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European Heart Journal logoLink to European Heart Journal
. 2020 Feb 20;41(40):3913–3920. doi: 10.1093/eurheartj/ehaa070

Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study

Milad Nazarzadeh 1,2,3, Ana-Catarina Pinho-Gomes 4,5, Zeinab Bidel 6,7,8, Abbas Dehghan 9, Dexter Canoy 10,11,12,13, Abdelaali Hassaine 14,15, Jose Roberto Ayala Solares 16,17, Gholamreza Salimi-Khorshidi 18,19, George Davey Smith 20, Catherine M Otto 21, Kazem Rahimi 22,23,24,
PMCID: PMC7654932  PMID: 32076698

Abstract

Aims 

Aortic valve stenosis is commonly considered a degenerative disorder with no recommended preventive intervention, with only valve replacement surgery or catheter intervention as treatment options. We sought to assess the causal association between exposure to lipid levels and risk of aortic stenosis.

Methods and results 

Causality of association was assessed using two-sample Mendelian randomization framework through different statistical methods. We retrieved summary estimations of 157 genetic variants that have been shown to be associated with plasma lipid levels in the Global Lipids Genetics Consortium that included 188 577 participants, mostly European ancestry, and genetic association with aortic stenosis as the main outcome from a total of 432 173 participants in the UK Biobank. Secondary negative control outcomes included aortic regurgitation and mitral regurgitation. The odds ratio for developing aortic stenosis per unit increase in lipid parameter was 1.52 [95% confidence interval (CI) 1.22–1.90; per 0.98 mmol/L] for low density lipoprotein (LDL)-cholesterol, 1.03 (95% CI 0.80–1.31; per 0.41 mmol/L) for high density lipoprotein (HDL)-cholesterol, and 1.38 (95% CI 0.92–2.07; per 1 mmol/L) for triglycerides. There was no evidence of a causal association between any of the lipid parameters and aortic or mitral regurgitation.

Conclusion 

Lifelong exposure to high LDL-cholesterol increases the risk of symptomatic aortic stenosis, suggesting that LDL-lowering treatment may be effective in its prevention.

graphic file with name ehaa070f3.jpg

Keywords: Blood cholesterol, Lipid profile, Heart valve diseases, Mendelian randomization analysis


See page 3921 for the editorial comment on this article (doi: 10.1093/eurheartj/ehaa225)

Introduction

A marked shift in the epidemiology of valvular heart disease has been observed in the past century.1 Degenerative valve disease, typically manifesting as aortic stenosis or mitral regurgitation, has replaced rheumatic valve disease as the leading cause of valvular heart disease, a trend fuelled by population ageing and increased prevalence of cardiovascular risk factors.2  ,  3 However, medical treatment for valvular heart disease remains limited and many patients would eventually need valve surgery or catheter-based valve repair or replacement.4 Such procedures are associated with significant complications and are costly, with recent estimates suggesting that procedural costs amount to £10 000 and £16 000 for surgical and catheter-based interventions in the UK.5

Poor understanding of the underlying mechanisms and risk factors for initiation and progression of valvular heart disease has hindered the development of effective medical treatment for primary and secondary prevention. Considering the shared aetiological pathways between different types of cardiovascular disease,6–8 several risk factors have been investigated, but findings for dyslipidaemia have been inconsistent. Whilst observational studies have suggested a potential association between dyslipidaemia and risk of aortic stenosis,9 randomized controlled trials (RCTs) have not demonstrated any effect of statin therapy on progression of aortic stenosis.10–12 However, RCTs have been based on mostly small sample sizes, relatively short follow-up, and inclusion of patients with established disease.

With this limited evidence from observational and interventional studies, Mendelian randomization (MR) offers an opportunity to efficiently and reliably investigate the potential causal association between dyslipidaemia and valvular heart disease. Mendelian randomization uses instrumental variable analysis to mimic the randomization process that underpins causal inference in RCTs. It is an approach that takes advantage of the naturally occurring random allocation of alleles inherited by offspring from their parents during the formation of the zygote (Supplementary material online, Figure S1). This process is similar to the random allocation of treatment in RCTs and could therefore overcome the problems of reverse causation and confounding inherent in observational studies.13 We aimed to use MR techniques to test the hypothesis that elevated plasma lipids are causally related to the risk of incident aortic stenosis.

Methods

Data for exposure

Our main exposure was genetically determined plasma lipids as instrumental variable. This was estimated from genetic variants that were associated with levels of low density lipoprotein (LDL)-cholesterol, high density lipoprotein (HDL)-cholesterol, triglycerides, and total cholesterol at genome-wide significance level. We retrieved summary estimations of 157 genetic variants that have been shown: (i) to be associated with plasma lipid levels in the Global Lipids Genetics Consortium (GLGC) genome-wide association study (P <5 × 10−8) that included 188 577 participants, mostly European ancestry and (ii) were independently associated with plasma lipid levels (linkage disequilibrium threshold of r  2 < 0.01 and located 1 Mb apart from each other) (Supplementary material online, Datasets S1–S4).14 A detailed description of the statistical methods and quality control is provided in a previous publication by the GLGC.14 Briefly, as in most studies included in the GLGC, plasma lipid concentrations had been measured after at least 8 h fasting, and the estimations were adjusted for age, age squared, sex, and population stratification. Participants with known lipid-lowering medication use have been excluded from study.14 Selected genetic variants together explained 10–14% of the total trait variance.14 Additive genetic models using linear regression on the inverse normal transformed traits were fitted for individual variant association estimates, and a weighted meta-analysis using Stouffer method was conducted for combined estimates.14 The effect sizes were calculated with respect to the minor allele per 1 SD increase in plasma lipid levels (1 SD is equal to 0.98 mmol/L for LDL-cholesterol, 0.41 mmol/L for HDL-cholesterol, 1 mmol/L for triglycerides, and 1.10 mmol/L for total cholesterol).14

Data for outcome

We used the UK Biobank data, a large prospective cohort study including 502 602 participants aged 40–69 years and recruited between 2006 and 2010 from 22 assessment centres across the UK. Details of the study design have been published elsewhere.15  ,  16 UK Biobank genotype data were imputed with IMPUTE4 using the Haplotype Reference Consortium and the UK10K + 1000 Genomes panel17 to identify ∼96 million variants for 487 381 participants. We excluded 55 208 individuals who were outliers based on heterozygosity, had a variant call rate <98%, or were not recorded as ‘white British’. The remaining participants (n = 432 173) were included in the estimation of genetic variants-outcome association in this study. The protocol of the present study was approved by UK Biobank (#22207).

Aortic stenosis was the primary outcome, with aortic regurgitation and mitral regurgitation as the negative control secondary outcomes (Supplementary material online, Text S1). We calculated corresponding summary statistics for the outcomes using logistic regression model adjusted for age, sex, assessment centre, genetic batch, the first 10 genetic principal component (for addressing population stratification), and up to third-degree relatedness based on kinship coefficients (>0.044).

