Impact of body-composition methodology on the composition of weight loss and weight gain

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DOI: 10.1038/ejcn.2013.35 · Source: PubMed
Cite this publication
Abstract
Background/objectives: We intended to (i) to compare the composition of weight loss and weight gain using densitometry, deuterium dilution (D₂O), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI) and the four-compartment (4C) model and (ii) to compare regional changes in fat mass (FM), fat-free mass (FFM) and skeletal muscle as assessed by DXA and MRI. Subjects/methods: Eighty-three study participants aged between 21 and 58 years with a body mass index range of 20.2-46.8 kg/m(2) had been assessed at two different occasions with a mean follow-up between 23.5 and 43.5 months. Body-weight changes within < 3% were considered as weight stable, a gain or a loss of >3% of initial weight was considered as a significant weight change. Results: There was a considerable bias between the body-composition data obtained by the individual methods. When compared with the 4C model, mean bias of D₂O and densitometry was explained by the erroneous assumption of a constant hydration of FFM, thus, changes in FM were underestimated by D₂O but overestimated by densitometry. Because hydration does not normalize after weight loss, all two-component models have a systematic error in weight-reduced subjects. The bias between 4C model and DXA was mainly explained by FM% at baseline, whereas FFM hydration contributed to additional 5%. As to the regional changes in body composition, DXA data had a considerable bias and, thus, cannot replace MRI. Conclusions: To assess changes in body composition associated with weight changes, only the 4C model and MRI can be used with confidence.
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REVIEW
Impact of body-composition methodology on the composition
of weight loss and weight gain
M Pourhassan
1
, B Schautz
1
, W Braun
1
, C-C Gluer
2
, A Bosy-Westphal
1,3
and MJ Mu¨ ller
1
BACKGROUND/OBJECTIVES: We intended to (i) to compare the composition of weight loss and weight gain using densitometry,
deuterium dilution (D
2
O), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI) and the four-compartment
(4C) model and (ii) to compare regional changes in fat mass (FM), fat-free mass (FFM) and skeletal muscle as assessed by
DXA and MRI.
SUBJECTS/METHODS: Eighty-three study participants aged between 21 and 58 years with a body mass index range of
20.2–46.8 kg/m
2
had been assessed at two different occasions with a mean follow-up between 23.5 and 43.5 months.
Body-weight changes within o3% were considered as weight stable, a gain or a loss of 43% of initial weight was considered
as a significant weight change.
RESULTS: There was a considerable bias between the body-composition data obtained by the individual methods. When
compared with the 4C model, mean bias of D
2
O and densitometry was explained by the erroneous assumption of a constant
hydration of FFM, thus, changes in FM were underestimated by D
2
O but overestimated by densitometry. Because hydration does
not normalize after weight loss, all two-component models have a systematic error in weight-reduced subjects. The bias between
4C model and DXA was mainly explained by FM% at baseline, whereas FFM hydration contributed to additional 5%. As to
the regional changes in body composition, DXA data had a considerable bias and, thus, cannot replace MRI.
CONCLUSIONS: To assess changes in body composition associated with weight changes, only the 4C model and MRI can be
used with confidence.
European Journal of Clinical Nutrition (2013) 67, 446–454; doi:10.1038/ejcn.2013.35; published online 20 February 2013
Keywords: body composition; deuterium dilution (D
2
O); air-displacement plethysmography (ADP); dual-energy X-ray
absorptiometry (DXA); magnetic resonance imaging (MRI); four-compartment model
(4C model)
INTRODUCTION
Increased body fat is associated with high risk of metabolic
disorders and cardiovascular disease, thus, weight loss is recom-
mended in obese subjects.
1
However, some studies suggest that
weight loss itself is associated with increased mortality.
2–4
This
association appears to be independent of the underlying disease
5
and maybe due to the loss of fat-free mass (FFM). Because diet-
induced weight loss results in both losses in fat and lean tissue,
6
the preservation of FFM is a specific concern of weight-loss
therapy in the obese patient.
7
In addition, weight maintenance is
challenging to the majority of patients who had lost weight
successfully, but undesirable weight regain and weight cycling is a
common phenomenon.
8
It is still unclear whether weight cycling
carries an independent health risk.
9
Adverse health consequences
of weight cycling might be because of a different composition of
weight regain when compared with weight loss, that is, weight
cycling results in a higher ratio of fat mass (FM) to FFM.
2
At present, there is limited information about the composition
of body-weight changes with weight regain after weight loss.
6
To our knowledge, body-composition analysis addressing long-
term changes of body weight has rarely been performed using
state-of-the-art methodologies in previous studies. A recent study
showed that among 2163 older men and women, significantly
more lean mass (as measured by dual-energy X-ray absor-
ptiometry (DXA)) was lost during weight loss than was regained
over periods of 2–4 years.
5
In contrast, data from a prospective
study using a low calorie diet in 24 obese women (age 49–67
years) found that, although the mean body weight 4 years after
weight-loss intervention was no longer significantly different from
baseline, the mean lean mass significantly exceeded the baseline
value (44.9±1.0 kg vs 43.6±1.1 kg, respectively).
10
In another
longitudinal population study on 2436 Danish adults aged 35, 45,
55 or 65 years, changes in body composition were measured by
bioelectrical impedance analysis.
2
In this study, FFM made up 41%
and 35% of weight loss and 24% and 15% of weight regain in men
and women, respectively. However, after adjustment for age-
related changes in body composition, gain and loss in FFM were
not significantly different.
2
Discrepant results among different studies may not only be
explained by differences in age but may also partly be due to
differences in the methods used for body-composition analysis.
Dilution techniques, densitometry and DXA are among the most
commonly used methods to assess changes in body composition.
However, a major limitation to the application of these techniques
1
Institute of Human Nutrition and Food Science, Christian-Albrechts University, Kiel, Germany;
2
Sektion Biomedizinische Bildgebung, Klinik fu¨ r Diagnostische Radiologie, MOIN
CC, Universita
¨tsklinikum (University Medical Center) Schleswig Holstein (UKSH), Kiel, Germany and
3
Institute of Nutritional Medicine, University of Hohenheim, Stuttgart,
Germany. Correspondence: Professor Dr MJ Mu¨ ller, Institut fu¨ r Humanerna
¨hrung und Lebensmittelkunde, Agrar-und Erna
¨hrungswissenschaftliche Fakulta
¨t, Christian-Albrechts-
Universita
¨t zu Kiel, Du¨ sternbrooker Weg 17-19, Kiel D-24105, Germany.
E-mail: mmueller@nutrfoodsc.uni-kiel.de
Received 22 January 2013; accepted 22 January 2013; published online 20 February 2013
European Journal of Clinical Nutrition (2013) 67, 446– 454
&
2013 Macmillan Publishers Limited All rights reserved 0954-3007/13
www.nature.com/ejcn
in measuring changes is the inherent assumption of a constant
density of FFM based on a fixed proportion of water, mineral and
protein in this compartment.
11
These assumptions may not be
valid in obese patients mainly because of a higher water content
of FFM, and especially during the dynamic state of weight
changes,
7
that is, because of a change in body water that is most
exclusively from lean mass or FFM. Therefore, the variability in the
density of FFM can affect the accuracy of these methods.
12
To
take into account the water and mineral content of FFM, a four-
compartment (4C) model has been recognized as the most
accurate method (that is, the gold standard) to assess FFM.
13
To
our knowledge, only a few studies have assessed the validity of
different methods to measure body-composition changes during
weight loss or regain in overweight and obese subjects using the
reference 4Cmodel.
7,11,14
In these studies, FM and FFM measured
by alternative techniques differed significantly from the reference
method, thus raising questions about their value in assessing
changes in body composition during weight changes.
In addition, regional changes and distributions of fat and lean
mass during weight loss and weight gain remain a controversy
and may affect disease risk.
15
Evaluation of regional body
composition is of great importance because changes in FM and
FFM may specifically and differentially affect the limbs and
trunk.
16
In addition to diet and lifestyle, age may also have an
impact on the regional changes in FM and FFM. With advancing
age, bone mineral and lean mass preferentially decrease,
17
whereas body FM concomitantly increases and is more prone to
be in the abdominal region.
18
Magnetic resonance imaging (MRI)
is the gold standard for assessment of regional body composition.
In addition, DXA, anthropometric measures and bioelectrical
impedance analysis have been applied to assess body compo-
sition in different parts of the body, but none of these techniques
had been systematically used to address changes in body
composition with weight changes.
The aims of the present study were (i) to compare the compo-
sition of weight loss and weight gain using different methods
including the gold standard, that is, the 4C model and (ii) to
compare regional changes in FM
DXA
and adipose tissue
MRI
(AT
MRI
),
as well as lean soft tissue
DXA
(LST
DXA
) and skeletal muscle
MRI
(SM
MRI
), with weight loss and weight gain. We have used
deuterium dilution (D
2
O), DXA and air-displacement plethysmo-
graphy (ADP) for evaluating the composition of weight loss and
weight gain in 83 healthy people with intentional diet-induced
weight loss and participants with spontaneous weight gain, aged
between 21 and 58 years. A 4C model was used as a reference.
To understand how regional fat and lean mass in the limbs are
altered during weight loss and weight gain, we measured the
amount of regional fat and LST (LST of the trunk, arms and legs) by
DXA with weight loss and weight gain using regional AT and SM
volumes as assessed by whole-body MRI as the reference.
