August 3, 2023 — Drs. Kelly Diehl and Audrey Ruple discuss the Golden Retriever Lifetime Study and the Dog Aging Project, and what we can learn about dog health from these large, prospective studies.
The Golden Retriever Lifetime Study
0:00:09.9 Dr. Kelly Diehl: Welcome to Fresh Scoop Episode 59, The Dog Aging Project, the Golden Retriever Lifetime Study and Beyond: What we can learn about dog health from large studies. I'm your host, Dr. Kelly Diehl, Morris Animal Foundation Senior Director of Science Communication.
0:00:26.4 DD: And today we'll be talking with Dr. Audrey Ruple. Dr. Ruple is the Dorothy A. And Richard G. Metcalf Professor of Veterinary Medical Informatics at Virginia Tech, and a member of the Golden Retriever Lifetime Study Scientific Steering Committee. She's also a member of the executive operations team and the director of the Environmental Core for the Dog Aging Project.
0:00:50.5 DD: Welcome, Audrey.
0:00:52.3 Dr. Audrey Ruple: Thanks for having me, Kelly. I'm really happy to be here.
0:00:54.2 DD: Yeah, Audrey and I go way back. So, we've known each other. Audrey was, you had funding from Morris Animal Foundation for part of your graduate training.
0:01:03.7 DR: For my PhD. That's exactly right. So, I have been affiliated with Morris Animal Foundation for a very long time.
0:01:09.5 DD: Right. Which is part of the reason that Audrey is also on our steering committee. But before we get started, tell us a little bit more about yourself, what led you to become a veterinarian and ultimately in this weird space of epidemiology?
0:01:23.7 DR: Yes, it is such a strange space and it's a very circuitous career path. I think like many people, my career trajectory only makes sense in retrospect.
0:01:35.5 DR: As I was doing things, it was kind of, everything was just like the next right fit. So, I was a social worker before I went to vet school, and I worked primarily with HIV positive individuals, so people who are immunocompromised, and was really fascinated with the human-animal bond at that point in my career.
0:01:55.8 DR: Because what I experienced was that many people that were immunocompromised had also lost a lot of their social support networks, and so their closest companions were actually their pets, so primarily dogs and cats. And those pets really became part of that health, like that overall health and that social health and wellness for those individuals.
0:02:16.8 DR: And so, I kind of went into vet med with a very human-centric perspective, which I think is the opposite of about 99% of people that go to vet school. [chuckle]
0:02:27.6 DR: During vet school became pretty fascinated with this idea of large-scale studies in epidemiology and learning how to prevent diseases from occurring, and that was how I first got the epi bug, but was able to, because of Morris Animal Foundation, do a PhD after graduation from veterinary school in Cancer Biology.
0:02:51.1 DR: So really marrying all of these different pathways that interest in human health really came into play in that comparative medicine aspect of cancer and epidemiology, so really looking at disease within the population. And truly with a mind to prevent it, which is, that's how I ended up with the College of Veterinary Preventive Medicine Board Certification.
0:03:14.8 DD: That is, it's pretty interesting, and I think people have heard about epidemiology because of COVID and we started to all get a little educated about what epidemiologists do. But I also want to ask you because I think we hear words like "big data", "bioinformatics", etcetera.
0:03:33.6 DD: You're a Professor of Veterinary Medical Informatics, can you help me understand exactly what that means and what you study? What do you look at every day?
0:03:45.4 DR: Yeah, so I look at data every day. [chuckle] So bioinformatics and big data, you're right, these are terms that we use in, especially with AI and machine learning and those types of things that have become really present in our news cycles, we hear these terms, but we don't often talk about what they mean.
0:04:04.0 DR: In my work, informatics, and so that veterinary medical informatics is really this transdisciplinary work that happens at this intersection of computer science and veterinary medicine.
0:04:15.0 DR: So, I have this massive amount of data, both from Golden Retriever Lifetime Study, also from the Dog Aging Project, I also do some work with insurance data sets. So, this massive amount of data that we have in veterinary medicine is typically and historically has been pretty untapped.
0:04:33.5 DR: And so, we're now really bringing these computer science and data analytics skills into this informatics space, this informatics to the veterinary medicine space, and really learning a lot about cause of disease outcomes, but also potential treatments and cures for diseases based on information that we've collected through the years.
0:04:55.8 DD: And adjacent to that, just so I understand, I think people think like you just feed data into computers and they do the work, but it's not that simple, you need people to formulate questions and put the data in a certain way.
0:05:14.8 DD: So, talk a little bit very quickly about that and why that's important?
0:05:17.8 DR: Absolutely. And actually, I'm really glad you brought this up because I think that it is one of the big misnomers around computer learning, is we think about computers is just doing this work on their own. That's dangerous.
0:05:30.1 DR: We can't trust computers to discern things that are true versus things that are false, like every data point is equally weighted from a computer's perspective, but we also can't trust a computer to understand whether something is biologically relevant.
0:05:43.7 DR: So just because something is statistically relevant or something that the computer can detect is having an association, doesn't mean that is something that makes any sense on a biological level.
0:05:53.5 DR: So, when I talk about things being transdisciplinary, and this is where I think the medical informatics is a really special and key place, it's that we have people like myself who are experts on this veterinary side, really integrating with this machine learning and the computer science side, so that both of us are working together, it isn't just a computer doing all the work.
0:06:15.8 DD: And I want... We're going to get back to this, but I wanted to talk now a bit about... This is a big question, study design, as we move into talking about these giant, I mean, cohort longitudinal studies.
0:06:36.6 DD: So, can you tell us what a longitudinal study is and briefly compared to other stuff that we may know, like you get the placebo and you don't get the placebo, right? You get a treatment.
0:06:48.1 DD: Those kinds of studies. And what these big studies can or can't tell us?
0:06:54.1 DR: Oh, that's a... So, this is a great question, and it is a big one, but I'll try to break it down and make it... Make it as straightforward as possible.
0:07:01.1 DR: So, the study that you just talked about like with placebo, so that's an interventional type of study where we might do a controlled trial, and that's the type of study we do to look for efficacy of a drug, so whether a drug works or doesn't work.
0:07:16.7 DR: Cohort studies and longitudinal studies are actually parts of observational studies, and I like to think about observational studies, or the way that we define them, as based upon two time points, exposure and disease outcome.
0:07:31.6 DR: And if you think about that, exposure obviously has to happen before disease outcome can occur, so there's really a timeline built into the way that I like to think about these studies.
0:07:42.7 DR: So, we can have studies that are based upon disease outcomes, so like case control studies where the entire population is defined by whether or not they have the disease or don't have the disease.
0:07:52.2 DR: Very powerful studies for things like cancer outcomes, which are still relatively rare in the overall population, so we can basically define based upon outcome and then look backwards and try to define what those exposures were that might be related to those outcomes.
0:08:06.7 DR: Cohort studies are kind of the opposite, where we define based on exposure and then follow forward, so we can look at lots of different disease outcomes in those studies. And then case-control studies are... Not case-control, I'm sorry. Cross-sectional studies, which is actually... I'll get to longitudinal from this point. Cross-sectional studies to find both exposure and disease status at the same time.
0:08:30.0 DR: A longitudinal study is essentially a bunch of cross-sectional studies measured over the course of time. So, like for instance, doing a survey every year where you're capturing all of the exposure information and all of the health outcome information in a population of animals, but doing that over time, that becomes the longitudinal study.
0:08:52.9 DR: We have a really rich data set that has that timeline captured, but also was looking through... You can look at both multiple exposures and multiple disease outcomes within that one study population.
0:09:08.0 DD: Okay, that's really helpful. So now let's go to Dog Aging Project. So, tell us a little bit about it, like how it started, what you're looking at and how it's structured?
0:09:21.2 DD: Because for people who are from the Golden Retriever Lifetime Study and may have heard our previous podcast, Dog Aging Project is structured a little differently. So, see, I know another big ask, Audrey, but you're so good at explaining. You're good at it.
0:09:37.0 DR: I hope so. [chuckle] Thanks, Kelly, appreciate that bit of confidence. So, Dog Aging Project is actually a project that had its origins prior to my involvement with it.
0:09:47.4 DR: So, there was a gerontologist and a veterinarian that went into a bar. And I'm kidding about the bar part.
