Effects of COVID-19 on Heart Failure and Its Risk Factors: A Bidirectional Mendelian Randomization Study

doi:10.21203/rs.3.rs-4704292/v1

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Effects of COVID-19 on Heart Failure and Its Risk Factors: A Bidirectional Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-4704292/v1

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Introduction

Previous studies suggest a potential link between COVID-19 and cardiovascular diseases, particularly heart failure (HF), but causal relationships remain unclear. This study uses bidirectional Mendelian randomization (MR) to investigate these associations.

Materials and methods

We selected genome-wide association study (GWAS) datasets detailing COVID-19 phenotypes as exposures, while traits associated with HF and its risk factors constituted the outcomes. Our primary estimation method involved utilizing the inverse-variance weighted technique for it could provide a precise estimation by giving more weight to studies with less variance. Alongside, we deployed the weighted median, MR-Egger, MR-PRESSO global test, and Cochran’s Q statistic to address biases, detect and correct for pleiotropy, and assess heterogeneity. Furthermore, we conducted bidirectional MR analysis to ascertain the directional causality.

Results

Based on the bidirectional MR analysis, our findings indicate a higher likelihood of developing into HF among hospitalized COVID-19 patients, as well as an increased propensity for HF development in individuals with all COVID-19 patients. Furthermore, the presence of HF increases the prevalence of COVID-19, escalates the severity of COVID-19, and raises the hospitalization rate in those affected by COVID-19. COVID-19 does not directly escalate the incidence of the risk factors of HF, but type 2 diabetes mellitus (T2DM) could increases the hospitalization and severity rates of COVID-19 patients. Meanwhile, chronic kidney disease (CKD) contributes to increased hospitalization rates among COVID-19 patients.

Conclusion

This study provides evidence of a bidirectional causal link among COVID-19 and HF and its risk factors. These findings suggest clinical implications for early detection and management of HF in COVID-19 patients and highlight the need for targeted healthcare resources.

Heart Failure

COVID-19

Mendelian Randomization Analysis

Risk Factors

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant global health challenges, with over 772 million cases and 6 million deaths worldwide (WHO, 2023; https://covid19.who.int/). COVID-19 often results in pneumonia and severe cases can lead to ARDS[1,2]. Additionally, the cardiovascular system is notably affected, with studies indicating that COVID-19 can lead to cardiovascular complications like left and right ventricular systolic impairment, pericardial effusions, and myocardial scarring[3,4].indicated that Patients with COVID-19 who have elevated cardiac troponin levels show more ventricular impairment and myocardial scarring during early recovery compared to contemporary controls. A study conducted in 2023 revealed that patients with COVID-19 who have elevated cardiac troponin levels show more ventricular impairment and myocardial scarring during early recovery compared to contemporary controls[4]. The impact of COVID-19 on public health systems has been profound, affecting healthcare delivery, resource allocation[5].

Heart failure (HF) is a chronic condition characterized by the heart's inability to pump sufficient blood to meet the body's needs. And HF is influenced by numerous cardiovascular conditions[6,7]. Despite efforts in prevention, its prevalence continues to rise, especially post-COVID-19 pandemic[7,8]. The inflammatory response triggered by COVID-19 can lead to increased strain on the heart, exacerbating pre-existing cardiovascular conditions or precipitating cardiovascular complications.

The development of COVID-19 and heart failure may mutually exacerbate each other, but current research rarely considers the bidirectional impacts of both simultaneously[9]. This study aims to explore the potential bidirectional causal relationship between COVID-19 and HF including its risk factors, through Mendelian randomization (MR), which leverages genetic variations to infer causality and mitigate biases from confounding factors and reverse causation[10]. We chose MR as our experimental method for it allows for scientific inference of causal relationships, with lower time and economic costs compared to conducting an RCT. The principle of genetic randomization leverages genetic variations to effectively mitigate biases related to reverse causation.

MR leverages genetic variations as instrumental variables (IVs) to infer causality. This study used SNPs as IVs to evaluate the causal impact of COVID-19 on HF and vice versa[11-13].

Study Design

The MR analysis was guided by three assumptions: (1) SNPs must be strongly associated with the exposure (COVID-19); (2) The genetic variants (IVs) should affect the outcome (HF) solely through the exposure (COVID-19); (3) IVs must be independent of confounders[14] (Figure 1).

