Associations of genetic and infectious risk factors with coronary heart disease

  1. Flavia Hodel
  2. Zhi Ming Xu
  3. Christian Wandall Thorball
  4. Roxane de La Harpe
  5. Prunelle Letang-Mathieu
  6. Nicole Brenner
  7. Julia Butt
  8. Noemi Bender
  9. Tim Waterboer
  10. Pedro Manuel Marques-Vidal
  11. Peter Vollenweider
  12. Julien Vaucher
  13. Jacques Fellay  Is a corresponding author
  1. Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland
  2. Swiss Institute of Bioinformatics, Switzerland
  3. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Switzerland
  4. Department of Medicine, Internal medicine, Lausanne University Hospital and University of Lausanne, Switzerland
  5. Division of Infections and Cancer Epidemiology, German Cancer Research Center, Germany

Abstract

Coronary heart disease (CHD) is one of the most pressing health problems of our time and a major cause of preventable death. CHD results from complex interactions between genetic and environmental factors. Using multiplex serological testing for persistent or frequently recurring infections and genome-wide analysis in a prospective population study, we delineate the respective and combined influences of genetic variation, infections, and low-grade inflammation on the risk of incident CHD. Study participants are enrolled in the CoLaus|PsyCoLaus study, a longitudinal, population-based cohort with baseline assessments from 2003 through 2008 and follow-up visits every 5 years. We analyzed a subgroup of 3459 individuals with available genome-wide genotyping data and immunoglobulin G levels for 22 persistent or frequently recurring pathogens. All reported CHD events were evaluated by a panel of specialists. We identified independent associations with incident CHD using univariable and multivariable stepwise Cox proportional hazards regression analyses. Of the 3459 study participants, 210 (6.07%) had at least one CHD event during the 12 years of follow-up. Multivariable stepwise Cox regression analysis, adjusted for known cardiovascular risk factors, socioeconomic status, and statin intake, revealed that high polygenic risk (hazard ratio [HR] 1.31, 95% CI 1.10–1.56, p=2.64 × 10−3) and infection with Fusobacterium nucleatum (HR 1.63, 95% CI 1.08–2.45, p=1.99 × 10−2) were independently associated with incident CHD. In a prospective, population-based cohort, high polygenic risk and infection with F. nucleatum have a small, yet independent impact on CHD risk.

Editor's evaluation

This study is an important contribution to the understanding of cardiovascular disease aetiology based on solid design and methodology. It is a useful independent replication of the effects of traditional risk factors in a large prospective cohort and a valuable investigation of the role of past infection with a commensal bacterium F. nucleatum which warrants validation.

https://doi.org/10.7554/eLife.79742.sa0

Introduction

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.

Results

Demographic and serological characteristics

A total of 3459 CoLaus|PsyCoLaus participants with available phenotypic, serological, and genotypic data were included. Their characteristics are presented in Table 1.

Table 1
Baseline characteristics of 3459 CoLaus|PsyCoLaus participants by coronary heart disease (CHD) cases and controls.

p-Values are based on the t-test for continuous variables and Fisher’s exact test for categorical variables comparing the CHD cases and controls group.

