Major Report Update and Omic Age Algorithm

hi everyone thank you so much for joining us tonight I&#39;m Hannah went I&#39;m the director of operations here at true Diagnostics and before we get started I am just going to go over a couple Logistics regarding this major report update that is just as equally exciting um so first and foremost uh before I go over this slide that you see here I just want to talk about the reports that you all should have in your portal now this is for everyone who&#39;s ever taken our test both direct to Consumer and healthcare provider and patience of healthcare providers so regardless if you&#39;ve ever taken our test you should now see um two new reports if you&#39;re a direct consumer and a patient of a healthcare provider this is our new 12 cell immunity convolution method and our physical fitness report that includes V2 Max F1 grip strength and gate speed if you are a health care provider you should be getting those two reports for every single patient you&#39;ve ever tested historically along with our new uh inflammation cognition report which is the D methylation CRP and il6 so those reports are currently live now of course we&#39;re going to dive into our OMC M age today but if you want to upgrade any kits that you&#39;ve ever run as a consumer you will have to log into your account and you&#39;ll get a popup message and you can purchase that way or purchase directly on Shopify if you&#39;re a healthcare provider you can see here when you log into your portal this message that you&#39;re going to get it&#39;s going to explain a little bit about what that OMC MH report looks like and then you can actually click in the bottom leftand corner of that message uh that says click here to upgrade the patient results from previous samples um so if you purchase that way that will be the quickest way doing that in your portal it will be a quick um 24 48 hour even shorter than that turnaround time remember if you&#39;ve taken the test once it would just be $1 199.99 if you&#39;ve taken the test uh twice or more no matter how many times it would be $29.99 for that upgrade so um if you can go ahead and go to the next slide Ryan please oh all right and then once you click that bottom right hand cor or lefthand corner Healthcare Providers you&#39;re going to get a list of all all of your patients and every single sample that you&#39;ve ever tested with us to date you&#39;ll then select the upgrade on the left hand side of the patients that you want to upgrade and you&#39;ll click that button on the bottom um that says basically heading to payment and submitting those and then we can charge your card IR L on file so it&#39;s super turn key the way we set it up for you um if you don&#39;t want to go through all of that hassle I am open via email and so is our support team and we can do this for you um let us know which patients you want to upgrade which specific kit IDs if not all of them um and then uh go ahead and give us the okay to charge that card on file as well um if you&#39;ve already purchased these and they&#39;re not in your portal yet give us until early next week um out of the 35,000 samples we&#39;ve tested to date we&#39;ve had a massive demand so we&#39;re working as hard as we can to go ahead and get these out um directed consumers if you want an interpretation here reach out to um our support team directly and we can set you up with a consult um Healthcare Providers this is the webinar to learn everything you can about OMC age um we will send this out afterwards as a recording um but of course you can set up a call with me or Ryan Smith to dive into some of your historic patient Resorts uh reports so without further Ado I&#39;ll introduce Ryan Smith and um if you stay till the end we are going to be raffling off three triage complete kits um I&#39;ll just put the names of those winners in the comments and then you can email me directly at Hannah true diagnostic.com and send me your shipping address um and information so um we hope that you all stay to the end and enjoy the rest of the presentation yeah thanks Hannah for that introduction and thank you all for attending uh we couldn&#39;t be more excited to finally launch this new report uh this report has been a long time coming um and so so we want to start with uh just some thank yous um particularly our entire team at true diagnostic this has been really a three-year effort um with uh Dr Jessica lisu and chinguen Chen at Harvard as well they&#39;ve been instrumental in helping us procure samples find the necessary data that we need to generate such an amazing algorithm and so I can&#39;t uh start this without giving thanks and again as Hannah mentioned we will be giving three kits to anyone who stays till the end it will be a science heavy discuss especially toward the beginning and toward the end um but uh but we want to talk about all the ways that this algorithm is so much better than anything that&#39;s been on the market before um and that requires some some specifics so um I hope that we can keep you all engaged and and tell you just why we think this algorithm is so much better and so useful to measure your aging process um so before we we get into the actual specifics we got to go over some Basics again first um so obviously why is it important to measure aging um and the answer is that aging is the biggest risk factor for every chronic disease and death and by a wide margin again it pales in comparison to smoking obesity and many of you all have heard us say this before but um uh but it&#39;s it goes uh it&#39;s important to reiterate just how important this process is and so oftentimes we also like to talk about some of these definitions biological versus chronological age but the reason we also like to emphasize this is because the reason we test is to predict outcomes we want to measure your aging process so that we can quantify your risk of disease and then hopefully change it and so some of the examples like we had in that dunan pace trial where we can see all of those people in the in the pictures on your right are the same chronological age um they&#39;re all 45 but they look vastly different because aging makes our bodies function worse and predisposes US disease and risk and that&#39;s really what we&#39;re trying to avoid and we say you cannot measure uh you can&#39;t change what you can&#39;t measure um and so that&#39;s what we&#39;re really trying to do is to provide the best way to quantify aging um and so again again uh what we&#39;ll have as an output is much like the other biological clocks um and so if you&#39;re accelerating epigenetic aging that means you&#39;re going to have generally a shorter lifespan and healthspan and those who are decelerated mean longer lifespan and and better healthspan so we really want uh to use this as a diagnostic tool to influence the way that we age and improve our aging process um and and so it&#39;s always important to reiterate that if you&#39;re healthy your biggest risk factor is age um and that&#39;s really the one we want to start with um so with that we also want to go over the history of some of these clocks uh because we&#39;re going to be badmouthing a little bit of these first generation clocks these clocks which we you know honestly have have even been using in this intrinsic and extrinsic age um so these first generation clocks as a recap for people are clocks that have been trained to predict the actual chronologic age of a patient so the day that they were born essentially um and these clocks again started off in 2013 with the Horvath and Hanam clock um and uh those Clocks Were were a great start but lots of time has passed since then um we&#39;ve essentially had over a decade and so what has happened in that time and the answer is better ways to measure the aging process and so this really started in in 2017 2018 with these biological clocks that were trained to phenotypes of Aging um and that&#39;s really what we want to predict right we don&#39;t necessarily care about someone&#39;s chronological age we want to quantify how their body is is changing with the age um and so that we can go ahead and reverse it and so the most notable algorithm here were Morgan lavine&#39;s pheno age this again um many of you might be familiar with because it started with nine blood-based biomarkers and if you&#39;re even to type in pheno ag calculation or calculator on um Google you&#39;d find a couple places where you can actually calculate this based off those blood Labs um and so that was a really good start because it started to capture more of that biological aging process but in the case of pheno age it still used things like someone&#39;s chronological age and so um the next big breakthrough on those biological clocks uh the second generation clocks was uh grimage um by by Dr horbath and Lou um as well um and that again was trained to predict time until death but it did so by also predicting certain proteomic biomarkers um and by by doing that it was able to capture again more of that biological signal and it was more predictive of even pheno age uh of certain types of outcomes um and then lastly we have these third generation clocks really led by uh Terry mofet Dan bsky and a whole host of other individuals who are certainly worth naming um but this clock was able to be trained off longitudinal data in this dunan pace cohort so um it it certainly took the clinical biomarkers a step further because instead of just using nine it used around 21 blood-based biomarkers um but it also used a longitudinal approach so measuring the same patients across time rather than just different patients across different time points um and this again performed extremely well you know compared for instance to horvat&#39;s clock which one standard deviation acceleration of a horbath clock would represent a 2% increased risk IM mortality while one standard deviation of that third generation D needan Pace would represent a 64% increased risk of mortality so we are getting better by designing more clocks um but it&#39;s important to mention that the clocks which are the best so in particular Grim age and denan Pace have been trained with the most robust and relevant clinical data um so in grimage it was these proteomic biomarkers in dunan Pace it was 21 blood-based markers but those are the clocks which tend to perform best so we know that the clocks which capture the best aging signal are the one that measure the most aging biomarkers um and that&#39;s a really important piece of information because it really launched what we call omic age um and and really uh I always like to to prove this point a little bit further by showing you what&#39;s happened to just the Hallmarks of Aging um since we started in 2020 um and so when we started in 2020 there were nine recognized Hallmarks of Aging as you see here on the left um however now um in in 2023 we know that there are 14 or 15 uh different Hallmarks of Aging which have been defined and and readily accepted by the scientific literature and so if there&#39;s one thing to mention it&#39;s that aging is complex which is exactly why these clocks which capture the most phenotypic information the most information about our our current biological State perform the best and so we we understood this you know we understood this even when we started and we knew that we wanted to create the clock and that meant lots of information and so this is why we took a multiomic approach and so um for those of you who are unfamiliar with sort of these omix um omix are different levels of measurement that we would consider within the body um so most everyone is familiar with uh you know genomics uh the study of DNA obviously we&#39;re talking a lot about epigenomics so if you&#39;re here you&#39;re very familiar with that but we also have transcriptomics where we look at at RNA um and we have proteomics which peptides and proteins that are made in the ribosomes after we we we sort of process that RNA related information um then we also have metabolites in the body that are created from processes these are things like hormones neurotransmitters um even you know gut microbiome metabolites um and then lastly we have that that phenome right so what diseases are some people diagnosed with um you know what are their physical performance characteristics and and so together we wanted to measure all of this information um because we wanted to get the most comprehensive biological aging signal possible so we did this in our cohort with Harvard and partners biobank we were able to quantify almost every single one of these levels and I think this is important because you know I always like to link to some of the Articles which have been published um over the years about uh the Human Genome Project um I think so many people thought the Human Genome Project would solve so many parts of medicine um and unfortunately you that didn&#39;t really happen like we thought it would um and so people call this the failure of the genome or or why did it fail to predict so many big disease breakthroughs um and the answer is again it&#39;s just one SM small part of a very big puzzle um and so genomics is is might have predispositions but epigenetics is about the expression of those predispositions and and epigenomics is still not the entire puzzle as well so what we really wanted to do in this particular