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Ketel IJ, Volman MN, Seidell JC, Stehouwer CD, Twisk JW, Lambalk CB. Superiority of skinfold measurements and waist over waist-to-hip ratio for determination of body fat distribution in a population-based cohort of Caucasian Dutch adults. Eur J Endocrinol 6, 655-661

Division of Reproductive Medicine, Department of Obstetrics & Gynaecology, de Boelelaan 1117, 1081 HV, room 0Z120, Amsterdam, The Netherlands.
European Journal of Endocrinology (Impact Factor: 4.07). 06/2007; 156(6):655-61. DOI: 10.1530/EJE-06-0730
Source: PubMed
ABSTRACT
To determine which anthropometric measurement is the most reliable alternative for fat distribution as measured by dual-energy X-ray absorptiometry (DXA).
Population-based survey carried out in Amsterdam, The Netherlands.
A total of 376 individuals (200 women) with a mean age of 36.5 years and mean body mass index (BMI) of 24.0 (+/-3.1) kg/m2 underwent various anthropometric and DXA measurements of central (CFM) and peripheral fat mass (PFM). Furthermore, for the assessment of apple-shaped body composition, CFM-to-PFM ratio was calculated. Anthropometric measurements were waist and hip circumference, waist-to-hip ratio (WHR), BMI, waist/length and the skinfold thickness of biceps, triceps, suprailiacal (SI), subscapular (SS) and upper leg. We determined whether equations of combined anthropometrics were even more reliable for the assessment of fat mass.
In both women and men, reliable alternatives for CFM are central skinfolds and waist (Pearson's correlation (r) >or= 0.8). Peripheral skinfolds are the best predictors of PFM (r >or= 0.8). In contrast, WHR correlated only marginally with any of the DXA measurements. Equations based on several anthropometric variables correlate with CFM even better (R2 >or= 0.8). CFM-to-PFM ratio has the highest correlation with the ratio (SS+SI)/BMI in women (r = 0.66) and waist/length in men (r = 0.71). Equations are reasonable alternatives of CFM-to-PFM ratio (R2 >or= 0.5).
Waist and skinfolds are reliable alternatives for the measurement of body fat mass in a cohort of Caucasian adults. WHR is not appropriate for the measurement of fat distribution.

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Available from: Jaap Seidell, Dec 27, 2013
CLINICAL STUDY
Superiority of skinfold measurements and waist over waist-
to-hip ratio for determination of body fat distribution in a
population-based cohort of Caucasian Dutch adults
Iris J G Ketel, Mariken N M Volman, Jacob C Seidell
1
, Coen D A Stehouwer
2
, Jos W Twisk
3
and Cornelis B Lambalk
Division of Reproductive Medicine, Department of Obstetrics & Gynaecology, de Boelelaan 1117, 1081 HV, room 0Z120, Amsterdam, The Netherlands,
1
Department of Nutrition and Health, Faculty of Earth and Life Sciences, de Boelelaan 1117, 1081 HV Amsterdam, The Netherlands,
2
Department of
Internal Medicine, Academic Hospital Maastricht, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands and
3
Department of Public and Occupational
Health and Institute for Research in Extramural Medicine of VU University medical centre, de Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
(Correspondence should be addressed to C B Lambalk; Email: cb.lambalk@vumc.nl)
I J G Ketel and M N M Volman contributed equally to this work
Abstract
Objective: To determine which anthropometric measurement is the most reliable alternative for fat
distribution as measured by dual-energy X-ray absorptiometry (DXA).
Design: Population-based survey carried out in Amsterdam, The Netherlands.
Subjects and methods: A total of 376 individuals (200 women) with a mean age of 36.5 years and mean
body mass index (BMI) of 24.0 (G3.1) kg/m
2
underwent various anthropometric and DXA
measurements of central (CFM) and peripheral fat mass (PFM). Furthermore, for the assessment of
apple-shaped body composition, CFM-to-PFM ratio was calculated. Anthropometric measurements
were waist and hip circumference, waist-to-hip ratio (WHR), BMI, waist/length and the skinfold
thickness of biceps, triceps, suprailiacal (SI), subscapular (SS) and upper leg. We determined whether
equations of combined anthropometrics were even more reliable for the assessment of fat mass.
