hannah_went:
In today’s episode, we talk with Dr. Sarah Voisin. Welcome to the Everything Epigenetics podcast, Dr. Voisinn. I’m extremely excited to have you.
sarah_voisin:
Thank you very much for inviting me. I’m really, really pleased to be here.
hannah_went:
Yeah, I’m very interested in your journey in particular. I know you’re using, you know, statistics and bioinformatics and I really want to understand what piqued your interest in that. I don’t think I’ve had any bioinformaticists or biostatisticians on the show quite yet. So you’re really using these skills in the epigenetic space. Can you tell me a little bit about your story and how you got where you are today?
sarah_voisin:
Yeah, absolutely. I think that my interest in statistics and bioinformatics stems from my love for math and logic that I think I developed from an early age by playing a lot of video games growing up. In middle and high school, I discovered the world of genetics and that fascinated me enormously. And I understood that I could study biology to try to understand human beings because they fascinate me. And I think that moving towards bioinformatics and statistics was just a… perfect marriage of those two passions of mine.
hannah_went:
Definitely, no. Super interesting background, because I can really resonate with the biology space or the science world, but I think then we really need more of the interpretations of what that actually means on more of a mathematical level. So your skillset is definitely valued, especially in our epigenetic space here as we’re using predictors of all sorts of different things as we’re applying them to the DNA methylation data.
So… What I wanted to have you on this podcast is you have an amazing paper that was published a couple of years back titled An Epigenetic Clock for Human Skeletal Muscle. I loved reading your paper. I encourage anyone that is listening to give that paper a read. Lets first start discussing the why behind this paper. Can you give some background regarding the importance of skeletal muscle tissue and aging? Why do we care in the first place?
sarah_voisin:
So it’s really important to understand that skeletal muscle tissue is the tissue that allows us to perform daily tasks and activities that give us independence and life enjoyment. You know, you are able to walk, to live, to run, to hike, to dance, and to do all of these things because you have muscles. And as we age, unfortunately, we lose considerable muscle mass and muscle strength, and therefore we lose a great deal of quality of life because muscle deteriorate as we age. So it’s very important to nurture muscle during aging.
hannah_went:
Yeah, absolutely. And would you encourage then to, you know, our listeners, I know a big question that I get is, you know, when should I start weightlifting or even moving my body a little bit more? It doesn’t have to be heavy weightlifting, right? You could be moving boxes or different things of the sort. When should I start that? Or what’s really the age? How should I, you know, they really want a workout plan from start to finish. What would be your recommendation based off of that question?
sarah_voisin:
So I think that the recommendation I would give is you start now. Like whether you’re young or old and as early as you can. It’s a bit like playing an instrument. The earlier you start, the better it is. And you need to keep doing it throughout your life. I see the nurturing of your muscles through exercise as a hygiene sort of routine. Just like you brush your teeth. I think you should exercise and move your body in the same way. And weightlifting in particular is very important. Like we underestimate the importance of weightlifting for men and women alike and at all ages.
hannah_went:
Yeah, that’s interesting because I’ve become an avid weightlifter in the past couple of years. I’ve always been super athletic. I played three different sports in high school, played a little bit of club soccer and college as well and have really always been active, I would say, but more cardio, right? You think cardio is going to burn the most calories and I think that’s a flaw in our thinking and how we’ve been raised and what we know about exercise today. So I’ve since shifted to this. this weight lifting model and it’s great. It’s almost like a weight lifting high like when people say they get their runners high, right? And this morning I definitely didn’t want to go to the gym and workout.
