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International Parkinson and Movement Disorder Society
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Heterogeneity of Parkinson's Disease | Congress 2023

September 18, 2023
Series:MDS Congress 2023
Why is Parkinson's disease heterogeneity important to research. Dr. Connie Marras met with Dr. Eduardo Fernandez to discuss why understanding the heterogeneity of this disease may help with prevention. 2023 Congress virtual access

2023 Congress virtual access

[00:00:00] Eduardo Fernandez: Welcome to a new episode of the MDS podcast, the official podcast of the International Parkinson's and Movement Disorder Society. We have a special episode today done during the International Congress in Copenhagen, and we are gonna discuss the research highlights on Parkinson's Disease.

I have a special guest here, professor Connie Marras from the University of Toronto. Thank you very much for your time and welcome to the podcast. 

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[00:00:31] Connie Marras: Thank you for the invitation. 

[00:00:32] Eduardo Fernandez: We talk a lot and there is a lot of talks in the program about Parkinson's heterogeneity or variability. What does it exactly mean and why is it important to do research on it?

[00:00:42] Connie Marras: Parkinson's disease is heterogeneous on multiple levels. We can think about from the very beginning even what is likely ideological heterogeneity in Parkinson's disease. We've heard many talks about the various underpinnings of Parkinson's disease, which [00:01:00] range from many, many genetic factors, both with low and high relative effects as well as environmental factors. And so there's heterogeneity in what causes Parkinson's disease. There's also biological heterogeneity when we try to measure what's happening in the body in Parkinson's disease. And then there's phenotypic heterogeneity as well, which is extremely broad from anything from age of onset to the original clinical manifestations, to the way it progresses fast and slow.

So there's heterogeneity in all of those levels. I think about heterogeneity being important at the etiological and biological levels because those understanding, those are really key to prevention, ultimately prevention of the illness, understanding the clinical heterogeneity has important research and clinical implications.

 Patients want to understand how they're going to progress and so we want to be able to predict that. For clinical research, if we're [00:02:00] going to do clinical trials that are going to be efficient, we also need to be able to predict progression, to be able to get the right people into the clinical trials that are going to manifest outcomes within a reasonable timeframe.

And so from that perspective we need also to understand, and measure and predict heterogeneity at the clinical level as well, so for multiple reasons. 

[00:02:23] Eduardo Fernandez: All very important for the ultimate role of finding better treatments and more individualized treatment for people with Parkinson's.

We have seen in the Congress some lectures talking about this and presenting some interesting findings. What are your highlights of the last year on the topic of Parkinson's heterogeneity. 

[00:02:43] Connie Marras: On a general level, the history of PD subtyping research is long, but I think within the last couple of years at least we've seen more analyses that try to tackle the aspect of Parkinson's progression more [00:03:00] directly.

So for example, more sophisticated analyses using machine learning and analogous techniques that actually incorporate multiple time points of observation into the actual model of the clustering and data-driven analyses, whereas previous analyses mainly took one point in time and then they may or may not even see if it relates to progression, but now incorporating progression right into the mixture. I think also the increasing incorporation of biological measures into the data that goes into subtyping. So we've seen more of that, both fluid biomarkers as well as imaging biomarkers.

So I think that's important to leverage what we know. And from a clinical point of view also predicting some aspects of treatment effects too. So, for example, a recently published prediction model for impulse control disorders, they can separate out those people who will and won't progress to get [00:04:00] those types of outcomes.

So that's some of the nature of the research.

[00:04:03] Eduardo Fernandez: You are also the chair of the MDS task force on Parkinson's disease sub types. What have you been working on and what are your challenges ahead for the task force? 

[00:04:14] Connie Marras: The last year in the task force life has been really devoted to two things really.

One is defining why we subtype, which I alluded to earlier in terms of why heterogeneity is important. But as a group, the subtyping task force came up with three main purposes of subtyping, and one was for predicting disease progression, both for clinical care and clinical trials.

The second was predicting responses to treatment, and the third was identifying targets for disease modification. Based on those purposes, then we've developed recommendations for the field to produce whatever product would be used. Whether it be a clinical tool like the A B C D score we use [00:05:00] for stroke, for example, or on some kind of an algorithm that one might use at baseline in a clinical trial to enroll patients. Whatever that tool is, to work backwards then towards the research we need to produce the tool. It's very purpose-driven recommendations and hopefully will be submitted soon for peer review.

So that's what we've been working on and I can share some of those recommendations if that would be of interest. 

[00:05:23] Eduardo Fernandez: Excellent. Looking forward to it. I feel like the field has moved so quickly over the years, as you mentioned before, so the sort of more clinically based, subtyping based on motor symptoms without hypothesis driven to more data driven subtyping incorporating biological biomarkers. Where do you see the future of Parkinson's subtyping or heterogeneity and how is that going to impact the ultimate goal of prevention or a more individualized treatment and precision medicine? 

[00:05:58] Connie Marras: I think that subtyping [00:06:00] is an intermediate step towards individualized prediction and I think that probably sooner rather than later we will be able to, with the size of data sets we have, and also the measures, especially the biological ones that are being developed, we will be able to predict at the individual level rather than need to subtype. And I realized for the purpose of, say, clinical trial enrollment, you want to take certain people that meet a certain set of characteristics, and maybe that's a subtype, but in any case, you have to apply that to the individual first in order to figure out which type they are.

 And so in the end it comes down to individualized predictions, and I think that's where the field of subtyping has been somewhat stalled in the essence that we've got a lot of descriptions at the group level, but validating that in terms of [00:07:00] applying it to an individual and seeing whether that individual is true to whatever outcome you're trying to predict.

A group you're assigning that person to really has not been done very much and I think that's where we need to go. I'm hopeful in 10 years from now, we'll be talking more about individualized prediction for the purpose of precision medicine than we will be subtyping.

[00:07:22] Eduardo Fernandez: Hopefully, as you said, in the near future, that will be a reality and during the Congress in some of the talks, there has been discussions about this future scenario where gps do some screening for prevention of neurodegenerative diseases and hopefully that will come in the next several years. Thank you very much for coming and having the interview with us and thank you for the listeners for the attention to the episode. 

[00:07:46] Connie Marras: My pleasure. [00:08:00] 

Special thank you to:

Dr. Connie Marras
Associate Professor
University Health Network, University of Toronto
Toronto, Canada

Eduardo de Pablo-Fernández, MD, PhD 

Department of Movement and Clinical Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom

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