Results: In both women and men, reliable alternatives for CFM are central skinfolds and waist
(Pearson’s correlation (r) R0.8). Peripheral skinfolds are the best predictors of PFM (rR0.8). In
contrast, WHR correlated only marginally with any of the DXA measurements. Equations based on
several anthropometric variables correlate with CFM even better (R
2
R0.8). CFM-to-PFM ratio has the
highest correlation with the ratio (SSCSI)/BMI in women (rZ0.66) and waist/length in men (rZ
0.71). Equations are reasonable alternatives of CFM-to-PFM ratio (R
2
R0.5).
Conclusion: Waist and skinfolds are reliable alternatives for the measurement of body fat mass in a
cohort of Caucasian adults. WHR is not appropriate for the measurement of fat distribution.
European Journal of Endocrinology 156 655–661
Introduction
Central fat mass (CFM) is increasingly recognized as an
independent risk factor for cardiovascular disease (1–5)
and metabolic disease as well as overall mortality. In
contrast, peripheral fat mass (PFM) may independently
contribute to a lower risk for cardiovascular disease
(4–10). Therefore, measuring fat distribution, i.e. CFM/
PFM ratio, is a topic of great interest, especially
population-based cohort studies, in which large
numbers of subjects are measured, simple anthropo-
metric measurements are needed to determine fat
distribution. Dual-energy X-ray absorptiometry (DXA)
is a well-accepted method and has a good concordance
with computed tomography (CT) for the assessment of
body fat (11). However, CT and DXA are costly and time
consuming. Moreover, these techniques involve
exposing the subject to ionizing radiation. As a
consequence, the use of these techniques cannot be
extended to studies that involve large numbers of
subjects. In these kinds of studies, body mass index
(BMI, for overall body fatness) and waist-to-hip ratio
(WHR, for body fat distribution) are most often used
(12). There is doubt whether WHR, which is partly
dependent on pelvic skeletal structure and muscle
distribution, is a valid anthropometric measure for the
assessment of body composition (13–15). Measurement
of skinfold thickness in combination with waist
circumference may be reasonable alternatives.
However, there are no larger studies so far that correlate
both skinfold measurements and waist circumference
with DXA for determining body fat. Therefore, the
purpose of the present study was to assess the predictive
value of various simple anthropometric measures of
body fat compared with DXA measurements in a
population-based cohort of Dutch Caucasian adults.
European Journal of Endocrinology (2007) 156 655–661 ISSN 0804-4643
q 2007 Society of the European Journal of Endocrinology DOI: 10.1530/EJE-06-0730
Online version via www.eje-online.org
Page 1
Besides the commonly used anthropometric and DXA
measures for the estimation of CFM, we had particular
interest in ratios. Since CFM is positively and PFM is
negatively correlated with cardiovascular disease, the
CFM-to-PFM ratio might be a stronger predictor for the
assessment of cardiovascular risk. The CFM-to-PFM
ratio is known to be associated with increased risk of
mortality (16, 17). Furthermore, we analysed a number
of skinfold ratios which have been associated with
cardiovascular risk factors in previous studies (18–21).
In addition, we aimed to develop easy-to-use regression
equations consisting of various combinations of anthro-
pometric measurements that predict body fat distri-
bution as measured with DXA.
Methods
Subjects and design
The Amsterdam Growth and Health Longitudinal Study
(AGAHLS), which started in 1976, was designed to
investigate the development of indicators of growth and
health in a cohort of Dutch adolescents. For that
purpose, 615 healthy boys and girls were recruited from
two secondary schools in and around Amsterdam. All
participants were born between 1961 and 1965, and
were residents of the Netherlands. In 2000, at a mean
age of 36 years, 376 Caucasian subjects (200 women)
visited the Vrije Universiteit for assessment of, amongst
others, anthropometrics and DXA. More detailed
information about the AGAHLS has been described in
previous publications (19, 22, 23). The Medical Ethical
Committee of the Vrije Universiteit Medical Centre
approved the protocol. All subjects gave informed
consent.
Data collection
A whole-body DXA scan was performed using the
Hologic QDR-2000 (S/N 2513; Hologic Inc., Waltham,
MA, USA). With the use of specific anatomic landmarks,
regions of the head, trunk, arms and legs were
distinguished as shown in Fig. 1. Total body fat, fat
mass of the trunk, legs and arms (kg) were used for
analysis. Fat mass of the trunk, further referred to as
CFM, includes both the subcutaneous and the visceral
fat of this anatomical region. PFM was estimated by the
sum of the fat mass of both arms and legs. Furthermore,
the CFM-to-PFM ratio was calculated.
