Background
Several studies have focused on the presence of early biological abnormalities in excess-weight youths, including elevated fasting glycaemia, insulin resistance, hypertriglyceridemia, high-density lipoprotein cholesterol (HDL-cholesterol), elevated blood pressure and causing several comorbidities in adults [
1‐
5]. Furthermore, some adipokines, namely leptin, adiponectin and resistin, have been identified as potential risk markers for a systemic low-grade inflammation state, which might lead to insulin resistance, type-2 diabetes and cardiovascular (CV) diseases [
6‐
8].
Moreover, beyond global excess weight, the role of the abdominovisceral adiposity as independent cardiometabolic risk factor has been underlined from children onwards [
9], while more peripheral fat has been considered as protective [
10].
Magnetic Resonance Imaging (MRI), Computed Tomography-Scan (CT-Scan) and Dual-energy X ray Absorptiometry (DXA) have been described as the gold standard of adiposity measurement and used to accurately assess body fat distribution and related comorbidities [
9,
11,
12]. However, these techniques are still no accessible because of their high cost and irradiation in the case of CT-Scan measurements as well [
11,
12].
Therefore, in order to assess the comorbidities associated with overweight and obesity and abdomino-visceral adiposity in youths, the identification of simple and accurate anthropometric methods that can be used with efficiency as clinical and research tools is essential.
Studies analysing the relationship between the easy-to-use anthropometric measures for total fat mass, body fat distribution and cardiometabolic risk factors are highly controversial when it comes to youths. Several authors suggested that only body mass index (BMI) constitutes an accurate predictor of biological abnormalities and cardiometabolic impairments [
13‐
17], whereas others highlighted the role of the waist-to-hip ratio (WHR) [
18,
19], waist circumference (Waist C) [
20,
21] and/or waist-to-height ratio (WHtR) [
22,
23]. Furthermore, certain studies showed no significant differences in the ability of BMI and WHR [
24], BMI and Waist C [
25], BMI and WHtR [
26], as well as Waist C and WHtR [
27] to predict cardiometabolic risk factors. Finally, in some other studies, differential associations were observed between CV risk factors and anthropometric measures [
28,
29].
In adults, extensive studies showed that adding anthropometric measures of body fat distribution such as WHR or Waist C, to BMI, allows predicting CV risk factors, diseases and death more accurately [
2,
30‐
34]. This type of associations has not really been developed in paediatric populations. Indeed, in an attempt to predict cardiometabolic risk factors in youths, some previous paediatric studies either tested the efficiency of a single anthropometric measurement [
14,
21,
23,
25] or assessed the contribution of BMI only as a potential confounder of other variables involved [
18,
20,
27,
29].
The present study investigated the ability of the “BMI and Waist C”, “BMI and WHR” and/or “BMI and WHtR” associations to predict cardiometabolic risk factors in overweight and obese youths. The consistency of our findings was evaluated by assessing the ability to predict the same risk factors presented by the associations between total fat mass and trunk fat mass, respectively total fat mass and trunk/legs fat mass as obtained by dual energy X-ray absorptiometry (DXA), which is the body-composition gold-standard analysis.
Results
The anthropometric, body composition and biological characteristics of the participants are summarized in Table
1.
