2026-07-22T09:50:34Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/254332025-12-18T12:59:07Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19608

00925njm 22002777a 4500 dc Shen, Yike author Domingo-Relloso, Arce author Kupsco, Allison author Kioumourtzoglou, Marianthi-Anna author Tellez-Plaza, Maria author Umans, Jason G author Fretts, Amanda M author Zhang, Ying author Schnatz, Peter F author Casanova, Ramon author Martin, Lisa Warsinger author Horvath, Steve author Manson, JoAnn E author Cole, Shelley A author Wu, Haotian author Whitsel, Eric A author Baccarelli, Andrea A author Navas-Acien, Ana author Gao, Feng author 2024-09-23 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. Brief Bioinform. 2024 Sep 23;25(6):bbae479. 10.1093/bib/bbae479 1477-4054 1467-5463 Briefings in bioinformatics 39323093 https://hdl.handle.net/20.500.12105/25433 Autoencoder survival analysis Cohort studies Coronary heart disease Deep learning Epigenetics AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease