Childhood overweight is a public health problem. Defining overweight in children and adolescents is not uniform with respect to obesity indices and cut-offs used. BMI is widely used as a measure of fat mass (FM) and international BMI reference values for children and adolescents have been published(Reference Cole, Bellizzi, Flegal and Dietz1). However, BMI is only an indirect parameter of total body fat and does not reflect body fat distribution(Reference Lobstein, Baur and Uauy2, Reference Seidell3). In addition, the association between BMI and disease risk is unproven in children and adolescents. In adults, BMI cut-offs for overweight and obesity were defined according to overweight- and obesity-associated co-morbidity(4). However, in children prospective data on the association between obesity indices and disease risk are rare; for example, longitudinal data of the Bogalusa Heart Study showed a relationship between childhood obesity and incidence of metabolic disorders in young adulthood(Reference Janssen, Katzmarzyk, Srinivasan, Chen, Malina, Bouchard and Berenson5). The International Obesity Taskforce Working Group recommended that BMI cut-offs for defining overweight and obesity in children should be linked to the adult disease-related cut-off points of 25 and 30 kg/m2(Reference Cole, Bellizzi, Flegal and Dietz1).
In addition to BMI, triceps skinfold thickness (TSF), waist circumference (WC) and percentage FM (%FM) as derived from bioelectrical impedance analysis have been recommended to identify individuals with increased overweight-associated disease risks(Reference Seidell3). However, the use of these obesity indices is limited because of methodological aspects as well as of the absence of adequate reference databases. Recently, reference databases have been published for WC and %FM in children(Reference Fredriks, van Buuren, Fekkes, Verloove-Vanhorick and Wit6, Reference McCarthy, Cole, Fry, Jebb and Prentice7). However, since these reference databases differ with respect to populations, data on prevalence, based on either direct or indirect measures of FM, are not comparable. In addition, comparing different indices of FM, WC showed the strongest association to CVD risks. WC correlates with visceral FM (in adults(Reference Janssen, Heymsfield, Allison, Kotler and Ross8) as well as in children(Reference Brambilla, Bedogni, Moreno, Goran, Gutin, Fox, Peters, Barbeau, De Simone and Pietrobelli9)) and visceral adipose tissue is associated with metabolic disorders and CVD risk factors(Reference Caprio, Hyman, McCarthy, Lange, Bronson and Tamborlane10).
At present, only a few studies have investigated the value of the combined use and the possible additive value of using different obesity indices in children and adolescents. This is of particular interest, since in adults two independent studies indicated that combining BMI and WC is a better predictor of metabolic risk than either measure alone(Reference Zhu, Heshka, Wang, Shen, Allison and Ross11, Reference Ardern, Katzmarzyk, Janssen and Ross12). In fact, in the Bogalusa Heart Study(Reference Janssen, Katzmarzyk, Srinivasan, Chen, Malina, Bouchard and Berenson13), the presence of elevated health risk among children and adolescents could also be better identified by a combination of BMI and WC. Following this idea, we compared BMI, TSF, WC and %FM with respect to their association with blood pressure (BP) and blood lipids in a greater group of children and adolescents using cross-sectional data of the Kiel Obesity Prevention Study (KOPS). We hypothesised that (i) a direct measure of FM or WC exceeds the value of BMI and (ii) combining BMI or WC with a second obesity index further improves the identification of children and adolescents with CVD risk.
Methods
Study populations
KOPS is an ongoing study which started in 1996 and will run up to 2009. The design of KOPS has been described previously(Reference Müller, Asbeck, Mast, Langnäse and Grund14–Reference Danielzik, Pust, Landsberg and Müller16). Up to now three samples of 4997 first graders (5–7 years old), 4487 fourth graders (9–11 years old) and 3196 eighth graders (13–16 years old) have been recruited. These samples have been shown to be representative for all children of the respective age groups in Kiel(Reference Danielzik, Pust, Landsberg and Müller16). In the present paper two samples of 9–11-year-old children and 13–16-year-old adolescents were analysed in which BP and blood lipid measurements have been performed. BP was measured in 4220 children and 3174 adolescents. Blood lipids were screened in a randomly selected subgroup of 15–20 % of subjects only (due to high costs, i.e. in 729 children and 536 adolescents). These samples are characterised and compared with the complete KOPS samples (Fig. 1) and overweight children ( ≥ 90th BMI percentile (90 P)) were under-represented. Regarding blood lipid screening, overfat children ( ≥ 90 P of %FM) were over-represented. At age 13–16 years, there was an over-representation of ‘overwaist’ adolescents ( ≥ 90 P of WC). All parents gave their informed written consent. The study protocol was approved by the local ethical committee.
