The association between rs4994 polymorphism of the Beta3-Adrenergic Receptor with obesity in Vietnamese preschool-age children, independently of eating behaviors

doi:10.21203/rs.3.rs-2593233/v1

Download PDF

Research Article

The association between rs4994 polymorphism of the Beta3-Adrenergic Receptor with obesity in Vietnamese preschool-age children, independently of eating behaviors

https://doi.org/10.21203/rs.3.rs-2593233/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 18 Sep, 2024

Read the published version in BMC Pediatrics →

You are reading this latest preprint version

Background: The Beta-3 adrenergic receptor (ADRB3) gene codes for the β₃ adrenergic receptor involved in control of energy balance by altering lipolysis and thermogenesis in adipocytes. A missense mutation in codon 64 of this gene caused the replacement Trp with Arg (Trp64Arg/rs4994). A cross-sectional study was performed with the joining of 708 non-obese children and 304 obese children in Hanoi. The objective of our study was to find out the association between rs4994 polymorphism of ADRB3 and obesity in Hanoi preschool-age children, considering their eating behaviors.

Results: The frequency of C allele in obese group was 16.4%, higher than that in the control group (11.7%, p = 0.003). After conducting univariate analysis, and multivariate analysis adjusted for eating behaviors, a significant association between rs4994 polymorphism and obesity was observed (p < 0.05).

Conclusions: This study indicated that ADRB3-rs4994 polymorphism can be considered as an independent risk factor for obesity in Vietnamese preschool children.

ADRB3

obesity

genetic variance

rs4994

Vietnamese children

The prevalence of childhood obesity has remarkably increased worldwide in recent decades. Within 40 years (1975–2016), the number of school-age children and adolescents with obesity rose more than 10-fold, from 11 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 most 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, cardiovascular diseases, and makes children feel inferiority complex and more likely to become involved in high-risk behaviors such as smoking or drinking alcohol [5].

Vietnam is facing a situation of the double burden of malnutrition [6, 7]. Results from a longitudinal study including 2,602 children from a cohort with an initial age of 3–6 years old showed that there was an increase in overweight and obese children between 2013 and 2016 [6]. A cross-sectional study was performed in 2018 on representing Kinh (n = 14421), Muong (n = 1307) and Tay (n = 449) ethnic groups had showed that malnutrition remains a major public health issue among children of minor ethnicities while overweight and obesity is an emerging challenge for the Kinh ethnic group [7].

Obesity is a multifactorial syndrome influenced by both genetic and environmental factors. To create a long-term and 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 food intake and energy consumption, which is closely related to lifestyle and dietary intake preferences [9]. One of the factors that have been shown to be closely related to childhood obesity is eating behavior. Steinsbekk et al. (2017) also showed that fat mass and muscle mass were strongly associated with specific eating behaviors [10]. Besides that, many other factors have been demonstrated that increase the risk of childhood obesity such as socioeconomic status [11], physiological, metabolic, and genetic factors [12, 13]... Our study in 2017 suggested the significant association of delivery method, birth weight, night sleep duration, and BDNF Val66Met polymorphism, with obesity in Vietnamese primary school children [14].

In recent years, many studies have focused on the β₂ adrenoreceptor and β₃ adrenoreceptor gene of the adrenergic receptor system, which plays an important role in stimulating thermogenesis and lipid mobilization in adipose tissue. The β₃-adrenergic receptor is expressed in visceral adipose tissue and plays a pivotal role in the regulation of human brown/beige adipocyte lipolysis and thermogenesis [15]. In 1997, a missense mutation in codon 64 of the ADRB3 gene, which led to alteration of the respective amino acid from arginine to tryptophan (Trp64Arg), was independently reported by three different groups [16]. According to some previous studies, the rs4994 variant of the ADRB3 gene is one of the candidates for obesity. A meta-analysis from 16 different studies indicated that this polymorphism might considerably contribute to both obesity in children and adults obesity, particularly in populations belonging to the East Asia regions [17]. Up to now, there is a paucity of published reports about the association between the ADRB3-rs4994 polymorphism 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 rs4494 polymorphism of the ADRB3 gene and obesity in 3-5-year-old Vietnamese children.