Table 1
Subject characteristics
N | 106 | 97 | 203 |
Age (years) | 12.2 ± 2.5 | 11.8 ± 2.4 | 12.0 ± 2.4 |
Pubertal status (Percentages) |
Yes | 84 (79.2 %) | 47 (48.5 %) | 131 (64.5 %) |
No | 22 (20.8 %) | 50 (51.5 %) | 72 (35.5 %) |
Anthropometry |
BMI (kg/m2) | 28.5 ± 5.6 | 28.2 ± 4.9 | 28.3 ± 5.3 |
BMI Z score | 2.42 ± 0.58 | 2.68 ± 0.53* | 2.54 ± 0.57 |
Waist C (cm) | 83.8 ± 12.4 | 86.5 ± 11.5 | 85.1 ± 12.0 |
Waist C Z score | 2.22 ± 0.63 | 2.46 ± 0.58* | 2.33 ± 0.62 |
WHtRa | 0.54 ± 0.06 | 0.56 ± 0.05* | 0.55 ± 0.06 |
WHRb | 0.84 ± 0.06 | 0.89 ± 0.05** | 0.86 ± 0.06 |
WHR Z score | 0.71 ± 0.89 | 0.85 ± 0.95 | 0.78 ± 0.92 |
Biology |
Fasting glucose (mg/dl) | 86.2 ± 6.8 | 86.9 ± 6.2 | 86.5 ± 6.5 |
Fasting insulin (mUI/l) | 17.5 ± 8.5 | 14.8 ± 8.3* | 16.2 ± 8.5 |
HOMA IR | 3.76 ± 1.98 | 3.21 ± 1.87* | 3.50 ± 1.94 |
QUICKI | 0.321 ± 0.024 | 0.330 ± 0.027* | 0.326 ± 0.026 |
Triglycerides (mg/dl) | 98.4 ± 58.4 | 90.0 ± 51.1 | 94.3 ± 55.1 |
HDL cholesterol (mg/dl) | 54.4 ± 12.7 | 52.9 ± 12.1 | 53.7 ± 12.4 |
LDL cholesterol (mg/dl) | 92.3 ± 29.0 | 93.0 ± 28.2 | 92.6 ± 28.6 |
CRP (mg/l) | 2.9 ± 4.1 | 3.2 ± 3.8 | 3.1 ± 4.0 |
Fibrinogen (g/l) | 3.7 ± 0.7 | 3.6 ± 0.6 | 3.6 ± 0.7 |
Adiponectin (μg/ml) | 8.0 ± 4.7 | 7.8 ± 4.5 | 7.9 ± 4.6 |
Leptin (ng/ml) | 38.7 ± 23.1 | 27.4 ± 16.1** | 33.3 ± 20.8 |
Resistin (ng/ml) | 5.3 ± 2.2 | 5.1 ± 2.0 | 5.2 ± 2.1 |
DXA |
Total fat mass (kg) | 32.51 ± 14.29 | 30.11 ± 10.85 | 31.37 ± 12.80 |
Trunk fat mass (kg) | 15.07 ± 7.14 | 14.17 ± 5.87 | 14.64 ± 6.57 |
Trunk/legs fat mass index | 1.22 ± 0.24 | 1.27 ± 0.28 | 1.24 ± 0.26 |
Blood pressure |
SBP (mmHg) | 117 ± 12 | 118 ± 14 | 117 ± 13 |
SBP Z score | 0.99 ± 1.04 | 0.91 ± 1.10 | 0.95 ± 1.07 |
DBP (mmHg) | 71 ± 9 | 72 ± 8 | 72 ± 9 |
DBP Z score | 0.75 ± 0.78 | 0.81 ± 0.64 | 0.78 ± 0.71 |
Relationships between single anthropometric variables and CV risk factors
BMI Z Score was the most accurate single predictor of fasting glucose, fasting insulin, HOMA IR, QUICKI, leptin and resistin. Triglycerides and HDL cholesterol were most accurately predicted by Waist C Z Score. Blood pressure, CRP and fibrinogen were most accurately predicted by WHtR. WHR Z Score was the most accurate single predictor of adiponectin (Table
2).
Table 2
Relationships between a single anthropometric measurement and biological variables
Pearson’s R |
Fasting glucose | 0.235* | 0.176* | 0.193* | 0.057 |
Fasting insulina | 0.490** | 0.483** | 0.463** | 0.295** |
HOMA IRa | 0.493** | 0.480** | 0.463** | 0.290** |
QUICKI | −0.475** | −0.463** | −0.444** | −0.283** |
Triglyceridesa | 0.205* | 0.270** | 0.250** | 0.249** |
HDL cholesterola | −0.205* | −0.293** | −0.252** | −0.273** |
LDL cholesterol | −0.047 | −0.013 | 0.003 | 0.018 |
SBP Z score | 0.385** | 0.389** | 0.433** | 0.198* |
DBP Z score | 0.392** | 0.353** | 0.418** | 0.186* |
CRPa | 0.374** | 0.388** | 0.472** | 0.261** |
Fibrinogena | 0.341** | 0.316** | 0.375** | 0.193* |
Adiponectina | −0.187* | −0.277** | −0.201* | −0.279** |
Leptina | 0.551** | 0.498** | 0.546** | 0.119 |
Resistina | 0.229* | 0.181* | 0.191* | 0.064 |
Prediction of CV risk factors using models adding anthropometric surrogates of body fat distribution to general adiposity measurements
The initial model including BMI Z Score, age, sex and pubertal status accounted for respectively 7.4, 43.7, 42.7, 41.4, 7.9, 4.3, 18.8, 17.5, 14.6, 19.9, 10, 50.2 and 9.5 % of the fasting glucose, insulin, HOMA IR, QUICKI, triglycerides, HDL-cholesterol, SBP Z Score, DBP Z Score, CRP, fibrinogen, adiponectin, leptin and resistin variances.
