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. 2026 Apr 20;20(5):820–833. doi: 10.4162/nrp.2026.20.5.820

Association of stress-related genetic risk and lifestyle factors with BMI in Korean adults

Ga Young Lee 1, Junghak Lee 2, Jeong-Han Kim 2, Kyong-Mee Chung 3, Sung Nim Han 1,4,✉
PMCID: PMC13669484  PMID: 42889467

Abstract

BACKGROUND/OBJECTIVES

Genetic susceptibility to stress may influence behavioral and metabolic responses involved in dietary choices and body weight regulation. While previous studies have focused on individual genetic variants related to dietary intake and obesity risk, a polygenic approach integrating multiple stress-related single nucleotide polymorphisms may better capture overall genetic susceptibility to stress. This study examined the relationship among stress-related polygenic risk, dietary intake, serum metabolite profiles, and body weight, and determined whether this relationship could be influenced by sleep duration and physical activity (PA).

SUBJECTS/METHODS

This study included 100 disease-free participants with a body mass index (BMI) of 18.5 to 25.0 kg/m2. The genetic scores for stress (GSstress) were determined using 4 genes: DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HTR2C rs3813929. Participants were classified into 4 categories: Low GSstress (n = 29), Normal GSstress (n = 16), Alert GSstress (n = 36), and Risk GSstress (n = 19). Anthropometric measurements, dietary intake, PA, sleep duration, serum cortisol, and serum metabolite levels were evaluated.

RESULTS

Serum cortisol concentration was significantly higher in the Risk GSstress group than in the Low GSstress and Alert GSstress groups. Participants with higher risk for stress consumed significantly higher sugar-sweetened beverages (SSB) than those at low risk. Additionally, there was a significant interaction effect between GSstress and sleep duration in relation to BMI (P = 0.041), with a positive association found only in individuals with short sleep duration (< 6 h/day).

CONCLUSION

This study demonstrated that individuals with a higher genetic risk for stress exhibited elevated cortisol levels and were associated with greater SSB intake and higher BMI. This relationship was more pronounced in those with insufficient sleep, emphasizing the importance of lifestyle factors in mitigating the effect of genetic stress risk on body weight.

Trial Registration

Clinical Research Information Service Identifier: KCT0004650

Keywords: Psychological stress, genotype, body mass index, sugar-sweetened beverages, physical activity, sleep duration

INTRODUCTION

Stress is defined as a state of disturbed homeostasis, accompanied by physiological and psychological changes [1,2]. In response to stress, the sympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis are activated, leading to the release of cortisol. Cortisol, often referred to as the stress hormone, plays a crucial role in helping the body cope with stress by regulating blood glucose levels through gluconeogenesis and promoting proteolysis and lipogenesis [3,4,5].

Stress can influence eating behavior by altering appetite regulation and reward-related responses. Exposure to stress often promotes the intake of energy-dense foods, resulting in excessive consumption and weight gain [6,7]. In addition, several studies have reported an association between stress response and sugar-sweetened beverages (SSB) intake [8,9]. Although the precise underlying factors contributing to stress-related eating behavior remain unclear, it is widely recognized as a multifactorial phenomenon, influenced by psychological, physiological, and genetic mechanisms [10]. Physiological factors, such as stress and negative emotions, can disrupt emotional regulation, while imbalances in appetite-regulating hormones such as ghrelin and leptin are known to induce overeating [11].

Furthermore, emerging evidence suggests that individual differences in genetic susceptibility to stress may contribute to variability in stress-related eating behaviors [12]. Specific genes, including COMT rs4680, BDNF rs6265, DRD2 rs1800497, and HTR2C rs3813929, have been implicated in stress sensitivity, binge eating, and body mass index (BMI) [13,14,15,16,17,18,19]. Individuals possessing the BDNF rs6265 gene ‘Met’ risk allele exhibited higher stress sensitivity levels than those with the ‘Val’ allele [16]. The COMT rs4680 and DRD2 rs1800497 genes are involved in the metabolism of dopamine and norepinephrine, contributing to stress-related eating behaviors [15,20], while HTR2C rs3813929 interacts with serotonin, influencing mood and food intake [21]. These genes are known to influence catecholamine metabolism and are involved in the regulation of the HPA axis, which regulates cortisol secretion [22,23,24,25]. To capture the combined effect of these genetic variants on stress sensitivity, we computed a genetic score of stress-related genes (GSstress), based on the 4 validated single nucleotide polymorphisms (SNPs) including COMT rs4680, BDNF rs6265, DRD2 rs1800497, and HTR2C rs3813929. Participants were then categorized according to their GSstress, and differences in dietary intake and body weight were assessed across categories.

Moreover, circulating metabolites have been recognized as critical intermediates linking dietary intake to metabolic responses [26]. Metabolomic profiles may reflect individual biochemical responses to both dietary factors and psychological stress, offering valuable insights into the biological pathways underlying stress-related eating behaviors. However, it remains unclear how circulating metabolites are related to eating behaviors associated with genetic susceptibility to stress.

In addition to genetic predisposition, lifestyle factors such as sleep duration and physical activity (PA) have been shown to modulate the impact of genetic risk on dietary intake and body weight [27,28]. Short sleep duration has been associated with an increased genetic susceptibility to weight gain [28], while PA levels have been found to influence the genetic impact on emotional eating and food preferences [27]. These findings underscore the need to consider both genetic and environmental factors in emotional eating. Therefore, it is crucial to conduct research exploring not only the significant impact of polygenic stress risk on dietary intake, but also how lifestyle factors may amplify or attenuate these genetic influences.

In this study, we aimed to investigate the interplay among stress-related genetic risk, dietary intake, serum metabolite profiles, and body weight, while also exploring whether this association is influenced by levels of sleep and PA. The findings of this study may provide insights into the associations between stress-related genetic susceptibility, dietary intake, serum metabolite profiles, and BMI, particularly among individuals with high susceptibility to stress.

SUBJECTS AND METHODS

Study participants

This study was a cross-sectional analysis using baseline data obtained as part of a previously reported randomized controlled trial [29,30]. For the present analysis, only data collected at the baseline assessment prior to randomization and intervention were used. Study participants included adults aged 25 to 35 yrs with a BMI ranging from 18.5 to 25 kg/m2, classified as normal weight to overweight according to the Asia-Pacific classification of BMI. To avoid potential confounding from ongoing treatment or medications in individuals with existing diseases, this study included only healthy participants without conditions such as cancer, diabetes, kidney disease, cardiovascular disease, or pulmonary disorders. Detailed inclusion and exclusion criteria are available in the Study Protocol. Recruitment was conducted through poster advertisements at Seoul National University from 27/12/2018 to 10/01/2019. The 100 eligible participants provided written consent. The original study was approved by the Seoul National University Institutional Review Board (IRB #1901/001-004) and registered in the Clinical Research Information Service database (CRIS; KCT0004650).

Genotyping and classification of participants

The genetic analysis was performed on peripheral blood using the Theragen Precision Medicine Research Array chip manufactured by TheragenEtex Bio Institute (TheragenEtex Inc., Suwon, Korea). The genotyping data showed a high call rate (> 99%) and Dish Quality Control values >0.92 across all samples. Based on the standard genetic report from a commercially available genetic testing service (TheragenEtex Bio Institute, Suwon, Korea), a comprehensive GSstress was calculated from 4 validated stress-related SNPs (DRD2 rs1800497, COMT rs4680, BDNF rs6265, HTR2C rs3813929). These genes are involved in key pathways related to stress regulation and neuropsychiatric responses such as dopaminergic, serotonergic, and neurotrophic signaling [31,32,33,34]. The GSstress score ranged from 0 to 10 and categorized as Low, Normal, Alert, or Risk according to predefined cut-off values standardized and validated by the manufacturer; Low (1.50–2.40), Normal (2.50–4.80), Alert (5.00–7.40), and Risk (7.50–8.80) (Supplementary Data 1).

Anthropometric measurements

The anthropometric measurements, including height (cm) and weight (kg), were obtained using a digital stadiometer (BSM 330; Biospace, Seoul, Korea), and skeletal muscle mass (kg), body fat mass (kg), and BMI (kg/m2) were measured using a bioelectrical impedance analyzer (Inbody 720; Biospace).

Assessment of nutrients and food intakes

A 3-day dietary record, including 2 weekdays and one weekend day, was collected from each participant. Participants were instructed to record all food intake using written diaries or food photographs. The adherence and accuracy of dietary records were evaluated by a clinical dietitian on the day of the visit. The nutrient intake from the diet was analyzed using the Computer Aided Nutritional Analysis Program (CAN-Pro) 5.0 (web version; The Korean Nutrition Society, Seoul, Korea). The foods consumed were classified into 23 groups based on the 2020 Dietary Reference Intakes for Koreans (Supplementary Table 1) [35]. Among them, SSB were defined as non-alcoholic drinks with added sugar, including soft drinks, sports drinks, coffee mix, and fruit juices.

Measurement of PA and sleep duration

The participants completed a self-reported survey derived from the International Physical Activity Questionnaire-short form (IPAQ-SF) (Supplementary Table 2). They were instructed to record the duration, frequency, and intensity of their activities. The IPAQ-SF data was then transformed into continuous variables based on the IPAQ-SF analysis guidelines and expressed as metabolic equivalent of task value-hour per week. Additionally, to accurately assess PA levels of the participants, they wore a Fitbit Charge 3 (Fitbit Inc., San Francisco, CA, USA) for the 10 days before each visit, with PA levels assessed using the sum of values over 7 consecutive days (h/week), excluding days with device recognition errors. Furthermore, the device also estimated total energy expenditure, which includes basal metabolic rate and activity-related energy expenditure. Based on recommended energy expenditure levels in adults (approximately 2,000–2,500 kcal/day, equivalent to 14,000–17,500 kcal/week) [36], participants were categorized into low (< 15,000 kcal/week) or high (≥ 15,000 kcal/week) energy expenditure groups. Sleep duration (h/day) measured by the mobile device was analyzed using the median of the data recorded for 7 consecutive days. Short sleep duration was defined as < 6 h/day based on previous evidence that defines fewer than 6 h of sleep as short sleep in adults and a risk factor for adverse health outcomes [37,38]. All participants wore the device throughout the 10-day tracking period, demonstrating full compliance and providing complete and reliable data for analysis.