SUBJECTS AND METHODS
Eighty-three study participants (59 women and 24 men), aged between 21
and 58 years with a body mass index (BMI) range of 20.2–46.8 kg/m
2
who
participated in previous studies and had been assessed at two different
occasions at the Institute for Human Nutrition at Christian-Albrechts-
University were recruited from the local community by advertisement in
newspapers and notice board postings. The present study focused on
comparison of weight-loss- and weight-gain-associated changes in body
composition. Body-weight changes withino3% were considered as weight
stable, whereas a gain or a loss of 43% of initial weight was considered as
a significant weight change.
19
Net body-weight change was calculated,
and subjects were grouped into three weight-change categories: 30
subjects with weight loss who participated in an intervention study on a
low-calorie diet with a follow-up of 23.5±22.4 months; 33 subjects gained
weight spontaneously with a mean period of 33.6±29.1 months; and 20
weight-stable subjects with a mean follow-up period of 43.5±25.0
months. All investigations have been performed between 2005 and
2011. Exclusion criteria were any use of medication that has an influence
on body composition, metallic implants, pregnancy, smoking and acute or
chronic disease (for example, diabetes). The study was approved by the
local ethical committee of the Christian-Albrechts-University zu Kiel, and all
participants provided informed written consent. Subjects arrived between
0700 and 0900 hours in the morning after an overnight fast at the
metabolic ward of the Institute for Human Nutrition.
ANTHROPOMETRIC MEASUREMENTS
Body weight was measured to the nearest 0.01 kg by using an
electronic scale coupled to the BOD POD device with participants
wearing light clothes (Tanita, Tokyo, Japan). Height was measured
to the nearest 0.5 cm by using a Seca stadiometer (Vogel & Halke,
Hamburg, Germany), with subjects standing erect and without
shoes. BMI was calculated as weight in kg/height in m
2
. Hip
circumference was measured at the level of the symphysis and
waist circumference was measured in a standing position using
the mean of two measures obtained midway between the lowest
rib and iliac crest at the end of normal exhalation.
DEUTERIUM DILUTION
The D
2
O procedure has been described in greater detail in
elsewhere.
13
Briefly, total body water (TBW) in liters was deter-
mined by D
2
O dilution (99.9%; Sigma-Aldrich Chemie GmbH,
Taufkirchen, Germany). After obtaining 40 ml venous blood
samples, each subject drank an oral dose of 0.4 g D
2
O per kg
body weight with an amount of 100 ml tap water. Four hours later,
a second blood sample was taken. Blood samples were centri-
fuged immediately after collection. Serum was stored at 40 1C.
Before infrared analysis, samples were centrifuged for 3 h in
ultrafiltration tubes (Vivaspin 4; VivaScience AG, Hannover,
Germany). The concentration of D
2
O was measured in ultra-
filtrate by fast-Fourier infrared spectroscopy. Infrared spectra of
the samples were measured in the range of 2200–2800 cm
1
using a FTS 2000 Series spectrophotometer (Digilab, Marlborough,
MA, USA) equipped with a CaF2 sample cell (omnicell Specac Ltd.,
Orpington, UK). A calibration curve with 0.5, 1.0 and 2.0 g D
2
O/l
distilled water was used for quantification. Peak height was
assessed by the manufacturer’s software (Version Merlin 3.4). The
D
2
O concentration in the sample before ingestion of the dose was
used as baseline value, which had to be subtracted from the result
of the second sample 4 h after ingestion of D
2
O. For one result,
four measurements each consisting of 16 scans were averaged. A
hydration of 73.2% was assumed for calculation of FFM. FM
D
2
O
was
calculated as body weight minus FFM
D
2
O
.
AIR-DISPLACEMENT PLETHYSMOGRAPHY
ADP was performed by the BOD POD device (Cosmed s.r.l., Rome,
Italy). A two-step calibration was carried out before each
measurement. In the first step, the volume of the empty chamber,
and in the second step, the volume of a 50-l calibration cylinder,
was measured. When entering the BOD POD device, all
participants wore tight-fitting underwear (that is, brassiere and
pants) and a swim cap. There were instructed to sit motionless
during the 50-s body-volume measurement. Two repeated volume
measurements were performed, averaged and corrected for
predicted body surface area and measured thoracic gas volume
using the BOD POD software (version 4.5.0). FM% was calculated
from body mass and volume via body density.
20
FFM
ADP
was
calculated as body weight minus FM
ADP
.
DUAL-ENERGY X-RAY ABSORPTIOMETRY
DXA whole-body measurement was performed (QDR4500A
Hologic Inc., Bedford, MA, USA). Subjects lay supine with arms
Body composition during weight loss and weight gain
M Pourhassan et al
447
&2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) 446 – 454
and legs at their sides during the 10-min scan. Scans were
performed by a licensed radiological technician. Manufacturer’s
software (version V8.26a:3) was used for analysis of bone mineral
content (BMC) and FM, respectively. FFM
DXA
was calculated as
body weight minus FM
DXA
.
FOUR-COMPARTMENT MODEL
A 4C model, which divides the body into lipids, water, mineral and
protein, was used as the criterion method. Measurement of BMC
(from DXA), body volume (from ADP) and TBW (from D
2
O) were
combined to yield a criterion 4C model estimation of FM; the
errors in each measurement are aggregated/propagated into the
4C model, proportionally modulated by the constants:
21
FM
4C
(kg) ¼2.7474 body volume
ADP
(l) 0.7145 TBW (l)
þ1.4599 BMC
DXA
(kg) 2.0503 weight (kg)
The 4C model is based on the assumption of a fixed ratio of
osseus- to non-osseus-mineral content of the body. Total body
mineral (density ¼3.0375 g/cm
3
) is acquired by BMC multiplied
with 1.2741.
22
The densities of water, protein and fat are assumed
to be 0.99371, 1.34 and 0.9007 g/cm
3
, respectively. FFM
4C
was
calculated as the difference of body weight and FM
4C
.
REGIONAL BODY COMPOSITION WITH DXA AND MRI
Regional body composition was determined in subpopulation of
64 subjects using DXA and MRI. The measured amounts of LST and
fat tissue (using DXA) and muscle mass and AT (using MRI) in the
regions of trunk, arms and legs were determined. MRI protocols
have been described in greater detail elsewhere.
23,24
Briefly,
volume of SM (SM
MRI
), subcutaneous AT (SAT
MRI
) and visceral AT
(VAT
MRI
) were obtained by a 1.5-T scanner (Magnetom Vision at
baseline and 6-year follow-up or Avanto at 6-year follow-up;
Siemens, Erlangen, Germany) using a T1-weighted gradient echo
sequences (TR (time to repeat) 575 ms and TE (time to echo) 15 ms
for Magnetom Vision and TR 157 ms and TE 4 ms for Siemens
Avanto). The two MRI devices have been validated cross-sectional.
Subjects were examined in a supine position with their arms
extended above their heads. Continuous transversal images with
8-mm slice thickness and 2-mm interslice gaps were obtained (in
subpopulation of 36 subjects at baseline, continuous transversal
images with 10-mm slice thickness and 10-mm interslice gaps
were obtained) and analyzed from wrist to ankle using the
SliceOmatic software (version4.3; Tomovision, Montreal, Canada).
Images in abdominal and thoracic regions were measured with
subjects holding their breath. Arms and legs were segmented
from wrist to humerus heads and from femur heads to ankle,
respectively. Trunk was defined as the region between femur
heads and humerus heads. VAT
MRI
was segmented from the top of
the liver to femur heads. SM
MRI
(kg) was calculated for muscle
volume using a density of 1.04 kg/l.
25
STATISTICAL ANALYSIS
Statistical analysis was performed using SPSS statistical software
(SPSS 17.0, Inc., Chicago, IL, USA). All data are given as means±s.d.
Differences between parameters of body composition assessed by
different methods and differences between baseline and follow-
up within each of the three weight-change groups (weight lost,
weight stable and weight gained) were analyzed by paired sample
t-test. After multiple comparison adjustments, differences
between men and women and between weight-change groups
at baseline were analyzed by unpaired t-test.
Bland–Altman analysis was performed to compare body-
composition variables assessed by different methods and 4C
model as a criterion method.
26
Stepwise regression analysis was
used to determine the relationships between bias of changes in
FM and FFM (assessed by comparison between 4C model and
alternative methods) as dependent variable, and age, BMI, waist
circumference, hip circumference, change in waist circumference
and hip circumference, FM% at baseline, FM% at follow-up, %
change in FM and change in FFM hydration as independent
variables. Pearson’s correlation coefficient was calculated for
relationships between variables. All tests were two-tailed, and a
P-value o0.05 was accepted as the limit of significance.
RESULTS
Basal characteristics of the study participants are shown in Table 1.
Of 83 participants, 71% were female and 29% were male, with an
age range between 21 and 58 years. The study population showed
a wide BMI range with no sex differences. Prevalence of normal
weight, overweight and obesity were 20%, 30% and 50%,
respectively. In all, 27% of female and 37% of male subjects were
overweight.
Descriptive characteristics of the study population at baseline
and follow-up are given in Table 2. In all, 36% of the study
population were weight losers. Weight loss ranged between 3.3
and 25.4 kg. In contrast, 40% of the study participants gained
weight. Weight gain ranged between 3.5 and 14.5 kg. The
reminder of the participants (24%) maintained their weight
within 3% of baseline values. At baseline, weight losers had a
significantly higher body weight, BMI, waist circumference and
hip circumference compared with weight-gain and weight-stable
groups. There was no difference in age between the three weight-
change categories.