OverallN = 3249 (100%)ControlsN = 3249 (93.93%)CHD casesN = 210 (6.07%)p
Baseline characteristics
Age (mean [SD])52.83 [10.48]52.34 [10.36]60.34 [9.53]<0.001
BMI (mean [SD])25.51 [4.31]25.41 [4.27]27.11 [4.62]<0.001
Systolic blood pressure (mean [SD])129.04 [18.40]128.32 [18.01]140.18 [20.60]<0.001
HDL cholesterol (mean [SD])1.66 [0.43]1.67 [0.43]1.49 [0.41]<0.001
LDL cholesterol (mean [SD])3.34 [0.92]3.33 [0.92]3.48 [0.91]0.018
Total cholesterol (mean [SD])5.60 [1.03]5.59 [1.02]5.73 [1.04]0.051
Sex = male (%)1592 (46.0)1448 (44.6)144 (68.6)<0.001
Statin = yes (%)296 (8.6)242 (7.4)54 (25.7)<0.001
Average gross monthly income (in CHF):0.029
<2999 (%)178 (5.1)161 (5.0)17 (8.1)
3000–4999 (%)452 (13.1)425 (13.1)27 (12.9)
5000–6999 (%)552 (16.0)517 (15.9)35 (16.7)
7000–9499 (%)504 (14.6)468 (14.4)36 (17.1)
9500–13,000 (%)338 (9.8)323 (9.9)15 (7.1)
>13,000 (%)344 (9.9)335 (10.3)9 (4.3)
Refused or missing (%)1091 (31.5)1020 (31.4)71 (33.8)
Smoking = yes (%)883 (25.5)820 (25.2)63 (30.0)0.146
Genetics
CHD-PRS (mean [SD])0.00 [1.00]−0.02 [0.99]0.26 [1.01]<0.001
Biomarkers of inflammation
hs-CRP (mean [SD])0.09 [0.46]0.08 [0.46]0.22 [0.44]<0.001
TNF-α (mean [SD]) – 63 NAs0.46 [0.38]0.46 [0.38]0.53 [0.35]0.013
IL-1β (mean [SD]) – 1’319 NAs0.17 [0.64]0.17 [0.64]0.14 [0.67]0.637
IL-6 (mean [SD]) – 294 NAs0.24 [0.58]0.24 [0.58]0.28 [0.55]0.398
Persistent pathogens
Human polyomaviruses:
BKPyV = seropositive (%)2912 (84.2)2735 (84.2)177 (84.3)1.000
JCPyV = seropositive (%)1812 (52.4)1696 (52.2)116 (55.2)0.434
HPyV6 = seropositive (%)2948 (85.2)2759 (84.9)189 (90.0)0.056
WUPyV = seropositive (%)3309 (95.7)3105 (95.6)204 (97.1)0.362
Human herpes viruses:
HSV-1 = seropositive (%)2547 (73.6)2373 (73.0)174 (82.9)0.002
HSV-2 = seropositive (%)601 (17.4)564 (17.4)37 (17.6)0.998
CMV = seropositive (%)1868 (54.0)1756 (54.0)112 (53.3)0.897
EBV = seropositive (%)3350 (96.8)3147 (96.9)203 (96.7)1.000
HHV-6A = seropositive (%)865 (25.0)800 (24.6)65 (31.0)0.049
HHV-6B = seropositive (%)1373 (39.7)1295 (39.9)78 (37.1)0.480
HHV-7 = seropositive (%)1846 (53.4)1746 (53.7)100 (47.6)0.099
KSHV = seropositive (%)141 (4.1)133 (4.1)8 (3.8)0.983
VZV = seropositive (%)3047 (88.1)2853 (87.8)194 (92.4)0.061
Parvovirus:
PVB-19 = seropositive (%)2420 (70.0)2277 (70.1)143 (68.1)0.595
Bacteria:
C. trachomatis = seropositive (%)1213 (35.1)1120 (34.5)93 (44.3)0.005
F. nucleatum = seropositive (%)520 (15.0)473 (14.6)47 (22.4)0.003
H. pylori = seropositive (%)685 (19.8)645 (19.9)40 (19.0)0.846
S. gallolyticus = seropositive (%)135 (3.9)130 (4.0)5 (2.4)0.322
Parasite:
T. gondii = seropositive (%)1445 (41.8)1349 (41.5)96 (45.7)0.262

During the follow-up of 4500 days (12.3 years), at least one CHD event occurred in 210 individuals (6.07%). The number of participants with one, two, and three coronary events was 140, 47, and 14, respectively. Nine individuals had between four and eight coronary events. Eligible study participants were on average 52.8 (standard deviation [SD] ± 10.5) years of age at baseline, 54% were women and 25.5% were smokers. On average, their body mass index (BMI) was 25.5 (±4.3) kg/m2, their systolic blood pressure was 129 (±18) mmHg, and their HDL cholesterol level was 1.66 (±0.43) mmol/L. The percentages of participants by average gross monthly income are 7.5 (<CHF 2999), 19.1 (CHF 3000–4999), 23.3 (CHF 5000–6999), 21.3 (CHF 7000–9499), 14.3 (CHF 9500–13,000), and 14.5% (CHF > 13,000).