Statistical analysis

Two-sample Mendelian randomization to assess total causal effect

We harmonized summary data based on a previously described method.18 Then, we used four different methods of two-sample MR [inverse-variance weighted (random-effects model), weighted median, MR-Egger, and MR-PRESSO] in order to address between variants heterogeneity and pleiotropy effect. The inverse-variance weighted method assumes that either all the instruments are valid or any horizontal pleiotropy is balanced.19 We provided an estimation using the weighted median method, which is consistent if at least 50% of the weight comes from valid instrumental variables.20 The MR-Egger regression method was used as the main estimation to account for potential pleiotropy.21 In addition, the MR pleiotropy residual sum and outlier (MR-PRESSO) method was used to test, and correct, if needed, for possible horizontal pleiotropic outliers in the analysis.22

We considered the association as causal when at least three methods provided consistent results. This approach reduces the risk of false-positive interpretation, and demonstration of consistent findings across the various models is likely to strengthen the case for a causal association. We used a predefined approach to select the best statistical estimation from these four methods (see Supplementary material online, Figure S2 for details). A leave-one-out sensitivity analysis was conducted by removing a single variant from the analysis in turn. The fluctuation of the estimates in response to excluding each variant reflects the possibility of outlier variant in the causal estimation. We examined the heterogeneity of the estimates using a scatter plot and applying the Cochran’s Q-test.23 We also assessed the probable directional pleiotropy using a funnel plot similar to that being used to assess for publication bias in meta-analysis.23

The minimum detectable odds ratio (OR) was calculated using the method reported by Brion et al.24, and implemented in a web-based application (Supplementary material online, Table S1). In addition to using negative control outcomes, we tested the validity of the instrumental variable by examining the causal association between plasma lipids and coronary heart disease as a positive outcome for LDL-cholesterol, total cholesterol, and triglycerides and a negative outcome for HDL-cholesterol.25 For this control analysis, we used two-sample MR using an analytical platform.26 We used the same genetic variants for plasma lipids, but the variants-outcome association was extracted from a large genome-wide association study meta-analysis including 22 233 individuals with coronary heart disease and 64 762 controls of European population.27 To address the possible mediating effect of myocardial infarction and heart failure on the association between lipid profile and aortic stenosis, we performed sensitivity analysis that excluded individuals with myocardial infarction and/or heart failure. In addition, to assess the robustness of the findings, we restricted cases to those with aortic stenosis and aortic valve replacement surgery. All the statistical analyses were performed using R software (‘MendelianRandomization’28 and ‘TwoSampleMR’26 packages).

Multivariable Mendelian randomization to assess the direct causal effect

We used multivariable MR through inverse-variance weighted method to estimate the direct causal effect of lipid profile on the outcomes. We excluded total cholesterol from this analysis because of observable overlap between total and LDL-cholesterol. In cases where the exposures of interest are correlated, such as total and LDL-cholesterol, the multivariable MR is useful to estimate direct causal effect of each lipid profile component, independently of any other lipid profile variables.29  ,  30 Given that a causal link between elevated lipoprotein-a [LP(a)] and aortic stenosis has been reported,31 we repeated the multivariable MR additionally adjusted for LP(a) to further check the possible effect of LP(a) on the associations. The biochemistry and genetic data for LP(a) have been obtained from the UK Biobank resource.

Results

Main findings

The characteristics of the populations included in the GLGC and UK Biobank are shown in Table 1. In the UK Biobank, we identified 1961 participants with aortic stenosis, 736 with aortic regurgitation, and 2213 with mitral regurgitation. Table 2 shows the results of MR for aortic stenosis. There was clear evidence of a causal effect of LDL-cholesterol, total cholesterol, and triglycerides on aortic stenosis (P < 0.05 in the three MR methods) (Table 2 and Supplementary material online, Figure S3). Considering the best causal estimation, the OR was 1.64 [95% confidence interval (CI) 1.28–2.11] per 0.98 mmol/L increase in LDL-cholesterol, 1.82 (95% CI 1.32–2.53) per 1.10 mmol/L increase in total cholesterol, and 1.55 (95% CI 1.20–2.00) per 1 mmol/L increase in triglycerides. The findings were also concordant on the lack of association between HDL-cholesterol and aortic stenosis. There was no evidence in favour of an association between plasma lipid parameters and aortic or mitral regurgitation, other than for a weak association between triglycerides and mitral regurgitation (Tables 3 and 4). However, the latter finding was only supported by one of the methods whilst all other methods consistently showed null associations between all lipid parameters and aortic and mitral regurgitation (Supplementary material online, Figure S3). Figure 1 compares the risk estimates from the MR analyses separately for each outcome. There was no evidence of directional pleiotropy except for total cholesterol (beta = −0.009; P = 0.04 in MR-Egger intercept) (Table 2). The funnel plots show an absence of directional pleiotropy, with a symmetrical distribution of variants effects (Supplementary material online, Figures S4–S11). However, there was significant heterogeneity for all lipid parameters. The control analysis with coronary heart disease as the outcome showed positive and significant association with each of lipid parameters other than for HDL-cholesterol, confirming that the selected genetic variants were valid instruments (Supplementary material online, Figure S12).

Table 1.

Characteristics of Global Lipids Genetics Consortium and UK Biobank datasets

Exposures Consortium No. SNPs Sample size Population
 HDL-cholesterol GLGC 71 92 860 90% European
 LDL-cholesterol 57 83 198
 Total cholesterol 73 92 260
 Triglycerides 40 91 598

Main outcomes Dataset No. cases/sample size Population

 Aortic stenosis UK Biobank 1961/432 173 100% European
 Aortic regurgitation 736/432 173
 Mitral regurgitation 2213/432 173
Outcomes for sensitivity analysis
 Myocardial infarction 15 391/432 173
 Heart failure 5161/432 173
 Aortic valve replacement 1233/432 173
Demographic variables
 Age (years), mean (SD) 56.8 (8.0)
 Male gender, n (%) 198 623 (45.9)

GLGC, Global Lipids Genetics Consortium; HDL, high density lipoprotein; LDL, low density lipoprotein; SD, standard deviation; SNP, single nucleotide polymorphism.

Table 2.

