Hanh et al. BMC Pediatrics(2024) 24:594
https://doi.org/10.1186/s12887-024-05073-7

BMC Pediatrics

RESEARCH

Open Access

Association between rs4994 variant in β3Adrenergic receptor and obesity in Vietnamesepreschool-age children, independentof eating behaviors
Nguyen Thi Hong Hanh1, Do Thi Nhu Trang1, Nguyen Thi Trung Thu1 and Le Thi Tuyet1*
Abstract
Background The Arg64 allele of the rs4994 (Trp64Arg) variant in the β3-adrenergic receptor (ADRB3) gene is involvedin the control of energy balance by altering lipolysis and thermogenesis in adipocytes, ultimately contributing to thedevelopment of obesity. The objective of our study was to investigate the association between the rs4994 variant of
the ADRB3 gene and obesity in Hanoi preschool-age children, adjusting for their eating behaviors.
Methods A cross-sectional study was performed involving 708 children with normal weight and 304 children
with obesity aged 3–5 years from 36 kindergartens in Hanoi, Vietnam. Cheek mucosa cell samples were used for
DNA extraction, and genotyping at the ADRB3-rs4994 locus was performed using the polymerase chain reaction–
restriction fragment length polymorphism method (PCR–RFLP). Eating behaviors were assessed using the Children’s
Eating Behaviour Questionnaire (CEBQ). Binary logistic regression analysis was employed to examine the association
between the rs4994 variant and obesity, adjusting for confounding factors such as age, sex, residence, birth weight,
and eating behaviors.
Results The frequency of the C allele in the group with obesity was 16.4%, which was higher than in the control
group (11.7%, P = 0.003). Children with the CC genotype exhibited significantly greater weight and weight-for-age
Z-score compared to those with the TT and TC genotypes (P = 0.004 and 0.03, respectively). Following univariate andmultivariate analyses adjusted for age, sex, residence, birth weight, and eating behaviors, a significant association
between the rs4994 variant and obesity was observed (P < 0.05).
Conclusions This study indicated that the ADRB3-rs4994 variant can be considered as an independent risk factor forobesity in Vietnamese preschool children.
Keywords ADRB3, Obesity, Genetic variance, rs4994, Vietnamese children*Correspondence:
Le Thi Tuyet
tuyetlt@hnue.edu.vn
1
Hanoi National University of Education, 136 Xuan Thuy Street, Hanoi,
Vietnam© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Hanh et al. BMC Pediatrics(2024) 24:594

Background
The prevalence of childhood obesity has remarkably
increased worldwide in recent decades. Within 40 years(1975–2016), the number of school-age children andadolescents with obesity rose more than 10-fold, from11 million to 124 million [1]. Obesity had been considered a huge problem in high-income countries before,
but now it is also a serious problem in low-income countries, especially in urban areas [2]. The childhood obesity
rate in developing countries has increased by more than
30% compared to developed countries [3].
According to data from World Health Organization(WHO), childhood obesity has become globally one of
the biggest health threats of the 21st century due to its
serious consequences [3]. Childhood obesity adversely
affects not only the physical health but also the mental
development of children [4]. Obesity is a major factor
that contributes to metabolic syndrome, hypertension,
type II diabetes mellitus, dyslipidemia, and cardiovascular diseases, and makes children feel an inferiority complex and more likely to engage in high-risk behaviors
such as smoking or drinking alcohol [5].
The incidence of overweight and obesity is on therise among children of all age groups in Vietnam [6, 7].
A cross-sectional study conducted in 2018 showed that
overweight and obesity were emerging challenges amongpreschool Kinh ethnic children, with 10.3% of boys and5.9% of girls experiencing overweight or obesity [6]. In
2020, the prevalence of overweight-for-height among
infants and children under the age of 5 years was 7.4%.
Among children and adolescents aged 5–19 years, theprevalence of overweight was 19.0% and the prevalenceof obesity was 8.1% [7].
Obesity is a multifactorial syndrome influenced by both
genetic and environmental factors. To create a long-termand effective way to prevent this morbidity, it is essential
to have a comprehensive understanding of factors that
contribute to childhood obesity [8].
Obesity is the result of an imbalance between foodintake and energy consumption, which is closely related
to lifestyle and dietary intake preferences [9]. One of
the factors that are closely related to childhood obesity
is eating behavior. Steinsbekk et al. (2017) also showedthat fat mass and muscle mass were strongly associated with specific eating behaviors [10]. A study was
conducted on 2,049 preschool children aged 3–6 years
in Taizhou, China, in 2022, utilizing the Children’s Eating Behavior Questionnaire. The findings indicate thatwithin the “Food Avoidant” subscales, satiety responsiveness (P < 0.001) and slowness in eating (P = 0.001) scores
were inversely correlated with body mass index Z-scoresamong preschool children of both genders. In the “FoodApproach” subscales, the enjoyment of food score exhibited a positive association with body mass index Z-score

