Ginny Robards
Professor Pankaj Kapahi, PhD
91
Ralph Waldo Emerson once wrote, “The eyes indicate the antiquity of the soul.”
But we now know that your eyes may also provide a remarkably accurate measure of the true age of your body. Indeed, perhaps more accurate than the number of years that you’ve been alive (i.e., your chronological age).
How can this be?
Well, it has been known for some time that the microvasculature of the retina can offer a window into the health of the circulatory system as a whole. Subtle changes in the retinal capillaries have been shown to provide the earliest signs of a vast array of diseases, even conditions that are not specific to the eye, long before symptoms emerge. This includes neurodegenerative disorders like Parkinson’s and Alzheimer’s disease, as well as hypertension HIV, and even cancer metastasis.
Recently, researchers at Google have used deep-learning models, trained on data from nearly 300,000 patients, to predict markers of cardiovascular health that were previously thought to be impossible to glean from retinal imaging. For instance, this algorithm, when applied to retinal images, could accurately distinguish between smokers and non-smokers, as well as predict systolic blood pressure within 11 mmHg. Most importantly, it could identify patients who experienced adverse cardiovascular events (like heart attacks or strokes) with 70% accuracy.
Incredibly, a new study suggests that images of your eyes might soon be able to yield insight into how long you have left to live – in time for you to do something about it.
Guest
On this episode of humanOS Radio, we welcome Pankaj Kapahi back to the show. Dr. Kapahi is a professor at the Buck Institute, an independent biomedical research institute that is devoted to research on aging. His lab has been exploring how nutrient status influences health and disease, and particularly how nutrients affect age-related changes in tissues and disease processes.
Pankaj has become a perennial guest on the show, largely because he’s been involved in some pretty innovative projects. In our previous interviews, we discussed his work examining how advanced glycation end products (also known as AGEs) drive the aging process. To that end, Pankaj has developed a novel formulation that combats the endogenous formation of AGEs in the body, known as GLYLO, which you can now purchase for yourself.
Preliminary testing of GLYLO in rodent models has been encouraging. Pankaj and his team found that the formulation reduced glycolytic byproducts, improved insulin sensitivity, reduced caloric intake, promoted fat loss, and extended lifespan by 30-40% when administered late in life.
But how can we gauge the effectiveness of these sorts of interventions in humans? To that end, Dr Kapahi has turned his attention to techniques for measuring biological age (as opposed to chronological age).
The passage of time is inexorable, and we are all getting older at the same rate from a strictly chronological standpoint. However, it is clear that the ravages of age do not affect people equally. There are many 50-year-olds who seem more like 30-year-olds, in terms of their appearance and their physical and mental performance, as well as others who more closely resemble 70-year-olds. And while chronological age is not modifiable, biological age can be changed.
To capture this phenomenon objectively, there has been extensive work developing aging clocks, which are usually based on calculations of epigenetic signatures from saliva or blood samples. These clocks are able to predict future death from any cause more accurately than chronological age.
In response to the aforementioned analyses of retinal imaging, Pankaj and his colleagues have developed a retinal aging clocking, which they have dubbed “eyeAge.” They found that eyeAge could predict changes in aging at a granularity of less than a year – a much shorter timescale than existing clocks.
Retinal fundus imaging offers some major advantages over current methods. For one thing, retinal scans tend to be more reliable than biomarkers found in blood and saliva, which can be influenced by confounding factors like recent diet, infections, etc. Even better, it is highly practical. Retinal imaging is inexpensive and non-invasive, and widely accessible (if you’ve ever had a standard eye exam where they dilated your pupils, you have already experienced this diagnostic tool yourself).
It’s not hard to imagine a future in which annual retinal scans could be used to tell you your current biological age, as well as the rate at which your tissues are aging. With this information, you could figure out whether your current lifestyle approach or medical interventions are working, and make changes as needed. And on a population level, we could use accumulated longitudinal data from retinal scans to identify new avenues for combating physiological aging.
To learn more, check out the interview below!
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Transcript
Pankaj Kapahi
We first fed it a subset of images from the UK Biobank and now based on this learn how to correlate age. This is the age of the person and his image. Basically, the AI is coming up with the model to see if it can differentiate younger and older, and then we put it in a test case and see if it can predict age in the next 50,000 thousand images and it was very good at doing all. What is very interesting about AI is we don’t know which features it picks up to predict age. That remains a black box, and that’s one of the complexities of AI.
