Added values of DXA-derived visceral adipose tissue to discriminate cardiometabolic risks in pre-pubertal children

Li-Wen Lee; Chu-Jung Hsieh; Yun-Hsuan Wu; Yu-San Liao

doi:10.1371/journal.pone.0233053

Abstract

Background

The new generation of dual energy X-ray absorptiometry (DXA) scanners provide visceral adipose tissue (VAT) estimates by applying different algorithms to the conventional DXA-derived fat parameters such as total fat, trunk fat and android fat for the same image data.

Objective

This cross-sectional study aimed to investigate whether VAT estimates from Hologic scanners are better predictors of VAT than conventional DXA parameters in pre-pubertal children, and to explore the discrimination ability of these VAT methods for cardiometabolic risks.

Methods

Healthy pre-pubertal children aged 7–10 years were recruited for basic anthropometric, DXA and magnetic resonance imaging (MRI) measurements. Laboratory tests included lipid profile, glycaemic tests and blood pressure.

Results

All VAT methods had acceptable to excellent performance for the diagnosis of dyslipidaemia (area under the curve [AUC] = 0.753–0.837) and hypertensive risk (AUC = 0.710–0.821) in boys, but suboptimal performance for these risks in girls, except for VAT by MRI in the diagnosis of dyslipidaemia. In both sexes, all VAT methods had no or poor discrimination ability for diabetes risk.

Conclusions

DXA-derived VAT estimates are very highly correlated with standard methods but has equivalent discrimination abilities compared to the existing DXA-derived fat estimates.

Citation: Lee L-W, Hsieh C-J, Wu Y-H, Liao Y-S (2020) Added values of DXA-derived visceral adipose tissue to discriminate cardiometabolic risks in pre-pubertal children. PLoS ONE 15(5): e0233053. https://doi.org/10.1371/journal.pone.0233053

Editor: Kai Sun, University of Texas Health Science Center at Houston, UNITED STATES

Received: November 14, 2019; Accepted: April 27, 2020; Published: May 13, 2020

Copyright: © 2020 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The first author (LWL) received funding from the Chang Gung Medical Foundation (grant no. CMRPG6C0052 and CMRPG6G0531). UPL of the funder website: https://www.cgmh.org.tw/. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Excess fat or obesity is associated with an increased risk of cardiometabolic diseases such as dyslipidaemia, diabetes and cardiovascular disease. In addition to the total fat mass, visceral adipose tissue (VAT) distribution and ectopic fat in non-adipose tissues are regarded as more reliable predictors than total fat mass alone for obesity-related health consequences [1, 2]. The most common approach to assessing adiposity is body mass index (BMI). In children, body adiposity is influenced by sex, age, race and pubertal stage [3, 4]; therefore, a child’s weight status needs to be assessed relative to children of the same age and sex (BMI z-score). BMI and BMI z-score represent indices of excess relative weight rather than fat masses, and there is concern that they may be insufficiently sensitive markers for the evaluation of fat distribution. Anthropometric indices such as waist circumference (WC), hip circumference and sagittal abdominal diameter (SAD) are regarded as better indicators of abdominal fatness. However, these methods are indirect measures of VAT and may not be accurate under some circumstances. Computed tomography (CT) and magnetic resonance imaging (MRI) are standard methods for determining VAT which allow direct measurement of VAT, but they have the disadvantages of high cost and being time-consuming. Dual energy X-ray absorptiometry (DXA) provides total body and regional body composition analysis at lower cost, radiation equivalent to the daily background radiation and short scan time but high precision [5, 6] and for these reasons is a more suitable tool for population screening.

DXA methodology is based on applying two different energies of X-rays to decompose an object into two components with known x-ray attenuation coefficients [7]. With an assumption of the fat fraction of the soft tissue mass in the bone-containing pixels, DXA can further partition the body into lean, fat and bone masses [8]. It is worth noting that the basic methodologies are similar but the algorithms used to partition soft tissues may differ among DXA manufacturers [9, 10]. Conventional DXA also provides cut lines by which to divide the scanned images into the head, trunk, arms and legs, allowing for regional body composition analysis [11]. With the region of interest (ROI) placed in the upper body and low body, DXA can also provide android and gynoid fat estimates, respectively. In DXA images, the android region is defined as the area with its inferior margin at the iliac crest and the superior border defined as 20% of the distance between the iliac crest and the chin [12]. The gynoid region includes the hips and upper thighs, with its superior margin at 1.5 times the android height below the iliac crest and its height two times the height of the android region [12]. Android obesity is known to be more correlated with cardiometabolic risk than gynoid fat deposition [13].

Conventional DXA analysis is based on a 2-dimensional projection image method and cannot be used to differentiate visceral fat from subcutaneous fat in the range of interest. However, the new generation of DXA scanners are equipped with additional VAT assessment options, i.e., Lunar’s CoreScan and Hologic’s InnerCore, that enable automated assessment of the VAT area and volume using specific algorithms. The VAT estimation equations for both DXA manufacturers include similar variables such as the anterior-posterior thickness of the abdomen and the width of subcutaneous layer on lateral abdomen, and the prediction model has been validated against CT-derived VAT in adults [14, 15]. Although VAT_DXA volume is measured from a block above iliac crest for both DXA manufacturers, the height of the block varies. The height of the block is 5 cm for the Hologic scanner [14] but 20% of the distance between the iliac crest and the chin (within the android region) for the Lunar scanner [15].

Studies report that metabolic alterations are already detectable in prepubertal boys and girls and the risks are associated with VAT [16–18]. Therefore, validation of VAT methods for early detection and intervention of obesity-related health consequences is appropriate in prepubertal age. VAT_DXA has been extensively validated in adults, showing very high correlation with VAT derived from cross-sectional images (r > 0.9) [14, 15, 19]. However, the limited studies in the pediatric population have substantial variation (r = 0.55–0.79) [20, 21], and all were performed with the Lunar scanner. There is high correlation but poor agreement between body composition measures using different DXA systems [22]. Further studies are still needed to investigate the values of VAT_DXA estimates provided by the Hologic DXA scanner. This study aimed first to investigate whether VAT_DXA estimates in pre-pubertal children from the Hologic scanner are better predictors of VAT than anthropometric indices and the other conventional DXA parameters. Another aim was to explore the discrimination ability of these VAT methods for cardiometabolic risks.

Methods

Study design

This cross-sectional prospective study was approved by the Institutional Review Board of the Chang Gung Medical Foundation (No: 101-2952A3) and written informed consent was obtained from all subjects and their parents or guardians after explaining the study procedure. Subjects were recruited via hospital advertisements, word of mouth and outpatient clinic referrals between July 2013 and January 2015.

Participants

The inclusion criteria were healthy Taiwanese children aged 7–10 years in Tanner stage I. Pubertal stage was assessed by a single physician according to Tanner and Marshall [23]. Children were excluded if they had congenital anomalies, chronic illness, contraindications for an MRI scan or use of regular medication in the last six months. Subjects who presented with a family history of type 2 diabetes, hypertension or gestational diabetes mellitus were also excluded from the study. On the study day, participants presented to an 8:30 am appointment, following an 8- to 12-hour fast. On arrival, participants were asked to void and change into a light hospital gown. All measurements were completed at the same morning session with a total study time of about 2 hours.

Anthropometric measurement

All anthropometric measurements were carried out by the same investigator throughout the study. Height and weight were measured using a digital scale, with subjects wearing a hospital gown and no shoes. BMI was calculated by dividing weight (kg) by height squared (m²). The z-scores for BMI were computed using the WHO AnthroPlus software according to the WHO Reference 2007 [24]. WC was measured at the midpoint between the lowest rib and iliac crest, with the tape parallel to the floor.

Body composition by DXA

Whole body DXA was performed using a Hologic Delphi A scanner equipped with software version 12.5 (Hologic, Bedford, MA, USA). Then, the obtained DXA images were reanalyzed using Apex version 5.6 (Hologic, Bedford, MA, USA), with the National Health and Nutrition Examination Survey (NHANES) option enabled. Subject position and ROI placement were performed in accordance with the manufacturer’s instructions [25]. Estimates, including total body fat mass, trunk fat mass, android fat mass, total percentage fat (PBF), trunk percentage fat (PBF_TR) and VAT, were generated and compared to VAT obtained by using MRI (VAT_MRI).

Body composition by MRI

Breath-hold T1-weighted images of the abdomen and pelvis were acquired on a 3 Tesla Siemens Verio scanner equipped with software syngo MR B17 (Siemens Medical System, Erlangen, Germany). Details of the parameters were as follows: repetition time = 514 ms, echo time = 8.8 ms, field of view = 500×500 mm², matrix = 384×384 and number of excitations = 1. Patients were placed in a supine position and a series of 10–12, 1-cm-thick axial images with a 1 cm gap between images were acquired. VAT_MRI volume (cm³) was measured between the diaphragm and the pubic symphysis; VAT_MRI area (cm²) was measured from single-slice MRI at the L4 vertebral level, using image analysis software (SliceOmatic 5.0, TomoVision, Montreal, Quebec, Canada) by the same radiologist. SAD was measured at the level of the L4 vertebral body by measuring the distance from the anterior to the posterior part of the body using ImageJ software [26].

Outcome definitions

Blood pressure was measured twice on the right arm using an automated sphygmomanometer with an appropriately-sized cuff after the children had rested in the sitting position for five minutes. A third measure was taken if the first two measures differed by ≥ 10 mmHg. The average of the measures was used for further analysis. Blood pressure percentile was categorized on the basis of individual age, sex and height percentile according to the American Academy of Pediatrics Pediatric Hypertensive Guidelines [27]. Children with blood pressure in the < 90^th percentile were classified as having normal blood pressure, whereas children with blood pressure in the ≥ 90^th percentile were considered at hypertensive risk.

Venous blood samples were collected following body composition measures. Blood glucose and lipid profile were analyzed using a Hitachi 7600 analyzer (Hitachi, Tokyo, Japan) with enzymatic procedure. Glycated hemoglobin (HbA₁c) values were measured by Premier Hb9210 (Trinity Biotech, Kansas City, MO, USA). Participants with total cholesterol ≥ 170 mg/dl, triglycerides ≥ 75 mg/dl, low-density lipoprotein cholesterol ≥ 110 mg/dl, high-density lipoprotein cholesterol ≤ 45 mg/dl or non-high-density cholesterol ≥ 120 mg/dl were considered at lipid risk [28]. Participants with HbA₁c ≥ 5.7% or fasting blood glucose ≥ 100 mg/dl were considered at diabetes risk.

Statistical analysis

All analyses were carried out using SPSS Statistics version 22 (IBM Corp., Armonk, NY, USA). A Student t-test was used to calculate the difference between measurements for both sexes. Pearson’s correlation analysis was used to test the degree of linear relationship between two variables. The Pearson correlation coefficients (r) were classified into perfect (r = 1), very high (0.9 ≤ r < 1), high (0.7 ≤ r < 0.9), moderate (0.5 ≤ r < 0.7) and low (0.3 ≤ r < 0.5). Receiver operating characteristics (ROC) analysis was used to judge how accurately a VAT method could discriminate between children with and without cardiometabolic risk factors. The area under the ROC curve (AUC) was used to quantify the predictive ability of a VAT method as a biomarker for cardiometabolic risk. ROC curves were generated using the same set of subjects for all VAT measurements. The values of the AUC were defined as reflecting acceptable discrimination if 0.7 ≤ AUC < 0.8; excellent discrimination if 0.8 ≤ AUC < 0.9; and outstanding discrimination if AUC ≥ 0.9. MRI was considered as the gold standard for VAT measurements and the other VAT methods were compared to VAT_MRI. Statistical significance was defined at α = 0.05. Boxplot was plotted for graphically illustration of the data distribution. Heat map was plotted using Microsoft Excel 2016 (Microsoft Corporation, Redmond, WA, USA) to provide a graphical representation of the correlation matrix.

Results

A total of 112 children aged 7–10 years were enrolled in the study; of these, one was excluded due to refusal to take venous sampling, and thus 111 individuals comprised the final study sample. Of the 111 children, 57 were boys (51.4%). The average age was 8.6 years and the mean BMI z-score was 0.8 (range: -2.4 to 4.0). Subject characteristics are shown in Table 1 and Fig 1. Boys were significantly older, taller and had greater VAT_DXA than girls. For the other variables, there were no statistically significant differences between the sexes. Children were considered as “at risk” of cardiometabolic components, according to the methodology section. In boys, there were 14, 27 and 24 subjects with diabetes, lipid and hypertensive risks, respectively. There were 20 boys with at least two risks. In girls, there were 5, 23 and 25 subjects with diabetes, lipid and hypertensive risks, respectively. There were 18 girls with at least two risks. In both sexes, the prevalence of lipid and hypertensive risk components were higher than that of diabetes risk.

Download:

Fig 1. Visceral fat estimates among the study population.

In the boxplot, the thick line in the middle of the box is the median. The left and right edges of the box are located at the first and third quartiles. The whiskers show the maximum and minimum values, with the exceptions of outliers (circles) and extremes (asterisks).

https://doi.org/10.1371/journal.pone.0233053.g001

Download:

Table 1. Descriptive characteristics of children.

https://doi.org/10.1371/journal.pone.0233053.t001

Tables 2 and 3 show the correlation coefficients between VAT methods in boys and girls, respectively. All the test VAT methods were positive correlated with VAT_MRI volume with high to perfect correlations (r ≥ 0.790). It is generally agreed that VAT_MRI volume is the reference measure for VAT. In our study, the single-slice VAT area obtained at the L4 level were shown to highly correlate with VAT_MRI volume in both sexes, with a correlation coefficient of r = 0.930 for boys and r = 0.843 for girls, indicating that single-slice VAT_MRI area is a good surrogate for VAT_MRI volume. The correlation between VAT_MRI and the other VAT methods was higher in boys than in girls, except for SAD. All VAT methods were better correlated with VAT_MRI volume than area. DXA estimates were better correlated with VAT_MRI volume than basic indices, except for PBF and PBF_TR. It is also worth noting that BMI was better correlated with VAT_MRI than BMI z-score. As expected, perfect positive correlations (r = 1.000) were found between VAT_DXA area and volume in both sexes, indicating that the values of VAT_DXA volume were derived from those of VAT_DXA area. The correlation coefficients between VAT_DXA and VAT_MRI volume were 0.954 for boys and 0.951 for girls.

Download:

Table 2. Pearson correlation between VAT_MRI and other predictors in boys.

https://doi.org/10.1371/journal.pone.0233053.t002

Download:

Table 3. Pearson correlation between VAT_MRI and other predictors in girls.

https://doi.org/10.1371/journal.pone.0233053.t003

Sensitivity and specificity are common metrics for quantifying the diagnostic accuracy of a test. A ROC curve plots the sensitivity against 1-specificity for all possible cut-off values and the AUC is a scalar measure of the overall performance of diagnostic tests with continuing variables. To assess of performance of VAT tools for different genders and cardiometabolic risks, subjects were stratified by sex and risk factors for ROC analysis. The AUC of different VAT tools for the diagnosis of a range of cardiometabolic risk factors in boys and girls are shown in Table 4 and Table 5, respectively. For the diagnosis of lipid risk, all VAT methods had acceptable to excellent performance in boys (AUC = 0.753–0.837), whereas all methods yielded poor performance in girls, except for VAT_MRI volume (AUC = 0.722). For the diagnosis of hypertensive risk, all VAT methods had acceptable to excellent performance in boys (AUC = 0.710–0.821), but poor or no discrimination ability in girls. In both sexes, all VAT methods had no or poor discrimination ability for diabetes risk. For the diagnosis of at least two obesity-related risks, all VAT methods had excellent to outstanding discriminating ability in boys (AUC = 0.861–0.945), but no or poor discrimination ability in girls.

Download:

Table 4. ROC analysis (boys).

https://doi.org/10.1371/journal.pone.0233053.t004

Download:

Table 5. ROC analysis (girls).

https://doi.org/10.1371/journal.pone.0233053.t005

Discussion

In this study, DXA-derived VAT area and volume were very highly correlated with MRI-derived VAT, indicating that VAT_DXA can act as a predictor of VAT in healthy pre-pubertal children. In addition, all other VAT measures and total VAT_MRI volume were high to perfectly correlated in pre-pubertal children, indicating a very strong tendency for these methods to estimate VAT. The two VAT methods that had the highest correlation coefficient were VAT_DXA area and VAT_DXA volume (r = 1.000), suggesting the values of VAT_DXA volume were derived from the VAT_DXA area. Further, using the VAT_DXA value to predict cardiometabolic risks was explored. In contrast to what previous work has shown, we found that VAT area and volume by DXA were no better at predicting VAT_MRI and cardiometabolic risks than the standard DXA-derived total body, trunk fat and android fat masses. Furthermore, VAT measures helped distinguish lipid risk, hypertensive risk and at least two cardiometabolic risk factors in boys, but not in girls. However, our results suggested no or poor discrimination ability to diagnose diabetes risk in children using VAT estimates in both sexes.

Volumetric CT and MRI measurements are the gold standard methods for VAT quantification in vivo. However, single-slice VAT area is more commonly used in body composition research than the VAT volume, due to the labour-intensive and time-consuming process of image segmentation. Validation of single-sliced VAT area has been done previously. In adults, the single-slice VAT area showed high to very high correlation with total VAT volume, with the highest correlation in slices taken at 5–10 cm above L4-L5 in healthy adults (r = 0.97–0.99) [29–32]. The correlations between single-slice VAT area and total VAT volume was also high to very high in school-aged children (r ≥ 0.874), but the best single-slice location to represent total VAT volume varied between studies [33–35]. It is not clear why the best single-slice location for VAT differs between children and adults. One possible explanation is that patterns of adiposity may vary with age. Consistent with previous studies, our study showed that single-slice MRI at the L4 level was highly to very highly correlated with VAT_MRI volume in healthy children (r = 0.930 in boys and r = 0.843 in girls).

As far as we know, only two studies have sought to validate VAT_DXA against CT or MRI in children; both studies were performed using Lunar scanners and neither took gender differences into account [20, 21]. Lee et al. [21] showed that VAT_DXA was highly correlated with VAT_MRI at multiple lumbar disc levels in 32 girls aged 9–13 years (r = 0.70–0.79). In another study, Bosch et al. [20] reported that the correlations between VAT_DXA and VAT_CT were 0.55 and 0.63 for children aged 6–11 years and 12–18 years, respectively. In that study, the correlation was high for children with BMI above the 85^th percentile (r = 0.859), but negligible in children with lower BMI (r = 0.226). Indeed, Lunar DXA scanners provided much lower correlation coefficients between VAT_DXA and standard methods in children than those of adults in previous studies (r > 0.9) [14, 15, 19]. Using a Hologic scanner, our results were consistent with previous studies, showing very high correlations between VAT_DXA and VAT_MRI in children with a wide range of BMI (r = 0.954 in boys and r = 0.951 in girls). The estimation models of VAT_DXA include similar variables and perform well in adults for both Hologic and Lunar scanners [14, 15]. It is not clear why the performance differed between two DXA models in children.

This study analysed gender differences for predicting cardiometabolic risks using VAT methods. According to AUC results, VAT methods were useful in accurately stratifying cardiometabolic risks for boys but not for girls. Body composition and cardiometabolic risks are affected by sex hormones and it is well known that gender differences present in discriminating cardiometabolic risks using VAT in adults [31, 36, 37]. In children, there is sexual dimorphism in fat distribution and the distribution is present in early life but emerges more dramatically during puberty [3, 38]. Our study suggested that gender differences in the ability to differentiate cardiometabolic risks using VAT were already present in prepubertal children.

The ability of a range of VAT methods to predict cardiometabolic risks was investigated in the present study. For the diagnosis of hypertensive risks, all methods showed acceptable to excellent performance in boys, but sub-optimal performance in girls. The issue of gender differences in the prediction of hypertensive risk using VAT has been observed in previous studies, but with inconsistent results. Daniels et al. [39] reported a significant positive relationship between android fat by DXA and hypertensive risk in 127 healthy children aged 9–17 years, but the study did not account for sex differences. Zhao et al. [40] reported a significant correlation between WC and hypertensive risk in Chinese children aged 7–16 years (n = 1626), regardless of sex. Similar to our results, He et al. [41] reported that the total fat and trunk fat as measured by DXA were predictors of blood pressure in boys at all pubertal stages, but not in girls, in 920 healthy children aged 5–18 years. Syme et al. [42] also reported that the positive association between VAT_MRI and blood pressure was less in girls compared to that in boys in healthy adolescents (n = 425). In this study, all VAT methods had acceptable to excellent performance for the accurate diagnosis of lipid risk in boys, whereas only VAT_MRI had an acceptable diagnostic performance in girls. Previous studies have reported associations between VAT and lipid risk in children, but the authors did not report gender differences [34, 39]. Daniels et al. [39] showed that android fat by DXA was associated with lipid risk in children aged 9–17 years, whereas Lee et al. [34] demonstrated that single-slice VAT_MRI areas could be used to predict lipid risk. For the diagnosis of diabetes risks, all VAT methods in this study showed sub-optimal performance, regardless of sex, suggesting that VAT may not be appropriate to identify pre-pubertal children at risk of diabetes. Our results contradicted those of previous studies which showed a correlation of VAT with diabetes risks in children [20, 43–45]. Although the reason for this discrepancy is unclear, it might be caused by the younger age of the subjects in our study.

The fundamental differences between DXA-derived total fat, trunk fat and android fat versus DXA-derived VAT lie in their different methodologies of calculating fat masses. DXA-derived total fat, trunk fat and android fat are based on solving the attenuation equations obtained at two energy levels of X-ray [7]. In contrast, the VAT_DXA area is calculated as the total fat minus the subcutaneous fat, where subcutaneous fat is derived from regression analysis, with the transverse diameter and the inner abdominal muscle wall diameter as independent variables [14, 15]. In spite of the methodological differences, our study showed that these estimates were all very highly correlated with VAT_MRI volume and their prediction abilities for obesity-related risks were compatible in pre-pubertal children. Simple anthropometric indices are widely used measures, but our study showed that these indices were highly to very highly correlated with VAT_MRI, and their ability to discriminate obesity-related risks were compatible to VAT_MRI, indicating that they are also very useful VAT indicators.

This study had the strengths of a homogeneous subject population in terms of ethnicity and pubertal stage, but a wide range of body fatness, as well as providing discrimination abilities for cardiometabolic risks among common VAT methods. However, our study has certain methodological limitations. First, because this is a cross-sectional study, we could not show causality between VAT and risk factors. Second, we did not use the standard definitions of abnormal values for cardiometabolic risks, as there is currently no universal agreement on the definition of childhood metabolic risk factors for pre-pubertal children. Third, the results of this study were limited to pre-pubertal children aged 7–10 years using a Hologic scanner, and might not be applicable to children at a different age group or studies using a Lunar DXA scanner.

Accurate VAT assessment is challenging. This study demonstrated that VAT estimates derived from the new generation of DXA scanners were very highly correlated with current standard methods, indicating that VAT_DXA can be an accurate VAT predictor in healthy pre-pubertal children. However, the discrimination abilities for cardiometabolic risks were equivalent to conventional DXA-derived fat estimates, indicating that VAT_DXA may not be a more robust VAT surrogate than the existing DXA-derived fat estimates such as total fat, trunk fat and android fat.

Supporting information

S1 Fig.

https://doi.org/10.1371/journal.pone.0233053.s001

(PPTX)

S1 Dataset.

https://doi.org/10.1371/journal.pone.0233053.s002

(CSV)

References

1. Thomas EL, Frost G, Taylor-Robinson SD, Bell JD. Excess body fat in obese and normal-weight subjects. Nutr Res Rev. 2012;25(1):150–61. pmid:22625426
- View Article
- PubMed/NCBI
- Google Scholar
2. Smith U. Abdominal obesity: a marker of ectopic fat accumulation. J Clin Invest. 2015;125(5):1790–2. pmid:25932676
- View Article
- PubMed/NCBI
- Google Scholar
3. Taylor RW, Grant AM, Williams SM, Goulding A. Sex differences in regional body fat distribution from pre- to postpuberty. Obesity (Silver Spring). 2010;18(7):1410–6.
- View Article
- Google Scholar
4. He Q, Horlick M, Thornton J, Wang J, Pierson RN Jr., Heshka S, et al. Sex and race differences in fat distribution among Asian, African-American, and Caucasian prepubertal children. J Clin Endocrinol Metab. 2002;87(5):2164–70. pmid:11994359
- View Article
- PubMed/NCBI
- Google Scholar
5. Toomey CM, Cremona A, Hughes K, Norton C, Jakeman P. A Review of Body Composition Measurement in the Assessment of Health. Topics in Clinical Nutrition. 2015;30(1):16–32.
- View Article
- Google Scholar
6. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15(2):95–101. pmid:24461239
- View Article
- PubMed/NCBI
- Google Scholar
7. Lukaski HC. Soft tissue composition and bone mineral status: evaluation by dual-energy X-ray absorptiometry. J Nutr. 1993;123(2 Suppl):438–43.
- View Article
- Google Scholar
8. Farrell TJ, Webber CE. The error due to fat inhomogeneity in lumbar spine bone mineral measurements. Clin Phys Physiol Meas. 1989;10(1):57–64. pmid:2785442
- View Article
- PubMed/NCBI
- Google Scholar
9. Kohrt WM. Body composition by DXA: tried and true? Med Sci Sports Exerc. 1995;27(10):1349–53. pmid:8531604
- View Article
- PubMed/NCBI
- Google Scholar
10. Tothill P, Reid DM, Avenell A, Fenner JA. Comparisons between Hologic, Norland and Lunar dual-energy X-ray bone absorptiometers. Basic Life Sci. 1993;60:385–8. pmid:8110157
- View Article
- PubMed/NCBI
- Google Scholar
11. Lorente Ramos RM, Azpeitia Arman J, Arevalo Galeano N, Munoz Hernandez A, Garcia Gomez JM, Gredilla Molinero J. Dual energy X-ray absorptimetry: fundamentals, methodology, and clinical applications. Radiologia. 2012;54(5):410–23. pmid:22285678
- View Article
- PubMed/NCBI
- Google Scholar
12. Schousboe JT, Kats AM, Langsetmo L, Vo TN, Taylor BC, Schwartz AV, et al. Central Obesity and Visceral Adipose Tissue Are Not Associated With Incident Atherosclerotic Cardiovascular Disease Events in Older Men. J Am Heart Assoc. 2018;7(16):e009172. pmid:30369326
- View Article
- PubMed/NCBI
- Google Scholar
13. Despres JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126(10):1301–13. pmid:22949540
- View Article
- PubMed/NCBI
- Google Scholar
14. Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL. Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat. Obesity (Silver Spring). 2012;20(5):1109–14.
- View Article
- Google Scholar
15. Kaul S, Rothney MP, Peters DM, Wacker WK, Davis CE, Shapiro MD, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring). 2012;20(6):1313–8.
- View Article
- Google Scholar
16. Gower BA, Nagy TR, Goran MI. Visceral fat, insulin sensitivity, and lipids in prepubertal children. Diabetes. 1999;48(8):1515–21. pmid:10426367
- View Article
- PubMed/NCBI
- Google Scholar
17. D'Adamo E, Marcovecchio ML, Giannini C, Capanna R, Impicciatore M, Chiarelli F, et al. The possible role of liver steatosis in defining metabolic syndrome in prepubertal children. Metabolism. 2010;59(5):671–6. pmid:19913850
- View Article
- PubMed/NCBI
- Google Scholar
18. Nier A, Brandt A, Baumann A, Conzelmann IB, Ozel Y, Bergheim I. Metabolic Abnormalities in Normal Weight Children Are Associated with Increased Visceral Fat Accumulation, Elevated Plasma Endotoxin Levels and a Higher Monosaccharide Intake. Nutrients. 2019;11(3).
- View Article
- Google Scholar
19. Neeland IJ, Grundy SM, Li X, Adams-Huet B, Vega GL. Comparison of visceral fat mass measurement by dual-X-ray absorptiometry and magnetic resonance imaging in a multiethnic cohort: the Dallas Heart Study. Nutr Diabetes. 2016;6(7):e221. pmid:27428873
- View Article
- PubMed/NCBI
- Google Scholar
20. Bosch TA, Dengel DR, Kelly AS, Sinaiko AR, Moran A, Steinberger J. Visceral adipose tissue measured by DXA correlates with measurement by CT and is associated with cardiometabolic risk factors in children. Pediatr Obes. 2015;10(3):172–9. pmid:24990328
- View Article
- PubMed/NCBI
- Google Scholar
21. Lee V, Blew R, Hetherington-Rauth M, Blew D, Galons JP, Hagio T, et al. Estimation of visceral fat in 9- to 13-year-old girls using dual-energy X-ray absorptiometry (DXA) and anthropometry. Obes Sci Pract. 2018;4(5):437–47. pmid:30338114
- View Article
- PubMed/NCBI
- Google Scholar
22. Shepherd JA, Fan B, Lu Y, Wu XP, Wacker WK, Ergun DL, et al. A multinational study to develop universal standardization of whole-body bone density and composition using GE Healthcare Lunar and Hologic DXA systems. J Bone Miner Res. 2012;27(10):2208–16. pmid:22623101
- View Article
- PubMed/NCBI
- Google Scholar
23. Marshall WA, Tanner JM. Variations in pattern of pubertal changes in girls. Arch Dis Child. 1969;44(235):291–303. pmid:5785179
- View Article
- PubMed/NCBI
- Google Scholar
24. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660–7. pmid:18026621
- View Article
