Our long-term goal is to identify medications, or combinations of medications, that may affect incidence of amyotrophic lateral sclerosis (ALS) or alter the progression of ALS. In vitro efforts to exploring single compounds for efficacy in ALS therapeutics based on proposed biological mechanisms for the disease is a valuable and warranted approach, but it is inherently slow because of the need to test compounds one at a time. It also does not explore effects of combinations and it cannot test the role of these compounds in ALS incidence. Given the wide array of medications that older adults take, and the possibility that different combinations of medications may be relevant, we propose that a valuable parallel approach would be an epidemiological screening process to test whether any currently used medications are related to ALS incidence or survival. This would be akin to in vitro high throughput screening, but using novel statistical approaches to explore high dimensional ?big? epidemiological data (many people, many medications) for associations with ALS and ALS survival: specifically, boolean logic regression and random forests in a nested case-control and survival analysis framework. These approaches allow for efficiently exploring high-dimensional, likely correlated, data for associations between individual medications and different combinations of medications and an outcome, here ALS. In order to accomplish this, we propose to use two parallel very large data sets with prospectively and objectively collected pharmaceutical and health data: The Danish Registry System and the Clalit Health System in Israel, with a total of approximately 4,300 ALS cases and over 300,000 controls. By using the two data sets in different populations we will increase the probability of identifying causally related compounds by identifying those that screen positive in both populations. The results of this work have the possibility to identify currently used medications or combinations of these medications that can affect ALS and survival with ALS. Any positive results could open up new research avenues that include targeted clinical trials as well as potentially new directions for epidemiological studies and laboratory research into underlying mechanisms.
Our long-term goal is to identify medications, or combinations of medications, that may affect incidence of amyotrophic lateral sclerosis (ALS) or alter the progression of ALS. To do this we will use very large health databases from Denmark and Israel with over 4300 ALS cases and objectively identified medication purchasing data. We will apply novel statistical approaches to carry out a screen of currently used prescription medications to identify those that appear related to ALS incidence or survival.
| Weisskopf, Marc G; Seals, Ryan M; Webster, Thomas F (2018) Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures. Environ Health Perspect 126:047003 |