Anthropometric measurements of body height, body
mass, waist-hip and skinfolds were performed according
to standard procedures (19). BMI was calculated by
dividing body mass (kg) by body height squared (m
2
).
Skinfolds were measured with a Harpenden caliper
(Holtain, UK) to the nearest 0.1 mm according to the
recommendations of the International Biological
Programme (24). Measurements were performed
according to standard procedures (25, 26) and with
the same instruments throughout the study. Over the
entire period of study, one trained examiner performed
the measurements of the skinfolds. The DXA and
skinfold measurements were done on the same day.
The correlation coefficients were calculated for every
skinfold. For the single skinfolds, these reproducibility
coefficients were R0.8.
The following skinfolds were measured: subscapular
(SS), suprailiacal (SI), biceps (BI), triceps (TRI) and
upper leg (LEG). Two measurements were performed
and the mean was used for the analyses. For the
assessment of CFM, the skinfolds SS and SI and the sum
of these were used. Several combinations of skinfolds
were calculated to assess PFM: BICTRICLEG, BICTRI,
TRICLEG, BICLEG and LEG. The anthropometrics that
Figure 1 Standard regions of a dual-energy X-ray absorptiometry
scan. 1, head; 2, trunk; 3, arms; 4, legs.
656 I J G Ketel, M N M Volman and others EUROPEAN JOURNAL OF ENDOCRINOLOGY (2007) 156
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Page 2
were correlated best with either central or peripheral fat
as measured with DXA were used to calculate CFM-
to-PFM ratios. In women, the ratio (SSCSI)/BMI, and in
men, the ratio waist/(BICTRI) were calculated. Two
other well-known ratios were used for analysis:
SS/(SSCTRI) (20) and SS/TRI (18). Furthermore, the
best correlated central-to-peripheral skinfold ratio was
also analysed (i.e. (SSCSI)/(TRICLEG)).
To determine WHR, we measured the circumferences
of the waist (at umbilicus height) and hip (at the level of
widest circumference over the buttocks) with a flexible
steel tape to the nearest 0.1 cm.
Statistical analysis
Data were analysed with a statistical software package
(SPSS 11 for Windows, SPSS Inc., Chicago, IL, USA).
Results are given as meansG
S.D. The Pearson correlation
coefficient (r) was calculated to estimate the predictive
value of various anthropometrics. To assess significance,
a P!0.05 was considered to be statistically significant.
For multiple comparisons, the significance level was
adjusted by multiplying the number of comparisons of
each DXA measurement according to Bonferroni
correction (27). Linear regression analysis was used to
develop equations containing anthropometric measure-
ments, which correlate with body fat distribution. The
anthropometric measurements of body fat considered
were (the combination of) skinfolds, waist or hip
circumference, BMI, WHR, waist-to-length ratio, weight
and length; and the DXA measurements considered were
CFM and CFM-to-PFM ratio. Selection of variables was
done in a forward stepwise fashion with strict variable
entry (P!0.05) and elimination criteria (PR0.05).
Comparing R
2
values of the models obtained from each
set assessed the associative value of each set of
measurements. Incremental additive value was judged
by the increase in R
2
obtained when anthropometric
measurements were added to the model. Three types of
association models of CFM and CFM-to-PFM ratio were
constructed (Table 3). The first type is developed for
situations in which skinfold measurements are not
available. The second type is the first equation combined
with the best associative skinfold. The third equation
represents the optimal model. All analyses were
performed separately for males and females.
Results
Characteristics of the study group are presented in
Table 1 and Fig. 2. Fourteen women and eight men
refused to undergo DXA measurements, resulting in
minor loss of subjects.
Women
Table 2 shows the correlation coefficients regarding the
anthropometric indices and DXA measurements for
both men and women. CFM is highly correlated with the
sum of two skinfolds SSCSI (r Z0.82), skinfold SS (rZ
0.82), BMI (rZ0.82) and waist (rZ0.82). BMI has a
good correlation with both CFM and PFM. The ratio
CFM-to-PFM ratio, determined by DXA, is correlated
best with the ratio (SSCSI)/BMI (rZ0.66) and SSCSI
(rZ0.65). The skinfold ratio (SSCSI)/(TRICLEG) has a
correlation coefficient of 0.62. Just as CFM, PFM shows
good correlations with anthropometry: BMI (rZ0.85),
weight (rZ0.84), BICTRI (rZ0.82) and BICTRIC
LEG (rZ 0.82). In contrast, WHR correlates only
marginally with any of the DXA ratios.