For the measured biomarkers of inflammation, the log10-transformed mean (SD) values for high-sensitive CRP (hs-CRP), IL-1β, IL-6, and tumor necrosis factor α (TNF-α) were 0.09 (±0.46), 0.17 (±0.64), 0.24 (±0.58), and 0.46 (±0.38), respectively.

We also investigated participants’ serostatus for the following 22 human pathogens: 15 viruses (human polyomaviruses BK [BKV], JC [JCV], 6 [HPyV6], and WU [WUPyV], herpes simplex virus [HSV]-1, HSV-2, varicella zoster virus (VZV), Epstein–Barr virus [EBV], cytomegalovirus [CMV], human herpes virus 6A [HHV-6A], HHV-6B, HHV-7, Kaposi’s sarcoma-associated herpes virus [KSHV], parvovirus B19 [PVB-19], and rubella virus); six bacteria (Chlamydia trachomatis [C. trachomatis], Clostridium tetani [C. tetani], Cornybacterium diphteriae [C. diphteriae], F. nucleatum, H. pylori, and S. gallolyticus); and one parasite (T. gondii) (Appendix 1—table 1). The overall seropositivity ranged from 3.99% (S. gallolyticus) to 96.80% (EBV). The overall serostatus split between CHD-positive (with at least one CHD event during follow-up) and CHD-negative individuals are shown in Figure 1. Rubella, C. tetani and C. diphteriae were excluded from further analyses as the antibodies detected against these pathogens were most likely induced by vaccination.

Prevalence of tested pathogens in CoLaus|PsyCoLaus study in participants with and without coronary heart disease (CHD).

Overall serostatus for the 22 pathogens are shown in the CHD-positive group (individuals with at least one CHD event during follow-up) or CHD-negative group. The y-axis indicates the relative percentage within each group. Pathogens are ranked in ascending order of overall seropositivity (all individuals combined).

Univariable predictors of CHD incidence

To validate the utility of SCORE2 in our cohort, we tested its association with CHD. SCORE2 was significantly and positively associated with CHD (HR 1.72, 95% CI 1.61–1.85, p = 2.87×10−61) (Appendix 1—table 2). We also observed a significant inverse association between average gross monthly income and CHD risk (HR 0.85, 95% CI 0.76–0.96, p = 7.27×10−3).

To investigate the relationship between CHD and humoral response to infectious agents, we tested the association of serostatus for each of the included 19 persistent or frequently occurring pathogens with CHD. We found significant positive associations for six of them, including three herpes viruses, namely HSV-1 (HR 1.88, 95% CI 1.30–2.68, p = 6.52×10−4), HHV-6A (HR 1.39, 95% CI 1.03–1.86, p = 2.89×10−2), and VZV (HR 1.70, 95% CI 1.02–2.82, p = 4.25×10−2), one polyomavirus, HPyV6 (HR 1.66, 95% CI 1.06–2.61, p = 2.74×10−2), and two bacteria, F. nucleatum (HR 1.66, 95% CI 1.20–2.29, p = 2.32×10−3), and C. trachomatis (HR 1.45, 95% CI 1.11–1.91, p = 7.22×10−3) (Appendix 1—table 2).

