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Effects of a Meal Replacement on Body Composition and Metabolic Parameters among Subjects with Overweight or Obesity

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Meal replacement plans are effective tools for weight loss and improvement of various clinical characteristics but not sustainable due to the severe energy restriction. The aim of the study was to evaluate the impact of meal replacement, specifically 388 kcal in total energy, on body composition and metabolic parameters in individuals with overweight and obesity from a Chinese population. A parallel, randomized controlled trial was performed with 174 participants (ChiCTR-OOC-17012000). The intervention group ( N=86 ) was provided with a dinner meal replacement, and the control group ( N=88 ) continued their routine diet as before. Body composition and blood parameters were assessed at 0, 4, 8, and 12 weeks. A post hoc analysis (least significant difference (LSD) test), repeated measurements, and paired T -test were used to compare each variable within and between groups. Significant ( p<0.001 ) improvements in body composition components were observed among the intervention group, including body weight (−4.3 ± 3.3%), body mass index (−4.3 ± 3.3%), waist circumference (−4.3 ± 4.4%), fat-free mass (−1.8 ± 2.9%), and body fat mass (−5.3 ± 8.8%). Body composition improvements corresponded with significant metabolic improvements of blood glucose (−4.7 ± 9.8%). Further improvements in visceral fat area (−7.7 ± 10.1%), accompanying with improvements in systolic (−3.7 ± 6.9%) and diastolic (−5.3 ± 7.7%) blood pressure, were only found in male subjects. To conclude, meal replacement intake with 388 kcal in total energy at dinner time for 12 weeks contributed to improvement in body composition and clinically significant metabolic parameters in both male and female participants with overweight/obesity. Additionally, glucose and blood pressure reduction were gender-specific highlighting the importance of gender stratification for design of nutritional intervention studies for improvement of health.
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Clinical Study
Effects of a Meal Replacement on Body Composition and
Metabolic Parameters among Subjects with
Overweight or Obesity
Xiaohui Guo ,
1
Yifan Xu ,
1
Hairong He ,
1
Hao Cai ,
1
Jianfen Zhang,
1
Yibin Li ,
1
Xinyu Yan,
1
Man Zhang ,
1
Na Zhang,
1
Rolando L. Maddela,
2
Jessie Nicodemus-Johnson,
2
and Guansheng Ma
1
,
3
1
Department of Nutrition and Food Hygiene, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District,
Beijing 100191, China
2
USANA Health Sciences Inc., 3838 W Parkway Boulevard, West Valley City, UT 84120, USA
3
Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health,
Peking University, 38 Xue Yuan Road, Haidian District, Beijing 100191, China
Correspondence should be addressed to Guansheng Ma; mags@bjmu.edu.cn
Received 7 August 2018; Revised 18 October 2018; Accepted 27 November 2018; Published 26 December 2018
Guest Editor: Assim A. Alfadda
Copyright ©2018 Xiaohui Guo et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Meal replacement plans are effective tools for weight loss and improvement of various clinical characteristics but not sustainable
due to the severe energy restriction. e aim of the study was to evaluate the impact of meal replacement, specifically 388 kcal in
total energy, on body composition and metabolic parameters in individuals with overweight and obesity from a Chinese
population. A parallel, randomized controlled trial was performed with 174 participants (ChiCTR-OOC-17012000). e in-
tervention group (N86) was provided with a dinner meal replacement, and the control group (N88) continued their routine
diet as before. Body composition and blood parameters were assessed at 0, 4, 8, and 12 weeks. A post hoc analysis (least significant
difference (LSD) test), repeated measurements, and paired T-test were used to compare each variable within and between groups.
Significant (p<0.001) improvements in body composition components were observed among the intervention group, including
body weight (4.3 ±3.3%), body mass index (4.3 ±3.3%), waist circumference (4.3 ±4.4%), fat-free mass (1.8 ±2.9%), and
body fat mass (5.3 ±8.8%). Body composition improvements corresponded with significant metabolic improvements of blood
glucose (4.7 ±9.8%). Further improvements in visceral fat area (7.7 ±10.1%), accompanying with improvements in systolic
(3.7 ±6.9%) and diastolic (5.3 ±7.7%) blood pressure, were only found in male subjects. To conclude, meal replacement intake
with 388 kcal in total energy at dinner time for 12 weeks contributed to improvement in body composition and clinically
significant metabolic parameters in both male and female participants with overweight/obesity. Additionally, glucose and blood
pressure reduction were gender-specific highlighting the importance of gender stratification for design of nutritional intervention
studies for improvement of health.
1. Introduction
Obesity has increased dramatically worldwide over the past
few decades [1]. According to the World Health Organization,
more than one-third of adults in China are overweight, while
7% of adults are obese [2], with some provinces reaching
epidemic proportions [3]. Chronic obesity increases indi-
vidual risks for many diseases such as cardiovascular disease,
type 2 diabetes, and hypertension [4, 5], which are also in-
creasing in prevalence among Chinese populations at an
alarming rate [6]. From a health-care perspective, addressing
overweight and obesity is an important strategy that con-
tributes to the prevention or reduced risk of developing these
diseases within the Chinese population [7].
A chronic imbalance between energy intake and energy
expenditure plays an important role in the development of
Hindawi
Journal of Obesity
Volume 2018, Article ID 2837367, 10 pages
https://doi.org/10.1155/2018/2837367
obesity. Long-term negative energy balance is necessary for
individuals with overweight and obesity to lose weight.
Energy-restricted meal replacements are a safe and effective
strategy for weight control that has been implemented in
many studies [8, 9]. In addition, lifestyle changes, accom-
panied by partial or whole meal replacements, in which
some aspects of the individuals’ daily routine are altered may
be more preferable. However, physiological response to
specific meal replacement diets is heterogeneous and may
vary by individual or population. Specifically, there are few
studies looking at the effects of low-calorie meal replacement
in Chinese populations, for which environmental, nutri-
tional, and physiological differences are known to exist [10].
Weight-control and weight-loss studies performed with
meal replacements were always accompanied by very low
calorie intake (<800 kcal/day) or low calorie intake (800–
1500 kcal/day), which improved weight reduction rapidly
but not sustainably [11, 12]. A key problem is the high
attrition rate. A meta-analysis and pooling analysis reported
a 16% and 47% drop out rate after 3-month and 1-year
intervention with low-calorie meal replacement, respectively
[9]. Another meta-analysis shows (49%) studies reported
30% attrition in commercial weight-loss programs [13]. One
of the possible explanations is that very low calorie/low
calorie intake is not easy to maintain in a real-world setting.
Mild energy-restricted meal replacement may be able to
solve this problem, but few studies have reported significant
results with this type of diet plan.
Inverse associations between weight loss, normally
expressed by reduction in BMI, and metabolic parameters in
individuals with overweight and obesity have been found in
previous studies [14, 15]. Abdominal fat reduction has been
shown to be a better predictor of metabolic risk than BMI.
Generally, abdominal fat was determined by waist cir-
cumference and waist-to-hip height ratio; however, waist
circumference is not precise enough because it is a function
of both the subcutaneous adipose tissue and visceral adipose
tissue compartments [16, 17]. Actually, visceral fat area,
another indicator of abdominal fat, shows better associations
with metabolic parameters. is may be explained by a
higher amount of visceral fat related to extraadipocyte fat
storage, reduced insulin sensitivity and increased proin-
flammatory adipokine concentrations [18]. However, little is
known about the effect of meal replacement on visceral fat in
a clinical trial setting, and there is a need to explore the
effects of weight control by meal replacement on visceral fat
and body composition [18, 19].
Despite the fact that gender-specific physiological dif-
ferences may result in variable responses to weight loss, they
are rarely highlighted in meal replacement weight-loss
studies. ere are a number of characteristics that differ
between men and women during weight-loss programs due
to the difference in fat distribution and the hormone level
[18]. Women tend to have more fat on subcutaneous area,
and men are more likely to exhibit central obesity [20]. Men
tend to lose weight faster, although both genders lose weight
overall [21]. Also, men and women exhibit different attitudes
and behaviors surrounding weight and weight management,
which may contribute to variation in weight-loss program
efficacy [22]. e gender differences in diet and body
composition mentioned above, as well as many others, are
likely to play a role in meal replacement processing and
manifestation of clinically relevant health improvements,
such as reductions in blood pressure, blood lipids, etc. Al-
though many studies have addressed gender in weight loss,
gender assessment in meal replacement trials is limited
[22, 23].
e aim of the current study was to evaluate the effects of
a mild restricted meal replacement on body composition and
metabolic parameters in male and female subjects with
overweight or obesity from a Chinese population. We hy-
pothesized that a slight reduction in energy intake by partial
meal replacement can achieve clinically relevant weight loss
and metabolic profile improvements over a 12-week in-
tervention period and that a subset of these effects will vary
by gender.
2. Materials and Methods
2.1. Ethics Statement. e Ethics Committee of Peking
University Health Science Center approved the study pro-
tocol on 6 July 2017. e authors confirm that all ongoing
and related trials for this drug/intervention are carried out
by following the rules of the Declaration of Helsinki of 1964
and registered (ChiCTR-OOC-17012000).