Assessment of anxiety and depression

During the visit, participants completed online self-report surveys using SurveyMonkey (http://www.surveymonkey.com) to assess psychological factors. The surveys were based on the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) (Supplementary Table 3). The GAD-7 is a self-report questionnaire consisting of 7 items designed to assess the severity of anxiety symptoms, with total scores ranging from 0 to 21. It has been reported that the GAD-7 demonstrated high internal consistency with a Cronbach’s α of 0.92 in Korean validation study [39], and the Cronbach’s α observed in the present study was 0.89. The PHQ-9 is a 9-item self-report tool for assessing depressive symptoms, with total scores ranging from 0 to 27. The PHQ-9 is widely used to test symptoms of depression in medical or primary care settings [40,41]. The PHQ-9 has also shown high reliability in Korean populations, with a Cronbach’s α of 0.95 in a validation study [42], and a coefficient of 0.88 in the current analysis.

Measurement of blood metabolites

Blood samples were collected and centrifuged at 2,000 rpm for 20 min. The separated serum was stored at −80°C until analysis. Serum metabolites were analyzed using Gas Chromatography-Mass Spectrometry/Mass Spectrometry (GC-MS/MS; Shimadzu GCMS-TQ8040) with multiple reaction monitoring ions at the Pesticide Chemistry and Toxicology laboratory of the Department of Agricultural Biotechnology of Seoul National University. Out of 304 metabolites measured, 93 were statistically analyzed, including 28 carbohydrates, 20 amino acids, 11 lipids, 3 nucleotides, 19 organic acids, 1 alcohol, 2 amides, 1 amine, 1 ester, 1 indole, 3 phenols, 2 purines, and 1 tocopherol, which were a combination of different forms of isomers or derivatives. The relative area under the curve for each metabolite was calculated by comparing the peak area of the metabolites with 2-isopropylmalic acid, an internal standard. The conditions for analyzing serum metabolites using GC-MS/MS are described in a previous study [43].

Measurement of serum cortisol concentration

Blood samples were collected in the morning between 9:00 and 10:00 and then centrifuged at 2,000 rpm for 20 min to prepare serum samples. These samples were stored at −80°C until analysis. The serum cortisol concentrations were quantified using a cortisol-competitive enzyme-linked immunosorbent assay kit (Invitrogen, Frederick, MD, USA) following the manufacturer’s instructions.

Statistical analysis

The sample size was calculated based on a previous study that examined the correlation between rs10767664 BDNF gene polymorphism and body weight [44]. Considering a 1.7 cm waist circumference change between the T allele carriers and AA genotype carriers over 3 mon, with a SD of 3.1 and a power of 95% at a significance level of 0.05, the estimated sample size was 90. However, 10% of the sample size was added to accommodate the subgroup analysis and enhance statistical power. Differences in changes in anthropometric data, dietary intake, PA, and metabolites among the groups were analyzed using 1-way analysis of variance (ANOVA) tests for normally distributed data and Kruskal−Wallis tests for skewed data, followed by Bonferroni-corrected multiple comparison tests for post hoc analysis. Group differences in categorical variables, including sex, were evaluated using χ2 tests. Multivariable linear regression analyses were performed with or without adjustment for confounders such as, age (continuous), sleep duration (continuous or categorized as < 6 h/day and ≥ 6 h/day), energy expenditure (< 15,000 kcal/week or ≥ 15,000 kcal/week), and PHQ-9 and GAD-7 scores (1, 2, 3, or 4 categories), to minimize potential sources of bias. A 2-way ANOVA was conducted to assess the interaction between sleep or PA and GSstress on BMI or body weight. All statistical analyses were performed using SPSS Statistics version 26 (IBM SPSS Statistics, Chicago, IL, USA), and the statistical significance level was set at P < 0.05 for 2-sided tests.

RESULTS

Characteristics of the study participants

Fig. 1 shows the flow chart of participants according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline. The characteristics of the 100 study participants are presented in Table 1. There were no significant differences among participants with varying levels of GSstress in terms of age, anthropometric measures, consumption of energy (kcal/day), carbohydrate and fat (% energy/day), PA-related measures, sleep duration, or psychological factors such as GAD-7 and PHQ-9 scores. However, the protein intake (% energy/day) was significantly lower in the participants with the Risk GSstress and Low GSstress compared to those with the Normal GSstress.

Fig. 1. Study flow chart according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline.

Fig. 1

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Table 1. The characteristics of participants based on GSstress .

Parameter Total participants (n = 100) Low GSstress (n = 29) Normal GSstress (n = 16) Alert GSstress (n = 36) Risk GSstress (n = 19) P-value
Gender (men, %) 50 (50) 11 (38) 8 (50) 18 (50) 13 (68) 0.234
Age (yrs) 28.1 ± 2.1 27.9 ± 2.1 28.2 ± 2.3 28.2 ± 2.2 28.2 ± 2.2 0.928
BMI (kg/m2) 22.3 ± 2.0 22.0 ± 2.0 22.0 ± 2.1 22.2 ± 1.8 23.0 ± 2.3 0.313
Skeletal muscle mass (kg)
Men 32.1 ± 3.7 31.8 ± 3.9 32.2 ± 4.2 31.8 ± 3.0 32.7 ± 4.5 0.926
Women 21.2 ± 1.9 21.2 ± 2.3 21.8 ± 1.9 21.0 ± 1.7 21.3 ± 1.1 0.810
Body fat (%)
Men 15.5 ± 4.0 23.4 ± 4.8 18.8 ± 3.3 20.5 ± 5.0 22.5 ± 4.8 0.125
Women 15.8 ± 3.2 28.5 ± 3.4 26.1 ± 4.4 30.0 ± 4.0 27.2 ± 4.7 0.115
Energy intake (kcal/day)
Men 2,106.3 ± 464.3 2,227.7 ± 574.2 2,211.3 ± 653.3 1,998.8 ± 394.8 2,087.7 ± 311.8 0.555
Women 1,670.9 ± 413.6 1,733.5 ± 316.2 1,561.5 ± 326.8 1,601.8 ± 439.2 1,835.9 ± 669.3 0.500
Carbohydrate intake (% energy/day) 47.8 ± 8.0 48.6 ± 9.2 46.2 ± 8.1 47.4 ± 8.2 48.8 ± 5.6 0.719
Protein intake (% energy/day) 16.3 ± 3.7 15.6 ± 3.5b 19.1 ± 5.6 a 16.4 ± 2.7ab 14.9 ± 2.7b 0.003
Fat intake (% energy/day) 32.5 ± 5.9 31.0 ± 6.5 33.1 ± 4.9 33.8 ± 6.0 31.9 ± 5.2 0.270
VPA (MET-h/week) 11.3 ± 11.5 9.6 ± 11.1 14.4 ± 14.1 11.3 ± 11.4 11.3 ± 10.4 0.649
MPA (MET-h/week) 6.0 ± 9.1 6.4 ± 10.1 7.9 ± 7.6 3.8 ± 4.8 7.9 ± 13.6 0.295
WPA (MET-h/week) 22.0 ± 24.2 27.0 ± 24.0 20.4 ± 21.2 18.6 ± 20.2 22.1 ± 33.2 0.578
TPA (MET-h/week) 34.3 ± 22.7 37.4 ± 22.4 37.6 ± 19.1 30.5 ± 19.8 34.0 ± 30.4 0.598
Energy expenditure (kcal/week) 16,525.5 ± 3,873.7 15,775.9 ± 3,799.4 16,152.0 ± 3,508.2 16,488.2 ± 3,477.7 18,015.2 ± 4,788.2 0.263
Sleep duration (h/day) 6.4 ± 1.0 6.4 ± 0.9 6.2 ± 0.9 6.4 ± 1.0 6.6 ± 1.2 0.603
GAD-7 score 3.7 ± 3.5 3.7 ± 3.1 3.5 ± 2.8 3.6 ± 3.5 3.9 ± 4.7 0.985
PHQ-9 score 5.1 ± 4.6 5.7 ± 5.1 3.9 ± 3.1 4.9 ± 4.1 5.7 ± 5.6 0.570
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Values are presented as mean ± SD.

Means with different superscripts indicate the significant differences in the changes in nutrient intake among the groups, assessed with 1-way analysis of variance tests for normally distributed, followed by Bonferroni tests for multiple comparisons. Group differences in categorical variables, including sex, were evaluated using χ2 tests.

GSstress, genetic scores for stress; BMI, body mass index; VPA, vigorous physical activity; MET, metabolic equivalent of task; MPA, moderate physical activity; WPA, walking physical activity; TPA, total physical activity; GAD-7, Generalized Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

Comparison of genotype frequencies between study participants and the East Asian population

The genotype frequencies of the study participants for COMT rs4680, BDNF rs6265, DRD2 rs1800497, and HTR2C rs3813929 are presented in Supplementary Table 4. Overall, the genotype distributions were comparable to those reported for the East Asian population, with differences within approximately 5% across genotypes [45].

Food intake according to GSstress

The analysis of food intake showed no significant differences across 23 food groups among the groups with different stress-related genetic scores, including rice, bread, meat, processed meat, pork, fish, oil, vegetables, seaweed, fruits, and fast foods. However, the intake of SSB (g/day) was significantly higher in the Risk GSstress compared to the Low GSstress (104.5 ± 34.7 vs. 27.3 ± 11.6 g/day) (Table 2).