0:09:53.6 DR: But they actually met at the University of Georgia and there had just been this recent study about the genome of dogs, and they'd been on the cover of Science, and there was a lot of talk about dogs as this model for human health outcomes.
0:10:08.1 DR: So, the gerontologist, Dr. Daniel Promislow and the veterinarian, Dr. Katie Creevy, had an initial kind of set of conversations around what would it look like to use the dog as a model for aging in human populations.
0:10:19.3 DR: Up until this point really with the Dog Aging Project, aging is typically studied in flies, worms and mice and yeast, for human health outcomes, so we hadn't really had a lot of experience with using dogs as a model for human health outcomes.
0:10:35.8 DR: And kind of fast-forward a few years, they had attempted to get an R01, which is a type of NIH grant funding study going and had failed to do... To get that RO1 funded.
0:10:54.0 DR: But my understanding is that NIH, though the National Institute of Aging actually came back and said, "Have you thought about doing something bigger and really building an infrastructure, so helping think about a whole population of dogs that can be used with lots of different projects to study aging in human populations?"
0:11:14.9 DR: Part of that work required use of a veterinary epidemiologist and a funny thing, there's not a whole lot of people that do what I do. [chuckle] So I think on their... I think I might have been the only person on their shortlist of people to approach to become involved with the Dog Aging Project.
0:11:31.8 DR: Which is truly now a project that has more than 100 people involved in it, we have researchers at multiple institutions, and there is a small group of us that are on that operations team, that executive operations team who are leading that core work of infrastructure, so building an infrastructure upon which multiple other studies can be built.
0:11:53.4 DD: So, compare Dog Aging Project to the Golden Retriever Lifetime Study, because you know both of them. And they're similar and yet different at the same time.
0:12:07.6 DD: So yeah, like compare and contrast, the old high school essay from your literature class.
0:12:14.6 DD: Compare and contrast. So, tell us a little bit about both of them?
0:12:18.6 DR: Sure. So, one of the most obvious ways in which they're similar is that these are populations of dogs that are being studied over time, so these are true longitudinal studies in dogs, and the both of them large for veterinary medicine.
0:12:30.7 DR: The Golden Retriever Lifetime Study, of course, started with over 3000 dogs and they have been studied from puppyhood, so from under the age of 2 up to the age of 2 at the time of enrollment, throughout their entire lifetime.
0:12:44.4 DR: That is a closed population of dogs though, which is one of the big differences between the Golden Retriever Lifetime Study and the Dog Aging Project, which is that the Dog Aging Project is an open population, as in dogs of all ages, all sizes and all breeds are being enrolled over time.
0:13:04.4 DR: So, we are continuously enrolling new dogs into the project with the same goal of following dogs across the course of their lifetime, but really had started enrolling dogs at the end of 2019 and have already had dogs that have had end-of-life outcomes because they were enrolled at an older age.
0:13:20.7 DR: So those are some of the bigger differences. Obviously the Golden Retriever Lifetime Study is only with golden retriever dogs as well. Dog Aging Project is all breeds including mixed breed dogs.
0:13:32.6 DD: Okay. And one thing that I forgot to ask you about, if you don't mind, and this is personal to me, I know there are people listening who are in Dog Aging Project and Golden Retriever Lifetime Study, I know there are folks who are in Golden Retriever Lifetime Study who are in both.
0:13:47.9 DD: I am in the Dog Aging Project, but my dog is in the TRIAD arm. Would you like to talk about TRIAD? [chuckle]
0:13:55.2 DR: Sure. So, as I said earlier, that the dog gene project has this core bit of infrastructure, and so that core part of the Dog Aging Project really starts with this health and life experience survey. So, every dog owner who completes that survey becomes part of this Dog Aging Project pack, so their dogs become part of the pack.
0:14:14.8 DR: Now, some parts of those dogs in our pack are used for different parts of studies, so there's multiple projects that are happening simultaneously, many of which involve sampling of things like genetic information, so we do that DNA swabs, these cheek swabs for dogs to get the genomic information about them.
0:14:34.2 DR: We also have some cohorts of our dogs that are getting blood samples, hair samples, toenail samples, very similar to the types of samples that are done in the Golden Retriever Lifetime Study, only that is done on all of the dogs involved in that study, and with the Dog Aging Project it's only a subset of dogs.