Two-sample Mendelian randomization estimations showing the effect of plasma lipids on aortic stenosis

Methods Exposure Odds ratioa 95% CI P-value Ph Q-statistics
Inverse-variance weighted HDL-cholesterol 0.86 0.69 1.06 0.17 <0.001 117.1
MR-Egger 0.98 0.70 1.37 0.91
Weighted median 0.99 0.76 1.29 0.93
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.009 −0.026 0.008 0.31

Inverse-variance weighted LDL-cholesterol 1.58 1.30 1.91 <0.001 0.01 81.6
MR-Egger 1.63 1.19 2.24 <0.001
Weighted median 1.64 1.28 2.11 <0.001
MR-PRESSO 1.59 1.34 1.90 <0.001
MR-Egger interceptc −0.002 −0.022 0.017 0.80

Inverse-variance weighted Total cholesterol 1.60 1.33 1.92 <0.001 0.04 93.7
MR-Egger 1.82 1.32 2.53 <0.001
Weighted median 1.73 1.33 2.25 <0.001
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.009 −0.026 0.009 0.04

Inverse-variance weighted Triglycerides 1.52 1.12 2.03 0.006 <0.001 77.5
MR-Egger 1.49 0.95 2.33 0.08
Weighted median 1.39 1.00 1.92 0.05
MR-PRESSO 1.55 1.20 2.00 0.002
MR-Egger interceptc 0.001 −0.024 0.026 0.91

The best causal estimation highlighted in bold .

CI, confidence interval; HDL, high density lipoprotein; LDL, low density lipoprotein; NA, not applicable; Ph, P-value for heterogeneity.

a

Odds ratio per 1 SD increase.

b

No significant outliers.

c

Regression coefficient (95% CI).

Table 3.

Two-sample Mendelian randomization estimations showing the effect of plasma lipids on aortic regurgitation

Methods Exposure Odds ratioa 95% CI P-value Ph Q-statistics
Inverse-variance weighted HDL-cholesterol 0.87 0.66 1.15 0.35 0.40 72.1
MR-Egger 0.82 0.53 1.25 0.35
Weighted median 0.73 0.47 1.13 0.15
MR-PRESSOb NA NA NA NA
MR-Egger interceptc 0.005 −0.017 0.027 0.65

Inverse-variance weighted LDL-cholesterol 0.97 0.73 1.30 0.88 0.09 70.2
MR-Egger 0.94 0.59 1.51 0.80
Weighted median 1.10 0.73 1.66 0.63
MR-PRESSOb NA NA NA NA
MR-Egger interceptc 0.003 −0.027 0.33 0.83

Inverse-variance weighted Total cholesterol 0.87 0.67 1.13 0.32 0.51 70.9
MR-Egger 1.06 0.67 1.69 0.80
Weighted median 1.11 0.74 1.69 0.61
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.013 −0.038 0.013 0.33

Inverse-variance weighted Triglycerides 1.01 0.70 1.45 0.94 0.19 46.4
MR-Egger 1.21 0.70 2.09 0.50
Weighted median 1.26 0.77 2.06 0.36
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.013 −0.044 0.018 0.39

The best causal estimation highlighted in bold.

CI, confidence interval; HDL, high density lipoprotein; LDL, low density lipoprotein; NA, not applicable; Ph, P-value for heterogeneity.

a

Odds ratio per 1 SD increase.

b

No significant outliers.

c

Regression coefficient (95% CI).

Table 4.

Two-sample Mendelian randomization estimations showing the effect of plasma lipids on mitral regurgitation

Methods Exposure Odds ratioa 95% CI P-value Ph Q-statistics
Inverse-variance weighted HDL-cholesterol 0.84 0.70 1.02 0.08 0.0009 100.4
MR-Egger 0.96 0.72 1.29 0.80
Weighted median 0.97 0.76 1.23 0.79
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.009 −0.024 0.006 0.25

Inverse-variance weighted LDL-cholesterol 1.10 0.93 1.30 0.23 0.12 68.5
MR-Egger 1.07 0.81 1.40 0.65
Weighted median 1.08 0.85 1.37 0.52
MR-PRESSOb NA NA NA NA
MR-Egger interceptc 0.003 −0.014 0.020 0.73

Inverse-variance weighted Total cholesterol 1.12 0.95 1.32 0.14 0.14 84.7
MR-Egger 1.19 0.88 1.60 0.24
Weighted median 1.08 0.85 1.39 0.50
MR-PRESSOb NA NA NA NA
MR-Egger interceptc −0.004 −0.020 0.012 0.65

Inverse-variance weighted Triglycerides 1.31 1.04 1.65 0.02 0.05 54.0
MR-Egger 1.30 0.92 1.85 0.13
Weighted median 1.29 0.96 1.73 0.09
MR-PRESSOb NA NA NA NA
MR-Egger interceptc 0.000 −0.019 0.020 0.97

The best causal estimation highlighted in bold.

CI, confidence interval; HDL, high density lipoprotein; LDL, low density lipoprotein; NA, not applicable; Ph, P-value for heterogeneity.

a

Odds ratio per 1 SD increase.

b

No significant outliers.

c

Regression coefficient (95% CI).

Figure 1.

Figure 1

Comparison of the total causal estimations considered heterogeneity and pleiotropic effect between plasma lipids and valvular heart disease risk using two-sample Mendelian randomization. SD, standard deviation; 1 SD is equal to 0.98 mmol/L for low density lipoprotein-cholesterol, 0.41 mmol/L for high density lipoprotein-cholesterol, 1 mmol/L for triglycerides, and 1.10 mmol/L for total cholesterol.

Sensitivity analysis

To assess the potential mediating effect of a presentation with myocardial infarction or heart failure on detection of valve disease, we repeated the analysis excluding all the participants with documented myocardial infarction and/or heart failure. The findings were broadly similar to the overall analysis, other than for a reduction in pleiotropy in total cholesterol analysis after excluded participants with myocardial infarction (MR-Egger intercept = −0.005; P = 0.59) (Supplementary material online, Tables S2–S4 and Figure S13). There were also no substantial differences in the results after excluding participants with heart failure (Supplementary material online, Figure S14). Sensitivity analysis by restricting the outcome only to include those with valve replacement therapy was consistent with the main results (Supplementary material online, Figure S15). In the leave-one-out analysis, we found that no single genetic variant was strongly driving the overall effect of plasma lipids on aortic stenosis (Supplementary material online, Figures S16–S19).

In the multivariable MR that adjusted for the effect of each lipid profile component, the strong positive association between LDL-cholesterol and aortic stenosis persisted, whereas the association with triglycerides was attenuated. The multivariable-adjusted ORs were 1.52 (95% CI 1.22–1.90; per 0.98 mmol/L increase) for LDL-cholesterol, 1.38 (95% CI 0.92–2.07; per 1 mmol/L increase) for triglycerides, and 1.03 (95% CI 0.80–1.31; per 0.41 mmol/L increase) for HDL-cholesterol (Figure 2). Additional adjustment for LP(a) did not change the results (Supplementary material online, Figure S20).

Figure 2.