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

Table 1 shows the values for anthropometric measurements, genotypes and allele frequencies of ADRB3 rs4994 in the non-obese and obese groups. There was no significant difference in gender, age, weight between the two groups (p > 0.05). Meanwhile, height, BMI, height-for-age Z-score, weight-for-age Z-score, weight-for-height Z-score, BMI-for-age Z-score of cases were significantly higher than those of controls (p < 0.05).

Table 1

**Characteristics of the study subjects**
Parameter	Controls (n = 708)	Cases (n = 354)	p
Male (n, %)	532 (75.1)	266 (75.1)	1
Age (months)^b	59.5 (51.6–64.6)	59.8 (54.0–64.8)	0.408
Weight (kg)^b	16.4 (14.6–18.3)	24.2 (22.2–26.9)	0.518
Height (cm)^a	106.7 ± 7.2	110.2 ± 7.9	< 0.0001
BMI (kg/m²)^b	15.4 (14.6–16.5)	21.4 (20.9–22.6)	< 0.0001
Height-for-age Z-score^b	-0.42 (-1.06–0.26)	0.48 (-0.56–0.91)	< 0.0001
Weight-for-age Z-score^a	-0.23 (-0.84–0.56)	2.71 (2.19–3.28)	< 0.0001
Weight-for-height Z-score	0 (-0.7–0.82)	3.60 (3.3–4.16)	< 0.0001
BMI-for-age Z-score	-0.06 (-0.68–0.63)	3.35 (2.77–3.91)	< 0.0001
Genotype frequency
+ TT (n, %)	555 (78.4)	253 (71.5)	0.016
+ TC (%)	139 (19.6)	86 (24.3)
+ CC (%)	14 (2.0)	15 (4.2)
Allele frequency
+ T (%)	1249 (88.2)	592 (83.6)	0.003
+ C (%)	167 (11.7)	116 (16.4)
BMI, body mass index; WHR, waist to hip ratio.
^aData 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.

Results from Table 1 indicated that the genotype and allele frequencies were statistically different between two groups (p = 0.016 and p = 0.003, respectively). For the entire study sample, TT genotype had the highest frequency (78.4% in controls, 71.5% in cases). The frequency of CC genotype in the obese group was twice as much as non-obese group (4.2% and 2.0%, p = 0.016). Although both groups were in Hardy-Weinberg equilibrium (p = 0.134 for control group and p = 0.059 for 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 obese group, the frequency of allele C was 16.4%, which was higher compared with 11.7% in the normal group.

Table 2 showed the characteristics of obesity-related traits in case and control groups. Among these characteristics, only height in the cases was the index that differed between three genotypes. Children with the CC genotype had the highest height, while the TT genotypes had the lowest (p = 0.02). No differences were observed in all 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).

Table 2

The characteristics of obesity-related traits in cases and controls according to *ADRB3-rs4994* genotypes in Vietnamese children
Controls (n = 708)
Characteristics	TT (n = 555)	TC (n = 139)	CC (n = 14)	p
Birth weight (kg)	3.2 ± 0.4	3.2 ± 0.44	3.3 ± 0.38	0.84^a
Age (months)	57.3 ± 10.6	56.6 ± 10.1	54.5 ± 8.9	0.16^a
Height (cm)	106.7 ± 7.3	106.8 ± 6.9	105.5 ± 7.6	0.64^a
Weight (kg)	17.6 ± 2.9	17.8 ± 3.1	17.8 ± 4.5	0.77^a
BMI	15.2 (14.4–16.2)	15.2 (14.4–16.2)	15.6 (14.4–16.2)	0.87^b
Weight-for-age Z-score	-0.22 (-0.89–0.43)	-0.28 (-0.73–0.44)	-0.16 (-1.16–0.16)	0.92^b
Height-for-age Z-score	-0.42 (-1.01–0.27)	-0.36 (-0.90–0.27)	-0.61 (-1.07–-0.03)	0.60^b
BMI-for-age Z-score	0.03 ± 0.99	0.07 ± 1.12	0.18 ± 1.3	0.88^a
Weight-for-height Z-score	0.04 ± 1.4	0.3 ± 1.6	0.4 ± 1.8	0.87^a
Cases (n = 354)
Characteristics	TT (n = 235)	TC (n = 86)	CC (n = 15)	p
Birth weight (kg)	3.4 ± 0.4	3.3 ± 0.4	3.4 ± 0.36	0.91^a
Age (months)	57.2 ± 10.2	59.0 ± 9.3	61.4 ± 8.8	0.08^a
Height (cm)	109.7 ± 7.7	110.8 ± 8.7	114.4 ± 8.1	0.02^a
Weight (kg)	25.8 ± 3.9	26.0 ± 3.9	27.8 ± 3.9	0.07^a
BMI	21.0 (20.2–22.0)	21.0 (19.5–22.1)	21.0 (19.8–22.6)	0.54^b
Weight-for-age Z-score	2.55 (1.89–3.26)	2.44 (1.90–2.98)	3.00 (2.12–3.46)	0.36^b
Height-for-age Z-score	0.47 (-0.35–1.08)	0.45 (-0.22–1.04)	1.20 (0.66–1.92)	0.09^b
BMI-for-age Z-score	0.48 ± 0.89	0.38 ± 1.0	0.51 ± 0.72	0.40^a
Weight-for-height Z-score	0.09 ± 1.9	0.5 ± 2.5	0.6 ± 1.4	0.53^a
^aVariables that according to the standard distribution are represented by the mean±standard deviation, p obtained from the One-way ANOVA test.
^bVariables that are not according to standard distribution are represented by median and 25th − 75th percentile, p obtained from the Krukall-Wallis test.