We conducted our research in European participants for the following reasons. Firstly, most GWAS only included European participants, the data for GWAS basing on non-European populations were rare. Secondly, GWAS of European Ancestry is relatively higher in sample size and data quality. Thirdly, GWAS data for European populations are readily available and easy to access for the phenotypes we plan to study, while GWAS data for non-European populations are only found in a few phenotypes. For the sake of consistency in experimental results, we choose to utilize GWAS data from European populations. However, it's of significant to acknowledge that focusing on European populations may limit the generalizability of our findings to non-European populations. Future studies incorporating diverse populations will be crucial to validate and extend our results.

Data Source

GWAS datasets were used, with data on COVID-19 phenotypes from the COVID-19 GWAS release 7 (https://www.covid19hg.org/results/) and HF traits from a comprehensive GWAS meta-analysis[15]. We selected four phenotypes from this GWAS: (1) hospitalized patients with COVID-19 compared with the not hospitalized patients with COVID-19 including 16,512 patients and 71,321 control participants(B1); (2) patients with very severe respiratory-confirmed COVID-19 compared with the general population including 18,152 patients and 1,145,546 control participants (A2); (3) hospitalized patients with COVID-19 compared with the general population including 44,986 patients and 2,356,386 control participants (B2); (4) patients with COVID-19 relative to the general population including 159,840 patients and 2,782,977 control participants (C2). All GWAS summary statistics associated with COVID-19 were based on populations of European ancestry excluding 23andme. The GWAS for HF comprised 47,309 heart failure cases and 930,014 controls, incorporating adjustments for age, gender, and additional principal components.

For coronary artery disease (CAD) and myocardial infarction (MI), data were extracted from the CARDIoGRAMplusC4D Consortium, which included 60,801 CAD patients and 43,676 MI patients. Type 2 diabetes mellitus (T2DM) data were collected from the IEU OpenGWAS project, including 12,931 patients. Atrial fibrillation (AF) and atrial flutter data were from the FinnGen Research Project, including 45,766 patients. CKD data came from the CKDGen Consortium, with 1,046,070 samples. Hypertension data were from the FinnGen Research Project, with 92,462 diagnosed cases. Valvular heart disease (VHD) data included 20,929 patients. Myocarditis data included 1,521 patients. Non-ischemic cardiomyopathy (NICM) data included 9,926 patients. CAD, MI, T2DM, AF, CKD, hypertension, VHD, myocarditis, and NICM are risk factors of HF. The detail for the included GWAS is provided in Table 1[16-18].

Selecting of IVs

SNPs were selected using a significance threshold of p < 5 × 10^-6, an LD threshold of r^2 < 0.001, and kb = 10,000. These specific thresholds were chosen to ensure that the selected SNPs are strongly associated with the traits of interest while minimizing the risk of linkage disequilibrium and confounding factors. F-statistics were calculated to ensure the strength of the instruments. The F value below 10 was deemed as weakly correlated with exposure[19]. An F-statistic below 10 indicates a weak instrument because it suggests that the SNPs do not explain a significant portion of the variance in the exposure, which can lead to biased and inconsistent estimates. The F-statistics was tested by using F = R² × (N−2)/(1−R²)[20], where N represents the number of the sample size for exposures, and R²represents the proportion of variance by each selected SNP (Figure 2).

Statistical analysis

MR uses genetic variants as IVs to infer causal effects and control for confounding factors. The IVW method was chosen as the primary statistical approach due to its robustness and ability to combine results from different studies. However, given that IVW may introduce potential horizontal pleiotropy, we introduced several additional statistical methods as supplements.

Inverse Variance Weighted (IVW) Method

Horizontal pleiotropy occurs when a genetic variant independently affects multiple traits. our MR investigation primarily relied on the IVW method for it could avoid horizontal pleiotropy.

[21]. A conservative random-effects model was applied in the process, providing robust estimates especially when heterogeneity exist among studies[22].