Men
Just as in women, CFM in men is highly correlated with
waist (rZ0.85) and the skinfold SSCSI (rZ0.84)
(Table 2). In contrast to women, waist/length (rZ
0.71) has the highest correlation with CFM-to-PFM
ratio. Correlations of PFM with anthropometry are
slightly lower in men than in women; the best relation is
found with the same combination of skinfolds as in
women: BIC TRICLEG (rZ0.78) and BICTRI (rZ
0.78). As in women, WHR correlates only marginally
with any of the DXA ratios.
Table 1 Descriptive statistics for age, height, body mass, body mass index (BMI) and waist-to-hip ratio (WHR).
Women (nZ200) Men (nZ176)
Mean
S.D. Range Mean S.D. Range
Age 36.1 0.7 36.0–37.0 36.0 0.8 35.0–36.0
Height (m) 1.7 0.0 1.7–1.7 1.8 0.1 1.8–1.9
Body mass (kg) 67.9 10.2 60.7–73.2 83.9 10.8 76.5–90.5
BMI (kg/m
2
) 23.4 3.3 21.2–24.9 24.8 2.7 22.8–26.4
Waist (cm) 73.1 8.4 67.4–76.4 85.3 8.0 79.4–90.0
Hip (cm) 89.1 8.6 82.8–93.7 89.3 7.3 84.1–93.5
WHR 0.8 0.1 0.8–0.8 0.9 0.1 0.9–1.0
DXA, dual-energy X-ray absorptiometry; CFM, central fat mass; PFM, peripheral fat mass; S.D., standard deviation.
Waist and skinfold for body fatness 657EUROPEAN JOURNAL OF ENDOCRINOLOGY (2007) 156
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Linear regression analyses
As shown in Table 3, association models consisting of
simple anthropometric measurements are a good
alternative for the measurement of CFM. The optimal
model of CFM in women is a combination of waist, BMI,
hip circumference and the skinfold SS, which accounted
for 82% of the variance in CFM measured by DXA
(R
2
Z0.82). In men, the combination of waist, BMI and
SSCSI is the best correlation with CFM (R
2
Z0.83). In
both women and men, a reliable model of simple
anthropometric measurements, when skinfolds are not
available, is the combination of waist and BMI (R
2
Z
0.75 and R
2
Z0.77 respectively). The additional
variance explained when the skinfold SS is added to
the models was 4 and 5% respectively (R
2
Z0.79 in
women and R
2
Z0.82 in men). The optimal model of
CFM-to-PFM ratio for women and men is the com-
bination of the following anthropometrics: the waist/
length ratio and the skinfold ratio [(SSCSI)/(TRIC
LEG); R
2
Z0.50 and R
2
Z0.56 respectively]. Simple,
commonly used anthropometric measurements, such as
waist and BMI, account for 30% in women and 38% in
men of the variance in CFM-to-PFM ratio measured by
DXA. The skinfold ratio (SSCSI)/BMI in women and the
skinfold ratio (SSCSI)/(TRICLEG) in men explained a
further 17 and 12% of variance respectively (R
2
Z0.47
and R
2
Z0.50 respectively).
Discussion
The results of this study indicate that in a population-
based cohort of Dutch Caucasians, CFM and PFM
Figure 2 Bar chart of DXA measurements and skinfold thickness in
men and women. Values are presented as means; DXA, dual-
energy X-ray absorptiometry.
$
nZ168 men and nZ186 women.
Table 2 Correlation between DXA and anthropometric measurements in women and men.
