New Results

Genetic determinants of lipids and cardiovascular disease outcomes: a wide-angled Mendelian randomization investigation

Elias Allara, Gabriele Morani, Paul Carter, Apostolos Gkatzionis, Verena Zuber, Christopher N Foley, Jessica MB Rees, Amy M Mason, Steven Bell, Dipender Gill, Adam S Butterworth, Emanuele Di Angelantonio, James Peters, Stephen Burgess
doi: https://doi.org/10.1101/668970

ABSTRACT

Aims To systematically investigate causal relationships between circulating lipids and cardiovascular outcomes, using a Mendelian randomization approach.

Methods and Results In the primary analysis, we performed two-sample multivariable Mendelian randomization using data from participants of European ancestry. We also conducted univariable analyses using inverse-variance weighted and robust methods, and gene-specific analyses using variants that can be considered as proxies for specific lipid-lowering medications. We obtained associations with lipid fractions from the Global Lipids Genetics Consortium, a meta-analysis of 188,577 participants, and genetic associations with cardiovascular outcomes from 367,703 participants in UK Biobank.

For LDL-cholesterol, in addition to the expected positive associations with coronary artery disease (CAD) risk (odds ratio per 1 standard deviation increase [OR], 1.45; 95% confidence interval [95%CI] 1.35-1.57) and other atheromatous outcomes (ischemic cerebrovascular disease and peripheral vascular disease), we found independent associations of genetically-predicted LDL-cholesterol with abdominal aortic aneurysm (OR 1.75; 95%CI 1.40-2.17), and aortic valve stenosis (OR 1.46; 95%CI 1.25-1.70). Genetically-predicted triglyceride levels were positively associated with CAD (OR 1.25; 95%CI 1.12-1.40), aortic valve stenosis (OR 1.29; 95%CI 1.04-1.61), and hypertension (OR 1.17; 95%CI 1.07-1.27), but inversely associated with venous thromboembolism (OR 0.79; 95%CI 0.67-0.93). The positive associations of genetically-predicted LDL-cholesterol and triglycerides with heart failure and aortic stenosis appeared to be mediated by CAD.

Conclusion Lowering LDL-cholesterol is likely to prevent abdominal aortic aneurysm and aortic stenosis, in addition to CAD and other atheromatous cardiovascular outcomes. Lowering triglycerides is likely to prevent CAD and aortic valve stenosis, but may increase risk of thromboembolism.

INTRODUCTION

Evidence from randomized trials has shown that therapies that lower low-density lipoprotein (LDL)-cholesterol, such as statins, are beneficial for preventing or treating several atheromatous diseases such as coronary artery disease (CAD),1 ischaemic stroke,2 and peripheral vascular disease,3,4 as well as both postoperative atrial fibrillation5 and heart failure in the presence of underlying CAD.6,7 However, for other cardiovascular outcomes, such as thromboembolic disease,8 haemorrhagic stroke9,10 and aortic aneurysms,11,12 the effects of LDL-cholesterol lowering are less clear. Recently, the REDUCE-IT trial has shown that therapies that predominantly lower triglycerides can reduce major cardiovascular events.13 However, the effects of triglyceride lowering on non-atheromatous cardiovascular outcomes are largely unknown.

Although a randomized trial is the gold standard of evidence and is required to conclusively establish the effectiveness of a treatment, naturally-occurring genetic variants can be used to help predict the outcome of a randomized trial in an approach known as Mendelian randomization.14 For example, this approach has been used successfully to validate the effects of statins and predict the effects of PCSK9 inhibitors on CAD risk seen in randomized trials.15,16 Mendelian randomization investigations have also shown positive associations of genetically-predicted LDL-cholesterol with abdominal aortic aneurysm17, ischaemic stroke,18 and aortic stenosis,19 and positive associations of triglycerides with CAD.20

However, Mendelian randomization approaches have not yet been used to investigate the relationship between circulating lipids and some major cardiovascular conditions such as venous thromboembolism and heart failure. Additionally, approaches to estimate the independent effects of LDL-cholesterol, triglycerides, and high-density lipoprotein (HDL)-cholesterol on cardiovascular disease while accounting for genetic pleiotropy have only been performed for a limited set of outcomes.17,19,20

Our objective in this paper is to assess which cardiovascular outcomes could be treated by lipid-lowering therapies. We performed a systematic Mendelian randomization analysis of atheromatous and non-atheromatous cardiovascular disease outcomes. We considered disease outcomes in a single dataset (UK Biobank) to ensure a consistent approach to the analysis across different outcomes. We carried out polygenic analyses for each of LDL-cholesterol, HDL-cholesterol, and triglycerides, based on all common genetic variants associated with at least one of these risk factors, as well as gene-specific analyses based on variants in or near gene regions that mimic specific pharmaceutical interventions.

METHODS

Study design and data sources

We performed two-sample Mendelian randomization analyses, taking genetic associations with lipid concentrations from one dataset, and genetic associations with cardiovascular disease outcomes from an independent dataset.

We obtained genetic associations with lipid concentrations (LDL-cholesterol, HDL-cholesterol, and triglycerides) from the Global Lipids Genetics Consortium (GLGC) on up to 188,577 participants of European ancestry.21 We estimated genetic associations with disease outcomes on 367,703 unrelated participants of European ancestry from UK Biobank, a population-based cohort recruited between 2006-2010 at 22 assessment centres throughout the UK and followed-up until 31st March 2017 or their date of death (recorded until 14th February 2018).22 We defined 19 outcomes based on electronic health records (ICD-9 or ICD-10 diagnosis), hospital procedure codes, and self-reported information validated by interview with a trained nurse (Supplementary Table S1). We included CAD as a positive control outcome, and chronic kidney disease as a negative control outcome as it is thought to be influenced by some cardiovascular risk factors (such as hypertension and diabetes) but not dyslipidaemia.23

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Supplementary Table S1:

Summary of disease outcomes considered: sources of information

Statistical analysis

We carried out polygenic analyses based on 184 genetic variants previously demonstrated to be associated with at least one of LDL-cholesterol, HDL-cholesterol or triglycerides at a genome-wide level of significance (p < 5 × 10−8) in the GLGC. These variants explained 13.7% of the variance in HDL-cholesterol, 14.6% in LDL-cholesterol, and 11.7% in triglycerides in GLGC.

To obtain the associations of genetically-predicted values of these lipids with each cardiovascular outcome while accounting for measured genetic pleiotropy via other major lipids, we performed multivariable Mendelian randomization analyses.24 As sensitivity analyses, we also performed (i) univariable Mendelian randomization using the inverse-variance weighted method for each lipid fraction based on all variants associated with that lipid fraction at a genome-wide level of significance,25 (ii) MR-Egger regression to account for unmeasured pleiotropy,26 and (iii) weighted median regression to assess robustness to invalid genetic instruments.27 To account for between-variant heterogeneity, we used random-effects models in all analyses. All Mendelian randomization estimates are expressed per 1 standard deviation increase in the corresponding lipid fraction in GLGC (1 standard deviation was 39.0 mg/dL for LDL-cholesterol, 15.8 mg/dL for HDL-cholesterol, and 90.5 mg/dL for triglycerides).

For cardiovascular diseases for which it is possible that effects of lipid fractions on that outcome may act via other conditions (e.g. via CAD or type 2 diabetes), we also performed multivariable Mendelian randomization analyses adjusted for the possible mediating condition(s). To validate results for thromboembolism outcomes, we performed additional analyses for traits related to thrombosis and coagulation that have publicly-available genetic association data.28

We also performed gene-specific analyses for variants in gene regions that can be considered as proxies for existing or proposed lipid-lowering therapies. We conducted separate analyses for the HMGCR, PCSK9, LDLR, APOC3, and LPL gene regions (Supplementary Table S2). These regions were chosen as they contain variants that explain enough variance in lipids to perform adequately powered analyses.

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Supplementary Table S2:

Genetic variants included in gene-specific analyses for each region.

Further information on the study design and statistical analysis is available in the Supplementary Methods.

RESULTS

Participant characteristics

Baseline characteristics of the participants in the UK Biobank are provided in Table 1. Around 46% of participants were men, and the mean age was 57 years. Around 10% were smokers, 93% were alcohol drinkers, and 4% had a history of diabetes at baseline.

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Table 1:

Baseline characteristics of UK Biobank participants included in this study and numbers of outcome events

Polygenic analyses for all lipid-related variants

Multivariable Mendelian randomization estimates are displayed graphically in Figure 1 and summarized in Supplementary Table S3. Associations with the positive control outcome (CAD) were as expected for LDL-cholesterol and triglycerides. There were no associations with the negative control outcome (chronic kidney disease).

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Supplementary Table S3:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) from polygenic multivariable Mendelian randomization analyses for all lipid-related variants. Estimates with p < 0.05 are reported in bold.

Figure 1:
Figure 1:

Multivariable Mendelian randomization estimates (odds ratio with 95% confidence interval per 1 standard deviation increase in lipid fraction) from polygenic analyses including all lipid-associated variants

We found a strong association of genetically-predicted LDL-cholesterol with CAD risk (odds ratio per 1 standard deviation increase [OR], 1.45; 95% confidence interval [CI] 1.35-1.57), which was in the reference range of a previous study.20 We also saw positive associations for LDL-cholesterol with risk of transient ischaemic attack, peripheral vascular disease, aortic aneurysm, abdominal aortic aneurysm and aortic valve stenosis. Of note, we found a novel positive association with heart failure (OR 1.17; 95%CI 1.06-1.28). The strongest associations by magnitude were for abdominal aortic aneurysm (OR 1.75; 95%CI 1.40-2.17) and aortic valve stenosis (OR 1.46; 95% CI 1.25-1.70). We observed a positive association for the combined outcome of ischaemic stroke and transient ischaemic attack (OR 1.14; 95% CI 1.04-1.24). The association with ischaemic stroke was in the positive direction but non-significant (OR 1.10; 95% CI 0.98-1.23).

Genetically-predicted triglyceride levels were associated with increased risk of CAD, consistent with previous results,20 as well as with increased risk of hypertension, aortic valve stenosis, and heart failure. Inverse associations were observed for triglycerides with all of the thromboembolic diseases we analysed: deep vein thrombosis (OR 0.78; 95%CI 0.65-0.93), pulmonary embolism (OR 0.78; 95%CI 0.64-0.96), and any venous thromboembolism (OR 0.79; 95%CI 0.67-0.93). An inverse association was observed with haemorrhagic stroke, in particular with intracerebral haemorrhage (OR 0.65; 95%CI 0.49-0.86). Additionally, genetically-predicted triglyceride levels were associated with several traits related to thrombosis and coagulation, including red cell distribution width and tissue-type plasminogen activator, with the general direction of associations corresponding to an anti-thrombotic effect of increased triglycerides (Supplementary Table S4). Genetically-predicted HDL-cholesterol was weakly associated with coronary heart disease (OR 0.91; 95%CI 0.83-1.00), and more strongly inversely associated with abdominal aortic aneurysm, hypertension and haemorrhagic stroke, in particular intracerebral haemorrhage (OR 0.65; 95%CI 0.51-0.82).

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Supplementary Table S4:

Associations of genetically-predicted triglyceride levels with traits relating to thrombosis and coagulation.

Univariable Mendelian randomization estimates for each lipid risk factor in turn are displayed in Supplementary Figures S1-S3 and summarized in Supplementary Tables S5-S7. Similar results were generally obtained from each of the univariable methods as for the multivariable methods, although confidence intervals were slightly wider in some cases, especially for the MR-Egger method. The association between HDL-cholesterol and CAD became null in MR-Egger regression (OR 0.99; 95%CI 0.83-1.19). Similarly, the associations of triglycerides with haemorrhagic stroke and its subtypes attenuated to the null in almost all univariable analyses (Supplementary Table S7). Another notable difference was a positive association of increased genetically-predicted triglycerides with aortic aneurysm risk, particularly for abdominal aortic aneurysm. While the association with aortic aneurysm was not strongly apparent in the multivariable analysis, it was evident for each of the univariable analysis methods.

