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Causal association between epigenetic age acceleration and two pulmonary vascular diseases: pulmonary arterial hypertension and pulmonary embolism—a bidirectional Mendelian study

Clinical Epigenetics volume 16, Article number: 172 (2024) Cite this article

Abstract

Background

Pulmonary arterial hypertension (PAH) is a relatively rare but severe disease with a poor prognosis. Pulmonary embolism (PE) is a serious condition that can cause sudden death. Epigenetic age acceleration (EAA) is a robust indicator derived from the DNA methylation-based epigenetic clock, which can predict the extent of aging. It has been proved that the epigenetic clock and EAA are associated with many cardiovascular diseases, while their associations with PAH and PE remain inconclusive. Our study aims to investigate the associations among these factors.

Method

By harnessing summary-level data from large-scale genome-wide association studies (GWAS), we designed a two-sample bidirectional Mendelian randomization (MR) analysis to assess the causal associations between measures of three epigenetic clocks, including GrimAge acceleration (n = 34,467), Hannum Age acceleration (n = 34,449) and PhenoAge acceleration (n = 34,463) and PAH (including 125 cases and 162,837 controls), as well as PE (including 3940 cases and 480,658 controls). The inverse variance-weighted (IVW) method was used as the primary method for MR analysis. Other methods, such as MR egger and weighted mode, served as complements to the IVW approach, were also applied in the analyses. Then, the MR pleiotropy test and MR-PRESSO test, which are effective tools for quality control of MR analysis, were subsequently used to ensure the accuracy of the study.

Results

The forward MR analysis indicated that all three epigenetic clocks had no significant effects on PAH or PE. The reverse analysis indicated that the onset and progression of PAH and PE had insignificant effects on three epigenetic clocks. The results of the quality control assessment confirmed that our findings were reliable.

Conclusion

Our two-sample bidirectional MR analysis suggested that there is no significant association between epigenetic clocks and these two pulmonary vascular diseases.

Introduction

Pulmonary arterial hypertension (PAH) is a disease leading to continual elevation of pulmonary vascular resistance and pulmonary vascular pressure due to varieties of reasons, which can further lead to right heart failure and thus death. PAH was previously regarded as a rare disease. Currently, PAH is a global health issue affecting approximately 1% of the global population on the basis of recent epidemiological research. In individuals aged 65 years and above, the prevalence of PAH is greater due to various of comorbidities [1]. The prognosis of PAH patients is relatively poor, according to an Australian cohort whose mean survival time was 4.4 years [2]. PAH is a rapidly progressive and harmful disease that is used to be deemed as a ‘tumor’ of the cardiovascular system [3]. Pulmonary embolism (PE) is a disease related to the occlusion of one or several pulmonary arteries due to emboli, manifesting a range of hemodynamic effects varying from no symptoms at all to a critical and potentially fatal situation requiring immediate medical attention. The occurrence rate of symptomatic PE is approximately between 0.5 and 1 case per 1000 individuals annually, with a rising trend observed as populations continue to age. According to the results of the ICOPER cohort, the all-cause mortality of acute PE was 11.4% at 2 weeks and increased to 15.4% at 3 months [4,5,6].

Epigenetic clocks are mathematical age estimators based on the combination of methylation values that change with age at specific CpGs in the genome. The deviation between epigenetic age (EpiAge) and chronological age is referred to as epigenetic age acceleration (EAA), which is a robust predictor of aging [7, 8]. EAAs have been proved to be related to multiple adverse cardiovascular outcomes. A study using a multi-omics method revealed that the onset and progression of subclinical atherosclerosis in middle-aged asymptomatic individuals are associated with GrimAge acceleration. By measuring 4 epigenetic clocks and the level of PAI-1, which is a DNA methylation predictor, it was found that EAA is an independent risk factor for atrial fibrillation. A large-scale prospective study analyzing 2543 whole-blood samples indicated that EAA is an independent and potential risk factor for heart failure, peripheral vascular disease and other cardiovascular diseases. [9,10,11]. It is still ambiguous whether there is a correlation between EAA and two pulmonary vascular diseases—PAH and PE. Therefore, in this analysis, we performed a two-sample bidirectional MR analysis to investigate the causal associations between epigenetic clocks and the risk of PAH and PE.