CFM CFM/PFM PFM
rrr
DXA Women Men Women Men Women Men
Central anthropometrics
SSCSI 0.82* 0.84* 0.65* 0.63* 0.61* 0.73*
SS 0.82* 0.80* 0.62* 0.63* 0.64* 0.62*
SI 0.72* 0.78* 0.59* 0.56* 0.50* 0.66*
Waist 0.82* 0.85* 0.54* 0.66* 0.69* 0.68*
Anthropometric ratios
(SSCSI)/BMI 0.67* 0.66* 0.39*
Waist/(BICTRI) K0.48* K0.24
K0.48*
(SSCSI)/(TRICLEG) 0.38* 0.39* 0.62* 0.52* 0.02
0.07
SS/TRI 0.30* 0.39* 0.55* 0.48* K0.03
0.12
SS/(SSCTRI) 0.31* 0.33* 0.34* 0.46* K0.02
0.01
BMI 0.82* 0.81* 0.39* 0.59* 0.85* 0.68*
WHR 0.21
0.27* 0.28* 0.29* 0.04
0.15
Waist/length 0.80* 0.82* 0.58* 0.71* 0.64* K0.48*
Peripheral anthropometrics
BICTRICLEG 0.70* 0.61* 0.24
0.27* 0.82* 0.78*
BICTRI 0.62* 0.52* 0.36* 0.39* 0.82* 0.78*
TRICLEG 0.78* 0.70* 0.16
0.18
0.78* 0.73*
BICLEG 0.66* 0.59* 0.23
0.26
0.78* 0.74*
LEG 0.51* 0.44* 0.08
0.13
0.69* 0.73*
Hip circumference 0.78* 0.81* 0.39* 0.57* 0.78* 0.69*
Overall anthropometrics
Weight 0.77* 0.77* 0.30* 0.44* 0.84* 0.76*
Length 0.00
0.10
K0.17
K0.15
0.09
0.27*
r, Pearson’s correlation, *P value !0.001,
P value !0.05 and
not significant; SI, suprailiacal; SS, subscapular; TRI, triceps; BI, biceps; LEG, upper leg; DXA,
dual-energy X-ray absorptiometry; WHR, waist-to-hip ratio; BMI, body mass index; CFM, central fat mass; PFM, peripheral fat mass.
658 I J G Ketel, M N M Volman and others EUROPEAN JOURNAL OF ENDOCRINOLOGY (2007) 156
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Page 4
measured by DXA were highly correlated with central
and peripheral skinfolds respectively. Waist circumfer-
ence is a good alternative for the assessment of CFM
measured by DXA. In addition, BMI and weight had a
good correlation; however, these measurements do not
differentiate between central or peripheral fat measured
by DXA. The (SSCSI)/BMI ratio in women and the
waist/length ratio in men are the best anthropometric
alternative for CFM-to-PFM ratio. WHR does not seem
appropriate for the DXA measurement of body fat mass.
Equations based on several anthropometric variables
are correlated with CFM, PFM and CFM-to-PFM ratio
even better.
For the determination of central adiposity, DXA CFM
is most commonly used. Clearly, DXA CFM is related
with cardiovascular factors (2, 9, 28). Commonly used
standards for defining central versus peripheral
adiposity do not yet exist.
Central and peripheral measurements and, to a lesser
extent, DXA ratios are often used for the determination
of fat distribution (2, 4, 5, 9, 29). Due to the importance
of body fat distribution, specifically central in relation to
peripheral fat, the use of DXA ratios does seem
interesting. Moreover, several studies showed corre-
lations of DXA ratios with cardiovascular risk factors
(5, 17). DXA derived ratios in this study were aimed at
partitioning trunk fat from the remainder of the body.
The ratio (trunk/(armsClegs)) segments the body into
central and peripheral portions.
Traditionally, skinfold thickness has been used for the
assessment of body composition. Central skinfolds,
mainly the SS skinfolds (30), are positively linked to
cardiovascular risk factors in contrast to peripheral
skinfolds, which are negatively related to cardiovascular
disease (31, 32). Skinfold ratios have been widely used
for the assessment of fat distribution (17, 19, 20, 33)
and correlate well with several factors of cardiovascular
disease (18). Our study shows that these skinfold ratios
are moderately correlated with CFM-to-PFM ratio, as
measured with DXA. As far as we know, the specific
skinfold ratio (SSCSI)/BMI, which we found to be the
best anthropometric alternative for CFM-to-PFM ratio,
has not yet been correlated with cardiovascular disease.
The possible usefulness in the context of a relationship
with cardiovascular disease needs to be shown in future
studies. So far, each of the individual parameters of
which this ratio consists is related to cardiovascular risk
factors.