Kendall Kendrick
Human OS learn master achieve.
Dan Pardi
Hi everyone, welcome back to Human OS Radio. We are not releasing many shows right now, but that is not due to a lack of interest in doing that. Rather, we are spending our time working on a new health coaching program that we were very excited about. In fact, we plan to launch the program in the next week or two, so if you’re interested to learn more about it, just check out HumanOS dot me and click on our link for coaching. Anyway, we are delighted to produce another show, This time it’s with a familiar guest, Pankaj Kapahi, who has been on the show twice before. Pankaj is a professor at the Buck Institute, an independent biomedical Research Institute that is devoted solely to research on aging.
His lab explores how nutrient status influences health and disease, and particularly how nutrients affect age-related changes in tissue and disease processes. He, along with others, demonstrated that lifespan extension through inhibition of Tor pathways overlaps with the observed effects of dietary restriction on lifespan. And age-related diseases in animal models. His lab also explores the role of diet and circadian clocks in aging and neurodegeneration.
In the first podcast we did with Pankaj, we discussed the role of advanced glycation end products or AGEs on aging and disease. AGEs are formed when proteins and lipids become glycated due to exposure to sugars. Once formed, AGEs bind with cell surface receptors and cross-links with body proteins. Ages that are produced have a deleterious effect throughout the body by embedding themselves into tissues, thereby impairing their function.
In fact, their formation and accumulation are considered a hallmark of aging. We then discussed ways to reduce exposure to AGEs in food and control their formation inside the body. Check out that previous episode to learn more about this under-appreciated. Topic and Agent In the second show, we extensively explored the science behind a new product he has developed called Glylo, which stands for Glycation Lowering Agent.
To develop this product, Pankaj and team screened over 800 compounds to identify ones that prevent age formation. From the screening work, a combination of five compounds were identified to independently affect a g e ‘s but synergistically perform even better. Together, our discussion explored various experiments that he and his team performed to see what kind of health outcomes were achieved in various animals. While on Glylo, the results are impressive, In rodents, Glylo was shown to reduce glycolytic byproducts, improve insulin sensitivity, reduce caloric intake, and promote fat loss. For fat loss, Glylo appeared to lower the set point for appetite independent of peripheral hormones like Leptin and ghrelin, suggesting that glycation may contribute to obesity. We also discussed possible synergies combining Glylo with GLP1 agonists like Semaglutide and how that might lead to even greater weight loss results than seen with GLP-1 agonists alone. Lastly, when administered late in life, Glylo also extended lifespan by 30 to 40 %, which is equal to the most impressive lifespan extension. That has been observed with any compounds in rodents.
In this episode, we’re gonna discuss a new and interesting aging clock. What is an aging clock? Well, in the last 10 years, there’s been a lot of excitement in the field of biological age, which can be thought of as how fast you have aged in the years you have lived. And how to measure biological age? The technique that has garnered the most attention are attempts to measure biological age via a calculation of epigenetic signatures that can easily be captured through either saliva or blood via a finger prick. Earliest versions of these clocks came out around 2010 and since that time have undergone multiple upgrades in the forms of new-generation clocks. At the time of this episode, we are on 3rd generation clocks. And our awaiting fourth-generation clocks, hopefully within the next 12 months. The idea is that with each generational advancement, these clocks get better at their job of detecting true biological age. Why are we interested in clocks that measure biological age? Well, theoretically, these clocks can help us better understand our health status. They can identify insights on areas for us to focus on and ways to modify our health practice and understand what types of interventions or factors modify the biological aging process for good or bad. And this helps us understand how our efforts to be healthy and modify the aging processes are working. So without further ado, Pankaj, welcome back.
Pankaj Kapahi
Thank you very much for having me again.
Dan Pardi
So you and your team have developed a new agent clock. First, tell us about this team and how did this project start?
Pankaj Kapahi
Oh, this is one of my favorite projects because it’s all done over COVID and I never met any of the co-authors in person. It was all done over Zoom. So we partnered with Google Health who had access to over a hundred thousand images of the retina and also some of our partners at UCSF who were ophthalmologists And together basically I put forward this idea that could we use the retinal images to come up with a retinal aging clock? Could that predict? Biological age for the obvious reason that we want to see. Would this be a cheap and easy, non-invasive way to measure biological age and the effectiveness of an intervention?