Fig. 1 Numbers and bias of the samples with blood pressure (BP) measurements and lipid screening in comparison with the complete Kiel Obesity Prevention Study (KOPS) samples of 9–11-year-old children and 13–16-year-old adolescents. 90 P, 90th percentile; TSF, triceps skinfold; WC, waist circumference; FM, fat mass. * Significantly different from the complete KOPS samples. † Samples of which 90 P of BMI, TSF, WC and FM were calculated.
Measurement of obesity indices
Obesity indices were measured anthropometrically and by bioelectrical impedance analysis as described previously(Reference Müller, Asbeck, Mast, Langnäse and Grund14, Reference Danielzik, Pust, Landsberg and Müller16). Briefly, WC was measured midway between the lowest rib and the top of the iliac crest at the end of gentle expiration. FM was calculated from bioelectrical impedance analysis measurements using a population-specific algorithm derived and cross-validated in a randomly selected sample of 158 5–18-year-old children and adolescents (56 % girls) using air-displacement plethysmography as the reference method(Reference Bosy-Westphal, Danielzik, Becker, Geisler, Onur, Korth, Bührens and Müller17). The bioelectrical impedance analysis algorithm was:
FM was then calculated from the difference between body weight and fat-free mass.
The intra-observer CV were 0·5, 4·2, 0·4 and 1·5 % for BMI, TSF, WC and %FM, respectively (for three repeated measurements in forty-two (BMI, WC), 150 (TSF) and ten (%FM) children). The corresponding inter-observer CV were 0·5, 5·7, < 1 and 0·7 %, respectively (for three different observers in forty-two (BMI, WC), fifteen (TSF) and ten (%FM) children). To compare different obesity indices the population-, age- and sex-specific 90 P were used (Table 1). Subjects above the 90 P of BMI were defined as overweight; children exceeding the 90 P of TSF, WC or FM were described as overfat.
Table 1 Population-, age- and sex-specific 90th percentiles (10th percentile for high-density lipoprotein) as derived from the complete cohorts of Kiel Obesity Prevention Study
TSF, triceps skinfold; WC, waist circumference; FM, fat mass derived from bioelectrical impedance analysis.
Tanner stage (pubic hair stages for sexes, breast stage for girls, and genitalia stages for boys) was self-estimated by the adolescents using standard pictures(Reference Tanner18) on scales from 2 to 5. This procedure has been validated by Duke et al. (Reference Duke, Litt and Gross19).
Measurement of cardiovascular disease risk factors
BP was measured using a sphygmomanometer on the right arm after a 5 min rest. Hypertension was defined by the age-specific 95 P for systolic and diastolic BP as recommended by the Second Task Force on Blood Pressure Control in Children(20). Measurements of blood lipids were performed before noon in school. Blood lipids were analysed with the Cholestech L-D-X® (Cholestech, Hayward, CA, USA) from capillary blood. Since there are neither German reference data for blood lipids nor international reference data based on analyses with the Cholestech L-D-X®, our own age- and sex-specific 10 P of HDL-cholesterol and 90 P of TAG, LDL-cholesterol and total cholesterol were used to define low and high levels (Table 1). Measurements were done under fasting conditions in 18 and 12 % of the children and adolescents. A total of 82 and 88 % of the children and adolescents had breakfast at about 1·45 (range 1·00–2·25) h and 1·20 (range 0·34–2·10) h before measurement, respectively. Comparing fasting with non-fasting values, TAG levels in fasting children were only marginally lower (median and interquartile range (IQR) of fasting/non-fasting TAG (mg/l): 820 (IQR 560–1240)/880 (IQR 580–1240)). The prevalence of elevated TAG was 28 and 30 % in fasting and non-fasting children. At age 13–16 years, non-fasting adolescents had slightly higher values when compared with fasting adolescents (770 (IQR 600–1030)/850 (IQR 600–1240)). Of fasting and non-fasting adolescents, 10 and 23 % had elevated levels. Because of the small differences, fasting and non-fasting values were analysed together.