Results for testing the relationship between ADRB3-rs4994 polymorphism and obesity in different genetic models are shown in Table 3. Association of rs4994 with obesity was found in dominant model, co-dominant model and recessive model. Using 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) in the co-dominant model. In the recessive model, CC genotypes were associated with a 2.2-fold higher risk of obesity than the TC and TT genotypes (p = 0.039).

Table 3

Analysis of the best-fit model for *ADRB3*-rs4994 polymorphism for obesity
Models	Controls n (%)	Cases n (%)	OR (95% CI)	p	AIC
Co-dominant
TT	555 (78.4)	253 (71.5)	1	0.050 0.024	1349.90
TC	139 (19.6)	86 (24.3)	1.36 (1.00–1.85)
CC	14 (2.0)	15 (4.2)	2.35 (1.12–4.94)
Dominant
TT	555 (78.4)	253 (71.5)	1
TC + CC	153 (21.6)	101 (28.5)	1.45 (1.08–1.94)	0.013	1349.96
Recessive
TT + TC	694 (98.0)	339 (95.8)	1
CC	14 (2.0)	15 (4.2)	2.19 (1.05–4.60)	0.037	1351.73
Over-dominant
TT + CC	569 (80.4)	268 (75.7)	1
TC	139 (19.6)	86 (24.3)	1.31 (0.97–1.78)	0.08	1352.91
Additive for copy number of C allele			1.45 (1.08–1.94)	0.013	1350.24
p 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.

The most suitable inheritance model for the ADRB3-rs4994 polymorphism with obesity was evaluated by AIC values for all possible genetic models, and results showed that co-dominant model was the best-fit model with the lowest AIC values (AIC = 1349.90). For further multivariate analyses, we also use this model for analyzing the influence of SNP ADRB3-rs4994 on obesity in preschool children in Hanoi.

The combined effect of the ADRB3-rs4994 polymorphism and eating behaviors (FR, EOE, EF, DD, SR, SE, EUE, FF) on the risk of obesity was examined by logistic regression because these eating behaviors are correlated with each other 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. This result indicated that although these above eating behaviors were thought to be a significant association with obesity, the impact of ADRB3-rs4994 polymorphism does not depend on children’s eating behaviors in our studied population.

Table 4

Association of *ADRB3*-rs4994 polymorphism with obesity in models considering children's eating behaviors
Models	Genotype	Obesity
Models	Genotype	OR (95%CI)	p
Unadjusted	TT	1
	TC	1.36 (1.00–1.85)	0.050
	CC	2.35 (1.12–4.94)	0.024
Adjusted for age, gender, residence, birth weight	TT	1
	TC	1.37 (1.01–1.86)	0.048
	CC	2.36 (1.12–4.99)	0.024
Adjusted for age, gender, residence, birth weight, and FR	TT	1
	TC	1.33 (1.00–1.87)	0.049
	CC	2.71 (1.2–6.08)	0.016
Adjusted for age, gender, residence, birth weight, and EOE	TT	1
	TC	1.37 (1.03–1.88)	0.048
	CC	2.47 (1.16–5.24)	0.019
Adjusted for age, gender, residence, birth weight, and DD	TT	1
	TC	1.38 (1.00–1.88)	0.045
	CC	2.49 (1.17–5.30)	0.018
Adjusted for age, gender, residence, birth weight, and SR	TT	1
	TC	1.35 (1.01–1.90)	0.047
	CC	2.98 (1.22–7.27)	0.017
Adjusted for age, gender, residence, birth weight, and EUE	TT	1
	TC	1.37 (1.00–1.89)	0.049
	CC	2.15 (1.01–4.95)	0.046
Adjusted for age, gender, residence, birth weight, and FF	TT	1
	TC	1.37 (1.00–1.86)	0.048
	CC	2.38 (1.23–5.01)	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: food fussiness.