2.2. Subjects. We sent recruitment advertisement to free-
living communities in and around the Beijing area, and all
potential participants were asked to read and sign an in-
formed consent as well as complete a basic screening
questionnaire which aimed at assessing eligibility. Specifi-
cally, inclusion criteria were (1) generally healthy partici-
pants with BMI >24 kg/m
2
, aged between 18 and 55 years;
(2) currently, not participated in any weight-loss activity; (3)
did not have any allergies to any of the known food in-
gredients; (4) currently were not pregnant or did not intend
to be pregnant. Exclusion criteria were (1) diagnosis of
cognitive impairment, schizophrenia, or depression by a
physician; (2) alcoholism, defined as 61 g alcohol drinks per
day for male and 41g alcohol drinks per day for female; (3)
equipped with pacemaker or other internal electronic
medical device; (4) previously underwent weight-loss sur-
gery; and (5) irregular diet and work such as night shift.
e sample size was calculated based on an intervention
and control group difference in weight change of 2.4 kg,
approximately 3% for individual with 80 kg, and a 7.5 kg SD
of weight change [24]. e estimated 154 subjects provided a
power of 80% to detect this difference in weight change at a
two-tailed significance level of 0.05. Assuming that the at-
trition rate is 20% after the intervention, the final sample size
was estimated to 185 individuals.
2.3. Study Design. e study was a paralleled, randomized
controlled clinical trial. After screening and selection, par-
ticipants were randomized into 2 groups, the meal re-
placement group (intervention group) and the routine diet
group (control group). e intervention group was advised
2Journal of Obesity
to consume one liquid meal replacement (Nutrimeal©,
Fibergy©, USANA Health Sciences Inc.), which contains
22.6 g protein, 11.1 g fat, 39.3 g carbohydrate, 20.9 g dietary
fiber, and 388 kcal in total energy at dinner time during the
intervention, and the control group was advised to follow a
routine Chinese dinner as before. Additional nutrient profile
for meal replacement can be found in Supplementary
Table S1. e summarized nutritional profiles between the
control and intervention can be found in Table S2. In-
dividuals were advised to continue their regular physical
activity regimen. Participants were invited back to reser-
vation location for assessment of body composition and
blood parameters at 0 (initial), 4, 8, and 12 (post-
intervention) weeks during the intervention. After each visit,
the intervention group was given sufficient meal re-
placement sachets to last until the next visit at no charge. If a
participant failed to attend a scheduled appointment, they
were contacted, and if participants were unable or unwilling
to regularly attend the scheduled appointments after study
initiation, they were considered drop-outs.
2.4. Dietary Assessment. Dietary habits were assessed
through a self-administered 77-item Food Frequency Ques-
tionnaire (FFQ) at the first and last visit. Information about
lifestyle, health condition, education, history of illnesses, and
medication use was collected by a brief self-administered 17-
item general questionnaire. Physical activity was evaluated by
a self-administered 24-item questionnaire.
2.5. Body Composition. Body composition was measured at
baseline and at 4-week intervals throughout the study by
using multifrequency bioelectrical impedance analysis with
8-point tactile electrodes (InBody 720; Biospace, Seoul,
Korea) [25]. Bioelectric impedance was measured within 1-2
minutes with the subject standing in her/his bare feet and
grasping the hand electrodes with arms in the vertical po-
sition. Height was measured using a standard wall-mounted
stadiometer to the nearest 0.5 cm. BMI was calculated as
weight in kilograms divided by height in square meters.
Overweight is defined as a condition where a subject has a
BMI between 24 and 28 kg/m
2
, and obesity is defined as a
condition where a subject has a BMI higher than 28 kg/m
2
,
according to current definitions in China [26]. Body com-
position parameters also include waist-to-hip ratio (WHtR),
intracellular water (ICW), extracellular water (ECW), total
body water (TW), protein, minerals, fat-free mass (FFM),
body fat mass (BFM), visceral fat area (VFA), body cell mass
(BCM), and basal metabolic rate (BMR).
2.6. Metabolic Parameters Measurements. Metabolic pa-
rameters measured at initial and postintervention visit
included systolic blood pressure (SBP), diastolic blood
pressure (DBP), fasting glucose (GLU), total cholesterol
(TC), triglycerides (TG), high-density lipoprotein choles-
terol (HDL-C), and low-density lipoprotein cholesterol
(LDL-C). For the measurement of blood pressure, a
validated semiautomatic sphygmomanometer (Omron
HEM-705CP) was used by trained nurses. Two measure-
ments were taken at 5-minute intervals with participants in a
seated position. Data were reported as an average of 2
measurements [27]. Plasma glucose, total cholesterol, and
triglyceride concentrations were measured using standard
enzymatic automated methods. Levels of HDL cholesterol
were measured by an enzymatic procedure after pre-
cipitation, and LDL cholesterol was estimated by the Frie-
dewald formula [28].
In the current study, hypertension is defined as systolic
blood pressure (SBP) higher than 140 mmHg or diastolic
blood pressure (DBP) higher than 90 mmHg [29]. Diabetes
is defined as self-reported history of diabetes or fasting
plasma glucose (FPG) 7.8 mmol/L or 2 h plasma glucose
11.1 mmol/L on 75 g oral glucose tolerance test [30].
Dyslipidemia is defined by self-reported history of dyslipi-
demia or elevations in levels of TC, LDL-C, TG higher than
6.22 mmol/L, 4.14 mmol/L, 2.26 mmol/L, respectively, or
HDL-C less than 1.04 mmol/L [31].
2.7. Statistical Analysis. To ensure data met assumptions of
parametric tests, data were assessed for outliers, homosce-
dasticity, and normality using Kolmogorov and Levene tests.
Nutrients intake and food consumption according to the
FFQs were assessed at baseline and postintervention. A post
hoc analysis (least significant difference (LSD) test), repeated
measurements, and paired T-test were used to compare each
variable within and between groups.
All analyses were performed using SPSS software V24.0
(SPSS Inc., Chicago, IL, USA). Results were expressed
as mean ±SD for continuous variables or percentages for
categorical variables. All statistical tests were two-tailed, and
the threshold for significance level was p<0.05.
3. Results
3.1. Flowchart. Figure 1 represents a flowchart depicting
study design and participant dropout rate. A total of 220
subjects were recruited from Beijing and surrounding areas.
Twenty-eight were excluded because they did not meet the
inclusion criteria. After the intervention, 18 withdrew for
various reasons: 5 because they were not able to meet the
scheduled appointment, 8 due to noncompliance, 3 because
they were unhappy with the flavor, and 2 due to personal
reasons; hence, a total of 174 participants were finally
included.
3.2. Baseline Characteristics. e baseline characteristics of
participants are shown in Table 1. Eighty-six individuals
were assigned to the intervention groups (42 male and 44
female) and eighty-eight to the control groups (38 male and
50 female). Intervention and control groups were not found
to be significantly different for any of the parameters
measured. Male subjects had a mean age of 38.9 ±6.5 and
38.0 ±6.6 years, and body weight of 89.9 ±12.5 and 89.2 ±
10.2 kg for intervention and control groups, respectively. Of
the participants, 61.9% and 43.2% had hypertension, 10%
and 2.7% had diabetes, 26.2% and 39.5% had dyslipidemia,
Journal of Obesity 3
and 11.9% and 10.5% were current smokers in the in-
tervention and control groups, respectively. Female par-
ticipants had a mean age of 39.4 ±7.9, 37.0 ±7.8 years and
body weight of 73.3 ±7.9, 73.4 ±8.5 kg for the intervention
and control group, respectively. Moreover, 22.7% and 18.0%
had hypertension, 2.4% and 4.0% had diabetes, 13.6% and
24.0% had dyslipidemia, and 9.1% and 4.0% were current
smokers in the intervention and control groups, respectively.
3.3. Improvement in Body Composition Parameters.
Relevant body composition parameters (BW, BMI, WC,
WHtR, FFM, BFM, VFA, and BCM) stratified by time point
and treatment group are shown in Table 2. Additional body
composition parameters (ICW, ECW, TW, protein, min-
erals, and BMR) measured are shown in Table S3. In the
intervention group, significant (p<0.001) reductions were
found in BW, BMI, WC, ICW, ECW, TW, protein, minerals,
FFM, BFM, BCM, and BMR in the combined analysis.
Similar results were observed after gender stratification.
BFM was significantly decreased in both genders; however,
the percent decrease was double in males (7.7%) relative to
females (3.2%). Additionally, VFA was only significant in
males (p<0.001) relative to females (p0.012). is male-
specific reduction in VFA was 4.3 times greater than the
modest reduction observed in females. e absolute FFM
decreased significantly during the intervention in both
genders, while the relative FFM increased from 69.5% ±3.5%
to 71.4% ±4.7% (p<0.001 in male and from 60.5% ±4.7% to
62.1% ±5.4%, p<0.001) in females, respectively. Consis-
tently, absolute reduction in BCM was observed after in-
tervention for both genders, while a slight increase in relative
BCM was shown in the intervention group (from 39.2% ±
3.0% to 40.4% ±3.6% in female, p<0.001; from 45.5% ±
2.6% to 47.0% ±3.6% in male, p<0.001). Among the in-
tervention group, the FFM: BW was 0.33 ±1.83 overall
and 0.47 ±2.43 and 0.22 ±1.14 for males and females,
respectively.
