Causal Inference with Mendelian Randomization to Explore Risk Factors of Diabetes


  • Amulya Agrawal Jefferson City High School



Mendelian Randomization, MRBase, R Programming


Abstract—Since the dawn of time, health conditions have dictated life around the world. Gradually, through the advancement of medicine and technology, more and more treatments have been discovered to combat these conditions. One of these conditions is a disease known as diabetes. Even with a plethora of treatments being utilized by individuals internationally, diabetes continues to be the one of the leading causes of death worldwide [1]. Caused by a deficiency of insulin, a hormone created and released by the pancreas, diabetes renders individuals unable to effectively utilize glucose. With low amounts of insulin, cells cannot allow glucose to enter them and be used as energy for the body, leaving high amounts of glucose to build up in the bloodstream. Several factors have been suggested as a link to causing this insulin deficiency, resulting in diabetes. However, it is important to remember that there are two types of diabetes present, Type 1 and Type 2 diabetes. This report uses Mendelian Randomization to analyze contributing factors of Type 1 and 2 diabetes and explain the roles of confounds and genetics in the pathogenesis of the disease.


Download data is not yet available.

References or Bibliography


“The top 10 causes of death,” World Health Organization, 2018.

M. B. Nasr, S. Tezza, F. D’Addio, C. Mameli, V. Usuelli, A. Maestroni, D. Corradi, S. Belletti, L. Albarello, G. Becchi, G. P. Fadini, C. Schuetz, J. Markmann, C. Wasserfall, L. Zon, G. V. Zuccotti, and P. Fiorina, “PD-l1 genetic overexpression or pharmacological restoration in hematopoietic stem and progenitor cells reverses autoimmune diabetes,” Science Translational Medicine, vol. 9, p. eaam7543, Nov. 2017.

“About diabetes,” World Health Organization, 2014.

M. Monaghan, V. Helgeson, and D. Wiebe, “Type 1 diabetes in young adulthood,” Current Diabetes Reviews, vol. 11, pp. 239–250, July 2015.

S. Chatterjee, K. Khunti, and M. J. Davies, “Type 2 diabetes,” The Lancet, vol. 389, pp. 2239–2251, June 2017.

A. Bianco, F. Pomara, E. Thomas, A. Paoli, G. Battaglia, M. Petrucci, P. Proia, M. Bellafiore, and A. Palma, “Type 2 diabetes family histories, body composition and fasting glucose levels: a cross-section analysis in healthy sedentary male and female,” Iran. J. Public Health, vol. 42, no. 7, pp. 681–690, 2013.

D. Dayal, “Understanding diabetes from a new perspective: The role of free radicals,” Free Radicals in Biology and Medicine, vol. 77, 2005.

M. V. Holmes, M. Ala-Korpela, and G. D. Smith, “Mendelian randomization in cardiometabolic disease: challenges in evaluating causality,” Nature Reviews Cardiology, vol. 14, pp. 577–590, June 2017.

L. J. Corbin, R. C. Richmond, K. H. Wade, S. Burgess, J. Bowden, G. D. Smith, and N. J. Timpson, “BMI as a modifiable risk factor for type 2 diabetes: Refining and understanding causal estimates using mendelian randomization,” Diabetes, vol. 65, pp. 3002–3007, July 2016.

C. A. Emdin, S. G. Anderson, M. Woodward, and K. Rahimi, “Usual blood pressure and risk of new-onset diabetes,” Journal of the American College of Cardiology, vol. 66, pp. 1552–1562, Oct. 2015.

X. Tao Xie, Q. Liu, J. Wu, and M. Wakui, “Impact of cigarette smoking in type 2 diabetes development,” Acta Pharmacologica Sinica, vol. 30, pp. 784–787, May 2009.

P. Suter, R. Maire, D. Holtz, and W. Vetter, “Relationship between self-perceived stress and blood pressure,” Journal of Human Hypertension, vol. 11, pp. 171–176, Mar. 1997.

H. A. Lee, W. K. Lee, K.-A. Kong, N. Chang, E.-H. Ha, Y. S. Hong, and H. Park, “The effect of eating behavior on being overweight or obese during preadolescence,” Journal of Preventive Medicine and Public Health, vol. 44, pp. 226–233, Sept. 2011.

C. C. Hsu, C.-H. Lee, M. L. Wahlqvist, H.-L. Huang, H.-Y. Chang, L. Chen, S.-F. Shih, S.-J. Shin, W.-C. Tsai, T. Chen, C.-T. Huang, and J.-S. Cheng, “Poverty increases type 2 diabetes incidence and inequality of care despite universal health coverage,” Diabetes Care, vol. 35, pp. 2286–2292, Aug. 2012.



How to Cite

Agrawal, A. (2020). Causal Inference with Mendelian Randomization to Explore Risk Factors of Diabetes. Journal of Student Research, 9(2).



HS Research Articles