Results for FM and FFM at baseline and follow-up and
respective changes are summarized in Table 3. The 4C model
suggested significant losses or gains in FM as well as in FFM. With
weight loss, 79 and 21% were explained by FM and FFM
respectively. In contrast with weight gain, 90% was explained by
FM, with only 10% left for FFM. In weight-loss and weight-gain
groups, FM and FFM as measured by all methods significantly
differed from baseline values, except for FFM
ADP
that remained
unchanged in the weight-gain group. In the weight-stable group,
a significant gain in FM was observed using ADP and 4C model,
whereas FFM increased according to the DXA measurements.
In the weight-loss group, changes in FM and FFM determined
by ADP, DXA and D
2
O did not differ from the reference 4C model
(Table 4). In contrast, in the weight-gain group, DXA and D
2
O
significantly underestimated the gain in FM
4C
(Po0.001, Po0.05)
and overestimated the gain in FFM
4C
(Po0.001, Po0.05), whereas
Table 1. Characteristics of the study population stratified by gender
at baseline (mean±s.d.)
All (n¼83) Females
(n¼59)
Males
(n¼24)
Age (y) 36.36±8.90 34.50±7.69 40.91±10.15**
Height (m) 1.71±0.08 1.68±0.07 1.79±0.04***
Weight (kg) 88.86±19.06 87.09±19.91 93.21±16.34
BMI (kg/m
2
) 30.09±5.67 30.55±6.03 28.95±4.60
WC (cm) 98.03±14.40 97.47±14.82 99.40±13.53
Hip (cm) 109.86±13.42 112.24±14.23 104.00±9.00**
Prevalence of
normal weight
20.5% 20.3% 20.8%
Prevalence of
overweight
30.1% 27.1% 37.5%
Prevalence of
obesity
49.4% 52.5% 41.7%
Abbreviations: BMI, body mass index; Hip, hip circumference; WC, waist
circumference. **Po0.01 and ***Po0.001 difference between gender.
Body composition during weight loss and weight gain
M Pourhassan et al
448
European Journal of Clinical Nutrition (2013) 446 – 454 &2013 Macmillan Publishers Limited
ADP overestimated the gain in FM
4C
(Po0.05) and under-
estimated the gain in FFM
4C
(Po0.05).
We also estimated the FFM hydration by dividing TBW
D
2
O
by
FFM
4C
. At baseline, FFM hydration did not differ between weight-
loss and weight-gain groups. At follow-up, FFM hydration
significantly increased (Po0.05) with weight gain. In contrast,
there was no significant difference in FFM hydration with weight
loss (P¼0.781).
Mean result, bias and 95% limit of agreement for changes in fat
mass (DFM) and fat-free mass (DFFM) are shown in Table 4. In the
total study population, DFFM
DXA
was overestimated when
compared with DFFM
4C
(Po0.01). Figures 1 and 3 show good
absolute agreement between all methods and the 4C-model for
assessment of either DFM or DFFM. Limits of agreement (mean
bias and 95% confidence interval) were narrow for D
2
O and wider
for the other methods. Systematic errors were observed for the
assessment of DFM
DXA
,DFFM
ADP
,DFFM
DXA
and DFFM
D2O
. DXA
overestimated the loss and underestimated the gain in DFM
(Table 4, Figure 1). By contrast, DXA and D
2
O underestimated the
loss and overestimated the gain in DFFM (Table 4, Figure 3). In
addition, ADP overestimated the loss and underestimated the gain
in DFFM (Table 4, Figure 3).
The bias between DFM
4C
and DFM assessed by other methods
was correlated with BMI (r ¼0.23, Po0.05 for ADP, r ¼0.23,
Po0.05 for D
2
O), change in FFM hydration (r ¼0.85, Po0.001
for ADP, r ¼0.26, Po0.05 for DXA and r ¼0.98, Po0.001 for
D
2
O) change in waist or hip circumference (r ¼0.45, r ¼0.38,
both Po0.001 for DXA), DFM% and FM% at baseline (r ¼0.74,
r¼0.34, both Po0.001 for DXA). In a stepwise multiple
regression analysis with the bias between DFM
4C
and DFM
assessed by alternative methods as dependent variable and age,
WC, Hip, FM% at baseline, FM% at follow-up and DFM% and
change in FFM hydration as independent variables, BMI and
change in FFM hydration explained 5.1 and 73.1% of the variance
in bias between DFM
4C
and DFM
ADP
, respectively. BMI and change
in FFM hydration explained 5.1 and 96.3% of the variance in bias
between DFM
4C
and DFM
D2O
, respectively. DFM% and change in
FFM hydration explained 54.3 and 6.8% of the variance in bias
between DFM
4C
and DFM
DXA
, respectively. Other variables did not
contribute to the variance in bias between DFM
4C
and DFM
assessed by alternative methods.
The bias between DFFM
4C
and DFFM assessed by other
methods was also correlated with BMI (r ¼0.25, Po0.05 for
DXA, r ¼0.23, Po0.05 for D
2
O, r ¼0.24, Po0.05 for ADP),
change in FFM hydration (r ¼0.85, Po0.001 for ADP, r ¼0.22,
Po0.05 for DXA and r ¼0.98, Po0.001 for D
2
O), hip
circumference (r ¼0.28, Po0.01 for DXA), FM% at baseline
(r ¼0.47, Po0.001 for DXA) and change in waist or hip
circumference (r ¼0.45, r ¼0.35, both Po0.001 for DXA).
In a stepwise multiple regression analysis using the bias between
DFFM
4C
and DFFM assessed by alternative methods as dependent
variable and age, WC, Hip, FM% at baseline, FM% at follow-up and
DFM% and change in FFM hydration as independent variables,
baseline FM% and change in FFM hydration explained 11.9 and
73.5% of the variance in bias between DFFM
4C
and DFFM
ADP
,
respectively. Baseline FM% and change in FFM hydration
explained 54.3 and 5.0% of the variance in bias between DFFM
4C
and DFFM
DXA
, respectively.
BMI and change in FFM hydration explained 5.1 and 96.3%
of the variance in bias between DFFM
4C
and DFFM
D2O
.
Other independent variables did not contribute to the variance
in bias between DFFM
4C
and DFFM assessed by alternative
methods.
Table 5 compares changes in regional FM and LST using DXA
with changes in AT and SM using MRI. In the weight-loss group,
total weight loss was estimated to consist of 25.3% LST (mainly
Table 2. Characteristics of the study population stratified by weight change (mean±s.d.)
Weight loss (n¼30) Weight gain (n¼33) Weight stable (n¼20)
T0 T1 DT1 T0 T0 T1 DT1 T0 T0 T1 DT1 T0
Age (y) 36.93±8.44 38.93±9.78 2.00±2.21 36.18±10.00 39.03±10.42 2.84±2.42 35.80±8.01 39.45±8.74 3.65±2.10
Height (m) 1.72±0.08 1.71±0.08 1.71±0.07
Weight (kg) 99.84±18.89
a
88.65±17.23 11.19±4.92*** 86.52±16.63
c
93.01±18.14 6.49±3.326*** 76.23±13.75
b
77.09±14.45 0.86±1.41*
BMI (kg/m
2
) 33.57±5.41
a
29.66±4.64 3.90±1.74*** 29.49±4.68
d
31.56±5.20 2.07±1.15*** 25.86±4.32
b
26.01±4.60 0.15±0.52
WC (cm) 106.92±12.77
a
96.65±10.97 10.26±6.64*** 95.67±11.81
c
99.65±11.10 4.97±6.93** 88.60±13.55
b
88.56±14.06 0.04±5.01
Hip (cm) 117.60±13.22
a
110.16±12.11 7.44±3.64*** 108.19±11.75
c
114.41±11.81 6.22±7.00*** 101.00±9.79
b
103.15±10.19 2.14±5.00
Abbreviations: BMI, body mass index; Hip, hip circumference; WC, waist circumference. *Po0.05, **Po0.01 and ***Po0.001 difference between T0 and T1
within group;
a
Po0.01 difference between weight loss and weight gain within time point;
b
Po0.001 difference between weight loss and weight stable within
time point;
c
Po0.05 and
d
Po0.01 difference between weight gain and weight stable within time point.
Table 3. Changes in fat mass (FM) and fat-free mass (FFM) over time as measured with D
2
O, ADP, DXA and 4C within groups (mean±s.d.)
Weight loss (n¼30) Weight gain (n¼33) Weight stable (n¼20)
T0 T1 DT1 T0 T0 T1 DT1 T0 T0 T1 DT1 T0
FM (kg)
D
2
O 37.76±13.71 28.86±10.80 8.90±6.28*** 28.54±10.01 33.56±11.99 5.02±4.10*** 21.97±10.18 22.49±10.27 0.51±3.03
ADP 41.45±15.31 31.96±12.65 9.49±5.46*** 30.79±12.89 37.15±14.46 6.36±3.29*** 22.85±12.30 24.02±12.33 1.17±1.67**
DXA 37.55±12.95 29.68±11.34 7.86±3.84*** 29.28±10.33 33.20±11.63 3.91±2.51*** 22.08±10.74 22.42±10.58 0.34±1.47
4C 38.74±14.52 29.86±11.55 8.87±5.89*** 29.24±11.29 34.89±13.03 5.64±3.36*** 22.04±11.10 22.89±11.22 0.85±1.79*
FFM (kg)
D
2
O 62.07±12.95 59.78±11.13 2.28±4.06** 57.98±13.03 59.44±12.03 1.46±2.86** 54.26±8.61 54.60±9.39 0.34±2.94
ADP 58.36±11.11 56.69±9.93 1.67±2.08*** 55.69±12.52 55.83±12.09 0.13±1.60 53.39±10.24 53.07±10.25 0.31±1.44
DXA 62.28±12.63 59.44±11.80 2.84±3.10*** 57.24±12.79 60.47±13.52 3.23±2.25*** 54.03±9.89 55.48±9.90 1.45±1.23***
4C 61.10±12.44 58.78±10.72 2.31±3.02*** 57.27±12.92 58.12±12.18 0.84±1.82* 54.18±9.31 54.19±9.83 0.01±1.63
FFM
hydration
(%)
74.32±2.20 74.43±1.70 0.10±2.10 74.10±2.45 74.97±2.22 0.86±2.04* 73.49±2.98 73.87±1.94 0.38±2.49
Abbreviations: ADP, air-displacement plethysmography; 4C, four-compartment model; D
2
O, deuterium dilution; DXA, dual-energy X-ray absorptiometry; MRI,
magnetic resonance imaging. FFM
hydration
¼TBWD2O/FFM
4C
.*Po0.05, **Po0.01 and ***Po0.001 difference between T0 and T1 within group.