Supplementary methods for MR analyses

The weighted-median approach was used when up to 50% of the IVs were weak[23]. The MR-Egger approach was applied when pleiotropic effects were detected, as it can analyze horizontal pleiotropic effects if a nonzero intercept is identified in the MR-Egger regression line[24]. The weighted mode was used to strengthen the robustness of the results, especially when the majority of instruments are invalid[25]. We present the results using the aforementioned methods. When MR-Egger indicates pleiotropy, the results are generally considered not reliable. Additionally, when many IVs are weak or invalid, the results from the weighted-median and weighted mode approaches are prioritized over IVW.

Sensitivity Analyses

To ensure the reliability of our MR discoveries, we conducted a series of sensitivity analyses. The MR-Egger assessed horizontal pleiotropy, with an intercept of zero indicating no pleiotropic effect[26]. Cochran’s Q test measured diversity across IVs, with a p-value below 0.05 indicating significant heterogeneity[27]. The MR-PRESSO test was used to detect potential outliers, with a global test p-value below 0.05 indicating their presence[28]. We conducted leave-one-out (LOO) analysis where each SNP was individually removed to gauge its impact and identify any SNPs causing abnormal shifts in the causal correlation[28]. After completing the forward MR analysis, we proceeded with the reverse MR analysis using the same methodology. This reverse analysis is important as it examines the bidirectional relationship between COVID-19 and heart failure.

Software and packages

Causal relationship was revealed by using the TwoSampleMR package (version 0.5.7) in R (version 4.3.1)[29]. The MR-PRESSO analyses was conducted by MRPRESSO package (version 1.0). The causal influence was visualized the by ggplot2 package (version 3.4.4). Statistical significance was defined at a threshold of p < 0.05. The causal associations were depicted as odds ratios (OR) along with their corresponding 95% confidence intervals (95% CI). The research protocol and specifics were not registered preemptively in any repository or platform.

Genetic Instruments

A detailed description of the IVs associated with COVID-19, including effect allele, other allele, beta, standard error, p-value, and EAF, is presented in Tables S3A, S3B, S3C, and S3D. The number of IVs for groups B1, A2, B2, and C2 were 22, 68, 87, and 56, respectively. All F values exceeded 21, indicating the study's resilience against weak IVs. The genetic instruments for COVID-19 were selected from a population of approximately 220,000 cases and over 3 million controls. The COVID-19 Host Genetics Initiative (COVID-19 HGI) accumulating DNA samples from COVID-19 patients and comparing them to negative controls.

In the bidirectional MR, IVs connected with HF were extracted following the aforementioned MR assumptions (Figure 1). The number of SNPs varied from 50 to 52 (Tables S4A, S4B, S4C, and S4D), and the F values ranged from 21 to 84, indicating the robustness of the IVs. The genetic instruments for HF were selected from a population of 140,323 individuals, comprising 47,309 cases and 930,014 controls. The data was a meta-analysis including from GWAS data from 26 studies.

SNPs related to COVID-19 traits or HF were carefully chosen as IVs using a threshold of p < 5e-6. And the IVs was assessed by F value, with an F value exceeding 10 indicating reliability. IVs identified in exposure would be removed if they cannot be matched in the outcome.

The genetic instruments associated with COVID-19 traits may represent key biological pathways involved in disease susceptibility and progression. For example, SNPs related to severe respiratory-confirmed COVID-19 could implicate genes involved in lung function and immune response. Similarly, the genetic factors linked to the incidence rate of HF may involve genes associated with myocardial injury, inflammation, and endothelial dysfunction.

Causal Impact of COVID-19 on HF

COVID-19 influences the incidence of HF, although some results are not significant in the forward MR, the OR values share a similar trend.

We identified a statistically significant association between the genetic predisposition of hospitalized COVID-19 patients and HF compared to the general population (OR, 1.031; 95% CI, 1.001–1.061; p = 0.038) using the IVW method. Furthermore, we observed a noteworthy causal relationship between COVID-19 and HF (OR, 1.121; 95% CI, 1.020–1.232; p = 0.018). The MR Egger intercept test indicated no directional pleiotropy for these tests (p = 0.353 and p = 0.576, respectively). COVID-19 infection, regardless of hospitalization, affects the onset of HF. These two associations were not confirmed using the weighted median method, but a similar trend was observed with MR-Egger (all beta > 0, p = 0.748 and p = 0.081, respectively). Table 2 illustrated the causal relationship between COVID-19 and HF by using IVW, MR-Egger, Weighted median and Weighted mode method. Each statistical method has its own emphasis, but based on previous studies, the IVW results are usually considered as the main primary. Results from other statistical methods can serve as supplementary evidence, particularly when the direction of MR-Egger aligns, thus providing additional support for the study's findings[30].

