Associations of genetic and infectious risk factors with coronary heart disease

Worldwide, cardiovascular diseases (CVDs) are the leading cause of mortality (Roth et al., 2018). An estimated 17.9 million people die from CVD each year, accounting for 32% of all deaths. CVD is a broad term for medical conditions involving the heart and blood vessels, such as coronary heart disease (CHD), congenital heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, deep vein thrombosis, and pulmonary embolism (World Health Organization, 2009).

CHD is the most common type of heart disease (Roth et al., 2018). It is caused by atherosclerosis, a build-up of plaque inside the walls of the arteries that supply blood to the heart. CHD progresses over a long period of time and eventually evolves into symptoms such as chest pain (angina), tightness in the chest, breathing difficulties, and pain in the arms or shoulders (Ambrose and Singh, 2015). A complete blockage can cause a heart attack.

A combination of demographic, environmental, and genetic factors contribute to the development of CHD (Khot et al., 2003; Mendis et al., 2011). The main risk factors associated with the development of CHD – smoking, diabetes, hyperlipidemia, and hypertension – have been established by extensive epidemiological research (MacMahon et al., 1990; Stamler et al., 1993; Verschuren et al., 1995; Weintraub, 1990). Age is also an important risk factor for CHD (Castelli, 1984). Finally, the incidence of CHD is greater in males than in females (Castelli, 1984). Very recently, a new algorithm, named Systematic COronary Risk Evaluation 2 (SCORE2), was developed to predict the 10-year risk of first-onset CVD in European populations (SCORE2 working group and ESC Cardiovascular risk collaboration, 2021; SCORE2-OP working group and ESC Cardiovascular risk collaboration, 2021). This score has replaced the existing HeartScore scoring system, and incorporates most of the risk factors mentioned above (Conroy et al., 2003).

CHD also has an important genetic component. In 1938, the first familial risk model for CHD was described and later confirmed by clinical observations and large studies of twins and of longitudinal cohorts (Müller, 1938; Marenberg et al., 1994; Samani et al., 2007; Abraham et al., 2016). Based on whole-genome approaches, the heritability of CHD has been estimated at 40–60%, even after controlling for known risk factors (Vinkhuyzen et al., 2013).

Multiple clinical studies have identified inflammatory risk factors that are predictive of future cardiovascular events (Alfaddagh et al., 2020; Libby, 2006; Hansson, 2005). Endothelial dysfunction and subintimal cholesterol have been shown to trigger an inflammatory cascade, involving activated macrophages and leading to atherosclerotic lesions. At the molecular level, inflammasome formation in macrophages plays, through their production of interleukin (IL)-1β, an essential role in the propagation of inflammation. These cytokines are released, trigger various inflammatory cells, and produce IL-6 that in turn, stimulate C-reactive protein (CRP) production by the liver, which further enhances the inflammatory cascade within the vascular wall. Today, CRP is an established biomarker of systemic inflammation and a possible predictor of future cardiovascular events (Libby, 2006).

The recognition of atherosclerosis as an inflammatory disease has renewed interest in examining the role of pathogens in CHD and other CVDs. Nearly 150 years ago, acute infection with Bacillus typhosus was found to cause sclerosing changes in the arterial wall (Gilbert and Lion, 1889). A century later, the interest for a potential role of infection in atherosclerosis was renewed, with the discovery that CHD-positive individuals show an increased likelihood of having elevated levels of antibodies to Chlamydia pneumoniae (C. pneumoniae) (Saikku et al., 1988). This was followed by the discovery of the association with CHD of several other infectious agents, including bacteria and viruses, such as Helicobacter pylori (H. pylori), hepatitis C virus (HCV), and human herpes viruses (Adinolfi et al., 2018; Filardo et al., 2015; Wang et al., 2020; Zhang et al., 2008). The exact mechanisms linking infection to low-grade inflammation and atherosclerosis are still being studied, though some potential pathways have been proposed. One proposed mechanism involves the production of pro-inflammatory molecules in response to an infection (Campbell and Rosenfeld, 2015). These molecules, such as cytokines, can increase the activity of cells involved in atherosclerosis, such as macrophages and smooth muscle cells, leading to the formation of plaques and other changes in the walls of arteries (Campbell and Rosenfeld, 2015). Another mechanism is related to the inflammation at the site of vessel wall. Specifically, it is characterized by the presence of the infectious agents within the atherosclerotic plaques. The infectious consequences on the atherosclerotic plaque can be accelerated progression or a final complication like thrombosis and plaque rupture (Pedicino et al., 2013).

