Anastasia E. Zhilina
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Anastasia E. Zhilina has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Elena V. Valeeva
Kazan State Medical University, Ministry of Health of the Russian Federation
1 shared publication - Elvina R. Bikbova
Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia
1 shared publication - Emiliya S. Egorova
Central Research Laboratory, Kazan State Medical University, Kazan 420012, Russia
1 shared publication - Ildus I. Ahmetov
Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, Kazan, Russia
1 shared publication - Kamilla K. Aseyan
Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia
1 shared publication
Latest Publications
- Open Access
Effects of Gene–Lifestyle Interaction on Obesity Among Students
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.
Open AccessEffects of Gene–Lifestyle Interaction on Obesity Among Students
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.