Results from our case-control study in Hanoi preschool children found that the ADRB3-rs4994 polymorphism was strongly associated with obesity as an independent risk factor.

As a crucial component for regulating energy balance in mammals, the essential role of ADRB3 gene is due to its effects on lipid degradation, transport of fatty acid, thermogenesis [18, 19]… Binding to β₃-adrenergic agonists leads to the activation of ADRB3, which then activate adenylyl cyclase. Activated adenylyl cyclase initiates a signal pathway through intracellular signaling cascades including cyclic AMP, kinase proteins... that finally results in the production of heat in brown and white adipocytes [20, 21]. According to the research by Krief et al. (1993), ADRB3 also participates in the regulation of lipid metabolism including the absorption of fat during digestion, storage, and mobilization of lipids from adipocytes [18]. Thus, dysfunction of ADRB3 directly affects the process of lipolysis and energy expenditure, which consequently causes obesity as well as metabolic disorders through excessive accumulation of fat in adipose tissue.

ADRB3-rs4994 polymorphism was demonstrated that associated with some health problems such as being overweight, abdominal obesity, HDL-C levels, harder to lose weight, a reduced basal metabolic rate, type 2 diabetes in previous studies [16],[22],[23],[24]. Adipocytes with TC and CC genotypes exhibited a weaker response to β₃-adrenergic agonists as their cytosolic cAMP and glycerol were nearly 70% lower compared with that of TT genotypes [25, 26]. In addition, the existence of C allele in genotypes also leads to a lower rate of enzyme activity for the lipolysis induced by ADRB3 [27]. Thus, the replacement of T with C in codon 64 of ADRB3 gene significantly decreased 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 were demonstrated to associate with obesity in many populations such as Chinese [28], France [29], Japan [29]... According to research by Daghestani et al. in 329 unrelated persons (31% male and 66.9% female) with the age range from 18 to 36, there was a substantial correlation between the development of overweight and obesity in the Saudi population and the ADRB3-rs4994 variant. All anthropometric parameters in the overweight and obese group were higher than in the control group (p < 0.0001). In the obese group, the frequency of TC and CC genotypes was 15.8% and 4.8%, while in the control, the frequency of these genotypes was only 2.6% and 0%. Besides, the research also found that homozygotes for the C allele had a greater value for all anthropometric indices and almost biochemical parameters compared with two other genotypes (p < 0.05) [30]. Research by Sipilä et al. (1997) showed that the ADRB3-rs4994 polymorphism affects basal metabolic rate in obese Finns [31]. After adjustment for body weight and age, basal metabolic rate was lower in individuals with TC genotype than in TT genotypes (1,569 vs. 1,635 kcal/day, p < 0.05). Thus, it can be speculated that rs4994 polymorphism is associated with obesity through partly reducing basal metabolic rate, which results in an increase in fat accumulation and difficulty with losing weight [31].

Conversely, some research indicated that there was no association of ADRB3-rs4994 polymorphism with obesity. According to research by Kurokawa et al. on 87 Japanese children, BMI index and body fat percentage were not statistically different among three genotypes TC CC, and TT [32]. 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 polymorphism of ADRB3 gene. The frequency of TT, TC, and CC genotypes were 85%, 14.5%, and 0.5%, respectively [33]. Research by Chou et al., in 559 adolescent volunteers in Taiwan, was not found any association between obesity and rs4994 polymorphism of 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 Hardy-Weinberg equilibrium [34].

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 rs4994 variant was higher in obese girls, which can lead to weight gain. The frequencies of 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 two study groups. The frequency 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 [35].

