Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA

Department of Nutrition, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA

Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA 02115, USA

Department of Population Medicine, Obesity Prevention Program, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 3rd Floor, Boston, MA 02215, USA

Abstract

Background

Given that it is not feasible to use dual x-ray absorptiometry (DXA) or other reference methods to measure adiposity in all pediatric clinical and research settings, it is important to identify reasonable alternatives. Therefore, we sought to determine the extent to which other adiposity measures were correlated with DXA fat mass in school-aged children.

Methods

In 1110 children aged 6.5-10.9 years in the pre-birth cohort Project Viva, we calculated Spearman correlation coefficients between DXA (n=875) and other adiposity measures including body mass index (BMI), skinfold thickness, circumferences, and bioimpedance. We also computed correlations between lean body mass measures.

Results

50.0% of the children were female and 36.5% were non-white. Mean (SD) BMI was 17.2 (3.1) and total fat mass by DXA was 7.5 (3.9) kg. DXA total fat mass was highly correlated with BMI (r_{s}=0.83), bioimpedance total fat (r_{s}=0.87), and sum of skinfolds (r_{s}=0.90), and DXA trunk fat was highly correlated with waist circumference (r_{s}=0.79). Correlations of BMI with other adiposity indices were high, e.g., with waist circumference (r_{s}=0.86) and sum of subscapular plus triceps skinfolds (r_{s}=0.79). DXA fat-free mass and bioimpedance fat-free mass were highly correlated (r_{s}=0.94).

Conclusions

In school-aged children, BMI, sum of skinfolds, and other adiposity measures were strongly correlated with DXA fat mass. Although these measurement methods have limitations, BMI and skinfolds are adequate surrogate measures of relative adiposity in children when DXA is not practical.

Background

Gold standard measures of adiposity such as the four-compartment model, which calculates adiposity using independent measurements of total body water, body density, and bone mass

Body mass index (BMI) and waist circumference measure overall and central adiposity, respectively, relatively well in adults

While some previous studies have examined correlations of adiposity measurement methods in children, most were relatively small, spanned a large age range, and/or were limited to only a few measures of adiposity. We sought to examine the extent to which measures of total and central adiposity, including BMI, circumferences, skinfolds, bioimpedance, and DXA fat mass, were correlated with each other in a large study population of multiethnic school-aged children.

Methods

Subjects/study design

We studied 1110 children in Project Viva, an ongoing prospective pre-birth cohort study initiated in 1999. Women joined the study during their first prenatal visit at Harvard Vanguard Medical Associates, a large multi-specialty group practice in eastern Massachusetts. Eligibility criteria included fluency in English, gestational age less than 22 weeks at first prenatal visit, and singleton pregnancy. Additional details of recruitment and retention procedures have been published elsewhere

Measurements

At the in-person visit, trained research assistants measured height to the nearest 0.1 cm using a calibrated stadiometer (Shorr Productions, Olney, Maryland) and weight to the nearest 0.1 kg using a calibrated scale (Tanita model TBF-300A, Tanita Corporation of America, Inc., Arlington Heights, IL). We computed each child’s BMI using the following formula: BMI=weight/height^{2} (kg/m^{2}). We calculated age-sex-adjusted BMI z-score and BMI percentile using the 2000 Centers for Disease Control and Prevention reference data

We measured subscapular (SS) and triceps (TR) skinfold thicknesses to the nearest 0.1 mm using Holtain calipers (Holtain Ltd, Crosswell, Wales) and calculated the sum (SS + TR) and ratio (SS:TR) of the two thicknesses. The correlations of other measures of adiposity with subscapular or triceps thickness individually were very similar to the correlations with sum of the two, so we chose to show results for only sum of skinfolds. We measured hip and waist circumferences to the nearest 0.1 cm using a Hoechstmass measuring tape (Hoechstmass Balzer GmbH, Sulzbach, Germany), and calculated waist-to-hip circumference ratios. We measured middle upper arm circumference using a Ross measuring tape (Ross Products Division, Abbott Laboratories Inc., Columbus, OH).

Research assistants performing the measurements followed standardized techniques

We measured bipolar bioelectrical impedance using a Tanita scale model TBF-300A (Tanita Corporation of America, Inc., Arlington Heights, IL) foot-to-foot body composition analyzer. We calculated fat mass and fat-free mass indices for DXA and bioelectrical impedance measurements using the following formula: (mass in kg)/(height in meters)^{2}.