0:14:50.4 DR: But we do also have an interventional trial, so what we talked about earlier, so it's a randomized controlled trial where we're looking at the effects of a drug called rapamycin, and seeing whether or not it actually helps to slow aging at a cellular level in dogs.
0:15:06.0 DR: So, we have a placebo-controlled trial, so half of the dogs in that trial arm are getting a placebo, and the other half of the dogs that are getting a drug called rapamycin, and we are studying those dogs through the course of the rest of their lifetime and looking at different outputs like cardiac endpoints, so does it help improve cardiac function, obviously overall longevity.
0:15:29.6 DR: And in some cohorts of our dogs we have a newer sub-cohort that we're looking at neurological aging, so whether or not we have cognitive dysfunction happen in those dogs.
0:15:41.1 DD: Right. It's a little... That's a little different for people who are listening, and we just went through this whole thing about no intervention, but this is... But it's a real small group, as Audrey mentioned, that are kind of peeled off.
0:15:52.6 DD: Because I was in Dog Aging Project with my dog for several years before we were asked to join the TRIAD, and some of it had to do with the age of my dog and it had to be a healthy older beastie, but I think no breed restrictions?
0:16:09.7 DR: No breed restrictions. No. We are looking... Those are typically middle-aged kind of larger breed dogs, but generally healthy dogs at the time of enrollment in that trial.
0:16:18.1 DD: Right. And it's a commitment, and once we're done with this arm, we're still participating, for everyone who's in the giant Dog Aging Project, we still do do stuff for that. So, I'm going to ask you to walk us through...
0:16:34.5 DD: I'm going to give you a scenario, because I want people to kind of understand, you're confronted, as you said, with this giant mound of data, whether that's the insurance data you work with, Dog Aging Project, Golden Retriever Lifetime Study or people.
0:16:48.9 DD: So, let's say, but I'm going to save you the first step, which is I want to know... I'm going to create a question. So, suppose I want to know about the effect of eating green beans on health outcomes, like okay, now you're confronted with this data, Audrey.
0:17:07.4 DD: Walk us through how you think about a question and then approach this just massive data, like where to even start?
0:17:15.8 DR: Yeah, so that's a really good one. And I love that you went straight for the diet question. Dietary data is especially messy, and I'm actually going to take a little bit of a step back and just talk about one of the benefits of studying, studying dogs.
0:17:31.4 DR: There's many, not the least of which is that everything that we do in these dog populations, we're hoping to both better health of dogs and humans, but there's this other one, which is that most of us pay more attention to feeding our dogs than we do ourselves. [chuckle]
0:17:44.5 DR: So, when it comes to dietary information for you, Kelly, I might ask you what you ate last Wednesday, and you might have a hard time coming up with all the things that you ate that day, unless you were using some sort of a fitness track or diet tracker.
0:18:00.8 DR: Most of us don't have a really good recollection on what we've eaten, but if I ask you what you fed your dog, I bet you'd be able to come in within 95% accuracy of exactly what you find your dog that day. Because most of us feed our dogs a pretty consistent diet. So that's just a little side note about why dogs are so important.
0:18:17.0 DR: And diet stuff is just darned fascinating because it's something that is very difficult, because in human populations we do have such a diverse dietary input. So, finding the impact of something like a green bean in human health population becomes very difficult because of all that messiness in the data.
0:18:36.6 DR: But about 85% of dogs in the US are fed a dry, commercially prepared kibble diet. There's lots of other variables out there and lots of other varieties of food, but most dogs are eating the same type of food.
0:18:50.2 DR: They may have different protein and carbohydrate sources, or they may be grain-free, there are some differences, but for the most part, we know overall what these dogs are eating.
0:19:00.2 DR: So, when it comes to finding the impact of something like a green bean, we're going to have the ability to detect those differences because we have a better data source on what's actually going into these dogs.
0:19:09.7 DR: So in looking at something like impacts of eating green beans on health, the way that we would actually start is with looking at that cross-sectional study where we would take that entire population of data and we'd break the population of dogs down into those that have had green beans and those that do not have green beans, and then we'd look at what those health outcomes are in those different populations of dogs.
