Social impacts of cardiovascular disease risk assessed using the NIH-All-of-US-Data

Mentors:
Lead: Mette Olufsen (Mathematics, NC State)
Collaborator: Brian Carlson (Physiology, University of Michigan)

Intellectual merit and significance:
While cardiovascular disease (CVD) remains the leading cause of death in adults above 65, disparities in CVD risk and mortality exist by biological sex, race, and ethnicity [1, 2] . These disparities may partly be explained by social determinants of health, such as exposure discrimination. SDoH are defined as societal and environmental conditions that influence the health and behavior of individuals. Nevertheless, the social determinants of CVD risk across these different gender, racial, and ethnic groups are poorly characterized and understood. In addition, there are very few analytical statistical and predictive models to analyze quantitative data [3].

Objectives:
This study integrates modeling [4], machine learning [5], and biostatistics to examine how social stress is associated with CVD risk among racial and ethnic adults aged 45-75. We will test the validity of integrating markers extracted from predictive models in a moderated-mediation statistical
approach through modeling exercises and sensitivity analyses.

Outcomes:
Mathematical and statistical models integrating cardiovascular data, local and global sensitivity analysis, and statistical methodology to analyze available CVD. Development of data management and organization using a fully cloud-based platform to understand how to use advanced mathematical and statistical methods to examine the links between social stress and CVD risk.

References:

1. Jackson, J., Explaining intersectionality through description, counterfactual thinking, and mediation analysis. Soc Psychiatry Psychiatr Epidemiol, 2017. 52: p. 785-793.

2. Colunga, A., K. Kim, NP. Woodall, J. Gennari, MS. Olufsen, and BE. Carlson. Deep phenotyping of cardiac function in heart transplant patients using cardiovascular systems models. J Physiol, 2020.598: p. 3203-3222.

3. Colunga, A., MJ. Colebank, REU Program, and MS. Olufsen. Parameter inference in a computational model of haemodynamics in pulmonary hypertension. J R Soc Interface, 2023. 20:p. 20220735.

4. Jones, E., D. Cameron, D. Beard, S. Hummel, and BE. Carlson. Cardiovascular system model- based phenotyping of heart failure with preserved ejection fraction. J Cardiac Failure, 2019. 25:p. S33.