INTRODUCTION

Obesity is an important public health problem in the Hispanic/Latino population. Data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) demonstrated a high prevalence of obesity in both women and men (1,2). US-born individuals and those living longer in the United States have the highest prevalence of obesity, suggesting that environmental influences are at play (2). Economic hardship and stress are important determinants of obesity in HCHS/SOL (3,4). In particular, compared with those with no stressors, participants reporting three or more chronic stressors had higher odds of having obesity. This association was not fully explained by physical activity, energy intake, or depressive symptoms, suggesting that there may be a direct pathway linking psychosocial distress to obesity.

Body mass index (BMI) and obesity also have genetic influences (5,6). Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with BMI and obesity risk (6). There is strong interest in the interplay between environmental and genetic factors, and studies have demonstrated gene-environment interactions. One such study showed that the genetic effects on obesity were attenuated among individuals who were physically active (7). HCHS/SOL previously reported that the associations of a BMI-genetic risk score with BMI and other adiposity measures were stronger at low levels of physical activity and high levels of sedentary behaviors (8). However, gene-environment interactions of psychosocial factors have been explored less frequently (9,10). Because psychosocial stress is an important risk factor in Hispanic/Latinos (4,11–14), HCHS/SOL provides a valuable opportunity to examine the contribution of psychosocial factors and genetic risk to BMI and obesity, given the comprehensive psychosocial and biobehavioral characterization of participants in the cohort. In this analysis, we investigate whether the association of chronic stress is independent of genetic risk and whether there is an interaction between chronic stress and genetic risk in a large sample of Hispanic/Latino adults.

METHODS

HCHS/SOL is a population-based cohort study of 16,415 Hispanic/Latino adults (ages 18–74 years) who were selected using a multistage probability sampling design from four US communities (Chicago, IL; Miami, FL; Bronx, NY; San Diego, CA). Participants had an in-person baseline examination between 2008 and 2011, and a second examination took place approximately 6 years later (2014–2017). In this second examination, cardiometabolic assessments were repeated, and participants were also asked to complete a psychosocial questionnaire that included a measure of self-reported chronic stress. Details about the aims and methodology of HCHS/SOL are published elsewhere (15–17). Of the 11623 participants who completed the in-person second examination, 3056 were excluded because they were missing the stress measure (n = 619), genetic data (n = 2502), and BMI (n = 378), or had BMI <18.5 kg/m2 (n = 69), leaving a final analytic sample of N = 8567.

BMI and Obesity

Height and weight were obtained at each field center as part of the HCHS/SOL second examination. Height (in centimeters) was measured with a wall stadiometer (SECA 222, Hamburg, Germany), and weight (in kilograms) was obtained with a digital scale (Tanita Body Composition Analyzer; TBF 300, Tokyo, Japan). BMI was calculated as weight in kilograms divided by height in meters squared. BMI values were used to define presence of obesity according to National Heart Lung and Blood Institute guidelines (BMI ≥30.0 kg/m2) (18). Individuals who were in the underweight category (n = 69) were excluded because underweight may indicate the presence of severe disease.

Chronic Stress Burden

Participants completed the eight-item chronic burden scale where participants were asked about the presence of ongoing stressors in major life domains (e.g., health, job, financial, relationship, network problems), whether the stressor has been ongoing for 6 months or more, and the severity of the stressor. To indicate the severity of the stressor, participants reported on a 3-point scale if the event was “not very stressful,” “moderately stressful,” or “very stressful.” A total count score (range, 0–8) was calculated for the stressors endorsed as ongoing for 6 months and were reported as being moderately or very stressful (12,19–21).