Generally, the inconsistencies among the previous studies’ results may be due to differences in study populations (sex, age, socio-economic status, etc.), environmental or lifestyle factors (levels of energy intake and level 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 [36]. Japanese population had a high C allele frequency of 0.21, which is twice as common as in white populations [37]. 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 gradual replacement of healthy foods with various high-calorie but low-nutrient foods and beverages, which leads to a substantial change in eating behaviors of Vietnamese children, especially those living in big cities. When emotions change, students often have no control over the amount 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 [38]. Ashcroft et al. (2008) also showed that emotional overeating increased over time from 4 years of age to 10 years of age [39]. In addition, a study of twins in the UK has shown that overeating in response to negative emotions is a learned behavior rather than a genetic factor [40]. In addition, food response (such as demanding food, if allowed, students can eat a lot, when they are full, they can still eat…) is 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 with high BMI. When children were 10 years old, there was a positive 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.[38] Therefore, the association of ADRB3-rs4994 polymorphism 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, and also the effect of nutrition on gene expression. Based on the PubMed database, Pavlidis et al. (2015) conducted a meta-analysis to examine the association of 38 candidate genes with dietary intake and/or pathologies of nutrient-related 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 [41].

Since our results indicated that there was a significant association between ADRB3-rs4994 variant with obesity regardless of children's eating behaviors, it may be useful for doctors 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 cross-sectional study also had certain disadvantages due to not considering the contribution of lifestyle factors and their complicated interaction with other genetic factors to the development of childhood obesity. Therefore, further research needs to be conducted in the future to examine the roles of other factors in the epidemiology of obesity.

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

Study populations

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, and HIV/AIDS.

From 2019 to 2020, a case-control study was performed on 354 obese children and 708 controls recruited using WHO (2006) criteria for children under 5 years old. Number of children satisfied the sample size for a case-control study [42].

Children were classified as obese if their weight/height Z-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 and signed the written informed consent form. The personal information was strictly confidential and the data was only used 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, height was taken twice following the Loman standard method [43], 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, the body weight was calculated to the nearest 0.1 kg and the measurement of height was taken down to the nearest 0.1 cm. BMI indices were determined by the weight per square of height (kg/m²).

Investigation method

Children's Eating Behavior Questionnaire (CEBQ) was used in this study. This is a set of questions evaluated by experts with high reliability and consistency [44]. 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 of children's eating behavior are divided into 2 groups [45]: (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).

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 the study.

Genotyping method

Genotype of each child at the ADRB3 rs4994 locus were identified by the polymerase chain reaction-restriction fragment length polymorphism method (PCR–RFLP). The sequence of the primers used for PCR reaction was designed by the research group. The forward primers had the following sequence 5’- CGCCCAATACCGCCAACAC-3’, and the sequence of the reverse primers were 5’-CCACCAGGAGTCCCATCACC-3’ [46]. PCR mixture was initially heated to 94°C for 3 minutes, then 34 reaction cycles were repeated as follows: denaturation at 94°C in 30 seconds, lowering temperature to 64°C for annealing in 30 seconds, raising temperature to 72°C for extension in 30 seconds. The extension stage of the last cycle was performed in 8 minutes, and the products were kept cold at 4°C.

The amplified DNA segments with the length of 210 bp then were incubated with Fast Digest Mval restriction enzyme (Thermo Fisher Scientific, USA) at 37°C for 15 minutes. Products of restriction enzyme digestion were separated 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 ADRB3 rs4994 locus. The band patterns of CC, TC, and TT genotypes were (158 bp + 31 bp + 15 bp + 6 bp), (158 bp + 31 bp + 15 bp + 6 bp), (97 bp + 61 bp + 31 bp + 15 bp + 6 bp), respectively, but DNA fragments with the length of 31 bp, 15 bp, and 6 bp DNA could not be observed on the agarose gel due to their small sizes that made them move out of the gel plate.

The genotyping results from PCR–RFLP method were checked 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 two methods above were identical, this indicated that the designed method can precisely identify genotypes of the rs4994 polymorphism.

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 shown in the form of mean ± standard deviation if they were in the normal distribution, or were expressed in median and 25th − 75th percentiles in case of other types of distribution. Significant differences between data groups were identified by either χ² test, 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. Inheritance mechanism of alleles at 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 rs4994 polymorphism 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.

Acknowledgments

The authors wish to thank Mr. Binh TQ and colleagues at the National Institute of Nutrition, Hanoi Medical University and Hanoi National University of Education for their kind helps and supports.

