Computing a Synthetic Chronic Psychosocial Stress Measurement in Multiple Datasets and its Application in the Replication of G × E Interactions of the EBF1 Gene

Abstract

Chronic psychosocial stress adversely affects health and is associated with the development of disease [Williams, 2008]. Systematic epidemiological and genetic studies are needed to uncover genetic variants that interact with stress to modify metabolic responses across the life cycle that are the proximal contributors to the development of cardiovascular disease and precipitation of acute clinical events. Among the central challenges in the field are to perform and replicate gene-by-environment (GxE) studies. The challenge of measurement of individual experience of psychosocial stress is magnified in this context. Although many research datasets exist that contain genotyping and disease-related data, measures of psychosocial stress are often either absent or vary substantially across studies. In this paper, we provide an algorithm to create a synthetic measure of chronic psychosocial stress across multiple datasets, applying a consistent criterion that uses proxy indicators of stress components. We validated the computed scores of chronic psychosocial stress by observing moderately strong and significant correlations with the self-rated chronic psychosocial stress in the MESA Cohort (Rho = 0.23, P<0.0001) and with the measures of depressive symptoms in five datasets (Rho = 0.15 – 0.42, Ps=0.005 - <0.0001) and by comparing the distributions of the self-rated and computed measures. Finally, we demonstrate the utility of this computed chronic psychosocial stress variable by providing three additional replications of our previous finding of gene-by-stress interaction with central obesity traits [Singh et al., 2015].

The importance of gene–environment interactions in human obesity

“…The authors also replicated the interaction between psychosocial stress and three of the original five SNPs (rs17056278 C>G, rs17056298 C>G and rs17056318 T>C) in EBF1 in the Framingham Offspring cohort [161]. A subsequent analysis by the same research group replicated the EBF1 × psychosocial stress interaction on obesity (waist circumference or BMI) in the Family Heart Study Whites and at trend level in the Duke Caregiver study [162]. The direction of the interaction effect was consistent across each of the studies: chronic psychosocial stress amplified the effect of EBF1 variation on BMI [162].…”

“…A subsequent analysis by the same research group replicated the EBF1 × psychosocial stress interaction on obesity (waist circumference or BMI) in the Family Heart Study Whites and at trend level in the Duke Caregiver study [162]. The direction of the interaction effect was consistent across each of the studies: chronic psychosocial stress amplified the effect of EBF1 variation on BMI [162].…”

Lack of Association of a Functional Polymorphism in the Serotonin Receptor Gene With Body Mass Index and Depressive Symptoms in a Large Meta-Analysis of Population Based Studies

“…For the chronic stress measure, out of the 10 studies that we included in the present study only two, MESA ( Shivpuri et al, 2012 ) and JHS ( Johnson et al, 2016 ), had self-rated stress measures. In our prior work on data harmonization ( Singh et al, 2018 ), in the eight studies that lacked a self-rated stress measure we constructed a stress variable using an algorithm ( Singh et al, 2015 ) based on proxy indicators of five stress domains: financial, marital, work, health of spouse, and one’s own health. These domains were based on the chronic burden items from the MESA study ( Shivpuri et al, 2012 ) that were derived from a composite stress measure originally developed in the Study of Women’s Health Across the Nation ( Troxel et al, 2003 ).…”

“…These domains were based on the chronic burden items from the MESA study ( Shivpuri et al, 2012 ) that were derived from a composite stress measure originally developed in the Study of Women’s Health Across the Nation ( Troxel et al, 2003 ). Briefly, our algorithm ( Singh et al, 2015 ) searched for proxy indicators of each stress domain, scored each proxy item as 1 = stressful, 0 = not stressful. The item scores were then summed resulting in a single score.…”

“…In order to harmonize the differently scaled measures, we standardized the score within each study by transforming them to z-scores (mean of zero and a standard deviation of one). For additional details regarding this measure see ( Singh et al, 2015 , 2018 ).…”