Polygenic Risk Score

Genetic data were obtained from the HCHS/SOL Custom 15041502 B3 SNP array (Illumina Omni 2.5M array plus ∼150,000 custom SNPs); this was followed by imputation based on the TOPMed freeze 5b from diverse ethnicities including Hispanic/Latino populations. An iterative procedure to simultaneously estimate principal components reflecting population structure and kinship coefficients measuring familial relatedness has been described elsewhere (17). The Hispanic/Latino background was identified to be Cuban, Dominican, Puerto Rican, Mexican, Central American, and South American from the combination of self-identified Hispanic/Latino background and genetic principal components. BMI polygenic risk score (PRS) in HCHS/SOL was calculated by PRSice (22) based on summary statistics from GIANT and UK BioBank meta-analysis of BMI genome-wide association studies (23). After clumping the SNP set according to the LD structure in HCHS/SOL (clumping parameter R2 = 0.1 and distance of 1000 kbp), 22,715 BMI-associated SNPs were selected with p value in the summary statistics <0.01 and minor allele frequency <0.01. The BMI PRS in HCHS/SOL was calculated by taking the summation of the effect size per effective allele × number of effective alleles/number of alleles included for an individual. Different p value thresholds were considered, and the mean squared errors of a model with the constructed BMI PRS were compared. Because higher p value showed lower mean squared error, a p value cutoff of .01 was chosen, as described by Elgart et al. (24).

Sedentary Behavior and Physical Activity

Detailed information on objective measurement of sedentary behavior and physical activity in HCHS/SOL has been described elsewhere (25,26). Briefly, at the HCHS/SOL baseline examination, participants were asked to wear an omnidirectional accelerometer (Actical B-1 version; model 198-0200-03; Respironics Co. Inc., Bend, Oregon) above the iliac crest for 7 days, except for swimming, showering, and sleeping. The accelerometer measured omnidirectional accelerations in counts and steps in 1-minute epochs. Nonwear time was determined by at least 90 consecutive minutes of zero counts, allowing for 1 or 2 minutes of nonzero counts in a ±30-minute window, using the Choi algorithm (27). An adherent day was defined as at least 10 hours of wear time, and at least 3 adherent days was required for inclusion in this analysis. Accelerometer data were summarized as total minutes per day by activity level: sedentary time, <100 counts/min; light activity, 100–1534 counts/min; moderate activity, 1535–3961 counts/min; and vigorous activity, >3962 counts/min. To mitigate the influence of selection bias on our results and to account for missing or incomplete accelerometer data, inverse probability weighting was implemented as described previously (25). Because of a high correlation between sedentary time and wear time (r2 = 0.83), we used the standardized sedentary time to 16 hours of wear time per day (the approximate average of both daily wear time and awake time in our study), by regressing out wear time from the sedentary time (25). After standardization, sedentary time was not correlated with wear time (r2 = 0.08).

Sociodemographic Variables

Participants also reported their Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, South American, and other/mixed), date of birth, sex, place of birth, years living in the United States, household income, and educational attainment.

Statistical Analysis

Descriptive statistics of the study population were summarized by using the count and percentage for categorical variables and the mean and SE for continuous variables, accounting for the complex study design. We examined whether there was an interaction between chronic stress and BMI PRS on obesity using mixed-effect logistic models and linear mixed-effect models for BMI. Models accounted for genetic relatedness, same household, and sampling block. All models included sampling weights, age, field center, sex, and five genetic principal components as fixed effects. Genetic relatedness, household, and sampling block were included as random effects. These models were further adjusted for sedentary time, moderate/vigorous physical activity, and household annual income. All statistical tests were two-sided, and analyses were performed using R (version 3.6.1; R Foundation) (28).

RESULTS Study Characteristics

Among 5336 women and 3231 men, the average age was 47.9 years (range, 23–95 years) (Table 1). Individuals were predominantly born outside of the 50 US states (77%) and had low socioeconomic status: 36.5% reported an annual household income <$20,000, and 29.4% did not graduate from high school. The prevalence of obesity in this sample was 43.5% (46.9% in women and 38.9% in men). The number of chronic stressors ranged from 0 to 8, with financial and health-related stressors being the most common (Table 2).

