examined across body mass index categories. The Plourde and Karelis criteria were applied in relation to abdominal obesity. Results: Proportions of adults with metabolic health by body size categories were largely stable over time, except for an increasing proportion of metabolically healthy persons with pre-obesity and metabolically healthy women without abdominal obesity. In both surveys proportions of adults meeting ATPIII criteria ranged from approximately 30% among men and women with obesity, to about two thirds of those with pre-obesity to about 93% among those with normal weight. According to Plourde and Karelis criteria proportions ranged from almost 30% among men and women without abdominal obesity to less than 10% among those with abdominal obesity. Proportions were consistently higher among younger than older age groups and less consistently higher among women than men. Conclusions: Proportions of adults with metabolic health by body size categories were largely stable over time, except for an increasing proportion of metabolically healthy women without abdominal obesity. There is no evidence that metabolic health among adults with obesity increased in Germany over a period of ten years. [ABSTRACT FROM AUTHOR]

Background: Assessing skeletal muscle (SM) and visceral adipose tissue (VAT) by a single MRI slice at lumbar vertebra (L) 3 can replace whole-body MRI in young and middle-aged adults. However, this technique has not been proven in older adults.Objective: The aim of this analysis was to reinvestigate the best estimate for SM and VAT in an independent population of healthy elderly people.Methods: SM and VAT were assessed by whole-body MRI in 84 subjects ≥60 y [45 men; mean ± SD age: 68.4 ± 5.4 y, mean ± SD body mass index (in kg/m2): 25.5 ± 3.5]. SM and VAT areas of 9 slices at the lumbar spine were analyzed. The best estimate was investigated by Pearson correlations. Total volumes (in liters) were predicted by the area at lumbar vertebra 3 (AL3). Besides Bland-Altman analysis, linear regressions were performed to explain the variance of the bias by age, height, and percentage of fat mass (%FM). In a mixed population (healthy elderly plus reference population), linear regression with total SM and VAT volume as dependent variables and AL3, age, and height as independent variables was applied.Results: When comparing the correlation coefficients between the tissue areas and total volumes, L3 was identified as the best estimate (r range: 0.71-0.94; all P < 0.05). However, Bland-Altman analysis showed a positive SM bias in men (mean ± SD: -1.0% ± 9.0%; P < 0.05) and a negative SM bias in women (mean ± SD: 3.7% ± 9.6%; P < 0.05). Contrary to SM, no significant bias was observed for VAT. In the elderly, stepwise linear regression showed height as a predictor for SM bias (R2 = 0.21, SEE = 2.07 L; P < 0.05) and %FM and age as predictors of the nonsignificant VAT bias (R2 = 0.26, SEE = 0.22L, P < 0.05), in men only. In the mixed population, AL3 and height were predictors for total SM, and AL3 for total VAT, independent of sex.Conclusions: AL3 was confirmed as the best estimate for SM and VAT volumes in healthy elderly adults. Contrary to VAT, there is a bias for SM, and height has to be added to the algorithm. [ABSTRACT FROM AUTHOR]

Background: Adaptive thermogenesis (AT) is the fat-free mass (FFM)-independent reduction of resting energy expenditure (REE) to caloric restriction (CR). AT attenuates weight loss and favors weight regain. Its variance, dynamics, and control remain obscure. Objectives: Our aims were to address the variance and kinetics of AT, its associations with body composition in the context of endocrine determinants, and its effect on weight regain. Design: Thirty-two nonobese men underwent sequential overfeeding (1 wk at +50% of energy needs), CR (3 wk at 250% of energy needs), and refeeding (2 wk at +50% of energy needs). AT and its determinants were measured together with body composition as assessed with the use of quantitative magnetic resonance, whole-body MRI, isotope dilution, and nitrogen and fluid balances. Results: Changes in body weight were +1.8 kg (overfeeding), 26.0 kg (CR), and +3.5 kg (refeeding). CR reduced fat mass and FFM by 114 and 159 g/d, respectively. Within FFM, skeletal muscle (25%), liver (213%), and kidneys (28%) decreased. CR also led to reductions in REE (2266 kcal/d), respiratory quotient (215%), heart rate (214%), blood pressure (27%), creatinine clearance (212%), energy cost of walking (222%), activity of the sympathetic nervous system (SNS) (238%), and plasma leptin (244%), insulin (254%), adiponectin (249%), 3,5,3#-tri-iodo-thyronine (T3) (239%), and testosterone (211%). AT was 108 kcal/d or 48% of the decrease in REE. Changes in FFM composition explained 36 kcal, which left 72 kcal/d for true AT. The decrease in AT became significant at #3 d of CR and was related to decreases in insulin secretion (r = 0.92, P , 0.001), heart rate (r = 0.60, P , 0.05), creatinine clearance (r = 0.79, P , 0.05), negative fluid balance (r = 0.51, P , 0.01), and the free water clearance rate (r = 20.90, P , 0.002). SNS activity and plasma leptin, ghrelin, and T3 and their changes with CR were not related to AT. Conclusion: During early weight loss, AT is associated with a fall in insulin secretion and body fluid balance. This trial was registered at clinicaltrials.gov as NCT01737034. [ABSTRACT FROM AUTHOR]

Background: Whole-body magnetic resonance imaging (MRI) is the gold standard for the assessment of skeletal muscle (SM) and adipose tissue volumes. It is unclear whether single-slice estimates can replace whole-body data. Objective: We evaluated the accuracy of the best single lumbar and midthigh MRI slice to assess whole-body SM, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Design: Whole-body MRI was performed in 142 healthy adults aged 19-65 y [mean ± SD age: 37.0 ± 11.8 y; BMI (in kg/m²): 25.3 ± 5.9]. Single slices were taken at lumbar vertebrae L1-L5 plus intervertebral discs and the thigh (midthigh, 10 cm distally from the midthigh, and 10 cm proximally from the midthigh). The value of single-slice areas was also tested in a longitudinal study on 48 healthy volunteers during weight loss (8.2 ± 5.2 kg). Results: Cross-sectionally, all SM and adipose tissue single-slice areas correlated with total tissue volumes (P < 0.01). Because of the close associations between L3 areas and corresponding tissue volumes (r = 0.832-0.986, P < 0.01), this location was identified as the reference to estimate SM and adipose tissue in both sexes. SM, SAT, and VAT areas at L3 explained most of the variance of total tissue volumes (69-97%, with SEs of estimation of 1.96 and 2.03 L for SM, 0.23 and 0.61 L for VAT, and 4.44 and 2.47 L for SAT for men and women, respectively. There was no major effect on the explained variance compared with that for optimal slices. For SM, the optimal slice area was shown at midthigh. With weight-loss changes in total SM, VAT, and SAT, volumes were significantly different from those at baseline (SM changes: 22.8 ± 2.9 L; VAT changes: 20.7 ± 1.0 L; SAT changes: 25.1 ± 6.0 L). The area at L3 reflected changes in total VAT and SAT. To assess changes in total SM volumes, areas at midthigh showed the best evidence. Conclusion: In both sexes, a single MRI scan at the level of L3 is the best compromise site to assess total tissue volumes of SM, VAT, and SAT. By contrast, L3 does not predict changes in tissue components. This trial was registered at clinicaltrials.gov as NCT01737034. [ABSTRACT FROM AUTHOR]

Objective We investigated the impact of detailed body composition on aerobic fitness to determine whether regional components of fat mass have independent effects on VO2submax, and whether VO2submax and detailed body composition independently explain variation in REE. Methods 71 healthy adults (80% female, 20% male, BMI 28.2-43.8 kg/m2) were investigated. Body composition was measured by the four-compartment model together with whole body magnetic resonance imaging (MRI) to assess high and low metabolic rate organs and regional fat depots. VO2submax was estimated at 75% of predicted maximum heart rate. Results There was a strong association between VO2submax and FFM and all organ masses except for heart. Skeletal muscle mass accounted for 34.8% of the variance in VO2submax. In addition, subcutaneous adipose tissue (SAT) of extremities explained additional 14.4%. FFM and FM explained 71.3% of the variance in REE. Including the components of FFM and FM, the explained variance in REE increased by about 5.8%; skeletal muscle mass explained 70.0% of the variance in REE and kidney and liver masses explained additional 7.1%. VO2submax correlated with REE. Taking into account body composition, VO2submax did not add to the variance in REE. Conclusion FFM is a determinant of both VO2submax and REE. Modeling either REE or VO2submax from individual components of FFM, about 77.1% of variance in REE (by muscle, liver and kidneys mass) and 34.8% of variance in VO2submax (by skeletal muscle mass) could be explained. FM explained additional variance in REE, whereas SAT of extremities added to the variance in VO2submax only. Am. J. Hum. Biol. 27:397-406, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Weight change affects resting energy expenditure (REE) and metabolic risk factors. The impact of changes in individual body components on metabolism is unclear. Objective: We investigated changes in detailed body composition to assess their impacts on REE and insulin resistance. Design: Eighty-three healthy subjects [body mass index (BMI; in kg/m²) range: 20.2-46.8; 50% obese] were investigated at 2 occasions with weight changes between -11.2 and +6.5 kg (follow-up periods between 23.5 and 43.5 mo). Detailed body composition was measured by using the 4-component model and whole-body magnetic resonance imaging. REE, plasma thyroid hormone concentrations, and insulin resistance were measured by using standard methods. Results: Weight loss was associated with decreases in fat mass (FM) and fat-free mass (FFM) by 72.0% and 28.0%, respectively. A total of 87.9% of weight gain was attributed to FM. With weight loss, sizes of skeletal muscle, kidneys, heart, and all fat depots decreased. With weight gain, skeletal muscle, liver, kidney masses, and several adipose tissue depots increased except for visceral adipose tissue (VAT). After adjustments for FM and FFM, REE decreased with weight loss (by 0.22 MJ/d) and increased with weight gain (by 0.11 MJ/d). In a multiple stepwise regression analysis, changes in skeletal muscle, plasma triiodothyronine, and kidney masses explained 34.9%, 5.3%, and 4.5%, respectively, of the variance in changes in REE. A reduction in subcutaneous adipose tissue rather than VAT was associated with the improvement of insulin sensitivity with weight loss. Weight gain had no effect on insulin resistance. Conclusion: Beyond a 2-compartment model, detailed changes in organ and tissue masses further add to explain changes in REE and insulin resistance. [ABSTRACT FROM AUTHOR]

