Anja Bosy-Westphal

Body fluid volume determination via body composition spectroscopy in health and disease

The assessment of extra-, intracellular and total body water (ECW, ICW, TBW) is important in many clinical situations. Bioimpedance spectroscopy (BIS) has advantages over dilution methods in terms of usability and reproducibility, but a careful analysis reveals systematic deviations in extremes of body composition and morbid states. Recent publications stress the need to set up and validate BIS equations in a wide variety of healthy subjects and patients with fluid imbalance. This paper presents two new equations for determination of ECW and ICW (referred to as body composition spectroscopy, BCS) based on Hanai mixture theory but corrected for body mass index (BMI). The equations were set up by means of cross validation using data of 152 subjects (120 healthy subjects, 32 dialysis patients) from three different centers. Validation was performed against bromide/deuterium dilution (NaBr, D2O) for ECW/TBW and total body potassium (TBK) for ICW. Agreement between BCS and the references (all subjects) was -0.4 +/- 1.4 L (mean +/- SD) for ECW, 0.2 +/- 2.0 L for ICW and -0.2 +/- 2.3 L for TBW. The ECW agreement between three independent reference methods (NaBr versus D2O-TBK) was -0.1 +/- 1.8 L for 74 subjects from two centers. Comparing the new BCS equations with the standard Hanai approach revealed an improvement in SEE for ICW and TBW by 0.6 L (24%) for all subjects, and by 1.2 L (48%) for 24 subjects with extreme BMIs (<20 and >30). BCS may be an appropriate method for body fluid volume determination over a wide range of body compositions in different states of health and disease.

Quercetin reduces systolic blood pressure and plasma oxidised low-density lipoprotein concentrations in overweight subjects with a high-cardiovascular disease risk phenotype: a double-blinded, placebo-controlled cross-over study

Regular consumption of flavonoids may reduce the risk for CVD. However, the effects of individual flavonoids, for example, quercetin, remain unclear. The present study was undertaken to examine the effects of quercetin supplementation on blood pressure, lipid metabolism, markers of oxidative stress, inflammation, and body composition in an at-risk population of ninety-three overweight or obese subjects aged 25-65 years with metabolic syndrome traits. Subjects were randomised to receive 150 mg quercetin/d in a double-blinded, placebo-controlled cross-over trial with 6-week treatment periods separated by a 5-week washout period. Mean fasting plasma quercetin concentrations increased from 71 to 269 nmol/l (P < 0.001) during quercetin treatment. In contrast to placebo, quercetin decreased systolic blood pressure (SBP) by 2.6 mmHg (P < 0.01) in the entire study group, by 2.9 mmHg (P < 0.01) in the subgroup of hypertensive subjects and by 3.7 mmHg (P < 0.001) in the subgroup of younger adults aged 25-50 years. Quercetin decreased serum HDL-cholesterol concentrations (P < 0.001), while total cholesterol, TAG and the LDL:HDL-cholesterol and TAG:HDL-cholesterol ratios were unaltered. Quercetin significantly decreased plasma concentrations of atherogenic oxidised LDL, but did not affect TNF-alpha and C-reactive protein when compared with placebo. Quercetin supplementation had no effects on nutritional status. Blood parameters of liver and kidney function, haematology and serum electrolytes did not reveal any adverse effects of quercetin. In conclusion, quercetin reduced SBP and plasma oxidised LDL concentrations in overweight subjects with a high-CVD risk phenotype. Our findings provide further evidence that quercetin may provide protection against CVD.

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.

Bioelectrical phase angle and impedance vector analysis – Clinical relevance and applicability of impedance parameters

BACKGROUND & AIMS The use of phase angle (PhA) and raw parameters of bioelectrical impedance analysis (BIA) has gained attention as alternative to conventional error-prone calculation of body composition in disease. This review investigates the clinical relevance and applicability of PhA and Bioelectrical Impedance Vector Analysis (BIVA) which uses the plot of resistance and reactance normalized per height. METHODS A comprehensive literature search was conducted using Medline identifying studies relevant to this review until March 2011. We included studies on the use of PhA or BIVA derived from tetrapolar BIA in out- and in-patient settings or institutionalized elderly. RESULTS Numerous studies have proven the prognostic impact of PhA regarding mortality or postoperative complications in different clinical settings. BIVA has been shown to provide information about hydration and body cell mass and therefore allows assessment of patients in whom calculation of body composition fails due to altered hydration. Reference values exist for PhA and BIVA facilitating interpretation of data. CONCLUSION PhA, a superior prognostic marker, should be considered as a screening tool for the identification of risk patients with impaired nutritional and functional status, BIVA is recommended for further nutritional assessment and monitoring, in particular when calculation of body composition is not feasible.

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 (≈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.

Phase Angle From Bioelectrical Impedance Analysis: Population Reference Values by Age, Sex, and Body Mass Index

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.

Cutoff percentiles of bioelectrical phase angle predict functionality, quality of life, and mortality in patients with cancer

BACKGROUND The bioelectrical phase angle has shown predictive potential in various diseases, but general cutoffs are lacking in the clinical setting. OBJECTIVES This study evaluated the prognostic value of the fifth percentile of sex-, age-, and body mass index-stratified phase angle reference values in patients with cancer with respect to nutritional and functional status, quality of life, and 6-mo mortality. In a second step, we also studied the effect of the standardized phase angle (with a z score to determine individual deviations from the population average) on these variables. DESIGN A total of 399 patients with cancer were studied. Phase angle was obtained with bioelectrical impedance analysis; muscle function was assessed by handgrip strength and peak expiratory flow. Quality of life was determined by the European Organization for Research and Treatment of Cancer questionnaire. Nutritional status was assessed by using Subjective Global Assessment. Survival of patients was documented after 6 mo. RESULTS Patients with a phase angle of less than the fifth reference percentile had significantly lower nutritional and functional status, impaired quality of life (P lt 0.0001), and increased mortality (P lt 0.001). The standardized phase angle emerged as a significant predictor for malnutrition and impaired functional status in generalized linear model regression analyses. It was also a stronger indicator of 6-mo survival than were malnutrition and disease severity in the Cox regression model (P lt 0.0001) and according to the receiver operating characteristic curve. CONCLUSIONS The standardized phase angle is an independent predictor for impaired nutritional and functional status and survival. The fifth phase angle reference percentile is a simple and prognostically relevant cutoff for detection of patients with cancer at risk for these factors.

Influence of Partial Sleep Deprivation on Energy Balance and Insulin Sensitivity in Healthy Women

Background: Voluntary sleep restriction is a lifestyle feature of modern societies that may contribute to obesity and diabetes. The aim of the study was to investigate the impact of partial sleep deprivation on the regulation of energy balance and insulin sensitivity. Subjects and Methods: In a controlled intervention, 14 healthy women (age 23–38 years, BMI 20.0–36.6 kg/m2) were investigated after 2 nights of >8 h sleep/night (T0), after 4 nights of consecutively increasing sleep curtailment (7 h sleep/ night, 6 h sleep/night, 6 h sleep/night and 4 h sleep/night; T1) and after 2 nights of sleep recovery (>8 h sleep/night; T2). Resting and total energy expenditure (REE, TEE), glucose-induced thermogenesis (GIT), physical activity, energy intake, glucose tolerance and endocrine parameters were assessed. Results: After a decrease in sleep du-ration, energy intake (+20%), body weight (+0.4 kg), leptin / fat mass (+29%), free triiodothyronine (+19%), free thyroxine (+10%) and GIT (+34%) significantly increased (all p < 0.05). Mean REE, physical activity, TEE, oral glucose tolerance, and ghrelin levels remained unchanged at T1. The effect of sleep loss on GIT, fT3 and fT4 levels was inversely related to fat mass. Conclusion: Short-term sleep deprivation increased energy intake and led to a net weight gain in women. The effect of sleep restriction on energy expenditure needs to be specifically addressed in future studies using reference methods for total energy expenditure.

Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome

Body composition is related to various physiological and pathological states. Characterization of individual body components adds to understand metabolic, endocrine and genetic data on obesity and obesity‐related metabolic risks, e.g. insulin resistance. The obese phenotype is multifaceted and can be characterized by measures of body fat, leg fat, liver fat and skeletal muscle mass rather than by body mass index. The contribution of either whole body fat or fat distribution or individual fat depots to insulin resistance is moderate, but liver fat has a closer association with (hepatic) insulin resistance. Although liver fat is associated with visceral fat, its effect on insulin resistance is independent of visceral adipose tissue. In contrast to abdominal fat, appendicular or leg fat is inversely related to insulin resistance. The association between ‘high fat mass + low muscle mass’ (i.e. ‘sarcopenic adiposity’) and insulin resistance deserves further investigation and also attention in daily clinical practice. In addition to cross‐sectional data, longitudinal assessment of body composition during controlled under‐ and overfeeding of normal‐weight healthy young men shows that small decreases and increases in fat mass are associated with corresponding decreases and increases in insulin secretion as well as increases and decreases in insulin sensitivity. However, even under controlled conditions, there is a high intra‐ and inter‐individual variance in the changes of (i) body composition; (ii) the ‘body composition–glucose metabolism relationship’ and (iii) glucose metabolism itself. Combining individual body components with their related functional aspects (e.g. the endocrine, metabolic and inflammatory profiles) will provide a suitable basis for future definitions of a ‘metabolically healthy body composition’.

Determinants and impact of sleep duration in children and adolescents: data of the Kiel Obesity Prevention Study.

Background/Objectives: This study investigates determinants of sleep duration and its impact on nutritional status, resting energy expenditure (REE), cardiometabolic risk factors and hormones in children/adolescents.Subjects/Methods: In 207 girls and 207 boys (13.0±3.4 (6.1–19.9) years) body mass index standard deviation score (BMI SDS), waist circumference (WC) z-score, body composition (air-displacement plethysmography), REE (ventilated hood system; n=312) and cardiometabolic risk factors/hormones (n=250) were assessed. Greater than 90th percentile of BMI/WC references was defined as overweight/overwaist. Sleep duration, media consumption (TV watching/computer use), physical activity, dietary habits, parental BMI, socio-economic status and early infancy were assessed by questionnaire. Short sleep was defined as <10 h per day for children <10 years and otherwise <9 h per day.Results: Total 15.9% participants were overweight, mean sleep duration was 8.9±1.3 h per day. Age explained most variance in sleep (girls: 57.0%; boys: 41.2%) besides a high nutrition quality score (girls: 0.9%) and a low media consumption (boys: 1.3%). Sleep was inversely associated with BMI SDS/WC z-score (girls: r=−0.17/−0.19, P<0.05; boys: r=−0.21/−0.20, P<0.01), which was strengthened after adjusting for confounders. Short vs long sleep was associated with 5.5-/2.3-fold higher risks for obesity/overwaist (girls). After adjusting for age, REE (adjusted for fat-free mass) was positively associated with sleep in boys (r=0.16, P<0.05). Independently of age and WC z-score, short sleep was associated with lower adiponectin levels in boys (11.7 vs 14.4 μg/ml, P<0.05); leptin levels were inversely related to sleep in girls (r=−0.23, P<0.05). Homoeostasis model assessment–insulin resistance (r=−0.20, P<0.05) and insulin levels (r=−0.20, P<0.05) were associated with sleep (girls), which depended on WC z-score.Conclusions: Age mostly determined sleep. Short sleep was related to a higher BMI SDS/WC z-score (girls/boys), a lower REE (boys), higher leptin (girls) and lower adiponectin levels (boys).