Adding WHR Z Score improved fasting insulin (R2: 45.9 %; r2partial: 3.9 %), HOMA IR (R2: 44.7 %; r2partial: 3.6 %), QUICKI (R2: 43.3 %; r2partial: 3.3 %), HDL-cholesterol (R2: 9.6 %; r2partial: 5.6 %), triglycerides (R2: 11.7 %; r2partial: 4.2 %), adiponectin (R2: 14.3 %; r2partial: 4.7 %) and CRP (R2: 18.2 %.; r2partial: 4.3 %) prediction.
Associating Waist C Z Score with BMI Z Score, age, sex and pubertal status showed similar findings except for CRP. Indeed, Waist C Z Score accounted for 3.2 % of fasting insulin variance (R2: 45.5 %), respectively for 2.6 % of HOMA IR (R2: 44.2 %), 2.5 % of QUICKI (R2: 42.9 %), 6.8 % of HDL-cholesterol (R2: 10.8 %), 4.7 % of triglycerides (R2: 12.2 %) and 8.5 % of adiponectin (R2: 17.7 %) variances.
Associated with BMI Z Score, age, sex and pubertal status, WHtR accounted for 2.4 % of the HDL-cholesterol variance (R
2: 6.5 %), respectively for 4.4 % of the SBP Z Score (R
2: 22.4 %), 3 % of the DBP Z Score (R
2: 20 %), 10.2 % of the CRP (R
2: 23.3 %) and 2.4 % of the fibrinogen (R
2: 21.8 %) variances (Table
3).
Table 3
Multivariable anthropometric prediction of cardiovascular risk factors in youths
Fasting glucose | 0.074* | 0.074* | 0.042* | 0.000 | 0.080* | 0.027* | 0.007 | 0.074* | 0.014 | 0.000 |
Fasting insulina | 0.437** | 0.459** | 0.266** | 0.039* | 0.455** | 0.016 | 0.032* | 0.440** | 0.071** | 0.005 |
HOMA IRa | 0.427** | 0.447** | 0.262** | 0.036* | 0.442** | 0.019 | 0.026* | 0.430** | 0.070** | 0.005 |
QUICKI | 0.414** | 0.433** | 0.242** | 0.033* | 0.429** | 0.016 | 0.025* | 0.416** | 0.066** | 0.004 |
Triglyceridesa | 0.079* | 0.117** | 0.033* | 0.042* | 0.122** | 0.009 | 0.047* | 0.095* | 0.000 | 0.017 |
HDL cholesterola | 0.043* | 0.096* | 0.022* | 0.056** | 0.108** | 0.023* | 0.068** | 0.065* | 0.001 | 0.024* |
LDL cholesterol | 0.011 | 0.013 | 0.002 | 0.002 | 0.015 | 0.005 | 0.004 | 0.018 | 0.009 | 0.007 |
SBP Z score | 0.188** | 0.200** | 0.140** | 0.014 | 0.201** | 0.007 | 0.016 | 0.224** | 0.003 | 0.044* |
DBP Z score | 0.175** | 0.184** | 0.130** | 0.010 | 0.176** | 0.028* | 0.000 | 0.200** | 0.006 | 0.030* |
CRPa | 0.146** | 0.182** | 0.113** | 0.043* | 0.158** | 0.005 | 0.014 | 0.233** | 0.004 | 0.102** |
Fibrinogena | 0.199** | 0.208** | 0.116** | 0.012 | 0.199** | 0.021* | 0.001 | 0.218** | 0.006 | 0.024* |
Adiponectina | 0.100** | 0.143** | 0.011 | 0.047* | 0.177** | 0.040* | 0.085** | 0.107** | 0.000 | 0.007 |
Leptina | 0.502** | 0.502** | 0.412** | 0.001 | 0.502** | 0.111** | 0.001 | 0.511** | 0.101** | 0.019 |
Resistina | 0.095** | 0.095* | 0.044* | 0.000 | 0.097* | 0.017 | 0.002 | 0.095* | 0.016 | 0.000 |
Finally, as regards DXA measurements, apart from fasting glucose, LDL cholesterol, fibrinogen and leptin, the DXA prediction of every other cardiometabolic risk factor was improved when the trunk/legs fat mass index was added to total fat mass, as well as after the addition of trunk fat mass to total fat mass (models were adjusted on age, sex and pubertal status) (Table
4).