Figure 2

Comparison of the direct causal estimations between plasma lipids and valvular heart disease risk using multivariable Mendelian randomization. SD, standard deviation; 1 SD is equal to 0.98 mmol/L for low density lipoprotein-cholesterol, 0.41 mmol/L for high density lipoprotein-cholesterol, 1 mmol/L for triglycerides, and 1.10 mmol/L for total cholesterol. The multivariable Mendelian randomization was adjusted to estimate direct causal effect of each plasma lipids component, independently of any other plasma lipids variables.

Take home figure.

Take home figure

Schematic overview of the Mendelian randomization framework and key findings.

Discussion

This study showed that each standard deviation increase in LDL-cholesterol, total cholesterol, and triglycerides increases the risk of incident aortic stenosis by 64%, 82%, and 55%, respectively. In contrast, there was no evidence of a causal association between plasma lipids and aortic regurgitation or mitral regurgitation. After adjustment for each lipid profile component through multivariable MR, the result corroborated the association between LDL-cholesterol and risk of aortic stenosis. However, the findings for triglycerides were inconclusive and should be interpreted with caution. This is in part because of the small numbers of independent genetic variants available for triglycerides which could have led to a low statistical power and wide CIs. Nevertheless, the robustness and consistency of our results using different methods, together with the strength of the association, indicate an unconfounded relationship between elevated LDL-cholesterol with the risk of incident aortic stenosis, and suggest that this association is likely to be causal.

This MR study is in keeping with a previous population-based cohort study suggesting that dyslipidaemia was associated with an increased risk of incident aortic stenosis.9 However, the observational nature of this earlier report precluded drawing conclusions about causality and the binary categorization of dyslipidaemia limited the study from demonstrating any dose–response relationship. More recently, a one-sample MR study, which included 473 cases of aortic stenosis, demonstrated a causal association between LDL-cholesterol and aortic stenosis, with no evidence of a significant association with triglycerides.32 However, the latter may be due to lack of power to detect a small effect size, which is indeed a known limitation when conducting one-sample MR.33 Our two-sample analysis, based on data from two non-overlapping datasets, has a higher power than one-sample analysis to detect more modest associations.34 In addition, we were able to overcome the issue of weak instrument bias, which may underpin the underestimation of a causal association between triglycerides and aortic stenosis in the aforementioned study.35 However, the apparent causal association between raised triglycerides and aortic stenosis was attenuated by adjustment for the effect of other lipid markers. This adjustment substantially reduced the number of variants available as these variants needed to be associated with raised triglycerides but not with cholesterol markers. Therefore, the independent association between raised triglycerides and aortic stenosis remains uncertain. A similar issue is seen when assessing the direct causal effect of triglycerides on coronary heart disease, where the small number of genetic variants precluded precise causal estimation of the association using MR technique.30

Our findings are supported by pathophysiological studies which have shown the involvement of an atherosclerotic process of the valve cusps in aortic stenosis, similar to what happens in the arterial tree.36–38 It is thus biologically plausible that well-established causal factors in the development of atherosclerosis, particularly in the coronary arteries, maybe also be involved in the pathological process of aortic stenosis.39  ,  40 Cholesterol, and more specifically, LDL-cholesterol, is a clearly established risk factor of atherosclerotic diseases, whilst the role of triglycerides as an independent risk factor remains controversial.41–43 In addition, although experimental studies have suggested that components of HDL particles may have positive effects on aortic stenosis, we did not find an association between genetically determined HDL-cholesterol levels and risk of aortic stenosis.44 This is in keeping with the lack of effect of HDL-raising treatments for primary and secondary prevention of coronary artery disease consistently reported by both RCTs45  ,  46 and MR studies.47 Therefore, further evidence is warranted to understand the role of HDL-cholesterol in aortic stenosis pathogenesis and whether increasing HDL-cholesterol level could have a beneficial impact in delaying the disease progression.

To the best of our knowledge, no RCT has yet assessed the effect of lipid modification for primary prevention of aortic stenosis. However, three randomized trials have investigated the effects of statins in patients with mild to moderate aortic stenosis. Although these trials failed to show a clear benefit for LDL-lowering therapy in delaying the progression of aortic stenosis to eventually require aortic valve replacement,10–12  ,  48 they were mostly limited by a short follow-up duration and insufficient statistical power.10  ,  11  ,  48 Indeed, detecting a relatively small treatment effect on a slowly progressive disease will likely require a substantially large sample size. In addition, our MR analysis reflects the impact of lifelong exposure to higher levels of cholesterol and triglycerides, capturing long-term risks that may not be modifiable by short-term lipid-lowering treatment.49 Indeed, it is possible that cholesterol-induced atherosclerosis plays a more important role in initiation than in progression of aortic stenosis, which, for practical reasons, has been the main outcome of earlier RCTs. It is possible that an initial damage to the aortic cups disturbs valve function and flow, setting in motion an irreversible cycle of disturbed flow, abnormal pressure, endothelial damage, and calcification that eventually leads to severe stenosis requiring valve replacement.50 Once a certain threshold of valve damage has been crossed, cholesterol-lowering treatment might not be able to halt progression of aortic valve disease. As aortic stenosis has a long, silent clinically asymptomatic phase, it is plausible that treatment initiation after clinical manifestation might be too late to revert the pathologic process that has been triggered by prolonged exposure to raised lipid levels. A large randomized prospective, placebo vs. high-dose statin clinical trial in patients with subclinical aortic sclerosis or mild aortic stenosis would need to be conducted to test whether statin treatment can slow progression to overt aortic stenosis.

Given the established causal link between elevated LP(a) and aortic stenosis,31 and evidence showing that statin therapy increases LP(a) levels,51 it is also plausible that some of the expected beneficial LDL-lowering effects of statins in previous trials have been counteracted by a concomitant rise in LP(a). However, in our multivariable MR analysis, we adjusted for genetically determined LP(a) yet the risk estimate of LDL-cholesterol on aortic stenosis remained virtually unchanged.

In our study, the estimates used for outcomes are from valvular heart disease cases, which were obtained from linked hospital electronic health records, from which we could not assess disease progression or severity. Disease outcomes may also be affected by a degree of misclassification as we relied on using routinely collected data to identify cases, with no access to echocardiographic data for direct case ascertainment. However, previous studies that relied on electronic health records to identify outcomes have shown that the majority of clinically recorded valve disease codes were based on echocardiographic assessments, and the recorded cases were typically in the moderate to severe spectrum of the disease.31  ,  52 In addition, restricting cases to those with a valve replacement therapy as a proxy for valve severity yielded similar results. Also, our study assumed that the genetic variants selected as proxy for lipid levels influenced valvular heart disease only through the exposure of interest. Although it is impossible to be certain that the variants used in this study do not have pleiotropic effects, we did not find any evidence in favour of strong pleiotropy. Finally, the current study relied on genetic data conducted in a population mostly of European descent, which, despite the benefit of greater genetic homogeneity, limits the generalizability of the present findings to other ethnicities. It would be interesting to study whether the observed associations hold true in populations with different genetic backgrounds.