Body composition during weight loss and weight gain
M Pourhassan et al
449
&2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) 446 – 454
explained by a loss in LST
legs
) and 10.9% SM (mainly explained by
a loss in SM
trunk
), as well as 74.7% FM (mainly explained by a loss
in FM
trunk
) and 89.1% AT (mainly explained by a loss in SAT
trunk
). In
the weight gainer group, total weight gain consisted of 51.1% LST
(mainly explained by a gain in LST
trunk
) and 26.1% SM (mainly
explained by a gain in SM
legs
), as well as 48.9% FM (mainly
explained by a gain in FM
trunk
) and 73.9% AT (mainly explained by
a gain in SAT
trunk
).
Using DXA, subjects lost more FM than LST (Po0.001). With
weight gain, subjects gained approximately similar amounts of
LST and FM (P¼0.787). More LST was gained in the weight gainer
group than was lost in the weight loser group. Using MRI, subjects
lost more absolute AT than SM (Po0.001). Vice versa with weight
gain, subjects gained more AT compared to SM (Po0.01).
Approximately similar amounts of SM were gained in the weight
gainer group and was lost in the weight loser group.
In both weight change groups, the changes in FM were
proportionally greater than changes in FFM (Table 3). In the
weight loser group, the losses in FM as a percentage of initial FM
were similar between methods ( 22.3% (ADP), 20.9% (DXA)
and 21.8% (D
2
O)) and all not significantly different from 4C-
model ( 21.5%) (Figure 2). Losses in FFM as a percentage of
initial FFM were also similar between methods ( 2.6% (ADP),
4.3% (DXA) and 3.0% (D
2
O)) and all not significantly different
from 4C-model ( 3.3%) (Figure 2). By contrast, in the weight
gainer group, the gain in FM as a percentage of initial FM
significantly differed between ADP and DXA (23.8 vs 14.1%) and
between 4C and DXA (21.1 vs 14.1%). In addition, ADP estimated
lower (0.4%) and DXA estimated higher (5.7%) gains in FFM as a
percentage of initial FFM compared to 4C-model (1.9%). When
compared with the 4C-refrence method, all other methods
showed a different composition of weight gain (as a percentage
of initial body composition) (Figure 2).
DISCUSSION
Comparison of changes in body composition as assessed with
different methods
The primary aim of the present study was to compare the
composition of weight loss and weight gain between methods
using a 4C model as a reference. No significant differences were
observed in changes in FM and FFM as measured by ADP, DXA or
D
2
O when compared with the 4C-model (Table 4) with the
exception of changes in FFM
DXA
. DXA systematically under-
estimated the loss and overestimated the gain in FFM with weight
changes (Po0.01, Bland–Altman analysis in Figure 3b). This
resulted in a significant overestimation of FFM gain in weight
gainers (Table 4). Our findings are in line with Schoeller et al.
27
who
reported that QDR 4500 DXA overestimated FFM when compared
with a 4-C model. Accordingly, Minderico et al.
14
have shown that
DXA overestimated FM loss as measured with QDR-1500 (pencil-
beam mode) compared with a 4-C model in overweight and obese
women, the bias increased with the degree of overweight and
obesity. This might be due to tissue thickness that increases with
increasing body weight. Overestimation of %FM by DXA was
observed at a higher tissue thickness.
28,29
In case of a fan beam
technology, this leads to a magnification error. Williams et al.
30
found a higher error in obese subjects using fan beam technology.
In the present study, the bias between 4C-model and DXA was
mainly explained by FM% at baseline whereas the change in FFM
hydration contributed to additional 5% of the bias only (see
results). With increasing adiposity, FFM hydration increases due to
a higher water content of the lean compartment of AT.
31
However,
due to similar densities of fat and water, the attenuation coefficient
of X-rays for both compartments is similar. That would imply an
underestimation of lean mass with increasing FFM hydration in
obesity or with weight gain. Since the bias we found was in
opposite direction (for example, overestimation of lean mass with
weight gain), the erroneous assumption of a constant hydration of
FFM is unlikely to be the cause of the bias between DXA
and 4C-model. In addition, different instrument manufacturers and
software versions may also affect the accuracy of DXA results.
28,32
Accordingly, the limits of agreement were wider for DXA when
compared with the other methods; 5.39 to 7.60 kg DFFM for
DXA compared with 4.01 to 3.76 kg DFFM for ADP and 2.99
to 3.67 kg DFFM for D
2
O). Roemmich et al.
33
also found
that DXA produced larger limits of agreement than other 2C
age-adjusted models.
In our total population, changes in body composition measured
by D
2
O were not significantly different from the 4C-model
(Table 4). However, similar to DXA, Bland–Altman analysis revealed
a systematic bias (Figure 3c) and D
2
O significantly overestimated
the gain in DFFM
4C
in the subgroup of weight gainers (Po0.05;
Table 4). This may also be explained by the erroneous assumption
of a constant hydration of FFM.
34
In line with this hypothesis, the
bias between 4C-model and D
2
O correlated with BMI, as well as
with changes in FFM hydration (see results). About 15–30% of
TBW is present in AT, which increases with increasing adiposity.
35
Because FM is higher in women and obese individuals, the higher
hydration of FFM causes an underestimation of FFM and
overestimation of FM.
36
The present study population primarily
consists of women (71%) and overweight and obese subjects
(79.5%). Therefore, mean FFM hydration was higher (74%) at
baseline when compared with the hydration status assumed for
normal weight individuals (73.2%). By contrast, FFM hydration did
not normalize after weight loss (Table 3). These results are in line
with the study of Das et al.
37
who found no significant difference
in FFM hydration of extremely obese patients after massive weight
loss ( 44 kg) caused by gastric bypass surgery. Hence we postu-
late that 2-C models (FMs and FFMs) that assume a constant
hydration of FFM have a systematic error in weight-reduced
subjects.
Table 4. Results of the limit of agreement analysis: mean result
(±s.d.), bias and 95% limit of agreement for changes in fat mass (DFM)
and fat-free mass (DFFM) measured by ADP, DXA and D
2
O and
compared with results from the 4C model.
ADP DXA D
2
O
All (n¼83)
DFM (kg) 0.62±8.04 1.20±5.96 1.09±7.79
Bias vs DFM
4C
kg
a
0.14±1.94 0.45±2.87 0.34±1.67
Correlation,
b
r0.19 0.59** 0.08
DFFM (kg) 0.62±1.91 0.60±3.61 0.15±3.72
Bias vs DFFM
4C
kg
a
0.13±1.94 1.11±3.25ww 0.33±1.66
Correlation,
b
r0.42** 0.33** 0.64**
Weight loss (n¼30)
DFM (kg) 9.49±5.46 7.86±3.84 8.90±6.28
Bias vs DFM
4C
kg
a
0.62±2.19 1.01±3.16 0.03±1.76
Correlation,
b
r0.19 0.66** 0.22
DFFM (kg) 1.67±2.08 2.84±3.10 2.28±4.06
Bias vs DFFM
4C
kg
a
0.65±2.20 0.53±3.77 0.03±1.76
Correlation,
b
r0.46** 0.02 0.60**
Weight gain (n¼33)
DFM (kg) 6.36±3.29 3.91±2.51 5.02±4.10
Bias vs DFM
4C
kg
a
0.72±1.60
c
1.73±2.52
www
0.62±1.56
c
Correlation,
b
r0.04 0.37* 0.48**
DFFM (kg) 0.13±1.60 3.23±2.25 1.46±2.86
Bias vs DFFM
4C
kg
a
0.71±1.58
c
2.39±2.78
www
0.63±1.56
c
Correlation,
b
r0.16 0.19 0.69**
Abbreviations: ADP, air-displacement plethysmography; 4C, four-compart-
ment model; D
2
O, deuterium dilution; DXA, dual-energy X-ray absorptio-
metry. *Po0.05 and **Po0.01.
a
Bias was calculated as result obtained from
reference method (4C) minus ADP, DXA and D
2
O measurement. 95% limits
of agreement was calculated as ±2s.d.
b
Correlation was calculated as
Pearson correlation coefficient for the relationship between (result
reference
method
þresult
other methods
)/2 and the bias.
c
Significant difference between
reference method (4C) and results from ADP, DXA and D2O by paired
samples t-test (
w
Po0.05,
ww
Po0.01 and
www
Po0.001).
Body composition during weight loss and weight gain
M Pourhassan et al
450
European Journal of Clinical Nutrition (2013) 446 – 454 &2013 Macmillan Publishers Limited
In contrast to D
2
O and DXA, densitometry (ADP) systematically
overestimated the loss and underestimated the gain in FFM
(Table 4). This is in line with data of Fields et al.