Supplementary Figure S1:
Supplementary Figure S1:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for HDL-cholesterol from genome-wide Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction.

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Supplementary Table S5:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for HDL-cholesterol from polygenic univariable Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction. Estimates with p < 0.05 are reported in bold.

Supplementary Figure S2:
Supplementary Figure S2:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for LDL-cholesterol from genome-wide Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction.

Supplementary Figure S3:
Supplementary Figure S3:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for triglycerides from genome-wide Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction.

View this table:
Supplementary Table S6:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for LDL-cholesterol from polygenic univariable Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction. Estimates with p < 0.05 are reported in bold.

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Supplementary Table S7:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) for triglycerides from polygenic univariable Mendelian randomization analyses for all lipid-related variants associated with the target lipid fraction. Estimates with p < 0.05 are reported in bold.

We also assessed whether the associations of genetically-predicted lipids with outcomes that are comorbid with CAD (e.g. heart failure) may be mediated via CAD, and similarly for outcomes that are comorbid with type 2 diabetes (e.g. hypertension). On adjustment for CAD risk (Table 2), the associations of genetically-predicted LDL-cholesterol and triglycerides with heart failure attenuated sharply, suggesting that their effects on heart failure might be mediated via CAD. As a negative control, we also performed the same analysis for abdominal aortic aneurysm. The association with genetically-predicted LDL-cholesterol attenuated slightly, but was still clearly positive. We also performed multivariable Mendelian randomization analyses excluding participants with CAD for heart failure (2383 remaining cases). This analysis gave null estimates for all lipid fractions (Supplementary Table S8). This suggests that LDL-cholesterol is unlikely to be a causal risk factor for heart failure where there is no comorbidity with CAD. On adjustment for body mass index (BMI) and type 2 diabetes (Supplementary Table S9), associations between lipids and hypertension, abdominal aortic aneurysm, and haemorrhagic stroke did not change substantially compared to unadjusted analyses, suggesting that neither BMI nor type 2 diabetes is likely to mediate the effects of lipids on these outcomes.

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Supplementary Table S8:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) from polygenic multivariable Mendelian randomization analyses for all lipid-related variants for heart failure in participants without a CAD diagnosis.

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Supplementary Table S9:

Multivariable Mendelian randomization estimates (odds ratio with 95% confidence interval) for hypertension, peripheral vascular disease and abdominal aortic aneurysm without and with adjustment for body mass index (BMI) and type 2 diabetes (T2D).

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Table 2:

Multivariable Mendelian randomization estimates (odds ratio with 95% confidence interval) for heart failure without and with adjustment for coronary artery disease (CAD). Analyses were also performed for abdominal aortic aneurysm as a negative control.

Gene-specific analyses for drug proxy variants

Mendelian randomization estimates for specific gene regions are displayed graphically in Figure 2 and summarized in Supplementary Tables S10-S11. All the gene regions analysed showed clear associations with CAD risk, confirming their involvement in cardiovascular disease aetiology and the relevance of existing and proposed lipid-lowering therapies for CAD risk reduction.

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Supplementary Table S10:

Estimates (odds ratio per 1 standard deviation increase in lipid fraction and 95% confidence interval) from gene-specific Mendelian randomization analyses for four specified gene regions. Estimates with p < 0.05 are reported in bold. Estimates are calibrated per standard deviation increase in LDL-cholesterol for HMGCR, PCSK9, and LDLR regions, and per standard deviation increase in triglycerides for APOC3 and LPL regions.

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Supplementary Table S11:

P-values from gene-specific Mendelian randomization analyses for five specified gene regions.

Figure 2:
Figure 2:

Univariable Mendelian randomization estimates (odds ratio with 95% confidence interval per 1 standard deviation increase in lipid fraction) for variants in specific gene regions. Estimates are scaled to a unit standard deviation increase in LDL-cholesterol for the HMGCR, PCSK9, and LDLR regions, and to a standard deviation increase in triglycerides for the APOC3 and LPL regions.

Results for the LDLR gene regions, and to a lesser extent for HMGCR and PCSK9, were similar to those for LDL-cholesterol in the polygenic analyses, although with wider confidence intervals. Significant positive associations were obtained for LDLR with aortic and abdominal aortic aneurysm, venous thromboembolism, aortic valve stenosis, and heart failure, and an inverse association was observed for subarachnoid haemorrhage. Variants in the HMGCR gene region were inversely associated with intracerebral haemorrhage (p=0.022). Results for APOC3 and LPL followed a pattern more similar to the analysis for triglycerides, showing inverse associations with thromboembolic diseases (p=0.007 for APOC3, p=0.089 for LPL for any venous thromboembolism). In both regions, associations with aortic aneurysm, aortic valve stenosis and hypertension were all positive. Additionally, variants in the LPL region were also positively associated with all ischaemic cerebrovascular diseases.

DISCUSSION

In this study, we assessed the causal role of three major lipid fractions for a range of cardiovascular diseases in a large population-based cohort using the principle of Mendelian randomization while accounting for genetic pleiotropy between the lipid measures. Our most notable findings were the associations between genetically-predicted triglycerides and decreased risk of thromboembolic diseases both in polygenic analyses and in gene-specific analyses for the APOC3 gene region, suggesting that reducing triglycerides may increase risk for venous thromboembolism. Additionally, we found: 1) evidence supporting the current understanding of the aetiology of CAD, suggesting independent causal roles for LDL-cholesterol and triglycerides, both in the polygenic analyses and for all the drug-related gene-specific analyses20,29 2) positive associations of genetically-predicted LDL-cholesterol with abdominal aortic aneurysm and aortic valve stenosis, as well as atheromatous cardiovascular outcomes that are already addressed in clinical guidelines (e.g. peripheral vascular disease and the combined outcome of ischaemic stroke and transient ischaemic attack); 3) positive associations of genetically-predicted LDL-cholesterol and triglycerides with heart failure that appear to be mediated by CAD; 4) associations between genetically-predicted triglycerides and increased risk of aortic stenosis and hypertension; and 5) inverse associations between genetically-predicted HDL-cholesterol and abdominal aortic aneurysm, hypertension, and haemorrhagic stroke (in particular, intracerebral haemorrhage) that appear to be independent of BMI/T2D.