Table 2. Food and nutrient intakes of the study participants based on GSstress .

Parameter Total participants (n = 100) Low GSstress (n = 29) Normal GSstress (n = 16) Alert GSstress (n = 36) Risk GSstress (n = 19) P-value
Rice (g/day) 114.8 ± 51.5 118.2 ± 10.7 128.9 ± 13.3 108.0 ± 6.8 110.5 ± 13.5 0.559
Bread (g/day) 31.1 ± 41.5 34.1 ± 8.0 17.8 ± 6.2 25.9 ± 6.1 47.5 ± 12.4 0.149
Meat (g/day) 100.9 ± 73.5 93.1 ± 12.4 136.8 ± 18.7 87.2 ± 12.8 108.6 ± 16.2 0.132
Processed meat (g/day) 12.8 ± 18.1 16.2 ± 22.4 7.3 ± 10.4 12.5 ± 17.6 12.8 ± 16.8 0.481
Pork (g/day) 11.5 ± 28.7 7.0 ± 2.8 3.2 ± 2.6 20.7 ± 7.1 7.9 ± 3.9 0.108
Fish (g/day) 44.0 ± 40.4 43.6 ± 7.6 60.3 ± 10.3 40.5 ± 7.2 37.4 ± 7.5 0.343
Oil (g/day) 19.3 ± 16.5 18.7 ± 2.9 16.4 ± 2.2 19.3 ± 3.0 22.8 ± 4.5 0.713
Vegetables (g/day) 161.4 ± 102.0 137.2 ± 12.3 195.6 ± 21.0 165.5 ± 22.8 161.6 ± 18.1 0.325
Seaweed (g/day) 2.2 ± 4.8 1.8 ± 0.8 3.5 ± 1.7 1.9 ± 0.6 2.4 ± 1.3 0.656
Fruits (g/day) 90.2 ± 99.2 92.6 ± 13.4 135.7 ± 38.7 79.3 ± 14.5 69.0 ± 21.6 0.198
SSB (g/day) 56.1 ± 96.1 27.3 ± 62.7b 30.2 ± 12.0ab 65.3 ± 14.8ab 104.5 ± 34.7a 0.028
Fast foods (g/day) 56.1 ± 83.4 66.2 ± 21.9 39.9 ± 16.1 62.7 ± 12.4 42.1 ± 9.6 0.624
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Values are presented as mean ± SE of the mean.

Means with different superscripts indicate the significant differences in food intakes among Low GSstress, Normal GSstress, Alert GSstress, and Risk GSstress, determined by 1-way analysis of variance tests and Kruskal−Wallis tests, followed by Bonferroni-correction multiple comparison tests.

GSstress was determined by 4 genes, including DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HTR2C rs3813929, and categorized into 4 groups based on the levels of integrated genetic risk: low, normal, alert, risk.

GSstress, genetic scores for stress; SSB, sugar-sweetened beverage.

Differences in serum cortisol concentration among GSstress groups

In order to examine the biological stress levels based on GSstress, the serum cortisol concentrations were measured. The serum cortisol concentrations were significantly higher in the Risk GSstress group compared to the Low GSstress and the Alert GSstress groups. The mean cortisol concentration in the Risk GSstress group (263.8 ± 64.6 ng/mL) was above the upper reference limit for normal serum cortisol levels (normal reference range for serum cortisol: 50−250 ng/mL (Fig. 2) [46]. Furthermore, regression analysis showed that a one-unit increase in the GSstress is linked to a 13.5 ng/mL increase in serum cortisol levels (β = 13.2, P < 0.05), and this association remained significant after adjustment for age, sex, energy intake, and BMI (β = 13.5, P < 0.05) (Supplementary Table 5).

Fig. 2. The serum cortisol concentration (ng/mL) according to the GSstress.

Fig. 2

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Groups with different superscripts indicate significant differences in the average serum cortisol concentration, assessed using one-way analysis of variance followed by Bonferroni tests for multiple comparisons. Participants were categorized into 4 groups based on their GSstress; Low (n = 29), Normal (n = 16), Alert (n = 36), and Risk (n = 19). The mean serum cortisol concentrations (mean ± SD) were 205.5 ± 62.4, 218.6 ± 61.0, 203.6 ± 61.8, and 263.8 ± 64.6 ng/mL for the Low, Normal, Alert, and Risk GSstress groups, respectively.

GSstress, genetic scores for stress.

Comparison of serum metabolite levels among GSstress groups

The relative metabolite levels were examined to investigate the relationship between metabolite levels across the different GSstress groups. It was observed that the relative levels of serum amino acids, including isoleucine, methionine, valine, and phenylalanine were significantly higher in the Risk GSstress group compared to the Normal GSstress group (Supplementary Table 6). All other measured metabolites, including amino acids and fatty acid-related metabolites, did not show significant differences across the different GSstress groups.

