Influence of the -3826A/G Polymorphism UCP1 (rs1800592) and

Physical Activity on Obesity-related Traits in Russian Females with

Different Level of Physical Activity

Elvira Bondareva

, Olga Parfenteva

and Valentine Son’kin

2,3 c

Institute and Museum of Anthropology, Moscow State University, Mokhovaya st, 11/1, Moscow, Russia

Moscow Center of Advanced Sports Technologies, Sovietskoi armii st, 6, Moscow, Russia

Russian State University of Physical Education, Sports, Youth, and Tourism, Sirenevyi blv, 4, Moscow, Russia

Keywords: Athletes, Abdominal Obesity, UCP1, Gene-Environment Interactions, Physical Activity.

Abstract: The association between level of physical activity and -3826A/G polymorphism UCP1 (rs1800592) with

obesity-related traits was examined in the group of Russian females. A cross-sectional study of 124 adult

females aged of 18-30 years living in Moscow was performed. The genotype of the UCP1 rs1800592 variant

was determined. Height, body mass, waist, hip circumferences and body fat mass were measured. Waist to

hip ratio (WHR), waist to height ratio (WHtR), body mass index (BMI), and body adiposity index (BAI) were

calculated. Association analysis revealed that physical activity and the -3826A/G polymorphism of UCP1

(rs1800592) were significantly associated with obesity-related traits. However, physical activity had a greater

impact on obesity-related traits. Decreased level of physical activity is associated with increased waist to

height ratio, the amount of body fat and body adiposity index. Decreased level of physical activity enhanced

the effect of UCP1 gene polymorphism rs1800592 on obesity-related traits in the studied cohort.

1 INTRODUCTION

Over the last 50 years, the prevalence of obesity

among adults increased dramatically. According to

the last World Health Organization (WHO) report,

the number of overweight and obese individuals 18

years and older raised to 39% and 13%, respectively.

Obesity is defined as a multifactorial disease

which results from a combination of energy

imbalance, low physical activity and genetic

predisposition.

It was shown that the genetic effects may be

modified by various environmental factors

(Kilpeläinen et al., 2011; Rask-Andersen et al., 2017;

Bondareva et al., 2019). Physical activity, diet,

alcohol consumption, smoking, could enhance or

attenuate the influence of genetic factors on obesity-

related traits. For instance, physical activity

attenuated the effect of FTO common variants on

obesity risk (Kilpeläinen et al., 2011; Rask-Andersen

https://orcid.org/0000-0003-3321-7575

https://orcid.org/0000-0001-7895-6887

https://orcid.org/0000-0003-3834-8080

et al., 2017; Bondareva et al., 2019). Several studies

reported that the influence of other obesity-related

loci is diminished by physical activity. In physically

active adults, the minor T allele of the UCP3

rs1800849 (-55C/T) variant is associated with a lower

risk of obesity compared to C allele (Alonso et al.,

2005). Physical activity attenuated the influence of

the risk C allele of the UCP1 rs3811791 on type 2

diabetes risk (Dong et al., 2020).

Gene-lifestyle (gene-environment) interactions

can explain much of the variation in obesity-related

traits. The identification of gene-lifestyle interactions

is a promising method for understanding the etiology

of obesity and development of preventive strategies

(Lin et al., 2013).

In the current study, we investigate the effect of

physical activity level along with the common variant

of the UCP1 rs1800592 (-3826A/G) on obesity-

related traits in the female adults.

156

Bondareva, E., Parfenteva, O. and Son’kin, V.

Inﬂuence of the -3826A/G Polymorphism UCP1 (rs1800592) and Physical Activity on Obesity-related Traits in Russian Females with Different Level of Physical Activity.

DOI: 10.5220/0010130901560160

In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 156-160

ISBN: 978-989-758-481-7

2 MATERIALS AND METHODS

The study cohort included 124 healthy normal weight

Russian females aged 18-30 years with different level

of physical activity. The dataset included Russian

females who live in Moscow metropolitan area. The

average age of individuals was 18.1±4.2 and 18.4±0.9

years in physically active and inactive individuals,

respectively. The study cohort included individuals

with low physical activity level (N=54) and

professional athletes (N=70) who are engaged in

aerobic sports. The individuals in the first group

performed less than 150 minutes of moderate or

vigorous physical activity per week. Professional

athletes were included in the second group with a high

physical activity level. Physical activity level was

determined by a short questionnaire in which subjects

were asked about the number of minutes of moderate

(moderate physical effort) or vigorous (hard physical

effort) physical activity per week.

The study was approved by the Commission on

Bioethics of the Biological Faculty of Lomonosov

Moscow State University (Ref. 91-o from

24.05.2018). Each participant provided written

informed consent before the examination. The

examination was conducted at the Research Institute

and Museum of Anthropology of Lomonosov

Moscow State University. The anthropometric

examination included measurements of height (cm),

body weight (BW, kg), waist circumference (cm) and

hip circumference (cm). Height and weight were

measured by stable stadiometer Seca (SECA,

Germany) and flat scale Seca (SECA, Germany). The

following anthropometric indices were calculated:

body mass index (BMI, kg/m2), body adiposity index

(BAI, %), waist to hip ratio (WHR), waist to height

ratio (WHtR) The percentage of body fat mass was

measured by a bioimpedance analyzer ABC-01

“MEDASS” (Russia).

All genetic analysis was performed by

commercial company Lytekh (Moscow, Russia).

DNA was extracted and purified from buccal

epithelium following the manufacturer’s procedures

(COrDIS Sprint). The rs1800592 was determined

using matrix-assisted laser desorption/ionization -

time of flight mass spectrometry (MALDI-TOF MS).

The genotype data was checked for Hardy-Weinberg

equilibrium (HWE).

Statistical analysis was performed in the

computer environment R, version 3.5.1 (RStudio

Team, 2015). A comparison of the allele and

genotype frequencies rs1800592 between two studied

groups was carried out using the Fisher exact test

(Raymond M., Rousset F., 1995). A standard

exploratory analysis was carried out (Shapiro and

Wilk, 1965; Grubbs, 1969; Levene et al., 1960).

Based on the results of the exploratory analysis, we

decided to use quantile regression (Koenker et al.,

2001). Quantile regression has a few advantages

compared to ordinary least square regression.

Quantile regression is robust to outliers and does not

require normality assumption. Moreover, here, the

upper conditional quantiles functions were of interest.

It is assumed that the effect of the rs1800592 of the

UCP1 will be stronger at higher values of the

dependent variables. The models were built using

quantreg package (Available at: https://CRAN.R-

project.org/package=quantreg was used to build the

regression model. Accessed: 07/08/2020). The

bootstrap algorithm was used to calculate the standard

error of the quantile regression model. Regression

models were constructed for quantiles 10, 20, 30, 40,

50, 60, 70, 80, 90 to test the main effect of physical

activity and the variant rs1800592 of the UCP1 on the

dependent variables. The main effect of the variant

1800592 UCP1 on obesity-related traits was tested in

the whole study population as well as in physically

active (N=54) and inactive individuals (N=70). The

dominant model was used (AG+GG vs AA). The

main effect of physical activity was tested in the

whole sample (N=124). The model was used to test

the combined effect of the risk rs1800592 variant of

the UCP1 and physical activity. Age was added to the

models as covariates. The Benjamini-Hochberg

method was used for multiple testing comparison.

3 RESULTS AND DISCUSSION

The baseline phenotypic characteristics of the

individuals are presented in the table 1 and figure 1.

The minor allele frequency of the variant UCP1

rs1800592 (G allele) in the studied sample was 0.25.

In European populations, minor allele frequency

ranged from 0.15 to 0.27. The distribution of the

rs1800592 UCP1 in the study sample was in Hardy-

Weinberg equilibrium (χ

=0.31, p=0.57). Physically

active and inactive individuals did not significantly

differ in the allele frequency of the UCP1 rs1800592

(p=0.14).

Quantile regression revealed that physical activity

had a significant effect on obesity-related traits (Table

1). Individuals with a low level of physical activity

had significantly higher body adiposity index

(ß=4.16, p=2*10

-4

), waist to height ratio (ß=0.01,

p=1*10

-3

) and the percentage of body fat mass

(ß=6.30, p=1*10

-4

). The influence of physical activity

was higher at the upper quantile of waist to height

Inﬂuence of the -3826A/G Polymorphism UCP1 (rs1800592) and Physical Activity on Obesity-related Traits in Russian Females with

Different Level of Physical Activity

157

ratio, waist to hip ratio, and body adiposity index

(Fig. 2). Low level of physical activity led to increase

in waist to height ratio value by 0.01 at 25% quantile,

by 0.02 at 50% quantile (median) and by 0.04 at 90%

quantile (Fig. 2).

Figure 1: Boxplots of body fat percentage, body adiposity

index (BAI), body mass index (BMI), waist to hip ratio

(WHR), waist to height ratio (WHtR) in physically active

inactive individuals according to rs1800592 UCP1 risk

allele (AG+GG vs AA).

Insufficient physical activity increased the

amount of body fat. The influence of insufficient

physical activity is higher at the lower quantiles (Fig.

2). Moreover, a low level of physical activity led to

decrease in the amount of muscle mass (49.60% vs

51.1%, ß =-1.14, p=3*10

-3

Table 1: Baseline phenotypic characteristics of the studied

cohort (Mean, SD).