Methods

Study design

In our study, we first used three epigenetic clocks, namely GrimAge, PhenoAge and Hannum Age, and as exposures, PAH and PE were used as outcomes. In contrast, we subsequently used PAH and PE as exposures and epigenetic clocks as outcomes to finish the reverse analysis. Three key assumptions must be met in a Mendelian randomization study: (1) genetic variations must be reliably associated with exposure (Assumption of correlation); (2) genetic variations should not be associated with any known or unknown confounders (Assumption of independence); and (3) genetic variations affect the outcome only through epigenetic acceleration and not through any other direct causal pathway (Assumption of exclusivity) [12]. An overview of the study design is shown in Fig. 1. Because it is an analysis using previously collected and published data, no additional ethics approval is needed.

Fig. 1
figure 1

Study design and three key assumptions: (1) assumption of independence, (2) assumption of correlation, (3) assumption of exclusivity

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Genetic variants associated with epigenetic age acceleration and PAH

The genome-wide association studies (GWAS) summary data for epigenetic clocks were all from a study conducted by Daniel L. McCartney et al. [13]. A total of 34,710 individuals of European ancestry and 6195 African American individuals were included in the research to identify genetic variants associated with six methylation-based biomarkers. We chose three epigenetic clocks presented in the article: DNA methylation GrimAge (GCST90014288), DNA methylation Hannum Age (GCST90014289) and DNA methylation PhenoAge (GCST90014292). The GWAS summary data of epigenetic clocks and PE from a European population (GCST90038614) were downloaded from the GWAS catalog database. The GWAS summary data for PAH (I9_HYPTENSPUL) were downloaded from the FinnGen database [14,15,16]. The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organizations and biobanks within Finland and international industry partners. The detailed information of the GWAS data is shown in Table 1.

Table 1 Information of data source from GWAS catalog
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Instrument variables selection

The R package 'TwoSampleMR' (Version 0.6.6) was used in this study. First, significant single-nucleotide polymorphism (SNP) loci (P < 5 × 10–8) were extracted from the GWAS summary data of exposure. In the event that associated SNPs cannot be extracted, it would be advisable to set the threshold at 5 × 10–6. Subsequently, linkage disequilibrium (LD) was eliminated through the application of clumping. The parameter r2 was set to 0.001, and the window size was set to 10,000 kb [17]. The F value and R2 value of each SNP were subsequently calculated via the formula below:

$${R}^{2}=\frac{2{\beta }^{2}\times \text{MAF}\times (1-\text{MAF})}{2{\beta }^{2}\times \text{MAF}\times \left(1-\text{MAF}\right)+2N\times \text{MAF}\times (1-\text{MAF})\times {\text{SE}}^{2}}$$
$$F=\frac{{R}^{2}(N-2)}{1-{R}^{2}}$$

In the formula, β is the effect size, SE is the standard error, MAF is the minor allele frequency for each SNP, and N represents the sample size. All weak SNPs (F value < 10) were rejected from the results above. Furthermore, we harnessed the Pheweb UKBiobank TOPMed-imputed dataset (https://pheweb.org/UKB-TOPMed/) and the Pheweb UKBiobank HRC-imputed dataset (https://pheweb.org/UKB-SAIGE/) to query surrogate confounders and outcomes, such as alcohol intake, mental disorders, poor lung function, and obesity, to fulfill the assumption of independence and exclusivity [18,19,20]. Second, data for the exposed SNPs were extracted from the outcome GWAS, and missing SNPs were excluded from the analysis. Ultimately, the effect alleles pertaining to both exposure and outcome were harmonized. These SNPs served as instrumental variables (IVs) in the following Mendelian randomization analysis.

Two-sample bidirectional Mendelian randomization analysis

We investigated the causal effects by using five different MR methods: MR egger, weighted median, Inverse variance weighted (IVW), simple mode and weighted mode. The IVW method meta-analyzed the Wald ratio estimates for each SNP on the outcome, thereby providing precise estimates of causal effects when all selected SNPs are valid IVs [20]. However, it should be noted that the estimates of causal effects from the IVW method may be biased by the influence of pleiotropic IVs. In order to ascertain whether there was horizontal heterogeneity, Cochran’s Q test was applied in this study. If the P value of Cochran’s Q test is ≤ 0.05, which would indicate the presence of pleiotropy, a random-effects IVW MR analysis should be conducted [21]. Moreover, we employed MR‒Egger regression to evaluate the possibility of horizontal pleiotropy by examining the intercept value. A deviation from zero (with a P value less than 0.05) was considered indicative of the presence of pleiotropic bias [22]. Thus, the method of MR-PRESSO global tests was also utilized in the study to detect multi-effect outliers at any level for exposures with significant causal associations, and the causal associations’ estimates were reassessed after removing outliers [23, 24]. Furthermore, leave-one-out method analysis was conducted to detect the influence of a single SNP on the reliability of the MR results. A funnel plot was used to illustrate whether there was directional heterogeneity in this study.

Results

Instrumental variables selection

Totally, 4 SNPs, 9 SNPs and 11 SNPs were identified as IVs for GrimAge, Hannum Age and PhenoAge, respectively. Thus, for the reverse MR study, 5 SNPs and 8 SNPs were extracted as IVs for PAH and PE. After calculation, there was no SNP whose F value was less than 10, which means that all IVs were strongly associated with the phenotype. The detailed information is shown in Figure S1.

EAA to PAH

The initial IVW analysis using epigenetic clock data revealed that there was no causal association between EAA and PAH. The results of the IVW method for GrimAge (β: − 0.398, 95% CI: − 1.074–0.278), Hannum Age (β: − 0.181, 95% CI: − 0.608–0.246) and PhenoAge (β: − 0.251, 95% CI: − 0.540–0.037) were all insignificant. The MR‒Egger, weighted median, weighted mode and simple mode methods yielded similar results. The main results are shown in Table 2 and Figures S2–S4.

Table 2 Main results of EAA to PAH (*P < 0.05)
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PAH to EAA

The reverse IVW analysis of the PAH data revealed that there was no causal association between PAH and epigenetic clocks. The main results are shown in Table S1 and Figures S5–S7.

EAA to PE

Preliminary inverse variance-weighted (IVW) analysis, leveraging the GWAS data of epigenetic clocks, revealed no causal influence between the epigenetic clock and PE. The IVW method was applied to various measures of age acceleration—GrimAge (β: − 0.0002, 95% CI: − 0.001–0.001), Hannum Age (β: − 0.0003, 95% CI: − 0.001–0.0004) and PhenoAge (OR: 0.0002, 95% CI: − 0.0002–0.001)—all of which yielded statistically insignificant outcomes. Consistent with these findings, alternative Mendelian randomization methods, such as MR‒Egger and the weighted median, produced similar results. The comprehensive results are illustrated in Table 3 and Figures S8–S10.

Table 3 Main results of EAA to PE
Full size table

PE to EAA

By utilizing the GWAS data of PE in an IVW analysis, we found no evidence of a causal relationship between pulmonary embolism and epigenetic clocks. The key results are presented in Table S2 and Figures S11–S13.

Horizontal pleiotropy and heterogeneity test

After the sensitivity test was conducted, horizontal pleiotropy, heterogeneity and MR-PRESSO tests were conducted. The outcome of these analyses revealed that there was no horizontal or directional pleiotropy in the study, and no outlier SNPs were found in the study. The main results are presented in Table 4.

Table 4 Results of horizontal pleiotropy and heterogeneity test
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Discussion

In this MR study, we investigated the association between the epigenetic clock and PAH, together with PE. Our findings indicate that epigenetic clocks have no causal effect on PAH or PE.

Biological aging is characterized by a reduction in the reparative and regenerative potential in tissues and organs, leading to a decreased response to stress and a gradual failure of complex molecular mechanisms that cumulatively create dysfunction. One essential trait of aging is epigenetic alternation, including alterations in DNA methylation patterns, abnormal posttranslational modifications of histones, aberrant chromatin remodeling, and deregulated functions of noncoding RNAs (ncRNAs) [25, 26]. Hans T Bjornsson et al. reported that the DNA methylation patterns at special cite of CpG are strongly correlated with chronological age and enable accurate age estimates for any tissue across the entire life course [27]. Consequently, numerous ‘epigenetic clocks’ have been developed to predict the age of organisms. The Horvath clock and Hannum clock constituted the first generation of the epigenetic clock [28, 29]. Steve Horvath chose the data of 8000 samples from 82 DNA methylation arrays and selected 353 specific CpG sites to develop the Horvath clock which can predict the age of multiple tissues of the human body [30]. Gregory Hannum et al. constructed a model of the methylation clock by measuring 450,000 CpG sites from 650 people whose ages ranged from 19 to 101 years, revealing the relationships among aging, diseases and transcriptome shifts [31]. On the basis of the Horvath clock, Morgan E Levine et al. used data from the NHANES database and applied a Cox penalized regression model to develop a better epigenetic clock called PhenoAge. Moreover, after the characteristics of CpG methylation were identified, Lu et al. combined data on serum protein levels and smoking history to develop a composite epigenetic age clock–GrimAge. Therefore, GrimAge and PhenoAge have better abilities to predict the risk of death and other diseases than does the first generation of the epigenetic clock because of the inclusion of several biomarkers [32, 33].

EAA has been deemed as an indicator that can be used to measure long-term cardiovascular health, and it can help understand some extra risk factors and individual differences that chronological age cannot explain. Accelerated epigenetic age means that the predicted biological age is greater than the chronological age, which is related to a multitude of adverse health outcomes [34, 35]. Some studies have used Mendelian randomization to explore the relationship between epigenetic clocks and other cardiac diseases. EAA was associated with heart failure, atrial fibrillation, aortic valve calcification and CVH score. Surprisingly, there seems to be no association between coronary heart disease and EAA. At present, there is a lack of research on the causal relationship between primary hypertension and EAA [36,37,38,39]. More studies are needed to investigate casual association between EAA and cardiovascular diseases.

In this study, we use GWAS catalog and FinnGen databases to investigate the casual relationship between 3 epigenetic clocks and two pulmonary diseases—PAH and PE. We found that there is no association between them. In fact, we found GWAS data of primary pulmonary hypertension (Patient Inclusion Code: I27.0), which is an outdated classification of PAH, in the FinnGen database. According to the ICD10 disease catalog, the diagnosis 'I27.0: primary pulmonary hypertension' mainly encompasses two conditions: idiopathic pulmonary arterial hypertension (IPAH) and heritable pulmonary arterial hypertension (HPAH), both of which fall under pulmonary arterial hypertension. From the aspect of pathological mechanism, pulmonary arterial hypertension is a disease caused by various internal predisposing factors, such as genetic variations, and various external predisposing factors, such as inflammation, hypoxia, high shear force and infection, which can cause intimal injury and proliferation, media thickening and adventitial fibrosis of pulmonary blood vessels. Its internal and external predisposing factors show no casual association with aging. This may be one reason why a significant association between EAA and PAH was not detected in this study. However, this does not mean that there is no causal effect on PAH and epigenetic clocks. Except for IPAH and HPAH, connective tissue diseases, congenital heart diseases, portal hypertension and many other diseases can cause PAH, which may have casual association with EAA, for other groups of PH, according to classification of 2022 ESC/ERS guideline for pulmonary hypertension [1], such as group 2, which is PH associated with left heart disease, or group 3 PH, which is PH associated with lung disease and/or hypoxia, whose underlying diseases, such as HFpEF and COPD, are closely associated with aging. However, it can develop into pulmonary arterial hypertension after a long period of time. Unfortunately, owing to the lack of relevant GWAS summary data resources, we are unable to explore the causal associations between epigenetic clocks and these types of PAH. The mechanism by which the epigenetic clock and EAA influence the onset, progression and prognosis of PAH is still unknown.

For pulmonary embolism, we found that there was no causal relationship between PE and the 3 epigenetic clocks; however, it has been reported that age is a significant risk factor for venous thromboembolism (VTE) and PE. There is still a lack of detailed studies on pulmonary embolism during the aging process and epigenetic age acceleration. Meanwhile, there are many pathogenic mechanisms of PE, including genetic factors, blood hypercoagulability, endothelial injury, venous blood stasis and other factors. We speculated that PE caused by some of these etiologies may be strongly associated with the epigenetic clock while others may not. More studies are needed to confirm the role of these pathologic factors in pulmonary embolism [40].