clock decision-making is to integrate all of these different features and all of the relevant biology into one single marker um and so this is also important because if you tried to quantify each and every one of these um as much as you as we did in this study it would cost tens of thousands of dollars um to be able to do all this analysis on one person just isn&#39;t cost effective um and so what we wanted to do was to Trin it all back into the omic we think has can capture the most information and that is epigenetics um and so that&#39;s really what we did to start we we started measuring lots of data in a cohort of about 5,000 individuals um but we also had to choose what we trained it to predict so we have all this this uh information and data but we still have to Define how that data is put into a model for biologic age um and so uh if we look at clocks that have been happened before we have things like dun pace and pheno age which use blood-based biomarkers or other uh clinical biomarkers to create a a score of Aging um and then vice versa we also have the clocks that are trained um till time until death to predict the time you might pass away and and that was grimage for instance and so what we decided to do with omage was to split the two um I think both of these approaches have their benefits um and so what we wanted to do was to to split the difference and to do a little bit of both and so what we did was first we created a clinical time until death score so we started with time until death but we wanted to predict it with clinical blood-based values and so um in in Partners cohort uh they have samples which we pulled from their biobank and some of these samples have been as much as uh from as long as 30 years ago and so the good thing about these samples is we know what happened to them and their outcomes so we actually selected a large majority of this group for people who had already passed away and people had passed away for causes which we already have known and so we we selected those individuals and those samples but then we also looked at all of their health electronic medical records and so we extracted over 61 clinical phenotypes this included things that you might see on the genan pace calculations like you know cholesterol um or like you would see in Pho without v um but we wanted to create something that was a little bit more robust so not only did we use more people um we used over 60,000 samples to establish this um and then we sort of trained it all to predict time until death and what happened is we ended up creating around a 19 blood-based biomarker um uh panel which we call EMR age and so this EMR age is an alternative to even pheno age where you can even upload your your your biomarkers like your alkaline phosphatase or your albumin um and calculate your your EMR age um and so this was the first step in our algorithm and we trained that to predict time until death and what we saw was that it was better than pheno age in almost every category and we know it was better because first off the P values were better but in addition to that we had larger effect size so the hazard ratios how we predict the outcome of disease um was better in in almost every regard and so we knew that this method of quantifying age via EMR related records was better even than pheno AG version of em um EMR related data that you could pull out and so this was a good first step for us um and so uh the next step we wanted to do is we wanted DNA methylation to predict that bioage or that EMR age um and so we trained a methylation algorithm to predict that just like feno age would have and again what we saw was a really high correlation our bioage uh was very close to our our pheno bio AG it had uh sort of an R squar value around 082 which is excellent so we really saw that we were able to um to have a a really well performing methylation algorithm to estimate all of those clinical biomarkers um and so uh that was um a very good step in the process and so um the next thing that we did um in in step two so we had the beginning of our algorithm one that had been trained over on over 60,000 people and then created a a DNA methylation algorithm to predict that um but the next step of it was we wanted it to be informed by the other multiomics and so how do we incorporate all the metabolomic and proteomic data that we had in this cohort so the next step that we did was to develop something we&#39;re calling epigenetic biomarker proxies and so these proxies are ways to use DNA methylation data to predict uh different other values and I apologize for some of the formatting here um but the idea here is that we can take proteomic values so for instance alphascan or cow and proteins in Alzheimer&#39;s and we want to predict those with just DNA methylation alone and so we created what we these epigenetic biomarker proxies to estimate these factors and and we created a lot of these so in our training we only used about 400 um but with that being said we have tens of thousands of algorithms now that can just take in DNA methylation information um and output other values so we can estimate your igf-1 your cortisol your neurotransmitter levels like serotonin and dopamine we can look at the posos chemicals um that you&#39;ve been exposed to through these epigenetic signals um and and also a lot of clinical things which we&#39;ll talk a lot about in detail like hba1c V2 Max and lipids like HDL or LDL um and so uh this is how we actually Incorporated all these other omix into a single methylation based diagnostic is we trained the methylation to be able to predict them um and there&#39;s a history of this happening before in the literature so one of my favorite examples is Ricardo Ricardo Marion&#39;s Labs work um in the leithan birth Court in generation Scotland and what they did um was they actually use D methylation to predict several types of proteins um one of the best examples is their D methylation algorithm to predict CRP um and so the D methylation CRP actually shows benefits over regular HS CRP um and one of the reasons for that is because CRP is such an acute phase protein it can vary by up to a thousandfold within just an hour um whereas D methylation CRP is a little bit um longer lasting and because of that we can see associations we just wouldn&#39;t see with such a variable blood-based biomarker so here you can see in The leithan Birth Cohort that there is no Trend with hscp um with age but we do see it with DNA methylation CRP um in addition to that um we see um that uh sorry and this is a little bit of a tangent but CRP also is one of the reports that we&#39;re launching um and so uh we can actually predict your CRP and it is higher with obesity and drinking um and so this is one of the other outputs but and we also see associations with DNA methylation CRP compared to regular CRP and things like cognitive function if we look at the generation Scotland cohort and lethan Birth Cohort we see no significant associations with regular CRP or genetic scores to General cognitive function but we do see this with DNA methylation CRP um so higher DNA methylation CRPS are associated with poor cognitive ability in both cohorts um where we don&#39;t see that association with CRP in addition we can actually even see the comparison of how um much more predictive DNA methylation CRP is than regular serum HS CRP so here on the left you can see all these neuroimaging outputs and in the red you can see the effect size of our DNA methy predictor of CRP and in the white you can see the effect size of serum CRP and as you can see in every single one of these M um brain MRI related uh phenotypic outputs DNA methylation CRP is more effective at predicting or showing associations to cognitive function outcomes um and so again DNA methylation CRP here is showing us that it has benefits over even classical hscrp and not only that but we can also find out what&#39;s driving this process so we can actually see what genes are most associated with high inflammatory signals um and so in the case of CRP the the gene location which is most highly weighted is the sock S3 Gene and this stands for suppressor of cyto kind signaling so really it is meant to be a uh a gene which reduces inflammatory burden in cyto kind signaling um but unfortunately when it&#39;s upregulated um we we have inflammatory burden and we see that in these DNA methylation predictors of CRP um and so uh but but this understanding is also very relevant and important and so one of the other things that I&#39;ll come back to a little bit later is that these DNA methylation biomarkers usually perform um a little bit like an hba1c where they&#39;re sort of a longer average estimates rather than plasma biomarkers which represent uh sort of day-to-day variation oftentimes and so this longer average can be good in some cases by showing associations we wouldn&#39;t find on the day-to-day but for more acute things it might not be is good um and so we have to weigh that um those negatives and positives as we start to incorporate these um but this is really what we did in our our our omic age cohort we developed uh epigenetic biomarker proxies for about 107 proteins 21 clinical variables and 267 metabolites and we chose these approximately 400 um because they were the ones that were selected as most relevant to time until death um and so that&#39;s how we got these in the first place um and then we sort of and you can see their performance so um you can see that a lot of these what you see here is a piercing correlation um and this is basically how it performed um from the measured versus predicted so if we measured let&#39;s just say a CRP of two um and we predicted a CRP of two through methylation that would be a correlation of one that would be perfect um and as you can see here we have varying degrees of success um across many of these predictors um but generally most of them perform quite well um for instance in in DNA methylation Grim age um they use a threshold of around uh 35 r value um and almost all of our predictors exceed 35 um there are very few that do not and so we have a really really long list here um but again these are being chosen um by sort of the time until death algorithm so uh we did not pick these one by one we we threw them all the the board to see what was most predictive um and eventually what happened is we chose uh 38 were selected um as the final inputs into this omage algorithm so now in our omage algorithm we actually quantify each of these 38 biomarkers individually and so on the report you will see this but um just to show you how well these things actually perform we also did an analysis of how these things were related to uh both disease as well as lifestyle factors Within this Harvard cohort and what we found was really really positive so for instance um we saw that hba1c and glucose are methylation-based predictors of these things were very highly correlated with type two diabetes which again makes perfect sense because we know hba1c and fast and glucose are biomarkers for uh type two diabetes um and in addition to that we also saw things like igf-1 binding protein 2 having a differential effect size for underweight versus overweight and this is something again that we know is is classical from the literature that as we tend to become more insulin resistant our igf-1 binding protein values tend to go down um and and so uh so this again makes sense and and what it really shows us is that even our DNA methylation predictors are having this the similar correlation to what we&#39;d expect for those actual biomarkers um and so this is again a step in the right direction but we have even more examples of this so one of my favorite examples is from another study we&#39;ve done done on Twin pairs with Stanford um and in these twin pairs we actually took one twin and put them on a vegan diet and then we took another twin and put them on an omnivore diet um and this was to look at the biological aging effects of vegan versus omnivore diets um this study should be coming out sometime later this year um but we also wanted to to just look at some of the other methylation risk Wares we or the epigenic biomarker proxies that we did within this cohort as well because this cohort didn&#39;t get extensive metabol omics or it didn&#39;t get extensive proteom so they did get methylation though and so we really wanted to see if methylation could inform us about maybe other proteomic or metabolomic changes that weren&#39;t actually measured in this cohort and so we actually found three significant values all of which changed um and so one is an amino acid uh metabolite the other two are proteins particularly cxcl4 and pancreatic ribonuclease um and the great thing about all three of these is that every single one of them has been highly described in literature before um as changes which are associated with vegan or or vegetarian diets um and so uh so this again backs up our assumption that these epigenetic methylation predictors of these metabolites or proteins or clinical values are behaving just like their measured metabolites and that is a really good thing to see because it means we&#39;re capturing that biological signal um in methylation alone and this is fundamental to how we Design This multiomic algorithm because the end of the day after we created this time until death predictor with