Statistical analysis
All analyses were performed with SPSS (version 13.0; SPSS Inc., Chicago, IL, USA) for windows. Data are presented as medians and IQR. The Mann–Whitney U test was used to evaluate sex differences in subject characteristics. Levels of blood lipids were log-transformed to normalise the distribution. Pearson and partial correlation coefficients were used to determine the associations among the obesity indices and CVD risk factors. To compare the (correlated) correlation coefficients between different obesity indices, the test of significance according to Meng et al. (Reference Meng, Rosenthal and Rubin21) was performed. Stepwise linear regression analyses were done to estimate the explained variance of CVD risk factors by BMI and WC. Initially, the R 2 was determined for a base model based on age and pubertal development. Then, BMI and/or WC were added to the base model, to determine the additional variance above the base model that was explained by BMI and/or WC.
Logistic regression analyses were performed to estimate OR for elevations in BP and blood lipids from TSF, WC and %FM within children and adolescents having normal ( < 90 P) and elevated ( ≥ 90 P) BMI and WC values, respectively. Level of significance was set at P < 0·05 (two-sided).
Results
Subject characteristics
The main characteristics of the samples are shown in Table 2. All obesity indices increased with age with the exception of %FM in boys. Sex differences in obesity indices were seen in both samples: when compared with boys, BMI, %FM and TSF were higher in girls, whereas WC was greater in boys. At age 9–11 years, girls had higher total-cholesterol and LDL levels than boys. At age 13–16 years, boys had lower TAG, HDL and total-cholesterol levels but higher BP than girls.
Table 2 Main characteristics of the study samples of 9–11- and 13–16-year-old children and adolescents
(Medians and interquartile ranges (IQR))
TSF, triceps skinfold; WC, waist circumference; FM, fat mass derived from bioelectrical impedance analysis; BPsys, systolic blood pressure; BPdias, diastolic blood pressure.
* Significantly different from girls (P < 0·05; Mann–Whitney U test).
Correlation of obesity indices with cardiovascular disease risk factors
The correlation coefficients between individual obesity indices and either BP or blood lipids are shown in Table 3. In children, BMI and WC had higher correlation coefficients than %FM. When compared with BMI, WC had a closer correlation with BP in girls only. In adolescents, BMI showed higher correlation coefficients to BP than %FM and TSF; in boys this was also true for log HDL. Because individual obesity indices were highly inter-correlated (9–11/13–16-year-old boys: BMI-TSF, r 0·81/0·67; BMI-WC, r 0·86/0·87; BMI-%FM, r 0·86/0·75; girls: BMI-TSF, r 0·71/0·71; BMI-WC, r 0·80/0·75; BMI-%FM, r 0·85/0·82; for all: P < 0·001) BMI was further adjusted for TSF, WC or %FM. After adjustment for either TSF or %FM, BMI still had significant associations with BP, log TAG and log HDL. By contrast, adjustment of BMI for WC reduced the correlation coefficients, although correlation coefficients with BP remained significant in boys. Vice versa, WC adjusted for BMI still had significant associations with BP and blood lipids (in girls only). There were no associations between the obesity indices and either total cholesterol or LDL levels. These two variables were therefore excluded from further analyses.
Table 3 Correlation coefficients, adjusted for pubertal development, between obesity indices and blood pressure and blood lipids in 9–11- and 13–16-year-old children and adolescents†
TSF, triceps skinfold; WC, waist circumference; FM, fat mass derived from bioelectrical impedance analysis; BPsys, systolic blood pressure; BPdias, diastolic blood pressure.
Significant correlation coefficients: *P < 0·05, **P < 0·01.
† For 9–11- and 13–16-year-old boys, n 2112 and n 2108 for blood pressure and n 419 and n 348 for blood lipids; for 9–11- and 13–16-year-old girls, n 1534 and n 1640 for blood pressure and n 307 and n 350 for blood lipids.
‡ Significantly different to the correlation coefficient of BMI (Meng et al. (Reference Meng, Rosenthal and Rubin21)).
Prevalence of cardiovascular disease risk factors at normal or elevated obesity indices
The prevalences of elevated BP and blood lipids at normal ( < 90 P) and elevated ( ≥ 90 P) obesity indices are shown in Table 4. When compared with normal-weight or normal-fat subjects, overweight and overfat subjects had higher BP. The prevalence of elevated blood levels was not significantly different for all obesity indices between subjects with normal and high indices.
Table 4 Prevalence of elevations in systolic and diastolic blood pressure (BPsys and BPdias) and blood lipids in 9–11- and 13–16-year-old children and adolescents stratified by sex and different obesity indices being normal (<90th percentile; 90 P) and elevated (≥90 P)
(Percentages and 95 % confidence intervals)
TSF, triceps skinfold; WC, waist circumference; FM, fat mass derived from bioelectrical impedance analysis.
* Significantly different from subjects with elevated ( ≥ 90 P) levels in obesity indices (P < 0·05; Fisher's exact test).
Explained variance of cardiovascular disease risks by body mass index and waist circumference
Using stepwise regression analyses, a considerable variance in BP and blood lipids could be explained by BMI and WC (Table 5). The base model included age and pubertal development. The R 2 value for BMI and WC represented the additional variance (above the base model) explained by the obesity indices. BMI explained up to 10·9 % of the variance in CVD risk factors and WC explained up to 11·6 %. Combining BMI and WC explained up to 12·2 % of the CVD risks. Although the added variance above that predicted by BMI alone or WC alone was minimal, the combination of BMI and WC provided the best prediction.
Table 5 Explained variance (R 2) in systolic and diastolic blood pressure (BPsys and BPdias) and blood lipids by age, pubertal development, body mass index and waist circumference (linear regression analysis)
* The base model was a function of age and pubertal development.
Odds ratios of cardiovascular disease risks according to obesity indices
The results of the logistic regression models conducted within groups with normal ( < 90 P) and elevated ( ≥ 90 P) BMI are presented in Table 6. Prevalence of hypertension, high TAG and low HDL was higher in 9–11-year-old boys with normal BMI but elevated either TSF or WC or %FM when compared with those having normal values in all obesity indices. In 9–11-year-old girls this was also seen for BP in girls with elevated TSF and for low HDL in girls with elevated WC. In adolescents, the prevalence of elevated systolic BP was increased in boys with an elevated TSF but normal BMI. In addition, the prevalence of high TAG was higher in adolescent boys with elevated WC but normal BMI when compared with normal BMI and normal WC. At increased BMI, the additional use of TSF or %FM did not increase the estimation of CVD risk factors (except estimation for high diastolic BP in 13–16-year-old boys which improved by adding a high FM). By contrast, using increased WC as an additional obesity index further improved the estimation of elevated systolic BP in 9–11-year-old girls and adolescents.
Table 6 Prediction of elevated systolic and diastolic blood pressure (BPsys and BPdias) and blood lipids (BL) from elevations in obesity indices in children and adolescents with normal (<90th percentile; 90 P) and elevated (≥90 P) body mass index (logistic regression analyses)*
(Odds ratios and 95 % confidence intervals)
TSF, triceps skinfold; WC, waist circumference; FM, fat mass derived from bioelectrical impedance analysis.
* OR indicate TSF, WC or %FM ≥ 90 P relative to TSF, WC or %FM < 90 P (OR = 1) within subjects with BMI < 90 P and BMI ≥ 90 P.
† Numbers of subjects for BP and BL measurements with obesity indices < 90 P and ≥ 90 P.
‡ Significant OR (P < 0·05); models are adjusted for age and pubertal development.
OR of increases in BP and blood lipids for individual obesity indices stratified according to normal and elevated WC are shown in Table 7. Boys, aged 9–11 years, with normal WC had an increased prevalence of high BP when either TSF or %FM was elevated. This was contrary in girls of the same age, where the prevalence of hypertension was elevated when TSF or BMI was elevated. In addition, the prevalence of high TAG was elevated in children with normal WC but elevated BMI. Adolescents with normal WC but increased BMI had a higher diastolic BP. Systolic BP was increased in adolescent boys with elevated TSF but normal WC. In males of both age groups, increased TSF plus increased WC was associated with an increased prevalence of elevated systolic BP. In 9–11-year-old girls and adolescents, prevalence of high BP was elevated when BMI was elevated in addition to WC.