Page 2 of 9

in both boys (P = 0.007) and girls (P = 0.035). Additionally, the association between food responsiveness scoresand body mass index Z-score was observed exclusively
in girls (P = 0.001) [11]. Besides that, many other factors
have been demonstrated that increase the risk of childhood obesity such as socioeconomic status [12], physiological, metabolic, and genetic factors [13, 14]. Our
study in 2017 suggested a significant association between
delivery method, birth weight, night sleep duration, andBDNF Val66Met variant, with obesity in Vietnamese primary school children [15].
In recent years, many studies have focused on theβ2 adrenoreceptor and β3 adrenoreceptor genes of theadrenergic receptor system, which play an importantrole in stimulating thermogenesis and lipid mobilization in adipose tissues. The β3-adrenergic receptor isexpressed in visceral adipose tissue and plays a pivotal
role in the regulation of human brown/beige adipocyte
lipolysis and thermogenesis [16]. In 1997, the ADRB3rs4994 variant, characterized by a T to C substitution
resulting in the replacement of tryptophan by arginine atposition 64 (Trp64Arg), was independently reported bythree different groups [17]. As a crucial component for
regulating energy balance in mammals, the essential roleof ADRB3 gene is due to its effects on lipid degradation,
fatty acid transport, thermogenesis [18, 19]… Binding to
β3-adrenergic agonists leads to the activation of ADRB3,
which then activates adenylyl cyclase. Activated adenylyl
cyclase initiates a signal pathway through intracellularsignaling cascades including cyclic AMP, and kinase proteins. ultimately resulting in heat production in brown
and white adipocytes [20, 21]. According to the researchby Krief et al. (1993), ADRB3 gene also participates in theregulation of lipid metabolism including the absorptionof fat during digestion, storage, and mobilization of lipids
from adipocytes [18]. Thus, dysfunction of ADRB3 can
impair the processes of lipolysis and energy expenditure,
which may contribute to the development of obesity andmetabolic disorders through the excessive accumulationof fat in adipose tissue [22].
According to some previous studies, the rs4994 variantof the ADRB3 gene is one of the candidates for obesity. A
meta-analysis from 16 different studies indicated that thisvariant might considerably contribute to both obesity inchildren and adults, particularly in populations belonging to the East Asia regions [23]. Up to now, there has
been a paucity of published reports about the association
between the ADRB3-rs4994 variant and childhood obesity in Vietnamese populations.
Therefore, to prove whether the above results are correct for the Vietnamese population or not, the study
was conducted to determine the association between
the rs4494 variant of the ADRB3 gene and obesity in
3-5-year-old Vietnamese children.

Hanh et al. BMC Pediatrics(2024) 24:594

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We carried out this study to inquire into the association between the rs4994 variant belonging to the ADRB3
gene and childhood obesity, adjusted for children’s eating
behavior.

