Abstract
Many laboratory scientists are not trained in epidemiology and are lacking the ability to interpret the relationship of the observed results from the laboratory bench to the outcomes. For example, cytochrome p450 27B1 (CYP27B1) is an enzyme that converts 25-hydroxyvitamin D (Calcifediol) to 1,25-dihydroxyvitamin D (calcitriol), the bioactive hormonal form of Vitamin D in the kidney. Several studies reported that vitamin D insufficiency may facilitate development of cancers. This simplistic way of thinking asserts that lack of vitamin D will cause cancer. However, lipopolysaccharide (LPS) or Toll-like receptor2 increased the expression of CYP27B1. We know that precedent infections, obesity, or both would increase LPS and TLR2. We also know that the cause must occur before the outcome. Therefore, what happened earlier (infection and/or obesity) would be the real cause and the low level of vitamin D may be a marker for low immune responses. Unless we compare vitamin D, infection/obesity side by side in the same statistical models, we will never identify the real cause. This example clearly suggests that to be able to establish a causal relationship correctly, the bench scientists involved in translational research need to learn the basic epidemiologic principles. Otherwise, their conclusion might be incorrect or biased. In this chapter, we introduce the basic epidemiologic concepts and techniques needed to assess and infer causal relationships in translational research.