The importance of these findings is threefold. Firstly, they help us to better understand the aetiology and pathophysiology of common cardiovascular outcomes, and so to identify additional potential indications for lipid-lowering therapies. Secondly, association estimates provided in this paper can inform calculations on the risk-benefit and cost-benefit of these therapies. Thirdly, these findings can aid identification of which patients would most benefit from (or should avoid) lipid-lowering therapies.

Genetic predisposition to lower triglycerides was associated with an increased risk of thromboembolic diseases. In contrast, previous observational evidence did not suggest an association between triglycerides and venous thrombosis,30,31 although other lipid measures (in particular apolipoprotein B and lipoprotein(a)) were inversely associated with venous thrombosis mortality in a meta-analysis of over 700,000 participants from the Emerging Risk Factors Collaboration.31 Our result was consistent across 3 of the 4 Mendelian randomization methods. However, associations with thromboembolic outcomes using the MR-Egger method were slightly attenuated and compatible with the null, raising the possibility of unmeasured genetic pleiotropy. Variants in the APOC3 gene region were associated with thromboembolic events, suggesting that lowering triglyceride levels via intervening on this pathway may increase thromboembolism risk. Additionally, associations between genetically-predicted triglycerides and molecular traits relating to thrombosis, coagulation and fibrinolysis were generally concordant with this finding, providing some validation of this claim. Further investigation is needed to confirm this unexpected finding.

Both the positive association between genetic predictors of LDL-cholesterol and abdominal aortic aneurysm, and the inverse association for HDL-cholesterol, replicate a previous Mendelian randomization study which did not include UK Biobank participants.17 This evidence is complemented by a recent randomized trial that has shown benefit from screening and treating abdominal aortic aneurysm using various interventions including statins,11 by observational studies that have shown positive associations between LDL-cholesterol and abdominal aneurysm,32,33 as well as an animal study demonstrating that elevated LDL-cholesterol levels led to atherogenesis, abdominal aneurysm formation, and increased mortality in a murine model.34 However, no significant association was seen with thoracic aortic aneurysm. Discrepancies between the LDL-cholesterol estimates for abdominal and thoracic aortic aneurysm (p=0.006 for difference) are in line with the different pathophysiology of the two disease subtypes.35 Overall, these findings provide further support to the hypothesis that increased LDL-cholesterol has deleterious effects on abdominal aortic aneurysm, and suggest that LDL-cholesterol lowering therapies may be beneficial in preventing abdominal aortic aneurysm.

Similarly, the positive association between genetically-predicted LDL-cholesterol and aortic valve stenosis replicates a previous Mendelian randomization study.19 We also demonstrated a positive association for triglycerides, in line with the previous study (although their result did not achieve conventional levels of statistical significance). Our association was robust to all sensitivity analyses and evidenced in gene-specific analyses for both the APOC3 and LPL loci. These findings are consistent with previous observational and pathological studies suggesting a role of atherosclerotic processes in early valve lesions36,37 whereas clinical trials showed no benefit from LDL-cholesterol lowering via statins on aortic stenosis progression.38 Taken together, this suggests that increased LDL-cholesterol and triglycerides may facilitate initiation of early lesions, but that lipid-lowering therapies may be ineffective in preventing the progression of aortic stenosis.

CAD is a well-known risk factor for heart failure,39 as myocardial ischaemic damage reduces myocardial contractility and ventricular function. Associations of heart failure with genetically-predicted LDL-cholesterol and triglycerides disappeared after adjusting for CAD. The association was also absent when omitting participants with a CAD diagnosis from analyses. In contrast, associations with abdominal aortic aneurysm attenuated only slightly after adjustment for CAD. Overall, these results suggest that lipid-lowering therapies are only likely to influence risk of heart failure via their effects on CAD.

Our analyses also identified inverse associations of genetically-predicted HDL-C and triglyceride levels with risk of intracerebral haemorrhage, consistent with a recent observational analysis for triglycerides.40 Furthermore, we identified a detrimental effect of HMGCR inhibition on intracerebral haemorrhage risk, in keeping with a large randomized trial of atorvastatin in patients with recent transient ischaemic attack or stroke, which found statin therapy to lower risk of major cardiovascular events but increase the risk of haemorrhagic stroke.41 However, this finding has not been replicated in large meta-analyses.42 The mechanism relating lipid traits with risk of intracerebral haemorrhage requires further exploration.

Our investigation has both strengths and limitations. The large sample size of over 360,000 participants and the broad set of outcomes analysed render this one of the largest and most comprehensive Mendelian randomization analyses on cardiovascular disease conducted to date. Availability of multiple cardiovascular conditions within the same study enabled cross-comparisons between diseases, making this the first Mendelian randomization investigation to systematically assess the independent effects of circulating lipids on such a broad set of cardiovascular outcomes. This also enabled us to perform adjusted analyses in the same participants, to assess mediation of causal effects by CAD and other potential mediators. This investigation has a number of limitations. Lack of publicly-available summary data from external datasets prevented us from performing replication analyses for the novel associations, e.g. those for heart failure and venous thromboembolism. The two-sample design also hinders the possibility of conducting more detailed analyses such as subgroup stratification based on lipid levels. This design still allows sensitivity analyses such as MR-Egger and the weighted median estimator which enabled us to investigate and account for the possible presence of genetic pleiotropy, and gene-specific analyses that are less likely to be influenced by pleiotropy as they only include variants from a single gene region where the function is well-known. However, while we have taken steps to mitigate against results being driven by pleiotropy as far as possible, we acknowledge that the Mendelian randomization approach is subject to untestable assumptions, and that these may be violated.