Mendelian randomization serves as a pivotal epidemiological tool for identifying the intricate relationship between hereditary variations associated with exposure factors and disease outcomes, which encompass disease onset and mortality, showing great advantages, especially when studying some rare diseases. Its core lies in the strategic utilization of genomic information, mainly by harnessing the varieties of data from GWAS, as a bridge to discern the underlying causal link between particular exposures and their corresponding outcomes [8, 41]. The IVs following Mendel’s law of inheritance have been confirmed after birth and cannot be easily modified by other environmental factors, which can avoid endogeneity issues caused by missing variables and reverse causal relationships. The use of genetic variants as IVs avoids some of the limitations of observational studies (confounding, reverse causality, regression dilution bias) and RCTs (representativeness and feasibility issues) in making causal inferences and is easier to apply [42,43,44]. We also noticed that using the weighted mode method in Table 2 can infer that PAH is causally related to EAA. Because the SNPs in this study were not found to be heterogeneous and pleiotropic after analysis, we preferred to use the IVW method with higher statistical power. The weighted mode method will be used preferentially when the SNP has heterogeneity but no pleiotropy. Therefore, in this analysis, the IVW method is not significant, so we believe that there is no correlation between PhenoAge and PAH. The strength of this study is the application of the two-sample bidirectional Mendelian randomization method from GWAS summary data to investigate the genetic associations between epigenetic clocks and PAH for the first time. A series of sensitivity and pleiotropy tests were subsequently conducted to ensure the reliability of the study. However, there are several limitations in this study. First, the GWAS summary data are from individuals of European ancestry, which is not appropriate for extending the conclusions to populations of other races. Moreover, only 125 PAH patients were included in the PAH GWAS summary data. More PAH samples are needed in GWAS to guarantee the accuracy of MR studies. Finally, many other epigenetic clocks have been developed, such as the Horvath clock, and our research focuses solely on three of these models. Other epigenetic models may be related to the two diseases analyzed in the present study.

Conclusion

In conclusion, the two-sample bidirectional MR study revealed that there is no causal association between the epigenetic clock and PAH, together with PE. Further studies, such as large-scale cohort studies, are needed to validate the role of the epigenetic clock and EAA in both PAH and PE.

Data availability

No datasets were generated or analyzed during the current study.

Abbreviations

PAH:

Pulmonary arterial hypertension

PE:

Pulmonary embolism

EAA:

Epigenetic age acceleration

GWAS:

Genome-wide association studies

MR:

Mendelian randomization

SNP:

Single-nucleotide polymorphism

IV:

Instrumental variables

LD:

Linkage disequilibrium

IVW:

Inverse variance weighted

VTE:

Venous thromboembolism

IPAH:

Idiopathic pulmonary arterial hypertension

HAPH:

Heritable pulmonary arterial hypertension

RCT:

Randomized controlled trial

HFpEF:

Heart failure with preserved ejection fraction

COPD:

Chronic obstructive pulmonary disease

ncRNA:

Non-coding RNA

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Acknowledgements

This study used data from the FinnGen database. We want to acknowledge the participants and investigators of the FinnGen study.

Funding

Our study is Supported by National Natural Science Foundation of China (NSFC82270050).

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Authors and Affiliations

  1. Department of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 1630 Dongfang Road, Pudong New Area, Shanghai, 200127, China

    Jun Tong, Chuanxue Wan, An Wang, Mengqi Chen, Binqian Ruan & Jieyan Shen

  2. Department of Cardiology, Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China

    Mengqi Chen

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Contributions

Jun Tong is responsible for the design, data analysis and writing of the article, Chuanxue Wan is responsible for data collection and organization, Mengqi Chen, Binqian Ruan and An Wang read the article and provided revision suggestions, and Jieyan Shen, as the corresponding author, made a review and editing.

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Correspondence to Jieyan Shen.

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Tong, J., Wan, C., Wang, A. et al. Causal association between epigenetic age acceleration and two pulmonary vascular diseases: pulmonary arterial hypertension and pulmonary embolism—a bidirectional Mendelian study. Clin Epigenet 16, 172 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13148-024-01778-9

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Keywords

Clinical Epigenetics

ISSN: 1868-7083

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