But since I’ve been in this habit, it’s almost like I could go on autopilot and put my body through it and I’m so glad. I remembered I was speaking with you today and I was like I can’t not lift weights on the day that I’m interviewing Dr. Boycen. So how… How often should people lift weights? I know there’s a really big argument in the space as well that we should have these rest days. And I don’t think humans are meant to rest all the time. I think even in our rest days, we need to walk or do some stretching and be active. But what do you think in terms of how many times per week or how long? So, I think that’s a really good question. I think that’s a really good question. I think that’s a
sarah_voisin:
So I am in no means an expert on this. So take my advice with a grain of salt because I am not a registered, you know, personal trainer or anything like this. This is just secondhand knowledge that I gathered through my reading. But I think that the recommendation currently is weightlifting twice a week or something similar. Of course you can do more if you like. It’s based on personal preference as well. But definitely at least twice a week.
And as you said, Only cardio is going to give you great benefits, but limited. And in particular, in view of injuries, a lot of people underestimate how good weight lifting is to avoid injury, especially if you practice another sport that might be at a higher level. So I would say that as long any form of activity that allows you to move your body, and in particular with weight lifting, try to go as heavy as you can. But. Obviously, do not push yourself to the point of breaking. That’s the general
hannah_went:
Right.
sarah_voisin:
recommending.
hannah_went:
Definitely. That’s what holds me back. I would say, oh, you know, this feels pretty heavy. I could probably go a little bit heavier, but you know, I don’t want to because of X, Y, and Z or because I’m tired. So, you know, we always need to push ourselves a little bit more to our limits because you could probably lift a lot more than you can. It’s really just more of that mental block too. So, you know, I really encourage anyone who may be interested or on the fence regarding weightlifting to just at least start, you know, start with some cans of soup in your kitchen or two and a half pound weights because we’re going to lose that muscle mass as we age. So it’s never too late to start, you know, begin now and try and keep some of that muscle.
sarah_voisin:
Yes, and I also want to get one point across because I hear this a lot from people. Even older individuals can lift weights and it is not dangerous for them to lift weights. A lot of people are worried that they’re going to injure themselves and it is safe. I just want to state that out loud.
hannah_went:
Definitely, definitely. So yeah, again, I really wanna encourage my parents to even lift weights, to be a little bit more active too, because it really, really starts to decline as you become older chronologically. So Dr. Boycen, I wanna hear a little bit about the background of this paper. What started it? I’m always super interested to hear how you came about to do this question and how you started this paper. Can you give us a little bit of background about that?
sarah_voisin:
Yeah, absolutely. So this paper is basically building an epigenetic clock, which is a predictor of chronological age based on skeletal muscle epigenetic profiles. And the reason why I decided to build this clock is because there was at the time a clock that was available for all tissues that was known as the Horvath pan tissue clock that was developed in 2013. But I tried to apply this clock to my data in skeletal muscle, the data that I was handling at the time. I noticed that this clock performed rather poorly in skeletal muscle compared with the other tissues that were available out there, such as blood, adipose tissue, brain, et cetera.
And I didn’t really understand why. I dug into the paper and when I looked deeper, I realized that when Horvath developed his clock in 2013, there were virtually no DNA methylation data sets in muscle available at the time. when he developed his clock, he didn’t put any muscle data to develop the clock, which means that the clock performed rather poorly in this tissue.
Because you have to know that epigenetic patterns are rather tissue-specific, and there are some changes that are happening with age that are restricted to certain tissues. And so, by the time I became interested in this, in 2019, many datasets had become available. allowing me to build a muscle specific clock that could predict an individual’s chronological age based on their DNA methylation patterns in muscles.
hannah_went:
Perfect. Yeah. And I think, you know, some of our listeners will be familiar with that 2013 Horvath pan tissue or, or multi-tissue clock. Um, if, if, if that clock should work well in, in all tissues, why don’t you think it was represented well then with the skeletal muscle? Were there, were there us? Were there other, excuse me, tissue or organ groups that weren’t very well represented in the clock as well?
sarah_voisin:
I do not know. I haven’t looked specifically at which tissues were not really well represented, aside from muscle, because I was just handling that type of data. There might be some very specific tissues, such as, I don’t know, ovaries or some things that are pretty rare. I mean, like heart, for example, heart is also a tissue that’s difficult to get in humans. So I’m not too sure, but there are probably other tissues that are.