Trained research assistants performed whole body DXA scans on the children (n=875) using a Hologic model Discovery A (Hologic, Bedford, MA) that they checked for quality control daily by scanning a standard synthetic spine to check for machine drift. We used Hologic software QDR version 12.6 for scan analysis. A single trained investigator (CEB) checked all scans for positioning, movement, and artifacts, and defined body regions for analysis. Intrarater reliability on duplicate measurements was high (r=0.99).

Statistical analysis

We calculated Spearman correlation coefficients between pairs of adiposity measurements. We chose to focus on Spearman correlation coefficients because they do not assume normality and are conservative when linearity holds. However, we also ran Pearson correlations on natural log-transformed variables for comparison. We also examined correlations for boys and girls separately. We additionally calculated correlation coefficients among lean body mass measurements. Since children varied in exact age at study visit and measures differed by sex, we also calculated correlations adjusted for exact age at study visit and sex, but this adjustment did not substantially change correlations. Therefore we show only unadjusted correlations. We created Bland-Altman plots to assess the extent to which agreement between BMI and DXA fat mass index varied by amount of adiposity.

To assess whether measuring sum of skinfolds in addition to BMI improved associations with fat mass, we conducted linear regression of DXA fat mass index on BMI alone vs. BMI and sum of skinfolds. We compared the R-square values from the two models to assess the proportion of the variance in DXA fat mass explained by these measures.

We computed the difference between mean values of bioimpedance and DXA measures of fat mass and fat- free mass to assess any absolute difference between these measurement methods.

To assess how well BMI-defined obesity detects elevated DXA fat mass, we identified the children with BMI **≥**95^{th} age- and sex-specific percentile (“obesity”) and **≥**85^{th} percentile (“overweight plus obesity”) according to reference data from the Centers for Disease Control and Prevention

We conducted a complete case analysis. We repeated the analysis using multiple imputation and 95% of the adiposity correlations examined were within 0.02, so for simplicity we decided to use the complete case approach.

We conducted all analyses using SAS version 9.2 or higher (Cary, NC).

Results

Among the 1110 children in this analysis, 63.5% were white, 16.9% black, 4.2% Hispanic, 3.4% Asian, and 11.9% multiracial or other race/ethnicity, and 50.0% were female. Mean (SD) BMI was 17.2 (3.1) and BMI z-score was 0.4 (1.0). Bioelectrical impedance underestimated absolute fat mass (mean difference: -1.4 kg, 95% CI: -1.5, -1.4) and overestimated fat-free mass (mean difference: 1.1 kg, 95% CI: 1.0, 1.2) compared to DXA. Table

**Variable**

**N**

**Normal weight (n=826)**

**Overweight (n=149)**

**Obese (n=135)**

**Mean (SD)/%**

^{1} Abbreviations as follows:

**Mother/Family**

Smoked during pregnancy

1069

8.8%

7.1%

20.3%

Completed college

1104

71.3%

70.3%

45.9%

Multiparous

1110

51.7%

52.4%

56.3%

Annual household income > $70,000

1006

66.0%

69.3%

42.3%

Prepregnancy BMI (kg/m^{2})

1104

23.8 (4.5)

25.9 (5.2)

28.8 (7.3)

**Child**

Female

1110

49.5%

54.4%

48.2%

Race/ethnicity

1108

Asian

4.0%

3.4%

0.0%

Black

13.7%

15.4%

37.8%

Hispanic

3.5%

4.0%

8.9%

White

67.2%

66.4%

37.8%

More than one race/other

11.5%

10.7%

15.6%

Height (cm)

1110

127.6 (7.2)

130.9 (8.4)

134.0 (8.3)

Weight (kg)

1110

26.0 (4.1)

32.8 (5.6)

43.0 (10.5)

BMI (kg/m^{2})

1110

15.9 (1.2)

19.0 (1.0)

23.6 (3.5)

BMI z-score

1110

−0.05 (0.73)

1.31 (0.17)

2.07 (0.28)

Leg Length

1109

59.7 (4.6)

61.3 (5.5)

63.3 (5.8)

Waist circumference (cm)