0:19:31.4 DR: And this is something that becomes really complicated and difficult to do in large populations because of course there's lots that we would need to factor in. We would want to know how old is the dog, where does that dog live, how big is the dog.
0:19:44.2 DR: As we know, aging and health are very different experiences for big dogs versus little dogs, but we have the analytical techniques now to really control for those extraneous pieces and we can get to maybe true with a little "t", we're not maybe not yet at a place where we have truth with a bit "T". [chuckle] But we can get to a more true answer about what those answers are within that data set.
0:20:09.0 DR: Now, depending upon those findings, we may even want to do an interventional trial where we actually test what we think the impact of those green beans might be in a group of dogs, which is similar to that randomized control trial that we're doing in the Dog Aging Project with rapamycin.
0:20:23.2 DD: Okay. And it gets more complicated. Like then you could say, "Well, I want green beans who live next to a superfund site" [chuckle] Right?
0:20:32.5 DR: Exactly. Exactly. And I think that this is one of the real powers of having a data set as large as the Dog Aging Project, is we really get to think about all of those different environmental exposures in the context of the genomic environment that these are occurring, because we have kind of a cleaner data around dogs than we do with humans.
0:20:54.9 DR: And there's a lot of powerful data that we have from the Dog Aging Project, but we also have really powerful data sets that we're able to geo-code and link because we have things like EPA data sets that are super fund sites, but also NOAA where we can understand temperature and precipitation in those particular areas where the dogs are located.
0:21:13.8 DR: So, we end up with a really rich data set that really helps to allow us to hone in on what are the true drivers of disease occurrence.
0:21:22.4 DD: Right. And one of the things that my understanding with bioinformatics is, as we talk about this and the complexity, some of it had to do with the technology, like our computers had to catch up in their computational power?
0:21:37.4 DR: Absolutely correct. And it's not just the computers, but it's new techniques needed to be developed. Big data comes with big problems and you can make erroneous conclusions if you're not appropriately handling the data statistically.
0:21:50.3 DR: So yeah, so statistics needed to get involved, we needed to build new mechanisms of determining truth and accuracy using these big data sets.
0:22:00.4 DD: Right, so it's not as simple as the old ANOVA sort of that we used to do it in STAT 101.
0:22:06.1 DR: Correct, yup. This is a different... It's a whole new threshold. Whole new world.
0:22:10.9 DD: Wow. So, let's talk about, as we come off our platform of bioinformatics, computational, and to talk about are there things... Can you talk a little bit about what you found already in Dog Aging Project?
0:22:26.9 DD: Because I know your guys are publishing some things. She had a really great paper in Nature, right? Is it two years now or one year?
0:22:34.3 DR: A couple years ago, it's been two years ago that that paper came out. And yeah, so I think one of the biggest findings for the Dog Aging Project, and it's going to sound glib, but it's really true, it's that we can do this type of work.
0:22:48.3 DR: Our community scientists, because every one of these dogs, the vast majority of the information we're getting from them are coming from dog owners like you, Kelly, who are giving us this information and giving us these insights into their dog's life experiences and their health outcomes.
0:23:03.4 DR: This type of work would not be possible without this population of people, and we currently have over 45,000 dogs enrolled in the Dog Aging Project, which is mind-blowing. There was a point prior to launch of the project where we were worried whether or not we could get to 10,000 dogs. [chuckle]
0:23:21.5 DR: And so at this place where we have 45,000 dogs currently enrolled and that number is growing, I really feel like that is one of the big findings of our project, is that it can be done.
0:23:29.1 DR: We can collect these data, we can do it in a way that is private and secure, we do use HIPAA-like privacy, even though these are dogs and there's no HIPAA, no privacy laws for dog data, we use that type of privacy because these are people's pets and animals, but also their households that we're looking into, so we're very careful with how those data are stored, but also how they're shared or not shared.
0:23:54.3 DR: And I mention that because this is an open data science project, and so we do have curated data sets that go out, but we're very mindful and careful about what data gets released.
0:24:04.0 DR: Some of the publications we've been coming out with, which my gosh, there's quite a few of them, we have information where we talk about this environmental data, so we're talking about how we can structure environmental data in a large population, so really kind of setting that, setting the bar for what we expect these types of studies to look like.