clinical biomarkers um we also wanted to inform it with all of the biological change we would see across multiple omic levels um and so uh to do this we had to to to train the algorithm to see what actually helped predict um the uh omic Age and and some of these outcomes and so at the end of it we chose again 14 proteins 10 clinical um epigenetic biomar predictors and 14 metabolite EVPs um and together included them all into omage um and this is that&#39;s sort of how we trained it we&#39;ll talk about now why this is better and how it makes a difference for anyone who&#39;s interested in testing um or anyone who&#39;s interested in quantifying um age or the effect of treatment interventions we&#39;ll go through exactly why it matters and so that&#39;s what we want to talk about now now that you you know how we&#39;ve trained it and all of the data that went into it um again this data set is larger than any epigenetic algorithm that&#39;s been trained before um and includes metabolomics which has never been done um it includes clinical biomarker proxies which has never been done before and um you know grimage did use proteins but it only included um around 80 um we included over 15,000 in our analysis um and so uh this represents a much larger scale as well but let&#39;s talk about why this actually matters so one of the classical issues with biological clocks has been their reproducibility and precision um on the right here you can see um ICC values of some of these previous clocks um and and this has been a big issue so um if you measure the S one sample multiple times ideally you would see that sample be the exact same every time you ran it but unfortunately this isn&#39;t really what happens in real life sometimes variation on how you partition the sample might have an effect but this is how we judge the Precision of our test we really want a test to be precise which means that if we measure a sample multiple times we want it to be the same almost every single time um and so traditionally ICC values above 0.9 generally have excellent agreement um and sort of 0. n is the threshold but the ICC values that we&#39;re getting for omage are 0995 this is excellent this is means that that we&#39;re getting less than a 05% variance if we test the same sample multiple times um and so this is a really really good standard and definitely beats the 04 type of comparisons that were found in in some cohorts for the original horbath clock and this is important because if you test with us and you retest with us we want to make sure that any change that you&#39;re seeing in that algorithm is purely biologic and not driven by laboratory error um and and so if you take a test and see change you want to be able to make uh assumptions and actions based on that change you don&#39;t want to just chalk it up to some laboratory noise or err um and so that&#39;s really uh what we like to see here is omage is incredibly precise it has an IC value of 0995 or above um and so that talks about Precision in terms of accuracy um accuracy is a little bit harder to measure in those first generation chronological clocks we could just tell you how close it was to predicting accurately your chronological age but when we&#39;re using it to predict biological age we really have to look at how it affects our risk of disease right because we know aging is the biggest risk factor for every chronic disease and death and we&#39;ll do that a little bit later so one of the other B problems that we also solved was the inclusion of uh these immune deconvolution methods and this has also been a major problem for these epigenetic clocks so for instance um the buck Institute uh did some amazing research on individual cells from the same individuals and they found that human naive cd8 cells um exhibit a 15 to 20 year a difference than affector memory cdat cells from the same individual and this is a big problem because if we&#39;re testing your blood we&#39;re getting multiple different types of cells um and if someone has for instance an autoimmune disease a recent viral illness or even if they&#39;ve consum consumed caffeine that might affect their level of some of these values and that as a result then might change their biological age and we know that someone drinking caffeine you know once is not necessarily um you know a biological age change of multiple years so we shouldn&#39;t be seeing that on our algorithms and so this a big problem that multiple people have tried to correct we have done a webinar on this earlier year this year we talked about the buck institutes intrin clock um but we took a different approach and that is that we actually included weights for each of these immune cells within the algorithm so what we can do is actually quantify the relative percentage of each cells and then make sure our algorithm takes that into account so that we&#39;re getting very very precise ages that are not dependent on your immune cells that are independent of your immune cells and really capturing real biologic aging instead of just immune differences across individuals or even across yourself across time um so this is the other way that we&#39;ve made this more precise and less noisy so we&#39;re measuring true biological variation um one of the other things that&#39;s really exciting about our reports is that we actually even tell you um how your biological aging is affecting your risk of disease and so um in the reports we actually tell you uh how much what your current risk relative risk is based on aging profile and how that would change if you were to decrease your age by one year or three years or if you were to increase your age by one or three years and so now we we really wanted to gamify this because the reason that we&#39;re all interested in aging is reducing risk right um and so now we can actually tell you how much a one-year reduction or a three-year reduction would be so you can really shoot for those goals um and and really try and reduce your risk as much as possible so we&#39;re really excited that we can actually do that on this report too and we do that with the assumptions we saw in our Harvard cohort so we can can actually tell you how likely it was that someone with accelerated aging were to develop each of these outcomes and then apply that same assumption to your data um one of the other big um things that we&#39;re we&#39;re really excited to see is that there&#39;s much more intuitive connection between these outputs and what you might have seen earlier so if you used our testing before and you saw intrinsic age accelerated and you saw extrinsic age accelerated and then you saw the Gen Pace decelerated um you might have asked the question which one do I trust which one makes more sense which one should I actually use in my clinical or or personal assessment um and now uh we can actually show that that the D pace and our omic age are the highest correlated of all of these clocks which means you&#39;re going to see much more consistency between the rate of aging and the overall biological age but beyond that you&#39;re also going to see more consistency with what you know about your patient or what you know about yourself um and and so oftentimes you might have you know patient for instance that really looks unhealthy really has bad behaviors and they come back reading a great biologic age and that&#39;s because these first generation clocks have some big problems and I want to go through some of those problems so um before I actually give you case examples I want to reference a study that was published in 2020 by Jamie Justice who&#39;s now leading the X prize for uh longevity um and and Longevity treatments um and so in this article she sort of outlined the multiple things which need to be uh in place before this is used for FDA clinical trials um and and so these are the clocks that that we always talk about the horbath the hanom clock grimage feno age and then dunan Pace um and at this time none of them met the criteria um for you sort of FDA compliance studies and the reason being is that none of them had satisfied this last criteria which is are they responsive to interventions which beneficially affect the biology of Aging um and so none of them had satisfied this criteria um thankfully early this year uh Dan belsky and and the team of Columbia um actually showed that and improved one of these points for the Jan pace and what they showed is that chloric restriction was actually able to detect a significant reduction in biological aging which makes sense with what we know because chloric restriction is probably one of the most well validated therapies for for biological age reversal or um Improvement of longevity um the problem though is that first generation clocks like the horbath and hanm clock actually went the wrong Direction they actually showed increases um at 12 months um which we know doesn&#39;t quite stack up uh because caloric restriction is so well validated it might be giving us the wrong information especially when the newer generation clocks trained on biological signals like grimage and denan Pace show the right directionality um even though grimage didn&#39;t meet significance um and and so uh so this was finally proved in that calorie study earlier this year published in nature um with the dedan pace um however we also recently published a study looking at the epigenetic uh effects of distintive and cortin treatment so disad and cortin as a combo has been one of the things which has been shown to increase Mouse lifespan um and so we were expecting to see some type of epigenetic effect here but unfortunately what we saw with these first generation clocks is that cytic actually increase them much like we would have seen with these clocks with chloric restriction um and so again we&#39;re getting some conflicting information here these epigenetic first generation clocks are telling us they&#39;re going up where we&#39;re seeing you know studies with chloric restriction and and patinum and Corin showing in animal studies that they&#39;re increasing lifespan so this doesn&#39;t quite make sense right um and really it&#39;s because these first generation clocks are not providing us the right information they&#39;re capturing signals which are correlated to chronological AG not cpg methylation signals which are correlated to biological phenotypes and outcomes and so when we actually looked at un paac and grimage in the same cohort what we actually saw was that that on average they they did have some reversals um but it wasn&#39;t close to significance so while the jury was still out on calics for these second and third generation clocks what we saw in these first generation clocks is that they went up and that&#39;s probably not right which leads us to be very suspicious of any clock which is a first generation clock right because it&#39;s probably not picking out the signals which are most predictive or most well quantifying our biological aging um and so um so so that&#39;s a big part but we also want to come back to this idea of accuracy um and how we measure accuracy for biological clocks and generally the best way to predict accuracy is by seeing how well these clocks capture risk of disease and we do that by Hazard ratios um by looking at at things like um your likelihood of of getting a disease with accelerated aging or your likelihood of dying early with accelerated aging and what we see here is that our omic age outperforms every other clock um in in estimation of disease there&#39;s only one exception which is COPD um where grimage actually outperformed but in every other Disease Association omic age uh was significantly better um in the case of even 10year survival prediction we can actually predict your your uh rate of uh I should say risk of death over the the next 10 years with a 92% accuracy so um compared to chronological age which is around 75% accuracy this is a big Improvement because we&#39;re we&#39;re we&#39;re getting much closer to predicting um ultimate longevity and by capturing all of these different omic signals and all of the biology that goes into how we age um and again we can do it significantly better than most of the other clocks as you can see in that that testing cohort for their haard ratios and odds ratios so again our accuracy beats every other clock outside of grimage for one individual category of COPD and that&#39;s most likely because Grim&#39;s uh pack your smoking prediction is excellent um and generally outperformed RS in our cohort um and so that&#39;s the reason that COPD is probably a little bit better but um at the end of all of this uh you know we&#39;ve created a better clock but that might not change how you interact with it and so if there there&#39;s probably the biggest benefit of this omic age algorithm is that now we can give personalized guidance to why you as an individual are agent so previously if you had done our test we might have told you you were accelerated aging or you were decelerated in aging but we couldn&#39;t really tell you why um and this could happen for a lot of reasons some people might have had you know a lot of uh insulin resistance and visceral adiposity which is causing their aging other people might have high inflammatory burdens which is causing their aging um but we had no way of differentiating between the two or even telling you which was driving that