To evaluate the impact of the biomarkers of inflammation on CHD risk, we tested the association of log10-transformed levels of hs-CRP, IL-1β, IL-6, and TNF-α with CHD. We observed a positive relationship between individuals’ hs-CRP (HR 1.91, 95% CI 1.42–2.55, p = 1.51×10−5) and TNF-α (HR 1.43, 95% CI 1.05–1.96, p = 2.46×10−2) levels, and increased risk of CHD event (Appendix 1—table 2).

Finally, we calculated a CHD polygenic risk score (CHD-PRS) for each subject to investigate the effect of common human genetic variations on CHD. As expected, we observed a significant association between the PRS and CHD (HR 1.32, 95% CI 1.16–1.51, p = 4.29×10−5), confirming that genetic predisposition to CHD can be captured through CHD-PRS. The top three genetic principal components (PC1, PC2, and PC3) were not significantly associated with CHD (Appendix 1—table 2).

Co-linearity and proportional hazard assumption testing

We calculated pairwise correlations between all variables that were found to be significant in univariable analyses. Appendix 2—figure 1 and Appendix 2—figure 2 illustrate that no strong correlations exist between significant variables. The strongest correlation was observed between SCORE2 and hs-CRP, and between seropositivity to C. trachomatis and gross monthly household income, with Pearson’s and Cramer’s V coefficients of 0.22 and 0.15, respectively. The proportionality assumption was tested for all significant variables using the Schoenfeld residuals. The residual tests indicated that all variables satisfied the proportional hazards assumption, revealing that the effect of all covariates are constant in time (Appendix 2—figure 3). Finally, we also assessed potential co-linearity issues among predictors that could affect model fitting. No variance inflation factor (VIF) value was indicative of co-linearity.

Multivariable model

To identify the independent risk factors of CHD in our cohort, we performed backward stepwise selection on 2323 individuals with non-missing data using a multivariable Cox proportional hazards model, starting with all the significant factors from the univariable models. The final multivariable analysis confirmed that SCORE2 (HR 1.96 per SD increase, 95% CI 1.74–2.22, p = 2.42×10−27) is an independent prognostic factor of CHD (Figure 2). We also observed significant independent associations for statin intake (HR 2.24, 95% CI 1.50–3.35, p = 9.17×10−5) and for seropositivity to F. nucleatum infection (HR 1.63, 95% CI 1.08–2.45, p = 1.99×10−2). Comparing individuals who had a least one CHD event (CHD group) against those who had no event during the follow-up period (control group), 22.4% (47/210) of the individuals in the CHD group were seropositive to F. nucleatum, versus 14.6% (473/3249) in the control group (p = 0.003) (Figure 1, Table 1). Lastly, we also observed a significant association between CHD occurrence and elevated CHD-PRS with an HR of 1.31 (95% CI 1.10–1.56, p = 3.32×10−3) per SD increase.

Hazard ratio (HR) and 95% confidence intervals of coronary heart disease (CHD) occurrence according to associated factors.

HR > 1 indicates an increased risk of CHD, whereas HR < 1 indicates a protective effect. p-Values (p) for each factor based on the multivariable Cox regression are shown.

To assess if the overall burden of infections contributed to increased risk of CHD, study participants were stratified according to their overall seropositivity index for measured pathogens, calculated by summing the number of pathogens for which they show seropositivity (range: 0–16). The numbers of individuals in each pathogen burden stratum are shown in Appendix 2—figure 4. In the univariable Cox model, pathogen burden significantly increased the risk of CHD occurrence (HR 1.11, 95% CI 1.03–1.18, p = 3.25×10−3) (Appendix 1—table 2). However, after adjustment with multivariable Cox proportional hazards regression, pathogen burden did not meet the level of significance for staying in the model.

Discussion

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.

Conclusion

CHD is a multicomponent disease that is caused by demographic, environmental, and genetic factors. Inflammation, possibly caused by persistent or frequently recurring infections, can contribute to its development. We identified a statistically significant association between the incidence of CHD and F. nucleatum infection, after adjustment for all established risk factors. We also confirmed that the individual polygenic risk of CVD, calculated from genome-wide genotypes, represents an independent risk factor for incident CHD. Our results can help to better identify subjects at high risk for CHD and provide a rationale for future anti-infective prevention trials.

Methods

Study cohort

The CoLaus|PsyCoLaus study is a longitudinal population-based study initiated in Lausanne in 2003; it mainly investigates the biological, environmental, and genetic determinants of CVD (https://www.colaus-psycolaus.ch/) (Firmann et al., 2008). The study involves over 6500 participants of European ancestry, who were recruited at random from the general population and represent approximately 10% sample of Lausanne citizens. Of the participants, 47.5% are men, and age at enrolment ranged from 35 to 75 years (mean ± SD: 51 ± 10.9). The study participants provided detailed phenotypic information through questionnaires, interviews, clinical and biological data. Nuclear deoxyribonucleic acid (DNA) was also extracted from the blood for whole-genome genotyping data. Every 5 years, follow-up interviews on the participants’ lifestyle and health status are conducted. There are three completed follow-ups and a fourth follow-up began in January 2022. The institutional Ethics Committee of the University of Lausanne, which later became the Ethics Commission of the Canton Vaud (https://www.cer-vd.ch/), approved the CoLaus|PsyCoLaus study (reference 16/03, decisions of January 13 and February 10, 2003), and all participants gave written consent.

Cardiovascular phenotype

The medical records of the participants who reported a CHD event during their lifetime were collected and evaluated by an independent panel of specialists. Information on the cause of death was also collected prospectively during the study period. The full procedure was described previously (Beuret et al., 2021). Only first events occurring after the baseline and up to day 4500 after the baseline were included in the analysis, as only during this period were all participants reliably followed.

DNA genotyping data and PRS calculation for cardiovascular phenotypes

The BB2 GSK-customized Affymetrix Axiom Biobank array was used to genotype DNA samples from 5399 participants at approximately 800,000 single nucleotide polymorphisms (SNPs). After genotype imputation and quality control procedures, approximately 9 million SNPs were available for analysis (Hodel et al., 2021). We then calculated, based on the risk effects of common SNPs, the CHD-PRS for each study participant. We used validated PRS from Inouye et al., available in the polygenic score catalog (Inouye et al., 2018; Lambert et al., 2021). These scores and summary statistics were used to construct the CHD-PRS in our target cohort data by using the clumping and thresholding method of the PRSice-2 v2.2.7 software (Choi et al., 2020). A standardized method was used to obtain the PRS, by multiplying the risk allele dosage for each variant by the effect size and summing the scores across all selected variants. SNPs were clumped according to linkage disequilibrium (r2 < 0.1) within a 250 kb window.

CHD risk evaluation

The risk of CHD for each participant was also assessed using the very recent SCORE2 and SCORE2-Older Persons (SCORE2-OP, for individuals >65 years of age) algorithms (SCORE2-OP working group and ESC Cardiovascular risk collaboration, 2021; SCORE2 working group and ESC Cardiovascular risk collaboration, 2021). These two algorithms will be referred to as SCORE2. SCORE2 was derived, calibrated, and validated to predict the 10-year risk of first-onset CVD using data from 13 million individuals from >50 European prospective studies and national registries. To develop this algorithm, the authors used competing risk-adjusted and age- and sex-specific models including age, current smoking, systolic blood pressure, and total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol. The authors also defined four risk regions in Europe on the basis of country-specific CVD mortality. For CoLaus|PsyCoLaus participants, calculations were based on the low-risk region corresponding to Switzerland. The raw scores of participants were standardized to Z-scores with approximately zero mean and unit variance before data analysis.

Measurement of inflammatory biomarkers

Venous blood samples (50 mL) of the participants, in a fasted state, were drawn. Before cytokine assessment, the serum blood samples were stored at −80°C, then they were sent to the laboratory on dry ice. The measurements of hs-CRP, IL-1β, IL-6, and TNF-α cytokine levels were described previously in detail (Marques-Vidal et al., 2011). Briefly, hs-CRP levels were assessed by immunoassay and latex HS (IMMULITE 1000-High, Diagnostic Products Corporation, Los Angeles, CA, USA). Cytokine levels were measured using a multiplexed particle-based flow cytometric cytokine assay on the flow cytometer (FC500 MPL, BeckmanCoulter, Nyon, Switzerland), thus following the manufacturer’s instructions. The lower limits of detection for IL-1β, IL-6, and TNF-α were 0.2 pg/mL. Intra- and inter-assay coefficients of variation were, respectively, 15% and 16.7% for IL-1β, 16.9% and 16.1% for IL-6, and 12.5% and 13.5% for TNF-α. For quality control, repeat measurements were performed on 80 subjects randomly selected from the initial sample. Individuals with hs-CRP levels above 20 mg/L were assigned a value of 20 by the manufacturer therefore were removed from the hs-CRP analyses as indicative of acute inflammation.

Serological analyses

To assess the humoral responses to a total of 38 antigens derived from 22 persistent infectious agents, serum samples were analyzed by the Infections and Cancer Epidemiology Division at the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) in Heidelberg (Waterboer et al., 2005; Waterboer et al., 2006). Studied pathogens included 15 viruses (BKV, JCV, HPyV6, WUPyV, HSV-1, HSV-2, VZV, EBV, CMV, HHV-6A, HHV-6B, HHV-7, KSHV, PVB-19, and rubella virus); six bacteria (C. diphteriae, C. tetani, C. trachomatis, F. nucleatum, H. pylori, and S. gallolyticus); and one parasite (T. gondii) (for details, see Appendix 1—table 1). The seroreactivity was measured at a serum dilution of 1:1000 by using multiplex serology based on glutathione S-transferase fusion capture immunosorbent assays combined with fluorescent bead technology. For each infectious agent tested, the antibody responses were measured for one to six antigens and then expressed as a binary result (IgG positive or negative), based on the predefined median fluorescence intensity thresholds. To define overall seropositivity against infectious agents when more than one antigen was used, we applied the pathogen-specific algorithms suggested by the manufacturer (see references in Appendix 1—table 1).

Statistical analyses

Univariable and multivariable Cox proportional hazard models were used to explore the relationship between risk factors and CHD incidence in the CoLaus|PsyCoLaus study. Each variable was first screened in the univariable model. To identify potential confounding due to population structure, we also tested the top three genetic principal components (PC1, PC2, and PC3) for association with CHD. We then examined the proportional hazards assumption of the significant (p < 0.05) covariates by using the scaled Schoenfeld residuals. The residuals were plotted over time for each covariate to test for time independence. Risk factors significantly associated with CHD in the univariable model were further evaluated using pairwise correlations. Finally, the identified risk factors were assessed using multivariable stepwise Cox regression analysis, adjusted for competing risk (i.e., SCORE2), socioeconomic status (i.e., gross monthly household income), and statin intake. Potential multicollinearity between statistically significant factors (p < 0.05) were identified using VIFs. The existence of multicollinearity between co-variates was determined by a VIF value > 2. We performed all statistical analyses using R (version 4.2.1).