Although the principles of Mendelian randomization seek to emulate a randomized trial, the approach is fundamentally observational in nature. While there has generally been broad concordance between results from Mendelian randomization investigations and findings from randomized trials (as demonstrated by the results shown here for CAD), there are reasons why quantitative results could differ between the approaches.43 Genetic variants lead to small but long-term changes in the average levels of risk factors, whereas trials tend to be shorter in duration but assess larger changes in risk factors. Additionally, this investigation was conducted in UK-based middle- to late-aged participants of European ancestries. While this is recommended for Mendelian randomization to ensure that genetic associations are not influenced by population stratification, it means that results may not be generalizable to other ethnicities or nationalities.

In conclusion, multivariable Mendelian randomization analyses accounting for lipid-related genetic pleiotropy support the hypothesis that circulating lipids have causal effects on a wide range of cardiovascular diseases. Interventions that lower LDL-cholesterol are likely to be beneficial in preventing aortic aneurysm and aortic stenosis in addition to several other atheromatous diseases. Triglyceride lowering treatments are likely to be beneficial in preventing coronary artery disease and aortic valve stenosis, but caution is needed due to the possibility of increased risk of venous thromboembolism.

FUNDING/SUPPORT

The study’s coordinating centre has been underpinned by grants G0800270, MR/L003120/1 and MC_UU_12013/3 from the UK Medical Research Council, grants SP/09/002, RG/08/014, and RG13/13/30194 from the British Heart Foundation, grants from the National Institute for Health Research (NIHR) through the Cambridge Biomedical Research Centre, and grant HEALTH-F2-2012-279233 from the European Commission Framework 7 through the EPIC-CVD award. The NIHR Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics is supported by grant NIHR BTRU-2014-10024. Dr Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 204623/Z/16/Z). Dr Allara is supported by a NIHR BTRU PhD Studentship. Dr Gill is funded by the Wellcome 4i Clinical PhD Programme at Imperial College London. Dr Peters was funded by a UKRI Innovation Fellowship (MR/S004068/1). Aspects of the analysis were supported by the Cambridge Substantive Site of Health Data Research UK.

ROLE OF THE FUNDER/SPONSOR

The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Supplementary Online Materials