process um so now this is different and the way this is different is going back to those epigenetic biomarker proxies um and and so on our report now you will see um all all these epigenetic biomar proxies sort of categorized by organ system um but you can also see their relative impact on your score um and so you&#39;ll see it a little bit like here at the box at the bottom where you can see factors which are associated with better omic aging and those that are associated with worse omeg aging and we can actually tell you individually which effect size is is causing you to age faster um and so we do that with effect sizes but we actually also read out your individual scores so on this new reporting we will actually read out your alkaline phosphatases we&#39;ll read out your fasting glucose your HB andc your blood UA nitrogen um and uh we&#39;ll do that with 34 other individual biomarkers um and so uh you can actually now use this to quantify these biomarkers just with a single drop of blood um but again this is the waiting and I and I I want to give a couple examples of the waiting and some of the things that we found because as I mentioned we didn&#39;t select these ourselves they were selected by the algorithm to be the most predictive of these outcomes um and and so here you can see for instance some of the metabolites that were selected and and I want to go into a couple examples of these um so the first one that I want to give an example of is urine uh so urine plays a role in in cellular function and energy metabolism um it is also used in in nucleotide synthesis so making the things that make up our DNA um and uh and it&#39;s been linked to as a biomarker for a lot of different things um it&#39;s mostly present in the cere spinal fluid and people do this as a supplement particularly for neurolog iCal impact um but if you look at the literature we see that urine has lots of connections to aging um so for instance you can see this in the study we reference here where we see uh intestinal aging is alleviated by urine um it&#39;s also been selected as one of the most potent metabolites to help with regeneration um even in other species like salamanders um who are regrowing their tails um and as you can see in this plot we see our mrss score for urine decrease as we get older um tends to be a little bit higher in men um and a little bit lower in women um but uh but but this is one of the factors which was positively associated with better omage meaning that the higher your levels uh estimated levels of urine um generally the better you performed on biological aging um and so uh and and this is we&#39;re not the only studies to have selected this as an important factor multiple Studies have shown a significant Association of this with all cause mortality and one study in the world the Women&#39;s Health Initiative um found that this along with lysine and isocitrate um were associated with two to three times um higher odds of attaining longevity than those who didn&#39;t have high values of these these metabolites um and so uh this is not a new finding but but it is interesting that it was selected as one of the most important um and so some people might be able to supplement with this to improve their biological aging especially if they&#39;re low um and we can actually tell you if you&#39;re low based on our methylation analysis uh of yourself and our cohort um another good example is keratin dial so another name for ker keratin 33 dial is ludian um this is uh uh but but generally K keratinoid are found in you know your leafy green vegetables they&#39;re found in spinach kale but also most notably yellow carrots um and and ludian in particular has used a supplement for years to help improve cognitive function and Eye Health um and uh multiple studies big large metaanalyses of over 38 ,000 people have shown that higher intake of ludian is usually correlated with less coronary heart disease stroke and metabolic syndrome and lower levels of inflammation but our algorithm selected this as an important uh factor and in fact this is not the first algorithm to show us that carotenoids are really important for biological age in fact grimage also trained till time until death um had showed in association a significant Association um with coronoid levels um showing again the higher the carotenoid the lower the bi biological aging um and so again this is one thing that was selected but makes sense and shows us um sort of uh correlations to other epigenetic age algorithms which have been selected to predict time until death um and so uh again you could supplement with things like coronoids or or ludian in particular and probably improve your overall omag age especially if you&#39;re low um and so uh so that was certainly helpful um although this says metabolomic uh these are also a list of all the proteins that we included in our analysis um so so there are a couple proteins some of the most positively Associated proteins are carboxypeptidase B2 um and then serum peroxin aerol esterase one which we call paon one um and so we&#39;re to give you another example we&#39;ll look at Pon One so um Pon One is an enzyme which is encoded by the paon one gene um and it&#39;s found in almost every tissue but specifically secreted within the liver and it is the anti-os scoic component of HDL that good cholesterol um and so uh this Gene is activated by PPR gamma which uh generally is increased by things like exercise um and it is inversely Associated or correlated with things like leftin um hscrp and il6 which are all these inflammatory biomarkers and so uh what we want to see is high activities of things like Pawn One um and uh and generally that means lower levels of inflammation um and uh and generally lower odds of apos sclerosis um or cardiovascular complications and so again this was selected as our by our algorithm as being very very important till time until death which just so happens though there are also multiple dietary supplements which can help increase Pawn One activity so for instance uh there are things like pomegranate juice or um chokeberry which is this aronia micara extract which is probably the most promising for increasing Pawn One activity um and so again once if we see this as being elevated or or I should say low on your omic age we can then make recommendations specifically for you to increase these things and we only went through three examples with the carotenoids with um paon one and with iDine but we have 38 of these to make really specific recommendations and and plans to help improve that biological aging process so with all that being said I hope that you now can see that this algorithm does a couple things it&#39;s the most accurate and precise it is the most consistent with your clinical picture it is the most predictive of negative diseases a and aging outcomes um and it also answers the question as to why are we aging and provides resolution on an individualized basis so let&#39;s get into the reporting let&#39;s ask about how we should use this um and so uh as we get into this I I just want to mention that after this presentation you will have multiple resources available to you um one of the resources will actually be a sample uh uh om AG report which is annotated with uh information on every single part of the report which helps you understand or or know how to interpret these results um in addition to that we will give you an entire PowerPoint uh which introduces all of these epigenetic biomarker proxies so it&#39;ll tell you all about the proteins uh that you might not know about or the metabolites you might not know about and how it impacts sort of clinical treatment or it might impact your aging we also will give um links to uh Dr Jessica lais su&#39;s introduction to om AG um and her presentation slides as well as uh frequently asked questions and uh a sort of a a Layman&#39;s example for in in media um and so these will all be available after uh the discussion um and uh and uh I think will hopefully be very very helpful as you start to look at these results so let&#39;s sort of dive into it and see what we found um and so uh again we have two different cohorts here we have our 30,000 plus cohort in the true diagnostic uh but we also had that 5,000 cohort from Harvard which we used for training um and so uh these are some of the trends that we found um and so this is how the the actual biological age and omic age will be reported if you decide to do this report um we&#39;ll look at your calendar versus your biological age we&#39;ll show you that age acceleration and we will track it longitudinally so if you look at some of the trends here we sort of the the largest acceleration we had in our cohort was 29.4 years um so someone was 29.4 years older than their chronologic age it actually belonged to a young male at 18 um and uh conversely the largest deceleration that we had was almost negative 20 years and it belonged to someone uh a female who was aged 97.8 and so this brings up a good point which is that if you&#39;re younger you&#39;re more likely to see age accelerations and if you&#39;re older you&#39;re more likely to see age decelerations however um we we take sort of the guesswork out of this because we tell you where you are at in the population so what percentage percentile you are in um and we calculate your risk um so even if you&#39;re accelerated at younger ages so if you&#39;re in your 20s for instance um and you see age acceleration you still might have extremely low risk of developing these negative outcomes um and so this is just uh something we oftentimes see in the algorithm but if you&#39;re in your your low 30s if you&#39;re in your 20s you might see age acceleration but you also might see that your risk is significantly negative um and vice versa if if you&#39;re older you might see that age Gap start to shrink um and you might be younger even than than your your your chronological age but you still might see increased risk because the algorithm takes all that into account um and so uh so I just wanted to mention that if you&#39;re younger you might see age acceleration if you&#39;re you know particularly older you might see age deceleration but it it still is going to be calculating your risk uh uh all the same and what you really want to try to do is to perform the best out of our cohort so to to really try and score in the lowest percentiles um of of your rankings um and again this is where we actually give these rankings um one of the new features we also do do here is we actually separate it by sex so you can see where you uh relate to both men or women in the cohort and this is important because uh at age 50 men were around 4.35 years younger or sorry older in our Harvard cohort while women were um around 3.4 years younger um and so this is something we always see what we&#39;ve seen even classically with even the older algorithms is that men age a little bit worse than women but this makes sense because women live approximately 3 to four years longer on average average than men um so again I think that this makes a little bit of intuitive sense um but the other thing to mention here in this population level cohort that we report on our reports is that this is made up of our population this means that uh generally uh people are very healthy uh you&#39;re sort of competing against the best of the best because there everyone here is you know taking this test because they&#39;re interested in preventative medicine and in aging so if you&#39;re doing well on this cohort you&#39;re going to be doing well in almost any cohort um and so uh and again this is where we see that the men were 4.35 years younger than Harvard cohort while the women um were around 3.4 years younger so uh there is a big difference between a disease data set like Harvard and our internal data set um again we do report out how your risk is increased or decreased um and how you can actually interpret these for for your relative risk of multiple diseases um again we we&#39;re using this based on assumptions made in the Harvard cohort so often times you might see that uh your risk uh is maybe a little bit more elevated um because we&#39;re using the assumptions of a diseased population um but again this is uh hopefully a way to to gamify this process and try and get your risk as low as possible um so this is again what I think is probably one of the biggest parts of this report and these are the clinical factors or those clinical epigenetic biomarker proxies which are associated with Better or Worse aging so in this particular person we can see that you know the biggest negative impacts on their biological aging are red cell distribution withd and hba1c um and vice versa probably the most protective is albumin and we know albumin is protective against mortality and decreases as we age so for this uh we probably would want to focus on um hb1c and red cell distribution withth as ways to improve this person&#39;s omic age um and so this is how it&#39;s going to be read out but we&#39;ll do this for clinical we&#39;ll do this for metabolomic and we&#39;ll do this for proteomic so all three categories will be listed on your reports each with their relative effect sizes so you can see what&#39;s impacting you in the best or worst way but we also report out all of these things individually and and sort of plot where you are at on