Journal article
AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease
Briefings in bioinformatics, v 25(6), 479
23 Sep 2024
PMID: 39323093
Abstract
Coronary heart disease (CHD) is one of the leading causes of mortality and morbidity in the United States. Accurate time-to-event CHD prediction models with high-dimensional DNA methylation and clinical features may assist with early prediction and intervention strategies. We developed a state-of-the-art deep learning autoencoder survival analysis model (AESurv) to effectively analyze high-dimensional blood DNA methylation features and traditional clinical risk factors by learning low-dimensional representation of participants for time-to-event CHD prediction. We demonstrated the utility of our model in two cohort studies: the Strong Heart Study cohort (SHS), a prospective cohort studying cardiovascular disease and its risk factors among American Indians adults; the Women's Health Initiative (WHI), a prospective cohort study including randomized clinical trials and observational study to improve postmenopausal women's health with one of the main focuses on cardiovascular disease. Our AESurv model effectively learned participant representations in low-dimensional latent space and achieved better model performance (concordance index-C index of 0.864 +/- 0.009 and time-to-event mean area under the receiver operating characteristic curve-AUROC of 0.905 +/- 0.009) than other survival analysis models (Cox proportional hazard, Cox proportional hazard deep neural network survival analysis, random survival forest, and gradient boosting survival analysis models) in the SHS. We further validated the AESurv model in WHI and also achieved the best model performance. The AESurv model can be used for accurate CHD prediction and assist health care professionals and patients to perform early intervention strategies. We suggest using AESurv model for future time-to-event CHD prediction based on DNA methylation features.
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Details
- Title
- AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease
- Creators
- Yike Shen - The University of Texas at ArlingtonArce Domingo-Relloso - Universitat de ValènciaAllison Kupsco - Columbia UniversityMarianthi-Anna Kioumourtzoglou - Columbia UniversityMaria Tellez-Plaza - Instituto de Salud Carlos IIIJason G. Umans - Georgetown-Howard Universities Center for Clinical and Translational ScienceAmanda M. Fretts - University of WashingtonYing Zhang - University of Oklahoma Health Sciences CenterPeter F. Schnatz - Reading HospitalRamon Casanova - Wake Forest UniversityLisa Warsinger Martin - George Washington UniversitySteve Horvath - University of California, Los AngelesJoAnn E. Manson - Harvard UniversityShelley A. Cole - Texas Biomedical Research InstituteHaotian Wu - Columbia UniversityEric A. Whitsel - University of North Carolina at Chapel HillAndrea A. Baccarelli - Harvard UniversityAna Navas-Acien - Columbia UniversityFeng Gao - University of California, Los Angeles
- Publication Details
- Briefings in bioinformatics, v 25(6), 479
- Publisher
- Oxford University Press
- Number of pages
- 9
- Grant note
- 75N92019D00027; 75N92019D00028; 75N92019D00029; 75N92019D00030; R01HL090863; R01HL109315; R01HL109301; R01HL109284; R01HL109282; R01HL109319; U01HL41642; U01HL41652; U01HL41654; U01HL65520; U01HL65521 / National Heart, Lung, and Blood Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) R01ES021367; R01ES025216; R01ES032638; P42ES033719; P30ES009089; R35ES031688 / National Institute of Environmental Health Sciences; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering and Science
- Web of Science ID
- WOS:001321026700002
- Scopus ID
- 2-s2.0-85204941048
- Other Identifier
- 991022202103704721