Associations of GSstress and lifestyle factors, including sleep duration, energy expenditure, and SSB intake, with BMI

Higher GSstress was significantly associated with higher BMI after adjustment for the covariates such as age, education, total PA, PHQ-9 score, GAD-7 score, and energy intake (β = 0.35, P < 0.05). In addition, a significant linear relationship was observed between GSstress and energy expenditure (β = 808.0, P < 0.05) as well as SSB intake (β = 24.6, P < 0.01) after adjusting for the same covariates, whereas no significant association was observed between GSstress and sleep duration (Table 3).

Table 3. Multivariable regression analysis of the associations among the GSstress and BMI, sleep duration, energy expenditure, and SSB intake1) .

Model BMI (kg/m2) Sleep duration (h/day) Energy expenditure (kcal/day) SSB intake (g/day)
β SE t P-value β SE t P-value β SE t P-value β SE t P-value
Model 1
GSstress 0.28 0.18 1.56 0.122 0.07 0.09 0.77 0.441 642.8 351.5 1.83 0.070 24.9 8.43 2.96 < 0.01
Model 2
GSstress 0.35 0.18 2.00 < 0.05 0.04 0.09 0.50 0.616 808.0 314.4 2.57 < 0.05 24.6 8.35 2.95 < 0.01
Age (yrs) 0.10 0.10 1.08 0.282 −0.05 0.05 −1.06 0.292 251.4 166.7 1.51 0.135 −9.85 4.47 −2.21 < 0.05
Education 0.08 0.33 0.25 0.800 0.08 0.16 0.46 0.644 −669.6 581.3 −1.15 0.252 −5.89 15.5 −0.38 0.706
Total PA (MET-h/week) 0.02 0.01 1.64 0.104 −0.004 −0.10 −1.01 0.314 30.9 15.8 1.95 0.054 −0.86 0.42 −2.03 < 0.05
GAD-7 score −0.18 0.09 −1.95 0.054 −0.01 0.05 −0.24 0.811 −182.8 165.9 −1.10 0.274 6.20 4.44 1.40 0.166
PHQ-9 score 0.11 0.07 1.47 0.146 0.02 0.04 0.46 0.644 62.1 127.8 0.49 0.628 −5.98 3.42 −1.75 0.083
Energy intake (kcal/day) 0.0 0.00 2.67 < 0.01 0.00 0.00 −2.59 < 0.05 2.33 0.53 4.36 < 0.01 0.02 0.01 1.12 0.268
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GSstress was determined using 4 genes including DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HTR2C rs3813929.

GSstress, genetic scores for stress; BMI, body mass index; SSB, sugar-sweetened beverage; PA, physical activity; MET, metabolic equivalent of task; GAD-7, Generalized Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

1)Sample size (n = 100).

Interaction effects of GSstress with sleep duration, energy expenditure, and SSB intake on BMI

To assess whether GSstress and lifestyle factors, including sleep duration, PA, and SSB intake, influence BMI, a 2-way ANOVA was conducted. As shown in Table 4, there was an interaction between GSstress and sleep duration on BMI after adjusting for age, energy intake, GAD-7 score, PHQ-9 score, education, and energy expenditure. Specifically, higher GSstress was linked to greater BMI among participants with short sleep duration (< 6 h/day) when adjusted for age, gender, energy intake, energy expenditure, PHQ-9 score, and GAD-7 score (β = 1.14, P < 0.01). In contrast, no significant association was found among participants who had sufficient sleep durations (> 6 h/day).

Table 4. Interaction effects of GSstress with sleep duration, energy expenditure, and SSB intake on BMI1)2).

Variables BMI (kg/m2)
Low GSstress (n = 29) Normal GSstress (n = 16) Alert GSstress (n = 36) Risk GSstress (n = 19) P for trend1)
Sleep duration (h/day)
Short sleep duration (< 6 h/day) 21.4 ± 0.8 21.8 ± 0.8 23.0 ± 0.5 23.6 ± 0.6 0.016
Sufficient sleep duration (≥ 6 h/day) 22.2 ± 0.4 22.2 ± 0.8 21.9 ± 0.4 22.8 ± 0.7 0.432
GSstress × sleep duration interaction (P-value) = 0.0262)
Energy expenditure (kcal/week)
Low energy expenditure (< 15,000 kcal/week) 21.0 ± 0.4 21.2 ± 0.8 21.2 ± 0.3 20.8 ± 1.0 0.951
High energy expenditure (≥ 15,000 kcal/week) 23.2 ± 0.5 22.8 ± 0.6 23.0 ± 0.4 24.1 ± 0.3 0.101
GSstress × energy expenditure interaction (P-value) = 0.3902)
SSB intake (g/day)
Non-consumer (0 g/day) 21.9 ± 0.4 21.5 ± 0.6 22.0 ± 0.4 23.5 ± 0.7 0.107
Consumer (> 0 g/day) 22.4 ± 0.8 23.0 ± 1.0 22.3 ± 0.4 22.6 ± 0.7 0.642
GSstress × SSB intake interaction (P-value) = 0.4842)
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The data are presented as mean ± SE of the mean. Participants were categorized according to predefined cut-off values as follows: short sleep duration (≤ 6 h/day, n = 32) and sufficient sleep duration (> 6 h/day, n = 68); low energy expenditure (< 15,000 kcal/week, n = 47) and high energy expenditure (≥ 15,000 kcal/week, n = 53); non-consumers of SSB (0 g/day, n = 56) and consumers (> 0 g/day, n = 44).

GSstress was determined by 4 genes, including DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HTR2C rs3813929, and categorized into 4 groups based on the levels of integrated genetic risk: low, normal, alert, risk.

GSstress, genetic scores for stress; SSB, sugar-sweetened beverage; BMI, body mass index; GAD-7, Generalized Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

1)P for trend was determined using a general linear regression after adjusting for age, energy intake, GAD-7 score, PHQ-9 score, and education to evaluate the effects of genetic risk on BMI at low or high levels of sleep duration and energy expenditure.

2)Interaction effects between GSstress and sleep duration, energy expenditure, and SSB intake on BMI were examined using 2-way analysis of variance with adjustment for confounding factors, including age, energy intake, GAD-7 score, PHQ-9 score, and education level.

However, there was no significant interaction between the GSstress and total daily energy expenditure or SSB intake on BMI after adjusting for age, energy intake, GAD-7 score, PHQ-9 score, education, and sleep duration.

DISCUSSION

This study demonstrated that individuals with higher stress-related genetic risk showed significantly elevated cortisol, greater SSB intake, and higher BMI, and that the link between GSstress and BMI appeared to be stronger among individuals with shorter sleep duration.

This study investigated the comprehensive genetic impact of stress-related genes, including the COMT rs4680, BDNF rs6265, DRD2 rs1800497, and HTR2C rs3813929, on the biological stress levels, which may contribute to binge eating and subsequent increase in BMI. Previous studies have shown that these genes may affect the metabolism of neurotransmitters, including serotonin, dopamine, and epinephrine [21,47,48], which can regulate the secretion of the corticotropin-releasing hormone and arginine vasopressin from the hypothalamus, thereby influencing cortisol secretion and stress responses [49]. In line with this, the results of this study indicated that individuals with a higher polygenic risk for stress exhibited elevated serum cortisol levels compared to those with lower genetic risks. This indicates that individuals with high genetic susceptibility to stress may be more likely to experience elevated stress levels in real-life situations, and that genetic polymorphisms associated with stress-related genes could serve as potential indicators for stress vulnerability.

However, studies examining the relationship between genetic predisposition to stress and actual stress responses remained limited [50]. It has been shown that perceived stress has a hereditary component, with genetic factors accounting for approximately half of the correlation with stress measurements [51]. It was also reported that the significance of this association varies depending on the specific questionnaires used, such as the Perceived Stress Scale and the Measure for the Assessment of Stress Susceptibility [51]. In contrast to previous studies, the present study evaluated physiological stress levels by measuring blood cortisol levels, which provide objective data. Cortisol measurement provides valuable objective data; however, it may not account for individual variations in biological rhythms or effectively differentiate between acute and chronic stress. Future research could further enhance stress assessment by incorporating both subjective evaluations and physiological measurements.

In the present study, levels of several amino acids, including isoleucine, methionine, valine, and phenylalanine, were significantly higher in the Risk GSstress group compared with the Normal GSstress groups. These findings suggest that amino acid metabolism may vary according to stress-related genetic susceptibility. In particular, the relatively higher amino acid levels observed in the Risk GSstress group may reflect enhanced metabolic activity under stress conditions, accompanied by increased cortisol-driven protein breakdown [49]. Cortisol is known to stimulate gluconeogenesis, promote the breakdown of proteins into amino acids, and inhibit the synthesis of phospholipid and cholesterol [4,49,52].

Excessive consumption of SSB has been shown to be associated with weight gain and obesity [53,54]. Similarly, this study found that individuals with high genetic susceptibility to stress and elevated cortisol levels consumed more SSBs and had higher BMI than those with a low genetic risk. While the relationship between stress responses and increased SSB intake has been consistently reported [8,9], there is a lack of previous studies examining the association between the stress-related genes, SSB consumption, and BMI. Previous research has focused on obesity-related genes and their associations with SSB intake and obesity outcomes [55,56]. However, this study examined genetic variants related to stress susceptibility rather than those related to obesity such as FTO and MC4R. Obesity-related genes directly affect appetite regulation and energy metabolism [57,58,59], while stress-related genes are associated with neurobiological processes involved in stress and reward systems, making individuals more susceptible to environmental factors such as dietary habits [60,61,62]. Therefore, consideration of environmental and lifestyle factors, such as psychological factors, exercise, and sleep duration, may be important when interpreting the association between stress-related genes on BMI.