REFERENCES

  1. 1.↵
    Cholesterol Treatment Trialists C, Mihaylova B, Emberson J, Blackwell L, Keech A, Simes J, Barnes EH, Voysey M, Gray A, Collins R, Baigent C. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012;380(9841):581–90.
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    Kernan WN, Ovbiagele B, Black HR, Bravata DM, Chimowitz MI, Ezekowitz MD, Fang MC, Fisher M, Furie KL, Heck DV, Johnston SC, Kasner SE, Kittner SJ, Mitchell PH, Rich MW, Richardson D, Schwamm LH, Wilson JA, American Heart Association Stroke Council CoC, Stroke Nursing CoCC, Council on Peripheral Vascular D. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45(7):2160–236.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    Gerhard-Herman MD, Gornik HL, Barrett C, Barshes NR, Corriere MA, Drachman DE, Fleisher LA, Fowkes FG, Hamburg NM, Kinlay S, Lookstein R, Misra S, Mureebe L, Olin JW, Patel RA, Regensteiner JG, Schanzer A, Shishehbor MH, Stewart KJ, Treat-Jacobson D, Walsh ME. 2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2017;69(11):e71–e126.
    OpenUrlFREE Full Text
  4. 4.↵
    Aboyans V, Ricco JB, Bartelink MEL, Bjorck M, Brodmann M, Cohnert T, Collet JP, Czerny M, De Carlo M, Debus S, Espinola-Klein C, Kahan T, Kownator S, Mazzolai L, Naylor AR, Roffi M, Rother J, Sprynger M, Tendera M, Tepe G, Venermo M, Vlachopoulos C, Desormais I, Group ESCSD. 2017 ESC Guidelines on the Diagnosis and Treatment of Peripheral Arterial Diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: the European Stroke Organization (ESO)The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J 2018;39(9):763–816.
    OpenUrlCrossRefPubMed
  5. 5.↵
    January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC, Jr.., Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW, American College of Cardiology/American Heart Association Task Force on Practice G. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2014;64(21):e1–76.
    OpenUrlFREE Full Text
  6. 6.↵
    Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Jr.., Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, Johnson MR, Kasper EK, Levy WC, Masoudi FA, McBride PE, McMurray JJ, Mitchell JE, Peterson PN, Riegel B, Sam F, Stevenson LW, Tang WH, Tsai EJ, Wilkoff BL, American College of Cardiology F, American Heart Association Task Force on Practice G. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;62(16):e147–239.
    OpenUrlFREE Full Text
  7. 7.↵
    Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, Falk V, Gonzalez-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GM, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P, Authors/Task Force M, Document R. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2016;18(8):891–975.
    OpenUrlCrossRefPubMed
  8. 8.↵
    Kunutsor SK, Seidu S, Khunti K. Statins and primary prevention of venous thromboembolism: a systematic review and meta-analysis. Lancet Haematol 2017;4(2):e83–e93.
    OpenUrl
  9. 9.↵
    Van Matre ET, Sherman DS, Kiser TH. Management of intracerebral hemorrhageuse of statins. Vasc Health Risk Manag 2016;12:153–61.
    OpenUrl
  10. 10.↵
    Ma Y, Li Z, Chen L, Li X. Blood lipid levels, statin therapy and the risk of intracerebral hemorrhage. Lipids Health Dis 2016;15:43.
    OpenUrl
  11. 11.↵
    Lindholt JS, Sogaard R. Population screening and intervention for vascular disease in Danish men (VIVA): a randomised controlled trial. Lancet 2017;390(10109):2256–2265.
    OpenUrl
  12. 12.↵
    Stein LH, Berger J, Tranquilli M, Elefteraides JA. Effect of statin drugs on thoracic aortic aneurysms. Am J Cardiol 2013;112(8):1240–5.
    OpenUrlCrossRefPubMed
  13. 13.↵
    Bhatt DL, Steg PG, Miller M, Brinton EA, Jacobson TA, Ketchum SB, Doyle RT, Jr.., Juliano RA, Jiao L, Granowitz C, Tardif JC, Ballantyne CM, Investigators R-I. Cardiovascular Risk Reduction with Icosapent Ethyl for Hypertriglyceridemia. N Engl J Med 2018.
  14. 14.↵
    Smith GD, Ebrahim S. ’Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003;32(1):1–22.
    OpenUrlCrossRefPubMedWeb of Science
  15. 15.↵
    Ference BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, Voros S, Giugliano RP, Davey Smith G, Fazio S, Sabatine MS. Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes. N Engl J Med 2016;375(22):2144–2153.
    OpenUrlCrossRefPubMed
  16. 16.↵
    Cohen JC, Boerwinkle E, Mosley TH, Jr.., Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354(12):1264–72.
    OpenUrlCrossRefPubMedWeb of Science
  17. 17.↵
    Harrison SC, Holmes MV, Burgess S, Asselbergs FW, Jones GT, Baas AF, van ’t Hof FN, de Bakker PIW, Blankensteijn JD, Powell JT, Saratzis A, de Borst GJ, Swerdlow DI, van der Graaf Y, van Rij AM, Carey DJ, Elmore JR, Tromp G, Kuivaniemi H, Sayers RD, Samani NJ, Bown MJ, Humphries SE. Genetic Association of Lipids and Lipid Drug Targets With Abdominal Aortic Aneurysm: A Meta-analysis. JAMA Cardiol 2018;3(1):26–33.
    OpenUrl
  18. 18.↵
    Hindy G, Engstrom G, Larsson SC, Traylor M, Markus HS, Melander O, Orho-Melander M, Stroke Genetics N. Role of Blood Lipids in the Development of Ischemic Stroke and its Subtypes: A Mendelian Randomization Study. Stroke 2018;49(4):820–827.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    Smith JG, Luk K, Schulz CA, 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 H, Aging Research in Genetic Epidemiology Extracoronary Calcium Working G. Association of low-density lipoprotein cholesterol-related genetic variants with aortic valve calcium and incident aortic stenosis. JAMA 2014;312(17):1764–71.
    OpenUrlCrossRefPubMed
  20. 20.↵
    Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, Dale CE, Padmanabhan S, Finan C, Swerdlow DI, Tragante V, van Iperen EP, Sivapalaratnam S, Shah S, Elbers CC, Shah T, Engmann J, Giambartolomei C, White J, Zabaneh D, Sofat R, McLachlan S, consortium U, Doevendans PA, Balmforth AJ, Hall AS, North KE, Almoguera B, Hoogeveen RC, Cushman M, Fornage M, Patel SR, Redline S, Siscovick DS, Tsai MY, Karczewski KJ, Hofker MH, Verschuren WM, Bots ML, van der Schouw YT, Melander O, Dominiczak AF, Morris R, Ben-Shlomo Y, Price J, Kumari M, Baumert J, Peters A, Thorand B, Koenig W, Gaunt TR, Humphries SE, Clarke R, Watkins H, Farrall M, Wilson JG, Rich SS, de Bakker PI, Lange LA, Davey Smith G, Reiner AP, Talmud PJ, Kivimaki M, Lawlor DA, Dudbridge F, Samani NJ, Keating BJ, Hingorani AD, Casas JP. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J 2015;36(9):539–50.