In the control group, significant, although modest (<1%),
changes were found in BW, BMI, WC, FFM, BCM, ICW,
protein, minerals, and BMR in the combined analysis. ICW,
TW, and BMR were not significant in the combined analysis.
WHtR, BFM, and VFA increased significantly for both
genders when compared at baseline and postintervention.
Furthermore, BMI, BFM, and VFA in male and WC in both
genders showed significant differences between the in-
tervention and control group at the end of intervention.
3.4. Improvement in Metabolic Parameters. Metabolic pa-
rameters stratified by time point and the treatment group are
shown in Table 3. Systolic blood pressure improvements
were observed among males (mean reduction 3.7%) but not
females (p0.678; mean reduction 1.3%). Also, diastolic
Assessed for
eligibility (n= 220)
Excluded for not
meeting the criteria
(n= 28)
Randomization
(n= 192)
Intervention group
(n= 95)
Control group
(n= 97)
Intervention group
(n= 93)
Intervention group
(n= 88)
Intervention group
(n= 86)
Control group
(n= 93)
Control group
(n= 90)
Control group
(n= 88)
0 week
4 weeks
8 weeks
12 weeks
2 withdrew due to
noncompliance; 4
were lost to follow up
2 withdrew due to
noncompliance, 4 due to,
“unhappiness with avor” 2
due to personal reasons
2 withdrew due to noncompliance,
1 due to, “unhappiness with
avor” and 1 was lost to follow-up
Statistical analysis
Figure 1: Flowchart of study participants.
4Journal of Obesity
blood pressure improvements were observed among males
(mean reduction 5.3%) but not females (p0.060; mean
reduction 2.5%). Among the intervention group, significant
reductions were observed for glucose in the combined
dataset. e mean improvement in glucose levels was 4.7%.
Similar trends in improvement were observed separately in
male and female glucose data. For the control group, in-
crement in SBP (mean increment 3.2%), HDL (mean in-
crement 6.4%), and LDL-C (mean increment 6.7%) was
found in females, and reduction in DBP (mean reduction
3.9%) and TG (mean reduction 11.1%) and increment in
LDL-C (mean increment 4.5%) were found in males. No
significant difference was found between the intervention
group and the control group at the 12thweek.
3.5. Nutrients and Food Intake. Table S2 shows nutrients
intake and food consumption during the intervention. At
baseline, total daily energy intakes were 2162 ±390 kcal, 2108
±306 kcal for males, 1832 ±294 kcal, 1911 ±399 kcal for
females in the intervention groups and control group, re-
spectively. ere is no difference between the intervention
group and the control group at baseline (p0.495 for male;
p0.284 for female). However, energy intakes decreased by
220 kcal/d in the intervention group but not in the control
group after intervention. Additionally, physical activity,
expressed as MET-min per day, did not show any difference
within and between groups (p0.639).
ere is a significant increase in total protein and fiber
intake in the intervention group relative to controls. is
may partially be explained by consumption of the meal
replacement which contains large amounts of fiber and
protein (p<0.001). Additionally, we observed a significant
reduction in carbohydrate intake in the intervention group
relative to the control.
As for food intakes, differences were found in milk, meat,
legume, cereals, and beverage intake after intervention be-
tween the two groups. Significant reductions (p<0.001)
were shown in milk, meat, legume, and beverage intakes in
the intervention group.
4. Discussion
A 12-week meal replacement with mild caloric restriction
study conducted in a group of Chinese participants with
overweight and obesity showed significant reduction or
improvement in 14 body composition parameters as well as
3 out of 7 metabolic parameters assessed. Our findings
implemented a slight energy reduction design, likely making
it more acceptable and sustainable to participants in the
context of lifestyle modifications [13].
Table 1: Characteristics of participants at baseline.
Male pFemale p
Intervention group Control group Intervention group Control group
No. of subjects 42 38 44 50 0.454
Age (y), mean (SD) 38.9 ±6.5 38.0 ±6.6 0.539 39.4 ±7.9 37.0 ±7.8 0.134
Body weight (kg), mean (SD) 89.9 ±12.5 89.2 ±10.2 0.916 73.3 ±7.9 73.4 ±8.5 0.852
BMI (kg/m
2
), mean (SD) 29.8 ±3.1 28.7 ±3.0 0.474 28.9 ±3.2 28.7 ±3.0 0.909
Systolic BP (mmHg), mean (SD) 136.6 ±16.4 132.6 ±12.3 0.104 121.7 ±15.2 120.1 ±17.1 0.914
Diastolic BP (mmHg), mean (SD) 89.6 ±11.6 88.6 ±11.9 0.445 79.5 ±11.0 77.9 ±12.9 0.717
Hypertension, n(%) 26 (61.9) 16 (43.2) 0.097 10 (22.7) 9 (18.0) 0.463
Diabetes, n(%) 4 (10) 1 (2.7) 0.204 1 (2.4) 2 (4.0) 0.651
Dyslipidemia, n(%) 11 (26.2) 15 (39.5) 0.152 6 (13.6) 12 (24) 0.221
Smoking status, n(%) 0.629 0.280
Current 5 (11.9) 4 (10.5) 4 (9.1) 2 (4.0)
Former 3 (7.1) 1 (2.6) 0 (0) 0 (0)
Never 34 (81) 33 (86.8) 40 (90.9) 48 (96.0)
Medication, n(%)
Aspirin 0 (0) 0 (0) 0 (0) 0 (0)
Antihypertension 7 (16.7) 3 (7.9) 0.200 1 (2.3) 2 (4.0) 0.548
Hypolipidemic drugs (%) 3 (7.1) 2 (5.3) 0.548 0 (0) 2 (4.0) 0.280
Insulin 0 (0) 0 (0) 0 (0) 0 (0)
Oral hypoglycemic drugs 1 (2.4) 1 (2.6) 0.728 1 (2.3) 1 (2.3) 0.720
Vitamins 4 (9.5) 4 (10.5) 0.586 5 (11.4) 4 (8.0) 0.418
Minerals 3 (7.1) 3 (7.9) 0.613 3 (6.8) 4 (8.0) 0.572
Education level (%) 0.593 0.496
University 33 (78.6) 30 (78.9) 36 (81.8) 42 (84)
High school 9 (21.4) 8 (21.1) 8 (18.2) 8 (16.0)
Primary school 0 (0) 0 (0) 0 (0) 0 (0)
Marital status (%) 0.613 0.144
Single 3 (7.1) 3 (7.9) 3 (6.8) 8 (16.0)
Married 39 (92.9) 35 (92.1) 41 (93.2) 42 (84)
Widowed 0 (0) 0 (0) 0 (0) 0 (0)
BMI: body mass index; BP: blood pressure. Data are given as mean (SD) for continuous variables and percentages for categorical variables; p<0.05 indicates
statistical significance. pvalues calculated by analysis of variance or χ
2
tests.
Journal of Obesity 5
Table 2: Characteristics of body composition before and after intervention
a
.
Intervention group (n86) Mean
percent
reduction
(%)
pfor
trend
b
p
c
Control group (n88) Mean
percent
reduction
(%)
pfor
trend
b
p
c
p
d
Time 1 Time 2 Time 3 Time 4 Time 1 Time 2 Time 3 Time 4
BW (kg)
Male 89.9 ±12.5 88.3 ±11.8 87.6 ±12.2 85.9 ±12.4 4.5 ±3.4 <0.001 <0.001 89.2 ±10.2 90.1 ±10.0 90.0 ±9.8 89.6 ±9.9 0.2 ±3.1 0.013 0.353 0.149
Female 73.3 ±7.9 72.6 ±8.3 71.6 ±7.3 70.1 ±8.4 4.2 ±3.2 <0.001 <0.001 73.4 ±8.5 74.1 ±9.0 73.9 ±9.5 72.9 ±9.0 0.6 ±2.9 <0.001 0.145 0.101
Total 81.0 ±13.2 79.8 ±12.8 78.9 ±12.7 77.5 ±13.0 4.3 ±3.3 <0.001 <0.001 80.1 ±12.1 80.9 ±12.3 80.7 ±12.4 80.0 ±12.5 0.3 ±3.0 <0.001 0.698 0.137
BMI (kg/m
2
)
Male 29.8 ±3.1 29.3 ±2.9 29.1 ±3.1 28.5 ±3.2 4.5 ±3.4 <0.001 <0.001 30.1 ±2.6 30.4 ±2.5 30.4 ±2.4 30.3 ±2.5 0.2 ±3.1 0.012 0.117 0.013
Female 28.9 ±3.2 28.6 ±3.3 28.2 ±3.2 27.7 ±3.3 4.2 ±3.2 <0.001 <0.001 28.7 ±3.0 29.0 ±3.1 28.9 ±3.3 28.5 ±3.2 0.6 ±2.9 <0.001 0.134 0.150
Total 29.3 ±3.2 28.9 ±3.1 28.6 ±3.2 28.1 ±3.3 4.3 ±3.3 <0.001 <0.001 29.3 ±2.9 29.6 ±3.0 29.5 ±3.0 29.3 ±3.0 0.3 ±3.0 <0.001 0.576 0.009
WC (cm)
Male 102.3 ±7.4 100.2 ±7.0 99.1 ±6.6 97.4 ±6.8 4.7 ±3.7 <0.001 <0.001 102.8 ±7.2 103.1 ±7.0 103.0 ±7.0 103.5 ±7.2 0.7 ±2.8 0.392 0.487 <0.001
Female 93.1 ±7.5 92.1 ±8.4 90.7 ±8.6 89.5 ±8.9 3.9 ±5.0 <0.001 <0.001 93.1 ±7.7 93.3 ±6.9 92.9 ±7.7 93.6 ±7.0 0.7 ±3.1 0.409 0.186 0.014
Total 97.5 ±8.8 96.0 ±8.7 94.7 ±8.7 93.0 ±8.9 4.3 ±4.4 <0.001 <0.001 97.3 ±8.9 97.5 ±8.4 97.2 ±8.9 97.9 ±8.6 0.7 ±2.9 0.134 0.042 0.001
WHtR
Male 0.97 ±0.06 0.97 ±0.06 0.97 ±0.07 0.97 ±0.06 0.3 ±3.0 0.361 0.600 0.96 ±0.06 0.98 ±0.06 0.98 ±0.06 0.99 ±0.09 3.6 ±6.1 0.006 0.010 0.203
Female 0.93 ±0.05 0.94 ±0.05 0.94 ±0.05 0.95 ±0.06 2.5 ±3.1<0.001 <0.001 0.92 ±0.05 0.95 ±0.05 0.95 ±0.06 0.97 ±0.05 4.1 ±2.7 <0.001 <0.001 0.618
Total 0.95 ±0.06 0.96 ±0.06 0.95 ±0.06 0.96 ±0.06 1.3 ±3.3 0.002 0.002 0.94 ±0.06 0.96 ±0.06 0.96 ±0.06 0.98 ±0.07 3.9 ±4.5 <0.001 <0.001 0.507
FFM (kg)
Male 62.2 ±6.8 61.1 ±6.4 60.7 ±6.6 60.9 ±6.3 2.0 ±2.2 <0.001 <0.001 60.5 ±6.3 60.6 ±6.8 60.0 ±6.9 60.7 ±7.2 0.3 ±4.0 0.225 0.560 0.987
Female 44.2 ±4.3 43.2 ±4.3 43.1 ±4.1 43.4 ±4.4 1.7 ±3.4 <0.001 <0.001 44.2 ±4.2 43.9 ±4.6 43.8 ±4.6 43.8 ±4.6 0.8 ±2.9 0.050 0.078 0.551
Total 52.5 ±10.6 51.5 ±10.4 51.2 ±10.3 51.5 ±10.3 1.8 ±2.9 <0.001 <0.001 51.0 ±9.6 50.9 ±10.0 50.6 ±9.8 51.0 ±10.2 0.3 ±3.4 0.085 0.650 0.940
BFM (kg)
Male 27.7 ±6.8 27.2 ±6.5 26.0 ±7.4 25.7 ±7.1 7.7 ±9.1 <0.001 <0.001 28.7 ±6.8 29.5 ±6.5 28.7 ±6.9 29.9 ±7.3 3.2 ±13.2 0.021 0.025 0.022
Female 29.1 ±5.9 29.3 ±6.1 27.5 ±5.9 28.2 ±6.3 3.2 ±7.1<0.001 0.005 29.2 ±5.8 30.2 ±6.1 29.1 ±6.2 30.1 ±6.1 3.2 ±6.8 0.001 0.002 0.112
Total 28.5 ±6.3 28.3 ±6.3 26.8 ±6.6 27.1 ±6.8 5.3 ±8.3 <0.001 <0.001 29.0 ±6.2 29.9 ±6.2 29.0 ±6.5 30.0 ±6.6 3.2 ±10.0 <0.001 <0.001 0.005
VFA (cm
2
)
Male 124.7 ±33.4 122.3 ±32.7 117.5 ±37.5 115.7 ±36.0 7.7 ±10.1 <0.001 <0.001 128.2 ±36.0 133.6 ±36.0 131.1 ±38.6 137.5 ±44.9 5.7 ±16.9 0.021 0.012 0.036
Female 144.9 ±32.5 148.6 ±34.1 138.7 ±35.1 143.2 ±37.5 1.8 ±10.40.012 0.389 145.2 ±30.7 152.9 ±32.8 147.5 ±34.3 154.2 ±33.0 6.5 ±8.5 <0.001 <0.001 0.104
Total 135.6 ±34.2 136.5 ±35.7 128.9 ±37.5 130.5 ±39.1 4.5 ±10.6 <0.001 <0.001 138.0 ±33.9 144.8 ±35.3 140.6 ±36.8 147.2 ±39.1 6.2 ±12.7 <0.001 <0.001 0.008
BCM (kg)
Male 40.7 ±4.5 40.1 ±4.2 39.8 ±4.3 40.1 ±4.2 1.4 ±2.1 <0.001 <0.001 39.6 ±4.1 39.8 ±4.4 39.4 ±4.5 40.0 ±4.7 0.7 ±4.2 0.132 0.257 0.960
Female 29.6 ±2.8 28.1 ±2.8 28.0 ±2.6 28.2 ±2.8 1.2 ±3.5 <0.001 0.014 28.7 ±2.8 28.6 ±3.0 28.5 ±3.0 28.6 ±3.0 0.3 ±2.8 0.271 0.473 0.469
Total 34.2 ±7.1 33.6 ±6.9 33.5 ±6.9 33.7 ±6.9 1.3 ±2.9 <0.001 <0.001 33.3 ±6.4 33.3 ±6.7 33.1 ±6.5 33.4 ±6.8 0.1 ±3.5 0.077 0.524 0.980
a
Data are given as means (SD);
b
data were analyzed by repeated measurements;
c
data analyzed by paired-samples T-test between time 1 and time 4;
d
data analyzed by T-test between the intervention group and
control group at time 4. Asterisk shows difference between genders. BW: body weight; BMI: body mass index; WC: waist circumference; WHtR: waist-to-hip ratio; FFM: fat-free mass; BFM: body fat mass; VFA:
visceral fat area; BCM: body cell mass. p<0.05 indicates statistical significance.
6Journal of Obesity
Table 3: Characteristics of metabolic parameters factors before and after intervention
a
.
Intervention group (n86) Mean
percent
reduction
(%)
pfor
trend
b
p
c
Control group (n88) Mean
percent
reduction
(%)
pfor
trend
b
p
c
p
d
Time 1 Time 2 Time 3 Time 4 Time 1 Time 2 Time 3 Time 4
SBP (mmHg)
Male 136.6 ±16.4 130.8 ±15.9 133.3 ±15.5 130.3 ±15.0 3.7 ±6.9 <0.001 <0.001 132.6 ±12.3 131.7 ±9.6 130.0 ±12.3 129.7 ±12.7 1.3 ±7.4 0.184 0.104 0.923
Female 121.7 ±15.2 122.3 ±14.0 123.8 ±19.7 121.5 ±14.7 1.3 ±18.4 0.678 0.954 120.1 ±17.1 122.3 ±15.7 123.7 ±15.6 123.6 ±16.4 3.2 ±11.1 0.084 0.039 0.407
Total 128.9 ±17.4 126.5 ±15.4 128.4 ±18.3 125.8 ±15.4 1.1 ±14.4 0.078 0.055 125.2 ±16.4 126.2 ±14.2 126.3 ±14.6 126.1 ±15.2 1.4 ±9.9 0.760 0.479 0.695
DBP (mmHg)
Male 89.6 ±11.6 85.3 ±9.6 86.2 ±10.4 83.8 ±9.4 5.3 ±7.7 <0.001 <0.001 88.6 ±11.9 86.1 ±8.3 85.7 ±8.8 83.8 ±9.7 3.9 ±10.4 0.024 0.012 0.755
Female 79.5 ±11.0 79.4 ±10.7 79.8 ±11.4 76.3 ±10.4 2.5 ±16.7 0.060 0.053 77.9 ±12.9 78.4 ±11.8 79.5 ±10.7 79.2 ±11.9 2.0 ±12.8 0.527 0.285 0.228
Total 84.4 ±12.3 82.3 ±10.5 82.9 ±11.3 79.9 ±10.6 3.8 ±13.3 <0.001 <0.001 82.3 ±13.5 81.8 ±11.1 82.0 ±10.4 81.1 ±11.2 0.4 ±12.1 0.599 0.271 0.368
GLU
(mmol/L)
Male 5.8 ±1.2 5.6 ±1.1 5.4 ±1.3 5.5 ±1.5 5.1 ±10.4 0.011 0.006 5.4 ±1.1 5.6 ±0.9 5.5 ±0.9 5.4 ±1.2 0.7 ±10.6 0.079 0.670 0.743
Female 5.5 ±1.7 5.3 ±1.2 5.3 ±1.5 5.2 ±1.2 4.4 ±9.4 0.015 0.011 5.5 ±1.2 5.5 ±1.0 5.4 ±1.0 5.3 ±1.3 4.2 ±9.0 0.090 0.004 0.733
Total 5.6 ±1.5 5.4 ±1.2 5.4 ±1.4 5.3 ±1.3 4.7 ±9.8 <0.001 <0.001 5.5 ±1.1 5.5 ±1.0 5.4 ±1.0 5.4 ±1.3 2.7 ±9.8 0.033 0.020 0.964
TC (mmol/L)
Male 4.9 ±1.0 4.8 ±1.0 4.7 ±1.0 4.9 ±1.0 0.5 ±17.6 0.190 0.448 4.7 ±0.9 4.8 ±0.7 4.7 ±0.8 4.8 ±0.8 2.4 ±9.9 0.462 0.302 0.667
Female 4.8 ±0.8 4.7 ±0.8 4.6 ±1.0 4.7 ±0.8 0.7 ±8.6 0.042 0.146 4.6 ±0.9 4.6 ±0.9 4.6 ±1.0 4.7 ±1.0 3.9 ±12.2 0.240 0.558 0.984
Total 4.9 ±0.9 4.8 ±0.9 4.7 ±1.0 4.8 ±0.9 0.2 ±13.3 0.007 0.177 4.6 ±0.9 4.7 ±0.9 4.7 ±0.9 4.7 ±0.9 3.3 ±11.3 0.200 0.042 0.734
TG (mmol/L)
Male 1.9 ±0.8 1.9 ±1.3 2.0 ±1.1 1.9 ±1.2 3.8 ±35.8 0.585 0.653 2.4 ±1.4 1.9 ±1.4 2.1 ±1.3 2.0 ±1.1 11.1 ±34.6 0.015 0.003 0.848
Female 1.5 ±0.7 1.3 ±0.5 1.3 ±0.6 1.4 ±0.9 1.6 ±38.3 0.425 0.396 1.5 ±0.8 1.4 ±0.8 1.4 ±09 1.4 ±1.0 3.3 ±39.1 0.710 0.768 0.705
Total 1.7 ±0.8 1.6 ±1.0 1.7 ±1.0 1.6 ±1.1 2.6 ±36.9 0.566 0.397 1.9 ±1.2 1.6 ±1.1 1.7 ±1.1 1.7 ±1.1 3.0 ±37.7 0.028 0.014 0.903
HDL-C
(mmol/L)
Male 1.2 ±0.4 1.4 ±0.6 1.1 ±0.3 1.2 ±0.2 9.2 ±38.4 0.023 0.543 1.1 ±0.3 1.5 ±0.8 1.2 ±0.4 1.1 ±0.2 5.7 ±11.4 0.006 0.403 0.785
Female 1.3 ±0.2 1.4 ±0.4 1.2 ±0.2 1.3 ±0.3 2.0 ±11.5 0.111 0.451 1.2 ±0.2 1.3 ±0.5 1.3 ±0.3 1.3 ±0.2 6.4 ±14.4 0.188 0.019 0.438
Total 1.2 ±0.3 1.4 ±0.5 1.2 ±0.3 1.2 ±0.3 3.0 ±27.4 0.004 0.747 1.2 ±0.3 1.4 ±0.6 1.2 ±0.4 1.2 ±0.3 6.1 ±13.1 0.003 0.021 0.584
LDL-C
(mmol/L)
Male 3.0 ±0.8 3.1 ±0.8 2.9 ±0.6 3.0 ±0.7 1.7 ±18.7 0.236 0.857 2.8 ±0.6 3.0 ±0.6 2.8 ±0.6 2.9 ±0.6 4.5 ±18.5 0.003 0.004 0.648
Female 2.9 ±0.6 3.1 ±0.6 2.8 ±0.7 2.9 ±0.6 0.3 ±10.7 <0.001 0.132 2.7 ±0.7 2.9 ±0.7 2.8 ±0.7 2.9 ±0.8 6.7 ±19.3 0.007 0.004 0.681
Total 3.0 ±0.7 3.1 ±0.7 2.9 ±0.7 2.9 ±0.6 0.6 ±14.7 0.001 0.385 2.8 ±0.7 3.0 ±0.7 2.8 ±0.6 2.9 ±0.7 5.7 ±18.9 <0.001 <0.001 0.992
a
Data are given as means (SD);
b
data were analyzed by repeated measurements;
c
data analyzed by paired-samples T-test between time 1 and time 4;
d
data analyzed by T-test between the intervention group and
control group at time 4. SPB: systolic blood pressure; DBP: diastolic blood pressure; GLU: fasting glucose; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density
lipoprotein cholesterol. p<0.05 indicates statistical significance.
Journal of Obesity 7
Associations between weight loss and improvements in
metabolic parameters have been demonstrated by numerous
studies [9, 14, 32]. Although weight loss reported in the
present study did not reach clinical significance, defined as
5% reduction in weight over a period of at least 12 weeks,
even small increments of weight loss have been shown to be
important for the prevention of cardiovascular diseases
[13, 33] and thus confer meaningful health benefits, such as
improvements in glucose and blood pressure [34]. Fur-
thermore, it has been suggested that the threshold for
clinically significant weight loss is variable depending on the
clinical phenotypes in question. For example, while a 5%
weight loss has been suggested to be a marker of clinically
significant changes in blood pressure, HDL, and LDL, a 3%
weight loss has been shown to be clinically relevant for blood
sugar and triglyceride levels [35]. Regardless of the weight
loss cut-off, in the present study, the overall loss of 4.3%
body weight coincides with clinically significant improve-
ments in blood pressure (males only) and glucose (males and
females) levels. Moreover, due to the moderate duration of
this study (only 12 weeks), we believe additional studies with
longer durations will reach the 5% benchmark, resulting in
greater improvements in clinical risk factors.
Maximizing fat loss while preserving muscle mass is the
central goal of obesity treatments. As such, attention to
body composition parameters such as free fat mass are
critical to the assessment of the clinical utility of weight-loss
programs, wherein the ratio of free-fat mass loss to weight
loss may serve as a biomarker of clinical efficacy [36, 37]. In
the current study, the modest weight loss observed in this
program is associated with FFM/BW ratio of 0.3, con-
sistent with the commonly reported ratio of 0.25 lending
to the efficacy of this weight-loss approach. In addition,
while the absolute FFM and BCM decreased in the end of
intervention, the relative FFM and BCM increased, in-
dicating a satisfactory overall development of muscle mass
despite weight reduction. Our finding is in line with a long-
term weight-loss maintenance by a meal replacement pro-
gram [38] and concordant with results of a similar study
in a 40-week randomized, controlled clinical trial, which
aimed at evaluating the impact of a portion-controlled
meal replacement diet plan on body weight and body
composition [39].
Gender is known to modify both nutritional intake and
phenotypic response [40–42]. erefore, studying the dif-
ference between men and women in response to in-
terventions is essential for providing optimal care. e
present study demonstrated modest improvements in VFA
(4.5% reduction); however, when stratified by gender, the
majority of this effect was observed in the male subset of our
population (7.7% reduction), while females demonstrated a
marginal reduction (1.8%). Gender differences in VFA may
be due to sex hormone-driven differences in body com-
position and fat distribution with men having a more central
distribution of fat, i.e., preferentially storing and losing fat in
visceral deposits, while women of child-bearing age have
more subcutaneous adipose tissue deposits [43].
Male-specific effects were extended to SBP measure-
ments in which males demonstrated a 3.7% improvement
relative to 1.3% reduction in females. is result is consistent
with previous studies that demonstrate VFA is a better
predictor of metabolic risk parameters, such as SBP [16, 17].
e observed reductions in body weight and visceral fat
are known to contribute to correlative reductions in blood
pressure, as observed in this study, might through im-
provement in glucose tolerance [44]. e gender-specific
effects in VFA and blood pressure in response to weight loss
suggest men and women should adopt different weight-loss
strategies.
Notably, although VFA improvement and blood pres-
sure reduction were found in males only, improvements in
overall weight parameters (BMI, BW, and WC; 4.3%) co-
incided with a 4.7% reduction in blood glucose levels,
irrespective of gender thus identifying two different body
composition parameters that are accompanied with different
cardio metabolic risk parameters in the present study.
erefore, our findings highlight the fact that different body
composition parameters may be better predictors of specific
clinical phenotypes. e gender-neutral results correspond
with those of other published studies. For example, a cross-
sectional study also found inverse associations between
visceral fat and blood glucose, and a longitudinal study
showed changes in VFA were significantly correlated with
changes in fasting plasma glucose [45, 46].
Some limitations of this study should be noted. First, the
length of the intervention was only 12 weeks, a longer
duration may be more informative and enhance nominally
significant associations in light of our modest caloric in-
tervention. Our short-term intervention cannot assess the
long-term intervention associations with body composition
and metabolic parameters. Second, the sample size is rela-
tively small under stratified analysis; however, our results are
robust to multiple testing, and the paired longitudinal design
of the study increases our power to detect reported asso-
ciations. ird, food and nutrient intake were evaluated by
self-administered food frequency questionnaire using food
recall methods, which maybe not be precise enough for
nutritional inference. Fourth, the observed physiological
improvements may result from alteration to meal patterns or
specific micronutrient intake; however, this is out of the
scope of the current study.
5. Conclusions
Overall, our study reports the results of a 12-week ran-
domized controlled intervention in a Chinese population
with overweight and obesity. We demonstrate clinically
relevant improvements in metabolic parameters with
modest weight loss (<5%). Additionally, we demonstrate
both gender-specific and gender-independent body com-
position improvements that accompanied independent
metabolic parameters. For future research, gender-specific
associations between weight loss in subcutaneous fat and
visceral fat and improvement in metabolic parameters need
to be further explored. Finally, compared with other energy
restricted programs, our trial was conducted based on a
slight energy reduction design, making it more acceptable
and sustainable in the context of lifestyle modifications.