Results
Table 1 showed the values for anthropometric measurements, genotypes and allele frequencies of ADRB3rs4994 in the non-obese and obese groups. There was no
significant difference in gender, age, or weight between
the two groups (P > 0.05). Meanwhile, height, BMI,
height-for-age Z-score, weight-for-age Z-score, weightfor-height Z-score, and BMI-for-age Z-score of cases
were significantly higher than those of controls (P < 0.05).
Results from Table 1 indicated that the genotype andallele frequencies were statistically different between thetwo groups (P = 0.016 and P = 0.003, respectively). For theentire study sample, the TT genotype had the highest frequency (78.4% in controls, 71.5% in cases). The frequencyof CC genotype in the group with obesity was twice asmuch as the group with normal weight (4.2% and 2.0%,
P = 0.016). Although both groups were in Hardy-Weinberg equilibrium (P = 0.134 for the control group and
P = 0.059 for the case group, P value acquired from Fisher’s exact test), there was a significant difference in allele
frequencies between two groups (P = 0.003). In groupwith obesity, the frequency of allele C was 16.4%, which
was higher compared with 11.7% in group with normal
weight.
Table 2 showed the characteristics of obesityrelated traits in total, case, and control groups. In theoverall sample, distinctions in weight and weight-forage Z-score were observed based on the genotype at the
ADRB3-rs4994 variant. Specifically, children with the
CC genotype exhibited significantly greater weight andweight-for-age Z-score compared to those with the TT
and TC genotypes (P < 0.05). When examining individually in the case group and the control group, among thesecharacteristics, only height in the cases was the index
that differed between the three genotypes. Children with
the CC genotype had the highest height, while the TT
genotype had the lowest height (P = 0.02). In the control
group, no differences were observed in any other anthropometric indices, including weight, BMI, weight-for-age
Z-score, height-for-age Z-score, BMI-for-age Z-score,
and weight-for-height Z-score (P > 0.05).
The results of testing the relationship between the
ADRB3-rs4994 variant and obesity in different genetic
models are shown in Table 3. Association of rs4994 withobesity was found in the dominant model, co-dominantmodel and recessive model. Using the TT genotype as
the reference, statistical analysis indicated that the TC
and CC genotypes increased the risk of obesity by 1.36
times (P = 0.05) in the dominant model, the CC genotype
increased the risk of obesity by 2.35 times (P = 0.024) inthe co-dominant model. In the recessive model, the CC
genotype was associated with a 2.2-fold higher risk ofobesity than the TC and TT genotypes (P = 0.039).
The most suitable inheritance model for the ADRB3rs4994 variant with obesity was evaluated by AIC values
for all possible genetic models, and results showed thatthe co-dominant model was the best-fit model with theTable 1 Characteristics of the study subjects
Parameter
Male (n, %)Age (months)
Weight (kg)Height (cm)BMI (kg/m2)
Height-for-age Z-score
Weight-for-age Z-score
Weight-for-height Z-score
BMI-for-age Z-score
Genotype frequency+ TT (n, %)+ TC (n, %)+ CC (n, %)Allele frequency+ T (n, %)+ C (n, %)Controls(n = 708)
532 (75.1)
59.5 (51.6 – 64.6)
16.4 (14.6 – 18.3)
106.7 ± 7.2
15.4 (14.6 – 16.5)-0.42 (-1.06 – 0.26)-0.23 (-0.84 – 0.56)
0 (-0.7 – 0.82)-0.06 (-0.68 – 0.63)

Cases(n = 354)
266 (75.1)
59.8 (54.0 – 64.8)
24.2 (22.2 – 26.9)
110.2 ± 7.9
21.4 (20.9 – 22.6)
0.48 (-0.56 – 0.91)
2.71 (2.19 – 3.28)
3.60 (3.3 – 4.16)
3.35 (2.77 – 3.91)

P

555 (78.4)
139 (19.6)
14 (2.0)253 (71.5)
86 (24.3)
15 (4.2)

0.0161249 (88.2)
167 (11.7)

592 (83.6)
116 (16.4)

0.003

BMI: body mass indexaData are mean ± SD. bData are median (interquartile range)

P obtained by Student T test or Mann-Whitney U test or Chi-square test
Bold values indicate significant difference between cases and controls

10.408b
0.518b
< 0.0001a< 0.0001b< 0.0001b< 0.0001b< 0.0001b< 0.0001b

Hanh et al. BMC Pediatrics(2024) 24:594

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Table 2 The characteristics of obesity-related traits in total, controls and cases according to the ADRB3-rs4994 genotypes inVietnamese children
Total (n = 1062)
Characteristics
Birth weight (kg)Age (months)
Height (cm)Weight (kg)BMI (kg/m2)
Weight-for-age Z-score
Height-for-age Z-score
BMI-for-age Z-score
Weight-for-height Z-score
Controls (n = 708)
Characteristics
Birth weight (kg)Age (months)
Height (cm)Weight (kg)BMI (kg/m2)
Weight-for-age Z-score
Height-for-age Z-score
BMI-for-age Z-score
Weight-for-height Z-score
Cases (n = 354)
Characteristics
Birth weight (kg)Age (months)
Height (cm)Weight (kg)BMI (kg/m2)
Weight-for-age Z-score
Height-for-age Z-score
BMI-for-age Z-score
Weight-for-height Z-score

TT(n = 808)
3.3 ± 0.4
57.3 ± 10.5
107.6 ± 7.5
20.2 ± 5.0
16.1 (14.8 – 20.0)
0.35 (-0.62–2.05)-0.19 (-0.86 – 0.58)
1.11 ± 1.87
1.22 ± 1.93

TC(n = 225)
3.3 ± 0.4
57.5 ± 9.8
108.3 ± 7.9
20.9 ± 5.3
16.5 (14.9 – 20.3)
0.65 (-0.41–2.41)
0.02 (-0.73 – 0.68)
1.33 ± 1.94
1.51 ± 1.94

CC(n = 29)
3.3 ± 0.3
58.1 ± 9.3
110.1 ± 8.9
22.9 ± 6.5
19.3 (15.6 – 21.1)
2.03 (-0.16 – 3.25)-0.01 (-0.97 – 1.51)
1.74 ± 1.85
1.38 ± 1.99

PTT(n = 555)
3.2 ± 0.4
57.3 ± 10.6
106.7 ± 7.3
17.6 ± 2.9
15.2 (14.4 – 16.2)-0.22 (-0.89 – 0.43)-0.42 (-1.01 – 0.27)
0.03 ± 0.99
0.10 ± 1.08

TC(n = 139)
3.2 ± 0.44
56.6 ± 10.1
106.8 ± 6.9
17.8 ± 3.1
15.2 (14.4 – 16.2)-0.28 (-0.73 – 0.44)-0.36 (-0.90 – 0.27)
0.07 ± 1.12
0.37 ± 1.20

CC(n = 14)3.3 ± 0.38
54.5 ± 8.9
105.5 ± 7.6
17.8 ± 4.5
15.6 (14.4 – 16.2)-0.16 (-1.16 – 0.16)-0.61 (-1.07 – -0.03)
0.18 ± 1.3
0.35 ± 1.25

PTT(n = 253)
3.4 ± 0.4
57.2 ± 10.2
109.7 ± 7.7
25.8 ± 3.9
21.0 (20.2 – 22.0)
2.55 (1.89 – 3.26)
0.47 (-0.35 – 1.08)
3.48 ± 0.89
3.73 ± 0.57

TC
(n = 86)
3.3 ± 0.4
59.0 ± 9.3
110.8 ± 8.7
26.0 ± 3.9
21.0 (19.5 – 22.1)
2.44 (1.90 – 2.98)
0.45 (-0.22 – 1.04)
3.38 ± 1.0
3.84 ± 0.64

CC(n = 15)3.4 ± 0.36
61.4 ± 8.8
114.4 ± 8.1
27.8 ± 3.9
21.0 (19.8 – 22.6)
3.00 (2.12 – 3.46)
1.20 (0.66 – 1.92)
3.51 ± 0.72
3.94 ± 0.51

P

0.56a
0.89a
0.13a
0.004a
0.06b
0.03b
0.09b
0.07a
0.36a

0.84a
0.16a
0.64a
0.77a
0.87b
0.92b
0.60b
0.88a
0.87a

0.91a
0.08a
0.02a
0.07a
0.54b
0.36b
0.09b
0.40a
0.53a

a

Variables that are according to the standard distribution are represented by the mean ± standard deviation, P value obtained from the One-way ANOVA testb

Variables that are not according to standard distribution are represented by median and 25th − 75th percentile, P value obtained from the Krukall-Wallis test

lowest AIC values (AIC = 1349.90). For further multivariate analyses, we also used this model for analyzing theinfluence of SNP ADRB3-rs4994 on obesity in preschool
children in Hanoi.
The combined effect of the ADRB3-rs4994 variant andeating behaviors (FR, EOE, EF, DD, SR, SE, EUE, FF) on
the risk of obesity was examined by logistic regression
because these eating behaviors were correlated with eachother as well as with weight and BMI (P < 0.01) (Table 1S).
As shown in Table 4, after adjustment for age, gender, living area, and eating behaviors, the odd ratios for CC and
TC genotypes vs. TT reference genotype were still significantly associated with obesity.

Discussion
Results from our case-control study in Hanoi preschool
children indicated that the ADRB3-rs4994 variant wasstrongly associated with obesity as an independent risk
factor.
The replacement of tryptophan by arginine at position 64 in the ADRB3 protein affects the function of the
β3-adrenergic receptor. This variant leads to reduced
receptor sensitivity and impaired ability to stimulate
lipolysis, which is the breakdown of fat in adipose tissue.
As a result, individuals with the Trp64Arg variant have adecreased capacity for energy expenditure and fat burning. This can contribute to an increased risk of developing obesity and associated metabolic disorders [17, 24].

