Abstract

In prior work, we identified a novel gene-by-stress association of EBF1’s common variation (SNP rs4704963) with obesity (i.e., hip, waist) in Whites, which was further strengthened through multiple replications using our synthetic stress measure. We now extend this prior work in a precision medicine framework to find the risk group using harmonized data from 28,026 participants by evaluating the following: (a) EBF1 SNPxSTRESS interaction in Blacks; (b) 3-way interaction of EBF1 SNPxSTRESS with sex, race, and age; and (c) a race and sex-specific path linking EBF1 and stress to obesity to fasting glucose to the development of cardiometabolic disease risk. Our findings provided additional confirmation that genetic variation in EBF1 may contribute to stress-induced human obesity, including in Blacks (P = 0.022) that mainly resulted from race-specific stress due to “racism/discrimination” (P = 0.036) and “not meeting basic needs” (P = 0.053). The EBF1 gene-by-stress interaction differed significantly (P = 1.01e−03) depending on the sex of participants in Whites. Race and age also showed tentative associations (Ps = 0.103, 0.093, respectively) with this interaction. There was a significant and substantially larger path linking EBF1 and stress to obesity to fasting glucose to type 2 diabetes for the EBF1 minor allele group (coefficient = 0.28, P = 0.009, 95% CI = 0.07-0.49) compared with the same path for the EBF1 major allele homozygotes in White females and also a similar pattern of the path in Black females. Underscoring the race-specific key life-stress indicators (e.g., racism/discrimination) and also the utility of our synthetic stress, we identified the potential risk group of EBF1 and stress-induced human obesity and cardiometabolic disease.

Highlights

  • Understanding the precise role of genetic, demographic, and environmental variations on the expression of complex biological mechanisms contributing to the development and course of major medical disorders is critical for next-generation medicine, often referred to as precision medicine[1,2]

  • We present the utility of our synthetic stress in harmonized data sets; additional efforts to show, including in Black samples, that common variation in EBF1 may contribute to inter-individual differences in human obesity in the presence of stress; a systematic evaluation of sex, race, and age interactions with EBF1 gene-by-stress association to identify the precise risk group; and the evaluation of its clinical implication, i.e., a path linking EBF1 and stress to obesity to fasting blood glucose to the development type 2 diabetes mellitus in the risk group

  • The public-access data sets were from the Jackson Heart Study (JHS)[13]; The Women’s Health Initiative (WHI) Study[14]; The Coronary Artery Risk Development in Young Adults Study (CARDIA)[15]; Atherosclerosis Risk in Communities Study (ARIC)[16]; Framingham Offspring Cohort[17]; and Multi-Ethnic Study of Atherosclerosis (MESA)[18]

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Summary

Introduction

Understanding the precise role of genetic, demographic, and environmental variations on the expression of complex biological mechanisms contributing to the development and course of major medical disorders is critical for next-generation medicine, often referred to as precision medicine[1,2]. Its framework defines the human diseases at greater resolution by focusing on a particular target risk group or subpopulation[3] based on several factors, such as individual’s genetic and molecular makeup; complex physiological aspects of race, sex, and age; lifestyle and environmental factors; and their interactions. Singh et al Translational Psychiatry (2020)10:351 in achieving clinical utility of the precision medicine framework are accomplishing data pooling (i.e., data harmonization) in order to reconcile the evidence from multiple ongoing investigations[7] and developing robust estimates of the interactions among an individual’s genetic makeup and complex physiological aspects of race, sex, and age. Our past and current work have focused on both of these challenges. In the current work we focus on developing generalizable robust estimates within specific strata of the interactions using this large harmonized data set

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