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. 2024 Sep 10;6(5):731-742.
doi: 10.1016/j.jaccao.2024.07.014. eCollection 2024 Oct.

Multicohort Epigenome-Wide Association Study of All-Cause Cardiovascular Disease and Cancer Incidence: A Cardio-Oncology Approach

Arce Domingo-Relloso  1   2   3 , Angela L Riffo-Campos  4   5   6 , Naisi Zhao  7 , Guillermo Ayala  5 , Karin Haack  8 , Carlos Manterola  4 , Dorothy A Rhoades  9 , Jason G Umans  10 , M Daniele Fallin  11 , Miguel Herreros-Martinez  12 , Marina Pollan  3 , Eric Boerwinkle  13   14 , Elizabeth A Platz  15 , Miranda R Jones  15 , Jan Bressler  13 , Roby Joehanes  16   17 , Calen P Ryan  18 , Juan R Gonzalez  19 , Daniel Levy  16   17 , Daniel W Belsky  20 , Shelley A Cole  8 , Dominique S Michaud  7 , Ana Navas-Acien  1 , Maria Tellez-Plaza  2   3
Affiliations

Multicohort Epigenome-Wide Association Study of All-Cause Cardiovascular Disease and Cancer Incidence: A Cardio-Oncology Approach

Arce Domingo-Relloso et al. JACC CardioOncol. .

Abstract

Background: Emerging evidence reveals a complex relationship between cardiovascular disease (CVD) and cancer, which share common risk factors and biological pathways.

Objectives: The aim of this study was to evaluate common epigenetic signatures for CVD and cancer incidence in 3 ethnically diverse cohorts: Native Americans from the SHS (Strong Heart Study), European Americans from the FHS (Framingham Heart Study), and European Americans and African Americans from the ARIC (Atherosclerosis Risk In Communities) study.

Methods: A 2-stage strategy was used that included first conducting untargeted epigenome-wide association studies for each cohort and then running targeted models in the union set of identified differentially methylated positions (DMPs). We also explored potential molecular pathways by conducting a bioinformatics analysis.

Results: Common DMPs were identified across all populations. In a subsequent meta-analysis, 3 and 1 of those DMPs were statistically significant for CVD only and both cancer and CVD, respectively. No meta-analyzed DMPs were statistically significant for cancer only. The enrichment analysis pointed to interconnected biological pathways involved in cancer and CVD. In the DrugBank database, elements related to 1-carbon metabolism and cancer and CVD medications were identified as potential drugs for target gene products. In an additional analysis restricted to the 950 SHS participants who developed incident CVD, the C index for incident cancer increased from 0.618 (95% CI: 0.570-0.672) to 0.971 (95% CI: 0.963-0.978) when adjusting the models for the combined cancer and CVD DMPs identified in the other cohorts.

Conclusions: These results point to molecular pathways and potential treatments for precision prevention of CVD and cancer. Screening based on common epigenetic signatures of incident CVD and cancer may help identify patients with newly diagnosed CVD at increased cancer risk.

Keywords: DNA methylation; cancer; cardio-oncology; cardiovascular disease; multicohort.

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Conflict of interest statement

This work was supported by grants from the National Heart, Lung, and Blood Institute (under contracts 75N92019D00027, 75N92019D00028, 75N92019D00029, and 75N92019D00030) and previous grants (R01HL090863, R01HL109315, R01HL109301, R01HL109284, R01HL109282, and R01HL109319 and cooperative agreements U01HL41642, U01HL41652, U01HL41654, U01HL65520, and U01HL65521); by the National Institute of Health Sciences (R01ES021367, R01ES025216, P42ES033719, and P30ES009089); by the Spanish Funds for Research in Health Sciences, Instituto de Salud Carlos III, cofunded by European Regional Development Funds (PI22CIII/00029, PI15/00071 and CP12/03080); and the by Spanish Agency for Research (PID2023-147163OB-C22, PID2019-108973RB-C21 and PID2020-117114GB-I00 from Ministerio de Ciencia e Innovación) by “Ministerio de Ciencia e Innovación,” Maria Zambrano grant ZA21-063, funded by the Ministry of Universities of the Government of Spain, financed by the European Union, NextGeneration EU, to Dr Riffo-Campos; ANID–Millennium Science Initiative Program—NCS2021_013 and ANID FONDAP 152220002 (CECAN) and a fellowship from the “La Caixa” Foundation (ID 100010434), code LCF/BQ/DR19/11740016. The ARIC study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services, under contracts 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. Funding was also supported by grants 5RC2HL102419 and R01NS087541. Studies on cancer in ARIC are also supported by the National Cancer Institute (U01 CA164975). Cancer data were provided by the Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health, with funding from the State of Maryland and the Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data are also supported by cooperative agreement NU58DP007114, funded by the Centers for Disease Control and Prevention. The FHS is funded by National Institutes of Health contract N01-HC-25195. The laboratory work for this investigation was funded by the Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, and a National Institutes of Health Director’s Challenge Award (Daniel Levy, principal investigator). Dr Belsky is a fellow of the CIFAR CBD Network. The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Centers for Disease Control and Prevention, the U.S. Department of Health and Human Services, or Instituto de Salud Carlos III. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

None
Graphical abstract
Central Illustration
Central Illustration
Flowchart of the Multicohort Analysis Differentially methylated positions (DMPs) in the untargeted epigenome-wide association study (EWAS) were obtained by fitting separate elastic-net models for each cohort. Targeted EWAS DMPs were obtained by fitting an elastic-net model, separately for each cohort, to the union set of DMPs identified in the untargeted EWAS for all cohorts. DMPs in the targeted EWAS were annotated to their closest gene to perform a protein-protein interaction network in common nodes for at least 3 cohorts (highlighted in black lines in Venn diagram; Supplemental Figure 2). Subsequently, a gene-set analysis was carried out on the network. ARICb = African American participants of the Atherosclerosis Risk In Communities study; ARICw = European American participants of the Atherosclerosis Risk In Communities study; Ca-CVD = cancer and cardiovascular disease; CVD = cardiovascular disease; FDR = false discovery rate; FHS = Framingham Heart Study; GO = Gene Ontology; SHS = Strong Heart Study.
Figure 1
Figure 1
Protein-Protein Interaction Network for Protein-Coding Genes Associated With at Least 3 Cohorts The network was analyzed using Cytoscape and included 147 nodes and 217 interactions. The sizes of the nodes are proportional to the number of connections. Increasingly darker solid edge lines indicate protein interactions with increasing confidence scores. The interactions and their confidence scores (0.5 or greater) were obtained from the STRING database. The circle shape indicates that the node was common for 3 cohorts, whereas the triangle shape indicates that it was common for all 4 cohorts. Different colors are used for each outcome. CpG = cytosine followed by a guanine with a phosphate link; CVD = cardiovascular disease.
See this image and copyright information in PMC

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