Hanh et al. BMC Pediatrics(2024) 24:594

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Table 3 Analysis of the best-fit model for the ADRB3-rs4994
variant for obesity

Table 4 Association of the ADRB3-rs4994 variant with obesity inmodels considering children’s eating behaviors

ModelsModelsControls Cases
n (%)
n (%)
Co-dominantTT
555 (78.4) 253 (71.5)
TC
139 (19.6) 86 (24.3)
CC
14 (2.0)
15 (4.2)
Dominant
TT
555 (78.4) 253 (71.5)
TC + CC
153 (21.6) 101 (28.5)
Recessive
TT + TC
694 (98.0) 339 (95.8)
CC
14 (2.0)
15 (4.2)
Over-dominant
TT + CC
569 (80.4) 268 (75.7)
TC
139 (19.6) 86 (24.3)
Additive for copy number of C
alleleOR (95% CI)

11.36 (1.01–1.85)
2.35 (1.12–4.94)
1
1.45 (1.08–1.94)
1
2.19 (1.05–4.60)

P value AIC

Unadjusted
0.050
0.024

1349.90
Adjusted for age, gender,
residence, birth weight

0.013

0.037

1
1.31 (0.97 − 1 .78) 0.08
1.45 (1.08–1.94) 0.013

1349.961351.731352.911350.24

P value obtained by univariate logistic regression
Bold values indicate a statistically significant
95% CI: 95% Confidence interval, OR: odd ratio, AIC: Akaike’s Information
Criterion, BIC: Bayesian Information Criterion

ADRB3-rs4994 variant was demonstrated to be associated with some health problems such as being overweight, abdominal obesity, HDL-C levels, harder to lose
weight, a reduced basal metabolic rate, and type 2 diabetes in previous studies [17, 25–27]. Adipocytes with the
TC and CC genotypes exhibited a weaker response to β3adrenergic agonists as their cytosolic cAMP and glycerol
were nearly 70% lower compared with that of TT genotype [27, 28]. In addition, the existence of the C allele ingenotypes also leads to a lower rate of enzyme activity forthe lipolysis induced by ADRB3 [29]. Thus, the replacement of T with C in codon 64 of the ADRB3 gene significantly decreased the lipolysis rate in brown adipocytes.
Much research has been carried out to clarify the relationship between ADRB3-rs4994 and obesity in humans,
but the outcomes are still controversial. ADRB3-rs4994
was demonstrated to be associated with obesity in many
populations such as Chinese [30], France [31], andJapan [31]. A study involving 714 preschool children in
Wuhan, China, revealed a significant difference in the
β3-AR gene variant between girls with varying degreesof obesity and normal children. In the homozygous variant, elevated levels of triglycerides and decreased levelsof high-density lipoprotein were observed, and thesedifferences were statistically significant. The study concluded that the distribution of the Trp64Arg variant in
the ADRB3 gene among preschool children is associatedwith simple obesity [32]. A meta-analysis incorporating
data from 16 studies, encompassing 5,147 cases of overweight/obesity and 7,350 non-obese controls, with a substantial proportion (69.9%) of the subjects were sourced

Adjusted for age, gender,
residence, birth weight,
and FR
Adjusted for age, gender,
residence, birth weight, andEOE
Adjusted for age, gender,
residence, birth weight, andDD
Adjusted for age, gender,
residence, birth weight,
and SR
Adjusted for age, gender,
residence, birth weight, andEUE
Adjusted for age, gender,
residence, birth weight,
and FF

Genotype

TT
TC
CC
TT
TC
CC
TT
TC
CC
TT
TC
CC
TT
TC
CC
TT
TC
CC
TT
TC
CC
TT
TC
CC

Obesity
OR (95%CI)
11.36 (1.01 – 1.85)
2.35 (1.12 – 4.94)
1
1.37 (1.01 – 1.86)
2.36 (1.12 – 4.99)
1
1.33 (1.01 – 1.87)
2.71 (1.2 – 6.08)
1
1.37 (1.03 – 1.88)
2.47 (1.16 – 5.24)
1
1.38 (1.01 – 1.88)
2.49 (1.17 – 5.30)
1
1.35 (1.01 – 1.90)
2.98 (1.22 – 7.27)
1
1.37 (1.01 – 1.89)
2.15 (1.01 – 4.95)
1
1.37 (1.01 – 1.86)
2.38 (1.23 – 5.01)

P
value
0.050
0.024
0.048
0.024
0.049
0.016
0.048
0.019
0.045
0.018
0.047
0.017
0.049
0.046
0.048
0.023