In this study, we showed that genetically determined exposure to raised lipid levels, specifically LDL-cholesterol, total cholesterol, and triglycerides, significantly increased the risk of aortic stenosis. There was no evidence that such exposure to raised lipid levels were associated with aortic regurgitation and mitral regurgitation. After adjustment for other lipid components, the finding further confirmed the causal association between LDL-cholesterol and risk of aortic stenosis. In the absence of high-quality evidence from clinical trials, this study provides the most compelling evidence that lipids play a role in the aetiology of aortic stenosis. Considering the substantial ethical and practical implications of conducting large scale RCTs, particularly for primary prevention, this study could guide clinical decision-making regarding lipid-lowering treatment, which may contribute to curb the global epidemic of aortic valve stenosis.

Supplementary Material

ehaa070_supplementary_data

Acknowledgements

This research has been conducted using the UK Biobank Resource under Application Number 22207.We would like to acknowledge The Global Lipids Genetics Consortium for providing the summary statistics (http://lipidgenetics.org/).

Funding

Nazarzadeh (grant number: FS/19/36/34346), Pinho-Gomes (grant number: FS/19/64/34673) and Rahimi (grant number: PG/18/65/33872) are supported by the British Heart Foundation. Rahimi and Canoy are supported by the National Institute of Health Research (NIHR) Oxford Biomedical Research Centre, a grant from the Oxford Martin School, as well as the PEAK Urban programme, from the UKRI’s Global Challenge Research Fund Grant Ref: ES/P011055/1. George Davey Smith works in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/1.

Conflict of interest: The authors declare that there is no conflict of interest. The funding organizations had no role in design or conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Contributor Information

Milad Nazarzadeh, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK; The Collaboration Center of Meta-Analysis Research, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.

Ana-Catarina Pinho-Gomes, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.

Zeinab Bidel, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK; The Collaboration Center of Meta-Analysis Research, School of Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.

Abbas Dehghan, Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, London, UK.

Dexter Canoy, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Faculty of Medicine, University of New South Wales, Sydney, Australia.

Abdelaali Hassaine, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.

Jose Roberto Ayala Solares, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.

Gholamreza Salimi-Khorshidi, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.

George Davey Smith, MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Catherine M Otto, University of Washington, Seattle, WA, USA.

Kazem Rahimi, The George Institute for Global Health, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