hannah_went:
Absolutely. And how did you get, um, you know, the samples that you were working with, with that skeletal muscle?
sarah_voisin:
That was one of the interesting parts of this paper, at least in also in my journey as a researcher, because it involved a lot of data mining. So I felt like I was Sherlock Holmes trying to find all the cues that was out there to be able to gather all the data that I could to build this clock, which meant looking first of all in online repositories. So all the online databases that are now… overflowing with molecular data, such as the GeneXpression Omnibus platform, the dbGaP database of genotypes and phenotypes, ArrayXpress, and all those great platforms where people just dump their data.
sarah_voisin:
And I just dug into it and I found a lot of data sets that I could use, but it was not sufficient to build a really good clock. So I also reached out to my network of collaborators around the world from Europe, from the US, from Australia. because I knew that they had some data at some point, some sitting somewhere that I could potentially use. And this is one of the things that I love about academia and science. People are so collaborative and so open and they allowed me to use their data very generously, allowing this big effort, which led to the clock. The muscle.
hannah_went:
Yeah, that’s amazing. And I think that’s one of the things, you know, that’s great with all the technology that we have today is we’re able to collaborate and come together. And you know, I’m sure you’ll be able to return the favor for all of those people who sent you information to use for your skeletal muscle clock as well, right? Because we can only get so much of that data from the online data mining and then having some collaborators around the world is always, always helpful too. So you know, diving deeper a little bit into that. point when people discuss epigenetic clocks, they get really excited because they always go to this idea of biological aging and how they may be aging on a cellular level.
And that’s super exciting in the field, right? We know if your biological age in any sense, whether it’s a first generation, second generation, or third generation clock is above your chronological age, you are at risk for almost every single chronic disease and death as well. But what I don’t think people understand is really how these epigenetic age clocks are created. There’s a lot of work, a lot of time that goes into this and effort put into the development of, again, these epigenetic clocks or these predictors. So I want to spend some time really diving deeply into this process. How did you create this skeletal muscle clock? Can you describe the process? So I think you just went over the first step, which was actually getting that data. So you have the data. What do you do with it from there?
sarah_voisin:
So once I got my hands on the data, the first thing that I needed to check was that I had sufficient sample size because you need a substantial amount of data to build a clock. And the reason for this is because human molecular data is usually very noisy. You have a lot of, you have a signal that you’re trying to capture, but a lot of other factors can influence your DNA methylation levels and make them vary to a degree that is masking the signal that you’re trying to detect. So I needed at least a thousand samples.
So I, you know, I managed to get there, but it was challenging. Second thing that was very important for me to check was that each cohort or each data set that I had my hands on displayed a broad age range. To be able to detect changes in DNA methylation that are associated with age, I need… individuals in my datasets to vary in age. I cannot build a clock if my dataset only contains people who are 50 years old because they don’t vary in age. So my data is not going to vary at all. So it was very important to check that the age was variable between datasets and inside datasets as well.
And then I just decided to go with the method that Horath had implemented in his 2013 paper, which was to use a machine learning algorithm called ElasticNet. So what you have to know is that there are new developments in the field of epigenetic clocks, especially work by Morgan Levine. And she developed, I think a better way to use DNA methylation data to build a clock that does not use necessarily ElasticNet, but I am not entirely familiar with it, but I just know that these perform better now. With regards to ElasticNet, it is actually I mean, I was really scared to use it at first because I didn’t know anything about machine learning.
But thanks to Steve Horvath, who actually put his code completely open access, I could reuse his code and try to dig into it and understand exactly what he did. And ElasticNet is a really beautiful piece of algorithm that actually tries to find the best combination of features in your data. I
n this case, the features are DNA methylation sites called the CPG sites. So it tries to find the best combination of those sites that can predict the outcome of interest as accurately as possible. So you can ask ElasticNet to predict chronological age. You can ask the algorithm to predict anything you want. You can predict sex, you can predict anything. And each feature is actually assigned a specific weight. And then all the features are summed up together. they are linearly combined into one particular measure that predicts with high precision somebody’s age based on their DNA methylation level.
hannah_went:
Yeah, that’s a beautiful example. Like I said, I don’t think we’ve had anyone on the show yet, really dig into elastic net regression modeling or how these are actually created. And that’s a really just definition by book of how we’re diving into this data. So you go through this elastic net regression modeling. And what do you find? How many CPGs actually make up the skeletal clock? And remember, a CPG is going to be that cytosine, phosphate, and guanine. So you have your d***. two nucleotides with that phosphate bond holding it together and that’s going to be the location of methylation in that genome. Correct me if I’m wrong on any of that Dr. Royston, but
sarah_voisin:
Thanks.
hannah_went:
how many, yeah, CPGs did you find and give us some insight there.
sarah_voisin:
So, I mean, if I’m really honest, I don’t remember the number of CPG sites in the clock, because actually, the right, I mean, the CPGs that are selected to build the clock are, in my opinion, they’re nothing special. The clock could have selected a different set of CPGs altogether, and, you know, it could have arrived at a prediction of age that would be just as accurate or slightly less accurate, ever so slightly. And it could be a completely different set of CPGs.
So there’s nothing special about this particular 100 or 200 CPG sites that are contained in the clock. Because what you have to know is the clock itself, it’s a very artificial process. It’s a machine learning, it’s artificial intelligence. And it’s by definition artificial. It doesn’t give you, I don’t think, much insight into the entire biology of the aging muscle. It is purely a program to predict age. So the number of scientists didn’t really matter to me, to be honest.
hannah_went:
And that is such a great answer because a lot of times people think if a clock has a higher number of CPGs then it’s a better clock and that is just not correct, right? I know off the top of my head the the 2013 Horvath one has 353 the dr. Gregory Hannum one has 71 You know, I think phenol age is around the the 500 area which is dr. Morgan Levine second-generation clock. So They’re all varying. There are some clocks that only have three CPGs, right? So they’re all varying and I think that’s a very big misconception in the space is that people think with more positions it’s better. But like you said, that’s machine learning.
That is just a predictor. It’s a prediction outcome. We don’t really necessarily know on the biological level what those CPGs are meaning or what that methylation is actually telling us. There can be prediction based. predictions based off of that and that there are some groups out there, some really great research groups who are looking more at the causation of aging and what positions might actually be causing us to age. So that’s a completely different question and, you know, completely different interview to have, but really appreciate the honesty and the feedback there regarding the number of sites. And your muscle clock is going to outperform the pan tissue clock. correct in reporting out that age. So can you explain this further? How does that perform better than Dr. Horvath’s 2013 clock?
sarah_voisin:
It performs better just because if you use a particular sample of muscle from a person who was, let’s say, 50 years of age, the skeletal muscle clock that we developed actually together with Steve Horvath is just going to predict that person’s age with better accuracy. There is a smaller error in the prediction of the age. So the person might be predicted to be 52 years old with the mid clock, so the skeletal muscle clock, versus something like 60. with the Horvath’s Pentishrew clock. So it’s just more accurate, but there’s nothing magical about it, I would say.
hannah_went:
Perfect, yeah, and it’s a first generation clot, correct? So
sarah_voisin:
Correct,
hannah_went:
it’s using
sarah_voisin:
absolutely.
hannah_went:
to predict the chronological age within using that skeletal muscle. So the second part of your study, I found this super interesting as well. So if you’re reading Dr. Voicen’s study and you move on to more of the second half, you also performed an epigenome-wide association study or what people call an EWAS study of age in your paper. Let’s start with describing what an EWAS study is.
sarah_voisin:
Yes, absolutely. So EWAS actually is very similar to a GWAS that you might be familiar with. It’s actually a hypothesis-free approach that is used to identify the DNA methylation loci, so the epigenetic loci in this case, that are statistically associated with a particular trait of interest. And in our case, it was age, chronological age. So the way that this works is that it… It tries for every single CPG site that is in your data. It tries just to align your model to associate the methylation level at this site with age, adjusting for other potential confounders, such as sex and disease and whatever you want. And then it does a particular type of correction to avoid false positive findings. And then you end up with a list of DNA methylation loci that are called DMPs. that are statistically significantly associated with age.
hannah_went:
Perfect. And what does the DMP stand for again?
sarah_voisin:
differentially methylated position.
hannah_went:
position. Okay, and is that the same as the DMR, the differentiated methylated region?
sarah_voisin:
So a DMR, actually, it’s a contiguous stretch of DNA that harbor multiple DNPs. So a DMR is composed of multiple DNPs.
hannah_went:
Perfect. Thank you for that clarification there. And what did you find in your EWAS study?
sarah_voisin:
So we found actually a balanced number of DNA methylation sites, so DMPs, that increased in methylation with age or decreased in methylation with age. And we also found that those loci that change with age in the genome are not randomly distributed. They are enriched in specific regions of the genome that perform specific functions, such as enhancers. into gene ecregions and promoters, etc. So I won’t go maybe into all the details of it, but there is a very specific distribution of where these changes happen with age in the genome. It’s not just randomly distributed, which gives an insight as to what is the upstream reason why the CP genome changes in the first place during aging and what is the actual cause of those changes. So I think this is the interesting part.
hannah_went:
Sure. Yeah. Let’s go into the details. No, I want to hear
sarah_voisin:
You
hannah_went:
them. So if you don’t mind, what sites were they more related to, you know, skeletal muscle and aging or kind of what were those groupings?
sarah_voisin:
Yes, so when looking first at the function of the genes that displayed a difference in methylation during aging, I found that those genes were particularly, they were, I will use the term enriched. So what I mean by that is that many of those genes that changed with aging in muscle at the epigenetic level were involved in skeletal muscle structure and function, such as myosin, troponin, and all those kinds of proteins that make up the muscle itself. So it just confirmed to me that the signal that I was detecting during aging might be functionally involved in the degradation of muscle during aging and especially the decline in muscle strength and muscle mass during aging.
hannah_went:
Sure. Yeah. Thank you for that description. Again, I would want to use the muscle clock on myself. And I know you mentioned the meat package, which we’ll talk about how people can access that at the end here. So that leads me to what’s the utility or application of your clock? If someone’s tested their methylation and they have a 450K, they have their IDAT files or they have the 850K or some of their raw data, can they use it? Where do you think this is you’re going to see your clock used most widely?
sarah_voisin:
Yes, so I think that the first development that I see using the first, I mean, it’s a first generation clock. So it can give you, unfortunately, little insight into the biological aging of your muscle. Because I asked my algorithm to predict age with as much accuracy as possible, it selected those methylation sites that change with age and nothing else. Like it didn’t select the methylation sites that change with exercise or diet or anything else that counters. degradation of your muscle as you age.
So it’s a first generation clock, so we need to take this with a grain of salt. But I think that the application of this clock might be in future reprogramming experiments that are currently being tested in mice and that I see could be applied to human muscle culture to try to, for example, treat those muscle with OSKM factors or reprogramming factors or maybe with molecules such as NAD. to try to understand whether those particular molecules that are known to have anti-aging properties at least in animal models or in other cells could potentially rejuvenate the epigenetic age of skeletal muscle and to what degree and what that actually means for the function of the muscle, etc. The second, yeah,
hannah_went:
Perfect.
sarah_voisin:
no, I was going to say the second potential application, but that’s like far-fetched is in forensics.
hannah_went:
Thank you. Bye.
sarah_voisin:
Because a piece of muscle found on a crime scene, you could look at the DNA methylation level of this piece of muscle and determine the age of the person to whom this piece of muscle belonged. But I think this is farfetched because blood is more readily available and more useful in this context.
hannah_went:
Definitely. Talking about the second application, that’s very interesting because that’s really how I heard of or a part of how I heard of DNA methylation testing and its utility, you know, back in 2011 and 2013 when these first generation clocks really came out is you could use blood from a crime scene or even muscle now. And you know, they even were really used at the beginning for… people to seek asylum, to see if they were old enough to seek asylum.
So they’re really great at predicting chronological age, and those first-generation clocks still have utility for those purposes if you accurately want to predict that specific interest and outcome, which would be that chronological age. Going back to your first application, so for a study there, you could do…and let me know if this is what you’re imagining, but you could do a…you could start with mice, right? And you could take a… muscle tissue and you could test their age based on that muscle and then you could intervene with something like NAD or I think you were talking about the reprogramming factors, right? Like the Yamanaka factors, correct? And then do, you know, wait a certain amount of time and then retest afterwards to see how their chronological age based on their skeletal muscle is being affected. Would that be something you were thinking along the lines of?
sarah_voisin:
Yeah, absolutely. It’s exactly what I was thinking about, knowing that the clock that I built is specific for humans. So I would
hannah_went:
Thank
sarah_voisin:
not
hannah_went:
you.
sarah_voisin:
go through mice at all. It would be directly applied to human muscle cultures. But yes, this is exactly the line of thinking.
hannah_went:
So in humans, perfect. And can you just explain a little bit more? And again, forgive me, this is maybe not your specialty, but can you describe just the Yamanaka factors for our listeners? I think that may be a little bit of a foreign subject for them or even just some of the reprogramming factors.
sarah_voisin:
Yeah, so I know little about it, but I just know that these factors, these are four proteins, four transcription factors, whose combination was actually found to turn a differentiated adult cell back into a baby stage of a sort of
hannah_went:
I’m sorry.
sarah_voisin:
a differentiated cell, which actually got Shinya Yamanaka, who found those factors, the Nobel Prize, This discovery has many applications for organ transplant, for trying to rejuvenate tissues and in the aging field in particular. So the combination of these four proteins turns back the clock, if you will, to a baby stage, with the caveat that there is one of the four I know that tends to turn the tissue cancerous and I know that one of them has been removed lately in experiments to try to rejuvenate the tissue without turning it into a teratoma. But once again, I’m not super familiar with it.
hannah_went:
No, understood. I appreciate that answer. That just gives our audience at least a picture or some insight. And I know that’s really what Altos Labs is working on now, a lot of those cellular reprogramming options to see how we can turn the age of those cells back to stage zero or those baby cells. So it’ll be interesting to see what they come up with in the years to come. So Dr. Forysen, what’s next for you? What have you currently been studying or looking at? Tell us your interests. Tell us what we can expect in the next coming years.
sarah_voisin:
So I have actually a big project that I’m working on at the moment that is probably going to take me another two years to complete. But once it’s completed, I’m going to be very happy with it because it is absolutely a mammoth task. So my goal is to build an atlas of epigenetic aging across all human tissues at the cell type resolution by combining something like 75,000 DNA methylation profiles across 18 tissues. I want to build that huge atlas so people can understand exactly what type of DNA methylation changes happen in which cell type, in which organs.
And further down the line, what I want to do as well is to investigate sex differences in the aging profiles because we know that men and women do not age similarly or at the same rate. And I want to understand whether this is true at the epigenetic level and in which tissues and whether this actually explains why… For example, women develop more Alzheimer’s, while men Parkinson’s, and whether it has some actual application to understand sex differences in age-related diseases.
hannah_went:
Wow, I’m excited for that. And I’ll have to have you back on when you complete that project, too. So will the Atlas, will that be publicly available to researchers
sarah_voisin:
Oh, absolutely.
hannah_went:
or anyone? Perfect.
sarah_voisin:
So a big part of the project is to build an open access, user-friendly, transparent website where people can browse anything they want, a bit like the GTEX portal, clicking on a tissue and understanding which side changes to what degree with age, because this is only when we build an atlas of this kind that then we can test experiments in human tissues to know whether the reprogramming factors can actually affect this tissue or that tissue. So once we have the base, the atlas, then we can move on to understanding the effect
hannah_went:
Right.
sarah_voisin:
of anti-aging therapies.
hannah_went:
Yeah, and that’s your gift back to the community, right? Again, all those
sarah_voisin:
I’ll
hannah_went:
people
sarah_voisin:
get.
hannah_went:
share their data with you regarding the skeletal muscle clock for you to build that. So that’s great. I love when the community comes together and we’re sharing all this information and data. I’m particularly interested in the male versus female information and what you’ll find from that because there’s always that sex paradox where men typically age a little bit quicker than women and they die younger too. A recent article came out that shows… forgive me as I don’t remember which clock they actually used, but it showed that men are typically about four years older biologically compared to women.
I think it was one of the first generation clocks. So that would be super interesting at a cellular level to understand that a little bit further. I think a lot of the questions stem from that, especially most of these interventional trials you’ll see done on men, for example. So women I think are a little bit underrepresented there and we can find out why and why we’re developing diseases earlier too.
sarah_voisin:
Exactly.
hannah_went:
Perfect. And my last question for you, it’s a curve ball. I ask everyone this at the end of the podcast,
sarah_voisin:
Yeah.
hannah_went:
Dr. Orson, if you could be any animal in the world, what would you be and why?
sarah_voisin:
Oh my God, if I could be an animal, I think. Okay, this is a random one, but this is a question that I’ve thought about in the past and I would be a cat. You have the best
hannah_went:
Really?
sarah_voisin:
of everything. Oh yeah, you have the best of everything. You hunt as much as you want. You’re being fed, pet, and I would love to be a cat, I think.
hannah_went:
I love that you’ve thought about that question because I’ve thought about the question too. I haven’t given my answer out yet, but really a handful of people have said cat. Cat is winning for the same reasons that you just said. They have cats and they are sleeping or they’re fed all the time and they’re pet. Do you have any cats at home then? Let’s see.
sarah_voisin:
I used to, I actually, yeah, I used to, but yeah, I love cats, they’re my spirit animal, I think.
hannah_went:
Yeah. Favorite animal. Well, I really appreciate your time. We’ve, we’ve come to the end of this amazing podcast interview for listeners who want to connect with you, where can they find you? And, and can you talk about where they can find your, your code as well?
sarah_voisin:
Yes, so I do have a GitHub account with my name Sarah Voisin. So you can find all the code that I upload on my GitHub regularly as I move on to different projects. I’m also, I can be reached with my work email that is now at the University of Copenhagen. So I’m pretty sure you will share that email with the listeners. So.
hannah_went:
Absolutely. I’ll put everything in the show notes. And why is it called the meat package? What does that stand for? I love it. I think it’s a great fit, but what does it stand for again?
sarah_voisin:
So MEAT stands for Muscle Epigenetic Age Test. To be fair, I am not the one who found that acronym, but it was one of my colleagues whom I presented the clock to and he looked at me and he says, you should call it MEAT. And I’m like, what are you talking about? And then he gave me the acronym and I thought it was so spot on. I had to use it.
hannah_went:
Yeah. You said muscle epigenetic age and what does the T stand for?
sarah_voisin:
Yes.
hannah_went:
Test test. Perfect. I love it. Um, well, thank you so much for, for joining us, Dr. Boyce and at the everything epigenetics podcast. And remember you have control over your epigenetics. So stay tuned next time to learn more. Thank you so much.
sarah_voisin:
Thank you.