P values obtained by multivariate logistic regression
Bold values indicate a statistically significant
95% CI: 95% Confidence interval, OR: odd ratio. FR: food responsiveness, EOE:
emotional overeating, EF: enjoyment of food, DD: desire to drink, SR: satiety
responsiveness, SE: slowness in eating, EUE: emotional undereating, FF: foodfussinessfrom East Asia. In the overall population meta-analysis,
statistically significant associations were identified, indicating an increased risk of childhood and adolescentoverweight/obesity in the allele model (OR = 1.23, 95%
CI = 1.10 – 1.38), heterozygote model (OR = 1.39, 95%
CI = 1.16 – 1.68), and dominant model (OR = 1.31, 95%
CI = 1.12 – 1.54). Further stratified analysis based on
geographical regions revealed that statistical significancewas observed exclusively in the East Asia subgroup forthe allele model, homozygote model, heterozygote model,
and dominant model. In summary, the meta-analysis
indicates a significant association between the ADRB3rs4994 variant and an elevated risk of childhood and adolescent with overweight/obesity, particularly within the
East Asian population [23].
Conversely, some research indicated that there was no
association of the ADRB3-rs4994 variant with obesity.
According to research by Kurokawa et al. on 87 Japanesechildren, BMI and body fat percentage were not statistically different among the TC, CC, and TT genotypes

Hanh et al. BMC Pediatrics(2024) 24:594[33]. Research by Porto et al. (2004) on 934 high school
students (121 normotensive and 54 hypertensive students) showed that there was no association between
increasing BMI and rs4994 variant of the ADRB3 gene.
The frequencies of TT, TC, and CC genotypes were 85%,
14.5%, and 0.5%, respectively [34]. Research by Chou etal., on 559 adolescent volunteers in Taiwan, did not found
any association between obesity and the rs4994 variantof ADRB3 gene in Taiwanese adolescents. The frequencies of TT, TC, and CC genotypes were 72.3%, 26.1%, and
1.6%, respectively. And these genotypes were found to be
in Hardy-Weinberg equilibrium [35].
A study by Yilmaz et al. (2019) conducted on 441 children and adolescents aged 6–18 years in Turkish also
resulted in no association of ADRB3-rs4994 with obesity.
However, the frequency of the rs4994 variant was higher
in obese girls, which can lead to weight gain. The frequencies of the TT, TC, and CC genotypes in the obese
group were 84.8%, 14.4%, and 0.8%, while in the control
group, the frequencies of these genotypes were 89.4%,
10.6%, and 0.0%, respectively (P = 0.247). There was no
significant difference in terms of allele and genotype frequencies between the two study groups. The frequencies of T and C alleles were 92.0% and 8.0% in the obese
group, whereas the frequencies of these alleles were
94.7% and 5.3% (P = 0.127) in the control group [36].
Generally, the inconsistencies among the previous
studies’ results may be due to differences in study populations (sex, age, socio-economic status, etc.), and environmental or lifestyle factors (levels of energy intake andlevel of physical activity). In addition, the allele frequency
at locus rs4994 varies in different ethnic populations.
With a frequency of 0.38 in Eskimos and 0.31 in Indians,
the C allele was very common in both populations [37].
Japanese population had a high C allele frequency of 0.21,
which is twice as common as in white populations [38].
In our study, the C allele accounted for 0.12 in the controls and 0.16 in the cases.
Along with the development of social life, Vietnam
has experienced a sharp rise in the prevalence of overweight and obesity in recent years, particularly in children. This phenomenon may be caused by the gradualreplacement of healthy foods with various high-calorie
but low-nutrient foods and beverages, which leads to a
substantial change in the eating behaviors of Vietnamese children, especially those living in big cities. Whenemotions change, students often have no control over theamount of food they eat. According to Derks et al. (2018),
EOE has a two-way relationship with BMI, both as a predictor and as a result of high BMI [39]. Ashcroft et al.
(2008) also showed that emotional overeating increased
over time from 4 years of age to 10 years of age [40]. In
addition, a study of twins in the UK showed that overeating in response to negative emotions was a learned

Page 6 of 9

behavior rather than a genetic factor [41]. In addition,
food responses (such as demanding food, if allowed,
students can eat a lot, when they are full, they can stilleat…) were also one of the risk factors for increasing BFP.
A longitudinal study of 3,331 children in the Netherlands
showed that, by the age of 4, food response index (FR)and food preference (EF) were higher in children withhigh BMI. When children were 10 years old, there was apositive relationship between food response index (FR),food preference (EF) and mood swings (EOE) with BMI
and body fat mass. Meanwhile, satiety response (SR) was
negatively associated with BMI and body fat mass [39].
Therefore, the association of the ADRB3-rs4994 variant
with childhood obesity was adjusted for children’s eating
behaviors.
Nutrigenomics is a new field of study whose objective is
to clarify the relationship between the genetic factors of
an individual and the corresponding dietary intake, andalso the effect of nutrition on gene expression. Based on
the PubMed database, Pavlidis et al. (2015) conducted ameta-analysis to examine the association of 38 candidate
genes with dietary intake and/or pathologies of nutrientrelated diseases. Although his results showed that there
was no significant association in any of these 38 genes, it
is clear that additional research on nutrigenomics needs
to be carried out because this is a potential tool that provides many benefits for medicine [42].
Since our result indicated that there was a significant
association between the ADRB3-rs4994 variant withobesity regardless of children’s eating behaviors, it may
be useful for doctors, professional nutritionists andother healthcare professionals to categorize children
into groups with different risks of obesity, thus providing
suggestions for their parents about specific diets or exercise routines to prevent obesity. Despite this, our crosssectional study also had certain disadvantages due tonot considering the contribution of lifestyle factors andtheir complicated interaction with other genetic factors
to the development of childhood obesity. Furthermore,
although the CEBQ has been tested in various groups ofchildren, it has not been formally validated in the Vietnamese children population through published studies.
Therefore, further research needs to be conducted in the
future to examine the roles of other factors in the epidemiology of obesity.

