Corinna Geisler

Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors

Objective:To compare the value of body fat mass (%FM) to indirect measures of general (body mass index (BMI)) and central adiposity (waist circumference (WC); waist-to-height ratio (WC/ht)) for the prediction of overweight- and obesity-related metabolic risk in a study population with a high prevalence of metabolic syndrome (MSX).Methods:BMI, WC, WC/ht, body composition (by air-displacement plethysmography) and metabolic risk factors: triglycerides, cholesterol, HDL-cholesterol (HDL-C), uric acid, systolic blood pressure (BPsys), insulin resistance by homeostasis model assessment (HOMA-IR) and C-reactive protein (CRP) were measured in 335 adults (191 women, 144 men; mean age 53 ±13.9 years, prevalence of MSX 30%).Results:When compared with BMI and WC, %FM showed weaker associations with metabolic risk factors, except for CRP and BPsys in men. In women, HDL-C and HOMA-IR showed the closest correlations with BMI. For all other risk factors, WC or WC/ht were the best predictors in both sexes. Differences in the strength of correlations between an obesity index and different risk factors exceeded the differences observed between all obesity indices within one risk factor. In stepwise multiple regression analyses, WC/ht was the main predictor of metabolic risk in both sexes combined. However, analysis of the area under receiver operating characteristic curves for prediction of the prevalence of ⩾2 component traits of the MSX revealed a similar accuracy of all obesity indices.Conclusions:At the population level, measurement of body FM has no advantage over BMI and WC in the prediction of obesity-related metabolic risk. Although measures of central adiposity (WC, WC/ht) tended to show closer associations with risk factors than measures of general adiposity, the differences were small and depended on the type of risk factor and sex, suggesting an equivalent value of methods.

Common familial influences on clustering of metabolic syndrome traits with central obesity and insulin resistance: the Kiel obesity prevention study.

Objective:The phenotypic heterogeneity of metabolic syndrome (MSX) suggests heterogeneity of the underlying genotype. The aim of the present study was to examine the common genetic background that contributes to the clustering between the two main features (insulin resistance, central obesity) and different MSX component traits.Methods:In all, 492 individuals from 90 families were investigated in a three-generation family path study as part of the Kiel Obesity Prevention Study (KOPS, 162 grandparents, 66.1±6.7 years, 173 parents, 41.3±5.4 years and 157 children, 10.8±3.4 years). Overall heritability was estimated and common familial (genetic and environmental) influences on insulin resistance (HOMA-IR) or central obesity (elevated waist circumference, WC), respectively, and different MSX traits were compared in a bivariate cross-trait correlation model.Results:Prevalence of MSX (according to NCEP criteria) was 27.2% (f) and 27.8% (m) in adults and 3.5% (f) and 8.5% (m) in children and adolescents, respectively. MSX phenotype was found to be highly variable, comprising 16 subtypes of component trait combinations. Within-trait heritability was 38.5% for HOMA-IR and 53.5% for WC, cross-trait heritability was 53.4%. As much as 6–18% and 3–10% of the shared variance between different MSX component traits (lipid profile, blood pressure) and WC or HOMA-IR, respectively, may be genetic. With the exception of HDL-C, the shared genetic variance between MSX component traits and WC was higher than the genetic variance shared with HOMA-IR.Conclusion:A common genetic background contributes to the clustering of different MSX component traits and central obesity or insulin resistance. Common genetic influences favour central obesity as a major characteristic linking these traits.

What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?

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/m(2)): 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: -2.8 ± 2.9 L; VAT changes: -0.7 ± 1.0 L; SAT changes: -5.1 ± 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.

Assessment and definition of lean body mass deficiency in the elderly.

Although the effect of age on body composition has been intensively discussed during the past 20 years, we do not have a uniform definition of sarcopenia. A suitable definition of low, lean body mass should be based on magnetic resonance imaging (MRI) estimates of muscle mass. Using recent MRI data of a population of 446 healthy free-living Caucasian volunteers (247 females, 199 males) age 18–78 years, a low skeletal muscle mass and sarcopenia were defined as a skeletal muscle mass >1 and >2 s.d. below the mean value obeserved in younger adults at age 18–39 years. The cutoffs for low muscle mass according to the skeletal muscle index (skeletal muscle mass/(height)2) or the appendicular skeletal muscle mass index (skeletal muscle mass of the limbs/(height)2) were 6.75 or 4.36 kg/m2 for females and 8.67 or 5.54 kg/m2 for males, respectively. On the basis of these cutoffs, prevalences of sarcopenia in the group of adults at >60 years are calculated to be 29% in females and 19.0% in males. Faced with different sarcopenic phenotypes (that is, sarcopenia related to frailty and osteopenia; sarcopenic obesity related to metabolic risks; cachexia related to wasting diseases), future definitions of sarcopenia should be extended to the relations between (i) muscle mass and adipose tissue and (ii) muscle mass and bone mass. Suitable cutoffs should be based on the associations between estimates of body compositions and metabolic risks (for axample, insulin resistance), inflammation and muscle function (that is, muscle strength).

Application of standards and models in body composition analysis.

The aim of this review is to extend present concepts of body composition and to integrate it into physiology. In vivo body composition analysis (BCA) has a sound theoretical and methodological basis. Present methods used for BCA are reliable and valid. Individual data on body components, organs and tissues are included into different models, e.g. a 2-, 3-, 4- or multi-component model. Today the so-called 4-compartment model as well as whole body MRI (or computed tomography) scans are considered as gold standards of BCA. In practice the use of the appropriate method depends on the question of interest and the accuracy needed to address it. Body composition data are descriptive and used for normative analyses (e.g. generating normal values, centiles and cut offs). Advanced models of BCA go beyond description and normative approaches. The concept of functional body composition (FBC) takes into account the relationships between individual body components, organs and tissues and related metabolic and physical functions. FBC can be further extended to the model of healthy body composition (HBC) based on horizontal (i.e. structural) and vertical (e.g. metabolism and its neuroendocrine control) relationships between individual components as well as between component and body functions using mathematical modelling with a hierarchical multi-level multi-scale approach at the software level. HBC integrates into whole body systems of cardiovascular, respiratory, hepatic and renal functions. To conclude BCA is a prerequisite for detailed phenotyping of individuals providing a sound basis for in depth biomedical research and clinical decision making.

Gender-Specific Associations in Age-Related Changes in Resting Energy Expenditure (REE) and MRI Measured Body Composition in Healthy Caucasians

BACKGROUND The effect of gender as well as gender-specific changes of fat free mass (FFM) and its metabolic active components (muscle mass and organ masses [OMs]) and fat mass (FM) on age-related changes in resting energy expenditure (REE) are not well defined. We hypothesized that there are gender differences in (1) the age-specific onset of changes in detailed body composition (2); the onset of changes in body composition-REE associations with age. METHODS Using a cross-sectional magnetic resonance imaging database of 448 Caucasian participants (females and males) with comprehensive data on skeletal muscle (SM) mass, adipose tissue (AT), OMs, and REE. RESULTS We observed gender-specific differences in the onset of age-related changes in metabolic active components and REE. Declines in body composition and REE started earlier in females than in males for SM (29.4 vs 39.6 years), AT (38.2 vs 49.9 years), OM (34.7 vs 45.7 years), and REE (31.9 vs 36.8 years). The age-related decrease of AT was significantly higher in females than in males (-5.69kg/decade vs -0.59kg/decade). In females adjusted REEmFFM&FM (resting energy expenditure measured adjusted for FFM and FM) and REEmSM/OM/AT (resting energy expenditure measured adjusted for skeletal muscle and organ mass and adipose tissue) decreased by -145 kJ/d/decade and -604.8 kJ/d/ decade after the age of 35.2 respectively 34.3 years. SM was main determinant of REEm in females (R (2) = .67) and males (R (2) = .66) with remaining variance mainly explained by kidney mass (R (2) = .07) in females and liver mass (R (2) = .09) in males. CONCLUSION We concluded that gender affects the age-related changes in body composition as well as changes in body composition-REE relationship. This trial was registered at linicaltrials.gov as NCT01737034.

Use of height3:waist circumference3 as an index for metabolic risk assessment?

Current anthropometric indices for health risk assessment are indirect measures of total or visceral body fat mass that do not consider the inverse relationship of lean body mass to metabolic risk as well as the non-linear relationship between central obesity and insulin resistance. We examined a new anthropometric index that reflects the relationship of waist circumference (WC) as a risk factor to fat-free mass (FFM) as a protective parameter of body composition. In a population of 335 adults (191 females and 144 males; mean age 53 (SD 13.9) years) with a high prevalence of obesity (27%) and metabolic syndrome (30%) we derived FFM:WC(3) from the best fit of the relationship with metabolic risk factors (plasma triacylglycerol levels and insulin resistance by homeostasis model assessment index). Because FFM is known to be proportional to the cube of height, FFM was subsequently replaced by height(3) yielding height(3):WC(3) as an easily applicable anthropometric index. Significant inverse relationships of height(3):WC(3) to metabolic risk factors were observed for both sexes. They slightly exceeded those of conventional anthropometric indices such as BMI, WC or WC:hip ratio in women but not in men. The exponential character of the denominator WC(3) implies that at a given FFM with gradually increasing WC the increase in metabolic risk is lower than proportional. Further studies are needed to evaluate height(3):WC(3) as an anthropometric index for health risk assessment.

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.

Normalizing resting energy expenditure across the life course in humans: challenges and hopes

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.

Inadequacy of Body Weight-Based Recommendations for Individual Protein Intake—Lessons from Body Composition Analysis

Current body weight-based protein recommendations are ignoring the large variability in body composition, particularly lean mass (LM), which drives protein requirements. We explored and highlighted the inter-individual variability of weight versus body composition-adjusted protein intakes by secondary analysis in three cohorts of (1) 574 healthy adults (mean ± SD age: 41.4 ± 15.2 years); (2) 403 cirrhotic patients (age: 44.7 ± 12.3 years) and (3) 547 patients with lung cancer (age: 61.3 ± 8.2 years). LM was assessed using different devices (magnetic resonance imaging, dual-energy X-ray absorptiometry, computer tomography, total body potassium and bioelectrical impedance), body weight-based protein intake, its ratio (per kg LM) and mean protein requirement were calculated. Variability in protein intake in all cohorts ranged from 0.83 to 1.77 g protein per kg LM per day using (theoretical protein intake of 60 g protein per day). Calculated mean protein requirement was 1.63 g protein per kg LM per day; consequently, 95.3% of healthy subjects, 100% of cirrhotic and 97.4% of cancer patients would present with a low protein intake per kg LM. Weight-adjusted recommendations are inadequate to address the LM specific differences in protein needs of healthy subjects or clinical populations. Absolute protein intake seems to be more relevant compared to the relative proportion of protein, which in turn changes with different energy needs.