Mendelian randomization for cardiovascular diseases: principles and applications

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Abstract

Large-scale genome-wide association studies conducted over the last decade have uncovered numerous genetic variants associated with cardiometabolic traits and risk factors. These discoveries have enabled the Mendelian randomization (MR) design, which uses genetic variation as a natural experiment to improve causal inferences from observational data. By analogy with the random assignment of treatment in randomized controlled trials, the random segregation of genetic alleles when DNA is transmitted from parents to offspring at gamete formation is expected to reduce confounding in genetic associations. Mendelian randomization analyses make a set of assumptions that must hold for valid results. Provided that the assumptions are well justified for the genetic variants that are employed as instrumental variables, MR studies can inform on whether a putative risk factor likely has a causal effect on the disease or not. Mendelian randomization has been increasingly applied over recent years to predict the efficacy and safety of existing and novel drugs targeting cardiovascular risk factors and to explore the repurposing potential of available drugs. This review article describes the principles of the MR design and some applications in cardiovascular epidemiology.

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Mendelian randomization findings on major cardiometabolic and lifestyle factors and common cardiovascular diseases.

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Theo Vos, Stephen S Lim, Cristiana Abbafati … (2023)

Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

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Author and article information

Contributors

Susanna C Larsson:

ORCID: https://orcid.org/0000-0003-0118-0341

Adam S Butterworth:

ORCID: https://orcid.org/0000-0002-6915-9015

Stephen Burgess:

ORCID: https://orcid.org/0000-0001-5365-8760

Journal

Journal ID (nlm-ta): Eur Heart J

Journal ID (iso-abbrev): Eur Heart J

Journal ID (publisher-id): eurheartj

Title: European Heart Journal

Publisher: Oxford University Press (US )

ISSN (Print): 0195-668X

ISSN (Electronic): 1522-9645

Publication date Collection: 14 December 2023

Publication date (Electronic): 03 November 2023

Publication date PMC-release: 03 November 2023

Volume: 44

Issue: 47 , Focus Issue on Epidemiology, Prevention, and Health Care Policies

Pages: 4913-4924

Affiliations

Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University , Uppsala, Sweden

Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet , Stockholm, Sweden

British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge, UK

Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge , Papworth Road, Cambridge, UK

British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital, University of Cambridge , Cambridge, UK

Health Data Research UK, Wellcome Genome Campus and University of Cambridge , Hinxton, UK

NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge , Cambridge, UK

British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge, UK

Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge , Papworth Road, Cambridge, UK

MRC Biostatistics Unit, University of Cambridge , Cambridge, UK

Author notes

Corresponding author. Tel: +44 1223 748651, Fax: +44 1223 748658, Email: sb452@ 123456medschl.cam.ac.uk

Author information

Susanna C Larsson https://orcid.org/0000-0003-0118-0341

Adam S Butterworth https://orcid.org/0000-0002-6915-9015

Stephen Burgess https://orcid.org/0000-0001-5365-8760

Article

Publisher ID: ehad736

DOI: 10.1093/eurheartj/ehad736

PMC ID: 10719501

PubMed ID: 37935836

SO-VID: 66ba51ee-86a0-4541-8729-52fd8cb425d9

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 01 August 2023

Date revision received : 13 September 2023

Date accepted : 17 October 2023

Page count

Pages: 12

Funding

Funded by: Swedish Heart-Lung Foundation, DOI 10.13039/501100003793;

Funded by: Hjärt-Lungfonden, DOI 10.13039/501100003793;

Award ID: 20190247

Funded by: Swedish Research Council, DOI 10.13039/501100004359;

Funded by: Vetenskapsrådet, DOI 10.13039/501100004359;

Award ID: 2019-00977

Funded by: Swedish Cancer Society, DOI 10.13039/501100002794;

Funded by: Wellcome Trust, DOI 10.13039/100010269;

Award ID: 225790/Z/22/Z

Funded by: United Kingdom Research and Innovation Medical Research Council, DOI 10.13039/100014013;

Award ID: MC_UU_00002/7

Funded by: National Institute for Health Research Cambridge Biomedical Research Centre, DOI 10.13039/501100000272;

Award ID: NIHR203312

Funded by: NIHR Blood and Transplant Research Unit, DOI 10.13039/100009033;

Award ID: NIHR BTRU-2014-10024

Award ID: NIHR203337

Funded by: UK Medical Research Council, DOI 10.13039/501100000690;

Award ID: MR/L003120/1

Funded by: British Heart Foundation, DOI 10.13039/501100000274;

Award ID: SP/09/002

Award ID: RG/13/13/30194

Award ID: RG/18/13/33946

Funded by: NIHR Cambridge BRC, DOI 10.13039/501100018956;

Award ID: BRC-1215-20014

Award ID: NIHR203312

Funded by: EC-Innovative Medicines Initiative;

Funded by: Health Data Research UK, DOI 10.13039/501100023699;

Funded by: UK Medical Research Council, Engineering and Physical Sciences Research Council;

Funded by: Economic and Social Research Council, DOI 10.13039/501100000269;

Funded by: Department of Health and Social Care, DOI 10.13039/501100000276;

Funded by: Chief Scientist Office of the Scottish Government Health and Social Care Directorates;

Funded by: Health and Social Care Research and Development Division, DOI 10.13039/501100010756;

Funded by: Public Health Agency, DOI 10.13039/501100001626;

Funded by: British Heart Foundation and Wellcome;

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Mendelian randomization for cardiovascular diseases: principles and applications

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Cardiovascular Innovations and Applications

Most cited references 121

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

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