Weight, which is easy to measure and investigator
independent, does not make any differentiation between
CFM and PFM. Moreover, weight alone does not
represent a person’s body fat mass, if one is not taking
length into account. BMI, WHR and waist circumfer-
ence are all markers of body composition and therefore
considered as predictive measurements for cardiovas-
cular disease. BMI, a ratio of weight and length, is often
used and assumed to represent the degree of body fat
(and muscle in adolescents), but does not capture body
fat distribution (34). In particular, WHR is considered
the traditional anthropometric technique for assessing
CFM-to-PFM ratio (12). It is widely used and established
in cross-sectional (35), longitudinal (36) and interven-
tion studies, and it is a robust predictor of disease risk
and mortality (35, 37, 38). In this context, WHR, rather
than BMI, has therefore recently been recommended for
the assessment of body fatness (38). However, in the
present study, we could not validate WHR against DXA
data for the distribution of body fat. The likely
explanation for this is that WHR, being largely
dependent on pelvic bone structures, contains varia-
bility that makes differentiation between distribution of
fat and fat-free tissues less accurate and thus less
reliable (39, 40). In this context, the importance of
waist instead of WHR has recently been recognized (29,
41, 42). Remarkably, the waist circumference, however,
is not always a stronger predictor of cardiovascular risk
than the WHR (34). In the present study, the waist
indeed showed good relation with CFM.
Skinfolds and waist circumference are easy to perform
in daily practice, cost effective, non-invasive, non-time
consuming and widely applicable, especially for large
cohort studies. Moreover, with respect to skinfolds,
different sites of the body can be measured, which is an
advantage in the assessment of body fat distribution.
Table 3 Association models containing simple anthropometrics
correlated with central fat mass and body fat distribution (i.e. central
(CFM)-to-peripheral fat mass (PFM) ratio) measured with dual-
energy X-.
R
2
CFM
Women
K18.53C0.20 waist C0.51 BMI 0.75
K12.90C0.13 waist
C0.36 BMI C0.23 (SS)
0.79
K17.67C0.13 waist C0.16 BMI
C0.11 hip circumferenceC0.20 (SS)
0.82
Men
K33.00C0.34 waist C0.48 BMI 0.77
K25.40C0.25 waist C0.32 BMI
C0.31 (SS)
0.82
K22.80C0.22 waist
C0.31 BMI C0.16 (SSCSI)
0.83
CFM/PFM
Women
WaistCBMI
a
0.30
a
K0.21C0.01 waist C0.25 ((SSCSI)/BMI) 0.47
K0.17C1.35 (waist/length)
C0.37 ((SSCSI)/(TRICLEG))
0.50
Men
K1.25C0.01 waistC0.04 BMI
a
0.38
a
K1.17C0.02 waistC0.22 ((SSCSI)
/(TRICLEG))
0.50
K1.42C4.36 (waist/length)
C0.20 ((SSCSI)/(TRICLEG))
0.56
R
2
, explained variance.
a
Not significant. SI, suprailiacal; SS, subscapular;
TRI, triceps; BI, biceps; LEG, upper leg; BMI, body mass index.
Waist and skinfold for body fatness 659EUROPEAN JOURNAL OF ENDOCRINOLOGY (2007) 156
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A limitation of the present study is that mostly lean
persons (BMI%25) were measured (151 women and
101 men), who might not have an increased risk of
cardiovascular diseases because of their weight.
However, people with a BMI in the normal weight
range can still be at increased risk of metabolic
disturbances if their WHR or waist circumference is
increased. To obtain an impression as to whether the
results could be applicable to an overweight population,
the analyses were done on a subset of individuals (BMI
between 25 and 30, n Z99 (36 women)). Just as in the
entire cohort, in overweight women, DXA CFM was
highly correlated with central skinfolds (SSCSI;
rZ0.84). Peripheral skinfolds were correlated slightly
less with DXA PFM (BICTRI; rZ0.69), whereas the
skinfolds ratios were correlated slightly higher with
DXA ratio ((SSCSI)/(TRICLEG); rZ0.72). In over-
weight men, DXA CFM was highly correlated with waist
circumference (rZ0.83). Of the skinfold measurements,
SSCSI had the highest correlation rZ 0.69. The
waist/length ratio in men was again the best anthro-
pometric alternative for CFM-to-PFM ratio (rZ0.69). As
in the entire cohort, WHR does not seem appropriate for
the measurement of body fat mass distribution (r%0.4).