8Journal of Obesity
Data Availability
e database of participants’ data used to support the findings
of this study are included within the Supplementary File S2.
Database of participants. Individual identified participant
data are available, including basic information, body com-
position, and metabolic parameters. Other documents, such
as study protocol and statistical analysis plan, are not avail-
able. All the readers can review the database with attachment
File S2.
Conflicts of Interest
e resident authors in China declare no conflicts of interest.
e funding sponsors had no contact with the researchers of
the study and thus had no role in the design of the study; in
the data collection, analyses, or and interpretation of data; in
the writing of the manuscript; or in the decision to publish
the results. Some assistance in writing and interpretation has
been provided by J.N-J. and R.M. who are scientists of
USANA Health Sciences, Inc., USA.
Acknowledgments
e authors thank the BabyCare Ltd., which is the subsidiary
of USANA in China, which donated the meal replacement
(USANA NUTRIMEAL and Fibergy) used in the study. is
research received funding from BabyCare Ltd.
Supplementary Materials
Table S1: ingredients of protein powder and fibergy; Table
S2: dietary characteristics before and after the meal re-
placement intervention; Table S3: additional body compo-
sition characteristic before and after intervention; File S1:
consort-2010-checklist. File S2: database of participants.
(Supplementary Materials)
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10 Journal of Obesity

Supplementary resource (1)

... Our searches identified 15,478 potentially eligible studies of body fatness and insulin signalling. After removing duplicates and screening studies for eligibility, we identified seven eligible studies [32][33][34][35][36][37][38] where a reduction in body fatness was the exposure and at least one biomarker of insulin sensitivity was reported as an outcome (Figure 1). ...
... We identified 3152 potentially eligible studies on insulin sensitivity and prostate cancer but only six of these studies ( Figure 2) met our inclusion criteria [39][40][41][42][43][44]. Table 1 shows the characteristics of the included studies for the body fatness-insulin association [32][33][34][35][36][37][38]. All seven eligible studies were RCTs carried out in men only, the largest of which had 80 participants and the smallest only 22 participants. ...
... We identified 3152 potentially eligible studies on insulin sensitivity and prostate c cer but only six of these studies ( Figure 2) met our inclusion criteria [39][40][41][42][43][44]. Table 1 shows the characteristics of the included studies for the body fatness-insu association [32][33][34][35][36][37][38]. All seven eligible studies were RCTs carried out in men only, the la est of which had 80 participants and the smallest only 22 participants. ...
Article
Full-text available
Excess body weight is thought to increase the risk of aggressive prostate cancer (PCa), although the biological mechanism is currently unclear. Body fatness is positively associated with a diminished cellular response to insulin and biomarkers of insulin signalling have been positively associated with PCa risk. We carried out a two-pronged systematic review of (a) the effect of reducing body fatness on insulin biomarker levels and (b) the effect of insulin biomarkers on PCa risk, to determine whether a reduction in body fatness could reduce PCa risk via effects on the insulin signalling pathway. We identified seven eligible randomised controlled trials of interventions designed to reduce body fatness which measured insulin biomarkers as an outcome, and six eligible prospective observational studies of insulin biomarkers and PCa risk. We found some evidence that a reduction in body fatness improved insulin sensitivity although our confidence in this evidence was low based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations). We were unable to reach any conclusions on the effect of insulin sensitivity on PCa risk from the few studies included in our systematic review. A reduction in body fatness may reduce PCa risk via insulin signalling, but more high-quality evidence is needed before any conclusions can be reached regarding PCa.
... [27][28][29][30] Otherwise, the impact of patient-related factors has not been reported in most other clinical trials investigating the efficacy of MR plans for weight loss. [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] In terms of the number of MR meals per day, a characteristic of MR interventions, Guo and colleagues 31 reported in a clinical trial that although total diet replacement was more effective for weight loss, partial meal replacement was more beneficial as a long-term treatment when considering individual adherence to dietary regimens. In another clinical trial, Leader and colleagues 32 showed that 2 MR meals per day are more effective than 1 MR meal per day for weight loss, regulation of blood glucose, and compliance with dietary prescriptions in patients with obesity and diabetes. ...
... [27][28][29][30] Otherwise, the impact of patient-related factors has not been reported in most other clinical trials investigating the efficacy of MR plans for weight loss. [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] In terms of the number of MR meals per day, a characteristic of MR interventions, Guo and colleagues 31 reported in a clinical trial that although total diet replacement was more effective for weight loss, partial meal replacement was more beneficial as a long-term treatment when considering individual adherence to dietary regimens. In another clinical trial, Leader and colleagues 32 showed that 2 MR meals per day are more effective than 1 MR meal per day for weight loss, regulation of blood glucose, and compliance with dietary prescriptions in patients with obesity and diabetes. ...
... Multiple meta-regression analysis using the hierarchical regression analysis approach, for the impact of patient-and treatment-related moderators of meal replacementebased, calorie-restricted vs conventional calorie-restricted diets on weight loss comparing the effect of MR plans according to MR intake frequency on weight loss. As reported by Guo and colleagues, 31 dietary compliance can vary, depending on the frequency of MR intake regardless of the effect; as MR products are generally not provided for free, these factors must be considered when attempting MR plans for weight loss. The dropout rate increases otherwise. ...
Article
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Background Portion control is a useful component of weight reduction interventions and meal replacement (MR) plans represent a promising strategy for portion control. Research performed with pooled data on the effect of MR plans according to various characteristics of MR interventions remains scarce. Objective Our aim was to assess the effects of MR-based diets compared with food-based diets on weight loss, according to calorie-restriction types and energy intake proportions from MR. Methods Electronic databases (Cochrane Central Register of Controlled Trials, PubMed, Embase, and Research Information Sharing Service) were searched for randomized controlled trials on weight loss results of MR-based calorie-restricted diets compared with food-based calorie-restricted diets from January 2000 to May 2020. Standardized mean differences (Hedges' g) from all study outcomes were calculated using a random-effects model. Heterogeneity was quantified by Q test and I². Publication bias was assessed using a funnel plot and a trim and fill method. Both interventions (MR and control) were separated into very-low-energy diets and low-energy diets (LEDs). A meta-analysis of variance was conducted by dividing patient-related factors and treatment-related factors into subgroups. In multivariable meta-regressions, background variables were selected first, after which main independent variables were included. Results Twenty-two studies involving 24 interventions and 1,982 patients who were overweight or obese were included. The effect size in which MR-based LED was compared with food-based LED for weight loss was small, favoring MR (Hedges’ g = 0.261; 95% CI 0.156 to 0.365; I² = 21.9; 95% CI 0.0 to 53.6). Diets including ≥60% of total daily energy intake from MR had a medium effect size favoring MR with regard to weight loss among the groups (Hedges’ g = 0.545; 95% CI 0.260 to 0.830; I² = 42.7; 95% CI 0.0 to 80.8). Conclusions The effect of MR-based LED on weight loss was superior to the effect of food-based LED, and receiving ≥60% of total daily energy intake from MR had the greatest effect on weight loss.
... This is an observational study that looked at data from a randomized controlled clinical trial that was conducted among free-living 168 overweight or obese subjects over a period of 3 months. The details of the original study have been published [23]. Briefly, the participants in the study were randomly assigned into 2 groups, the meal replacement group (intervention group) and the routine diet group (control group). ...
... Overweight was defined as 24 ≥ BMI < 28 kg/m 2 and obesity was defined as BMI ≥ 28 kg/m 2 according to the current definitions for Chinese adults [24]. Body composition was measured by using multi-frequency bioelectrical impedance analysis with 8-point tactile electrodes (InBody 720; Biospace, Seoul, Korea) [23]. Bioelectrical impedance was measured within 1-2 min with the subject standing in her/his bare feet and grasping the hand electrodes with arms in the vertical position. ...
Article
Full-text available
Background: Visceral adiposity has been reported to play a key role in hypertension compared with other measurements of regional or general obesity. The aim of current study was to evaluate the relationship between visceral fat reduction and changes in blood pressure in a group of overweight or obese Chinese individuals. Methods: An observational study was conducted with 168 participants (ChiCTR-OOC-17012000). Body composition, blood parameters and blood pressure were assessed at the beginning and end of the intervention. Males and females were categorized separately into quartiles according to changes in visceral fat during the intervention. Multiple linear regression models were used to assess the associations of changes in systolic and diastolic blood pressure with changes of visceral fat area, adjusted for potential confounders. Results: Changes in visceral fat was significantly associated with systolic and diastolic blood pressure in men for systolic (β = 0.234, 95% CI: 0.103, 0.365; p = 0.001) and diastolic blood pressure (β = 0.237; 95% CI: 0.127, 0.346; p <0.001), but not in women after adjustment for the same potential confounders for systolic blood (β = - 0.003, 95% CI: - 0.260, 0.255; p = 0.984) and diastolic blood pressure (β = 0.101, 95% CI: - 0.072, 0.273; p = 0.249). Conclusions: A positive association was observed between reduction in visceral fat and improvements in both systolic blood and diastolic blood pressures in males but not females in a 12-week meal replacement intervention. Trial registration: The Ethics Committee of Peking University Health Science Center approved the study protocol on 6 July 2017. The authors confirm that all ongoing and related trials for this intervention were carried out following the rules of the Declaration of Helsinki of 1975 and registered (ChiCTR-OOC-17012000). http://www.chictr.org.cn/showprojen.aspx?proj=20426.