However, we did not find evidence for a direct causal effect between COVID-19 hospitalization (versus non-hospitalized cases) and the onset of HF (OR, 1.014; 95% CI, 0.978–1.051; p = 0.867). This finding suggests that COVID-19 hospitalization may not be a significant independent risk factor for developing HF. And the MR-Egger test indicated no directional pleiotropy (p = 0.655). Similarly, there was no genetic link observed between severe-confirmed COVID-19 and HF (OR, 1.017; 95% CI, 0.992–1.043; p = 0.983). This finding revealed that the severity of COVID-19 infection is not related to HF.

Cochrane’s Q test showed that there was heterogeneity in group B1, A2 and C2 (p=0.001, p=0.001 and p=0.000 respectively), and there was no heterogeneity in the liberal instrument for group B2 (p=0.227). Therefore, the IVW method under random effect was used to evaluate the causal associations of COVID-19 on HF

As for group B1, A2 and B2, the absence of conspicuous outliers among the IVs in this study was indicated by the MR-PRESSO test. But the test showed that there were outliers in group C2, but the results were robust after removing the outliers by IVW method (OR, 1.131; 95% CI, 1.029–1.029; p = 0.010) (Figure 3). The symmetrical distribution of SNPs in the funnel plots suggests a reduced susceptibility to potential biases in causal associations. The result of the LOO analysis showed that where no single SNP was driving the whole effect.

Causal Association of HF with COVID-19

The reverse MR analysis indicated that HF was associated with COVID-19. Hospitalized cases compared to non-hospitalized cases by IVW method (OR, 1.144; 95% CI, 1.013–1.293; p = 0.030), very severe respiratory-confirmed COVID-19 compared with the general population (OR, 1.274; 95% CI, 1.106–1.472; p = 0.001), hospitalized COVID-19 compared with the general population (OR, 1.169; 95% CI, 1.058–1.292; p = 0.002), and COVID-19 compared with the general population (OR, 1.081; 95% CI, 1.013–1.153; p = 0.018) were all affected by HF (Table 3 and Figure 4).

MR-PRESSO indicated that there were outliers in group C2, but when outliers were removed the results remained stable. No obvious directional pleiotropy among COVID-19 associated instrument variants was identified by the MR-Egger intercept (all p >0.05). Due to the random effects model employed, the heterogeneity present in the experimental results can be adequately explained.

The relationship between COVID-19 and Risk Factors of HF

CKD and T2DM, risk factors for HF, were found to be correlated with hospitalized patients with COVID-19 compared to the general population (OR, 0.666; 95% CI, 0.445–0.997; p = 0.048 and OR, 0.959; 95% CI, 0.926–0.993; p = 0.019, respectively). And T2DM had the same effect on patients with very severe respiratory-confirmed COVID-19 when compared with the general population (OR, 0.949; 95% CI, 0.902–1.153; p = 0.041) (Figure 5). Reversely, it was obvious that COVID-19 did not increase the incidence rate of risk factors for HF (Figure 6).

Summary of Findings

Our bidirectional two-sample MR analysis using GWAS data reveals a significant interaction between COVID-19 and HF, along with its risk factors. COVID-19 infection increases the risk of HF (OR, 1.121; 95% CI, 1.020–1.232; p = 0.018), particularly for hospitalized patients (OR, 1.031; 95% CI, 1.001–1.061; p = 0.038). Conversely, HF impacts COVID-19 severity (OR, 1.274; 95% CI, 1.106–1.472; p = 0.001) and hospitalization rates (OR, 1.144; 95% CI, 1.013–1.293; p = 0.030). Additionally, CKD (OR, 0.666; 95% CI, 0.445–0.997; p = 0.048) and T2DM (OR, 0.959; 95% CI, 0.926–0.993; p = 0.019) influence the rate of hospitalization among COVID-19 patients compared to controls.