TABLE 1 - Characteristics of the Study Population (N = 8567), HCHS/SOL (2014–2017) All, Mean (SE) or % Without Obesity a (n = 4761) With Obesity a (n = 3806) p Age, y 47.9 (0.3) 47.8 (0.4) 48.0 (0.4) .79 Sex, % female 51.2 48.1 55.2 <.001 Hispanic background, % .009  Dominican 9.6 9.5 9.8  Cuban 23.4 23.1 23.7  Mexican 36.6 37.5 35.4  Puerto Rican 16.5 15.5 17.7  Central American 8.2 7.6 9.0  South American 5.8 6.8 4.4 Less than high school education, % 29.4 28.6 30.3 .25 Annually family income .001   < $20,000 34.8 32.9 37.3  $20,000–$50,000 41.0 41.6 40.1   >$50,000 19.7 21.5 17.4  Missing 4.5 4.0 5.2 Born within US 50 states 22.9 21.5 24.6 .055 Born outside US 50 states and with ≥10 yr in the United States 84.2 84.6 87.1 .067 No. stressors, % <.001  0 26.1 30.0 21.0  1–2 44.5 44.1 45.1  3+ 29.4 26.0 34.0

HCHS/SOL = Hispanic Community Health Study/Study of Latinos; SE = standard error.

a Obesity is defined as body mass index ≥30 kg/m2.

TABLE 2 - Distribution of Each Reported Chronic Stressor, HCHS/SOL (2014–2017) Chronic Stressor % Without Obesity With Obesity p Personal serious health problem 29.2 26.1 33.2 <.001 Close person with health problem 33.3 29.8 37.9 <.001 Ability to work/difficulty at work 12.9 10.4 16.1 <.001 Financial strain 34.7 30.8 39.8 <.001 Relationship difficulties 19.7 18.5 21.3 .048 Someone close: alcohol/drug use 16.8 17.3 16.0 .28 Help someone close: sick/limited/frail 28.6 26.8 31.0 .002 Other problem not listed 5.3 4.9 5.8 .22

HCHS/SOL = Hispanic Community Health Study/Study of Latinos.

Chronic Stress, Genetic Risk, BMI, and Obesity

Greater number of chronic stressors and higher BMI-PRS were both associated with higher BMI (β [log odds] = 0.28 [95% confidence interval [CI] = 0.20–0.37] and β [log odds] = 1.52 [95% CI = 1.37–1.67], respectively) after adjustment for potential confounders including sedentary behavior and time spent in moderate/vigorous physical activity (Table 3). Chronic stress explained 0.79% of the variance of BMI, whereas the PRS explained 5.9%. Chronic stress and the PRS were also associated with higher log odds of obesity (β [log odds] = 0.10 [95% CI = 0.07–0.13] and β [log odds] = 0.47 [95% CI = 0.42–0.52], respectively), adjusting for sedentary behavior and moderate/vigorous physical activity, which did not change the magnitude of effects. No interactions between chronic stress and PRS were observed for BMI (β for interaction = 0.01 [95% CI = −0.06 to 0.08]; p = .77) or obesity (β for interaction = −0.01 [95% CI = −0.04 to 0.01]; p = .32).

TABLE 3 - Association of Obesity (or BMI) With Chronic Stress and Polygenic Risk Score (N = 8567), HCHS/SOL (2014–2017) Obesity BMI β (95% CI) p β (95% CI) p Model 1  Chronic stress (per stressor) 0.10 (0.07–0.13) <.001 0.31 (0.23–0.38) <.001  PRS (per 1 SD) 0.47 (0.42–0.52) <.001 1.54 (1.41–1.68) <.001 Model 2  Chronic stress (per stressor) 0.10 (0.07–0.13) <.001 0.28 (0.20–0.37) <.001  PRS (per 1 SD) 0.47 (0.41–0.53) <.001 1.52 (1.37–1.67) <.001

HCHS/SOL = Hispanic Community Health Study/Study of Latinos; BMI = body mass index, CI = confidence interval; PRS = polygenic risk score; SD = standard deviation.