... While meal replacement can lead to greater initial weight loss and glucose control, there is still a lack of evidence on the long-term feasibility of the sustained use of meal replacements [14]. Once the meal replacement intervention is reduced or discontinued, its benefit of glycemic control and weight reduction may not be sustained [15]. ...
Article
Full-text available
Abstract: Meal replacement (MR) is widely used in weight and diabetes management programs due to its ease of compliance and handling. However, little is known about its impact on outcomes other than glycaemic control and weight loss. Furthermore, not many studies evaluate its cost-effectiveness and sustainability. This study aimed to evaluate the efficacy of a diabetes-specific MR for the weight reduction and glycaemic controls of overweight and obese T2DM patients, as compared to routine dietary consultation. Other health outcomes, the cost effectiveness, and the sustainability of the MR will also be evaluated. Materials and Methods: This randomised controlled clinical trial will involve 156 participants who have been randomised equally into the intervention and control groups. As a baseline, both groups will receive diet consultation. Additionally, the intervention group will receive an MR to replace one meal for 5 days a week. The duration of intervention will be 12 weeks, with 36 weeks of follow-up to monitor the sustainability of the MR. The primary endpoints are weight and Hemoglobin A1c (HbA1c) reduction, while the secondary endpoints are anthropometry, biochemical measurements, satiety, hormone changes, quality of life, and cost-effectiveness. The impact of the COVID-19 pandemic on study design is also discussed in this paper. This study has obtained human ethics approval from RECUKM (JEP-2019-566) and is registered at the Thai Clinical Trials Registry (TCTR ID: TCTR20210921004).
... Par ailleurs, une diminution de la proportion de VAT entraine une meilleure sensibilité à l'insuline (Goodpaster et al., 1999). Le VAT est également plus sensible à la perte de poids et est métaboliquement plus actif que le SCAT (Choe et al., 2016;Guo et al., 2018;Serra et al., 2017). ...
Thesis
Les monocytes font partie des acteurs majeurs de l’immunité innée. Ils sont générés dans la moelle osseuse et contribuent à garantir le maintien de l’homéostasie de l’organisme en cas de blessure ou d’inflammation chronique par leur mobilisation dans le sang et leur recrutement dans les tissus où ils ont la capacité de se différencier en macrophages. Depuis les années 1970, l’étude du contrôle exercé par le métabolisme sur la survie, la fonction et le devenir des cellules immunitaires, et plus particulièrement des macrophages, impliqués dans de nombreuses pathologies cardio-métaboliques, a gagné un grand intérêt auprès de la communauté scientifique. Cependant, les données recueillies sur l’impact du métabolisme sur les monocytes restent très limitées. De façon intéressante à l’état physiologique, le nombre de monocytes dans la circulation sanguine varie au cours de la journée. De plus, nous avons mis en évidence une corrélation entre le nombre de monocytes présents dans la circulation et les taux d’acides gras circulants au cours de la journée. Ainsi, durant ma thèse je me suis intéressée à l’étude de la potentielle causalité qui unit ces deux phénomènes et plus particulièrement à l’impact des métabolites énergétiques tels que les acides gras sur le contrôle des monocytes. Pour ce faire, nous avons utilisé un modèle murin génétiquement déficient pour l’enzyme clé responsable de la libération des acides gras à travers la lipolyse du tissu adipeux, appelée Atgl. Nous avons pu mettre en évidence que l’altération de la lipolyse du tissu adipeux entraine un stress et une inflammation chronique spécifique du tissu adipeux brun, associés à un recrutement local massif des monocytes de façon dépendante de l’expression du récepteur aux chimiokines CCR2. Le recrutement accru des monocytes dans le tissu adipeux brun est également associé à une diminution des taux de monocytes dans la circulation sanguine. Cependant chez ces souris, nous n’avons pas observé d’altération du compartiment médullaire, siège de l’hématopoïèse et de la production des cellules sanguines. Nos résultats permettent ainsi de mettre en évidence l’importance de la lipolyse ainsi que du recrutement des monocytes sur le maintien de l’homéostasie du tissu adipeux brun.
... Men had more success with HLC diets than HLF diets, while women did not demonstrate a significant difference in success between the two diets. Multiple studies also assessed gender differences following low energy diets or very-low energy diets, with many findings that men were more successful in achieving higher percent weight loss [72][73][74]. However, these studies are post hoc analyses and were not powered or designed to specifically test differences based on sex/gender. ...
Article
Full-text available
Purpose of Review Obesity is a heterogeneous condition, yet sex/gender is rarely considered in the prevention or clinical care of this disease. This review examined and evaluated recent literature regarding the influence of sex and gender on obesity prevalence, comorbidities, and treatment in adults. Recent Findings Obesity is more prevalent in women than men in most countries, but in some countries and population subgroups, this gap is more pronounced. Several obesity-related comorbidities, including type 2 diabetes and hypertension, demonstrate sex-specific pathways. Women, compared to men, are more likely to be diagnosed with obesity and seek and obtain all types of obesity treatment including behavioral, pharmacological, and bariatric surgery. Men tend to have greater absolute weight loss, but this difference is attenuated once accounting for baseline weight. Summary Obesity is a multifactorial condition with complex interactions among sex/gender, sociocultural, environmental, and physiological factors. More sex/gender research is needed to investigate mechanisms underlying sex/gender differences in prevalence, comorbidities, and treatment, identify ways to increase men’s interest and participation in obesity treatment, and examine differences in obesity prevalence and treatments for transgender and gender non-conforming individuals.
... This outcome will be assessed with multi-frequency bioelectrical impedance analysis with 8-point tactile electrodes (InBody 720; Biospace, Seoul, Korea) [21]. Bioelectrical impedance will be measured within 2 min, standing on bare foot and grasping the hand electrodes with arms in the vertical position as is described in an article of our team [22]. ...
Article
Full-text available
Background: Promotion of a healthy lifestyle is considered a good strategy for dealing with chronic diseases. Mobile-based lifestyle interventions have shown beneficial effects in the control and treatment of chronic diseases such as diabetes, obesity and metabolic syndrome. Current clinical trials for mobile-based lifestyle intervention were mainly conducted among non-elderly populations, thus well-designed trials performed among the elderly who are more susceptible to chronic diseases are needed. The study aims to assess the effect of the mobile-based lifestyle intervention on the improvement of body weight, glucose and lipid metabolism among overweight and obese elderly adults in China. Materials and Methods: Participants aged 60-80 years who are overweight or obese will be randomly assigned to receive mobile-based nutrition and exercise intervention, mobile-based exercise intervention and no intervention for 3 months. Before the intervention, participants will receive the training of the mobile application and sports bracelet. The primary outcome will be the between-group (three groups) difference in body mass index at the end of intervention. The secondary outcomes will include body composition, parameters of glucose and lipid metabolism, blood pressure, dietary data and physical activity data. All these outcomes will be assessed at baseline, day 45 and day 90. Ethics and dissemination: The trial has been approved by the Ethics Committee of Peking University Health Science Center (IRB00001052-18039).
... In accordance with the current study, dinner meal replacement with reduced calories equaling 388 kcal for twelve weeks was reported improve the body composition and metabolic parameters in obese patients. (18) It is interesting to note that a relatively short period of intervention for two weeks with portion controlled meal replacement with cereal also resulted in effective weight loss in obese/overweight females (BMI 29.2 ± 2.4 kg/m 2 ). (19) In addition, treatment option with meal replacement in combination with drug therapy (20) can also be considered for longer-term treatment or in the management of morbid obese patients. ...
Article
Full-text available
Metabolic syndrome is well known to increase the risk of cardiovascular diseases. We have reported that phytochemicals rich black rice with giant embryo reduced fat mass and metabolic disorders in an animal model. However, such effects have not been evaluated in humans. Subjects with metabolic syndrome (n = 49, 38 male, 44.3 ± 6.1 years) were randomly assigned into two groups and ingested roasted black-rice with giant embryo (BR, n = 26, 20 male) or white-rice (WR, n = 23, 18 male) powders mixed with water for breakfast for three months. Subjects were evaluated for various metabolic parameters before and after intervention. All parameters were not significantly different between groups before starting the intervention. After three months of consumption of either BR or WR, changes of body weight in BR vs WR groups (–1.54 kg vs –1.29 kg, p = 0.649) as well as waist circumference (–1.63 cm vs –1.02 cm, p = 0.365) were not significantly different between groups. However, changes in highly-sensitive C reactive proteins in BR vs WR groups (–0.110 mg/dl vs 0.017 mg/dl, p = 0.003) had significant differences. Three months of meal replacement with BR had a significant reduction of highly-sensitive C reactive protein compared to those with WR in adults with metabolic syndrome.