Comparison with Previous Studies

Initial studies during the pandemic focused on acute cardiovascular complications of COVID-19, often with small sample sizes. For instance, a 2021 study reported a high OR for acute myocardial infarction in COVID-19 patients[31]. Retrospective analyses also highlighted a substantial percentage of COVID-19 fatalities among HF patients without prior cardiovascular conditions[32]. Our findings align with these early observations but extend them by using MR to infer causality, providing a more robust evidence base. The MR approach offers distinct advantages over traditional observational studies by leveraging genetic variants as IVs to infer causal relationships. This methodology helps mitigate biases from confounding factors and reverse causation, thereby providing more reliable causal estimates in epidemiological research[33].

Existing GWAS related to this topic suffer from small sample sizes and focus primarily on critically ill COVID-19 patients which supports the causal association[34]. Moreover, current GWAS findings are inconsistent, with some studies suggesting that there is no relationship between genetically instrumented COVID-19 and the risk of HF[35]. The current inconsistency in GWAS results is mainly due to the gradual increase in sample sizes as the disease progresses. We believe that studies incorporating larger populations using the latest databases bears more credibility.

By using the latest and largest GWAS for COVID-19, we found more credible evidence indicating that COVID-19 indeed contributes to the incidence of HF. Research suggests that hospitalized COVID-19 patients often have more comorbidities, but they receive timely treatment during hospitalization compared with non-hospitalized COVID patients[36]. We additionally explore the incidence rate of HF in those two groups hoping to identify high-risk patients and guide future allocation of medical resources.

Mechanistic Insights

The pathophysiological link between COVID-19 and HF likely involves direct viral invasion via ACE2 receptors, prevalent in both lung and heart tissues, and subsequent immune responses, including cytokine release and endothelial dysfunction[37-41]. The expression of ACE2 is reduced and complement system is activated at the same time. Additionally, dysregulation of the renin-angiotensin-aldosterone system, potential myocarditis, and fibrosis of cardiac tissue are also potential contributing factors[42]. This mechanism is supported by the presence of SARS-CoV-2 particles in cardiomyocytes and elevated inflammatory markers in severe cases[43](Figure 7).

Implications for Clinical Practice

Our results suggest that monitoring COVID-19 patients, especially those hospitalized, for signs of HF could be crucial in improving outcomes. For example, regular cardiac function monitoring is needed for COVID-19 patients, including echocardiography and blood biomarkers like B-type natriuretic peptide (BNP). Additionally, for high-risk patients, such as the elderly and those with cardiovascular diseases, medication and lifestyle interventions should be used to prevent and mitigate HF. Moreover, the findings underscore the importance of COVID-19 vaccination in reducing cardiovascular complications in HF patients, as evidenced by studies from New York City and the Czech Republic showing lower hospitalization and mortality rates among vaccinated individuals[44,45]. The application of COVID-19 vaccines is becoming increasingly widespread[46]. However, the population we included did not have grouping based on whether they received the COVID-19 vaccine, which we think it necessary to carry out accordingly in designing research in the future[47]. Future studies could stratify patients basing on the type of vaccine administered and the timing of vaccination, which could help better assess the long-term and short-term impacts of vaccination on cardiovascular health.

Our study emphasizes the importance of heightened monitoring of cardiac function in COVID-19 patients to promptly identify early symptoms of HF. Simultaneously, healthcare resources should be prioritized for COVID-19 patients at high risk, such as those with pre-existing cardiovascular diseases or chronic kidney disease.

The occurrence of HF in this study is commonly associated with the incidence of COVID-19, elevating the risk of contracting the virus. It also escalates the severity, hospitalization rates, and prevalence of the existing condition. This has been corroborated in previous experiments, indicating that patients with underlying heart failure are relatively more susceptible to infection[48]. In a comprehensive database analysis encompassing over 90,000 patients diagnosed with COVID-19, a history of HF correlated with escalated mortality rates. Among individuals with chronic stable HF, the 30-day and 90-day mortality rates stood at 8.0% and 9.0%, respectively. Notably, patients experiencing a deterioration in HF exhibited the highest mortality rates, reaching 15.6% at 30 days and 16.5% at 90 days[49]. However, due to the inability of this study to stratify based on age and other factors, and considering that a majority of HF patients are elderly individuals, this confounding factor becomes challenging to eliminate.