0:24:25.7 DR: But then we've also got some smaller, I'm actually just going to look at... We have a Google Scholar page where you can look at all of the project papers that have come out from the Dog Aging Project, and many of them are about this foundational work that we've been doing with how to build the project, how to structure data, also how to create things like our biobank, which Marta Castelhano is running out of Cornell University.
0:24:47.9 DR: But then we're finding things like associations between physical activity and cognitive dysfunction in older dogs, so in other words, dogs that get more exercise typically have better cognitive function, dogs with cognitive disorder are much like humans with Alzheimer's.
0:25:04.5 DR: So, we do see that cognitive decline happen in dogs were sometimes that cognitive decline is at a different level than the body is, but obviously mind-body connection there.
0:25:14.3 DR: We also know that we find some associations between feeding frequency and health outcomes and dogs in that particular study, and mind you, this was the cross-sectional data, so this was not longitudinal.
0:25:27.1 DR: Once a day feeding, so that calorie restriction was actually associated with better health outcomes in dog populations. Lots of asterisks around how to interpret that data, but it's still really interesting, and nothing like that had been published previously.
0:25:41.3 DR: And then also looking at things like neighborhood disadvantage and walking scores. So, in other words, we have this environmental piece of data that we can look at called the neighborhood disadvantage, and it's something that you might see in like a Zillow or Realtor.com when you're looking at houses, where they talk about the walkability score of a neighborhood. We can also look at socio-demographic information of the humans in that area.
0:26:07.6 DR: And what we're finding is that dogs really are good sentinels for the types of environment that we live in. So those neighborhoods that our dogs are living in really do have impact on things like how much physical activity that they're getting and what their overall health outcomes might be, similar to what we would see in human health populations and human populations and health outcomes.
0:26:27.6 DD: Wow. That's really interesting. So again, so it sounds like rural like do you have a sidewalk? Right? Is it a high crime neighborhood? Because it can be, I think... And are you finding cities...
0:26:41.0 DD: I spent time in New York City and I grew up outside, so a lot of people walk dogs in certain sections of the city, so it doesn't have to be just a city that could be a disadvantage. It could be, it sounds like neighborhoods even?
0:26:53.3 DR: Absolutely. And I think... And we've just recently published a paper looking at the social determinants of health in dog populations, and really that is so closely associated to what we see in human populations. That it's not just about...
0:27:07.8 DR: There's obviously a lot of factors that influence health, but it's, there really is this social determination of what types of health outcomes that we have, and we see that reflected, really mirrored in our dog population.
0:27:21.3 DD: Okay. Since we're talking about environment, I'm going to take a slight cul-de-sac and ask you a question. Audrey wrote a paper actually using Golden Retriever Lifetime Study data on characteristics of our participants and what predicted their staying in the study.
0:27:41.4 DD: So, can you talk a little, very quickly Audrey, about that paper?
0:27:44.9 DR: Yeah. Actually, I will talk about that, and this is one of those examples of that phrase about, "If I can see further than others, it's because I've sat on the shoulders of giants." This work that we did with Golden Retriever Lifetime Study actually really influenced how we structured the Dog Aging Project.
0:28:00.5 DR: So, we were asking the question of, what helps people stay involved with longitudinal studies with dog owners when they've enrolled their dog in a study? And we used the Golden Retriever Lifetime Study to look at that information, and we found there was a couple of different variables that we were able to find that were associated with people staying involved and attached or involved and committed to those research projects.
0:28:22.1 DR: And one of the ones that I think is, I think it's really interesting, is where the dog slept at night, was the variable that was associated with how likely someone was to stay involved in this study.
0:28:35.2 DR: And I really think that what that is, is a surrogate data point where we... How we feel about our dog and what the relationship with our dogs are, that actually impacts whether or not we're going to be willing to commit to doing these longitudinal studies where we're committing...
0:28:50.4 DR: As you mentioned with the TRIAD study that your dog is involved in, it takes time, it takes effort. It's a true commitment on the part of the dog owner.
0:28:58.1 DR: But if that dog isn't just a dog, and I'm doing air quotes around that, if that dog is seen as a member of your family, that dog is sleeping in your bed at night or in your bedroom because it is so much a part of who you are and part of your family life, that's going to be an indicator that you're going to be more likely to stay engaged in a project.
0:29:18.2 DR: And actually, as part of our nomination form for the Dog Aging Project, we actually ask the question, "Where does your dog sleep at night?"
0:29:25.3 DR: So, it's something that really did find its way into the core part of how we're operating the Dog Aging Project.
0:29:32.9 DD: Yeah, that was really... I think that was really interesting, and I'm glad that that data from one project was able to help people in a different project, and I think that collaboration is so very important when we work together.
0:29:50.1 DD: What are you working on right now? And that could be Dog Aging Project or the work you do with all the insurance databases. What's going on for you?
0:29:58.0 DR: You know, I'll talk about both of those things, because I'm really excited about where I am right now in my career. I love what I do, and it's one of those career paths that tells me, on the daily, I get that affirmation I'm on the right path.
0:30:09.9 DR: So, with Dog Aging Project, one of the things that we're working on right now is actually doing direct environmental measurements within dogs' homes and environments.
0:30:18.2 DR: So, by that I mean we have a cohort of dogs that we're having water samples, we're actually collecting water samples from their tap that they're getting their water from. We have a cohort of dogs that we've attached silicone tags to, and those silicone tags can be used to monitor chemical environment for those dogs' households.
0:30:36.7 DR: And we have a cohort of dogs that we've put these actigraphy monitors on, so like pedometers, so we're getting step count and information about how those dogs are really interacting within those environments.
0:30:47.0 DR: And that's really exciting work, because first of all, we wouldn't be able to do this type of work without that infrastructure that Dog Aging Project has provided, but secondly, it really helps us to validate and understand the accuracy of information that we're getting from dog owners.
0:31:03.6 DR: So, most of us can tell you something about the water that we're drinking, but we can't tell you about the mineral content, typically. There's very few people that have that kind of information about their dog's water source. And so being able to collect that information is something that's really valuable for the study going forward. So that's on the Dog Aging Project side.
0:31:23.9 DR: On the insurance side we actually just had a publication come out this week, so on Monday of this week. It's in Nature's Scientific Reports and it's about using artificial intelligence and machine learning on this large insurance data set in order to build predictive models. So, in other words, in order to be able to predict the health outcomes in dogs living in the US and Canada.
0:31:48.7 DR: And that is really powerful work that we're doing, and again, just not possible without these large data sets that have been accumulated over a long period of time. In this case, this is Fetch Insurance. They've got a 17 years’ worth of claims data on dogs from all over the US and Canada. So really fascinating stuff and fun stuff to work on.
0:32:09.0 DD: That sounds really good. I think that is important data that's been out there that we're looking... We just funded someone who looked at cats actually in Sweden. And it's cats in Sweden and you could say, "Well it's cats in Sweden," but it was a lot of cats. But it's a start, right?
0:32:31.2 DR: Yeah, exactly.
0:32:31.9 DD: It's a start to try to get this kind of data. So, what are some of the big questions... I know that's really hard. What are some of the big things that you hope maybe we first learned from these big studies?
0:32:46.8 DR: So, my global answer to that is, I hope that we can learn how to prevent disease occurrences. In both of these types of data sets I think that that's really my goal.
0:32:56.4 DR: And again, I'm a preventive medicine specialist, that's my area of specialty, and that will always be my perspective is to look at how we can prevent things from happening.
0:33:08.2 DR: More specifically, and especially with this really rich environmental data set that we're building in the Dog Aging Project, dogs are our sentinels, they are the canaries in the coal mine. Because they are living in our homes and they are drinking our water and they're experiencing our same chemical environments.
0:33:23.2 DR: Those dogs that we are living with are the canaries for us, and so if there are things that we have introduced into our environments or if there's elements that are in our water that can cause health outcomes, we're going to see that faster in dogs than we will in human populations.
0:33:41.2 DR: And so, I really think that being able to look at the dogs in our population and look for those early warning signs, for us, I think that is a really important part of the work that we're doing.
0:33:53.8 DR: I've been thinking a lot about things like BPA, this is that element that's in plastics that we didn't know it was a problem until we knew it was a problem. And I feel like there's, a lot of the stuff that we're studying right now are things that we don't know yet whether or not these things are issues.