... This data showed us that, despite the caloric restriction, the maintenance of an adequate protein intake could help prevent an excess of muscle loss, which could enhance the sarcopenia situation detected in most patients. The predominant decrease in fat mass with maintenance or the relative increase in the percentage of muscle mass and fat mass has been observed in different studies with hyperproteic diets replacing one or more meals [10,27] or in studies replacing a meal versus a usual diet [28]. ...
Article
Full-text available
Background and aims: Meal replacement diets consist of replacing one or more meals with an artificial nutritional supplement. The objective of this study was to compare the effect of one against two meal replacement strategies on body composition and cardiovascular risk parameters in patients with obesity. Methods: A randomized clinical trial was designed with a modified hypocaloric diet with an artificial nutritional preparation replacing one or two meals for three months in patients with obesity and osteoarthritis pending orthopedic surgery. An anthropometric evaluation and a measurement of the body composition were done with bioelectrical impedance measurement at the beginning and at three months. Results: A total of 112 patients were recruited. Fifty-two patients (46.4%) were randomized to one replacement and 60 patients (53.6%) to two meal replacements. Eighty-one patients (72.3%) were women, and the average age was 61 (11.03) years. The percentage of weight loss at three months was 8.27 (4.79)% (one meal replacement: 7.98 (5.97)%; two meal replacements: 8.50 (3.48)%; p = 0.56). A decrease in fat mass measured by the fat mass index (FMI) was detected (one meal replacement: -2.15 (1.45) kg/m2 vs. two meal replacements: -2.78 (2.55) kg/m2; p > 0.05), and a relative increase in fat-free mass was observed (one meal replacement: +3.57 (4.61)% vs. two meal replacements: +2.14 (4.45)%; p > 0.05). A decrease in HOMA-IR, systolic blood pressure (SBP), and total cholesterol was observed in both groups without differences between them. Conclusions: The substitution strategies of one or two meal replacements were effective in weight loss and fat mass decrease without differences between the two groups. An improvement in lipid parameters, glycemic control, and systolic blood pressure was observed without differences between strategies.
... The analysis of insulin signaling pathways in human viscera and SAT shows that VAT expresses higher levels of proteins specific to the insulin signaling pathway and a higher sensitivity [28]. In summary, VAT is more sensitive to weight loss, more metabolically active, more lipolytic and produces more adipokines than SAT [29,30]. ...
Article
Full-text available
Obesity is considered to significantly increase the risk of the development of a vast range of metabolic diseases. However, adipogenesis is a complex physiological process, necessary to sequester lipids effectively to avoid lipotoxicity in other tissues, like the liver, heart, muscle, essential for maintaining metabolic homeostasis and has a crucial role as a component of the innate immune system, far beyond than only being an inert mass of energy storage. In pathophysiological conditions, adipogenesis promotes a pro-inflammatory state, angiogenesis and the release of adipokines, which become dangerous to health. It results in a hypoxic state, causing oxidative stress and the synthesis and release of harmful free fatty acids. In this review, we try to explain the mechanisms occurring at the breaking point, at which adipogenesis leads to an uncontrolled lipotoxicity. This review highlights the types of adipose tissue and their functions, their way of storing lipids until a critical point, which is associated with hypoxia, inflammation, insulin resistance as well as lipodystrophy and adipogenesis modulation by Krüppel-like factors and miRNAs.
Article
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Dyslipidemia is a precursor to a myriad of cardiovascular diseases in the modern world. Age, gender, and diet are known modifiers of lipid levels, however they are not frequently investigated in subset analyses. Food and nutrient intakes from National Health and Nutrition Examination Study 2001–2013 were used to assess the correlation between lipid levels (high-density lipoprotein (HDL) cholesterol, triglycerides (TG), low-density lipoprotein (LDL) cholesterol, and total cholesterol (TC):HDL cholesterol ratio) and nutritional intake using linear regression. Associations were initially stratified by gender and significant gender correlations were further stratified by age. Analyses were performed at both the dietary pattern and nutrient level. Dietary pattern and fat intake correlations agreed with the literature in direction and did not demonstrate gender or age effects; however, we observed gender and age interactions among other dietary patterns and individual nutrients. These effects were independent of ethnicity, caloric intake, socioeconomic status, and physical activity. Elevated HDL cholesterol levels correlated with increasing vitamin and mineral intake in females of child bearing age but not males or older females (≥65 years). Moreover, increases in magnesium and retinol intake correlated with HDL cholesterol improvement only in females (all age groups) and males (35–64), respectively. Finally, a large amount of gender-specific variation was associated with TG levels. Females demonstrated positive associations with sugar and carbohydrate while males show inverse associations with polyunsaturated fatty acid (PUFA) intake. The female-specific association increased with the ratio of carbohydrate: saturated fatty acid (SFA) intake, suggesting that gender specific dietary habits may underlie the observed TG-nutrient correlations. Our study provides evidence that a subset of previously established nutrient-lipid associations may be gender or age-specific. Such discoveries provide potential new avenues for further research into personalized nutritional approaches to treat dyslipidemia.
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This study aimed to investigate the prevalence of overweight and obesity and its relationship with cardiovascular risk diseases among different sex and age groups in an urban Chinese adult population. A retrospective analysis was performed on 384,061 Chinese adults aged 20 years and older in Nanjing. The age-standardized prevalence of overweight and obesity was 42.8% and 13.2% in men and 23.9% and 6.6% in women. A gradually increasing trend was observed in the prevalence of overweight and obesity from 2008 to 2016, especially in individuals aged 20~39 years. Overweight and obesity were significantly associated with increased risks of dyslipidemia, diabetes mellitus, hypertension, and hyperuricemia. Age weakened such relationship for both genders, which spiked in individuals aged 20~39 years. For men and women aged 20~39 years, the OR (95% CI) of obesity reached 4.23 (4.01–4.47) and 5.29 (4.63–6.04) for dyslipidemia, 3.70 (2.97–4.60) and 6.38 (3.86–10.55) for diabetes mellitus, 6.19 (5.76–6.64) and 9.36 (7.86–11.13) for hypertension, and 3.66 (3.45–3.88) and 6.65 (5.70–7.74) for hyperuricemia, respectively. The increasing trend in the epidemic of overweight and obesity is a risk factor for cardiovascular risk diseases in Chinese adults, especially in individuals aged 20~39 years.
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This review will examine topical issues in weight loss and weight maintenance in people with and without diabetes. A high protein, low glycemic index diet would appear to be best for 12-mo weight maintenance in people without type 2 diabetes. This dietary pattern is currently being explored in a large prevention of diabetes intervention. Intermittent energy restriction is useful but no better than daily energy restriction but there needs to be larger and longer term trials performed. There appears to be no evidence that intermittent fasting or intermittent severe energy restriction has a metabolic benefit beyond the weight loss produced and does not spare lean mass compared with daily energy restriction. Meal replacements are useful and can produce weight loss similar to or better than food restriction alone. Very low calorie diets can produce weight loss of 11-16 kg at 12 mo with persistent weight loss of 1-2 kg at 4-6 years with a very wide variation in long term results. Long term medication or meal replacement support can produce more sustained weight loss. In type 2 diabetes very low carbohydrate diets are strongly recommended by some groups but the long term evidence is very limited and no published trial is longer than 12 mo. Although obesity is strongly genetically based the microbiome may play a small role but human evidence is currently very limited.
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Objectives The purpose of this study is to assess the prevalence of overweight/obesity, abdominal obesity and obesity-related risk factors in southern China. Methods A cross-sectional survey of 15,364 participants aged 15 years and older was conducted from November 2013 to August 2014 in Jiangxi Province, China, using questionnaire forms and physical measurements. The physical measurements included body height, weight, waist circumference (WC), body fat percentage (BFP) and visceral adipose index (VAI). Multivariate logistic regression analysis was performed to evaluate the risk factors for overweight/obesity and abdominal obesity. Results The prevalence of overweight was 25.8% (25.9% in males and 25.7% in females), while that of obesity was 7.9% (8.4% in males and 7.6% in females). The prevalence of abdominal obesity was 10.2% (8.6% in males and 11.3% in females). The prevalence of overweight/obesity was 37.1% in urban residents and 30.2% in rural residents, and this difference was significant (P < 0.001). Urban residents had a significantly higher prevalence of abdominal obesity than rural residents (11.6% vs 8.7%, P < 0.001). Among the participants with an underweight/normal body mass index (BMI), 1.3% still had abdominal obesity, 16.1% had a high BFP and 1.0% had a high VAI. Moreover, among obese participants, 9.7% had a low /normal WC, 0.8% had a normal BFP and 15.9% had a normal VAI. Meanwhile, the partial correlation analysis indicated that the correlation coefficients between VAI and BMI, VAI and WC, and BMI and WC were 0.700, 0.666, and 0.721, respectively. A multivariate logistic regression analysis indicated that being female and having a high BFP and a high VAI were significantly associated with an increased risk of overweight/obesity and abdominal obesity. In addition, living in an urban area and older age correlated with overweight/obesity. Conclusion This study revealed that obesity and abdominal obesity, which differed by gender and age, are epidemic in southern China. Moreover, there was a very high, significant, positive correlation between WC, BMI and VAI. However, further studies are needed to explore which indicator of body fat could be used as the best marker to indirectly reflect cardiometabolic risk.
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