Parameter

Physically

active (n=54)

Physically

inactive (n=70)

Age 18.1 (4.2) 18.4 (0.9)

Waist circumference,

65.8 (3.8) 69.2 (6.0)

Hip circumference, cm 90.9 (4.7) 96.5 (5.8)

Body weight, kg 56.3 (7.1) 57.7 (7.3)

Height, cm 165.2 (7.2) 163.7 (5.7)

BMI, kg/m

20.6 (2.2) 21.6 (2.8)

Body fat content, % 20.3 (4.8) 28.1 (4.8)

Body adiposity index,

25.1 (3.6) 28.1 (3.5)

Waist to hip ratio 0.72 (0.03) 0.72 (0.04)

Waist to height ratio 0.40 (0.03) 0.42 (0.04)

Muscle mass, % 50.5 (2.1) 48.6 (1.4)

Body mass index did not significantly differ

between physically active and inactive individuals

(ß=0.84, p=0.07). Anthropometric indices such as

waist to height ratio and body adiposity indices may

be better predictors of body fat accumulation in

individuals with different levels of physical activity

than body mass index (Sayeed et al., 2003; Lee et al.,

2008). In physically active individuals, an increase in

body mass index may be due to an increase in the

muscle mass rather than fat mass (Freedman et al.,

2005; Torstveit et al., 2012).

Figure 2: Changes of the beta (ß) value of the coefficients

(in y-axis) at different quantiles (in x-axis) of body fat

percentage, body adiposity index (BAI), body mass index

(BMI), waist to hip ratio (WHR), waist to height ratio

(WHtR) according to physical activity level.

In the studied sample, the risk G allele (AG+GG

vs AA) of the UCP1 rs18008592 was associated with

a higher body mass index (ß=1.89, p=0.04), waist to

hip ratio (ß=0.05, p=0.01), and waist to height ratio

(ß=0.05, p=0.05). Several studies reported that the

risk G allele increased the risk of obesity in different

populations (Cha et al., 2008; Chathoth et al., 2018).

In physically inactive individuals, the risk G allele

increased waist to height ratio, body mass index, the

percentage of body fat mass and waist to hip ratio

(table 2). In physically active individuals, the

significant effect of the risk variant UCP1 rs1800592

on waist to hip ratio (ß=0.03, p=0.05) and waist to

height ratio (ß=0.01, p=0.01) was confirmed at the

upper quantiles (table 2).

Physical activity and the UCP1 rs1800592 risk

variant significantly modified the risk of body fat

accumulation. However, physical activity has a

greater influence on obesity-related traits compared

to the UCP1 rs180592 risk variant. Physical activity

modified the influence of the UCP1 1800592 risk

variant on obesity-related traits. The influence of the

UCP1 rs180592 risk variant on obesity-related traits

was higher in individuals with a low level of physical

activity compared to physically active individuals.

Several studies reported that physical activity is an

effective way to control weight gain even in

individuals with genetic predisposition (Kilpeläinen

et al., 2011; Young et al., 2016; Rask-Andersen et al.,

icSPORTS 2020 - 8th International Conference on Sport Sciences Research and Technology Support

158

2017; Bondareva et al., 2019). For instance, the risk

FTO rs9939609 had a significant effect on body fat

accumulation only in individuals with a low level of

physical activity (Bondareva et al., 2019).

Table 2: Association of the risk G allele of the rs1800592

UCP1 and obesity-related traits in physically active and

inactive females (ß – regression coefficient,

* - p-value<0.05, ** - p-value<0.01, Q- quantile).

Q BF BMI WHR WHtR BAI

Physically inactive individuals

0.1 0.54 0.17 0.01 0.01 1.4**

0.2 0.95 0.4 0.02 0.02* 1.51*

0.3 0.51 0.97* 0.01 0.01 1.97*

0.4 1.26 0.76* 0.00 0.01 2.2*

0.5 1.65* 0.48 0.00 0.01 1.64*

0.6 1.41 1.18 0.00 0.01 1.24

0.7 2.63** 1.94** 0.01 0.03** 1.07

0.8 2.44** 2.09** 0.02 0.02* 0.49

0.9 6.13* 3.31** 0.04 0.07** 1.47

Physically active individuals

0.1 3.73* 0.03 0.001 0.01 1.55*

0.2 2.56* 0.01 0.001 0.001 -0.7*

0.3 0.31 0.01 0.001 0.001 0.32

0.4 0.85 0.01** 0.001 0.001 0.37

0.5 -0.22 0.66 0.001 0.01 0.65

0.6 0.03 0.6 0.001 0.01 0.59

0.7 0.29 0.8* 0.001 0.01 1.51

0.8 1.48 0.46 0.01 0.02** 1.65

0.9 0.10 1.30 0.01 0.03* 1.18

The study has several limitations. First, the UCP1

rs1800592 risk variant explained a small amount of

the variance of the obesity-related traits. Thus, it

cannot be a significant predictor of obesity. However,

recent study revealed around 1000 common obesity-

related loci accounted for 6% of the variance of

obesity-related traits (Yengo et al., 2018). Second, the

studied sample included only female individuals, so

the findings are not generalizable to other population,

i.e. male. Third, the conducted study is cross-

sectional, that does not take into account changes

across the life course.

4 CONCLUSIONS

Physical activity and the UCP1 rs1800592 risk

variant significantly influence the risk of fat

accumulation and obesity. However, physical activity

is a better predictor of fat accumulation and obesity

compared to the UCP1 rs1800592 risk variant.

However, to confirm the effect of the interaction,

additional studies are needed in adult males, as well

as in the group of children and adolescents.

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