References

  • 1. Coffey  S, Cairns  BJ, Iung  B.  The modern epidemiology of heart valve disease. Heart  2016;102:75–85. [DOI] [PubMed] [Google Scholar]
  • 2. Watkins  DA, Johnson  CO, Colquhoun  SM, Karthikeyan  G, Beaton  A, Bukhman  G, Forouzanfar  MH, Longenecker  CT, Mayosi  BM, Mensah  GA, Nascimento  BR, Ribeiro  A, Sable  CA, Steer  AC, Naghavi  M, Mokdad  AH, Murray  C, Vos  T, Carapetis  JR, Roth  GA.  Global, regional, and national burden of rheumatic heart disease, 1990–2015. N Engl J Med  2017;377:713–722. [DOI] [PubMed] [Google Scholar]
  • 3. Nkomo  VT, Gardin  JM, Skelton  TN, Gottdiener  JS, Scott  CG, Enriquez-Sarano  M.  Burden of valvular heart diseases: a population-based study. Lancet  2006;368:1005–1011. [DOI] [PubMed] [Google Scholar]
  • 4. Baumgartner  H, Falk  V, Bax  JJ, De Bonis  M, Hamm  C, Holm  PJ, Iung  B, Lancellotti  P, Lansac  E, Rodriguez Muñoz  D, Rosenhek  R, Sjögren  J, Tornos Mas  P, Vahanian  A, Walther  T, Wendler  O, Windecker  S, Zamorano  JL; ESC Scientific Document Group. 2017 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J  2017;38:2739–2791. [DOI] [PubMed] [Google Scholar]
  • 5. Fairbairn  TA, Meads  DM, Hulme  C, Mather  AN, Plein  S, Blackman  DJ, Greenwood  JP.  The cost-effectiveness of transcatheter aortic valve implantation versus surgical aortic valve replacement in patients with severe aortic stenosis at high operative risk. Heart  2013;99:914–920. [DOI] [PubMed] [Google Scholar]
  • 6. Nazarzadeh  M, Pinho-Gomes  A-C, Smith Byrne  K, Canoy  D, Raimondi  F, Ayala Solares  JR, Otto  CM, Rahimi  K.  Systolic blood pressure and risk of valvular heart disease. JAMA Cardiol  2019;4:788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Otto  CM, Kuusisto  J, Reichenbach  DD, Gown  AM, O'Brien  KD.  Characterization of the early lesion of ‘degenerative’ valvular aortic stenosis. Histological and immunohistochemical studies. Circulation  1994;90:844–853. [DOI] [PubMed] [Google Scholar]
  • 8. Collins  R, Armitage  J, Parish  S, Sleight  P, Peto  R.  MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20 536 high-risk individuals: a randomised placebo-controlled trial. Lancet  2002;360:7–22. [DOI] [PubMed] [Google Scholar]
  • 9. Yan  AT, Koh  M, Chan  KK, Guo  H, Alter  DA, Austin  PC, Tu  JV, Wijeysundera  HC, Ko  DT.  Association between cardiovascular risk factors and aortic stenosis: the CANHEART Aortic Stenosis Study. J Am Coll Cardiol  2017;69:1523–1532. [DOI] [PubMed] [Google Scholar]
  • 10. Chan  KL, Teo  K, Dumesnil  JG, Ni  A, Tam  J.  Effect of Lipid lowering with rosuvastatin on progression of aortic stenosis: results of the aortic stenosis progression observation: measuring effects of rosuvastatin (ASTRONOMER) trial. Circulation  2010;121:306–314. [DOI] [PubMed] [Google Scholar]
  • 11. Cowell  SJ, Newby  DE, Prescott  RJ, Bloomfield  P, Reid  J, Northridge  DB, Boon  NA.  A randomized trial of intensive lipid-lowering therapy in calcific aortic stenosis. N Engl J Med  2005;352:2389–2397. [DOI] [PubMed] [Google Scholar]
  • 12. Rossebo  AB, Pedersen  TR, Boman  K, Brudi  P, Chambers  JB, Egstrup  K, Gerdts  E, Gohlke-Barwolf  C, Holme  I, Kesaniemi  YA, Malbecq  W, Nienaber  CA, Ray  S, Skjaerpe  T, Wachtell  K, Willenheimer  R.  Intensive lipid lowering with simvastatin and ezetimibe in aortic stenosis. N Engl J Med  2008;359:1343–1356. [DOI] [PubMed] [Google Scholar]
  • 13. Smith  GD, Ebrahim  S. ‘ Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?  Int J Epidemiol  2003;32:1–22. [DOI] [PubMed] [Google Scholar]
  • 14. Willer  CJ, Schmidt  EM, Sengupta  S, Peloso  GM, Gustafsson  S, Kanoni  S, Ganna  A, Chen  J, Buchkovich  ML, Mora  S, Beckmann  JS, Bragg-Gresham  JL, Chang  H-Y, Demirkan  A, Den Hertog  HM, Do  R, Donnelly  LA, Ehret  GB, Esko  T, Feitosa  MF, Ferreira  T, Fischer  K, Fontanillas  P, Fraser  RM, Freitag  DF, Gurdasani  D, Heikkilä  K, Hyppönen  E, Isaacs  A, Jackson  AU, Johansson  Å, Johnson  T, Kaakinen  M, Kettunen  J, Kleber  ME, Li  X, Luan  J, Lyytikäinen  L-P, Magnusson  PKE, Mangino  M, Mihailov  E, Montasser  ME, Müller-Nurasyid  M, Nolte  IM, O'Connell  JR, Palmer  CD, Perola  M, Petersen  A-K, Sanna  S, Saxena  R, Service  SK, Shah  S, Shungin  D, Sidore  C, Song  C, Strawbridge  RJ, Surakka  I, Tanaka  T, Teslovich  TM, Thorleifsson  G, Van den Herik  EG, Voight  BF, Volcik  KA, Waite  LL, Wong  A, Wu  Y, Zhang  W, Absher  D, Asiki  G, Barroso  I, Been  LF, Bolton  JL, Bonnycastle  LL, Brambilla  P, Burnett  MS, Cesana  G, Dimitriou  M, Doney  ASF, Döring  A, Elliott  P, Epstein  SE, Ingi Eyjolfsson  G, Gigante  B, Goodarzi  MO, Grallert  H, Gravito  ML, Groves  CJ, Hallmans  G, Hartikainen  A-L, Hayward  C, Hernandez  D, Hicks  AA, Holm  H, Hung  Y-J, Illig  T, Jones  MR, Kaleebu  P, Kastelein  JJP, Khaw  K-T, Kim  E, Klopp  N, Komulainen  P, Kumari  M, Langenberg  C, Lehtimäki  T, Lin  S-Y, Lindström  J, Loos  RJF, Mach  F, McArdle  WL, Meisinger  C, Mitchell  BD, Müller  G, Nagaraja  R, Narisu  N, Nieminen  TVM, Nsubuga  RN, Olafsson  I, Ong  KK, Palotie  A, Papamarkou  T, Pomilla  C, Pouta  A, Rader  DJ, Reilly  MP, Ridker  PM, Rivadeneira  F, Rudan  I, Ruokonen  A, Samani  N, Scharnagl  H, Seeley  J, Silander  K, Stančáková  A, Stirrups  K, Swift  AJ, Tiret  L, Uitterlinden  AG, van Pelt  LJ, Vedantam  S, Wainwright  N, Wijmenga  C, Wild  SH, Willemsen  G, Wilsgaard  T, Wilson  JF, Young  EH, Zhao  JH, Adair  LS, Arveiler  D, Assimes  TL, Bandinelli  S, Bennett  F, Bochud  M, Boehm  BO, Boomsma  DI, Borecki  IB, Bornstein  SR, Bovet  P, Burnier  M, Campbell  H, Chakravarti  A, Chambers  JC, Chen  Y-DI, Collins  FS, Cooper  RS, Danesh  J, Dedoussis  G, de Faire  U, Feranil  AB, Ferrières  J, Ferrucci  L, Freimer  NB, Gieger  C, Groop  LC, Gudnason  V, Gyllensten  U, Hamsten  A, Harris  TB, Hingorani  A, Hirschhorn  JN, Hofman  A, Hovingh  GK, Hsiung  CA, Humphries  SE, Hunt  SC, Hveem  K, Iribarren  C, Järvelin  M-R, Jula  A, Kähönen  M, Kaprio  J, Kesäniemi  A, Kivimaki  M, Kooner  JS, Koudstaal  PJ, Krauss  RM, Kuh  D, Kuusisto  J, Kyvik  KO, Laakso  M, Lakka  TA, Lind  L, Lindgren  CM, Martin  NG, März  W, McCarthy  MI, McKenzie  CA, Meneton  P, Metspalu  A, Moilanen  L, Morris  AD, Munroe  PB, Njølstad  I, Pedersen  NL, Power  C, Pramstaller  PP, Price  JF, Psaty  BM, Quertermous  T, Rauramaa  R, Saleheen  D, Salomaa  V, Sanghera  DK, Saramies  J, Schwarz  PEH, Sheu  WH-H, Shuldiner  AR, Siegbahn  A, Spector  TD, Stefansson  K, Strachan  DP, Tayo  BO, Tremoli  E, Tuomilehto  J, Uusitupa  M, van Duijn  CM, Vollenweider  P, Wallentin  L, Wareham  NJ, Whitfield  JB, Wolffenbuttel  BHR, Ordovas  JM, Boerwinkle  E, Palmer  CNA, Thorsteinsdottir  U, Chasman  DI, Rotter  JI, Franks  PW, Ripatti  S, Cupples  LA, Sandhu  MS, Rich  SS, Boehnke  M, Deloukas  P, Kathiresan  S, Mohlke  KL, Ingelsson  E, Abecasis  GR; Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat Genet  2013;45:1274–1285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Sudlow  C, Gallacher  J, Allen  N, Beral  V, Burton  P, Danesh  J, Downey  P, Elliott  P, Green  J, Landray  M, Liu  B, Matthews  P, Ong  G, Pell  J, Silman  A, Young  A, Sprosen  T, Peakman  T, Collins  R.  UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med  2015;12:e1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.UK Biobank Coordinating Centre. UK Biobank: Protocol for a Large-Scale Prospective Epidemiological Resource UK Biobank Coordinating Centre. Design; 2007. p1–112. http://www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf (24 May 2018).
  • 17. Bycroft  C, Freeman  C, Petkova  D, Band  G, Elliott  LT, Sharp  K, Motyer  A, Vukcevic  D, Delaneau  O, O’Connell  J, Cortes  A, Welsh  S, Young  A, Effingham  M, McVean  G, Leslie  S, Allen  N, Donnelly  P, Marchini  J.  The UK Biobank resource with deep phenotyping and genomic data. Nature  2018;562:203–209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Hartwig  FP, Davies  NM, Hemani  G, Davey Smith  G.  Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol  2016;45:1717–1726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Hartwig  FP, Davey Smith  G, Bowden  J.  Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol  2017;46:1985–1998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Bowden  J, Davey Smith  G, Haycock  PC, Burgess  S.  Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol  2016;40:304–314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Bowden  J, Davey Smith  G, Burgess  S.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol  2015;44:512–525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Verbanck  M, Chen  C-Y, Neale  B, Do  R.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet  2018;50:693–698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Burgess  S, Bowden  J, Fall  T, Ingelsson  E, Thompson  SG.  Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology  2017;28:30–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Brion  M-J, Shakhbazov  K, Visscher  PM.  Calculating statistical power in Mendelian randomization studies. Int J Epidemiol  2013;42:1497–1501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Mach  F, Baigent  C, Catapano  AL, Koskinas  KC, Casula  M, Badimon  L, Chapman  MJ, De Backer  GG, Delgado  V, Ference  BA, Graham  IM, Halliday  A, Landmesser  U, Mihaylova  B, Pedersen  TR, Riccardi  G, Richter  DJ, Sabatine  MS, Taskinen  M-R, Tokgozoglu  L, Wiklund  O; ESC Scientific Document Group. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J  2020;41:111–188. [DOI] [PubMed] [Google Scholar]
  • 26. Hemani  G, Zheng  J, Elsworth  B, Wade  KH, Haberland  V, Baird  D, Laurin  C, Burgess  S, Bowden  J, Langdon  R, Tan  VY, Yarmolinsky  J, Shihab  HA, Timpson  NJ, Evans  DM, Relton  C, Martin  RM, Davey Smith  G, Gaunt  TR, Haycock  PC.  The MR-Base platform supports systematic causal inference across the human phenome. Elife  2018;7:e34408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Schunkert  H, König  IR, Kathiresan  S, Reilly  MP, Assimes  TL, Holm  H, Preuss  M, Stewart  AFR, Barbalic  M, Gieger  C, Absher  D, Aherrahrou  Z, Allayee  H, Altshuler  D, Anand  SS, Andersen  K, Anderson  JL, Ardissino  D, Ball  SG, Balmforth  AJ, Barnes  TA, Becker  DM, Becker  LC, Berger  K, Bis  JC, Boekholdt  SM, Boerwinkle  E, Braund  PS, Brown  MJ, Burnett  MS, Buysschaert  I, Carlquist  JF, Chen  L, Cichon  S, Codd  V, Davies  RW, Dedoussis  G, Dehghan  A, Demissie  S, Devaney  JM, Diemert  P, Do  R, Doering  A, Eifert  S, Mokhtari  NEE, Ellis  SG, Elosua  R, Engert  JC, Epstein  SE, de Faire  U, Fischer  M, Folsom  AR, Freyer  J, Gigante  B, Girelli  D, Gretarsdottir  S, Gudnason  V, Gulcher  JR, Halperin  E, Hammond  N, Hazen  SL, Hofman  A, Horne  BD, Illig  T, Iribarren  C, Jones  GT, Jukema  JW, Kaiser  MA, Kaplan  LM, Kastelein  JJP, Khaw  K-T, Knowles  JW, Kolovou  G, Kong  A, Laaksonen  R, Lambrechts  D, Leander  K, Lettre  G, Li  M, Lieb  W, Loley  C, Lotery  AJ, Mannucci  PM, Maouche  S, Martinelli  N, McKeown  PP, Meisinger  C, Meitinger  T, Melander  O, Merlini  PA, Mooser  V, Morgan  T, Mühleisen  TW, Muhlestein  JB, Münzel  T, Musunuru  K, Nahrstaedt  J, Nelson  CP, Nöthen  MM, Olivieri  O, Patel  RS, Patterson  CC, Peters  A, Peyvandi  F, Qu  L, Quyyumi  AA, Rader  DJ, Rallidis  LS, Rice  C, Rosendaal  FR, Rubin  D, Salomaa  V, Sampietro  ML, Sandhu  MS, Schadt  E, Schäfer  A, Schillert  A, Schreiber  S, Schrezenmeir  J, Schwartz  SM, Siscovick  DS, Sivananthan  M, Sivapalaratnam  S, Smith  A, Smith  TB, Snoep  JD, Soranzo  N, Spertus  JA, Stark  K, Stirrups  K, Stoll  M, Tang  WHW, Tennstedt  S, Thorgeirsson  G, Thorleifsson  G, Tomaszewski  M, Uitterlinden  AG, van Rij  AM, Voight  BF, Wareham  NJ, Wells  GA, Wichmann  H-E, Wild  PS, Willenborg  C, Witteman  JCM, Wright  BJ, Ye  S, Zeller  T, Ziegler  A, Cambien  F, Goodall  AH, Cupples  LA, Quertermous  T, März  W, Hengstenberg  C, Blankenberg  S, Ouwehand  WH, Hall  AS, Deloukas  P, Thompson  JR, Stefansson  K, Roberts  R, Thorsteinsdottir  U, O’Donnell  CJ, McPherson  R, Erdmann  JCARDIoGRAM ConsortiumSamani  NJ.  Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet  2011;43:333–338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Yavorska  OO, Burgess  S.  MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol  2017;46:1734–1739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Sanderson  E, Davey Smith  G, Windmeijer  F, Bowden  J.  An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol  2019;48:713–727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Burgess  S, Thompson  SG.  Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol  2015;181:251–260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Thanassoulis  G, Campbell  CY, Owens  DS, Smith  JG, Smith  AV, Peloso  GM, Kerr  KF, Pechlivanis  S, Budoff  MJ, Harris  TB, Malhotra  R, O'Brien  KD, Kamstrup  PR, Nordestgaard  BG, Tybjaerg-Hansen  A, Allison  MA, Aspelund  T, Criqui  MH, Heckbert  SR, Hwang  S-J, Liu  Y, Sjogren  M, van der Pals  J, Kälsch  H, Mühleisen  TW, Nöthen  MM, Cupples  LA, Caslake  M, Di Angelantonio  E, Danesh  J, Rotter  JI, Sigurdsson  S, Wong  Q, Erbel  R, Kathiresan  S, Melander  O, Gudnason  V, O'Donnell  CJ, Post  WS.  