Although enormous progress has been made in the understanding of CHD pathogenesis, the overall picture of the combined contribution of infectious, inflammatory, and genetic factors to the risk of developing CHD in the general population remains incomplete. We here use data from the CoLaus|PsyCoLaus study, a well-characterized, longitudinal, population-based study from Switzerland, to obtain a more comprehensive view of the evidence for the respective contributions of these factors to CHD.

CHD is a complex disease that is influenced by demographic, environmental, and genetic factors (Khot et al., 2003; Mendis et al., 2011). Infections have also been suspected to increase the risk of CHD, directly or through the induction of chronic inflammation (Vojdani, 2003). The present study investigated the independent and combined effects of these risk factors as possible prognostic indicators for the occurrence of CHD. We performed an event-free survival analysis of incident CHD using data from a longitudinal, population-based study, in which more than 6% of participants developed CHD over a 12-year study period.

We confirmed the utility of SCORE2 to predict CHD risk in our cohort (SCORE2 working group and ESC Cardiovascular risk collaboration, 2021). Of note, chronic inflammation reflected in hs-CRP level did not appear as an independent predictor of CHD in our analyses, as the univariable association signal was suppressed after adjustment for SCORE2 levels.

We studied the effect of human genetic determinants on CHD occurrence using PRS, and we reproduced previously observed effects: participants with a higher CHD-PRS have a greater risk of CHD, even after adjustment for all known factors (Ding et al., 2011; Kullo et al., 2016). This result confirms the existence of genetic susceptibility loci for CHD, and that the individual genetic background modulates CHD risk independently from age, sex, or co-morbidities. Our work confirms the potential interest in using PRS to improve the prediction of coronary events.

We also evaluated the potential contribution of multiple persistent or frequently recurring pathogens to CHD after controlling for conventional CHD risk factors, socioeconomic status, and human genetic variability. We observed an association of CHD with detection of antibodies against F. nucleatum. This pathogen is very prevalent in humans (Adams et al., 2004; Afra et al., 2013; Looker et al., 2015). F. nucleatum is an anaerobic bacterium that belongs to the normal flora of the oral cavity and plays an important role in the development and progression of oral diseases, such as gingivitis (gum inflammation) and periodontitis (infection of the gums). Under pathological conditions, the pathogen can spread by the hematogenous route to extra-oral systemic sites, including the gut and the female genital tract (Han and Wang, 2013; Han et al., 2004). Studies have also suggested the involvement of F. nucleatum in CVD. First, by its capacity to directly migrate into arterial plaques, thus exacerbating atherosclerosis, and more recently, through the association of periodontitis and CVD (Kholy et al., 2015; Elkaïm et al., 2008; Figuero et al., 2011; Ford et al., 2006; Han, 2015; Zardawi et al., 2020). Finally, it has been shown that periodontal pathogens are able to spread through the bloodstream from the buccal cavity to the arteries in patients with detectable coronary calcium, a very specific marker of atherosclerosis (Corredor et al., 2022). In summary, the relationship between oral inflammations and CVD could be explained by the colonization of arterial walls and atherosclerosis plaques by dental bacteria, as well as by increased systemic inflammation due to oral infection. However, to date, no direct causality has been established. Besides, no genome-wide association study on F. nucleatum has been published, neither on humoral immune response (i.e., IgG levels) nor on susceptibility to infection/colonization (i.e., serostatus).

HSV-1, HHV-6A, VZV, HPyV6, and C. trachomatis serologies, as well as total burden of infection, were associated with CHD occurrence in univariable models. However, these factors were not significantly associated in the multivariable analysis, suggesting that at least some of them could be indirect markers of socioeconomic status.

