This month we’re featuring Precision Health member Vincent Chen, MD. Dr. Vincent Chen is an assistant professor of internal medicine at the Medical School. His research interests are in precision health to improve the care of patients with chronic liver disease by using genetics and clinical data to identify who is at highest risk of developing complications of liver disease. Eventually, the goal of this research is to tailor treatment based on personalized risk profiles.

• Tell us a bit more about the details of your current research/projects

Steatotic liver disease (excess fat accumulation inside the liver) affects >30% of adults in the US. There’s no FDA-approved treatment for SLD, though there are some coming down the pipeline. There are a few big issues with managing SLD. First, we don’t know who is most likely to get advanced liver disease and therefore who needs the most attention. Certainly we can’t treat 30% of the population—even leaving cost aside, it may even cause as much harm (with side effects) as good if you treat people who are at very low risk of ever getting advanced liver disease. Second, we don’t know who’s most likely to respond to treatment and which treatment to offer.

What I’m interested in is combining different forms of big data, like genetic/genomic data, electronic medical record data, and so on, and applying it to steatotic liver disease to see if we can address these problems.

We had a couple recent studies which looked at these problems. There is a common allele in this gene called PNPLA3, which has been strongly associated with liver disease as well as advanced liver disease. We found that, first, people who carry this allele have much faster disease progression—it “bumps you up” a level of risk, so to speak. Second, people with this allele may respond more to treatment with a medication called semaglutide which is approved for diabetes and obesity and is being studied for SLD.

• How does your work apply to the field of precision health?

We’ve been asking the questions, “Who are the people who will benefit the most from treatment?” and “Can we tailor treatment to individuals based on their medical conditions?” Our recent work has looked at both of the questions. We’re trying to bring the many insights from genome-wide association studies and trying to apply them to patient care.

• What is innovative/new/exciting about these projects?

We’re interested in developing a roadmap for how genetics may be applicable in the clinic. Previously, if someone came in with direct-to-consumer genotyping results and asked me, “What does this mean for my health?” I could say “your risk of disease is X% higher because you have this mutation.” We’re trying to go beyond that. Hopefully in a few decades we’ll be able to say, “Based on your genetic testing showing X, your risk of developing a disease is Y, and the way to decrease that risk is with treatment Z.” Obviously it’s still too early to use PNPLA3 genotype or any other genetic mutations in clinical care of people with liver disease, but we’re working on a proof of concept.

• How is Precision Health supporting this research? 

The access to MGI data and the versatility of it has been incredibly valuable to this work. MGI lets you do huge genome-wide association studies, but being able to merge granular medical record data with genetics lets you also do small, granular studies. You can go from a study with 80,000+ people to our recent study on genetics influencing response to semaglutide treatment which had only 220 patients, and everything in between.

• What do you like to do when you aren’t doing research?

We have a 16 month old at home, so that keeps us pretty busy! Other than time with family, I enjoy reading, and try to stay active.

• Any other message you’d like to share related to precision health?

I’d like to thank my mentors, colleagues, and collaborators at University of Michigan, many of whom are members of Precision Health. Special shoutout to my primary research mentor Liz Speliotes who taught me everything I know about genetics.

• Links to recent/significant work:

https://www.sciencedirect.com/science/article/pii/S0016508523001130
https://www.gastrojournal.org/article/S0016-5085(23)05240-X/fulltext
https://www.nature.com/articles/s41467-020-20870-1
https://onlinelibrary.wiley.com/doi/10.1111/apt.17631