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308 Machine learning to predict genetic variation and cardiovascular risk in Hispanic patients with Systemic lupus erythematosus

Published online by Cambridge University Press:  24 April 2023

Ariana González-Meléndez
Affiliation:
University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
Abiel Roche-Lima
Affiliation:
Center for Collaborative Research in Helth Disparities-University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
Claudia P. Amaya Ardila
Affiliation:
Department of Epidemiology and Biostatistics, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
Luis M. Vilá
Affiliation:
University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
Elizabeth Brown
Affiliation:
Department of Epidemiology, Schools of Public Health and Medicine, University of Alabama at Birmingham, Birmingham, Alabama
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Abstract

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OBJECTIVES/GOALS: Cardiovascular disease (CVD) is the most common cause of death in systemic lupus erythematosus (SLE). Genome-wide association studies have identified single nucleotide polymorphisms linked with CVD risk, but the association with SLE is not well established. We aimed to determine associations using machine learning in a multi-ethnic lupus cohort. METHODS/STUDY POPULATION: We will use data from the established SLE cohort study named Genetic Profile Predicting the Phenotype (PROFILE). PROFILE was constituted in 1998 by combining existing cohorts at multiple sites which are also of defined ethnicity (Hispanics of Mexican ancestry and Puerto Rico, African American, and Caucasian). The cohort consists of 3,118 individuals and the database contains socioeconomic–demographic, clinical, laboratory, and genetic variables. Genetic data consist of 196,524 single nucleotide polymorphisms. To detect risk genes and predict an individual’s SLE risk will design a random forest classifier using SNP genotype data. Logistic regression models will be performed with CVD as the outcome, adjusted for age, sex, ethnicity, disease duration, and traditional and nontraditional risk factors for CVD. RESULTS/ANTICIPATED RESULTS: We expect to find several established and new susceptibility genes associated with CVD. DISCUSSION/SIGNIFICANCE: This approach offers an opportunity to characterize distinct genetic risk factors and the relationship of CVD with SLE. These data may be important in the identification of patients at high risk for such events and may allow the design of preventive strategies which may beneficially have an impact on the morbidity and mortality of SLE patients.

Type
Precision Medicine/Health
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2023. The Association for Clinical and Translational Science