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Citation: Egorova, E.S.; Aseyan, K.K.;
Bikbova, E.R.; Zhilina, A.E.; Valeeva,
E.V.; Ahmetov, I.I. Effects of Gene–
Lifestyle Interaction on Obesity
Among Students. Genes 2024,15, 1506.
https://doi.org/10.3390/genes15121506
Academic Editors: Eron Manusov,
Vincent P. Diego and Sarah
Williams-Blangero
Received: 1 November 2024
Revised: 21 November 2024
Accepted: 22 November 2024
Published: 24 November 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
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4.0/).
Article
Effects of Gene–Lifestyle Interaction on Obesity
Among Students
Emiliya S. Egorova 1, Kamilla K. Aseyan 1, Elvina R. Bikbova 1, Anastasia E. Zhilina 1, Elena V. Valeeva 1
and Ildus I. Ahmetov 1,2, *
1Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia;
jastspring@yandex.ru (E.S.E.); aseyan.kamilla@yandex.ru (K.K.A.); ytii4686@gmail.com (E.R.B.);
zhilina_anastasia01@mail.ru (A.E.Z.)
2Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 5AF, UK
*Correspondence: i.akhmetov@ljmu.ac.uk
Abstract: Background: Obesity is a global health issue influenced primarily by genetic variants
and environmental factors. This study aimed to examine the relationship between genetic and
lifestyle factors and their interaction with obesity risk among university students. Methods: A total
of 658 students from the same university participated in this study, including 531 females (mean age
(SD): 21.6 (3.9) years) and 127 males (21.9 (4.6) years). Among them, 550 were classified as normal
weight or underweight (456 females and 94 males), while 108 were identified as overweight or obese
(75 females and 33 males). All the participants underwent anthropometric and genetic screening and
completed lifestyle and sleep quality questionnaires. Results: The polygenic risk score, based on
seven genetic variants (ADCY3 rs11676272, CLOCK rs1801260, GPR61 rs41279738, FTO rs1421085,
RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597), explained 8.3% (p< 0.0001) of
the variance in body mass index (BMI). On the other hand, lifestyle factors—such as meal frequency,
frequency of overeating, nut consumption as a snack, eating without hunger, frequency of antibiotic
use in the past year, symptoms of dysbiosis, years of physical activity, sleep duration, bedtime,
ground coffee consumption frequency, and evening coffee consumption time—accounted for 7.8%
(p< 0.0001) of the variance in BMI. The model based on gene–environment interactions contributed
15% (p< 0.0001) to BMI variance. Conclusions: This study revealed that individuals with a higher
genetic predisposition, as defined by the seven polymorphic loci, are more susceptible to becoming
overweight or obese under certain lifestyle conditions.
Keywords: lifestyle genetics; DNA; polymorphism; genotype; GxE; gene–lifestyle interaction; obesity;
nutrition; eating behavior
1. Introduction
The World Health Organization (WHO) defines obesity as a condition characterized
by an excessive accumulation of adipose tissue, which adversely impacts human health [
1
].
It is estimated that approximately 2 billion adults aged 18 years and older worldwide are
overweight, with 650 million classified as obese [
2
,
3
]. Overweight and obesity are among
the leading risk factors for mortality, contributing to at least 2.8 million deaths annually [
1
].
Notably, the association between body mass index (BMI) and mortality is stronger in
younger age groups [
4
,
5
]. Epidemiological studies have shown that these conditions not
only reduce life expectancy but also increase the prevalence of functional limitations [6].
The rising prevalence of obesity is primarily due to lifestyle changes in an obesogenic
environment, characterized by reduced physical activity and increased consumption of
high-calorie foods. Additionally, substantial evidence supports the role of genetic factors in
body weight regulation [
7
–
9
]. However, recent genome-wide association studies (GWAS)
indicate that identified candidate genes account for only about 6% of the variability in
Genes 2024,15, 1506. https://doi.org/10.3390/genes15121506 https://www.mdpi.com/journal/genes
Genes 2024,15, 1506 2 of 13
BMI [
10
]. This limited contribution underscores the complex interplay between genetic
variants and environmental factors [11].
The conventional approach to investigating gene–environment–lifestyle interactions in
obesity has been to examine the impact of genes (exposure) on obesity (outcome) in groups
stratified by an environmental factor (e.g., physically active vs. inactive individuals). This
approach allows for examining how environmental factors modify the association between
exposure and outcome [
12
]. Gene-by-environment (GxE) interaction refers to how a per-
son’s genetic makeup interacts with environmental factors to influence their phenotype,
meaning the effect of genes can vary depending on the environmental context [13].
The concept of gene-by-environment interaction in obesity can be defined as the influ-
ence of a genetic variant on BMI, which may vary across different environmental conditions.
Recent studies have demonstrated that genetic predisposition to obesity can be influenced
by various lifestyle factors, including alcohol consumption, sweetened beverages, smoking,
diet, and physical activity. The impact of physical activity levels on obesity has been
confirmed in multiple studies [
14
–
17
]. Additionally, the role of television viewing habits
in obesity has been examined [
18
], as well as the relationship between sleep quality and
obesity [19].