Since our study was underrepresented for the purpose
of the prediction of body fat distribution in an obese
population (BMI O30), this needs further investigation.
Another limitation of the study is that the study group
was Caucasian. Ethnic variation of body fat distribution is
well known. Black women, for example, have more bone
and muscle mass, but less fat as a percentage of body
weight than white women, after controlling for ethnic
differences in age, body weight and height (43).Therefore,
heterogeneity of waist circumference (44) and skinfold
(45) cut-off points have been reported. Conclusions
derived from the present study should only be used in
Caucasian populations.
It should be noticed that our results are applicable on
a population level. Since DXA is expressed in kilograms
and skinfolds in millimetres, the Bland–Altman analysis
with standardized
S.D. of DXA and anthropometrics, to
circumvent problems with different units of the
measurements, will not gain more information than
Pearson’s correlation (46). In conclusion, our study
suggests that for predicting central and peripheral body
fat mass, anthropometric measurements such as waist
circumference and skinfolds are good alternatives in
large epidemiological studies that do not allow appli-
cation of DXA or CT. In daily practice, for the
assessment of CFM-to-PFM ratio, the ratio (SSCSI)/
BMI in women and the waist/length in men are the best
alternatives. According to our study, WHR should not
be used to determine fat distribution. These findings
might improve the prediction of diabetes and cardio-
vascular risk in men and women in future studies.
Therefore, longitudinal data must be collected to
establish the value of waist circumference and skinfold
thickness.
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Received 11 October 2006
Accepted 20 March 2007
Waist and skinfold for body fatness
661EUROPEAN JOURNAL OF ENDOCRINOLOGY (2007) 156
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  • Source
    • "Compared to the stylized MIRD5 phantoms, FAX06 and MAX06 are true to nature representations of humans, which make their CCs applicable to individual patients, at least to those with similar anatomical properties, if one neglects minor differences that still could exist with regard to organs without fixed positions, such as stomach, colon and small intestine. Among the anatomical properties of adult patients of a given age, the central fat mass (CFM), i.e. the subcutaneous and visceral fat mass of the trunk, can influence organ and tissue absorbed doses significantly, whereas the peripheral fat mass (PFM), i.e. the subcutaneous fat mass of the extremities, has negligible influence at least for the examinations mentioned in table 1. Ketel et al (2007) have reported that among the various anthropometric parameters they investigated, the waist circumference showed the best correlation with the CFM, which had been determined before independently by dual-energy x-ray absorptiometry. Body mass index and body weight also showed good correlation, but only with the total (CFM + PFM) fat mass. "
    Full-text · Dataset · Aug 2014
  • Source
    • "Compared to the stylized MIRD5 phantoms, FAX06 and MAX06 are true to nature representations of humans, which make their CCs applicable to individual patients, at least to those with similar anatomical properties, if one neglects minor differences that still could exist with regard to organs without fixed positions, such as stomach, colon and small intestine. Among the anatomical properties of adult patients of a given age, the central fat mass (CFM), i.e. the subcutaneous and visceral fat mass of the trunk, can influence organ and tissue absorbed doses significantly, whereas the peripheral fat mass (PFM), i.e. the subcutaneous fat mass of the extremities, has negligible influence at least for the examinations mentioned in table 1. Ketel et al (2007) have reported that among the various anthropometric parameters they investigated, the waist circumference showed the best correlation with the CFM, which had been determined before independently by dual-energy x-ray absorptiometry. Body mass index and body weight also showed good correlation, but only with the total (CFM + PFM) fat mass. "
    Full-text · Dataset · Aug 2014
  • Source
    • "On the other hand, our findings are in agreement with those of Yeong et al. (1997) who determined good correlations for somatotype d r a w i n g a n d B M I , w e i g h t , w a i s t a n d h i p c i r c u m f e r e n c e , b u t a r e l a t i v e l y l o w e r correlation for WHR. In addition, some authors have suggested that WHR is not appropriate for the measurement of body fat mass distribution for its lack of correlation with CT (Keller et al., 1999) and the marginal correlations observed with any of the DEXA measurements (Ketel et al., 2007). Therefore, the weak correlation obtained for the pairs form by WHR in this work, summed to the findings of other studies suggest that no decisive conclusions can be drawn when WHR is the only indicator of fat distribution considered in a specific investigation. "
    Dataset: Hum biol 12
    Full-text · Dataset · Dec 2013
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