Children With High Genetic Susceptibility Benefit More From Short-term Physical Activity Interventions
Phase: NA
Conditions: Physical Inactivity, Obesity, Childhood
Sponsor: China Medical University, China

Short-term effects of gene-physical activity interaction on obesity and
related metabolic indicators in children: a randomized controlled trial
1-February-2022

Research design
Ethics statement
This study was approved by the ethics committee of the China Medical
University (Ethics Approval No. [2020] 075). Every participant signed an informed
consent form. All methods were performed in accordance with the Declaration of
Helsinki and relevant guidelines.
Study Design and Participants
This study is based on a randomized controlled trial of a 4-week MVPA
intervention conducted among children aged 10-12 years with overweight or obesity
problems. We followed the established criteria of overweight and obesity in Chinese
children and adolescents proposed by the Working Group on Obesity in China
(WGOC) 14. Children with physical activity-limiting diseases and a lack of baseline or
post-intervention physical examination data were excluded. Then a total of 139
children with overweight or obesity were randomly allocated (2:1) to MVPA
intervention group (n=93) or a control group (n=46) after completing baseline
measurement in June 2022.
Exercise program
The 4-week MVPA intervention engaged in supervised aerobic exercise,
resistance training and interesting sport for 60 minutes, 3 days/week (Monday,
Wednesday and Friday). Participants completed four sessions including preparation
module (15 min), training module (30 min), and relaxation module (15 min). Another
aerobic module (15 min) was added in each Friday. Children in intervention group

were also encouraged to take exercise for 10 minutes together with their parents at
home two hours later after dinner including ten minutes’ walking (at least 1000 meters)
or at least 200 times rope skipping. Parents were invited to come to school at the
beginning of the intervention program to accept project training. In order to safeguard
the rights of the control groups, the same physical activity intervention was given to
the control group at the end of the research.
Strength
was
measured
by Tri-axial
body
motion
recorder
(ActiGraph,
Wgt3x-BT, USA) to maintain relative intensity. Children were required to wear
ActiGraph by a professional investigator on the left wrist, loosely tightened, and
required to wear it at all times except for the shower during the intervention for 7 days.
Valid data for each day were defined as: recording a wearing time of more than 8
hours, excluding sleep time, and recording counts per minute (CPM) of no more than
20 minutes of continuous 0 CPM. After the test, the raw data were analyzed using
Actilife V6.13.4 software, and after extracting and calculating the daily average MET
levels, CPMs per minute greater than 20,000 were excluded from the data, and the
average daily total MVPA time was accumulated and calculated based on different
physical activity intensity cut-offs (total MVPA time = total VPA time + total MPA
time). The vector data recorded by the ActiGraph were transformed into different
physical activity intensity times with cut-off points of 100 CBM for light physical
activity, 2296 CPM for moderate intensity, and 4012 CPM for high-intensity activity,
based on Evenson et al.’s 15 study about the proposed cut-off points.
At baseline, Physical Activity Questionnaire for Children (PAQ-C) was answered

by each participant. The PAQ-C reflects the overall physical activity level of children
and adolescents in the past 7 days and the MVPA levels16. The scale consists of 10
items, 9 of which are structured to investigate MVPA levels in children and
adolescents. A 1-5 scale was used to obtain a total score by summing the scores of the
individual items. The PAQ-C scale scores < 2.33 defined as low physical activity
levels, 2.33-3.66 as moderate physical activity levels, and >3.66 as high physical
activity levels17.
Obesity outcomes
Height was measured to the nearest 0.1cm using Height Measuring Ruler
(Selcom, Germany). Measurement was taken on the vertical distance from the apex of
the skull to the ground in the standing position. Body weight and fat mass was
measured to the nearest 0.1kg using Body Composition Analyzer (DC-13, Parade,
Japan). Subjects were measured barefoot, wearing light. Waist circumference was
measured to the nearest 0.1cm with circumference measuring tape. Measurement was
taken between the lowest rib and the iliac crest with the abdomen relaxed at the end of
a gentle expiration. Hip circumference was measured to the nearest 0.1cm with
circumference measuring tape. Measurement was taken on the most prominent part of
the buttocks and wraps it horizontally around the buttocks.
Preperitoneal fat and abdominal subcutaneous fat thickness were measured by
ultrasonography. The thickness and area of preperitoneal fat and abdominal
subcutaneous fat were measured with a linear array probe L12-5 (38 mm, 5-12 HZ)
according to the method of Suzuki18. The linear array probe was placed vertically in