Dan Pardi
Since we’re talking about biological clocks, what are some things that we would like to see in a good biological clock? What would a good biological clock do?
Pankaj Kapahi
Yeah, I think there’s, I mean, this is a strongly debated question and I don’t think I just want to say off the bat there’s not going to be a single biological. Clock The body ages in multiple ways, but what’s interesting is you can get a reflection of the aging of the Organism by looking at different processes like you can get from the physical strength. For example, a lot of functions are declining so you could use any of them as a biological clock. So what you want is less variability between measurements. You wanted to have a strong correlation with mortality, but also morbidity if possible. And it’s possible, like if we can get tissue health even better. You want the results to be rapid. This is sort of cheap and easy to administer, and it’s better if it is non-invasive.
Dan Pardi
Then what are some limitations that you see of trying to assess biological age? Now, you mentioned a few that we don’t age in this uniform fashion, that different tissues age different times. Are there some other? Issues that are discussed when discussing these clocks now, that kind of where you felt like there might be an opportunity to create something different and new.
Pankaj Kapahi
It’s early days in the field of aging clocks. We need more and more of these clocks and understand the relationship between these clocks. So for example, senescence is well known now to be important in the process of aging and also morbidity in certain tissues. You can’t quite capture that with epigenetic aid some of the popular clocks that are out there. So like there are many examples like that which are one dimension also through the epigenetic age also clocks we’re also learning that they’re different in different tissues. So we’re learning the overall. We kind of think that tissues age similarly in an Organism. But as you dig deeper and look more closer, you’re finding that’s not exactly true. And that’s the kind of, I guess resolution we want to now get at it with it like I said the newer generations of. Clocks where you can be more accurate in your prediction of health markers and biological age. One of the other big limitations I think is are this. Is it simply wrinkle counting or are some of them also going to be useful for early detection of diseases and even reveal something about the process of aging itself?
Dan Pardi
You mentioned earlier you’re looking at information from the eye. Where did the eye data come?
Pankaj Kapahi
From yeah, there’s two major sources. One is a UK Biobank and one is IPAX data set. And these are massive data sets with like I said hundred thousand plus images. And in a lot of cases we also had longitudinal data which was very useful. For this question we wanted to ask whether you could take images from the same person and see the directionality of aging.
Dan Pardi
And so for our audience that might not be familiar, what is fundus imaging? Just so people understand when we use the word fundus, what we’re actually looking at right i’ll use these terms interchangeably, but we’ll use the word retinal clock, which is looking at the age of the retina, and the way it’s done is through what’s called fundus photography. And fundus is the inner lining of a hollow organ. In this case, it’s the eye. So what you’re doing is taking a picture of the back of the eye. What you can see is the retina, the macula, the optic disc, the fovia which is responsible for this very sharp vision and has a high concentration of cones and blood vessels. One of the things you might remember when you go to a doctor, especially in the earlier days, the first thing the doctor would do is take the ophthalmoscope and look at the back of the eye, because it’s a very simple way to just gaze the general health of a person. With fundus imaging, you need a special camera to focus on the back of the eye, and there are newer versions that don’t even require pupil dilation. And then you can get this picture taken and then it becomes a record. And what we’re doing, what Google Health is doing, is finding ways to analyze this image. Much better. So you’re using AI. You’re doing a much better job of getting information from that image.
Dan Pardi
So just by looking at the back of the eye, what are some things that we can detect and look at that will correlate with a person’s health?
Pankaj Kapahi
Yeah, the beauty of looking at the back of the eye is there are what are called the retinal capillaries are there. So retinal (tissues) having some of the smallest aspects of microcirculation that can be directly observed is a window to the tissue. Gives you so much information. What’s amazing is that the AI can tell you a lot more than any human being can right now tell. One of the things they’re seeing is the changes in the vascular system and that’s why you can predict heart health. But also in some cases it’s also relevant for some things that are happening in the brain and certain debentures are being predicted now using the retinal imaging as well. It’s used for several types of eye diseases like age-related macular degeneration, diabetic retinopathy. And now, cardiovascular assessment and certain types of dementia.
Dan Pardi
It’s a interesting and unique window because we can detect the microvascular of the body, which is otherwise hard to detect. But since we’re looking right into the body, it’s a good place to do that. Now with AI, we can take these images and do more with them. You use deep learning models on the fungus images. So let’s first introduce what are deep learning models.
