O Korth

Publications (3)11.84 Total impact

• Article: Influence of methods used in body composition analysis on the prediction of resting energy expenditure.

  O Korth, A Bosy-Westphal, P Zschoche, C C Glüer, M Heller, M J Müller
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  ABSTRACT: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. In a cross-sectional design measurements of REE and body composition were performed. The study population consisted of 50 men (age 37.1+/-15.1 years, body mass index (BMI) 25.9+/-4.1 kg/m2) and 54 women (age 35.3+/-15.4 years, BMI 25.5+/-4.4 kg/m2). REE was measured by indirect calorimetry and predicted by either FFM or body weight. Measurement of FFM was performed by methods based on a 2-compartment (2C)-model: skinfold (SF)-measurement, bioelectrical impedance analysis (BIA), Dual X-ray absorptiometry (DXA), air displacement plethysmography (ADP) and deuterium oxide dilution (D2O). A 4-compartment (4C)-model was used as a reference. When compared with the 4C-model, REE prediction from FFM obtained from the 2C methods were not significantly different. Intercepts of the regression equations of REE prediction by FFM differed from 1231 (FFM(ADP)) to 1645 kJ/24 h (FFM(SF)) and the slopes ranged between 100.3 kJ (FFM(SF)) and 108.1 kJ/FFM (kg) (FFM(ADP)). In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM(BIA)) to 75% (FFM(DXA)) and was only 46% for body weight. Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population- and/or investigator specificity of algorithms for REE prediction.
  European Journal of Clinical Nutrition 06/2007; 61(5):582-9. · 2.46 Impact Factor
• Article: Common familial influences on clustering of metabolic syndrome traits with central obesity and insulin resistance: the Kiel obesity prevention study.

  A Bosy-Westphal, S Onur, C Geisler, A Wolf, O Korth, M Pfeuffer, J Schrezenmeir, M Krawczak, M J Müller
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  ABSTRACT: 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. 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. 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. 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.
  International Journal of Obesity 06/2007; 31(5):784-90. · 4.69 Impact Factor
• Article: Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors.

  A Bosy-Westphal, C Geisler, S Onur, O Korth, O Selberg, J Schrezenmeir, M J Müller
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  ABSTRACT: 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). 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%). 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 >or=2 component traits of the MSX revealed a similar accuracy of all obesity indices. 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.
  International Journal of Obesity 04/2006; 30(3):475-83. · 4.69 Impact Factor