The COVID-19 infection did not show a significant impact on many risk factors for HF, potentially due to the short duration of the disease onset. The COVID-19 pandemic has only been prevalent for a few years since its emergence, so the GWAS population included usually has insufficient sample size and lacks representativeness. As research majoring on COVID-19 continue to increase and the disease affects a broader population, it is possibly to make a more accurate assessment of the impact of COVID-19 on the risk factors of HF. This underscores the importance of long-term studies on COVID-19 and its effects to gain a more comprehensive understanding of its potential impact. It also indicates the necessity for broader exploration of other potential risk factors for HF.

Interestingly, the reverse MR partially suggests a negative impact of diabetes and CKD on COVID-19 occurrence with significant heterogeneity by using the RE-IVW method. Basing on the fact that CKD patients may demonstrate characteristics such as chronic inflammation and immune system dysfunction[50], which might lead to different immune regulatory pathways when infected with SARS-Cov-2. Patients with diabetes and CKD may receive long-term medication treatment, such as angiotensin-converting enzyme inhibitor (ACEI). While such medications upregulate ACE2, they negatively regulate the renin-angiotensin system (RAS). Research conducted by Zhang and colleagues have illustrated that compared to patients not taking these medications, COVID-19 patients receiving ACEI/ARB have a lower all-cause mortality rate[51]. Additionally, baseline information such as sex, age, smoking has a significant impact on the prognosis of COVID-19[52]. However, due to the limitations in the collection of GWAS data, subgroup analysis based on these factors was not feasible. Therefore, these outcomes should be considered for reference only. As a result, we advocate for more relevant clinical studies to enhance the experiment's credibility.

Limitations

Despite the strengths of MR in reducing confounding, our study has limitations. Firstly, the GWAS we included entirely consist of individuals of European descent, limiting the likelihood of a large sample size and diverse ethnicities. Therefore, the generalizability of the findings to other ancestries is challenging. Potential substantial differences in genetic makeup, lifestyle elements, and environmental exposures across diverse populations could significantly impact the results. Secondly, GWAS are inability to stratify data by age and other baseline characteristics due to GWAS data constraints, thus leading to potential bias. Moreover, the relatively short duration of the pandemic limits the ability to assess long-term impacts on HF risk factors fully.

Future Directions

Future research should focus on long-term, large-scale studies to better understand the chronic impacts of COVID-19 on cardiovascular health. Subgroup analyses based on demographic factors and vaccination status will be essential to refine our understanding and guide targeted interventions.

Our bidirectional MR analysis revealed a significant increase in the risk of HF among COVID-19 patients and demonstrated that pre-existing HF exacerbates the severity of COVID-19. These findings underscore the importance of early detection and monitoring of cardiac function in COVID-19 patients to promptly identify HF symptoms. Healthcare systems should prioritize resources for high-risk COVID-19 patients, particularly those with existing cardiovascular conditions. However, it is necessary to interpret the result with more caution due to the limitations of including only individuals of European ancestry and the inherent characteristics of the GWAS methodology, which could introduce bias. More cross-ethnicity studies and large-scale RCTs are needed to confirm and extend these findings.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of Interest

I have nothing to declare.

Declaration of Figures Authenticity

All figures submitted have been created by the authors who confirm that the images are original with no duplication and have not been previously published in whole or in part.

Availability of data and materials

The data used and analyzed during the present study are available from the corresponding author on reasonable request. Requests for access to these data should be made to Weiran Dai (e-mail: [email protected]).

Acknowledge

We do acknowledge the participants and investigators of the FinnGen study. Data on coronary artery disease / myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from www.CARDIOGRAMPLUSC4D.ORG.

Author contributions

Yiheng Liu: Formal analysis, Methodology, Project administration, Writing – original draft. Mingsheng Huang: Data curation, Formal analysis, Visualization. Yue Sun: Data curation, Methodology, Visualization. Weiran Dai: Methodology, Writing – review & editing.

Ethics statement

We used summary data from publicly available databases (FinnGen, MRC-IEU, HERMEs, CARDIoGRAMplusC4D, CKDGen, COVID-19 HGI) that had obtained participant consent and ethical approval.

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Effects of COVID-19 on Heart Failure and Its Risk Factors: A Bidirectional Mendelian Randomization Study

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