38
who suggested
that ADP underestimates FFM and overestimates FM in
overweight adults. Comparable to DXA and D
2
O, hydration of
FFM is likely the main source of the bias between ADP and a 4-C
model. However, the bias is in the opposite direction because
densities of fat and water are very similar and thus an increasing
water fraction of FFM in obese people or after weight gain is
misinterpreted as an increase in FM. In the present study, the bias
between 4C-model and ADP with weight loss or weight gain was
related to BMI and the change in FFM hydration (see results).
Comparison of the composition of weight loss and weight gain
Comparing FFM and FM, proportionally more FFM
4C
(3.3% as a
percentage of initial FFM
4C
) was lost during weight loss than was
gained (1.89% as a percentage of initial FFM
4C
) during weight gain
(Figure 2). A 1 kg weight loss consisted of 0.20 kg FFM whereas a
1 kg weight gain is explained by 0.12 kg FFM only; this was not
related to the time of follow up. Our data compare changes in two
different populations. In contrast to our protocol, Beavers et al.
6
investigated intra-individual changes in body composition (as
measured by DXA) with weight loss and regain in a 6 months trial
involving obese women. In that study, each 1 kg fat loss was
associated with a loss of 0.26 kg FFM whereas the regain of 1kg fat
was associated with a gain of 0.12 kg lean tissue.
6
Thus, these data
are similar to our results. The implication of these findings is that
in the long term weight cycling can promote sarcopenia which
may be even worse in elderly patients. In fact, the results of the
Health Aging and Body Composition Study have shown that for
every kg weight loss there was a 0.42 kg and 0.06 kg loss of FFM in
men and women, respectively, whereas for every kg of weight
gain 0.37 and 0.32 kg of FFM increased.
17
However, other
-15
-10
-5
0
5
10
15
-30 -25 -20 -15 -10 -5 0 5 10 15 20
ΔFM4C -ΔFMADP (kg)
(ΔFM4C + ΔFMADP) / 2 (kg)
-15
-10
-5
0
5
10
15
-30 -25 -20 -15 -10 -5 0 5 10 15 20
ΔFM4C -ΔFMD2O (kg)
(ΔFM4C + ΔFMD2O) / 2 (kg)
R2 = 0.3592
-15
-10
-5
0
5
10
15
-30 -25 -20 -15 -10 -5 0 5 10 15 20
ΔFM4C -ΔFMDXA (kg)
(ΔFM4C + ΔFMDXA) / 2 (kg)
4C - ADP 4C - DXA
4C - D2OFemale Male
Figure 1. Bland–Altman plots of limits of agreement for changes in FM (kg) between 4C and (a) ADP, (b) DXA and (c) D
2
O. Solid line indicates
the mean difference and dashed lines indicate ±2 s.d. Open symbols for females; closed symbols for males.
weight loser group
weight gainer group
-40
-30
-20
-10
0
10
20
30
40
4C ADP DXA D2O
Change in FM & FFM
(as percentage of initial FM & FFM)
Fat mass
Fat free mass
-40
-30
-20
-10
0
10
20
30
40
4C ADP DXA D2O
Change in FM & FFM
(as percentage of initial FM & FFM)
Fat mass
Fat free mass
**
***
*
*****
Figure 2. Mean (±s.d) changes in FM and FFM as a percentage of initial FM and FFM shown for (a) weight losers (n¼30) and (b) weight
gainers (n¼33). Changes in FM and FFM as a percentage of initial FM and FFM were significantly different in weight-gain group, *Po0.05, **P
o0.01 and ***Po0.001 (paired t-test). No significant differences were observed in changes in FM and FFM as a percentage of initial FM and
FFM in weight-loss group.
Body composition during weight loss and weight gain
M Pourhassan et al
451
&2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) 446 – 454
longitudinal studies of body composition in older adults show a
gain of FM and a loss of lean mass with time in weight stable
individuals.
39,40
In one of our recent studies we intra-individually
followed weight loss and re-gain over 6 months of follow up in
103 overweight and obese subjects.
41
The intra-individual
comparison between the different body components lost and
re-gained revealed that the regain was in proportion to weight
loss except for a higher regain in AT of the extremities in women
and a lower regain in extremity and visceral AT in man. There was
also a lag of SM regain in the trunk behind the extremities. These
data argued against the idea that after weight loss weight regain
adversely affects fat distribution. However, in that study weight
regain was not complete and body weight as well as body
composition differed between baseline and after the 6 months
observation period.
Comparison of regional changes between weight loser and weight
gainer groups
We have also compared the regional changes in FM
DXA
and AT
MRI
as well as LST
DXA
and SM
MRI
with weight loss and weight gain.
Using DXA, lean mass is mainly lost at the arms and legs and
gained at the trunk whereas; by contrast, using MRI lean mass is
mainly lost at the trunk and gained at the extremities (Table 5).
Table 5. Changes in regional LST and FM using DXA and SM and AT using MRI within groups (mean±s.d).
Weight loser (n¼17) Weight gainer (n¼29) Weight stable (n¼18)
T0 T1 DT1 T0 T0 T1 DT1T0 T0 T1 DT1 T0
DXA
LST
trunk
(kg) 29.98±6.14 30.63±5.67 0.64±2.38 26.09±5.32 29.43±6.22 3.33±2.85*** 23.91±4.51 26.30±4.64 2.39±1.54***
LST
arms
(kg) 7.61±2.27 6.71±1.86 0.89±1.18** 6.08±2.03 6.34±1.99 0.26±0.43* 5.68±1.57 5.51±1.59 0.16±0.51
LST
legs
(kg) 22.16±5.34 21.18±4.78 0.98±1.45** 18.93±4.73 19.86±4.91 0.92±0.94*** 17.35±3.06 17.54±3.13 0.18±0.76
MRI
SM
trunk
(kg) 11.01±2.83 10.30±2.32 0.71±1.15* 9.71±3.12 9.50±2.87 0.21±0.95 8.57±2.13 8.32±1.92 0.25±0.81
SM
arms
(kg) 4.39±1.34 4.42±1.28 0.02±0.42 3.64±1.09 3.93±1.38 0.29±0.52** 3.18±0.95 3.56±1.03 0.38±0.58*
SM
legs
(kg) 15.47±4.20 15.21±3.75 0.26±1.22 13.45±3.61 14.28±3.89 0.83±0.70*** 11.83±2.21 12.64±2.37 0.81±0.98**
DXA
FM
trunk
(Kg) 15.90±5.37 13.23±4.61 2.66±1.95*** 12.54±4.50 15.26±5.26 2.72±1.82*** 9.58±5.89 10.08±5.76 0.53±1.01*
FM
arms
(Kg) 3.94±1.58 3.02±1.15 0.91±0.54*** 3.40±1.32 3.77±1.53 0.36±0.39*** 2.76±1.47 2.68±1.44 0.07±0.26
FM
legs
(Kg) 11.92±6.03 9.88±5.45 2.04±1.33*** 11.00±4.91 12.25±5.61 1.24±1.05*** 9.06±3.92 9.08±3.88 0.02±0.71
MRI
AT
trunk
(l) 19.88±6.13 14.52±5.92 5.36±2.51*** 16.19±5.64 17.69±6.90 1.49±2.52** 12.86±6.90 11.53±7.61 1.32±1.58**
SAT
arms
(l) 3.61±1.23 3.05±1.05 0.56±0.32*** 3.12±1.14 3.56±1.39 0.43±0.56*** 2.52±1.00 2.57±1.12 0.04±0.24
SAT
legs
(l) 13.42±6.34 11.23±6.13 2.19±1.41*** 12.44±5.18 13.82±6.23 1.37±1.21*** 9.90±3.71 10.08±4.54 0.18±1.72
Abbreviations: AT, adipose tissue; AT
trunk
, AT of the trunk (subcutaneo us AT of the trunk þvisceral AT); DXA, dual X-ray absorptiometry; FM, fat mass; FM
arms
,FM
of the arms; FM
legs
, FM of the legs; FM
trunk
, FM of the trunk; LST, lean soft tissue; LST
arms
, LST of the arms; LST
legs
, LST of the legs; LST
trunk
, LST of the trunk; MRI,
magnetic resonance imaging; SAT
arms
, subcutaneous AT of the arms; SAT
legs
, subcutaneous AT of the legs; SM, skeletal muscle; SM
arms
, SM of the arms; SM
legs
,
SM of the legs; SM
trunk
, SM of the trunk. *Po0.05, **Po0.01 and ***Po0.001 difference between T0 and T1 within group.
R2 = 0.1798
-15
-10
-5
0
5
10
15
-10 -5 0 5 10 15
ΔFFM 4C - ΔFFM ADP (kg)
(ΔFFM 4C + ΔFFM ADP) / 2 (kg)
Female
Male
R2 = 0.1097
-15
-10
-5
0
5
10
15
-10
-
50 51015
ΔFFM4C - ΔFFMDXA (kg)
(ΔFFM 4C + ΔFFM DXA) / 2 (kg)
R2 = 0.4092
-15
-10
-5
0
5
10
15
-10 -5 0 5 10 15
ΔFFM 4C - ΔFFM D2O (kg)
(ΔFFM 4C + ΔFFM D2O) / 2 (kg)
4C - DXA4C - ADP
4C - D2O
Figure 3. Bland–Altman plots of limits of agreement for changes in FFM (kg) between 4C and (a) ADP, (b) DXA and (c)D
2
O. Solid line indicates
the mean difference and dashed lines indicate ±2 s.d. Open symbols for females, closed symbols for males.
Body composition during weight loss and weight gain
M Pourhassan et al
452
European Journal of Clinical Nutrition (2013) 446 – 454 &2013 Macmillan Publishers Limited
Using DXA, weight change consisted of a loss ( 47.4%),
and a gain in FM
trunk
(þ63.0%). When using MRI, weight change
consisted of a loss ( 66.0%) and a gain in AT
trunk
(þ45.3%).
These results suggest that there may be a regional redistribution
of fat and lean mass with weight loss and weight regain. However,
future research needs to be done using an intra-individual study
design. Because AT does not resemble FM and LST is higher than
SM (due to connective tissue and organ mass) absolute
differences in the composition of weight loss and weight gain
between both methods are obvious. However, this does not
explain the differences in fat and lean redistribution with weight
loss and weight gain that may partly be due to inherent
assumptions of DXA software. Taken together, data on DXA- and
MRI-derived changes in regional body composition cannot be
directly compared with each other. The limitations of the DXA
approach (and presumably the 2 component models) has to be
taken into account.
Study strengths and limitations
Some limitations to the present study should be discussed. The
number of men was small (n¼24), therefore sex differences
cannot be addressed with confidence. In addition, physical activity
and fitness have not been addressed which are known to have an
impact on the composition of weight change.
42
Since in this study
we did not assess intra-individual weight cycles, we cannot
directly compare the composition of weight loss and weight gain.
The strengths of this study is the concomitant use of a variety of
highly standardized body composition techniques including a
4C-model as a gold standard that avoids the assumptions of
different 2C-methods that may be violated during unstable
conditions of weight loss and weight gain. It should be
mentioned that 4C is balancing of the three measurements,
with amended measurement errors from all of them. In addition,
imaging technology allowed the evaluation of regional changes in
body composition with weight loss and weight gain.
CONCLUSIONS
When compared with the 4C model ( ¼gold standard), mean bias of
D
2
O and densitometry methods is explained by the erroneous
assumption of a constant hydration of FFM. This assumption leads to
an underestimation of FM change measured by D
2
Oandan
overestimation of FM change measured by densitometry. Because
hydration does not normalize after weight loss we can deduce that
all 2C-models that are based on the assumption of a constant
hydration of FFM have a systematic error in weight reduced subjects.
The bias between 4C-model and DXA was mainly explained by FM%
at baseline whereas the change in FFM hydration only contributed to
additional 5% of the bias. As to the regional changes in body
composition, MRI data cannot be replaced by DXA measurements.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
The authors wish to thank Britta Jux, Klinik fu¨ r Radiologische Diagnostik, UKSH Kiel,
for their help in MRI scanning. The study was funded by Deutsche Forschungsge-
meinschaft (DFG Mu¨ 714/ 8-3) BMBF Kompetenznetz Adipositas, Core domain ‘‘Body
composition’’ (Ko
¨rperzusammensetzung; FKZ 01GI1125)
DISCLOSURE
The sponsor of the study (DFG, BMBF) had no role in study design, the collection,
analysis and the interpretation of the data, writing the text or in the decision to
submit the manuscript.
AUTHOR CONTRIBUTIONS
ABW and MJM designed and supervised the study, ABW, MP and MJM wrote the
final version of the manuscript, MJM and ABW had primary responsibility for the
final content of the manuscript. ABW and WL performed all the investigations. BS,
WL, MP organized the study, collected the data, did the segmentations of whole
body MRI data and performed the statistical analyses. C-CG was responsible for
DXA and MRI examinations.
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  • ... High protein intake (1.0 g/kg/d), particularly consumption of leucine-rich proteins such as whey protein, is recommended to prevent age-associated muscle loss (16)(17)(18)(19)(20) and to mitigate the adverse effect of diet-induced weight loss on muscle mass (18,20,21) because protein ingestion stimulates muscle protein synthesis in a dose-dependent manner (22), leucine ingestion augments the anabolic effect of protein consumption (23), and high protein intake blunts the weight-loss-induced decline in lean body mass (24,25). However, it is not known whether high protein intake during weight loss actually prevents the loss of skeletal muscle because (1) the acute effect of protein ingestion on muscle protein synthesis might not predict the chronic effect of protein ingestion on muscle mass, which is determined by the balance between synthesis and breakdown; and (2) the weight-loss-induced change in lean body mass (determined by using dual-energy x-ray absorptiometry [DXA]) is not a reliable surrogate for changes in muscle mass (determined by using computed tomography or magnetic resonance imaging [MRI]) (26). ...
    ... However, we are not aware of any previous studies that have evaluated the effect of high protein intake or protein supplementation during weight loss on muscle mass or volume in people with obesity. It was reported that weight-loss-induced changes in DXA-derived total lean body mass did not accurately reflect changes in muscle mass (determined directly by using computed tomography or MRI) (26). Our results confirmed this observation because we found very weak correlations between the change in thigh muscle volume determined by MRI and the changes in total fat-free mass, lean body mass, and leg lean mass determined by DXA. ...
    Article
    Objective High protein (particularly leucine‐rich whey protein) intake is recommended to mitigate the adverse effect of weight loss on muscle mass. The effectiveness of this approach is unknown. Methods Seventy middle‐aged (50‐65 years old) postmenopausal women with obesity were randomized to (1) weight maintenance (WM), (2) weight loss and the recommended daily allowance for protein (0.8 g/kg/d) (WL group), or (3) weight loss plus whey protein supplementation (total protein: 1.2 g/kg/d) (WL‐PS group). Thigh muscle volume and strength were assessed at baseline and after 5% and 10% weight loss in the weight‐loss groups and after matched time periods (∼3 and 6 months, respectively) in the WM group. Results A 5% weight loss caused a greater decrease in thigh muscle volume in the WL group than the WL‐PS group (4.7% ± 0.7% vs. 2.8% ± 0.8%, respectively; P < 0.05). After 10% weight loss, there was no statistically significant difference in muscle mass loss in the two groups, and the total loss was small in both groups (5.5% ± 0.8% and 4.5% ± 0.7%, respectively). The dietary interventions did not affect muscle strength. Conclusions Whey protein supplementation during diet‐induced weight loss does not have clinically important therapeutic effects on muscle mass or strength in middle‐aged postmenopausal women with obesity.
  • ... Magnetic resonance imaging (MRI) is the gold standard for assessment of body composition, although its application is limited due to time-consuming assessment of whole-body tissue volumes and high costs [24][25][26]. Therefore, several studies have suggested to estimate muscle volumes from a single-slice section at mid-thigh [26][27][28], since lower limb power has been considered as a critical factor for mobility in older adults [29]. ...
    Preprint
    Full-text available
    Background We assessed the quantitative changes in muscle mass and strength during two weeks of hospitalization in immobile and mobile acutely ill hospitalized older adults. Methods 41 patients (82.4 ± 6.6 years, 73.0% females) participated in this prospective longitudinal observational study. Mobility status was defined according to walking ability as described in the Barthel-Index. Functional status, including handgrip strength and isometric knee-extension strength, and mid-thigh magnetic resonance imaging measurements of cross-sectional area (CSA) were conducted on admission and at discharge. Results Twenty-two participants (54%) were immobile and 19 (46%) mobile. In all, 54.0% and 12.0% were at risk of malnutrition and malnourished, respectively. The median time between baseline and follow-up for MRI scans were 13 days in mobile and immobile participants (P = 0.072). Mid-thigh muscle and subcutaneous fat CSA significantly decreased by 3.9 cm² (5.0%, P = 0.002) and 5.3 cm² (5.7%, P = 0.036) during hospitalization whereas intermuscular fat remained unchanged in immobile subjects. No significant changes were observed in mobile patients. In a regression analysis, mobility was the major independent risk factor for changes in mid-thigh muscle CSA as a percentage of initial muscle area (P = 0.022) whereas other variables such as age (P = 0.584), nutritional status (P = 0.835) and inflammation (P = 0.291) were not associated with muscle mass changes. There was a significant decrease in isometric knee extension strength (P = 0.002) and no change in handgrip strength (P = 0.167) in immobile patients whereas both parameters increased significantly over time in mobile patients (P = 0.048 and P = 0.012, respectively). Conclusions Two weeks of disease-related immobilization result in a significant loss of thigh muscle mass and muscle strength. Concomitantly, there was a significant reduction of subcutaneous adipose tissue whereas no changes were observed in intermuscular fat in frail older hospitalized patients.
  • ... Body fat mass increased by 0.4 ± 0.1 kg (p = 0.0015) during the ultra-processed diet and decreased by 0.3 ± 0.1 kg during the unprocessed diet (p = 0.05) ( Figure 3C), whereas fat-free mass tended to increase during the ultra-processed diet (0.5 ± 0.3 kg; p = 0.09) and decrease during the unprocessed diet (0.6 ± 0.3 kg; p = 0.08) ( Figure 3D). While the dual-energy X-ray absorptiometry (DXA) methodology used to measure body composition in our study tends to underestimate body fat changes (Pourhassan et al., 2013), the relatively large fat-free mass changes may be due to extracellular fluid shifts associated with differences in sodium intake between the diets. Indeed, individual differences in sodium intake between the diets were significantly correlated with changes in fat-free mass (r = 0.63; p = 0.004) and body weight (r = 0.64; p = 0.002). ...