1106

56.6 (4.1)

64.5 (5.0)

76.1 (9.4)

Hip circumference (cm)

1094

65.3 (4.7)

72.7 (6.0)

82.5 (8.5)

Subscapular skinfold thickness (mm)

1103

6.3 (2.0)

10.7 (7.5)

18.7 (7.2)

Triceps skinfold thickness (mm)

1106

9.6 (2.8)

14.5 (3.6)

20.6 (5.3)

BIA fat mass (kg)

1109

4.3 (1.7)

7.8 (2.3)

14.5 (5.9)

BIA % fat

1109

16.2 (4.3)

23.6 (4.2)

32.7 (6.5)

BIA fat-free mass (kg)

875

21.7 (3.1)

25.0 (3.9)

28.2 (5.3)

DXA total fat mass (kg)

875

5.8 (1.6)

9.1 (2.2)

15.0 (4.8)

DXA trunk fat (kg)

875

1.8 (0.6)

3.1 (1.0)

5.8 (2.3)

DXA % fat

875

22.2 (4.5)

27.8 (4.4)

34.8 (5.2)

DXA fat-free mass (kg)

875

20.3 (3.2)

23.7 (3.8)

27.5 (5.4)

Table _{s})=0.83; Pearson r (r_{p})=0.88], sum of skinfolds (r_{s}=0.90; r_{p}=0.93), and bioelectrical impedance body fat (r_{s}=0.87; r_{p}=0.89). DXA total fat mass was also highly correlated with middle upper arm circumference (r_{s}=0.87). DXA fat mass index was also highly correlated with BMI (r_{s}=0.80). Correlations of BMI with other adiposity indices were also high, for example with middle upper arm circumference (r_{s} =0.91) and sum of skinfolds (r_{s}=0.79). All other correlations of these measures with each other were >0.78. DXA trunk fat was highly correlated with waist circumference (r_{s}=0.79) but correlations with waist-to-hip and subscapular-to-triceps skinfold ratios were low (r_{s}=0.20 and r_{s}=0.16, respectively). Together, BMI and sum of skinfolds explained 89% of the variance of DXA fat mass index, as compared to 78% of the variance explained by BMI alone. Results were similar for BMI z-score. The Bland-Altman plot of BMI and fat mass index showed random scatter, indicating that the methods have good agreement at all levels of adiposity (plot not shown).

**BMI**

**BMI Z**

**Ht**

**Wt**

**Wt: ht**

**Waist circ**

**Waist: hip**

**SS: TR**

**SS+TR**

**BIA % fat**

**BIA fat**

**DXA trunk fat**

**DXA % fat**

**DXA fat**

^{1}Abbreviations are as follows:

**Spearman R**

**BMI**

1.00

0.98

0.38

0.84

0.93

0.86

0.23

0.22

0.79

0.81

0.88

0.81

0.63

0.83

**BMI Z**

1.00

0.28

0.77

0.88

0.81

0.26

0.21

0.76

0.81

0.85

0.78

0.63

0.80

**Height**

1.00

0.80

0.66

0.56

−0.07

0.15

0.33

0.33

0.53

0.44

0.14

0.47

**Weight**

1.00

0.98

0.87

0.11

0.23

0.69

0.70

0.86

0.76

0.49

0.80

**Wt:ht**

1.00

0.90

0.16

0.23

0.75

0.77

0.90

0.81

0.56

0.84

**Waist circ**

1.00

0.46

0.23

0.73

0.75

0.85

0.79

0.59

0.81

**Waist: hip**

1.00

0.19

0.19

0.25

0.20

0.20

0.21

0.17

**SS: TR**

1.00

0.14

0.18

0.21

0.16

0.08

0.13

**SS+TR**

1.00

0.80

0.81

0.89

0.84

0.90

**BIA % fat**

1.00

0.95

0.82

0.73

0.84

**BIA fat**

1.00

0.84

0.67

0.87

**DXA trunk fat**

1.00

0.89

0.98

**DXA % fat**

1.00

0.89

**DXA fat**

1.00

Correlations were slightly higher in females than in males, although differences were unlikely to be clinically meaningful. For example, the correlation between BMI and DXA fat total fat was 0.84 for boys and 0.89 for girls, while the correlation between DXA and sum of skinfolds was 0.88 for boys and 0.90 for girls. The correlation between waist circumference and DXA trunk fat was 0.79 for boys and 0.87 for girls.