Previous studies suggest that a low-glycaemic index (LGI) diet may improve insulin sensitivity (IS). As IS has been shown to decrease during refeeding, we hypothesised that an LGI- v. high-GI (HGI) diet might have favourable effects during this phase. In a controlled nutritional intervention study, sixteen healthy men (aged 26·8 (sd 4·1) years, BMI 23·0 (sd 1·7) kg/m2) followed 1 week of overfeeding, 3 weeks of energy restriction and of 2 weeks refeeding at ± 50 % energy requirement (50 % carbohydrates, 35 % fat and 15 % protein). During refeeding, subjects were divided into two matched groups receiving either high-fibre LGI or lower-fibre HGI foods (GI 40 v. 74, fibre intake 65 (sd 6) v. 27 (sd 4) g/d). Body weight was equally regained in both groups with refeeding (mean regain 70·5 (sd 28·0) % of loss). IS was improved by energy restriction and decreased with refeeding. The decreases in IS were greater in the HGI than in the LGI group (group × time interactions for insulin, homeostasis model assessment of insulin resistance (HOMAIR), Matsuda IS index (MatsudaISI); all P< 0·05). Mean interstitial glucose profiles during the day were also higher in the HGI group (ΔAUCHGI-LGI of continuous interstitial glucose monitoring: 6·6 mmol/l per 14 h, P= 0·04). At the end of refeeding, parameters of IS did not differ from baseline values in either diet group (adiponectin, insulin, HOMAIR, MatsudaISI, M-value; all P>0·05). In conclusion, nutritional stress imposed by dietary restriction and refeeding reveals a GI/fibre effect in healthy non-obese subjects. LGI foods rich in fibre may improve glucose metabolism during the vulnerable refeeding phase of a weight cycle. [ABSTRACT FROM PUBLISHER]

Age-related changes in leptin and adiponectin levels remain controversial, being affected by inconsistent normalisation for adiposity and body fat distribution in the literature. In a cross-sectional study on 210 Caucasians (127 women, eighty-three men, 18–78 years, BMI 16·8–46·8 kg/m2), we investigated the effect of age on adipokine levels independent of fat mass (FM measured by densitometry), visceral and subcutaneous adipose tissue volumes (VAT and SAT assessed by whole-body MRI). Adiponectin levels increased with age in both sexes, whereas leptin levels decreased with age in women only. There was an age-related increase in VAT (as a percentage of total adipose tissue, VAT%TAT), associated with a decrease in SATlegs%TAT. Adiposity was the main predictor of leptin levels, with 75·1 % of the variance explained by %FM in women and 76·6 % in men. Independent of adiposity, age had a minor contribution to the variance in leptin levels (5·2 % in women only). The variance in adiponectin levels explained by age was 14·1 % in women and 5·1 % in men. In addition, independent and inverse contributions to the variance in adiponectin levels were found for truncal SAT (explaining additional 3·0 % in women and 9·1 % in men) and VAT%TAT (explaining additional 13·0 % in men). In conclusion, age-related changes in leptin and adiponectin levels are opposite to each other and partly independent of adiposity and body fat distribution. Normalisation for adiposity but not for body fat distribution is required for leptin. Adiponectin levels are adversely affected by subcutaneous and visceral trunk fat. [ABSTRACT FROM PUBLISHER]

Abstract: The BIOPAC indirect calorimeter for measuring resting energy expenditure (REE) is less cumbersome than many other calorimeters. We tested the hypothesis that the BIOPAC calorimeter is as well suited for determining REE in older people as traditional calorimeters. In 50 healthy persons (24 men and 26 women; age range, 61-83 years), REE by BIOPAC was validated against Vmax Spectra indirect calorimeter as a criterion method. Resting energy expenditure by BIOPAC was recorded at 2 different time intervals to find optimal conditions for older persons. Also, REE by 7 published equations was validated. The Bland-Altman procedure was used to test agreement between methods. Multiple regression analysis was applied to develop a new equation for predicting REE from BIOPAC. The BIOPAC calorimeter and most empirical equations significantly overpredicted mean REE in both sexes. Mifflin''s equation best-predicted mean REE. Limits of agreement were wide for BIOPAC and narrow for most empirical equations. The Lührmann and Müller equations had smallest limits of agreement in men (±950 kJ/24 h) and the Harris-Benedict and Müller equations in women (±672 kJ/24 h). A new equation was developed for the BIOPAC device improving both predictions of mean and individual REE (R 2 = 0.671, standard error of the estimate = 136 kJ/24 h). Using this equation, 72.9% of subjects were lying within 10% of measured REE, compared with only 12.5% when using the manufacturer''s algorithm. In conclusion, the BIOPAC calorimeter is suitable for measuring REE in healthy older adults when the new prediction equation is applied. This calorimeter is not applicable to frail older persons. [Copyright &y& Elsevier]