  • ... Body fat mass increased by 0.4 ± 0.1 kg (p = 0.0015) during the ultra-processed diet and decreased by 0.3 ± 0.1 kg during the unprocessed diet (p = 0.05) ( Figure 3C), whereas fat-free mass tended to increase during the ultra-processed diet (0.5 ± 0.3 kg; p = 0.09) and decrease during the unprocessed diet (0.6 ± 0.3 kg; p = 0.08) ( Figure 3D). While the dual-energy X-ray absorptiometry (DXA) methodology used to measure body composition in our study tends to underestimate body fat changes (Pourhassan et al., 2013), the relatively large fat-free mass changes may be due to extracellular fluid shifts associated with differences in sodium intake between the diets. Indeed, individual differences in sodium intake between the diets were significantly correlated with changes in fat-free mass (r = 0.63; p = 0.004) and body weight (r = 0.64; p = 0.002). ...
    Article
    Full-text available
    We investigated whether ultra-processed foods affect energy intake in 20 weight-stable adults, aged (mean ± SE) 31.2 ± 1.6 years and BMI = 27 ± 1.5 kg/m2. Subjects were admitted to the NIH Clinical Center and randomized to receive either ultra-processed or unprocessed diets for 2 weeks immediately followed by the alternate diet for 2 weeks. Meals were designed to be matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. Subjects were instructed to consume as much or as little as desired. Energy intake was greater during the ultra-processed diet (508 ± 106 kcal/day; p = 0.0001), with increased consumption of carbohydrate (280 ± 54 kcal/day; p < 0.0001) and fat (230 ± 53 kcal/day; p = 0.0004), but not protein (-2 ± 12 kcal/day; p = 0.85). Weight changes were highly correlated with energy intake (r = 0.8, p < 0.0001), with participants gaining 0.9 ± 0.3 kg (p = 0.009) during the ultra-processed diet and losing 0.9 ± 0.3 kg (p = 0.007) during the unprocessed diet. Limiting consumption of ultra-processed foods may be an effective strategy for obesity prevention and treatment.
  • ... The DLW method has been validated during 30% caloric restriction with a 55% carbohydrate diet (25) and agrees with our result that EE DLW and EE DLW RQ were not significantly different from EE bal during the BD diet phase. Nevertheless, the calculated EE bal values are somewhat uncertain because DXA has a limited ability to precisely and accurately detect small changes in body energy stores (26). We cannot rule out the possibility that the KD resulted in increased activity-related energy expenditure that was undetected by accelerometers. ...
    Article
    Background: Low-carbohydrate diets have been reported to significantly increase human energy expenditure when measured using doubly labeled water (DLW) but not by respiratory chambers. Although DLW may reveal true physiological differences undetected by respiratory chambers, an alternative possibility is that the expenditure differences resulted from failure to correctly estimate the respiratory quotient (RQ) used in the DLW calculations. Objective: To examine energy expenditure differences between isocaloric diets varying widely in carbohydrate and to quantitatively compare DLW data with respiratory chamber and body composition measurements within an energy balance framework. Design: DLW measurements were obtained during the final 2 wk of month-long baseline (BD; 50% carbohydrate, 35% fat, 15% protein) and isocaloric ketogenic diets (KD; 5% carbohydrate, 80% fat, 15% protein) in 17 men with a BMI of 25-35 kg/m2. Subjects resided 2 d/wk in respiratory chambers to measure energy expenditure (EEchamber). DLW expenditure was calculated using chamber-determined RQ either unadjusted (EEDLW) or adjusted (EEDLWΔRQ) for net energy imbalance using diet-specific coefficients. Accelerometers measured physical activity. Body composition changes were measured by dual-energy X-ray absorptiometry (DXA) which were combined with energy intake measurements to calculate energy expenditure by balance (EEbal). Results: After transitioning from BD to KD, neither EEchamber nor EEbal were significantly changed (∆EEchamber = 24 ± 30 kcal/d; P = 0.43 and ∆EEbal = -141 ± 118 kcal/d; P = 0.25). Similarly, physical activity (-5.1 ± 4.8%; P = 0.3) and exercise efficiency (-1.6 ± 2.4%; P = 0.52) were not significantly changed. However, EEDLW was 209 ± 83 kcal/d higher during the KD (P = 0.023) but was not significantly increased when adjusted for energy balance (EEDLWΔRQ = 139 ± 89 kcal/d; P = 0.14). After removing 2 outliers whose EEDLW were incompatible with other data, EEDLW was marginally increased during the KD by 126 ± 62 kcal/d (P = 0.063) and EEDLW∆RQ was only 46 ± 65 kcal/d higher (P = 0.49). Conclusions: DLW calculations failing to account for diet-specific energy imbalance effects on RQ erroneously suggest that low-carbohydrate diets substantially increase energy expenditure. This trial was registered at clinicaltrials.gov as NCT01967563.
  • ... The 4-compartment model was used because it is the most accurate way to assess body composition under conditions (such as weight loss) where body composition is changing [49][50][51]. This gold-standard method can be explained by considering the body to be composed of a variety of "compartments" which include bone, fat, water, and a "residual" compartment that is largely made up of protein (mostly muscle) but which also includes non-bone mineral and glycogen. ...
    Article
    Full-text available
    Very low energy diets (VLEDs), commonly achieved by replacing all food with meal replacement products and which result in fast weight loss, are the most effective dietary obesity treatment available. VLEDs are also cheaper to administer than conventional, food-based diets, which result in slow weight loss. Despite being effective and affordable, these diets are underutilized by healthcare professionals, possibly due to concerns about potential adverse effects on body composition and eating disorder behaviors. This paper describes the rationale and detailed protocol for the TEMPO Diet Trial (Type of Energy Manipulation for Promoting optimal metabolic health and body composition in Obesity), in a randomized controlled trial comparing the long-term (3-year) effects of fast versus slow weight loss. One hundred and one post-menopausal women aged 45–65 years with a body mass index of 30–40 kg/m2 were randomized to either: (1) 16 weeks of fast weight loss, achieved by a total meal replacement diet, followed by slow weight loss (as for the SLOW intervention) for the remaining time up until 52 weeks (“FAST” intervention), or (2) 52 weeks of slow weight loss, achieved by a conventional, food-based diet (“SLOW” intervention). Parameters of body composition, cardiometabolic health, eating disorder behaviors and psychology, and adaptive responses to energy restriction were measured throughout the 3-year trial.
  • ... Numerous others have utilized the 4C model to assess body composition or serve as a criterion method (e.g. Gallagher et al., 1996;Williams et al., 2006;Chomtho et al., 2008;Deurenberg-Yap et al., 2001;Pourhassan et al., 2013;Wells et al., 2015). ...
    Conference Paper
    The ‘expensive-tissue’ hypothesis of Aiello and Wheeler is well-known in anthropology for positing that an increasingly small gut was a key factor in the evolution of the large hominin brain. The insight that organs and tissues in the body compete for energy resources was also central to the ‘thrifty phenotype’ hypothesis of Hales and Barker, which proposed that nutritional stress in fetal life resulted in differential growth of the brain and pancreas. Both hypotheses are consistent with life history theory, which assumes that energy allocation trade-offs occur in energylimited environments. The prediction that somatic traits trade off against one another in the context of the body’s fixed energy budget has, however, yet to be rigorously tested in humans. The current thesis project aimed to fill this gap by recruiting 70 healthy young women and obtaining comprehensive, high-quality data on their brain and body composition. This included, specifically, measures of brain gray and white matter volume, fat mass, skeletal muscle mass, and volumes of the heart, liver, kidneys and spleen. Additional outcomes included resting energy expenditure and two proxies of early-life growth: birth weight, a marker of fetal weight gain, and tibia length, a marker of linear growth indexing postnatal experience. With these data, three principal hypotheses were tested: 1) there is variation in the energy expenditure of tissues and organs; 2) trade-offs are observed between brain and body organs/tissues; and 3) trade-off relationships are mediated by early-life growth. Results suggest the metabolic cost of organs and tissues is variable, and that the brain – in particular its gray matter component – trades off against lean tissues in the body (i.e. skeletal muscle, the liver and kidneys), but not fat mass. However, less support was found for the prediction that trade-offs are mediated by fetal and infant growth.
  • ... FM and fat-free mass (FFM) were determined using a 4Cmodel as has been described in detail before [17,18]. Briefly, the model divided the body into lipids, water, mineral, and protein content. ...
    Article
    Background/objectives: We investigated whether fat mass (FM) and total adipose tissue (TAT) can be used interchangeably and FM per TAT adds to metabolic risk assessment. Subjects/methods: Cross-sectional data were assessed in 377 adults (aged 18-60 years; 51.2% women). FM was measured by either 4-compartment (4C) model or quantitative magnetic resonance (QMR); total-, subcutaneous- and visceral adipose tissue (TAT, SAT, VAT), and liver fat by whole-body MRI; leptin, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), C-reactive protein (CRP), and triglycerides; resting energy expenditure and respiratory quotient by indirect calorimetry were determined. Correlations and stepwise multivariate regression analyses were performed. Results: FM4C and FMQMR were associated with TAT (r4C = 0.96, rQMR = 0.99) with a mean FM per TAT of 0.85 and 1.01, respectively. Regardless of adiposity, there was a considerable inter-individual variance of FM/TAT-ratio (FM4C/TAT-ratio: 0.77-0.94; FMQMR/TAT-ratio: 0.89-1.10). Both, FM4C and TAT were associated with metabolic risks. Further, FM4C/TAT-ratio was positively related to leptin but inversely with CRP. There was no association between FM4C/TAT-ratio and VAT/SAT or liver fat. FM4C/TAT-ratio added to the variance of leptin and CRP. Conclusions: Independent of FM or TAT, FM4C/TAT-ratio adds to metabolic risk assessment. Therefore, the interchangeable use of FM and TAT to assess metabolic risks is questionable as both parameters may complement each other.