Conclusions
In summary, there was an association between the rs4994
variant of the ADRB3 gene and obesity in Vietnamesepreschool children, independently of their eating behaviors. As obesity is preventable, it is very important toconsider applying intervention through genotypic risk
assessment to prevent and counteract obesity and its
complications.

Hanh et al. BMC Pediatrics(2024) 24:594

Page 7 of 9

Methods

Assessment of eating behavior

Study populations

Children’s Eating Behavior Questionnaire (CEBQ) wasused in this study. This is a set of questions evaluated byexperts with high reliability and consistency [46]. Parents
or guardians choose the child’s eating habits according
to how often they occur (never, rarely, sometimes, often,
always). The questions that assess the current 8 factors ofchildren’s eating behavior are divided into 2 groups [47]:(1) Group 1: food approach includes the variables food
responsiveness (FR), emotional overeating (EOE), enjoyment of food (EF) and desire to drink (DD); (2) Group 2:
food avoidance includes the variables satiety responsiveness (SR), slowness in eating (SE), emotional undereating (EUE) and food fussiness (FF). Each item was rated
on a 5-point Likert scale from 1 (never) to 5 (always),with higher scores indicating stronger expressions of
the respective eating behaviors. Subscale scores werecomputed by averaging the scores of items within each
subscale.

The present study enrolled 1,062 3-5-year-old unrelated
Vietnamese children from 36 kindergartens in Hanoi,
Vietnam. Our study excluded all children with psychological issues, atherosclerosis, heart disease, diabetes
mellitus, endocrine disorders, infectious diseases, andHIV/AIDS.
From 2019 to 2020, a case-control study was performedon 354 children with obesity and 708 children with normal weight recruited using WHO (2006) criteria for children under 5 years old. The sample size for a case-control
study involving a qualitative variable was calculated [43]with a control-to-case ratio of 2:1. Based on a previous
study [44], the estimated proportion of exposure amongcases was 0.3, and among controls was 0.2. A standard
normal variate of 1.28 was used to achieve 90% power,
and the standard normal variate corresponding to a
95% confidence level was 1.96. Consequently, the study
required a minimum of 296 children in the case groupand 592 children in the control group.
Children were classified as obese if their weight/heightZ-score > + 3SD for children under 5 years old or BMI
Z-score by age and sex > + 2SD for children upper 5 years
old. To exclude children asymptotically malnourished or
overweight, children were considered normal weight as-1SD ≤ weight/height Z-score ≤ + 1SD for children under
5 years old or -1SD ≤ BMI Z-score ≤ Mean for children
upper 5 years old.
Ethics statement

The parents or guardians of all children were clearly
explained the purposes of the study. They were given andsigned the written informed consent form. The personal
information was kept strictly confidential, and the data
was used only for research purposes. All participants had
the right to quit the study whenever they wanted. Medical Ethics Council of the Institute of Nutrition with Decision No. 343/VDD-QLKH on July 27, 2018.
Measurements

Children’s anthropometric measurements, including
weight, and height were taken twice following the Loman
standard method [45], and the average was used for analysis. Children were dressed simply in lightweight clothes
and without shoes when they were taken their anthropometric indices. Using standardized medical scales, thebody weight was calculated to the nearest 0.1 kg and themeasurement of height was taken down to the nearest
0.1 cm. BMI indices were determined by the weight per
square of height (kg/m2).