    OpenUrlCrossRefPubMed
  21. 21.↵
    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 HY, 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, Heikkila K, Hypponen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikainen LP, Magnusson PKE, Mangino M, Mihailov E, Montasser ME, Muller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen AK, 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, Doring A, Elliott P, Epstein SE, Ingi Eyjolfsson G, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJP, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimaki T, Lin SY, Lindstrom J, Loos RJF, Mach F, McArdle WL, Meisinger C, Mitchell BD, Muller 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, Stancakova 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 YI, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrieres 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, Jarvelin MR, Jula A, Kahonen M, Kaprio J, Kesaniemi 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, Marz W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njolstad 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, 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 C. Discovery and refinement of loci associated with lipid levels. Nat Genet 2013;45(11):1274–1283.
    OpenUrlCrossRefPubMed
  22. 22.↵
    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(3):e1001779.
    OpenUrlCrossRefPubMed
  23. 23.↵
    Webster AC, Nagler EV, Morton RL, Masson P. Chronic Kidney Disease. Lancet 2017;389(10075):1238–1252.
    OpenUrlCrossRefPubMed
  24. 24.↵
    Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 2015;181(4):251–60.
    OpenUrlCrossRefPubMed
  25. 25.↵
    Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013;37(7):658–65.
    OpenUrlCrossRefPubMed
  26. 26.↵
    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(2):512–25.
    OpenUrlCrossRefPubMed
  27. 27.↵
    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(4):304–14.
    OpenUrlCrossRefPubMed
  28. 28.↵
    Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, Paul DS, Freitag D, Burgess S, Danesh J, Young R, Butterworth AS. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 2016;32(20):3207–3209.
    OpenUrlCrossRefPubMed
  29. 29.↵
    Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, Hindy G, Holm H, Ding EL, Johnson T, Schunkert H, Samani NJ, Clarke R, Hopewell JC, Thompson JF, Li M, Thorleifsson G, Newton-Cheh C, Musunuru K, Pirruccello JP, Saleheen D, Chen L, Stewart A, Schillert A, Thorsteinsdottir U, Thorgeirsson G, Anand S, Engert JC, Morgan T, Spertus J, Stoll M, Berger K, Martinelli N, Girelli D, McKeown PP, Patterson CC, Epstein SE, Devaney J, Burnett MS, Mooser V, Ripatti S, Surakka I, Nieminen MS, Sinisalo J, Lokki ML, Perola M, Havulinna A, de Faire U, Gigante B, Ingelsson E, Zeller T, Wild P, de Bakker PI, Klungel OH, Maitland-van der Zee AH, Peters BJ, de Boer A, Grobbee DE, Kamphuisen PW, Deneer VH, Elbers CC, Onland-Moret NC, Hofker MH, Wijmenga C, Verschuren WM, Boer JM, van der Schouw YT, Rasheed A, Frossard P, Demissie S, Willer C, Do R, Ordovas JM, Abecasis GR, Boehnke M, Mohlke KL, Daly MJ, Guiducci C, Burtt NP, Surti A, Gonzalez E, Purcell S, Gabriel S, Marrugat J, Peden J, Erdmann J, Diemert P, Willenborg C, Konig IR, Fischer M, Hengstenberg C, Ziegler A, Buysschaert I, Lambrechts D, Van de Werf F, Fox KA, El Mokhtari NE, Rubin D, Schrezenmeir J, Schreiber S, Schafer A, Danesh J, Blankenberg S, Roberts R, McPherson R, Watkins H, Hall AS, Overvad K, Rimm E, Boerwinkle E, Tybjaerg-Hansen A, Cupples LA, Reilly MP, Melander O, Mannucci PM, Ardissino D, Siscovick D, Elosua R, Stefansson K, O’Donnell CJ, Salomaa V, Rader DJ, Peltonen L, Schwartz SM, Altshuler D, Kathiresan S. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 2012;380(9841):572–80.
    OpenUrlCrossRefPubMedWeb of Science
  30. 30.↵
    Morelli VM, Lijfering WM, Bos MHA, Rosendaal FR, Cannegieter SC. Lipid levels and risk of venous thrombosis: results from the MEGA-study. Eur J Epidemiol 2017;32(8):669–681.
    OpenUrl
  31. 31.↵
    Gregson J, Kaptoge S, Bolton T, Pennells L, Willeit P, Burgess S, Bell S, Sweeting M, Rimm EB, Kabrhel C, Zoller B, Assmann G, Gudnason V, Folsom AR, Arndt V, Fletcher A, Norman PE, Nordestgaard BG, Kitamura A, Mahmoodi BK, Whincup PH, Knuiman M, Salomaa V, Meisinger C, Koenig W, Kavousi M, Volzke H, Cooper JA, Ninomiya T, Casiglia E, Rodriguez B, Ben-Shlomo Y, Despres JP, Simons L, Barrett-Connor E, Bjorkelund C, Notdurfter M, Kromhout D, Price J, Sutherland SE, Sundstrom J, Kauhanen J, Gallacher J, Beulens JWJ, Dankner R, Cooper C, Giampaoli S, Deen JF, Gomez de la Camara A, Kuller LH, Rosengren A, Svensson PJ, Nagel D, Crespo CJ, Brenner H, Albertorio-Diaz JR, Atkins R, Brunner EJ, Shipley M, Njolstad I, Lawlor DA, van der Schouw YT, Selmer RM, Trevisan M, Verschuren WMM, Greenland P, Wassertheil-Smoller S, Lowe GDO, Wood AM, Butterworth AS, Thompson SG, Danesh J, Di Angelantonio E, Meade T, Emerging Risk Factors C. Cardiovascular Risk Factors Associated With Venous Thromboembolism. JAMA Cardiol 2019;4(2):163–173.
    OpenUrl
  32. 32.↵
    Rizzo M, Krayenbuhl PA, Pernice V, Frasheri A, Battista Rini G, Berneis K. LDL size and subclasses in patients with abdominal aortic aneurysm. Int J Cardiol 2009;134(3):406–8.
    OpenUrlCrossRefPubMed
  33. 33.↵
    Hobbs SD, Claridge MW, Quick CR, Day NE, Bradbury AW, Wilmink AB. LDL cholesterol is associated with small abdominal aortic aneurysms. Eur J Vasc Endovasc Surg 2003;26(6):618–22.
    OpenUrlCrossRefPubMedWeb of Science
  34. 34.↵
    Tanaka H, Iida Y, Iwaki T, Suzuki Y, Sano H, Miyajima C, Zaima N, Sasaki T, Sumioka A, Hakamata S, Shimizu H, Umemura K, Urano T. Elevated Plasma Levels of LDL Cholesterol Promote Dissecting Thoracic Aortic Aneurysms in Angiotensin II-Induced Mice. Ann Vasc Surg 2018;48:204–213.
    OpenUrl
  35. 35.↵
    Isselbacher EM. Thoracic and abdominal aortic aneurysms. Circulation 2005;111(6):816–28.
    OpenUrlFREE Full Text
  36. 36.↵
    Paradis JM, Fried J, Nazif T, Kirtane A, Harjai K, Khalique O, Grubb K, George I, Hahn R, Williams M, Leon MB, Kodali S. Aortic stenosis and coronary artery disease: what do we know? What don’t we know? A comprehensive review of the literature with proposed treatment algorithms. Eur Heart J 2014;35(31):2069–82.
