Causal Inference with Mendelian Randomization to Explore Risk Factors of Diabetes

Abstract

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.