Appendix 1

Additional tables

Appendix 1—table 1
Characteristics of infectious agent-specific antigens used on the Multiplex Serology platform in CoLaus|PsyCoLaus.
FamilyPathogenAntigen(Predicted) functionDef. of seropositivity is based onReference
Human polyomavirusesBKVVP1Major capsid proteinNAKjaerheim et al., 2007; Gossai et al., 2016; Robles et al., 2015
JCVVP1Major capsid proteinNAKjaerheim et al., 2007; Gossai et al., 2016; Robles et al., 2015
HPyV6VP1Major capsid proteinNAKjaerheim et al., 2007; Gossai et al., 2016; Robles et al., 2015
WUPyVVP1Major capsid proteinNAKjaerheim et al., 2007; Gossai et al., 2016; Robles et al., 2015
Human herpes virusesCMVpp150Tegument proteinAt least two positiveBrenner et al., 2018
pp52DNA binding protein
pp28Capsid protein
EBVZEBRAReplication activatorAt least two positiveBrenner et al., 2018
EA-DReplication (polymerase accessory subunit)
VCA p18Capsid protein
EBNA1Replication, latent viral infection
HHV-6IE1BPotential transactivatorAny HHV-6=at least one positive HHV-6A=IE1A and/or p100HHV-6B=IE1B and/or p101KBassig et al., 2018; Engdahl et al., 2019; Freuer et al., 2020
IE1APotential transactivator
p101KPotential tegument protein
p100Potential tegument protein
HHV-7U14Potential tegument proteinNAValidation ongoing
HSV-1gGMembrane glycoproteinNABrenner et al., 2019a
HSV-2mgGMembrane glycoproteinNABrenner et al., 2019a
KSHVLANA3Replication and long-term persistenceAt least one positiveValidation ongoing
K8.1Structural glycoprotein
VZVgE/gIEnvelope glycoproteinNABrenner et al., 2019a
ParvovirusB19VP1uniqueMinor capsid proteinNABrenner et al., 2019b
Rubella virus*RVE1Class II viral fusion proteinNABrenner et al., 2019b
C. trachomatisCtpGP3Virulence factorNATrabert et al., 2019
C. tetani*CtTetXToxoid (heavy chain)NABrenner et al., 2019b
Corynebacterium diphtheriae*CdDTAToxoid (intracellular)NABrenner et al., 2019b
F. nucleatumFnFn0264Adhesin (FadA)At least one positive (experimental)Butt et al., 2019
Fn1449Type Va secretion system (Fap2)
Fn1859Porin (FomA)
H. pyloriHpHP 10 GroELChaperoninAt least three positiveMichel et al., 2009
HP 73 UreaseAUrease alpha subunit
HP 547 CagAPathogenesis
HP 875 CatalaseDetoxification
HP 887 VacAPathogenesis
HP 1564 OMPCell envelope
S. gallolyticusSgGallo2178Pil1 pilus subunit (major pilin)NAButt et al., 2016; Butt et al., 2017
T. gondiiTgp22Surface proteinAt least one positiveBrenner et al., 2019a
sag-1Surface protein
  1. *

    Pathogens not taken forward due to lack of vaccination history in CoLaus|PsyCoLaus and/or the difficulty in identifying target antigens to ensure specificity of the test.

Appendix 1—table 2
Association of risk factors with coronary heart disease (CHD) based on the univariable Cox proportional hazard analyses.
VariableHR* (95% CI)*p
Baseline characteristics
SCORE21.72 (1.61–1.85)2.87×10-61
Statin3.82 (2.80–5.22)3.13×10-17
Average gross monthly income0.85 (0.76–0.96)7.27×10-3
Genetics
CHD-PRS1.32 (1.16–1.51)4.29×10-5
PC174-28 (0.03–195096)0.28
PC20.12 (00.0–728)0.64
PC30.33 (0.00–1131)0.79
Biomarkers of inflammation
hs-CRP1.91 (1.42–2.55)1.51×10-5
TNF-α1.43 (1.05–1.96)2.46×10-2
IL-1β0.93 (0.70–1.25)0.64
IL-61.10 (0.88–1.37)0.42
Human polyomaviruses
BKPyV1.05 (0.72–1.52)0.80
JCPyV1.14 (0.87–1.50)0.35
HPyV61.66 (1.06–2.61)2.74×10-2
WUPyV1.45 (0.65–3.27)0.37
Human herpes viruses
HSV-11.88 (1.30–2.68)6.52×10-4
HSV-21.05 (0.74–1.50)0.78
CMV1.00 (0.76–1.31)0.99
EBV0.97 (0.46–2.06)0.94
HHV-6A1.39 (1.03–1.86)2.89×10-2
HHV-6B0.93 (0.70–1.23)0.59
HHV-70.79 (0.60–1.03)8.33×10-2
KSHV0.89 (0.44–1.80)0.74
VZV1.70 (1.02–2.82)4.25×10-2
Parvovirus
PVB-190.90 (0.68–1.21)0.49
Bacteria.
C. trachomatis1.45 (1.11–1.91)7.22×10-3
F. nucleatum1.66 (1.20–2.29)2.32×10-3
H. pylori0.95 (0.67–1.34)0.78
S. gallolyticus0.62 (0.26–1.51)0.29
Parasite
T. gondii1.17 (0.90–1.54)0.25
Pathogen burden1.11 (1.03–1.18)3.25×10−3
  1. *