    OpenUrlCrossRefPubMed
  37. 37.↵
    Pibarot P, Dumesnil JG. Improving assessment of aortic stenosis. J Am Coll Cardiol 2012;60(3):169–80.
    OpenUrlFREE Full Text
  38. 38.↵
    Zhao Y, Nicoll R, He YH, Henein MY. The effect of statins on valve function and calcification in aortic stenosis: A meta-analysis. Atherosclerosis 2016;246:318–24.
    OpenUrlCrossRefPubMed
  39. 39.↵
    Kemp CD, Conte JV. The pathophysiology of heart failure. Cardiovasc Pathol 2012;21(5):365–71.
    OpenUrlCrossRefPubMed
  40. 40.↵
    Sun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, Parish S, Millwood IY, Bian Z, Chen Y, Yu C, Lv J, Collins R, Chen J, Peto R, Li L, Chen Z, China Kadoorie Biobank Collaborative G, International Steering C, International Co-ordinating Centre O, National Coordinating Centre B, Regional Co-ordinating C. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nat Med 2019;25(4):569–574.
    OpenUrl
  41. 41.↵
    Amarenco P, Bogousslavsky J, Callahan A, 3rd, Goldstein LB, Hennerici M, Rudolph AE, Sillesen H, Simunovic L, Szarek M, Welch KM, Zivin JA, Stroke Prevention by Aggressive Reduction in Cholesterol Levels I. High-dose atorvastatin after stroke or transient ischemic attack. N Engl J Med 2006;355(6):549–59.
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.↵
    Ziff OJ, Banerjee G, Ambler G, Werring DJ. Statins and the risk of intracerebral haemorrhage in patients with stroke: systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2019;90(1):75–83.
    OpenUrlAbstract/FREE Full Text
  43. 43.↵
    Burgess S, Butterworth A, Malarstig A, Thompson SG. Use of Mendelian randomisation to assess potential benefit of clinical intervention. BMJ 2012;345:e7325.
    OpenUrlFREE Full Text
  44. 44.↵
    Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 2016;167(5):1415-1429. e19.
    OpenUrlCrossRef
  45. 45.↵
    Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007;39(7):906–13.
    OpenUrlCrossRefPubMedWeb of Science
  46. 46.
    Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, Oliver-Williams C, Kamat MA, Prins BP, Wilcox SK, Zimmerman ES, Chi A, Bansal N, Spain SL, Wood AM, Morrell NW, Bradley JR, Janjic N, Roberts DJ, Ouwehand WH, Todd JA, Soranzo N, Suhre K, Paul DS, Fox CS, Plenge RM, Danesh J, Runz H, Butterworth AS. Genomic atlas of the human plasma proteome. Nature 2018;558(7708):73–79.
    OpenUrlCrossRefPubMed
  47. 47.
    Lapchak PA, Chapman DF, Zivin JA. Metalloproteinase inhibition reduces thrombolytic (tissue plasminogen activator)-induced hemorrhage after thromboembolic stroke. Stroke 2000;31(12):3034–40.
    OpenUrlAbstract/FREE Full Text
  48. 48.
    Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA, Lambourne JJ, Sivapalaratnam S, Downes K, Kundu K, Bomba L, Berentsen K, Bradley JR, Daugherty LC, Delaneau O, Freson K, Garner SF, Grassi L, Guerrero J, Haimel M, Janssen-Megens EM, Kaan A, Kamat M, Kim B, Mandoli A, Marchini J, Martens JHA, Meacham S, Megy K, O’Connell J, Petersen R, Sharifi N, Sheard SM, Staley JR, Tuna S, van der Ent M, Walter K, Wang SY, Wheeler E, Wilder SP, Iotchkova V, Moore C, Sambrook J, Stunnenberg HG, Di Angelantonio E, Kaptoge S, Kuijpers TW, Carrillo-de-Santa-Pau E, Juan D, Rico D, Valencia A, Chen L, Ge B, Vasquez L, Kwan T, Garrido-Martin D, Watt S, Yang Y, Guigo R, Beck S, Paul DS, Pastinen T, Bujold D, Bourque G, Frontini M, Danesh J, Roberts DJ, Ouwehand WH, Butterworth AS, Soranzo N. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 2016;167(5):1415–1429 e19.
    OpenUrlCrossRef
  49. 49.
    Zoller B, Melander O, Svensson P, Engstrom G. Red cell distribution width and risk for venous thromboembolism: a population-based cohort study. Thromb Res 2014;133(3):334–9.
    OpenUrlCrossRefPubMed
  50. 50.
    Bucciarelli P, Maino A, Felicetta I, Abbattista M, Passamonti SM, Artoni A, Martinelli I. Association between red cell distribution width and risk of venous thromboembolism. Thromb Res 2015;136(3):590–4.
    OpenUrl
  51. 51.
    Deatrick KB, Obi A, Luke CE, Elfline MA, Sood V, Upchurch GR, Jr.., Jaffer F, Wakefield TW, Henke PK. Matrix metalloproteinase-9 deletion is associated with decreased mid-term vein wall fibrosis in experimental stasis DVT. Thromb Res 2013;132(3):360–6.
    OpenUrlCrossRefPubMed
  52. 52.
    Pepper MS. Role of the matrix metalloproteinase and plasminogen activator-plasmin systems in angiogenesis. Arterioscler Thromb Vasc Biol 2001;21(7):1104–17.
    OpenUrlAbstract/FREE Full Text
  53. 53.
    Wang J, Wang C, Chen N, Shu C, Guo X, He Y, Zhou Y. Association between the plasminogen activator inhibitor-1 4G/5G polymorphism and risk of venous thromboembolism: a meta-analysis. Thromb Res 2014;134(6):1241–8.
    OpenUrlCrossRefPubMed
  54. 54.
    Stavrou EX, Fang C, Merkulova A, Alhalabi O, Grobe N, Antoniak S, Mackman N, Schmaier AH. Reduced thrombosis in Klkb1-/- mice is mediated by increased Mas receptor, prostacyclin, Sirt1, and KLF4 and decreased tissue factor. Blood 2015;125(4):710–9.
    OpenUrlAbstract/FREE Full Text
  55. 55.
    Folkersen L, Fauman E, Sabater-Lleal M, Strawbridge RJ, Franberg M, Sennblad B, Baldassarre D, Veglia F, Humphries SE, Rauramaa R, de Faire U, Smit AJ, Giral P, Kurl S, Mannarino E, Enroth S, Johansson A, Enroth SB, Gustafsson S, Lind L, Lindgren C, Morris AP, Giedraitis V, Silveira A, Franco-Cereceda A, Tremoli E, group Is Gyllensten U, Ingelsson E, Brunak S, Eriksson P, Ziemek D, Hamsten A, Malarstig A. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet 2017;13(4):e1006706.
    OpenUrlCrossRefPubMed
  56. 56.
    Weimar W, Stibbe J, van Seyen AJ, Billiau A, De Somer P, Collen D. Specific lysis of an iliofemoral thrombus by administration of extrinsic (tissue-type) plasminogen activator. Lancet 1981;2(8254):1018–20.
    OpenUrlCrossRefPubMedWeb of Science
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Posted June 14, 2019.
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Genetic determinants of lipids and cardiovascular disease outcomes: a wide-angled Mendelian randomization investigation
Elias Allara, Gabriele Morani, Paul Carter, Apostolos Gkatzionis, Verena Zuber, Christopher N Foley, Jessica MB Rees, Amy M Mason, Steven Bell, Dipender Gill, Adam S Butterworth, Emanuele Di Angelantonio, James Peters, Stephen Burgess
bioRxiv 668970; doi: https://doi.org/10.1101/668970
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