While many other factors that appear to contribute to the risk of obesity have not
been studied in the context of gene–environment interactions, such factors include human
behaviors related to circadian rhythms (e.g., bedtime and coffee consumption patterns), as
well as antibiotic use and intestinal dysbiosis. It is therefore evident that further investi-
gation and replication of previous findings in diverse populations are required to gain a
deeper understanding of the influence of environmental factors on genetic predisposition to
obesity. Continued research on gene–environment interactions is essential to enhance our
comprehension of the etiology of obesity and to facilitate the development of personalized
prevention and treatment strategies.
The aim of this study was to examine the relationship between genetic and lifestyle
factors, as well as their interaction with obesity risk, in a homogeneous cohort of students
attending the same university and therefore living under partly identical environmental
conditions.
2. Materials and Methods
2.1. Ethical Approval
This research was conducted in accordance with the ethical standards established
by the Declaration of Helsinki of the World Medical Association and received approval
from the Local Ethical Committee of Kazan State Medical University (protocol #10, dated
21 November 2023). Prior to participation, all the students were thoroughly informed about
this study’s purpose and procedures, and each provided written consent to
participate voluntarily.
2.2. Participants
The study group comprised university students. This study included 658 students:
338 of Tatar ethnicity, 225 of Russian ethnicity, and 95 of mixed origin. The mean age
(SD) of the participants was 21.6 (4.0) years. The sample consisted of 531 females (age
21.6 (3.9) years) and 127 males (age 21.9 (4.6) years). Individuals engaged in professional
sports, pregnant or lactating, or suffering from serious medical conditions (e.g., cancer and
cerebral palsy) were excluded from the study. Additionally, individuals unable to engage
in physical activity due to underlying health conditions were also excluded. This study
included a total of 658 participants who met the eligibility criteria and provided informed
consent. Age was recorded directly from the participants during the bioimpedance analysis,
and ethnicity information was collected via a questionnaire.
Genes 2024,15, 1506 3 of 13
2.3. Anthropometric Measurement
All the participants underwent anthropometric measurements to assess overweight
and obesity status. Body weight and composition were measured using bioelectrical
impedance (Tanita MC-780 MAN, Tanita, Japan). The parameters collected included total
body weight (kg), fat-free mass (FFM, kg), fat mass (FM, kg), and body mass index (BMI,
kg/m2). The measurements were taken in a fasting state.
2.4. Lifestyle Variables
The participants completed a lifestyle questionnaire. To identify new factors associated
with obesity risk, we developed a customized questionnaire that included questions about
daily meal patterns, such as meal frequency and timing (e.g., the timing of the last meal),
frequency of overeating, and instances of eating without hunger. They also reported the
number of daily snacks and the frequency and timing of beverages consumed, including
coffee, caffeinated drinks, decaffeinated coffee, green tea, and black tea. The questionnaire
assessed the frequency of consumption of various food items such as white meat, red meat,
fish, dairy products, vegetables, fruits, juices, fast food, sausages, processed foods, and
confectionery, as well as daily water intake.
Additionally, the participants provided details on their outdoor activities, including
the frequency, duration, and intensity of physical exercise. To assess sleep quality, the
participants completed the Pittsburgh Sleep Quality Index (PSQI) [
20
], which consists of
19 questions covering aspects such as typical bedtimes, wake-up times, time taken to fall
asleep, and average hours of sleep per night.
2.5. SNP Selection and Genotyping
A total of 12 genetic variants were selected for inclusion in this study based on
their consistent association with obesity traits as reported in previous studies [
21
–
23
]. The
selected genetic variants included ADCY3 rs11676272, CLOCK rs1801260, GPR61 rs41279738,
FTO rs14210, RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597. These
gene polymorphisms were also associated with various circadian rhythm traits, such as
sleep duration, daytime sleepiness, and chronotype, based on summary statistics from the
UK Biobank (https://genetics.opentargets.org; accessed on 15 October 2024).
Buccal epithelium samples, collected with disposable sterile probes, were used for a
molecular genetic analysis. DNA was extracted using the sorbent method and the DNA-
sorb-AM reagent kit (FBUN Central Research Institute of Epidemiology of Rospotrebnadzor,
Moscow, Russia), following the manufacturer’s instructions. Gene polymorphisms were
identified via real-time polymerase chain reaction using Testgen kits (Ulyanovsk, Russia).
2.6. Genetic Risk Score Calculation
Each genetic polymorphism was weighted based on its relative effect size (coefficient
β
) obtained from a regression analysis of BMI and the polymorphic locus under study. The
weighted score of the genetic variants was calculated according to previously described
methods [
14
,
18
,
24
–
26
]. The genotypes were assigned values of 0, 1, or 2 based on the
number of risk alleles, which were then weighted by multiplying by the
β
coefficient.
Given the additive genetic architecture of the BMI, the weighted scores for the seven gene
variants were summed to estimate the cumulative effect of these gene polymorphisms
on BMI. The polygenic risk score for the seven gene variants was calculated using the
following formula:
GBMI =β1SNP1+ . . . β7SNP7,
where G—genetic risk and β—effect size of genotype.