Table 7 Prediction of elevated systolic and diastolic blood pressure (BPsys and BPdias) and blood lipids (BL) from elevations in obesity indices in children and adolescents with normal (<90th percentile; 90 P) and elevated (≥90 P) waist circumference (WC) (logistic regression analyses)*
(Odds ratios and 95 % confidence intervals)
TSF, triceps skinfold; FM, fat mass derived from bioelectrical impedance analysis.
* OR indicate TSF, BMI or %FM ≥ 90 P relative to TSF, WC or %FM < 90 P (OR = 1) within subjects with WC < 90 P and WC ≥ 90 P.
† Numbers of subjects for BP and BL measurements with obesity indices < 90 P and ≥ 90 P.
‡ Significant OR (P < 0·05); models are adjusted for age and pubertal development.
Discussion
In this cross-sectional study, we compared four obesity indices with respect to their association with CVD risk factors in children and adolescents. The main results of the present paper were that (i) all four obesity indices showed significant associations with CVD risk factors, (ii) correlation coefficients were similar for BMI and WC which both exceeded those of %FM and TSF and (iii) in children with normal BMI (especially in boys) or normal WC as well as in adolescents with elevated WC the additional use of a second obesity index further improved identification of CVD risk factors.
Body mass index v. other obesity indices
Although several anthropometric parameters have been shown to be associated with CVD risk factors, controversy still exists regarding the best anthropometric marker assessing the relationship between body fat (including fat distribution) and CVD risk factors. BMI is a well-established measure of relative FM in children and adolescents. However, BMI has some limitations: the relationship between BMI and fat and fat-free mass varies at different ages. Maynard et al. (Reference Maynard, Wisemandle, Roche, Chumlea, Guo and Siervogel22) could show that up to late adolescence, annual increases in BMI were driven primarily by increases in fat-free mass. Moreover, the sensitivity of BMI (in relation to %FM) is low(Reference Moreno, Blay, Rodriguez, Blay, Mesana, Olivares, Fleta, Sarria and Bueno23). Therefore, BMI is recommended for screening of overweight in population studies. By contrast, in the clinical setting, a more accurate measure of body fat is needed. Nevertheless, in children, overweight defined by BMI was shown to be a strong predictor of obesity and risk of CHD in young adulthood(Reference Janssen, Katzmarzyk, Srinivasan, Chen, Malina, Bouchard and Berenson5, Reference Raitakari, Juonala and Viikari24). In addition, there was a close relationship between increased body weight and elevated BP(Reference Wilson and Gaffney25). In contrast, in another study in children WC rather than BMI was the best predictor of the metabolic syndrome(Reference Moreno, Pineda, Rodriguez, Fleta, Sarria and Bueno26). Several studies have also shown that body size accounts for a greater proportion of variance in systolic BP than %FM(Reference Stallones, Mueller and Christensen27–Reference Al Sendi, Shetty, Musaiger and Myatt29). In adults, Shen et al. (Reference Shen, Punyanitya, Chen, Gallagher, Albu, Pi-Sunyer, Lewis, Grunfeld, Heshka and Heymsfield30) as well as Bosy-Westphal et al. (Reference Bosy-Westphal, Geisler, Onur, Korth, Selberg, Schrezenmeir and Müller31) have recently shown that %FM had no advantage over BMI and WC in predicting obesity-related metabolic risk factors. In adolescents, Steinberger et al. (Reference Steinberger, Jacobs, Raatz, Moran, Hong and Sinaiko32) showed that when compared with %FM, BMI had a closer correlation with CVD risk factors. Our data support this idea for children.
In contrast, our data did not support our hypothesis that direct measures of FM or WC exceed the value of BMI. WC and BMI were similarly correlated to CVD risk factors. In addition, the association of either WC or BMI with CVD risk factors remained even after controlling for each other (Table 3). The latter finding is in line with data of Lee et al. (Reference Lee, Bacha, Gungor and Arslanian33).