the median position of the upper abdomen, and the probe was moved 1-5cm above the
umbilicus to obtain an image that included the maximum preperitoneal fat thickness.
The probe was then placed vertically in the abdominal white line to obtain a
transverse image. The preperitoneal fat thickness is the maximum height in the
triangle. Abdominal subcutaneous fat thickness is the height above the triangle.
Blood pressure was measured by medical sphygmomanometer for 3 times. The
subjects were at rest for more than 15 minutes. The blood pressure was measured on
the right arm in a seated position with the elbow, sphygmomanometer and heart at the
same
level. All
subjects
were
fasted
for
more
than
12
hours.
3
ml
of
non-anticoagulated blood and 2 ml of EDTA anticoagulated blood were collected. The
same medical examiner examined before and after the intervention to ensure data
comparability.
Genotyping
The whole genomic DNA of blood mononuclear cells was performed using a
fully automated nucleic acid extractor (QIAGEN, QIAcube, Germany), the DNA
extraction reagents were used in a magnetic bead method DNA extraction kit
(QIAGEN, 51331, Germany), and the detailed steps were performed according to the
reagent and instrument instructions. 14 SNP loci associated with obesity and BMI
were reliably validated based on GWAS studies in large Asian populations19, 20.
Genome-wide typing of the 14 targeted SNP loci was performed using the
MassARRAY® system (AgenaBioscience Inc., USA) based on MALDI-TOF mass
spectrometry for typing each locus. Due to the differences in the contribution effect of

each allele to obesity or BMI, a weighted genetic risk score (WGRS) was used to
assess the genetic susceptibility to obesity in this study, calculated as follows:
WGRS = (β1×SNP1 + β2×SNP2 + ... + β14×SNP14) × (total SNP allele points / sum of
β-effect coefficients)
where βn coefficient is the contribution of each effect locus to BMI and obesity found
in the GWAS study.
Covariates
Children’s gender, age, date of birth, ethnicity and household income were
collected in questionnaires answered by children at baseline. Non-quantitative Food
Frequency Questionnaires (FFQ) was used to collect information on the dietary
frequency, including 24 types of daily dietary foods such as cereals, flour, meat from
livestock, seafood, vegetables, milk, snacks, and beverages. Dietary pattern factor
score was calculated for each participant and included as covariate21.
Statistical Analyses
Data from the questionnaires and physical examination data was entered using
EPIDATA 3.0 software, with trained entry clerks double-entering independently. All
analysis was processed using SPSS 20.0 software, and a two-sided P value of less
than 0.05 indicated a statistical difference with a test level of α=0.05.
Baseline participant characteristic differences between intervention groups were
evaluated by independent t-tests for continuous variables and chi-squared (χ2) tests for
categorical variables. The three possible genotypic frequencies for each gene studied
were evaluated by Chi-Squared tests and found to be in Hardy–Weinberg equilibrium.

Associations between WGRS and obesity outcomes were analyzed using generalized
linear models. Generalized linear models (GLM) were established to analyze two
main effects of interest (gene, MVPA) and one interaction (gene MVPA) on each
obesity outcome. Change in obesity outcome served as dependent variables.
Covariates included age, sex, ethnicity, and family income status. Estimated marginal
means of gene MVPA interaction, adjusted by the covariates, are presented in
figures.