Research Projects
Unraveling the Causes of Cardiovascular Disease and Type 2 Diabetes Using Nature’s Randomised Trial


Programme(s) to which this project applies:
☒ MPhil/PhD ☑ MRes[Med] ☒ URIS

Objective and Significance:

Cardiovascular diseases and type 2 diabetes are important contributors to global burden of diseases.1 However, the causes of these diseases are not clearly understood.2 The use of observational studies to identify causes are prone to biases and confounding, given the contradictory findings from randomized controlled trials on topics such as hormone replacement therapy and vitamins.3,4 Without credible sources to form the evidence base, it would be difficult for policy makers and clinicians alike to make informative judgement to improve health outcomes. Mendelian randomization is a design which makes use of genetics to infer causal relation.5 Given genetic makeup is randomly allocated at conception, this makes the design less susceptible to confounding. The findings from this design is also consistent with trial results, such as vitamins and blood pressure. Better use of this design will undoubtedly improve our understanding of disease causes, and hence identify potential targets of interventions. The objective of this project is to use Mendelian randomization to examine the impact of a potential risk factor on coronary artery disease and type 2 diabetes, using summary statistics from relevant genome wide association studies (GWAS).

Research Plan and Methodology:

This will be a 2 sample Mendelian randomization study.6 Genetic instruments will be extracted from the largest GWAS on relevant exposures of interest, and applied to the GWAS on coronary artery disease (CAD) and type 2 diabetes.7,8 F statistics of each instrument will be approximated to assess potential weak instrument bias.9 The causal estimate from each genetic instrument will be calculated based on the Wald ratio and then meta-analyzed, using inverse variance weighting (IVW), to obtain the causal effect of exposures on health outcomes. However, IVW relies on no overall horizonal pleiotropy, which is difficult to test. To assess the robustness of the results, sensitivity analyses will be conducted, which rely on different assumptions.10 Consistencies in the results across different sensitivity analyses would strengthen the certainty of evidence. These include MR-Egger,11 weighted median,12 MR-PRESSO,13 and exclusion of pleiotropic instruments (based on PhenoScanner14). Analysis will be done using R Version 3.5.2 (R Development Core Team, Vienna, Austria) using R packages (“TwoSampleMR”),15 and (“MRPRESSO”).13

This study will only use published or publicly-available data. No original data will be collected for the MR study. Ethical approval for each of the studies included in the investigation can be found in the original publications (including informed consent from each participant).

References

  1. Disease, G. B. D., Injury, I. & Prevalence, C. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1545-1602, doi:10.1016/S0140-6736(16)31678-6 (2016).
  2. Ezzati, M. et al. Contributions of risk factors and medical care to cardiovascular mortality trends. Nat Rev Cardiol 12, 508-530, doi:10.1038/nrcardio.2015.82 (2015).
  3. Lawlor, D. A., Davey Smith, G., Kundu, D., Bruckdorfer, K. R. & Ebrahim, S. Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence? Lancet 363, 1724-1727, doi:10.1016/S0140-6736(04)16260-0 (2004).
  4. Lawlor, D. A., Davey Smith, G. & Ebrahim, S. Commentary: the hormone replacement-coronary heart disease conundrum: is this the death of observational epidemiology? International Journal of Epidemiology 33, 464-467, doi:10.1093/ije/dyh124 (2004).
  5. Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362, k601, doi:10.1136/bmj.k601 (2018). 
  6. Lawlor, D. A. Commentary: Two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol 45, 908-915, doi:10.1093/ije/dyw127 (2016).
  7. Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association metaanalysis of coronary artery disease. Nat Genet 47, 1121-1130, doi:10.1038/ng.3396 (2015). 
  8. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using highdensity imputation and islet-specific epigenome maps. Nat Genet 50, 1505-1513, doi:10.1038/s41588-018-0241-6 (2018).
  9. Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol, doi:10.1093/ije/dyw220 (2016). 
  10. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36, 1783-1802, doi:10.1002/sim.7221 (2017). 
  11. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44, 512-525, doi:10.1093/ije/dyv080 (2015).
  12. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40, 304-314, doi:10.1002/gepi.21965 (2016).
  13. 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 50, 693-698, doi:10.1038/s41588-018-0099-7 (2018).
  14. Staley, J. R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207-3209, doi:10.1093/bioinformatics/btw373 (2016).
  15. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, doi:10.7554/eLife.34408 (2018).

Dr RSL Au Yeung, School of Public Health

Biography
ayslryan@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

  1. your CV,
  2. a brief description of your research interest and experience, and
  3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information. 

Research @ School of Public Health