2.7. Lifestyle Risk Score Calculation
The weighted environmental factors were calculated using a similar approach. A total
of 11 lifestyle parameters associated with BMI were included to evaluate the influence of
environmental factors on BMI. Additionally, lifestyle parameters that increased the model’s
Genes 2024,15, 1506 4 of 13
coefficient of determination were incorporated. To incorporate categorical variables into
the regression analysis, a matrix of dummy variables was constructed. Each environmental
factor was assigned a value of 1 if it contributed to the risk of overweight or obesity, or 0 if
it did not. A weighted assessment of the environmental factors was conducted using the
β
coefficient.
2.8. Statistical Analysis
Statistical analyses were performed using GraphPad InStat Version 3.05 (GraphPad Soft-
ware, Inc., San Diego, CA, USA) software. The differences in allele and genotype frequencies
between the samples and Hardy–Weinberg equilibrium compliance were tested using the
χ2
test. A pvalue < 0.05 was considered statistically significant. Multiple regression analysis was
used to assess the relationship between BMI and lifestyle parameters, including covariates
such as sex, age, ethnicity, and physical activity level. The Benjamini–Hochberg correction was
applied to control the false discovery rate (FDR) for multiple testing. The parametric data were
checked for Gaussian distribution using the Kolmogorov–Smirnov test, with a
pvalue < 0.05
indicating non-normality. For data not meeting the Gaussian distribution criteria, a Box–Cox
transformation was applied, followed by a normality check.
To evaluate the combined effect of genetic variants (G), environmental factors (E),
and gene–environment interactions (G
×
E) on BMI, a multiple regression analysis was
conducted. The dependent variable was BMI, and the independent variables included
sex, age, ethnicity, weighted genotype scores of seven polymorphic loci, and lifestyle
parameters. The coefficient of determination (R
2
) was calculated to estimate the model’s
influence on BMI. Additional covariates, such as age, sex, and ethnicity, were included in
the BMI model. A linear regression model was constructed to assess the combined effect of
genetic and environmental factors on BMI, adjusted for sex, age, and ethnicity.
3. Results
3.1. Anthropometric Characteristics
Among the 658 participants, 550 students (456 females and 94 males; mean age (SD):
21.4 (3.6) years) were classified as normal weight or underweight, while 108 students
(75 females and 33 males; mean age: 22.9 (5.6) years) were classified as overweight or
obese. Males classified as underweight or normal weight (i.e., non-obese) were significantly
younger than males categorized as overweight or obese (p= 0.0002). No age differences
were observed between the two subgroups of females. Both non-obese males and females
had significantly (p< 0.0001) lower BMI, fat mass, and fat-free mass compared to overweight
or obese individuals (Table 1).
Table 1. Anthropometric characteristics of participants.
Traits
Males Females
Non-Obese
(n= 94)
Overweight or
Obese
(n= 33)
Non-Obese
(n= 456)
Overweight or
Obese
(n= 75)
Age, years 21.5 (3.7) 24.9 (6.9) ** 20.8 (2.8) 22.0 (4.7)
BMI, kg/m221.0 (2.0) 28.3 (2.7) * 20.0 (2.0) 28.1 (2.8) *
Fat mass, kg 9.9 (4.6) 22.2 (5.7) * 14.1 (4.2) 26.8 (5.5) *
Fat-free mass, kg, 61.6 (7.3) 66.3 (6.5) * 30.1 (5.3) 49.5 (4.8) *
*p< 0.0001 and ** p= 0.0002; data are means (SD).
3.2. Association Between Genetic Variants and BMI
The genotype distributions for the ADCY3 rs11676272, CLOCK rs1801260, FTO rs1421085,
GPR61 rs41279738, RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597
polymorphisms were in Hardy–Weinberg equilibrium (p> 0.05). To identify genetic variants
associated with obesity, a case-control study was conducted, comparing allelic frequencies
between overweight or obese (cases) and non-obese (controls) participants. When allelic
Genes 2024,15, 1506 5 of 13
frequencies differed by ethnicity (Supplementary Table S1), separate analyses were per-
formed. After correcting for multiple testing, we identified associations between three
genetic variants and BMI that aligned with findings in the literature, where obesity-related
risk alleles were more prevalent among overweight or obese subjects. Specifically, the
frequencies of the ADCY3 rs11676272 G (60.3 vs. 41.0%, p= 0.03), FTO rs1421085 C (46.7 vs.
32.0%, p= 0.04), and SLC22A3 rs9364554 T (38.0 vs. 23.7%, p= 0.01) alleles were signif-
icantly higher in overweight or obese individuals compared to non-obese participants
(Supplementary Table S2). Furthermore, BMI (SD) significantly (p< 0.05) increased with an
increase in the number of risk alleles in the whole cohort (ADCY3: 20.7 (3.5)
→
21.7 (3.2)
→
22.3 (3.4) kg/m
2
;FTO: 20.9 (3.5)
→
21.7 (3.8)
→
22.6 (3.6) kg/m
2
; and SLC22A3: 21.3 (4.5)
→21.5 (4.3) →24.6 (5.6) kg/m2) (Figure 1).