- PubMed/NCBI
- Google Scholar
25. Diessel E, Fuerst T, Njeh CF, Tylavsky F, Cauley J, Dockrell M, et al. Evaluation of a new body composition phantom for quality control and cross-calibration of DXA devices. J Appl Physiol (1985). 2000;89(2):599–605.
- View Article
- Google Scholar
26. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676–82. pmid:22743772
- View Article
- PubMed/NCBI
- Google Scholar
27. Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3).
- View Article
- Google Scholar
28. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;8(18):003.
- View Article
- Google Scholar
29. Demerath EW, Shen W, Lee M, Choh AC, Czerwinski SA, Siervogel RM, et al. Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr. 2007;85(2):362–8. pmid:17284730
- View Article
- PubMed/NCBI
- Google Scholar
30. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, et al. Visceral adipose tissue: relations between single-slice areas and total volume. Am J Clin Nutr. 2004;80(2):271–8. pmid:15277145
- View Article
- PubMed/NCBI
- Google Scholar
31. Irlbeck T, Massaro JM, Bamberg F, O'Donnell CJ, Hoffmann U, Fox CS. Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the Framingham Heart Study. Int J Obes (Lond). 2010;34(4):781–7.
- View Article
- Google Scholar
32. Cheng X, Zhang Y, Wang C, Deng W, Wang L, Duanmu Y, et al. The optimal anatomic site for a single slice to estimate the total volume of visceral adipose tissue by using the quantitative computed tomography (QCT) in Chinese population. Eur J Clin Nutr. 2018;72(11):1567–75. pmid:29559725
- View Article
- PubMed/NCBI
- Google Scholar
33. Blitman NM, Baron LS, Berkenblit RG, Schoenfeld AH, Markowitz M, Freeman K. Feasibility of using single-slice MDCT to evaluate visceral abdominal fat in an urban pediatric population. AJR Am J Roentgenol. 2011;197(2):482–7. pmid:21785098
- View Article
- PubMed/NCBI
- Google Scholar
34. Lee S, Kuk JL, Kim Y, Arslanian SA. Measurement site of visceral adipose tissue and prediction of metabolic syndrome in youth. Pediatr Diabetes. 2011;12(3 Pt 2):250–7.
- View Article
- Google Scholar
35. O'Connor M, Ryan J, Foley S. Best single-slice location to measure visceral adipose tissue on paediatric CT scans and the relationship between anthropometric measurements, gender and VAT volume in children. Br J Radiol. 2015;88(1054):20140711. pmid:26317895
- View Article
- PubMed/NCBI
- Google Scholar
36. Bidulescu A, Liu J, Hickson DA, Hairston KG, Fox ER, Arnett DK, et al. Gender differences in the association of visceral and subcutaneous adiposity with adiponectin in African Americans: the Jackson Heart Study. BMC Cardiovasc Disord. 2013;13:9. pmid:23433085
- View Article
- PubMed/NCBI
- Google Scholar
37. Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol. 2015;402:113–9. pmid:25578600
- View Article
- PubMed/NCBI
- Google Scholar
38. Wells JC. Sexual dimorphism of body composition. Best Pract Res Clin Endocrinol Metab. 2007;21(3):415–30. pmid:17875489
- View Article
- PubMed/NCBI
- Google Scholar
39. Daniels SR, Morrison JA, Sprecher DL, Khoury P, Kimball TR. Association of body fat distribution and cardiovascular risk factors in children and adolescents. Circulation. 1999;99(4):541–5. pmid:9927401
- View Article
- PubMed/NCBI
- Google Scholar
40. Zhao Y, Wang L, Xue B, Wang Y. Associations between general and central obesity and hypertension among children: The Childhood Obesity Study in China Mega-Cities. Sci Rep. 2017;7(1):16895. pmid:29203818
- View Article
- PubMed/NCBI
- Google Scholar
41. He Q, Horlick M, Fedun B, Wang J, Pierson RN JrHeshka S, et al. Trunk fat and blood pressure in children through puberty. Circulation. 2002;105(9):1093–8. pmid:11877361
- View Article
- PubMed/NCBI
- Google Scholar
42. Syme C, Abrahamowicz M, Leonard GT, Perron M, Richer L, Veillette S, et al. Sex differences in blood pressure and its relationship to body composition and metabolism in adolescence. Arch Pediatr Adolesc Med. 2009;163(9):818–25. pmid:19736335
- View Article
- PubMed/NCBI
- Google Scholar
43. Lindsay RS, Hanson RL, Roumain J, Ravussin E, Knowler WC, Tataranni PA. Body mass index as a measure of adiposity in children and adolescents: relationship to adiposity by dual energy x-ray absorptiometry and to cardiovascular risk factors. J Clin Endocrinol Metab. 2001;86(9):4061–7. pmid:11549626
- View Article
- PubMed/NCBI
- Google Scholar
44. Korner A, Wiegand S, Hungele A, Tuschy S, Otto KP, l'Allemand-Jander D, et al. Longitudinal multicenter analysis on the course of glucose metabolism in obese children. Int J Obes (Lond). 2013;37(7):931–6.
- View Article
- Google Scholar
45. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362–74. pmid:15175438
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Thomas EL, Frost G, Taylor-Robinson SD, Bell JD. Excess body fat in obese and normal-weight subjects. Nutr Res Rev. 2012;25(1):150–61. pmid:22625426
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Smith U. Abdominal obesity: a marker of ectopic fat accumulation. J Clin Invest. 2015;125(5):1790–2. pmid:25932676
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Taylor RW, Grant AM, Williams SM, Goulding A. Sex differences in regional body fat distribution from pre- to postpuberty. Obesity (Silver Spring). 2010;18(7):1410–6.
View Article
Google Scholar