    HR = hazard ratio, CI = confidence interval.

  2. log10-transformed.

Appendix 2

Additional figures

Appendix 2—figure 1
Pairwise correlations between quantitative characteristics significantly associated with coronary heart disease (CHD) risk in the univariable Cox proportional hazard models.

Pearson’s correlation values are displayed, along with linear fits between variables.

Appendix 2—figure 2
Strength of association for each pair of categorical variables significantly associated with coronary heart disease (CHD) risk in univariable Cox proportional hazard models.

Cramer’s V values are displayed.

Appendix 2—figure 3
Graphical test of proportional hazards assumption (Schoenfeld test).

The graphs show the scaled Schoenfeld residuals over time. The p-values (p) of the variables and the model as a whole were shown in the plot. A significant p-value (< 0.05) indicates that the variable violates the proportional hazard assumption. The solid line represents the smoothing fitted spline, and the dashed lines the confidence bands at two standard errors. Global Schoenfeld test p=0.58.

Appendix 2—figure 4
Distribution of individuals according to their exposure to infectious agents (pathogen burden).

Bar plot showing the number of participants for each cumulative number of positive serological results, reflecting simultaneous ongoing chronic/latent infections. Sample sizes for each group are shown above the box.

Data availability

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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Article and author information

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

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (31003A_175603)

  • Jacques Fellay

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the participants in the CoLaus|PsyCoLaus study for their time and contribution to this study. We also thank all the clinical, academic, and administrative collaborators who helped with participant recruitment, study coordination, data collection, and storage. This project was supported by the Swiss National Science Foundation (grant 31003A_175603 to JF). The CoLaus|PsyCoLaus study was and is supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (grants 3200B0_105993, 3200B0_118308, 33CSCO_122661, 33CS30_139468, 33CS30_148401, and 33CS30_177535/1).

Ethics

Human subjects: The institutional Ethics Committee of the University of Lausanne, which later became the Ethics Commission of the Canton Vaud (www.cer-vd.ch), approved the CoLaus|PsyCoLaus study (reference 16/03, decisions of 13th January and 10th February 2003), and all participants gave written consent.

Version history

  1. Preprint posted: April 13, 2022 (view preprint)
  2. Received: April 25, 2022
  3. Accepted: January 22, 2023
  4. Version of Record published: February 14, 2023 (version 1)

Copyright

© 2023, Hodel et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Flavia Hodel
  2. Zhi Ming Xu
  3. Christian Wandall Thorball
  4. Roxane de La Harpe
  5. Prunelle Letang-Mathieu
  6. Nicole Brenner
  7. Julia Butt
  8. Noemi Bender
  9. Tim Waterboer
  10. Pedro Manuel Marques-Vidal
  11. Peter Vollenweider
  12. Julien Vaucher
  13. Jacques Fellay
(2023)
Associations of genetic and infectious risk factors with coronary heart disease
eLife 12:e79742.
https://doi.org/10.7554/eLife.79742

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https://doi.org/10.7554/eLife.79742

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