Model 1: chronic stress and the PRS are simultaneously entered in the models. Models are adjusted for sampling weight, age, center, sex, and 5 genetic principal components as fixed effects and relatedness, block group, and household as random effects.

Model 2: adjusted for variables in model 1 plus household income, time in sedentary behaviors, and moderate/vigorous physical activity.

DISCUSSION

In analyses that adjusted for genetic risk, the study showed that there were small but significant associations of chronic stress and BMI and obesity. However, contrary to expectations, the relationship between chronic stress and obesity was not modified by genetic risk. Although in this study, an interaction was not observed, the findings contribute to the emergent field of gene-environment interaction in obesity (29–33). Environmental influences of obesity are strong and are shown to be modified by genetics effects. Brandkvist et al. (34) in a large population-based Norwegian cohort showed that the increases in BMI from the 1960s to the 2000s were greater among those with greater genetic risk, suggesting an interaction between genetic risk and the obesogenic environment that has been more common in recent decades. However, there are limited reports of how psychosocial risk factors modify genetic influences. A study of Chinese children showed that higher hair cortisol levels, a biological correlate of chronic stress, exacerbated the genetic influence on BMI (35). The Multi-Ethnic Study of Atherosclerosis, a cohort of middle-aged adults, reported an interaction of the early B-cell factor 1 gene (EBF1; a gene involved in the development of the immune system) and chronic stress that resulted in greater adiposity measures among Whites only (10). A Korean study, on the other hand, found that one SNP (rs2239219) of the RGS6 gene, which regulates protein signaling in the brain and other tissues, was related to greater abdominal adiposity in the context of high psychosocial stress (9). These contradictory results may be related to differences in the type of psychosocial factors chosen for the analysis.

This study addresses an important research gap, as data on psychosocial factors in gene-environment interaction associations are still limited. This gap is particularly important for racial/ethnic minorities, who are generally underrepresented in genomic studies but have a greater disease burden due to the social and economic environments in which they live. However, results must be interpreted with caution because of several methodological limitations. A relatively small sample size may explain the nonsignificant interactions observed. Furthermore, the study did not have the power to explore sex differences (three-way interaction), which may be important (31). Another limitation is that chronic stress and the PRS explained a small proportion of the variance in BMI (<1%, for chronic stress and 5.9% for PRS). As the methods for deriving PRS evolve to better capture genetic risk in racial/ethnic minorities, we may be able to improve the genetic prediction of these conditions and perhaps improve the ability to detect interaction effects. Future studies may also need to adopt a multidimensional approach for defining psychosocial stress to better capture relationships between stress, obesity, and genetic risk.

In summary, our findings confirmed our prior report relating higher stressors with higher BMI and obesity. These associations, although small, were present even after the adjustment for the underlying genetic risk, which support continuing to include stress reduction strategies in obesity management programs. We did not find evidence for an interaction between chronic stress and the BMI-PRS, which was not consistent with other publications that showed greater BMI or obesity in the groups with high stressors and elevated genetic risk. Future studies may consider the use of other approaches for assessing psychosocial stress in gene-environment interaction studies.

Source of Funding and Conflicts of Interest: The authors do not report any conflict of interest. The Hispanic Community Health Study/Study of Latinos was supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), the University of Illinois at Chicago (HHSN268201300003I), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following institutes/centers/offices contributed to the Hispanic Community Health Study/Study of Latinos through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurologic Disorders and Stroke, and the Office of Dietary Supplements. Additional support was provided by grant R01MD013320 and the Life Course Methodology Core at Albert Einstein College of Medicine and the New York Regional Center for Diabetes Translation Research (P30 DK111022-8786 and P30 DK111022) through funds from the National Institute of Diabetes and Digestive and Kidney Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or the National Institutes of Health.

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