Table 4
Multivariable DXA prediction of cardiovascular risk factors in youths
Fasting glucose | 0.058* | 0.058* | 0.029* | 0.000 | 0.058* | 0.004 | 0.000 |
Fasting insulina | 0.376** | 0.425** | 0.249** | 0.078** | 0.395** | 0.000 | 0.030* |
HOMA IRa | 0.366** | 0.412** | 0.242** | 0.073** | 0.383** | 0.000 | 0.027* |
QUICKI | 0.349** | 0.394** | 0.213** | 0.069** | 0.365** | 0.000 | 0.025* |
Triglyceridesa | 0.046 | 0.095* | 0.029* | 0.052* | 0.075* | 0.016 | 0.031* |
HDL cholesterola | 0.015 | 0.105** | 0.014 | 0.091** | 0.066* | 0.037* | 0.051* |
LDL cholesterol | 0.010 | 0.011 | 0.005 | 0.001 | 0.010 | 0.001 | 0.000 |
SBP Z score | 0.201** | 0.226** | 0.183** | 0.032* | 0.230** | 0.004 | 0.037* |
DBP Z score | 0.144** | 0.178** | 0.127** | 0.039* | 0.181** | 0.010 | 0.042* |
CRPa | 0.165** | 0.198** | 0.164** | 0.039* | 0.186** | 0.001 | 0.025* |
Fibrinogena | 0.216** | 0.216** | 0.158** | 0.000 | 0.217** | 0.018 | 0.000 |
Adiponectina | 0.077* | 0.137** | 0.005 | 0.065** | 0.112** | 0.029* | 0.038* |
Leptina | 0.575** | 0.578** | 0.506** | 0.005 | 0.582** | 0.138** | 0.015 |
Resistina | 0.100** | 0.120** | 0.063** | 0.022* | 0.120** | 0.006 | 0.023* |
Discussion
Our study clearly showed that, in addition to global overweight and obesity, body fat distribution, as assessed by anthropometry, significantly and independently contributes to the prediction of CV risk factors in overweight and obese youth. Insulin resistance markers, in particular, were more accurately predicted by adding WHR Z Score or Waist C Z Score to BMI Z Score. HDL cholesterol was unanimously more accurately predicted by adding to BMI Z Score one of the three selected anthropometric surrogates for body fat distribution. Triglyceride concentration was more accurately predicted after adding either WHR Z Score or Waist C Z Score to BMI Z Score. Inflammation, as assessed by C-reactive protein, had its prediction improved when WHR Z Score and/or WHtR were added to BMI Z Score. WHtR played a similar role in the case of fibrinogen. WHtR played a role also in blood pressure prediction, after combination with BMI Z Score. Adiponectin concentrations seem to be better approached by combining WHR or Waist C Z Scores with BMI Z Score, while resistin and leptin predictions were not affected by the anthropometric measures for body fat distribution. This was also the case of glucose concentrations, the prediction of which was not affected beyond BMI neither by WHR and Waist C Z Scores nor by WHtR. On the other hand, our findings based on anthropometric measures were in coherence with the associations observed between the aforementioned CV risk factors and DXA combinations: total fat mass and trunk fat mass; respectively total fat mass and trunk/legs fat mass.
Significant relationships linking unfavourable CV profiles to body fat distribution measures, beyond BMI, have been observed in adults since the pioneer work of Vague. Vague pointed out abdominal fat toxicity to be responsible for severe obesities and serious associated prognosis in adults, in opposition to the gynoid shapes which do not expose to similar hazardous health complications [
44]. Since that study, several epidemiological investigations in adults showed in particular that, beyond fatness degrees as assessed by BMI, Waist C and/or WHR, measuring upper body fat distribution, were significantly correlated with blood pressure, total serum cholesterol, HDL-cholesterol, triglycerides level and/or serum insulin level [
30‐
33].