Genetic associations with valvular calcification and aortic stenosis. N Engl J Med  2013;368:503–512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Smith  JG, Luk  K, Schulz  C-A, Engert  JC, Do  R, Hindy  G, Rukh  G, Dufresne  L, Almgren  P, Owens  DS, Harris  TB, Peloso  GM, Kerr  KF, Wong  Q, Smith  AV, Budoff  MJ, Rotter  JI, Cupples  LA, Rich  S, Kathiresan  S, Orho-Melander  M, Gudnason  V, O’Donnell  CJ, Post  WS, Thanassoulis  G; Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Extracoronary Calcium Working Group. Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis. JAMA  2014;312:1764–1771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. VanderWeele  TJ, Tchetgen Tchetgen  EJ, Cornelis  M, Kraft  P.  Methodological challenges in Mendelian randomization. Epidemiology  2014;25:427–435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Lawlor  DA.  Commentary: two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol  2016;45:908–915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Taylor  AE, Davies  NM, Ware  JJ, VanderWeele  T, Smith  GD, Munafò  MR.  Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Econ Hum Biol  2014;13:99–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Kuusisto  J, Räsänen  K, Särkioja  T, Alarakkola  E, Kosma  V-M.  Atherosclerosis-like lesions of the aortic valve are common in adults of all ages: a necropsy study. Heart  2005;91:576–582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. O’Brien  KD, Reichenbach  DD, Marcovina  SM, Kuusisto  J, Alpers  CE, Otto  CM.  Apolipoproteins B, (a), and E accumulate in the morphologically early lesion of ‘degenerative’ valvular aortic stenosis. Arterioscler Thromb Vasc Biol  1996;16:523–532. [DOI] [PubMed] [Google Scholar]
  • 38. Olsson  M, Thyberg  J, Nilsson  J.  Presence of oxidized low density lipoprotein in nonrheumatic stenotic aortic valves. Arterioscler Thromb Vasc Biol  1999;19:1218–1222. [DOI] [PubMed] [Google Scholar]
  • 39. Stewart  BF, Siscovick  D, Lind  BK, Gardin  JM, Gottdiener  JS, Smith  VE, Kitzman  DW, Otto  CM.  Clinical factors associated with calcific aortic valve disease. Cardiovascular Health Study. J Am Coll Cardiol  1997;29:630–634. [DOI] [PubMed] [Google Scholar]
  • 40. Peeters  F, Meex  SJR, Dweck  MR, Aikawa  E, Crijns  H, Schurgers  LJ, Kietselaer  B.  Calcific aortic valve stenosis: hard disease in the heart. Eur Heart J  2018;39:2618–2624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Sarwar  N, Danesh  J, Eiriksdottir  G, Sigurdsson  G, Wareham  N, Bingham  S, Boekholdt  SM, Khaw  K-T, Gudnason  V.  Triglycerides and the risk of coronary heart disease: 10,158 incident cases among 262,525 participants in 29 Western prospective studies. Circulation  2007;115:450–458. [DOI] [PubMed] [Google Scholar]
  • 42. Ference  BA, Kastelein  JJP, Ray  KK, Ginsberg  HN, Chapman  MJ, Packard  CJ, Laufs  U, Oliver-Williams  C, Wood  AM, Butterworth  AS, Angelantonio  ED, Danesh  J, Nicholls  SJ, Bhatt  DL, Sabatine  MS, Catapano  AL.  Association of triglyceride-lowering LPL variants and LDL-C-lowering LDLR variants with risk of coronary heart disease. JAMA  2019;321:364–373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Qi  G, Chatterjee  N.  Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat Commun  2019;10:1941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Gebhard  C, Maafi  F, Stähli  BE, Bonnefoy  A, Gebhard  CE, Nachar  W, Moraes  O, de  AB, Mecteau  M, Mihalache-Avram  T, Lavoie  V, Kernaleguen  AE, Shi  Y, Busseuil  D, Chabot-Blanchet  M, Perrault  LP, Rhainds  D, Rhéaume  E, Tardif  JC.  Beneficial effects of high-density lipoproteins on acquired von Willebrand syndrome in aortic valve stenosis. Thromb Haemost  2018;118:288–297. [DOI] [PubMed] [Google Scholar]
  • 45. Schwartz  GG, Olsson  AG, Abt  M, Ballantyne  CM, Barter  PJ, Brumm  J, Chaitman  BR, Holme  IM, Kallend  D, Leiter  LA, Leitersdorf  E, McMurray  JJV, Mundl  H, Nicholls  SJ, Shah  PK, Tardif  J-C, Wright  RS; dal-OUTCOMES Investigators. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med  2012;367:2089–2099. [DOI] [PubMed] [Google Scholar]
  • 46. Lincoff  AM, Nicholls  SJ, Riesmeyer  JS, Barter  PJ, Brewer  HB, Fox  KAA, Gibson  CM, Granger  C, Menon  V, Montalescot  G, Rader  D, Tall  AR, McErlean  E, Wolski  K, Ruotolo  G, Vangerow  B, Weerakkody  G, Goodman  SG, Conde  D, McGuire  DK, Nicolau  JC, Leiva-Pons  JL, Pesant  Y, Li  W, Kandath  D, Kouz  S, Tahirkheli  N, Mason  D, Nissen  SE.  Evacetrapib and cardiovascular outcomes in high-risk vascular disease. N Engl J Med  2017;376:1933–1942. [DOI] [PubMed] [Google Scholar]
  • 47. White  J, Swerdlow  DI, Preiss  D, Fairhurst-Hunter  Z, Keating  BJ, Asselbergs  FW, Sattar  N, Humphries  SE, Hingorani  AD, Holmes  MV.  Association of lipid fractions with risks for coronary artery disease and diabetes. JAMA Cardiol  2016;1:692–699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Dichtl  W, Alber  HF, Feuchtner  GM, Hintringer  F, Reinthaler  M, Bartel  T, Sussenbacher  A, Grander  W, Ulmer  H, Pachinger  O, Muller  S.  Prognosis and risk factors in patients with asymptomatic aortic stenosis and their modulation by atorvastatin (20 mg). Am J Cardiol  2008;102:743–748. [DOI] [PubMed] [Google Scholar]
  • 49. Labrecque  JA, Swanson  SA.  Interpretation and potential biases of Mendelian randomization estimates with time-varying exposures. Am J Epidemiol  2019;188:231–238. [DOI] [PubMed] [Google Scholar]
  • 50. Parolari  A, Loardi  C, Mussoni  L, Cavallotti  L, Camera  M, Biglioli  P, Tremoli  E, Alamanni  F.  Nonrheumatic calcific aortic stenosis: an overview from basic science to pharmacological prevention. Eur J Cardiothorac Surg  2009;35:493–504. [DOI] [PubMed] [Google Scholar]
  • 51. Tsimikas  S, Gordts  P, Nora  C, Yeang  C, Witztum  JL.  Statin therapy increases lipoprotein(a) levels. Eur Heart J  2019;doi: 10.1093/eurheartj/ehz310. [DOI] [PubMed] [Google Scholar]
  • 52. Andell  P, Li  X, Martinsson  A, Andersson  C, Stagmo  M, Zöller  B, Sundquist  K, Smith  JG.  Epidemiology of valvular heart disease in a Swedish nationwide hospital-based register study. Heart  2017;103:1696–1703. [DOI] [PMC free article] [PubMed] [Google Scholar]

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