Genes 2024, 15, x FOR PEER REVIEW 5 of 13
Fat mass, kg 9.9 (4.6) 22.2 (5.7) * 14.1 (4.2) 26.8 (5.5) *
Fat-free mass, kg, 61.6 (7.3) 66.3 (6.5) * 30.1 (5.3) 49.5 (4.8) *
* p < 0.0001 and ** p = 0.0002; data are means (SD).
3.2. Association Between Genetic Variants and BMI
The genotype distributions for the ADCY3 rs11676272, CLOCK rs1801260, FTO
rs1421085, GPR61 rs41279738, RP11-775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B
rs734597 polymorphisms were in Hardy–Weinberg equilibrium (p > 0.05). To identify ge-
netic variants associated with obesity, a case-control study was conducted, comparing al-
lelic frequencies between overweight or obese (cases) and non-obese (controls) partici-
pants. When allelic frequencies differed by ethnicity (Supplementary Table S1), separate
analyses were performed. After correcting for multiple testing, we identified associations
between three genetic variants and BMI that aligned with findings in the literature, where
obesity-related risk alleles were more prevalent among overweight or obese subjects. Spe-
cifically, the frequencies of the ADCY3 rs11676272 G (60.3 vs. 41.0%, p = 0.03), FTO
rs1421085 C (46.7 vs. 32.0%, p = 0.04), and SLC22A3 rs9364554 T (38.0 vs. 23.7%, p = 0.01)
alleles were significantly higher in overweight or obese individuals compared to non-
obese participants (Supplementary Table S2). Furthermore, BMI (SD) significantly (p <
0.05) increased with an increase in the number of risk alleles in the whole cohort (ADCY3:
20.7 (3.5) → 21.7 (3.2) → 22.3 (3.4) kg/m2; FTO: 20.9 (3.5) → 21.7 (3.8) → 22.6 (3.6) kg/m2;
and SLC22A3: 21.3 (4.5) → 21.5 (4.3) → 24.6 (5.6) kg/m2) (Figure 1).
Figure 1. The mean BMI among individuals with different genotypes of the ADCY3 rs11676272, FTO
rs1421085, and SLC22A3 rs9364554 polymorphisms. Protective alleles: ADCY3 A, FTO T, and
SLC22A3 C. Risk alleles: ADCY3 G, FTO C, and SLC22A3 T. * p < 0.05.
Although the remaining four SNPs (CLOCK rs1801260, GPR61 rs41279738, RP11-
775H9.2 rs1296328, and TFAP2B rs734597) did not show significant associations with BMI,
we considered it justified to include all seven SNPs in the polygenic analysis, as each had
been previously identified through genome-wide association studies—a common ap-
proach to minimize false-negative results. Consequently, a weighted polygenic risk score
(Supplementary Table S3) based on these seven gene variants (ADCY3 rs11676272, CLOCK
rs1801260, GPR61 rs41279738, FTO rs1421085, RP11-775H9.2 rs1296328, SLC22A3
rs9364554, and TFAP2B rs734597) explained 8.3% (p < 0.0001) of the variance in body mass
index (BMI).
3.3. Association Between Lifestyle Parameters and BMI
A multiple regression analysis was conducted to examine the relationship between
BMI and various lifestyle parameters of the subjects, adjusted for sex, age, ethnicity, and
Figure 1. The mean BMI among individuals with different genotypes of the ADCY3 rs11676272,
FTO rs1421085, and SLC22A3 rs9364554 polymorphisms. Protective alleles: ADCY3 A, FTO T, and
SLC22A3 C. Risk alleles: ADCY3 G, FTO C, and SLC22A3 T. * p< 0.05.
Although the remaining four SNPs (CLOCK rs1801260, GPR61 rs41279738, RP11-
775H9.2 rs1296328, and TFAP2B rs734597) did not show significant associations with BMI,
we considered it justified to include all seven SNPs in the polygenic analysis, as each
had been previously identified through genome-wide association studies—a common
approach to minimize false-negative results. Consequently, a weighted polygenic risk
score (Supplementary Table S3) based on these seven gene variants (ADCY3 rs11676272,
CLOCK rs1801260, GPR61 rs41279738, FTO rs1421085, RP11-775H9.2 rs1296328, SLC22A3
rs9364554, and TFAP2B rs734597) explained 8.3% (p< 0.0001) of the variance in body mass
index (BMI).
3.3. Association Between Lifestyle Parameters and BMI
A multiple regression analysis was conducted to examine the relationship between
BMI and various lifestyle parameters of the subjects, adjusted for sex, age, ethnicity, and
physical activity level. This analysis identified eight lifestyle factors significantly associated
with BMI (Figure 2).
Data from the lifestyle questionnaires revealed several positive associations with BMI:
frequency of overeating (r
2
= 3.5;
β
= 0.006; and p= 0.03), consumption of food without
feeling hungry (r
2
= 4.6;
β
= 0.006; and p= 0.003), frequency of ground coffee consumption
(r
2
= 5.7;
β
= 0.007; and p= 0.0004), and evening coffee consumption (r
2
= 3.7;
β
= 0.003; and
p= 0.02). Additionally, individuals who consumed fewer meals had a higher BMI (r
2
= 4.5;
β
=
−
0.004; and p= 0.003), while those who preferred nuts as snacks exhibited lower BMI
Genes 2024,15, 1506 6 of 13
values (r
2
= 3.8;
β
=
−
0.0014; and p= 0.02). The analysis of the Pittsburgh Sleep Quality
Questionnaire indicated that shorter sleep duration was associated with a greater BMI
(r
2
= 3.9;
β
=
−
0.008; and p= 0.01) among men (n = 92), and later bedtime was associated
with higher BMI values (r2= 3.9; β= 0.003; and p= 0.009).