It has been reported that shorter sleep duration is associated with increased BMI and a higher risk for obesity [63]. In the present study, we found a significant association between stress-related genetic risk and BMI among participants with shorter sleep duration. This suggests that sleep duration may influence the association between stress-related genetic risk and BMI. Although the exact underlying mechanism is unclear, sleep deprivation has been shown to activate stress-related pathways such as the HPA axis, which releases adrenocorticotropic hormone that triggers the secretion of glucocorticoids by the adrenal cortex [64,65,66]. These hormonal changes can stimulate appetite and potentially lead to weight gain [64].

This study has several limitations. The sample size of this study is relatively smaller compared to other studies investigating genetic factors and associated traits, which may have limited statistical power and generalizability. Future studies with larger, multi-center studies are needed to confirm these findings. Second, the cross-sectional design precludes causal inference, leaving the possibility of reverse causation. Third, as a limitation of measurement methods, data on sleep duration and PA derived from Fitbits may be subject to measurement error. Additionally, cortisol levels alone may not fully capture stress status, highlighting the value of incorporating subjective measures such as perceived stress in future research. Finally, potential confounders, including socioeconomic factors and long-term dietary habits, were not fully accounted for. Overall, while this study provides initial insights into the interplay between genetic factors, lifestyle behaviors, and BMI, further research is needed to examine a broader range of factors and to conduct more comprehensive analyses.

Despite these limitations, our findings provide insights into the relationship between genetic factors, sleep, and SSB consumption, suggesting new hypotheses regarding stress-related metabolic risk and highlighting the need for larger, longitudinal studies using varied stress indicators. The observed frequencies of the assessed genetic polymorphisms were consistent with those reported in East Asian populations, supporting the potential generalizability of our findings to this population. Moreover, the inclusion of serum cortisol measurements allowed for an objective and reliable assessment of biological stress levels, strengthening the study’s ability to elucidate the physiological mechanisms underlying the interactions between genetic factors, sleep, and SSB intake.

Taken together, individuals with a high genetic risk for stress had higher intakes of SSBs and higher BMI. Furthermore, the link between genetic susceptibility to stress and body weight appeared to be more pronounced in those with shorter sleep durations. This finding may provide supportive evidence for the interplay among polygenic risk for stress, dietary intake, BMI, and sleep duration. Further research with a broader range of factors associated with BMI is necessary to fully elucidate the mechanisms of body weight regulation and inform future interventions.

Footnotes

Funding: This research was supported by the grant from the Seoul National University Research Grant in 2018 (350-20180049).

Conflict of Interest: The authors declare no potential conflicts of interests.

Author Contributions:
  • Conceptualization: Han SN, Lee GY.
  • Formal analysis: Lee GY, Lee J.
  • Funding Acquisition: Han SN.
  • Investigation: Han SN, Lee GY.
  • Methodology: Han SN, Lee GY.
  • Supervision: Han SN, Lee GY.
  • Writing - original draft: Lee GY.
  • Writing - review & editing: Han SN, Lee GY, Kim JH, Chung KM.

SUPPLEMENTARY MATERIALS

Supplementary Data 1

An example of the genetic test results on stress-related genes1)

nrp-20-820-s001.doc (104.5KB, doc)
Supplementary Table 1

Classification of foods into 23 food groups

nrp-20-820-s002.xls (29KB, xls)
Supplementary Table 2

Surveys on IPAQ-short form

nrp-20-820-s003.xls (31.5KB, xls)
Supplementary Table 3

Surveys on psychological factors including GAD-7 and PHQ-9

nrp-20-820-s004.xls (31KB, xls)
Supplementary Table 4

The frequencies of genotypes for DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HRT2C rs3813929 in participants of this study and the East Asian population1)

nrp-20-820-s005.xls (28KB, xls)
Supplementary Table 5

Linear regression analysis examining the association between GSstress and serum cortisol levels1)

nrp-20-820-s006.xls (29KB, xls)
Supplementary Table 6

Relative areas of serum metabolites of participants according to the GSstress groups1)

nrp-20-820-s007.xls (32.5KB, xls)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data 1

An example of the genetic test results on stress-related genes1)

nrp-20-820-s001.doc (104.5KB, doc)
Supplementary Table 1

Classification of foods into 23 food groups

nrp-20-820-s002.xls (29KB, xls)
Supplementary Table 2

Surveys on IPAQ-short form

nrp-20-820-s003.xls (31.5KB, xls)
Supplementary Table 3

Surveys on psychological factors including GAD-7 and PHQ-9

nrp-20-820-s004.xls (31KB, xls)
Supplementary Table 4

The frequencies of genotypes for DRD2 rs1800497, COMT rs4680, BDNF rs6265, and HRT2C rs3813929 in participants of this study and the East Asian population1)

nrp-20-820-s005.xls (28KB, xls)
Supplementary Table 5

Linear regression analysis examining the association between GSstress and serum cortisol levels1)

nrp-20-820-s006.xls (29KB, xls)
Supplementary Table 6

Relative areas of serum metabolites of participants according to the GSstress groups1)

nrp-20-820-s007.xls (32.5KB, xls)

Articles from Nutrition Research and Practice are provided here courtesy of Korean Society of Community Nutrition and the Korean Nutrition Society

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