Identification of a risk group with cardiovascular disease risk factors
The present data argue in favour of the idea that (i) WC as well as BMI are appropriate to estimate CVD risk factors but (ii) more detailed information may result from their combined use. Of children and adolescents with an elevated obesity index, 20–30 % also had elevated levels in BP and blood lipids (Table 4). The characterisation of a risk group with elevated BP, high TAG or low HDL levels was improved by using an additional obesity index (Tables 6 and 7). However, in adolescents with normal BMI or normal WC the additional use of a second obesity index did not exceed the value of an individual BMI only. In contrast, in adolescents with elevated WC, the identification of high BP was improved by the use of a second obesity index (Table 7). This is in line with other cross-sectional data of 5–18-year-old children where TAG levels as well as BP values were highest when both BMI and WC were elevated(Reference Janssen, Katzmarzyk, Srinivasan, Chen, Malina, Bouchard and Berenson13). However, using elevated BMI only, the additional use of a second obesity index did not improve risk estimation beyond that provided by BMI alone (Table 6). Regarding our second hypothesis the combined use of obesity indices can be recommended in subgroups only.
Faced with four different obesity indices we could not definitively say which index should be used. This is because similar risk estimations were observed for different indices. BMI and WC had the highest correlation to CVD risk factors; thus the combination of these two parameters can be recommended. In addition, they are easy to measure.
Study limitations and methodological considerations
It should be mentioned that we have used our own population-specific 90 P of individual obesity indices to define overweight and overfat. Using different reference databases and cut-offs would lead to different prevalences of elevated parameters and therefore to differences in the association with CVD risk factors. For example, in our 9–11-year-old children the use of American(34), German(Reference Kromeyer-Hauschild and Wabitsch35) and worldwide(Reference Cole, Bellizzi, Flegal and Dietz1) reference databases for BMI led to prevalences of overweight and obesity of 7·5, 17·6 and 23·3 %, respectively. However, focusing on the 10 % highest values of each obesity index allows a direct comparison of data.
Regarding measurements of blood lipids the use of fasting levels is preferable. However, in population studies blood sampling cannot always be performed under perfectly standardised conditions. Thus, we could not investigate all children and adolescents under fasting conditions. This obviously affects plasma TAG levels. However, reanalysis of TAG levels in our children showed only minor differences between fasting and non-fasting levels (see Methods). In contrast, larger differences were seen in adolescents, which may slightly bias the results.
We have used our own percentiles to define elevated or decreased levels in blood lipids. This was due to the lack of German reference data. Nevertheless, recalculating all analyses using American reference values(Reference Rifkind and Segal36) led to nearly the same significances.
In a linear regression analysis, we could only explain about 12 % of the variance in systolic BP and 2–9 % of the variance in blood lipids by BMI plus WC. These low numbers are comparable with data obtained in other cross-sectional studies; for example, explained variance of a lipid-related metabolic risk score was 8·5 and 6 % in 15-year-old boys and girls for BMI and 7·3 and 6 % for WC(Reference Mesa, Ortega, Ruiz, Castillo, Tresaco, Carreño, Moreno, Gutiérrez and Bueno37). The association between obesity indices and CVD risk factors should become more obvious using longitudinal data. In addition, genetic factors add to the manifestation of CVD risk factors, for example, in a group of KOPS families we have recently analysed heritabilities of single traits of the metabolic syndrome which varied between 18 % (systolic BP) and 39 % (HDL)(Reference Bosy-Westphal, Onur, Geisler, Wolf, Korth, Pfeuffer, Schrezenmeir, Krawczak and Müller38).
Conclusion
BMI and WC had similar associations with CVD risk factors. These associations exceeded those of other obesity indices. Risk estimations were improved by using a second obesity index in children with normal BMI (especially in boys) or normal WC and in adolescents with elevated WC.
Acknowledgements
The present study was supported by grants from Deutsche Forschungsgemeinschaft (DFG Mü 5–1, 5–2, 5–3, 5–5), Wirtschaftliche Vereinigung Zucker, Precon and WCRF. The sponsors of the study had no role in study design, data collection, data analyses, data interpretation or writing of the paper. The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication.
S. P.-D. had the original idea, did the statistical analyses and interpretation of the data, and wrote the manuscript. B. L., M. J. and D. L. acquired data. M. J. M. supervised the study, did the interpretation of the data and wrote the paper. All authors discussed the data and approved the final version of the paper.