Genes 2024, 15, x FOR PEER REVIEW 6 of 13
physical activity level. This analysis identified eight lifestyle factors significantly associ-
ated with BMI (Figure 2).
Figure 2. Coefficients of determination (r2) for lifestyle factors associated with BMI: blue indicates
positive relationships, while orange indicates negative relationships.
Data from the lifestyle questionnaires revealed several positive associations with
BMI: frequency of overeating (r2 = 3.5; β = 0.006; and p = 0.03), consumption of food without
feeling hungry (r2 = 4.6; β = 0.006; and p = 0.003), frequency of ground coffee consumption
(r2 = 5.7; β = 0.007; and p = 0.0004), and evening coffee consumption (r2 = 3.7; β = 0.003; and
p = 0.02). Additionally, individuals who consumed fewer meals had a higher BMI (r2 = 4.5;
β = −0.004; and p = 0.003), while those who preferred nuts as snacks exhibited lower BMI
values (r2 = 3.8; β = −0.0014; and p = 0.02). The analysis of the Pisburgh Sleep Quality
Questionnaire indicated that shorter sleep duration was associated with a greater BMI (r2
= 3.9; β = −0.008; and p = 0.01) among men (n = 92), and later bedtime was associated with
higher BMI values (r2 = 3.9; β = 0.003; and p = 0.009).
Additionally, we investigated the correlation between body mass index (BMI) and
the number of risky lifestyle parameters. As shown in Figure 3, the presence of two addi-
tional lifestyle risk factors was significantly associated with an increase in BMI (20.3 (2.5)
→ 21.1 (3.2) → 22.3 (4.2) → 22.6 (4.3) kg/m2; p < 0.0001 for the linear trend). When the
number of lifestyle factors ranged from six to eight, BMI reached its peak value of 22.6
kg/m2.
Figure 3. Relationship between the number of lifestyle risk factors and BMI. p < 0.0001 for the linear
trend.
Next, we developed a model that accounted for all these lifestyle risk factors. To en-
hance the model’s robustness, additional factors—including years of physical activity, an-
tibiotic intake over the past year, and frequency of dysbiosis symptoms—were included
Figure 2. Coefficients of determination (r
2
) for lifestyle factors associated with BMI: blue indicates
positive relationships, while orange indicates negative relationships.
Additionally, we investigated the correlation between body mass index (BMI) and the
number of risky lifestyle parameters. As shown in Figure 3, the presence of two additional
lifestyle risk factors was significantly associated with an increase in BMI (20.3 (2.5)
→
21.1
(3.2)
→
22.3 (4.2)
→
22.6 (4.3) kg/m
2
;p< 0.0001 for the linear trend). When the number of
lifestyle factors ranged from six to eight, BMI reached its peak value of 22.6 kg/m2.
Genes 2024, 15, x FOR PEER REVIEW 6 of 13
physical activity level. This analysis identified eight lifestyle factors significantly associ-
ated with BMI (Figure 2).
Figure 2. Coefficients of determination (r2) for lifestyle factors associated with BMI: blue indicates
positive relationships, while orange indicates negative relationships.
Data from the lifestyle questionnaires revealed several positive associations with
BMI: frequency of overeating (r2 = 3.5; β = 0.006; and p = 0.03), consumption of food without
feeling hungry (r2 = 4.6; β = 0.006; and p = 0.003), frequency of ground coffee consumption
(r2 = 5.7; β = 0.007; and p = 0.0004), and evening coffee consumption (r2 = 3.7; β = 0.003; and
p = 0.02). Additionally, individuals who consumed fewer meals had a higher BMI (r2 = 4.5;
β = −0.004; and p = 0.003), while those who preferred nuts as snacks exhibited lower BMI
values (r2 = 3.8; β = −0.0014; and p = 0.02). The analysis of the Pisburgh Sleep Quality
Questionnaire indicated that shorter sleep duration was associated with a greater BMI (r2
= 3.9; β = −0.008; and p = 0.01) among men (n = 92), and later bedtime was associated with
higher BMI values (r2 = 3.9; β = 0.003; and p = 0.009).
Additionally, we investigated the correlation between body mass index (BMI) and
the number of risky lifestyle parameters. As shown in Figure 3, the presence of two addi-
tional lifestyle risk factors was significantly associated with an increase in BMI (20.3 (2.5)
→ 21.1 (3.2) → 22.3 (4.2) → 22.6 (4.3) kg/m2; p < 0.0001 for the linear trend). When the
number of lifestyle factors ranged from six to eight, BMI reached its peak value of 22.6
kg/m2.
Figure 3. Relationship between the number of lifestyle risk factors and BMI. p < 0.0001 for the linear
trend.
Next, we developed a model that accounted for all these lifestyle risk factors. To en-
hance the model’s robustness, additional factors—including years of physical activity, an-
tibiotic intake over the past year, and frequency of dysbiosis symptoms—were included
Figure 3. Relationship between the number of lifestyle risk factors and BMI. p< 0.0001 for the linear trend.
Next, we developed a model that accounted for all these lifestyle risk factors. To
enhance the model’s robustness, additional factors—including years of physical activity,
antibiotic intake over the past year, and frequency of dysbiosis symptoms—were included
(Supplementary Table S4). Overall, these factors accounted for 7.8% of the variance in BMI
(p< 0.0001).
3.4. Gene–Lifestyle Interaction
To evaluate the combined influence of lifestyle and genetic factors on the risk of over-
weight and obesity, the following multiple regression model was constructed, incorporating
gene–lifestyle interactions:
Y = 1.5 + 0.001C1−0.01C2+ 0.003C3+ 68.4G ×E,
Genes 2024,15, 1506 7 of 13
where Y is BMI, C1is ethnicity, C2is sex, C3is age, and GxE is gene–lifestyle interaction.
This model explained 15.0% of the variance in BMI among subjects (p< 0.0001). Addi-
tionally, the interaction between individual lifestyle factors and the genetic risk score was
assessed. A significant interaction was identified between the polygenic score and several
lifestyle variables, including meal frequency, frequency of overeating, nut consumption as
a snack, years of physical activity, sleep duration, and bedtime (Supplementary Table S5).
As shown in Figure 4, the quartile analysis of the weighted gene–lifestyle interaction score
reveals a positive correlation between quartiles and BMI (p< 0.0001). Specifically, the mean
(SD) BMI for individuals in the fourth quartile was 23.6 (4.1) kg/m
2
, which is 1.8 kg/m
2
higher than that of individuals in the third quartile (21.8 (4.2) kg/m
2
), 2.5 kg/m
2
higher
than those in the second quartile (21.1 (3.3) kg/m
2
), and 4.0 kg/m
2
higher than individuals
in the first quartile (19.6 (2.7) kg/m
2
). This indicates that BMI increases with a higher
weighted score, reflecting the combined genetic and environmental risk factors for obesity.
Genes 2024, 15, x FOR PEER REVIEW 7 of 13
(Supplementary Table S4). Overall, these factors accounted for 7.8% of the variance in BMI
(p < 0.0001).
3.4. Gene–Lifestyle Interaction
To evaluate the combined influence of lifestyle and genetic factors on the risk of over-
weight and obesity, the following multiple regression model was constructed, incorporat-
ing gene–lifestyle interactions:
Y = 1.5 + 0.001C1 − 0.01C2 + 0.003C3 + 68.4G × E,
where Y is BMI, C1 is ethnicity, C2 is sex, C3 is age, and GxE is gene–lifestyle interaction.
This model explained 15.0% of the variance in BMI among subjects (p < 0.0001). Ad-
ditionally, the interaction between individual lifestyle factors and the genetic risk score
was assessed. A significant interaction was identified between the polygenic score and
several lifestyle variables, including meal frequency, frequency of overeating, nut con-
sumption as a snack, years of physical activity, sleep duration, and bedtime (Supplemen-
tary Table S5). As shown in Figure 4, the quartile analysis of the weighted gene–lifestyle
interaction score reveals a positive correlation between quartiles and BMI (p < 0.0001).
Specifically, the mean (SD) BMI for individuals in the fourth quartile was 23.6 (4.1) kg/m2,
which is 1.8 kg/m2 higher than that of individuals in the third quartile (21.8 (4.2) kg/m2),
2.5 kg/m2 higher than those in the second quartile (21.1 (3.3) kg/m2), and 4.0 kg/m2 higher
than individuals in the first quartile (19.6 (2.7) kg/m2). This indicates that BMI increases
with a higher weighted score, reflecting the combined genetic and environmental risk fac-
tors for obesity.
Figure 4. Distribution of BMI by quartile of weighted gene–lifestyle interaction score.
4. Discussion
Obesity results from a complex interplay of genetic and environmental influences.
Over the past decade, significant efforts have been made to explore how these factors in-
teract in the context of obesity. The objective of these studies is to elucidate the network
of interactions involved in the development of complex diseases such as obesity, where
multiple genes and environmental factors can modulate individual risk [27]. Traditional
methodologies for examining gene–environment interactions in obesity typically assess
the impact of genetic predisposition on the development of the condition, with
Figure 4. Distribution of BMI by quartile of weighted gene–lifestyle interaction score.
4. Discussion
Obesity results from a complex interplay of genetic and environmental influences.
Over the past decade, significant efforts have been made to explore how these factors
interact in the context of obesity. The objective of these studies is to elucidate the network
of interactions involved in the development of complex diseases such as obesity, where
multiple genes and environmental factors can modulate individual risk [
27
]. Traditional
methodologies for examining gene–environment interactions in obesity typically assess the
impact of genetic predisposition on the development of the condition, with environmental
factors acting as moderators of the relationship between genetic influence and BMI [12].