[10] View Article

[11] Google Scholar

[ref4] 4. He Q, Horlick M, Thornton J, Wang J, Pierson RN Jr., Heshka S, et al. Sex and race differences in fat distribution among Asian, African-American, and Caucasian prepubertal children. J Clin Endocrinol Metab. 2002;87(5):2164–70. pmid:11994359
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Toomey CM, Cremona A, Hughes K, Norton C, Jakeman P. A Review of Body Composition Measurement in the Assessment of Health. Topics in Clinical Nutrition. 2015;30(1):16–32.
View Article
Google Scholar

[17] View Article

[18] Google Scholar

[ref6] 6. Chen LK, Liu LK, Woo J, Assantachai P, Auyeung TW, Bahyah KS, et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc. 2014;15(2):95–101. pmid:24461239
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref7] 7. Lukaski HC. Soft tissue composition and bone mineral status: evaluation by dual-energy X-ray absorptiometry. J Nutr. 1993;123(2 Suppl):438–43.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref8] 8. Farrell TJ, Webber CE. The error due to fat inhomogeneity in lumbar spine bone mineral measurements. Clin Phys Physiol Meas. 1989;10(1):57–64. pmid:2785442
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref9] 9. Kohrt WM. Body composition by DXA: tried and true? Med Sci Sports Exerc. 1995;27(10):1349–53. pmid:8531604
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref10] 10. Tothill P, Reid DM, Avenell A, Fenner JA. Comparisons between Hologic, Norland and Lunar dual-energy X-ray bone absorptiometers. Basic Life Sci. 1993;60:385–8. pmid:8110157
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref11] 11. Lorente Ramos RM, Azpeitia Arman J, Arevalo Galeano N, Munoz Hernandez A, Garcia Gomez JM, Gredilla Molinero J. Dual energy X-ray absorptimetry: fundamentals, methodology, and clinical applications. Radiologia. 2012;54(5):410–23. pmid:22285678
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref12] 12. Schousboe JT, Kats AM, Langsetmo L, Vo TN, Taylor BC, Schwartz AV, et al. Central Obesity and Visceral Adipose Tissue Are Not Associated With Incident Atherosclerotic Cardiovascular Disease Events in Older Men. J Am Heart Assoc. 2018;7(16):e009172. pmid:30369326
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref13] 13. Despres JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126(10):1301–13. pmid:22949540
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref14] 14. Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL. Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat. Obesity (Silver Spring). 2012;20(5):1109–14.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref15] 15. Kaul S, Rothney MP, Peters DM, Wacker WK, Davis CE, Shapiro MD, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring). 2012;20(6):1313–8.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Gower BA, Nagy TR, Goran MI. Visceral fat, insulin sensitivity, and lipids in prepubertal children. Diabetes. 1999;48(8):1515–21. pmid:10426367
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref17] 17. D'Adamo E, Marcovecchio ML, Giannini C, Capanna R, Impicciatore M, Chiarelli F, et al. The possible role of liver steatosis in defining metabolic syndrome in prepubertal children. Metabolism. 2010;59(5):671–6. pmid:19913850
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref18] 18. Nier A, Brandt A, Baumann A, Conzelmann IB, Ozel Y, Bergheim I. Metabolic Abnormalities in Normal Weight Children Are Associated with Increased Visceral Fat Accumulation, Elevated Plasma Endotoxin Levels and a Higher Monosaccharide Intake. Nutrients. 2019;11(3).
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref19] 19. Neeland IJ, Grundy SM, Li X, Adams-Huet B, Vega GL. Comparison of visceral fat mass measurement by dual-X-ray absorptiometry and magnetic resonance imaging in a multiethnic cohort: the Dallas Heart Study. Nutr Diabetes. 2016;6(7):e221. pmid:27428873
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref20] 20. Bosch TA, Dengel DR, Kelly AS, Sinaiko AR, Moran A, Steinberger J. Visceral adipose tissue measured by DXA correlates with measurement by CT and is associated with cardiometabolic risk factors in children. Pediatr Obes. 2015;10(3):172–9. pmid:24990328
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref21] 21. Lee V, Blew R, Hetherington-Rauth M, Blew D, Galons JP, Hagio T, et al. Estimation of visceral fat in 9- to 13-year-old girls using dual-energy X-ray absorptiometry (DXA) and anthropometry. Obes Sci Pract. 2018;4(5):437–47. pmid:30338114
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref22] 22. Shepherd JA, Fan B, Lu Y, Wu XP, Wacker WK, Ergun DL, et al. A multinational study to develop universal standardization of whole-body bone density and composition using GE Healthcare Lunar and Hologic DXA systems. J Bone Miner Res. 2012;27(10):2208–16. pmid:22623101
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref23] 23. Marshall WA, Tanner JM. Variations in pattern of pubertal changes in girls. Arch Dis Child. 1969;44(235):291–303. pmid:5785179
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref24] 24. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660–7. pmid:18026621
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref25] 25. Diessel E, Fuerst T, Njeh CF, Tylavsky F, Cauley J, Dockrell M, et al. Evaluation of a new body composition phantom for quality control and cross-calibration of DXA devices. J Appl Physiol (1985). 2000;89(2):599–605.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref26] 26. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676–82. pmid:22743772
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref27] 27. Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3).
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref28] 28. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2018;8(18):003.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref29] 29. Demerath EW, Shen W, Lee M, Choh AC, Czerwinski SA, Siervogel RM, et al. Approximation of total visceral adipose tissue with a single magnetic resonance image. Am J Clin Nutr. 2007;85(2):362–8. pmid:17284730
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref30] 30. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, et al. Visceral adipose tissue: relations between single-slice areas and total volume. Am J Clin Nutr. 2004;80(2):271–8. pmid:15277145
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. Irlbeck T, Massaro JM, Bamberg F, O'Donnell CJ, Hoffmann U, Fox CS. Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the Framingham Heart Study. Int J Obes (Lond). 2010;34(4):781–7.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref32] 32. Cheng X, Zhang Y, Wang C, Deng W, Wang L, Duanmu Y, et al. The optimal anatomic site for a single slice to estimate the total volume of visceral adipose tissue by using the quantitative computed tomography (QCT) in Chinese population. Eur J Clin Nutr. 2018;72(11):1567–75. pmid:29559725
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref33] 33. Blitman NM, Baron LS, Berkenblit RG, Schoenfeld AH, Markowitz M, Freeman K. Feasibility of using single-slice MDCT to evaluate visceral abdominal fat in an urban pediatric population. AJR Am J Roentgenol. 2011;197(2):482–7. pmid:21785098
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref34] 34. Lee S, Kuk JL, Kim Y, Arslanian SA. Measurement site of visceral adipose tissue and prediction of metabolic syndrome in youth. Pediatr Diabetes. 2011;12(3 Pt 2):250–7.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref35] 35. O'Connor M, Ryan J, Foley S. Best single-slice location to measure visceral adipose tissue on paediatric CT scans and the relationship between anthropometric measurements, gender and VAT volume in children. Br J Radiol. 2015;88(1054):20140711. pmid:26317895
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref36] 36. Bidulescu A, Liu J, Hickson DA, Hairston KG, Fox ER, Arnett DK, et al. Gender differences in the association of visceral and subcutaneous adiposity with adiponectin in African Americans: the Jackson Heart Study. BMC Cardiovasc Disord. 2013;13:9. pmid:23433085
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref37] 37. Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol. 2015;402:113–9. pmid:25578600
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref38] 38. Wells JC. Sexual dimorphism of body composition. Best Pract Res Clin Endocrinol Metab. 2007;21(3):415–30. pmid:17875489
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref39] 39. Daniels SR, Morrison JA, Sprecher DL, Khoury P, Kimball TR. Association of body fat distribution and cardiovascular risk factors in children and adolescents. Circulation. 1999;99(4):541–5. pmid:9927401
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref40] 40. Zhao Y, Wang L, Xue B, Wang Y. Associations between general and central obesity and hypertension among children: The Childhood Obesity Study in China Mega-Cities. Sci Rep. 2017;7(1):16895. pmid:29203818
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref41] 41. He Q, Horlick M, Fedun B, Wang J, Pierson RN JrHeshka S, et al. Trunk fat and blood pressure in children through puberty. Circulation. 2002;105(9):1093–8. pmid:11877361
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref42] 42. Syme C, Abrahamowicz M, Leonard GT, Perron M, Richer L, Veillette S, et al. Sex differences in blood pressure and its relationship to body composition and metabolism in adolescence. Arch Pediatr Adolesc Med. 2009;163(9):818–25. pmid:19736335
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref43] 43. Lindsay RS, Hanson RL, Roumain J, Ravussin E, Knowler WC, Tataranni PA. Body mass index as a measure of adiposity in children and adolescents: relationship to adiposity by dual energy x-ray absorptiometry and to cardiovascular risk factors. J Clin Endocrinol Metab. 2001;86(9):4061–7. pmid:11549626
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref44] 44. Korner A, Wiegand S, Hungele A, Tuschy S, Otto KP, l'Allemand-Jander D, et al. Longitudinal multicenter analysis on the course of glucose metabolism in obese children. Int J Obes (Lond). 2013;37(7):931–6.
View Article
Google Scholar

[163] View Article

[164] Google Scholar

[ref45] 45. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362–74. pmid:15175438
View Article
PubMed/NCBI
Google Scholar

[166] View Article

[167] PubMed/NCBI

[168] Google Scholar

Figures

Abstract

Background

Objective

Methods

Results

Conclusions

Introduction

Methods

Study design

Participants

Anthropometric measurement

Body composition by DXA

Body composition by MRI

Outcome definitions

Statistical analysis

Results

Discussion

Supporting information

S1 Fig.

S1 Dataset.

References