Correlations were stronger in black and Hispanic children than in whites (e.g. for BMI vs. DXA fat mass, r_{s}=0.91 for blacks, 0.94 for Hispanics, 0.79 for whites).

DXA fat mass increased little for each age-sex-adjusted percentile of BMI until above the 85^{th} percentile (Figure

Age- and sex-specific BMI percentile at 6.5-10.9 years, by race/ethnicity

**Age- and sex-specific BMI percentile in relation to DXA fat mass index at 6.5-10.9 years, by race/ethnicity.** Data from 875 participants in Project Viva. BMI percentiles based on CDC growth charts

DXA fat-free mass was highly correlated with bioelectrical impedance fat-free mass (r_{s}=0.94) and height (r_{s}=0.83) (Table _{s}=0.54), as were bioimpedance fat and fat-free mass (0.65). BMI had lower correlation with DXA fat-free mass (r_{s}=0.69) than with fat mass (r_{s}=0.83).

**Height**

**Weight**

**BMI**

**Leg length**

**DXA fat-free mass**

**BIA fat-free mass**

**Spearman R**

^{1}Abbreviations are as follows:

**Height**

1.00

0.80

0.38

0.93

0.83

0.86

**Weight**

1.00

0.84

0.71

0.91

0.93

**BMI**

1.00

0.31

0.69

0.69

**Leg length**

1.00

0.74

0.77

**DXA fat-free mass**

1.00

0.94

**BIA fat-free mass**

1.00

BMI-defined overweight plus obesity (≥85^{th} percentile) had a sensitivity of 0.73 and specificity of 0.90 as a predictor of DXA percent body fat cutpoints of ≥30.10% in females and ≥24.63% in males (Table ^{th} percentile) had a sensitivity of 0.75, specificity of 0.96, and kappa of 0.71 as a predictor of DXA cutpoints of ≥34.00% body fat in girls and ≥29.00% in boys. the area under the ROC curve in this study was 0.90 for overweight plus obesity and 0.94 for obesity.

**BMI**

**DXA % body fat**

**Kappa**

**Sens**

**Spec**

**LR+**

**LR-**

**AUC**

^{1} Cutoffs defined by CDC growth reference curves

^{2} Based on the prevalence of BMI percentile-defined overweight and obesity in females and males in our dataset. Abbreviations are as follows:

**Females: ≥30.10% Males: ≥24.63%**
^{
2
}

**Females: <30.10% Males: <24.63%**
^{
2
}

0.63

0.73

0.90

7.3

0.31

0.90

**≥85**
^{
th
}**percentile**
^{
1
}

169

64

**<85**
^{
th
}**percentile**
^{
1
}

64

578

**Females: ≥34.00% Males: ≥29.00%**
^{
2
}

**Females: <34.00% Males: <29.00%**
^{
2
}

0.71

0.75

0.96

19.6

0.26

0.94

**≥95**
^{
th
}**percentile**
^{
1
}

85

29

**<95**
^{
th
}**percentile**
^{
1
}

29

732

Discussion

Among school-aged children enrolled in a US cohort study with research measures of adiposity, we found that BMI, sum of skinfolds, and bioimpedance total fat were all strongly correlated with total fat mass as measured by DXA, which many consider to be a gold standard for field research. Despite concerns that anthropometric measures such as BMI do not distinguish fat mass from lean mass, they were highly correlated with direct measures of adiposity in our study population. These findings suggest that in epidemiologic studies of school-aged children in which DXA is not available, more feasible anthropometric measures such as BMI and skinfold thicknesses are reasonable surrogate measures.