the population as well um and so here it&#39;s important to mention that some of these epigenic biomarker proxy weights might be a little bit misleading and that&#39;s because some of these things are weighted more important than others so for instance Serum alumin is very very highly weighted meaning that it&#39;s almost always going to be the most predictive um or I should say the most protective epigenetic biomarker proxy in your clinical values vice versa Red Cell distribution withd is weighted um very highly as well so it&#39;s almost always going to be the worst of your performing markers and so um if you look at this you might see overall how they&#39;re impacting but it might not tell you the biggest impact you can make on your score and so for this we recommend these different types of values and what you really want to look at here is your percentile ranking if you are uh you know in the bottom 10% or in the top 10 percent it&#39;s probably worth looking into this a little bit more so some things like fast and glucose we would want to be in the bottom 10% um whereas other things like albumin we would want to be in the top you know 90% um because it&#39;s weighted positively so depending on how this is weighted you you either want to be you know really good or really bad and where you are in the extremes are probably where you can impact your score the most um and so um so for instance I want to use this as an example so this is H actually someone who did our test and and collected their data from Lab Core at the exact same time so many of you might be thinking how accurate is this and so here we can actually look at an example um so you can see for instance High correlation to some things so for instance albumin uh we we predicted albumin to be 4.2 the actual clinical albumin was 4.1 um you know you look at uh things like fast and glucose we estimated that this would be a 94 um and in fact it was 9 won so we were really close even with blood URI nitrogen um we thought this would be 11.4 and it read is 11 on a lab core test um and so those are really consistent we see some that are less consistent so for instance with red cell distribution width this person read at 13.8 well in fact they were at 16.5 um and then uh you know even things like chatrit and and hemoglobin are relatively close um so this tends to be pretty accurate and I think this is hopefully a very good example um this is just one of the first ones that we pulled uh but I also want to highlight that these epigenetic biomarker proxies are not always going to be perfect or consistent with traditional lab measurements because they&#39;re measuring technically something different um and oftentimes these are longer averages than the individual biomarker themselves um and so that&#39;s important to mention if you see inconsistencies that&#39;s probably um something that that is noteworthy um but generally most of them are going to be very very close together if with the traditional blood markers that you would get um and so here&#39;s another example of the same individual here we can see their clinical biomarker um uh proxies um and what we can see jump out of this immediately is the most protective is albumin as we thought because it&#39;s weighted relatively well and probably the worst impact of their omic age is actually going to be that red cell distribution with um but if we look beyond that we see that the other two biggest factors are going to be fast and glucose and hba1c and so what jumps out to us immediately is that there&#39;s probably an insulin resistance type of issue um because we have the the some the two uh I would say outside of Red Cell distribution with the two highest categories have to do with uh uh insulin resistance and and and glucose um and so this gives us something to Target we probably want to do what we can to drive down this person&#39;s uh insulin resistance by seeing what we can do to improve glucose in hba1c um and and so that can be very very helpful um I should also mention that that red cell distribution with is almost always going to be highly weighted um and so you&#39;re going to see this is the most important aging factor in several different clinical analyses um and in in in an analysis of this we actually see that this is not surprising so this particular study found that uh uh High Red Cell distribution with um is linked to all cause mortality and then sort of asked the question as to why and so the findings were that things like Baseline values of VFR um igf-1 binding protein 2 temp one uh uh saw to some of these inflammatory genes were all involved but particularly igf1 binding protein 2 and and compliment another inflammatory Factor um were also associated with changes in Red Cell distribution with and so their interpretation in this paper was that cellular inessence might contribute to Red Cell distribution with and mortality um or it&#39;s mediated by inflammation so um and we know this because multiple Studies have shown us that excess oxidative stress inflammation and cells inessence um have been proposed as conditions which cancre Red Cell distribution with so this is something uh Red Cell distribution with in this particular output is probably a surrogate marker for just general inflammation um and uh and something that probably everyone is going to struggle with um and and something we definitely want to see people get lower um and so that&#39;s why you always see Red Cell Distribution withd on here um but that&#39;s also why you want to look at the population level graphs to see where someone is uh compared to others of of their same age and sex um and uh and again this is just another uh good example of all the different um uh I would say associations and while uh why this is highly linked to inflammation and mortality but uh but going back to it this is also why you want to look at this population level graph so this again is for the same patient we discussed earlier with those lab core results um and so uh what we really want is low percentages for things which are associated with lower omic age um uh and or I should say higher omic Age and and higher percentages for those ebps which are associated with uh with better aging so for instance we know urine is a positive metric higher rates are associated with improved omic age um and so uh so we want this to be high and in this patient it&#39;s actually really really high it&#39;s this patient is in the 97.6 percentile so for this patient we&#39;re probably not going to recommend supplementation with iDine because they&#39;re already so High um and uh vice versa we can look at um you know this plasminogen uh this uh uh sterile adrenal GPC um and we see that that this particular patient um is actually pretty low compared to people of their age group um and so with this being higher rates associated with improved doac AG we might want to supplement with a plasminogen for instance um if we and vice versa we look at Red Cell distribution withd again this is uh lower rates or associated with better omic age um and we see this patient at 91.22 per. so we really want to tackle inflammation to try and dive that down um versus we also have cysteine um which is uh you know lower weights are improved with omic age and this is really really low in this patient so their cysteine is actually doing very very well um and so again by looking at these population level graphs you can get an idea of how much you can impact each individual value um and uh the other great thing is this can actually help us identify causes of Aging uh again using these epigenic biomar proxies and aging studies might let us know mechanisms of action might let us know which which genes are are most important to predict certain features um and so here I I want to talk about some conflicting information um so uh so we know that lower rates of igf-1 binding protein 2 um are generally uh associated with better omic ages um however we also know that as your insulin sensitivity um improves your igf-1 binding protein tends to go down um and so this is a little bit paradoxical um we even know that igf1 binding protein 2 uh mediates some of the early metabolic improvements caused by bariatric surgery so is it good or is it bad um sometimes we don&#39;t know the answer to this because the data conflicts but if we look a little bit deeper we can see that what we&#39;re probably measuring here with igf-1 binding protein 2 is again probably related to inflammatory status rather than insulin sensitivity um and so ig1 binding protein 2 has been a biomarker for several different conditions here are just a few examples uh so we can see it as a marker for idiopathic pulmonary fibrosis which is characterized by a lot of ccent cells in the lungs um we can see that uh it&#39;s it predicts deterioration and renal function we see that it&#39;s a candidate for uh heart failure uh and we also know that it activates this this nfca beta pathway which is a highly inflammatory pathway so again what we&#39;re probably measuring with aone binding protein 2 is theic inflammation rather than this as a surrogate marker itself so again I think what if this just adds to this analysis is that um these biomarker proxies can give us really really good information um about how our biological system is behaving and how that&#39;s impacting our aging and then can give us specific recommendations to try and drive this process down so it really is for the first time ever um a a epigenetic age algorithm which can help explain the why um and we want to really help you understand this so we&#39;ve created lots of assets which explain each of these individual uh proteins each of these individual uh metabolites and I imagine most of the clinical biomarkers uh most of you will already be familiar with but we really want to give you a step-by-step approach um and so at the end of all this I think we&#39;re sort of at the end of our presentation um but this is um again an algorithm we are so excited to have on our platform um I will also mention that we do expect to have another algorithm on our platform in January which will actually give you the age of 11 different organ systems um and and so we&#39;re really excited to be debut that as well and eventually we hope to combine um this uh both of those algorithms into an amazing single algorithm uh and continue to make these things better across time um as Hannah mentioned earlier we are having to charge for this particular algorithm and Analysis uh the reason for that is because we have to pay a license fee from Harvard who we co-developed it with um if we can ever add new reporting to your report um we usually will do it free of charge so tomorrow um or even as of now you can upgrade this report in your portal uh but you&#39;ll also find three additional reports new reports in your portal which we&#39;ve provided Absol absolute for free even if you decide not to upgrade um your analysis for this omage algorithm um so that will include our inflammatory marker report which looks at il6 the CRP it&#39;ll include our fitness age report which will give you a fitness age but also then tell you um your grip strength prediction your your V2 Max and fpv1 prediction um and your walking speed prediction um and then we will also do an advanced immune offering which includes all 12 of those immune cell subsets which we just recently published with the Chinese Academy of Sciences Hopkins in Harvard um and found some interesting associations with immune cell subsets and different types of behaviors um so all those reports will be for free um but this report will be an additional charge um and uh and it&#39;s only 1999 for one report and 2999 for every report if you had multiple different types of tests um and again we&#39;ll offer this as a as an addition incorporated into the price in the future um as well so if anyone has any questions about this algorithm please feel free to reach out to me or to anyone on our team at support atre diagnostic.com or to me directly at Ryan atre diagnostic.com uh as I mentioned we will be giving um uh many resources you can use to interpret this report if anyone has questions or wants to know how to review these results with their patients directly or wondering how this might affect you personally um again feel free to reach out and we will try and put some time on the calendar to help walk you through um all the insights that you can generate from this report um and so um so with that I know that I miss quite a few answers question and answers as I was going um but uh would be absolutely happy to go through these uh one by one so if anyone has any other questions please let me know um but I&#39;m I will quickly look through um some of these as Hannah uh uh starts this raffle to give away the three kits um and then we&#39;ll answer any other questions which come up in the meantime uh Hannah feel free to uh to start the process if you&#39;d like awesome thanks so much for the wonderful presentation I just want to address one question um before before I announce um these winners um Curtis asked a good question if we can upgrade true age Pace kits so unfortunately not true AG Pace kits are on a smaller array that only measure 10,000 cpgs so you can only upgrade the complete kits that&#39;s a great question and um one that we should have addressed earlier so uh thanks so much Curtis but