Developing a synthetic psychosocial stress measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses

Evaluating the precision of EBF1 SNP x stress interaction association: sex, race, and age differences in a big harmonized data set of 28,026 participants

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.

Overview of Federated Facility to Harmonize, Analyze and Management of Missing Data in Cohorts

Cohorts are instrumental for epidemiologically oriented observational studies. Cohort studies usually observe large groups of individuals for a specific period of time to identify the contributing factors to a specific outcome (for instance an illness) and create associations between risk factors and the outcome under study. In collaborative projects, federated data facilities are meta-database systems that are distributed across multiple locations that permit to analyze, combine, or harmonize data from different sources making them suitable for mega- and meta-analyses. The harmonization of data can increase the statistical power of studies through maximization of sample size, allowing for additional refined statistical analyses, which ultimately lead to answer research questions that could not be addressed while using a single study. Indeed, harmonized data can be analyzed through mega-analysis of raw data or fixed effects meta-analysis. Other types of data might be analyzed by e.g., random-effects meta-analyses or Bayesian evidence synthesis. In this article, we describe some methodological aspects related to the construction of a federated facility to optimize analyses of multiple datasets, the impact of missing data, and some methods for handling missing data in cohort studies.

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

The serotonin receptor 5-HTR2C is thought to be involved in the function of multiple brain structures. Consequently, the HTR2C gene has been studied extensively with respect to its association with a variety of phenotypes. One coding variant in the HTR2C gene, Cys23Ser (rs6318), has been associated with depressive symptoms. and adiposity; however, these findings have been inconsistent. The reasons for this mixed picture may be due to low statistical power or due to other factors such as failure to account for possible interacting environmental factors, such as psychosocial stress. Further, the literature around this polymorphism is marked by limited inclusion of persons of African ancestry. The present study sought to overcome these limitations and definitively determine the relationship of this polymorphism with depressive and obesity phenotypes in a large sample meta-analysis. Thus, we harmonized individual level data from 10 studies including the Women’s Health Initiative, CARDIA, ARIC, Framingham Offspring, and the Jackson Heart Study, resulting in a sample of 27,161 individuals (10,457 Black women, 2,819 Black men, 7,419 White women, and 6,466 White men). We conducted a random effects meta-analysis using individual level data to examine whether the Cys23Ser variant—either directly, or conditionally depending on the level of psychosocial stress—was associated with depressive symptoms and body mass index (BMI). We found that psychosocial stress was associated with both depression and BMI, but that Cys23Ser was not directly associated with, nor did it modify the associations of psychosocial stress with depression or BMI. Thus, in the largest study of this polymorphism, we have determined that rs6318 is not associated with depression, or BMI.