The high correlations we found between DXA and BMI are similar to those in other studies in children _{s}=0.83 in our study)

BMI percentile appeared to correlate more highly with adiposity by DXA fat mass index among overweight and obese children than among normal or underweight children. However, this finding may be due to a wider spread of data among those with higher DXA fat mass than those with less fat mass, as the Bland-Altman plots indicated good agreement between BMI and DXA fat mass index at all levels of adiposity. Similarly, the higher correlations between adiposity measures in black and Hispanic children than white children may be due to differences in the distribution of the data. In our study population, blacks and Hispanics had higher mean and SD in DXA fat mass (9.1 [5.9] kg in blacks, 8.5 [3.9] kg in Hispanics), and wider spread of fat mass values whereas whites clustered together in a narrower range (7.0 [3.0] kg). There may truly be racial/ethnic differences in the relationship between BMI at DXA fat mass in youth, as suggested by Dugas et al.,

Using internally-defined DXA percent body fat cutoffs as a criterion standard for true obesity, we found that BMI-defined obesity had a very high specificity (0.96) and a moderate sensitivity (0.75). Under this scenario, 75% of school-aged children with BMI ≥95^{th} percentile would be correctly classified as obese, and 4% of those with BMI <95^{th} percentile would be incorrectly classified as nonobese. 96% of children whose BMI exceeds the 95^{th} percentile actually had excess adiposity. BMI-defined overweight plus obesity (≥85^{th} percentile) had similar sensitivity (0.73) but slightly lower specificity (0.90). In a review of several studies on this topic, Freedman et al. ^{th} percentile is a reasonably sensitive (0.7-0.9) and specific (0.95) cutoff for excess adiposity in children, consistent with our results. In addition, the area under the ROC curve was very high (0.90 for overweight plus obesity and 0.94 for obesity), indicating that overall, BMI percentile discriminates effectively between high and low DXA percent body fat.

The sum of skinfold thickness was highly correlated with DXA total fat and was strongly correlated with other measures of adiposity. Previous smaller studies have also found strong associations between reference methods and skinfold thickness using equations

As in previous studies, we found that bipolar bioelectrical impedance underestimated fat mass and overestimated fat-free mass compared to DXA _{s}=0.87) with DXA fat mass, consistent with the smaller studies of Eisenmann et al. (r=0.84)

We also examined markers of central adiposity. Trunk fat may be reflective of intra-abdominal fat, which may be more metabolically active than fat stored in other regions of the body. One study of 1987 children and adolescents in Cyprus found that waist circumference and waist-to-height ratio were more highly associated with risk factors for cardiovascular disease like cholesterol and blood pressure than was BMI

When determining which adiposity measurement technique to use, it is important to consider the feasibility and cost of each method in addition to its validity and precision

This study has several limitations. First, we based our analyses on DXA as a clinical gold standard, whereas a true gold standard is the four-compartment model. Nevertheless, other studies found associations between these two methods to be high (r or r-squared>0.84) among children _{s}=0.83), and the broader age range could increase generalizability.

Strengths of our study include the large number of participants, careful measurement by research assistants, and variety of measurement techniques used.

Conclusions

In school-aged children, BMI, sum of skinfold thicknesses, and other adiposity measures were strongly correlated with DXA fat mass. As anthropometry is less expensive and more feasible than DXA, BMI and skinfolds are reasonable surrogate measures of adiposity in clinical and population studies among children when DXA is not practical. Given the high specificity and predictive value of BMI **≥**95^{th} percentile for DXA-assessed adiposity, clinicians should be confident that using this definition of obesity correctly identifies school-age children with adiposity, even though children below the 95^{th} percentile may also have excess adiposity. Future research should assess whether these findings are consistent in preschool-age children and adolescents. Defining and validating cutoffs for excess adiposity will be important to assess the feasibility of their use in clinical practice.

Abbreviations

DXA: Dual x-ray absorptiometry; BMI: Body mass index; wt: Weight; ht: Height; BMI Z: Age-sex-adjusted BMI z-score; ROC: Receiver operating characteristic; waist circ: Waist circumference; hip circ: Hip circumference; SS: Subscapular skinfold thickness; TR: Triceps skinfold thickness; BIA: Bioelectrical impedance; Sens: Sensitivity; Spec: Specificity; LR+: Likelihood ratio positive; LR-: Likelihood ratio negative.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

MWG, EO, and CEB made substantial contributions to study conception and design. SLR and CEB conducted data analysis. All authors helped to interpret the data. CEB drafted the article. All authors revised the manuscript critically and approved the final version of the manuscript.

Acknowledgements

We thank the Project Viva participants and staff. This work was supported by NIH grants R01 HD 034568, R01 HL 064925, and K24 HD 069408. Caroline Boeke was supported by T32 CA 09001. There are no conflicts of interest.

Pre-publication history

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