In this study, we evaluated three models to assess the impacts of genetic, environ-
mental, and gene–environment interactions on BMI variability among students. The first
model included genetic variants known to be associated with obesity—specifically, ADCY3
rs11676272, CLOCK rs1801260, GPR61 rs41279738, FTO rs1421085, RP11-775H9.2 rs1296328,
SLC22A3 rs9364554, and TFAP2B rs734597—and explained 8.3% of the variance in BMI.
These genes are critical for regulating energy homeostasis and circadian rhythms, and
previous studies have associated these polymorphisms with obesity [21–23].
The second model focused on environmental factors, incorporating lifestyle param-
eters such as the frequency of overeating, consumption of food without feeling hungry,
meal frequency, ground coffee consumption, timing of coffee consumption (evening versus
morning), sleep duration, bedtime, consumption of nuts as a snack, years of physical activ-
ity, frequency of antibiotic use in the past year, and the presence of dysbiosis symptoms.
The findings of this study align with the existing literature, which links a high frequency of
Genes 2024,15, 1506 8 of 13
overeating [
28
,
29
], frequent consumption of food without hunger [
29
], short sleep dura-
tion [
30
,
31
], late bedtimes [
32
,
33
], and low levels of physical activity [
34
,
35
] to an increased
risk of obesity.
Notably, our study presents a novel finding: a high frequency of coffee consumption
and evening coffee consumption are linked to an increased risk of overweight and obesity.
Although evidence suggests that coffee consumption can have favorable effects on BMI
and weight loss [
36
,
37
], recent studies indicate it may also negatively impact body com-
position [
38
–
40
]. A recent large-scale genome-wide association study (GWAS) identified
genes associated with coffee intake, substance use, and obesity-related traits. Notably, these
genes are predominantly expressed in the brain and appear to influence human behavior.
This hypothesis is supported by findings from a recent randomized controlled trial, which
demonstrated a significant link between coffee consumption and an increased desire for
sweet foods, as well as higher fructose intake and triglyceride levels [
41
]. Furthermore, it
was shown that individuals with elevated genetic risk (based on three genetic variants)
experienced a 50% increase in insulin resistance when consuming high amounts of coffee
(over 10 cups per day) [
42
]. This suggests that the consumption of coffee may stimulate
additional intake of sweet or fatty foods, contributing to weight gain. Moreover, Costa
et al. [
43
] conducted a two-year prospective study revealing that coffee consumption is
linked to increased obesity, particularly abdominal obesity, as well as decreased muscle
quality. Mendelian randomization analyses highlighted differing effects of coffee consump-
tion and plasma caffeine levels on BMI [
44
]. The findings indicated that coffee consumption
correlates with increased BMI, while higher caffeine levels are associated with a decrease in
BMI. This multidirectional effect may arise from individuals with slow caffeine metabolism
resulting in lower plasma levels—compensating by increasing their caffeine intake.
The timing of coffee consumption may also mediate its effects. In the present study,
subjects with a high genetic predisposition exhibited a higher BMI when consuming coffee
in the evening. Data from mouse studies suggest that caffeine consumed early in the active
period inhibits weight gain associated with high-fat meals [
45
]. The lipolytic effects of
morning coffee intake are attributed to increased expression of circadian rhythm genes that
regulate metabolism and suppression of lipogenesis gene expression [45].
The third model, which estimated the impact of gene–lifestyle interaction on BMI,
demonstrated the most significant contribution at 15%. This study’s findings indicated that
individuals with a genetic predisposition (based on seven polymorphic loci) displayed a
heightened risk of overweight and obesity when exposed to specific lifestyle parameters.
Moreover, stratifying individuals into quartiles based on their weighted gene–lifestyle
interaction scores revealed a statistically significant increase in mean BMI values. Subjects
in the fourth quartile had a BMI 4.0 kg/m
2
higher than those in the first quartile. These
findings underscore the role of the interaction between identified genetic and lifestyle
factors in elevating obesity risk. Additionally, the complex interplay between all lifestyle
factors and genetic polymorphisms accounted for a greater proportion of BMI variability
than any single lifestyle factor alone. This reinforces the importance of adhering to all
aspects of a healthy lifestyle to minimize the risk of developing obesity.
Previous GWASs have demonstrated the association of the studied polymorphic
loci ADCY3 rs1167627272, CLOCK rs1801260, FTO rs1421085, GPR61 rs41279738, RP11-
775H9.2 rs1296328, SLC22A3 rs9364554, and TFAP2B rs734597 with obesity-related phe-
notypes
[25–27]
. Of these, three genetic variants (ADCY3 rs11676272, FTO rs1421085, and
SLC22A3 rs9364554) were significantly associated with BMI individually in our study, as
well as all seven loci in combination. The ADCY3 gene encodes adenyl cyclase 3, which
catalyzes the conversion of ATP to cAMP, a molecule involved in signaling pathways for
glucagon-like peptide 1, ghrelin, orexins,
α
-melanocyte-stimulating hormone, and lep-
tin [
46
,
47
]. The SLC22A3 gene (also known as organic cation transporter 3, OCT3) mediates
norepinephrine uptake in white adipose tissue, which activates
β
3-adrenergic receptors to
increase cAMP and protein kinase A levels, thereby enhancing lipolysis through hormone-
sensitive lipase [
48
,
49
]. In mice, Slc22a3 promotes adipose browning, thermogenesis, and
Genes 2024,15, 1506 9 of 13
mitochondrial biogenesis [
49
]. The FTO gene encodes
α
-ketoglutarate-dependent dioxyge-
nase, with many variants associated with obesity-related traits. Notably, previous research
has identified an interaction between the FTO rs1421085 locus and various environmental
factors. Specifically, studies indicated that dietary fiber intake [
50
], trans fat consump-
tion [
51
], and adherence to the Mediterranean diet [
52
] influenced obesity risk among
genetically predisposed individuals. Furthermore, factors such as fast food and sugary
beverage consumption [
53
], meal frequency [
54
], and physical activity levels [
55
] have also
been shown to affect obesity risk in this population.