  • ... Furthermore, the EE measurements were limited to the 2 weeks at the start and end of this period and may not truly reflect the average EE, especially in the early stages of calorie restriction (15). A systematic bias of the intake-balance method could possibly result from inaccurate assumptions of the DLW method (16) as well as systematic errors arising from DXA which is a 2compartment body composition method that makes assumptions about hydration status that may be violated with weight-loss (17). We assumed that such systematic biases were negligible in the CALERIE study. ...
    Article
    Background: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design: Mathematical models were initialized using baseline CALERIE data, and changes in body weight (ΔBW), fat mass (ΔFM), and energy expenditure (ΔEE) were simulated in response to time-varying changes in energy intake (ΔEI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results: The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ΔBW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that ΔEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions: The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193.
  • Article
    Background & Aims To assess the influence of acute water ingestion on body composition analyses via Dual-Energy X-Ray Absorptiometry (DXA). Methods One hundred (44 females; 56 males; Age = 24.2 ± 6.7 yrs; Height = 175.8±12.1 cm; Body Mass = 76.1±16.5 kg) volunteers took part in this study. Participants underwent an initial DXA scan. Immediately following the DXA scan, each participant consumed 500 ml of water. Participants body mass was assessed again and immediately completed a second DXA scan. Total body fat mass, fat free mass, and percent body fat were quantified. Paired sample t-test and Pearson correlations were utilized to determine mean differences and the relationship between initial and secondary measures. Results Paired sample t-test analyses revealed significant a increase in body mass of 0.46 ± 0.1 kg [t(99) = 42.6, p < .0001]. There were no significant changes in fat mass (–10.6 ± 493.4g). In contrast, there was a significant increase in lean mass (524.9 ± 615.1g) [t(99) = 8.5, p < .001]. Overall, there was a significant decrease in percent body fat of –0.16% [t(99) = 2.4, p = .02]. Conclusions Results indicate that acute water ingestion before a DXA analysis will significantly influence body composition. More precisely, acute ingestion of 500 ml of water will significantly inflate fat free mass as well as lower percent body fat. While the values were of small magnitude, these results highlight the importance of the control of liquid ingestion prior to DXA scans for body composition measurement.
  • Article
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    Background:Although weight cycling is frequent in obese patients, the adverse consequences on body composition and an increased propensity to weight gain remain controversial.Objective:We investigated the effect of intentional weight loss and spontaneous regain on fat distribution, the composition of lean mass and resting energy expenditure (REE).Design:Weight regainers (30% of loss, n=27) and weight-stable subjects (within <±20% of weight change, n=20) were selected from 103 overweight and obese subjects (body mass index 28-43 kg m(-2), 24-45 years) who passed a 13-week low-calorie diet intervention. REE and body composition (by densitometry and whole-body magnetic resonance imaging) were examined at baseline, after weight loss and at 6 months of follow-up.Results:Mean weight loss was -12.3±3.3 kg in weight-stable subjects and -9.0±4.3 kg in weight regainers (P<0.01). Weight regain was incomplete, accounting for 83 and 42% of weight loss in women and men. Regain in total fat and different adipose tissue depots was in proportion to weight regain except for a higher regain in adipose tissue of the extremities in women and a lower regain in extremity and visceral adipose tissue in men. In both genders, regain in skeletal muscle of the trunk lagged behind skeletal muscle regain at the extremities. In contrast to weight-stable subjects, weight regainers showed a reduced REE adjusted for changes in organ and tissue masses after weight loss (P<0.001).Conclusion:Weight regain did not adversely affect body fat distribution. Weight loss-associated adaptations in REE may impair weight loss and contribute to weight regain.International Journal of Obesity advance online publication, 5 February 2013; doi:10.1038/ijo.2013.1.
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    The objective was to compare measures from dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA) and anthropometry with a reference four-compartment model to estimate fat mass (FM) and fat-free mass (FFM) changes in overweight and obese women after a weight-loss programme. Forty-eight women (age 39.8 ± 5.8 years; weight 79·2 ± 11·8 kg; BMI 30·7 ± 3·6 kg/m2) were studied in an out-patient weight-loss programme, before and after the 16-month intervention. Women attended weekly meetings for the first 4 months, followed by monthly meetings from 4 to 12 months. Body composition variables were measured by the following techniques: DXA, anthropometry (waist circumference-based model; Antrform), BIA using Tanita (TBF-310) and Omron (BF300) and a reference four-compartment model. Body weight decreased significantly ( − 3·3 (sd 3·1) kg) across the intervention. At baseline and after the intervention, FM, percentage FM and FFM assessed by Antrform, Tanita, BF300 and DXA differed significantly from the reference method (P ≤ 0·001), with the exception of FFM assessed by Tanita (baseline P = 0·071 and after P = 0·007). DXA significantly overestimated the change in FM and percentage FM across weight loss ( − 4·5 v. − 3·3 kg; P < 0·001 and − 3·7 v. − 2·0 %; P < 0·001, respectively), while Antrform underestimated FM and percentage FM ( − 2·8 v. − 3·3 kg; P = 0·043 and − 1·1 v. − 2·0 %; P = 0·013) compared with the four-compartment model. Tanita and BF300 did not differ (P>0·05) from the reference model in any body composition variables. We conclude that these methods are widely used in clinical settings, but should not be applied interchangeably to detect changes in body composition. Furthermore, the several clinical methods were not accurate enough for tracking body composition changes in overweight and obese premenopausal women after a weight-loss programme.
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    Objective: Given that the repetitive loss and regain of body weight, termed weight cycling, is a prevalent phenomenon that has been associated with negative physiological and psychological outcomes, the purpose of this study was to investigate weight change and physiological outcomes in women with a lifetime history of weight cycling enrolled in a 12-month diet and/or exercise intervention. Methods: 439 overweight, inactive, postmenopausal women were randomized to: i) dietary weight loss with a 10% weight loss goal (N=118); ii) moderate-to-vigorous intensity aerobic exercise for 45 min/day, 5 days/week (n=117); ii) both dietary weight loss and exercise (n=117); or iv) control (n=87). Women were categorized as non-, moderate- (≥3 losses of ≥4.5 kg), or severe-cyclers (≥3 losses of ≥9.1 kg). Trend tests and linear regression were used to compare adherence and changes in weight, body composition, blood pressure, insulin, C-peptide, glucose, insulin resistance (HOMA-IR), C-reactive protein, leptin, adiponectin, and interleukin-6 between cyclers and non-cyclers. Results: Moderate (n=103) and severe (n=77) cyclers were heavier and had less favorable metabolic profiles than non-cyclers at baseline. There were, however, no significant differences in adherence to the lifestyle interventions. Weight-cyclers (combined) had a greater improvement in HOMA-IR compared to non-cyclers participating in the exercise only intervention (P=.03), but no differences were apparent in the other groups. Conclusion: A history of weight cycling does not impede successful participation in lifestyle interventions or alter the benefits of diet and/or exercise on body composition and metabolic outcomes.
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    To establish reference values for limb composition, fat-free mass (FFM) and fat mass (FM) in Italian adults for gender-specific age brackets 20-80 years old and to assess age-related regional changes in body composition. A multicenter, retrospective study was conducted on 1571 healthy subjects, 1240 women and 331 men. Regional FFM and FM were measured by dual-energy X-ray absorptiometry. FM was expressed as % of limb weight. FFM in men diminished with age in both arms and legs, with reference ranges (25th -75th percentile) of 3.8-4.6 kg and 10.4-12.2 kg, respectively for 20-29 year-olds, and 3.1-3.9 kg and 8.2-10.4 kg for 70-79 year-olds. Women's arm FFM remained stable with aging (reference values 1.7-2.2 kg), decreasing in their legs (6.2-7.2 kg for 20-29 year-olds, 5.5-6.5 kg for 70-79 year-olds). Limb FM% increased with age in both genders: the reference values were 9-15% (arms) and 12-21% (legs) for 20-29 year-old men, and 19-26% and 19-29%, respectively, for 70-79 year-olds; for women's arms, they were 25-36% for 20-29 year-olds and 36-48% for 70-79 year-olds, while their leg FM remained the same with aging, i.e. 32-40%. These data complete the published reference values for whole body composition, enabling physiological or pathological changes in limb composition to be identified in Caucasian populations living in the Mediterranean area.
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    Despite the well-known recidivism of obesity, surprisingly little is known about the composition of body weight during weight regain. The objective of this study was to determine whether the composition of body weight regained after intentional weight loss is similar to the composition of body weight lost. The design was a follow-up to a randomized controlled trial of weight loss in which body composition was analyzed and compared in 78 postmenopausal women before the intervention, immediately after the intervention, and 6 and 12 mo after the intervention. All body mass and composition variables were lower immediately after weight loss than at baseline (all P < 0.05). More fat than lean mass was lost with weight loss, which resulted in body-composition changes favoring a lower percentage of body fat and a higher lean-to-fat mass ratio (P < 0.001). Considerable interindividual variability in weight regain was noted (CV = 1.07). In women who regained ≥2 kg body weight, a decreasing trend in the lean-to-fat mass ratio was observed, which indicated greater fat mass accretion than lean mass accretion (P < 0.001). Specifically, for every 1 kg fat lost during the weight-loss intervention, 0.26 kg lean tissue was lost; for every 1 kg fat regained over the following year, only 0.12 kg lean tissue was regained. Although not all postmenopausal women who intentionally lose weight will regain it within 1 y, the data suggest that fat mass is regained to a greater degree than is lean mass in those who do experience some weight regain. The health ramifications of our findings remain to be seen.