However, the scarce published studies in children about the usefulness of adding anthropometric surrogates for body fat distribution to BMI remain controversial. Certain American paediatric studies reported, exactly as is shown in the present study, a significant impact of WHR in addition to BMI, to predict HDL-cholesterol and triglycerides, in youth aged 4–19 years [
19,
28]. Gillum [
18] also showed an improvement in blood pressure prediction in youths (6–17 y) by adding WHR to BMI. Maffeis et al. [
20] showed significant associations between Waist C and Apo lipoproteins, HDL-cholesterol, total/HDL cholesterol ratio, blood pressure, after BMI, age and sex adjustments in prepubertal children aged 3 to 11 years old.
Nevertheless, in 15–16 year-old youths, Lawlor et al. [
15] concluded with the superiority of BMI on Waist C in predicting blood pressure, fasting glucose and insulin, triglycerides, LDL and HDL-cholesterol. Only BMI was also highlighted by Garnett et al. to track CV risk between childhood and adolescence [
13]. Likewise, with a view to detecting arterial hypertension in 8–10 year-old children, Maximova et al. recommended the measurement of BMI rather than Waist C or WHtR [
45]. Gillum et al. [
24] showed no significant differences between BMI and WHR for the prediction of CRP in Mexican American children (6–11 y). Similar abilities of BMI-for-age and WHtR were also shown by Freedman et al. [
26] for the screening of fasting insulin, blood pressure, triacylglycerol, HDL, LDL and total-to-HDL cholesterol ratio in the Bogalusa Heart Study.
These controversies may be partly explained by the different methodologies applied in the studies. Actually, some studies used continuous data [
15,
18‐
20,
28], while others analysed categorical data [
13,
24,
26,
27,
45]. Indeed, using categorical rather than continuous data might result in information loss. The lack of standardized international thresholds to define weight status in children (e.g., for normal-weight versus overweight and obesity) may also impact data interpretations. In the current study, we showed different weight status frequencies according to two definitions suggested in the literature: 64 % of obesity and 36 % of overweight according to the IOTF definition [
35,
46] and L,M,S Dutch values [
42], respectively 80.8 % of obesity and 19.2 % of overweight according to the WHO definition [
47]. The lack of a specific national percentile distribution of anthropometric data in youths appears to be an undeniable issue. That constituted a limitation of the current study. However, thanks to the Dutch L, M, S values provided to us by Dr Van Buuren from the Department of Statistics, Quality of Life, Leiden, Netherlands [
42,
43], we were able to develop BMI, Waist C and WHR Z Scores after having checked that Luxembourgish and Dutch paediatric BMI means were similar.
The heterogeneity in the relationships between anthropometry and CV risk factors may also be attributed to the age groups considered in the different studies and/or to the few biological parameters tested. Our study sample was characterized by a broad age range and an exhaustive set of cardiovascular risk factors tested.
The selected nature and relatively small size of our sample, including only overweight and obese subjects, might be a limitation of the current study in that it does not allow the extrapolation of our findings to the general paediatric population. However, as young people who may be at higher risk for CV impairments are mostly the overweight and obese ones, the current findings might widely apply to this high-risk population subgroup.
Acknowledgements
We thank the children and the parents for their participation. We also thank Dr Van Buuren (Department of Statistics, TNO Quality of Life, 2301 CE Leiden, The Netherlands) who provided us with the L, M and S values initially developed in the Dutch population.
This study has been funded by the Ministry for Culture, Higher Education and Research, Luxembourg and by the National Research Fund, Luxembourg.
Competing interests
The author(s) declare that they have no competing interests.
Authors’ contributions
HS (co) conceived the study and (co) designed the protocol, carried out the anthropometric measurements, interpreted the DXA images, (co) analysed the data, interpreted the statistics and drafted the manuscript. CDB (co) conceived the study and (co) designed the protocol, included the participants and (co)interpreted the statistical analyses. BCG (co)interpreted the statistical analyses and have been involved in drafting the manuscript. GG performed the biological assessment and wrote the biological measurements protocol. MH designed the DXA protocol and managed the DXA collected data. JJ managed and (co) analysed and interpreted the data. MV (co) designed the protocol, calculated the sample size and gave statistical advices. FD (co) conceived the study, (co) designed the protocol and (co) interpreted the data analyses. SL performed the statistical analyses for the revision of the manuscript. CDB, SS, BCG, SL, MV and FD revised critically the manuscript for important intellectual content. All authors read and approved the final manuscript.