0:34:07.9 DR: We do know that things like the laminate floors that most of us have in our homes now, that there are off-gassing that are associated with that. Do they have health effects? We don't know. I think that our dog population is going to be the population where we really find those types of answers sooner rather than later.
0:34:24.5 DD: Right. This is the tin foil hat my husband and I are always arguing about, right?
0:34:31.7 DD: Because you're right, sometimes you go, "Oh, for sure that's going to be fine," right? Or we worry about things that maybe we shouldn't be worried about and we ignore things we shouldn't be ignoring. So, I think this is really, really important.
0:34:44.5 DD: And you mentioned this earlier, but as we're get ready to wrap up, Audrey, people can still enroll in the Dog Aging Project. And where do they need to go to do that?
0:34:55.1 DR: Absolutely. We do not anticipate stopping enrollment ever, we do see this as the forever project. So dogagingproject.org, and go to the landing site for that web page and there's a little button in the upper right that says, "Nominate my dog."
0:35:09.5 DD: That's cool. Are you ever going to do a cat one?
0:35:13.6 DR: No.
0:35:15.9 DR: We really are hopeful that someone else will start a cat aging project, and we certainly have built the infrastructure that could be used as a model. There's a couple of different reasons.
0:35:25.3 DR: Cats are also really good models, and cats are obviously living in our homes and people are just as attached to their cats as they are their dogs. But people, one, don't take their cats to the veterinarian as often as people take their dogs to the veterinarian.
0:35:39.9 DR: And two, because we don't have as rich of a data set on that background information with cats, it just made dogs a much easier starting point for us. But I would be happy to support the cat aging project that someone else would like to develop. [chuckle]
0:35:51.1 DD: We'll have to see. Yeah, cats are different there. I think the place to start is where we're seeing now, which is like, you know this all too well, is a lot of these... First insurance, we had the Swedish, we had, I think there's Italian, we have data from the UK.
0:36:09.6 DD: That was probably 10 years ago we first started to see those publications. So maybe with cats, now that we're seeing those, because cats are getting insured, that's the start, that's the springboard for other...
0:36:21.9 DR: I think it's really exciting. I think it's really exciting. And cats are obviously a really similar model for lots of health outcomes in humans.
0:36:30.8 DD: Yeah. It's kind of, it's something on the horizon for us. So, as we wrap up, Audrey, what's kind of your take-home message for our audience?
0:36:39.0 DR: Oh gosh. I think the big take-home message for the audience is, number one, you are a part of the scientific community as a dog or a cat owner, and these community-based projects like Dog Aging Project or Golden Retriever Lifetime Study, they are absolutely dependent upon dog owners committing the time, committing their energy and resources.
0:37:04.8 DR: And more importantly, committing their knowledge of their dog. There's no one that's going to know more about the health and life experiences of the dog than the person that loves them the most.
0:37:14.5 DR: So really that engagement piece is I think the big take-home. And because really this is information that can have big impacts in terms of health and disease outcomes for future generations, and we can all of us live longer, healthier lives because of the work that we're doing.
0:37:30.7 DD: Great, yeah. I'm really excited because we're finally seeing a lot of this come to fruition and more people tackling it. Well, that does it, yay, for this episode of Fresh Scoop.
0:37:43.2 DD: And once again, thanks to Dr. Audrey Ruple, my friend, for joining us. And we'll be back with another episode next month that we hope you'll find just as informative, because we know the science of animal health is ever changing, we need cutting edge research information, whether we're treating patients as veterinary caregivers or for all of us who are pet parents. And both, because there is no veterinarian who doesn't have a passel of animals. [chuckle]
0:38:10.2 DR: It's true.
0:38:11.4 DD: In their houses, in their pastures, in their backyard.
0:38:14.4 DD: And of course, that's why we're here. So, you can find us on iTunes, Spotify, Google Podcast and Stitcher. And if you like today's episode, please take a moment to rate us because that helps other people find our podcast.
0:38:26.6 DD: You can learn more about Morris Animal Foundation's work at morrisanimalfoundation.org, and there you'll see just how we bridge science and resources to advance the health of animals. You can also follow us on Facebook and Instagram.
0:38:38.4 DD: And I'm Dr. Kelly Diehl. We'll talk soon.