Pankaj Kapahi
So the deep learning models are a type of AI models, essentially artificial intelligence models. When a person looks at these images, and some people who are experts, they’re becoming essentially better at pattern recognition and classification. But computers can do this job much better. With our papers, AI had access to more images than most doctors would ever see in their career. And when the human eye looks at something, it’s filtering, and it’s focusing on a few aspects. With AI, you’re looking at all of this in an unbiased manner, right? So you’re using the structure of this image and machine learning so that you can predict what sort of changes in the image mean to process X. Then you train the images with an outcome. For example, we give the different ages of the people and ask, can you predict the age from this? So first you train the model, the different ages, these images, then you’ve trained the model and then you ask what it can predict and what’s very impressive and that that’s why. Ai technology is taking off is it’s very good at forming these patterns and understanding this complex relationship between this input of a very complex image and the output of can you predict age? Can you predict blood pressure? Can you predict eye diseases? And each time, we’re finding that this is doing a much better job than any single expert would do, essentially.
Dan Pardi
Tell us more about the details of those particular data sets and what the process was to access the data and then run the AI model.
Pankaj Kapahi
We first fed a subset of images from the UK Biobank and said now based on this learn how to correlate age. This is the age of the person and his image. Basically, the AI is coming up with the model to see if it can differentiate younger and older and then we put it in a test case and see if it can predict age in like next 50.000 thousand images and it was very good at doing all that. What is very interesting about AI is we don’t know which features did it pick up to predict age. That remains a black box and that’s one of the. Complexities of AI. And also then we asked, OK, now can we give you images 2 from the same individual but taken at different times, six months apart, one year apart. Can it tell which one is the image that it? We’re assuming here that the people who gave to the second image was slightly older. Now it’s possible that these people did things so to make themselves younger, It’s possible, but however, we are just assuming that those images were older. So now can the AI tell which ones were older and it did that with 71 % accuracy that this is the image that is from a later time point. All this gave us confidence that this could be a very useful tool. So it was as good as any of the other previous biomarkers that have been used. We know age and epigenetic age. They seem to be as good as those other biomarkers at predicting age. This is on just the beginning, but it could be useful. Marker that could complement other biomarkers that are out there.
Dan Pardi
You also did some validation studies. Tell us about the GWAS analysis that was done in the paper and why did you add this to the research?
Pankaj Kapahi
One of the big questions in the field of retinal engine clocks is really this question of how are these working, What’s the biology behind all this? What we did was called a GWAS, so genomide association study for each of these patients. The genetic information is also available at the UK Biobank. They have half a million, they have their genetic information. So then you can start asking if the gap between the eye age and the chronological age predicts your health. Other groups have also looked at the same clock, different algorithms, but they came to the similar conclusions. So if the eye age was. Worse than your chronological age, you had a higher risk of dying, essentially mortality. Then we did ran a GWAS (genome wide association study). What predicts this acceleration or deceleration of the retinal age? And we find these genes which reflect the processes which are behind this. We found the obvious genes relevant for macular degeneration and eye diseases. You’d expect that means I think the study was working. Secondly, we found some genes for cardiovascular risk. Maybe they’re influencing the vascular health. We also found genes for Alzheimer’s disease and dementia, and so certain cancers, cancers potentially could be relevant to the health of the microvasculature. So we found a number of interesting genes, and one of them we validated. This gene is called ALK is a kinase in relevant for small lung carcinoma. Now we found that when we inhibited this gene, it also slowed down retinal aging in a fly. That shows conservation of genes that are relevant for retinal aging, potentially between the humans and flies. That’s exciting because we’ve now not only identified the clock, but also by understanding what influences the clock you can. Identify novel pathways and genes that would influence not only I age but also organismal aging so it turns out this ALC inhibition of this ALC gene also extends lifespan in the flies.
Dan Pardi
How frequently did you test the flies to then assess if this knockdown of the ALC gene was having an impact on their aging according to your I age data?
Pankaj Kapahi
In the fly, what we look at is what’s called photo taxes. Flies have a natural ability to go toward light, but we have found. As they get older, they lose this ability. So we look at it almost every week and the flies die in like 5 to 6 weeks and you can see that’s sufficient to tell if there’s a difference between them because we employ hundreds of flies for an experiment like that.
Dan Pardi
Ok, so a little recap. You used data from IPACS and the UK Biobank that was your data source. You ran these deep learning models and you found that are correlated with chronological age and also seemed predictive of biological age. With over 70 % accuracy, you could determine if one sample was older or younger. Because of how AI works, you’re not exactly sure what it was detecting, but hopefully you can learn more about that over time. And then you did a GWAS analysis or a genome wide association study to then look at different genes that might be associated with aging. And then you knock those down in a fruit fly model and then assess to see if that was then something that you could capture with their eye health. Were you using? Images of my eyes, or just this phenotypic behavioral expression of how much they were going towards the light?
Pankaj Kapahi
Yeah, we did both, but mainly the phenotypic assay. But we can also look at the retinal image of the eye of the fly as well. The eye age clock was able to predict age within three years.
Dan Pardi
How does that compare to other clocks like third-generation epigenetic clocks?
Pankaj Kapahi
That was one of the most interesting things, I think, in the paper. For me, it seems like the predictability is in the same ballpark. It was almost too close to each other. But here’s the surprising thing. We asked the question, what is the correlation between the clock that we use and the other clock, which both have almost similar predictability? You’d expect they would be correlated with each other. There was no correlation between the two. I mean, it wasn’t even a small correlation. It was like no correlation. What it means is your body is aging in different ways, your skin aging a certain way, your face aging a certain way, blood vessels aging a whole different way, your muscle and nervous system’s all aging. There’s some people who are older cognitively they’re completely there, but physically they’re more frail and vice versa. So we know that when you look at the level of the individual, these things don’t correlate. But yet overall, broadly speaking, we think aging is accompanied by similar aging of multiple systems. But as we look closer, that doesn’t seem to be the case and I think that’s. Gonna be a very interesting thing to learn from these clocks in terms biological age. Let’s discuss.
Dan Pardi
Potential implications here Because you’re doing a retinal scan, do you foresee this as something that could eventually work its way into a iPhone camera so that people could do self-assessments?
Pankaj Kapahi
That’s my hope and we’re actually beginning to start do this ourselves, where we’re building an app so that someone can provide an image and we could assess. From that, image high age, cardiovascular risk and other aspects of health and see how much can just a single image from a person be useful in providing some feedback. Smartphones, there’s a lot more work needs to be done to prove that, but there are handheld cameras that you can buy that can be used for generating retinal images. So today is not far when this would be available for you. At home, where you do the simple test, follow it longitudinally, you would keep taking images of your. Retina for example, every month and sort of follow the trajectory of your health at.
Dan Pardi
Minimum when you go get an eye exam, one of the outcomes of that could not only be a vision test and anything that would be corrective, but also a biological age assessment which could come with every eye exam that’s being interesting knowing your biological age, but I think there’s something deeper here in that there is the potential for using a clock like this for early prediction of diseases. Just imagine. And we’re trying to see this for dementia, for example, if you could imagine, just like it is with cancer, if you catch it early, the chances of overcoming the cancer and morbidity much higher. Same thing applies to other diseases. So if you understand what’s happening to your health early, there’s huge potential to improve health. And despite how easy it is to get a glucose test, 70 to 80 % of the pre diabetic people don’t know they’re pre diabetic, right? That’s the time when you wanna catch. And try to improve someone’s health right so whether or not it’s necessarily a better biological age assessment than other clocks, if it is the best clock at detecting pre diabetes, then that alone would have tremendous value to helping people age better.
Pankaj Kapahi
Exactly, yeah. Let’s say three four things that you can use this clock for is 1 biological age. I age more than half of the people. By the time they’re aging, they’re gonna have some kind of eye problem thirdly it’s diabetic retinopathy. That’s a specific diabetic complication that accompanies diabetes. Now, not everybody gets it, but it’s very important. If you’re getting diabetic retinopathy, these clocks can tell you something about that before it happens. So for example, you give a score of the diabetic retinology one to four, and if you could catch it at an early time point, then you know there are certain things you should be working on to avoid the diabetic retinopathy score to go to a higher number. And finally then also there’s cardiovascular risk and a host of other eye diseases. So there’s a lot that can potentially be learned from.
Dan Pardi
I believe that prediabetic patients can develop diabetic retinopathy. They’re just at a lower risk than patients that have diabetes.
Panjak Kapahi
Which of course makes sense, but exactly that’s what you want to know, because diabetes is not one-to-one correlated with getting diabetic complications. And you’re absolutely right, prediabetic people could already be getting some of the complications. Those are the people who need to worry the most. Because it’s the complications that are the problem. Just having high glucose is not in itself a problem, it’s the complications that accompany that becomes a problem.
Dan Pardi
But it would be interesting to have 10 people do the test every hour for 24 hours. So that’s looking at within-day variability to see if that is a factor that matters. And secondarily, I’d love to see people doing this examination. Every day so or once a week for six months to see how much variability is there within an individual and if that is very low variability from test to test, you will have an understanding that changes to this score are relative to health than the normal variability that happens within that test. That is something that afflicts all other biological age tests. You’re doing a sampling in time and you don’t know variability had to do with time of day and if you get another test done and it’s a lower score or higher score. Is that because of score variability or is that actually a real change in your health status?
Pankaj Kapahi
This is where I think AI becomes as an essential tool for such research. Because we could not possibly go through these images, and I’ve looked at some of these images, it is not easy to make the sense of them, even if you’re an expert. For example, they were telling me at Google Earth that their AI algorithm can predict a male from a female retina, whereas doctors. You can’t, and what you’re suggesting is very important questions to get even more fine-tuned models to predict what’s going on.
Dan Pardi
There’s those that argue clocks, the interesting research tools, but they’re not really ready for commercialization. But if it was easy enough to do this at home with a camera and because of the rapid results compared to, let’s say, four to six weeks that you might have to take between measurement and results for epigenetic tests this.
Pankaj Kapahi
Included, to do that type of validation better than other systems that are out there. That’s absolutely true. Yeah, it would be exciting to get in position to do that.
Dan Pardi
How do you extend this so you publish this paper? It’s quite interesting. Is other research planned on using the I clock And tell us about that yeah it’s an exciting area in the lab. So one of the things we’re doing is understanding the biological mechanisms that drive I age, like understanding the genetic factors that are important for that. And other diseases that they are linked to, we want to understand better the role of IH and how well it predicts other diseases and others morbidities, right? For example, it might be good at predicting a certain aspect of cardiovascular disease or certain aspect of diabetes, which would be great to understand that. So we’re doing that and certain types of risk factors for cardiovascular disease might be captured here versus through another biomarker. These are kind of things we’re asking. Secondly, we’ve got an interventional trial we’re trying to hopefully start this year on Glilo. So advanced like Asian end products are another thing that drive eye aging. And our preliminary data from the mice show that when you treat the mice with Glilo, you also slow down the eye aging. The health of the eye looks way better. So we when we are trying this in humans, we want to see for example giving Glilo for three months, will that influence the eye age using this clock. Thirdly, we’re keen to develop an app so that this could be made more accessible to the average person and maybe helpful in predicting early diseases and a biomarker for age.
Dan Pardi
I would imagine Lilo would have an impact on retinal health, and I’m glad that you’re gonna look into that directly. I would love to see it done in rodents, since we’re testing so many interventions in them. This could be powerful at assessing whether or not interventions are working and in what direction.
Pankaj Kapahi
So the buck has purchased such a camera for imaging the eye of rodents and it’s going to take a while to get to the sort of images and that’s algorithm going for mice, but it’s definitely something very possible.
Dan Pardi
Is there anything that I haven’t asked yet that you want to make sure that you mentioned before we wrap up?
Pankaj Kapahi
I forgot to mention how we got more into this eye was from a study we were doing on diet restriction in flies. We found that upon diet restriction there is a change in the gene expression in the eye. Totally surprised us because you know, we were looking at gene expression all throughout the body and we found a very strong signature of gene expression changes in the eye which made us look here in the 1st place. We found that in response to changes in the diet, the eye is one of the first places to see changes and what we found was in diet restriction, you slow down the aging of the eye. What we think happens, evolutionary speaking, is that eye homeostasis improves in the sense that when an animal that doesn’t have as much food, it’s trying to optimize its vision so that it can find food, right? So what we’ve found was that eye homeostasis improves and the result of that is there’s a slowing of age-related visual decline. And this is opposite of what’s happening in diabetes. Dietary factories has such a strong impact on visual function. So I think the other side is also true that if you take good care of your diet you can really improve. I function, I just wanna leave with that something that’s reactionable.
Dan Pardi
It’s interesting to see how all your work ties together. I wanna thank you for coming back on and sharing this study. We love hearing about your work and appreciate you coming on to talk to us about it.
Pankaj Kapahi
Thank you so much and we have some exciting work in the store and I’m looking forward to coming back again.
Kendall Kendrick
Thanks for listening and come visit us soon at humanOS dot me.
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