One of the biggest questions facing ALS researchers is what causes most cases of the disease. While about 15% of ALS cases can be traced to inherited genetic mutations, the remaining 85%, referred to as sporadic ALS, have no known cause. Because of the disease’s diversity, researchers are confident that there are many different factors that could lead to disease onset.

An important step toward learning what’s behind sporadic ALS is discovering the risk factors that could make people more likely to develop the disease. In 2023, the ALS Therapy Development Institute (ALS TDI) received a grant from the Centers for Disease Control (CDC) to undertake an unprecedented study to identify these ALS risk factors. 

Specifically, the study aims to identify “lifestyle risk factors” – defined as personal behaviors or choices that could increase a person’s chance of developing a disease.

This project utilizes data from ALS TDI’s ALS Research Collaborative (ARC) Study, the longest running natural history study in ALS. Working alongside members of the ARC team will be collaborators Pat Dolan, a geospatial analysis expert who is living with ALS, and Dr. Danielle Boyce, MPH, DPA, a data scientist and researcher at Tufts University with extensive background in ALS research and knowledge of biomedical informatics.

We recently spoke to Dr. Boyce to delve deeper into the methodology behind the ongoing study and to explore the potential implications of uncovering lifestyle risk factors associated with ALS.

What is a Lifestyle Risk Factor?

Lifestyle risk factors cover a very broad umbrella of potential aspects of a person’s life history, such as:

  • Behaviors like smoking cigarettes or eating a particular diet.
  • Engaging in activities like strenuous exercise or contact sports.
  • Where they live, particularly in areas that might be exposed to pollutants from industrial or agricultural sources. 
  • What they do for work – whether their job involves strenuous physical labor or could expose them to certain chemicals.

However, because a disease appears to be more prevalent in a certain group does not necessarily mean there’s a direct connection. When researchers see a pattern, it’s important to understand the biological reasons why certain lifestyle factors might contribute to a disease.

“One of the challenges in ALS research is we don't fully understand what is triggering the disease to begin with,” says Dr. Boyce. “So even when we look at known risk factors, like service in the military, we need to ask, ‘What is it about service in the military?’ ‘Is it exposure to certain chemicals in the environment?’ ‘What about those exposures can contribute to ALS?’ Can we identify combinations of risk factors?  There must be some sort of pathway, something that makes sense in a biologically plausible way.”

Looking for Lifestyle Risk Factors in ARC Data

To aid in the discovery of new ALS risk factors and enhance our understanding of existing ones, the team will conduct a comprehensive analysis of data gathered from the ARC study. They will look at a wide range of data points such as participant’s locations, medical histories, current and past occupations, and genetics. Having all this information available in one dataset presents an opportunity to not only look for patterns that could point to ALS risk factors, but also to investigate how and why they could be related to ALS.

“ALS TDI’s ARC dataset is really uniquely suited to cut through what is anecdotal, or correlation vs. causation,” says Dr. Boyce. “For example, we have blood draws from some participants. This means we have information about what's going on inside them. We have electronic medical records for many people. We have a comprehensive view of all kinds of medical things that could have happened to them. We have information from surveys about their history, their occupation, their military service. And we also have the clinical information, like ALSFRS-r scores. For some we even have data that can be used to create digital biomarkers.”

Over three years, the team will analyze these data to look for patterns using techniques like geospatial mapping and machine learning analyses. Any potential factors that emerge from this initial analysis will be further studied – looking for possible biological mechanisms that could explain their relation to ALS and making sure that there is enough data available to support any conclusions.

One question of particular interest will be comparing lifestyle risk factors to participants’ genetic data. In many cases, researchers believe that interplay between genetic and lifestyle factors may be behind some cases for ALS. For example, strenuous exercise may not be a risk for everyone, but it could be behind an increased chance of ALS for people with certain genetic mutations.

These mutations might not mean carriers are guaranteed to develop ALS, like genes associated with familial ALS, but they could increase the risk of the disease in certain circumstances. By finding common mutations shared by people with ALS with similar lifestyle factors, researchers could pinpoint who is at risk – and possibly even recommend preventative measures.

What Finding ALS Risk Factors Would Mean for

Identifying these ALS risk factors would be an important step toward better understanding and treating ALS. Knowing who is at risk for developing the disease – and why – would help clinicians diagnose the ALS earlier. Identifying different root causes could pave the way for more targeted treatments for different kinds of ALS. With a better understanding of what risk factors might be relevant to certain people, we might even be able to take preventative measures to avoid ALS onset in the first place.

Dr. Boyce says she believes this study represents a unique opportunity to answer these questions in a way that will benefit people with ALS.

“The potential of this study, with this comprehensive dataset, could go beyond just knowing what the risk factors are and reporting them without learning anything actionable,” says Dr. Boyce. “If I see a group of people who seem to be clustering a certain way, to oversimplify, there is a possibility that their biological samples could be examined further. Or even that they have been examined already, we just haven't looked at it in the right way. From there, we could hopefully pinpoint what's going on, or at least develop hypotheses for the research community to explore with us in future studies. And that's just not something that many others have been able to do before.”

What to Do Next: