Chronic Disease Epidemiology

Explaining the Variance in Cardiovascular Disease Risk Factors

A Comparison of Demographic, Socioeconomic, and Genetic Predictors

Hamad, Rita^a,b; Glymour, M. Maria^c; Calmasini, Camilla^c; Nguyen, Thu T.^a; Walter, Stefan^d; Rehkopf, David H.^e

Author Information

From the ^aDepartment of Family and Community Medicine, University of California San Francisco, San Francisco, CA

^bPhilip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA

^cDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA

^dDepartment of Medicine and Public Health, Rey Juan Carlos University, Madrid, Spain

^eDepartment of Medicine, Stanford University, Stanford, CA

Editor’s Note: A related article appears on p. 34.

Submitted July 17, 2020; accepted September 17, 2021

Disclosure: This work was supported by grant K08-HL132106 from the National Institutes of Health to R.H. and by grant Talent Attraction Program 2018-T1/BMD-11226 from the Community of Madrid, Spain, to S.W. The other authors have no conflicts to report.

Data/Statistical Code Availability: The data used for our study can be requested by submitting an application for restricted data from the Health and Retirement Study at the University of Michigan (https://hrs.isr.umich.edu).

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Rita Hamad, Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF, 995 Potrero Avenue, Building 80, Ward 83; San Francisco, California 94110. E-mail: [email protected].

Epidemiology 33(1):p 25-33, January 2022. | DOI: 10.1097/EDE.0000000000001425

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Abstract

Background:

Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVD-related outcomes explained by genetic versus socioeconomic predictors.

Methods:

Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained.

Results:

The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes. Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF,

Conclusions:

Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings. See video abstract at, https://links.lww.com/EDE/B879.

Erratum

Regarding the article by Hamad et al.¹ in the January 2022 issue of Epidemiology, extraneous text was introduced into the Results section of the abstract as the result of a typesetting error. The corrected Results section is provided below.

Results: The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes.

Epidemiology. 33(3):e10, May 2022.