Conflict of Interest

All author declares that there is no conflict of interest.

Authors’ Contributions

NTHH performed the experiments, analyzed the data, and wrote the manuscript; NTTT, DTNT analyzed the data, discussed and edited of the final draft for publication; LTT, and NTTT collected the data, participated in manuscript draft preparation; LTT designed the research, analyzed the data, and edited of the final draft for publication.

Ethical approval and consent: 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 the study. They were given and signed the written informed consent form.

Consent to publish: NA

Availability of data and material: NA

Funding: NA

NCD Risk Factor Collaboration. : 2020 – [cited 10/10/2022]. Available from: https://ncdrisc.org/.
Johnson KA, Showell NN, Flessa S, Janssen M, Reid N, Cheskin LJ, Thornton RL. Do neighborhoods matter? A systematic review of modifiable risk factors for obesity among low socio-economic status Black and Hispanic children. Child Obes. 2019;15(2):71–86.
World Health Organization. : 2022 – [cited 10/01/2022]. Available from: https://www.who.int/.
Mirza NM, Yanovski JA. Prevalence and consequences of pediatric obesity. Handbook of Obesity: Epidemology, Etiology, and Physiopathology; Bray, GA, Bouchard,C, Eds. 2019:pp. 55–74.
Farhat T, Iannotti RJ, Simons-Morton BG. Overweight, obesity, youth, and health-risk behaviors. Am J Prev Med. 2010;38(3):258–67.
Minh Do L, Lissner L, Ascher H. Overweight, stunting, and concurrent overweight and stunting observed over 3 years in Vietnamese children. Global health action. 2018;11(1):1517932–2.
Le TT, Le TTD, Do NK, Nadezhda VS, Andrej MG, Nguyen TTT, Nguyen TTM, Vu TT, Le TH, Nguyen TTL. Ethnic variations in nutritional status among preschool children in Northern Vietnam: a cross-sectional study. Int J Environ Res Public Health. 2019;16(21):4060–70.
Phan HD, Nguyen TNP, Bui PL, Pham TT, Doan TV, Nguyen DT, Van Minh H. Overweight and obesity among Vietnamese school-aged children: National prevalence estimates based on the World Health Organization and International Obesity Task Force definition. PLoS ONE. 2020;15(10):e0240459.
Yang WY, Williams LT, Collins C, Swee CWS. The relationship between dietary patterns and overweight and obesity in children of Asian developing countries: a systematic review. JBI Evid Synthesis. 2012;10(58):4568–99.
Steinsbekk S, Llewellyn CH, Fildes A, Wichstrøm L. Body composition impacts appetite regulation in middle childhood. A prospective study of Norwegian community children. Int J Behav Nutr Phys Activity. 2017;14(1):1–7.
Mistry SK, Puthussery S. Risk factors of overweight and obesity in childhood and adolescence in South Asian countries: a systematic review of the evidence. Public Health. 2015;129(3):200–9.
Wang H, Xue H, Du S, Zhang J, Wang Y, Zhang B. Time trends and factors in body mass index and obesity among children in China: 1997–2011. Int J Obes. 2017;41(6):964–70.
Bahreynian M, Qorbani M, Khaniabadi BM, Motlagh ME, Safari O, Asayesh H, Kelishadi R. Association between obesity and parental weight status in children and adolescents. J Clin Res Pediatr Endocrinol. 2017;9(2):111–7.
Tuyet LT, Nhung BT, Dao DTA, Hanh NTH, Tuyen LD, Binh TQ, Thuc VTM. The brain-derived neurotrophic factor Val66Met polymorphism, delivery method, birth weight, and night sleep duration as determinants of obesity in vietnamese children of primary school age. Child Obes. 2017;13(5):392–9.
Cero C, Lea HJ, Zhu KY, Shamsi F, Tseng Y-H, Cypess AM. β3-Adrenergic receptors regulate human brown/beige adipocyte lipolysis and thermogenesis. JCI insight. 2021;6(11):e139160.