DNA extraction

DNA was extracted from the sample of the cheek mucosa
cell using the GeneJET Genomic DNA Purification kit(Thermo, USA) according to the manufacturer’s instructions. All the DNA samples had an A260/A280 ratio
ranging from 1.8 to 2 to ensure the purification of thestudy.
Genotyping method

The genotype of each child at the ADRB3-rs4994 locus
was identified by the polymerase chain reaction-restriction fragment length polymorphism method (PCR–
RFLP). The sequence of the primers used for the PCR
reaction was designed by the research group. The forward primers had the following sequence 5’- ​C​G​C​C​C​A​
A​T​A​C​C​G​C​C​A​A​C​A​C-3’, and the sequence of the reverse
primers was 5’-​C​C​A​C​C​A​G​G​A​G​T​C​C​C​A​T​C​A​C​C-3’[48]. The PCR mixture was initially heated to 94 °C for
3 min, then 34 reaction cycles were repeated as follows:denaturation at 94 °C in 30 s, lowering the temperature
to 64 °C for annealing in 30 s, raising the temperature
to 72 °C for extension in 30 s. The extension stage of thelast cycle was performed in 8 min, and the products were
kept cold at 4 °C.
The amplified DNA segments with a length of 210 bp
then were incubated with Fast Digest Mval restriction
enzyme (Thermo Fisher Scientific, USA) at 37 °C for
15 min. Products of restriction enzyme digestion wereseparated by gel electrophoresis on Redsafe-stained 2.5%
agarose gel at 100 V. The distribution of restriction fragments on the agarose gel was observed by UV illumination, and these results can be used to distinguish different
genotypes at the ADRB3-rs4994 locus. The band patternsof CC, TC, and TT genotypes were (158 bp + 31 bp + 15

Hanh et al. BMC Pediatrics(2024) 24:594

bp + 6 bp), (158 bp + 31 bp + 15 bp + 6 bp), (97 bp + 61 bp+ 31 bp + 15 bp + 6 bp), respectively, but DNA fragmentswith the length of 31 bp, 15 bp, and 6 bp could not be
observed on the agarose gel due to their small sizes thatmade them move out of the gel plate.
The genotyping results from PCR–RFLP method werechecked for accuracy by comparing them with genotyping results from dideoxy chain termination sequencing.
10% of the samples were randomly selected to identify
their genotypes using the BigDye® Terminator v3.1 cycle
sequencing chemistry kit (Axil Scientific Pte Ltd, Singapore). The results from the two methods above wereidentical, these indicated that the designed method canprecisely identify genotypes of the rs4994 variant.
Statistical analysis

The obtained data were statistically analyzed using SPSS
23.0 software. Measurements of study subjects were represented as percentages (%) for the qualitative variables.
For quantitative variables, the characteristics were shownin the form of mean ± standard deviation if they were in
the normal distribution, or were expressed in median and25th − 75th percentiles in case of other types of distribution. Significant differences between data groups wereidentified by either χ2 tests, the Kruskall-Wallis test, Student t-test, Fisher Exact test, Man-Whitney-U-test, or
ANOVA in each specific comparison. Hardy-Weinberg
equilibrium of the studied population was checked by
Fisher’s exact test. The inheritance mechanism of allelesat the ADRB3 rs4994 locus was tested in five genetic
models including dominant, recessive, co-dominant,additive and over-dominant models. SNPstats software
was used to choose the most suitable genetic model following Akaike’s Information Criterion (AIC). Binary
logistic regression analysis was used to examine the association between the rs4994 variant and obesity in preschool children, and the results were given as odds ratios(OR) with 95% Confidence intervals (CI). For the statistical analysis, 8 scales of CEBQ (FR, EF, DD, SR, SE, FF,
EUE, EOE), age, sex, and living area were adjusted. Differences were considered as statistical significance if the
P < 0.05.

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12887-024-05073-7.
Supplementary Material 1
Acknowledgements
The authors wish to thank Mr. Binh TQ and colleagues at the National Instituteof Nutrition, Hanoi Medical University and Hanoi National University of
Education for their kind help and support.

Page 8 of 9

Author contributions
NTHH performed the experiments, analyzed the data, and wrote themanuscript; NTTT, and DTNT analyzed the data, discussed and edited thefinal draft for publication; LTT, and NTTT collected the data, participated inmanuscript draft preparation; LTT designed the research, analyzed the data,and edited of the final draft for publication.
Funding
NA.
Data availability
Supplementary file data.

Declarations
Ethics approval and consent to participate
The project was approved by the Medical Ethics Council of the Institute of
Nutrition with Decision No. 343/VDD-QLKH on July 27, 2018. All methods
were carried out in accordance with relevant guidelines and regulations. The
parents or guardians of all children were clearly explained the purposes of thestudy. They were given and signed the written informed consent form.
Consent to publish
NA.
Conflict of interest
All author declares that there is no conflict of interest.
Competing interests
The authors declare no competing interests.
Received: 16 February 2023 / Accepted: 10 September 2024

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