Previous studies have shown that the interplay between genetic variants and factors
such as physical activity level, smoking, alcohol consumption, and socioeconomic status
contributes to BMI variability. Often, polygenic risk scores are calculated based on the
polymorphic loci identified by Locke et al. [
56
]. For instance, a large-scale study found
that genotype–smoking interactions accounted for 4.0% of BMI variability [
57
]. A recent
British Biobank investigation involving over 4 million single-nucleotide polymorphisms
reported a more modest contribution of environmental factors [
58
]. This study indicated
that the MET score, the number of pack-years of smoking, and the frequency of alcohol
consumption interacted with genetic variants to partially explain BMI variance, with
heritability contributions of 0.45% for the MET score, 0.52% for pack-years of smoking,
and 0.32% for alcohol frequency. Sulc et al. [
59
] reported a gene-mediated contribution of
1.9% to BMI variability using a model that treated environmental factors as random effects,
requiring only outcome and genetic data. Additionally, interactions between polymorphic
loci and age accounted for 0.4%, while neuroticism score, physical activity, and alcohol
consumption frequency contributed 0.7%, 0.3%, and 0.3%, respectively, to BMI variability.
In this study, we found no evidence that interactions between alcohol consumption and
smoking with genetic variants contributed to BMI variance. Additionally, the student
cohort was largely homogeneous regarding socioeconomic status, limiting our ability to
explore the potential influences of socioeconomic factors and genotype on BMI variability.
It should be noted that our study has limitations due to its cross-sectional design and
reliance on self-reported data. While self-reporting is common in epidemiological research,
it can introduce biases, such as recall and social desirability bias. Nonetheless, some of
the questionnaires used have been validated and shown to be reliable in previous studies,
lending confidence to our findings despite the limitations inherent to self-reported data. We
also acknowledge that this study used a limited number of genetic markers associated with
obesity, despite the existence of several hundred known obesity-related DNA polymor-
phisms [
10
,
56
]. In addition, numerous environmental factors influencing obesity were not
considered in this study, which represents a limitation. Finally, polygenic risk scores (PRSs)
have several limitations, including limited predictive accuracy for complex traits, poor
generalizability across different ancestries, inability to account for environmental factors,
difficulty in interpreting individual results, and a lack of robust clinical applications due to
the relatively small effect sizes of individual genetic variants [60].
5. Conclusions
This study revealed that individuals with a higher genetic predisposition, as defined
by the seven polymorphic loci, are more susceptible to becoming overweight or obese
under certain lifestyle conditions. The gene–lifestyle interaction model we developed
accounted for 15% of the variance in BMI. This model incorporated genetic variants that
have been underexplored in similar studies, alongside a broad range of environmental
factors encompassing various aspects of an individual’s life (dietary habits, physical activity,
circadian rhythms, antibiotic intake, etc.). In contrast, many prior studies have focused on
a narrower set of lifestyle factors. Our findings underscore that substantial contributions to
BMI variance arise from the complex interplay of genetic and environmental factors rather
than from any single factor. These insights may inform the development of prevention
strategies and non-pharmacological obesity management approaches that integrate genetic
Genes 2024,15, 1506 10 of 13
data with individual environmental contexts, potentially enhancing the effectiveness of
existing interventions [61–63].
Supplementary Materials: The following supporting information can be downloaded at https:
//www.mdpi.com/article/10.3390/genes15121506/s1: Table S1: Differences in genotype distribution
and allele frequencies across ethnicities. Table S2: Genotype and allele frequencies of significant
gene polymorphisms by ethnicity and BMI group. Table S3: Calculation of weighted polygenic risk
score for obesity. Table S4: Binary values assigned to risk factors based on questionnaire responses.
Table S5: Contribution of genetic risk score and lifestyle factor interactions to BMI variance.
Author Contributions: Conceptualization, E.S.E. and I.I.A.; methodology, E.S.E.; formal analysis,
E.S.E.; investigation, E.S.E., K.K.A., E.R.B., A.E.Z., and E.V.V.; writing—original draft preparation,
E.S.E.; writing—review and editing, I.I.A.; supervision, I.I.A. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: This study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of Kazan State Medical University (protocol #10,
dated 21 November 2023).
Informed Consent Statement: Informed consent was obtained from all the subjects involved in
this study.
Data Availability Statement: The data that support the findings of this study are available from the
corresponding author upon reasonable request due to the privacy of the students.
Conflicts of Interest: The authors declare no conflicts of interest.
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