Walston J, Silver K, Bogardus C, Knowler WC, Celi FS, Austin S, Manning B, Strosberg AD, Stern MP, Raben N. Time of onset of non-insulin-dependent diabetes mellitus and genetic variation in the β3-adrenergic–receptor gene. N Engl J Med. 1995;333(6):343–7.
Xie C, Hua W, Zhao Y, Rui J, Feng J, Chen Y, Liu Y, Liu J, Yang X, Xu X. The ADRB3 rs4994 polymorphism increases risk of childhood and adolescent overweight/obesity for East Asia’s population: an evidence-based meta-analysis. Adipocyte. 2020;9(1):77–86.
Krief S, Lönnqvist F, Raimbault S, Baude B, Van Spronsen A, Arner P, Strosberg AD, Ricquier D, Emorine LJ. Tissue distribution of beta 3-adrenergic receptor mRNA in man. J Clin Investig. 1993;91(1):344–9.
Lönnqvist F, Thöme A, Nilsell K, Hoffstedt J, Arner P. A pathogenic role of visceral fat beta 3-adrenoceptors in obesity. J Clin Investig. 1995;95(3):1109–16.
Zaagsma J, Nahorskii SR. Is the adipocyte β-adrenoceptor a prototype for the recently cloned atypical ‘β3-adrenoceptor?’. Trends Pharmacol Sci. 1990;11(1):3–7.
Lafontan M, Berlan M. Fat cell adrenergic receptors and the control of white and brown fat cell function. J Lipid Res. 1993;34(7):1057–91.
Yoshida T, Sakane N, Umekawa T, Sakai M, Takahashi T, Kondo M. Mutation of β3-adrenergic-receptor gene and response to treatment of obesity. The Lancet. 1995;346(8987):1433–4.
Brondani LA, Duarte GC, Canani LH, Crispim D. The presence of at least three alleles of the ADRB3 Trp64Arg (C/T) and UCP1 – 3826A/G polymorphisms is associated with protection to overweight/obesity and with higher high-density lipoprotein cholesterol levels in caucasian-brazilian patients with type 2 diabetes. Metab Syndr Relat Disord. 2014;12(1):16–24.
Hameed I, Masoodi SR, Afroze D, Naykoo NA, Bhat RA, Ganai BA. Trp homozygotes at codon 64 of ADRB3 gene are protected against the risk of type 2 diabetes in the Kashmiri population. Genetic Test Mol Biomarkers. 2013;17(10):775–9.
Piétri-Rouxel F, St John Manning B, Gros J, Strosberg AD. The biochemical effect of the naturally occurring Trp644→ Arg mutation on human β3‐adrenoceptor activity. Eur J Biochem. 1997;247(3):1174–9.
Umekawa T, Yoshida T, Sakane N, Kogure A, Kondo M, Honjyo H. Trp64Arg mutation of beta3-adrenoceptor gene deteriorates lipolysis induced by beta3-adrenoceptor agonist in human omental adipocytes. Diabetes. 1999;48(1):117–20.
Takenaka A, Nakamura S, Mitsunaga F, Inoue-Murayama M, Udono T, Suryobroto B. Human-specific SNP in obesity genes, adrenergic receptor beta2 (ADRB2), Beta3 (ADRB3), and PPAR γ2 (PPARG), during primate evolution. PLoS ONE. 2012;7(8):e43461.
Thomas G, Tomlinson B, Chan J, Young R, Critchley J. The Trp64Arg polymorphism of the β3-adrenergic receptor gene and obesity in Chinese subjects with components of the metabolic syndrome. Int J Obes. 2000;24(5):545–51.
Clément K, Vaisse C, Manning BSJ, Basdevant A, Guy-Grand B, Ruiz J, Silver KD, Shuldiner AR, Froguel P, Strosberg AD. Genetic variation in the β3-adrenergic receptor and an increased capacity to gain weight in patients with morbid obesity. N Engl J Med. 1995;333(6):352–4.
Daghestani M, Daghestani M, Daghistani M, Eldali A, Hassan ZK, Elamin MH, Warsy A. ADRB3 polymorphism rs4994 (Trp64Arg) associates significantly with bodyweight elevation and dyslipidaemias in Saudis but not rs1801253 (Arg389Gly) polymorphism in ARDB1. Lipids Health Dis. 2018;17(1):1–9.
Sipilä R, Uusitupa M, Heikkinen S, Rissanen A, Laakso M. Polymorphism of the β3-adrenergic receptor gene affects basal metabolic rate in obese Finns. Diabetes. 1997;46(1):77–80.
Kurokawa N, Nakai K, Kameo S, Liu Z-M, Satoh H. Relationship between the β3-adrenoceptor gene variant and body fat in Japanese children. Tohoku J Exp Med. 2003;201(4):271–6.