Our data do not support the existence of the previously identified associations between CHD and H. pylori, or CMV. The conflicting reports of possible associations between these pathogens and CHD could be due to sample size but remain questionable. Further extensive, and high-quality studies are needed to thoroughly examine these associations and provide firm conclusions.

Our study has several limitations. As is the case for most longitudinal studies, the absence of data on individuals who dropped out before the end of the follow-up implies that some CHD events could have gone undetected. Also, the demographic information, as well as the clinical and laboratory measurements, were obtained at baseline, and we do not know whether participant information changed over time. Adjustment for risk factors measured at baseline does not account for clinical or demographic changes that could influence CHD outcomes. Similarly, we do not know how the antibody responses against the various antigens evolved over the 12 years of the study. In addition, no significance adjustment was performed when using multiple univariable tests to determine the effect of single factors on CHD risk, although this may increase the false positive rate. Moreover, we were unable to replicate previously published observations of associations of CHD with C. pneumoniae and HCV as serologies for these pathogens were not available. From a more practical point of view, the identified association with F. nucleatum needs to be replicated and validated in independent cohorts and different populations. Finally, the clinical utility of including genetic and infection biomarkers in CHD prediction algorithms will need to be demonstrated.

The data of CoLaus|PsyCoLaus study used in this article cannot be fully shared as they contain potentially sensitive personal information on participants. According to the Ethics Committee for Research of the Canton of Vaud, sharing these data would be a violation of the Swiss legislation with respect to privacy protection. However, coded individual-level data that do not allow researchers to identify participants are available upon request to researchers who meet the criteria for data sharing of the CoLaus|PsyCoLaus Datacenter (CHUV, Lausanne, Switzerland). Any researcher affiliated to a public or private research institution who complies with the CoLaus|PsyCoLaus standards can submit a research application to research.colaus@chuv.ch or research.psycolaus@chuv.ch. Proposals requiring baseline data only, will be evaluated by the baseline (local) Scientific Committee (SC) of the CoLaus and PsyCoLaus studies. Proposals requiring follow-up data will be evaluated by the follow-up (multicentric) SC of the CoLaus|PsyCoLaus cohort study. Detailed instructions for gaining access to the CoLaus|PsyCoLaus data used in this study are available at https://www.colaus-psycolaus.ch/professionals/how-to-collaborate/. The underlying code used to analyze the data in this manuscript is publicly available on GitHub (https://github.com/flaviahodel/cox-chd-analysis, copy archived at swh:1:rev:317cf14e0fbf09f214b792cc5ea5a399739e15a1). Source Data files have been provided for all Figures and Figure Supplements. "Figure 1 - Source Data", "Figure 2 - Source Data", "Figure S1 - Source Data", "Figure S2 - Source Data", "Figure S3 - Source Data", and "Figure S4 - Source Data" contain the numerical data used to generate the figures.

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Author details

1. Flavia Hodel

   1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   Conceptualization, Software, Formal analysis, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7331-7357

2. Zhi Ming Xu

   1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   Conceptualization, Methodology

   Competing interests

   No competing interests declared

3. Christian Wandall Thorball

   Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

4. Roxane de La Harpe

   Department of Medicine, Internal medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

   Contribution

   Resources

   Competing interests

   No competing interests declared

5. Prunelle Letang-Mathieu

   1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

6. Nicole Brenner

   Division of Infections and Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7690-4925

7. Julia Butt

   Division of Infections and Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

   Contribution

   Resources

   Competing interests

   No competing interests declared

8. Noemi Bender

   Division of Infections and Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2542-9949

9. Tim Waterboer

   Division of Infections and Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

10. Pedro Manuel Marques-Vidal

    Department of Medicine, Internal medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    Resources, Data curation, Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4548-8500

11. Peter Vollenweider

    Department of Medicine, Internal medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Julien Vaucher

    Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

13. Jacques Fellay

    1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Swiss Institute of Bioinformatics, Lausanne, Switzerland
    3. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

    Contribution

    Supervision, Funding acquisition, Writing – original draft, Project administration

    For correspondence

    jacques.fellay@epfl.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8240-939X