Porto PI, García SI, Dieuzeide G, González C, Landa MS, Pirola CJ. Clinical features of the metabolic syndrome in adolescents: minor role of the Trp64Arg β3-adrenergic receptor gene variant. Pediatr Res. 2004;55(5):836–41.
Chou YC, Tsai CN, Lee YS, Pei JS. Association of adrenergic receptor gene polymorphisms with adolescent obesity in Taiwan. Pediatr Int. 2012;54(1):111–6.
Yılmaz R, Ateş Ö, Gül A, Kasap T, Özer S, Ensari E. Association between trp64arg polymorphism of the β3 adrenoreceptor gene and female sex in obese Turkish children and adolescents. Pediatr Gastroenterol Hepatol Nutr. 2019;22(5):460–9.
Biery A, Ebbesson S, Shuldiner A, Boyer B. The β3-adrenergic receptor TRP64ARG polymorphism and obesity in Alaskan Eskimos. Int J Obes. 1997;21(12):1176–9.
Yuan X, Yamada K, Koyama K-i, Ichikawa F, Ishiyama S, Koyanagi A, Koyama W, Nonaka K. β3-adrenergic receptor gene polymorphism is not a major genetic determinant of obesity and diabetes in Japanese general population. Diabetes Res Clin Pract. 1997;37(1):1–7.
Derks IP, Sijbrands EJ, Wake M, Qureshi F, van der Ende J, Hillegers MH, Jaddoe VW, Tiemeier H, Jansen PW. Eating behavior and body composition across childhood: a prospective cohort study. Int J Behav Nutr Phys Activity. 2018;15(1):1–9.
Ashcroft J, Semmler C, Carnell S, Van Jaarsveld C, Wardle J. Continuity and stability of eating behaviour traits in children. Eur J Clin Nutr. 2008;62(8):985–90.
Herle M, Fildes A, Steinsbekk S, Rijsdijk F, Llewellyn CH. Emotional over-and under-eating in early childhood are learned not inherited. Sci Rep. 2017;7(1):1–9.
Pavlidis C, Lanara Z, Balasopoulou A, Nebel J-C, Katsila T, Patrinos GP. Meta-analysis of genes in commercially available nutrigenomic tests denotes lack of association with dietary intake and nutrient-related pathologies. OMICS. 2015;19(9):512–20.
Charan J, Biswas T. How to calculate sample size for different study designs in medical research? Indian J Psychol Med. 2013;35(2):121–6.
Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Human kinetics books; 1988.
Sleddens EF, Kremers SP, Thijs C. The Children's Eating Behaviour Questionnaire: factorial validity and association with Body Mass Index in Dutch children aged 6–7. Int J Behav Nutr Phys Activity. 2008;5(1):1–9.
Wardle J, Guthrie CA, Sanderson S, Rapoport L. Development of the children's eating behaviour questionnaire. J Child Psychol Psychiatry Allied Disciplines. 2001;42(7):963–70.
Nguyen THH, Do TNT, Nguyen TNL, Tran QB, Do NK, Nguyen TTT, Le TT. Genotyping Method and Frequency of ADRB3-rs4994 Single Nucleotide Polymorphism Genotypes in Hanoi 3–5 Years Old Chidren. VNU J Science: Med Pharm Sci. 2019;35(1):104–11.

No competing interests reported.

SupplementaryTable1.docx

Download PDF

Journal Publication

published 18 Sep, 2024

Read the published version in BMC Pediatrics →

Editorial decision: Revision requested
10 Jan, 2024
Reviews received at journal
08 Jan, 2024
Reviewers agreed at journal
20 Dec, 2023
Reviews received at journal
10 Nov, 2023
Reviewers agreed at journal
01 Nov, 2023
Reviewers invited by journal
26 Oct, 2023
Editor assigned by journal
11 Oct, 2023
Editor invited by journal
10 Mar, 2023
Submission checks completed at journal
10 Mar, 2023
First submitted to journal
16 Feb, 2023

You are reading this latest preprint version

The association between rs4994 polymorphism of the Beta3-Adrenergic Receptor with obesity in Vietnamese preschool-age children, independently of eating behaviors

Status:

Journal Publication

Version 1

Abstract

Background

Results

Discussion

Conclusions

Methods

Study populations

Ethics statement

Measurements

Investigation method

DNA extraction

Genotyping method

Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1