without further Ado the three winners are William Stanford Nita Jane and Rick Cohen so you three um wanton a complete kit uh go ahead and email me your shipping address please um don&#39;t put it in the Q&amp;A um and we&#39;ll ship you those kits out uh free of charge tomorrow so Rick Cohen Nita Jane and William Stanford perfect thanks Hannah um and again if you have any uh issues uh uh you know with with some of this please let us know um we&#39;re happy happy to to help you through any of these different reports especially because it&#39;s so much new data um I do see a question out there which asks uh about the treatments which are most highly associated with improving biological aging um and I and and I want to address this because unfortunately it it has a a good answer and a bad answer the I think the good answer is that now that we have this tool we&#39;re seeing results that that are more expected so for instance you know with choric restriction we&#39;re seeing decreases in omag gauge where we didn&#39;t see it with some of those other CL um and so the answer is that we&#39;re really going to be doing this analysis on almost every published study that we can um so we&#39;ll be taking the systems age clock that was developed at Yale the omic age clock um and some other new algorithms and uh and really reviewing all the previous data to see if we can put together a full list of all of the Interventional things we know to change this result um in our actual report there&#39;s actually one thing we didn&#39;t mention which is that we do sort of a meta analysis on things which are positively or negatively associated um and again this isn&#39;t too groundbreaking but we see associations we&#39;ve typically seen before so again the more uh Fitness Behavior you do the better your omic age um if you do recreational drugs that&#39;s obviously negative for your omic age uh so we SE a lot of those those typical assumptions but we&#39;re going to start to provide a lot of data on things like Hyperbaric stem cells exosomes um uh vegan versus vegetarian diets uh senolytics um uh you know Mediterranean versus um uh other diets and so we&#39;re going to be in all of this data again um and uh and and hope to continue to update you so that we can give specific recommendations but we&#39;re also doing novel things like I mentioned urine and and carotenoid supplementation um and looking at how some of these other things fix it so we will continue to update our information on longitudinal interventions um to be able to make sure that you can make the right decision um and so um so I hope that that helps um you know I will say for anybody who is a researcher and interested in in getting this done we will make the code public uh to be used on on for any researcher who wants to investigate their cohort but if you ever need us to run samples for you we do so at a research price um to try and get this uh data out there and to learn more so we usually give substantially discounted pricing you can always reach us out to us directly at uh Ryan at true Diagnostic and we&#39;re happy to coordinate that we do have really cool studies coming out and we love working with researchers to certainly let us know um and uh and so I think that that&#39;s it for questions thank you all much again for attending um I hope it was worth it I know that the devil is in the details here so we are always going to be here to answer any questions um and help you use this um feel free to to share this webinar um if if you get the time and and tell people I think about these big breakthroughs and just how much has happened um and not every biological age algorithm or biological age test is created equal we always encourage you to look at the Publications and data look at the validation data because otherwise it&#39;s like going to a fortune teller you can choose to believe them but it&#39;s better to have proof uh so with that I think we&#39;ll try and end this call um always feel free to reach out to us and thank you so much for again for attending

hi everyone thank you so much for joining us tonight I&amp;#39;m Hannah went I&amp;#39;m the director of operations here at true Diagnostics and before we get started I am just going to go over a couple Logistics regarding this major report update that is just as equally exciting um so first and foremost uh before I go over this slide that you see here I just want to talk about the reports that you all should have in your portal now this is for everyone who&amp;#39;s ever taken our test both direct to Consumer and healthcare provider and patience of healthcare providers so regardless if you&amp;#39;ve ever taken our test you should now see um two new reports if you&amp;#39;re a direct consumer and a patient of a healthcare provider this is our new 12 cell immunity convolution method and our physical fitness report that includes V2 Max F1 grip strength and gate speed if you are a health care provider you should be getting those two reports for every single patient you&amp;#39;ve ever tested historically along with our new uh inflammation cognition report which is the D methylation CRP and il6 so those reports are currently live now of course we&amp;#39;re going to dive into our OMC M age today but if you want to upgrade any kits that you&amp;#39;ve ever run as a consumer you will have to log into your account and you&amp;#39;ll get a popup message and you can purchase that way or purchase directly on Shopify if you&amp;#39;re a healthcare provider you can see here when you log into your portal this message that you&amp;#39;re going to get it&amp;#39;s going to explain a little bit about what that OMC MH report looks like and then you can actually click in the bottom leftand corner of that message uh that says click here to upgrade the patient results from previous samples um so if you purchase that way that will be the quickest way doing that in your portal it will be a quick um 24 48 hour even shorter than that turnaround time remember if you&amp;#39;ve taken the test once it would just be $1 199.99 if you&amp;#39;ve taken the test uh twice or more no matter how many times it would be $29.99 for that upgrade so um if you can go ahead and go to the next slide Ryan please oh all right and then once you click that bottom right hand cor or lefthand corner Healthcare Providers you&amp;#39;re going to get a list of all all of your patients and every single sample that you&amp;#39;ve ever tested with us to date you&amp;#39;ll then select the upgrade on the left hand side of the patients that you want to upgrade and you&amp;#39;ll click that button on the bottom um that says basically heading to payment and submitting those and then we can charge your card IR L on file so it&amp;#39;s super turn key the way we set it up for you um if you don&amp;#39;t want to go through all of that hassle I am open via email and so is our support team and we can do this for you um let us know which patients you want to upgrade which specific kit IDs if not all of them um and then uh go ahead and give us the okay to charge that card on file as well um if you&amp;#39;ve already purchased these and they&amp;#39;re not in your portal yet give us until early next week um out of the 35,000 samples we&amp;#39;ve tested to date we&amp;#39;ve had a massive demand so we&amp;#39;re working as hard as we can to go ahead and get these out um directed consumers if you want an interpretation here reach out to um our support team directly and we can set you up with a consult um Healthcare Providers this is the webinar to learn everything you can about OMC age um we will send this out afterwards as a recording um but of course you can set up a call with me or Ryan Smith to dive into some of your historic patient Resorts uh reports so without further Ado I&amp;#39;ll introduce Ryan Smith and um if you stay till the end we are going to be raffling off three triage complete kits um I&amp;#39;ll just put the names of those winners in the comments and then you can email me directly at Hannah true diagnostic.com and send me your shipping address um and information so um we hope that you all stay to the end and enjoy the rest of the presentation yeah thanks Hannah for that introduction and thank you all for attending uh we couldn&amp;#39;t be more excited to finally launch this new report uh this report has been a long time coming um and so so we want to start with uh just some thank yous um particularly our entire team at true diagnostic this has been really a three-year effort um with uh Dr Jessica lisu and chinguen Chen at Harvard as well they&amp;#39;ve been instrumental in helping us procure samples find the necessary data that we need to generate such an amazing algorithm and so I can&amp;#39;t uh start this without giving thanks and again as Hannah mentioned we will be giving three kits to anyone who stays till the end it will be a science heavy discuss especially toward the beginning and toward the end um but uh but we want to talk about all the ways that this algorithm is so much better than anything that&amp;#39;s been on the market before um and that requires some some specifics so um I hope that we can keep you all engaged and and tell you just why we think this algorithm is so much better and so useful to measure your aging process um so before we we get into the actual specifics we got to go over some Basics again first um so obviously why is it important to measure aging um and the answer is that aging is the biggest risk factor for every chronic disease and death and by a wide margin again it pales in comparison to smoking obesity and many of you all have heard us say this before but um uh but it&amp;#39;s it goes uh it&amp;#39;s important to reiterate just how important this process is and so oftentimes we also like to talk about some of these definitions biological versus chronological age but the reason we also like to emphasize this is because the reason we test is to predict outcomes we want to measure your aging process so that we can quantify your risk of disease and then hopefully change it and so some of the examples like we had in that dunan pace trial where we can see all of those people in the in the pictures on your right are the same chronological age um they&amp;#39;re all 45 but they look vastly different because aging makes our bodies function worse and predisposes US disease and risk and that&amp;#39;s really what we&amp;#39;re trying to avoid and we say you cannot measure uh you can&amp;#39;t change what you can&amp;#39;t measure um and so that&amp;#39;s what we&amp;#39;re really trying to do is to provide the best way to quantify aging um and so again again uh what we&amp;#39;ll have as an output is much like the other biological clocks um and so if you&amp;#39;re accelerating epigenetic aging that means you&amp;#39;re going to have generally a shorter lifespan and healthspan and those who are decelerated mean longer lifespan and and better healthspan so we really want uh to use this as a diagnostic tool to influence the way that we age and improve our aging process um and and so it&amp;#39;s always important to reiterate that if you&amp;#39;re healthy your biggest risk factor is age um and that&amp;#39;s really the one we want to start with um so with that we also want to go over the history of some of these clocks uh because we&amp;#39;re going to be badmouthing a little bit of these first generation clocks these clocks which we you know honestly have have even been using in this intrinsic and extrinsic age um so these first generation clocks as a recap for people are clocks that have been trained to predict the actual chronologic age of a patient so the day that they were born essentially um and these clocks again started off in 2013 with the Horvath and Hanam clock um and uh those Clocks Were were a great start but lots of time has passed since then um we&amp;#39;ve essentially had over a decade and so what has happened in that time and the answer is better ways to measure the aging process and so this really started in in 2017 2018 with these biological clocks that were trained to phenotypes of Aging um and that&amp;#39;s really what we want to predict right we don&amp;#39;t necessarily care about someone&amp;#39;s chronological age we want to quantify how their body is is changing with the age um and so that we can go ahead and reverse it and so the most notable algorithm here were Morgan lavine&amp;#39;s pheno age this again um many of you might be familiar with because it started with nine blood-based biomarkers and if you&amp;#39;re even to type in pheno ag calculation or calculator on um Google you&amp;#39;d find a couple places where you can actually calculate this based off those blood Labs um and so that was a really good start because it started to capture more of that biological aging process but in the case of pheno age it still used things like someone&amp;#39;s chronological age and so um the next big breakthrough on those biological clocks uh the second generation clocks was uh grimage um by by Dr horbath and Lou um as well um and that again was trained to predict time until death but it did so by also predicting certain proteomic biomarkers um and by by doing that it was able to capture again more of that biological signal and it was more predictive of even pheno age uh of certain types of outcomes um and then lastly we have these third generation clocks really led by uh Terry mofet Dan bsky and a whole host of other individuals who are certainly worth naming um but this clock was able to be trained off longitudinal data in this dunan pace cohort so um it it certainly took the clinical biomarkers a step further because instead of just using nine it used around 21 blood-based biomarkers um but it also used a longitudinal approach so measuring the same patients across time rather than just different patients across different time points um and this again performed extremely well you know compared for instance to horvat&amp;#39;s clock which one standard deviation acceleration of a horbath clock would represent a 2% increased risk IM mortality while one standard deviation of that third generation D needan Pace would represent a 64% increased risk of mortality so we are getting better by designing more clocks um but it&amp;#39;s important to mention that the clocks which are the best so in particular Grim age and denan Pace have been trained with the most robust and relevant clinical data um so in grimage it was these proteomic biomarkers in dunan Pace it was 21 blood-based markers but those are the clocks which tend to perform best so we know that the clocks which capture the best aging signal are the one that measure the most aging biomarkers um and that&amp;#39;s a really important piece of information because it really launched what we call omic age um and and really uh I always like to to prove this point a little bit further by showing you what&amp;#39;s happened to just the Hallmarks of Aging um since we started in 2020 um and so when we started in 2020 there were nine recognized Hallmarks of Aging as you see here on the left um however now um in in 2023 we know that there are 14 or 15 uh different Hallmarks of Aging which have been defined and and readily accepted by the scientific literature and so if there&amp;#39;s one thing to mention it&amp;#39;s that aging is complex which is exactly why these clocks which capture the most phenotypic information the most information about our our current biological State perform the best and so we we understood this you know we understood this even when we started and we knew that we wanted to create the clock and that meant lots of information and so this is why we took a multiomic approach and so um for those of you who are unfamiliar with sort of these omix um omix are different levels of measurement that we would consider within the body um so most everyone is familiar with uh you know genomics uh the study of DNA obviously we&amp;#39;re talking a lot about epigenomics so if you&amp;#39;re here you&amp;#39;re very familiar with that but we also have transcriptomics where we look at at RNA um and we have proteomics which peptides and proteins that are made in the ribosomes after we we we sort of process that RNA related information um then we also have metabolites in the body that are created from processes these are things like hormones neurotransmitters um even you know gut microbiome metabolites um and then lastly we have that that phenome right so what diseases are some people diagnosed with um you know what are their physical performance characteristics and and so together we wanted to measure all of this information um because we wanted to get the most comprehensive biological aging signal possible so we did this in our cohort with Harvard and partners biobank we were able to quantify almost every single one of these levels and I think this is important because you know I always like to link to some of the Articles which have been published um over the years about uh the Human Genome Project um I think so many people thought the Human Genome Project would solve so many parts of medicine um and unfortunately you that didn&amp;#39;t really happen like we thought it would um and so people call this the failure of the genome or or why did it fail to predict so many big disease breakthroughs um and the answer is again it&amp;#39;s just one SM small part of a very big puzzle um and so genomics is is might have predispositions but epigenetics is about the expression of those predispositions and and epigenomics is still not the entire puzzle as well so what we really wanted to do in this particular clock decision-making is to integrate all of these different features and all of the relevant biology into one single marker um and so this is also important because if you tried to quantify each and every one of these um as much as you as we did in this study it would cost tens of thousands of dollars um to be able to do all this analysis on one person just isn&amp;#39;t cost effective um and so what we wanted to do was to Trin it all back into the omic we think has can capture the most information and that is epigenetics um and so that&amp;#39;s really what we did to start we we started measuring lots of data in a cohort of about 5,000 individuals um but we also had to choose what we trained it to predict so we have all this this uh information and data but we still have to Define how that data is put into a model for biologic age um and so uh if we look at clocks that have been happened before we have things like dun pace and pheno age which use blood-based biomarkers or other uh clinical biomarkers to create a a score of Aging um and then vice versa we also have the clocks that are trained um till time until death to predict the time you might pass away and and that was grimage for instance and so what we decided to do with omage was to split the two um I think both of these approaches have their benefits um and so what we wanted to do was to to split the difference and to do a little bit of both and so what we did was first we created a clinical time until death score so we started with time until death but we wanted to predict it with clinical blood-based values and so um in in Partners cohort uh they have samples which we pulled from their biobank and some of these samples have been as much as uh from as long as 30 years ago and so the good thing about these samples is we know what happened to them and their outcomes so we actually selected a large majority of this group for people who had already passed away and people had passed away for causes which we already have known and so we we selected those individuals and those samples but then we also looked at all of their health electronic medical records and so we extracted over 61 clinical phenotypes this included things that you might see on the genan pace calculations like you know cholesterol um or like you would see in Pho without v um but we wanted to create something that was a little bit more robust so not only did we use more people um we used over 60,000 samples to establish this um and then we sort of trained it all to predict time until death and what happened is we ended up creating around a 19 blood-based biomarker um uh panel which we call EMR age and so this EMR age is an alternative to even pheno age where you can even upload your your your biomarkers like your alkaline phosphatase or your albumin um and calculate your your EMR age um and so this was the first step in our algorithm and we trained that to predict time until death and what we saw was that it was better than pheno age in almost every category and we know it was better because first off the P values were better but in addition to that we had larger effect size so the hazard ratios how we predict the outcome of disease um was better in in almost every regard and so we knew that this method of quantifying age via EMR related records was better even than pheno AG version of em um EMR related data that you could pull out and so this was a good first step for us um and so uh the next step we wanted to do is we wanted DNA methylation to predict that bioage or that EMR age um and so we trained a methylation algorithm to predict that just like feno age would have and again what we saw was a really high correlation our bioage uh was very close to our our pheno bio AG it had uh sort of an R squar value around 082 which is excellent so we really saw that we were able to um to have a a really well performing methylation algorithm to estimate all of those clinical biomarkers um and so uh that was um a very good step in the process and so um the next thing that we did um in in step two so we had the beginning of our algorithm one that had been trained over on over 60,000 people and then created a a DNA methylation algorithm to predict that um but the next step of it was we wanted it to be informed by the other multiomics and so how do we incorporate all the metabolomic and proteomic data that we had in this cohort so the next step that we did was to develop something we&amp;#39;re calling epigenetic biomarker proxies and so these proxies are ways to use DNA methylation data to predict uh different other values and I apologize for some of the formatting here um but the idea here is that we can take proteomic values so for instance alphascan or cow and proteins in Alzheimer&amp;#39;s and we want to predict those with just DNA methylation alone and so we created what we these epigenetic biomarker proxies to estimate these factors and and we created a lot of these so in our training we only used about 400 um but with that being said we have tens of thousands of algorithms now that can just take in DNA methylation information um and output other values so we can estimate your igf-1 your cortisol your neurotransmitter levels like serotonin and dopamine we can look at the posos chemicals um that you&amp;#39;ve been exposed to through these epigenetic signals um and and also a lot of clinical things which we&amp;#39;ll talk a lot about in detail like hba1c V2 Max and lipids like HDL or LDL um and so uh this is how we actually Incorporated all these other omix into a single methylation based diagnostic is we trained the methylation to be able to predict them um and there&amp;#39;s a history of this happening before in the literature so one of my favorite examples is Ricardo Ricardo Marion&amp;#39;s Labs work um in the leithan birth Court in generation Scotland and what they did um was they actually use D methylation to predict several types of proteins um one of the best examples is their D methylation algorithm to predict CRP um and so the D methylation CRP actually shows benefits over regular HS CRP um and one of the reasons for that is because CRP is such an acute phase protein it can vary by up to a thousandfold within just an hour um whereas D methylation CRP is a little bit um longer lasting and because of that we can see associations we just wouldn&amp;#39;t see with such a variable blood-based biomarker so here you can see in The leithan Birth Cohort that there is no Trend with hscp um with age but we do see it with DNA methylation CRP um in addition to that um we see um that uh sorry and this is a little bit of a tangent but CRP also is one of the reports that we&amp;#39;re launching um and so uh we can actually predict your CRP and it is higher with obesity and drinking um and so this is one of the other outputs but and we also see associations with DNA methylation CRP compared to regular CRP and things like cognitive function if we look at the generation Scotland cohort and lethan Birth Cohort we see no significant associations with regular CRP or genetic scores to General cognitive function but we do see this with DNA methylation CRP um so higher DNA methylation CRPS are associated with poor cognitive ability in both cohorts um where we don&amp;#39;t see that association with CRP in addition we can actually even see the comparison of how um much more predictive DNA methylation CRP is than regular serum HS CRP so here on the left you can see all these neuroimaging outputs and in the red you can see the effect size of our DNA methy predictor of CRP and in the white you can see the effect size of serum CRP and as you can see in every single one of these M um brain MRI related uh phenotypic outputs DNA methylation CRP is more effective at predicting or showing associations to cognitive function outcomes um and so again DNA methylation CRP here is showing us that it has benefits over even classical hscrp and not only that but we can also find out what&amp;#39;s driving this process so we can actually see what genes are most associated with high inflammatory signals um and so in the case of CRP the the gene location which is most highly weighted is the sock S3 Gene and this stands for suppressor of cyto kind signaling so really it is meant to be a uh a gene which reduces inflammatory burden in cyto kind signaling um but unfortunately when it&amp;#39;s upregulated um we we have inflammatory burden and we see that in these DNA methylation predictors of CRP um and so uh but but this understanding is also very relevant and important and so one of the other things that I&amp;#39;ll come back to a little bit later is that these DNA methylation biomarkers usually perform um a little bit like an hba1c where they&amp;#39;re sort of a longer average estimates rather than plasma biomarkers which represent uh sort of day-to-day variation oftentimes and so this longer average can be good in some cases by showing associations we wouldn&amp;#39;t find on the day-to-day but for more acute things it might not be is good um and so we have to weigh that um those negatives and positives as we start to incorporate these um but this is really what we did in our our our omic age cohort we developed uh epigenetic biomarker proxies for about 107 proteins 21 clinical variables and 267 metabolites and we chose these approximately 400 um because they were the ones that were selected as most relevant to time until death um and so that&amp;#39;s how we got these in the first place um and then we sort of and you can see their performance so um you can see that a lot of these what you see here is a piercing correlation um and this is basically how it performed um from the measured versus predicted so if we measured let&amp;#39;s just say a CRP of two um and we predicted a CRP of two through methylation that would be a correlation of one that would be perfect um and as you can see here we have varying degrees of success um across many of these predictors um but generally most of them perform quite well um for instance in in DNA methylation Grim age um they use a threshold of around uh 35 r value um and almost all of our predictors exceed 35 um there are very few that do not and so we have a really really long list here um but again these are being chosen um by sort of the time until death algorithm so uh we did not pick these one by one we we threw them all the the board to see what was most predictive um and eventually what happened is we chose uh 38 were selected um as the final inputs into this omage algorithm so now in our omage algorithm we actually quantify each of these 38 biomarkers individually and so on the report you will see this but um just to show you how well these things actually perform we also did an analysis of how these things were related to uh both disease as well as lifestyle factors Within this Harvard cohort and what we found was really really positive so for instance um we saw that hba1c and glucose are methylation-based predictors of these things were very highly correlated with type two diabetes which again makes perfect sense because we know hba1c and fast and glucose are biomarkers for uh type two diabetes um and in addition to that we also saw things like igf-1 binding protein 2 having a differential effect size for underweight versus overweight and this is something again that we know is is classical from the literature that as we tend to become more insulin resistant our igf-1 binding protein values tend to go down um and and so uh so this again makes sense and and what it really shows us is that even our DNA methylation predictors are having this the similar correlation to what we&amp;#39;d expect for those actual biomarkers um and so this is again a step in the right direction but we have even more examples of this so one of my favorite examples is from another study we&amp;#39;ve done done on Twin pairs with Stanford um and in these twin pairs we actually took one twin and put them on a vegan diet and then we took another twin and put them on an omnivore diet um and this was to look at the biological aging effects of vegan versus omnivore diets um this study should be coming out sometime later this year um but we also wanted to to just look at some of the other methylation risk Wares we or the epigenic biomarker proxies that we did within this cohort as well because this cohort didn&amp;#39;t get extensive metabol omics or it didn&amp;#39;t get extensive proteom so they did get methylation though and so we really wanted to see if methylation could inform us about maybe other proteomic or metabolomic changes that weren&amp;#39;t actually measured in this cohort and so we actually found three significant values all of which changed um and so one is an amino acid uh metabolite the other two are proteins particularly cxcl4 and pancreatic ribonuclease um and the great thing about all three of these is that every single one of them has been highly described in literature before um as changes which are associated with vegan or or vegetarian diets um and so uh so this again backs up our assumption that these epigenetic methylation predictors of these metabolites or proteins or clinical values are behaving just like their measured metabolites and that is a really good thing to see because it means we&amp;#39;re capturing that biological signal um in methylation alone and this is fundamental to how we Design This multiomic algorithm because the end of the day after we created this time until death predictor with clinical biomarkers um we also wanted to inform it with all of the biological change we would see across multiple omic levels um and so uh to do this we had to to to train the algorithm to see what actually helped predict um the uh omic Age and and some of these outcomes and so at the end of it we chose again 14 proteins 10 clinical um epigenetic biomar predictors and 14 metabolite EVPs um and together included them all into omage um and this is that&amp;#39;s sort of how we trained it we&amp;#39;ll talk about now why this is better and how it makes a difference for anyone who&amp;#39;s interested in testing um or anyone who&amp;#39;s interested in quantifying um age or the effect of treatment interventions we&amp;#39;ll go through exactly why it matters and so that&amp;#39;s what we want to talk about now now that you you know how we&amp;#39;ve trained it and all of the data that went into it um again this data set is larger than any epigenetic algorithm that&amp;#39;s been trained before um and includes metabolomics which has never been done um it includes clinical biomarker proxies which has never been done before and um you know grimage did use proteins but it only included um around 80 um we included over 15,000 in our analysis um and so uh this represents a much larger scale as well but let&amp;#39;s talk about why this actually matters so one of the classical issues with biological clocks has been their reproducibility and precision um on the right here you can see um ICC values of some of these previous clocks um and and this has been a big issue so um if you measure the S one sample multiple times ideally you would see that sample be the exact same every time you ran it but unfortunately this isn&amp;#39;t really what happens in real life sometimes variation on how you partition the sample might have an effect but this is how we judge the Precision of our test we really want a test to be precise which means that if we measure a sample multiple times we want it to be the same almost every single time um and so traditionally ICC values above 0.9 generally have excellent agreement um and sort of 0. n is the threshold but the ICC values that we&amp;#39;re getting for omage are 0995 this is excellent this is means that that we&amp;#39;re getting less than a 05% variance if we test the same sample multiple times um and so this is a really really good standard and definitely beats the 04 type of comparisons that were found in in some cohorts for the original horbath clock and this is important because if you test with us and you retest with us we want to make sure that any change that you&amp;#39;re seeing in that algorithm is purely biologic and not driven by laboratory error um and and so if you take a test and see change you want to be able to make uh assumptions and actions based on that change you don&amp;#39;t want to just chalk it up to some laboratory noise or err um and so that&amp;#39;s really uh what we like to see here is omage is incredibly precise it has an IC value of 0995 or above um and so that talks about Precision in terms of accuracy um accuracy is a little bit harder to measure in those first generation chronological clocks we could just tell you how close it was to predicting accurately your chronological age but when we&amp;#39;re using it to predict biological age we really have to look at how it affects our risk of disease right because we know aging is the biggest risk factor for every chronic disease and death and we&amp;#39;ll do that a little bit later so one of the other B problems that we also solved was the inclusion of uh these immune deconvolution methods and this has also been a major problem for these epigenetic clocks so for instance um the buck Institute uh did some amazing research on individual cells from the same individuals and they found that human naive cd8 cells um exhibit a 15 to 20 year a difference than affector memory cdat cells from the same individual and this is a big problem because if we&amp;#39;re testing your blood we&amp;#39;re getting multiple different types of cells um and if someone has for instance an autoimmune disease a recent viral illness or even if they&amp;#39;ve consum consumed caffeine that might affect their level of some of these values and that as a result then might change their biological age and we know that someone drinking caffeine you know once is not necessarily um you know a biological age change of multiple years so we shouldn&amp;#39;t be seeing that on our algorithms and so this a big problem that multiple people have tried to correct we have done a webinar on this earlier year this year we talked about the buck institutes intrin clock um but we took a different approach and that is that we actually included weights for each of these immune cells within the algorithm so what we can do is actually quantify the relative percentage of each cells and then make sure our algorithm takes that into account so that we&amp;#39;re getting very very precise ages that are not dependent on your immune cells that are independent of your immune cells and really capturing real biologic aging instead of just immune differences across individuals or even across yourself across time um so this is the other way that we&amp;#39;ve made this more precise and less noisy so we&amp;#39;re measuring true biological variation um one of the other things that&amp;#39;s really exciting about our reports is that we actually even tell you um how your biological aging is affecting your risk of disease and so um in the reports we actually tell you uh how much what your current risk relative risk is based on aging profile and how that would change if you were to decrease your age by one year or three years or if you were to increase your age by one or three years and so now we we really wanted to gamify this because the reason that we&amp;#39;re all interested in aging is reducing risk right um and so now we can actually tell you how much a one-year reduction or a three-year reduction would be so you can really shoot for those goals um and and really try and reduce your risk as much as possible so we&amp;#39;re really excited that we can actually do that on this report too and we do that with the assumptions we saw in our Harvard cohort so we can can actually tell you how likely it was that someone with accelerated aging were to develop each of these outcomes and then apply that same assumption to your data um one of the other big um things that we&amp;#39;re we&amp;#39;re really excited to see is that there&amp;#39;s much more intuitive connection between these outputs and what you might have seen earlier so if you used our testing before and you saw intrinsic age accelerated and you saw extrinsic age accelerated and then you saw the Gen Pace decelerated um you might have asked the question which one do I trust which one makes more sense which one should I actually use in my clinical or or personal assessment um and now uh we can actually show that that the D pace and our omic age are the highest correlated of all of these clocks which means you&amp;#39;re going to see much more consistency between the rate of aging and the overall biological age but beyond that you&amp;#39;re also going to see more consistency with what you know about your patient or what you know about yourself um and and so oftentimes you might have you know patient for instance that really looks unhealthy really has bad behaviors and they come back reading a great biologic age and that&amp;#39;s because these first generation clocks have some big problems and I want to go through some of those problems so um before I actually give you case examples I want to reference a study that was published in 2020 by Jamie Justice who&amp;#39;s now leading the X prize for uh longevity um and and Longevity treatments um and so in this article she sort of outlined the multiple things which need to be uh in place before this is used for FDA clinical trials um and and so these are the clocks that that we always talk about the horbath the hanom clock grimage feno age and then dunan Pace um and at this time none of them met the criteria um for you sort of FDA compliance studies and the reason being is that none of them had satisfied this last criteria which is are they responsive to interventions which beneficially affect the biology of Aging um and so none of them had satisfied this criteria um thankfully early this year uh Dan belsky and and the team of Columbia um actually showed that and improved one of these points for the Jan pace and what they showed is that chloric restriction was actually able to detect a significant reduction in biological aging which makes sense with what we know because chloric restriction is probably one of the most well validated therapies for for biological age reversal or um Improvement of longevity um the problem though is that first generation clocks like the horbath and hanm clock actually went the wrong Direction they actually showed increases um at 12 months um which we know doesn&amp;#39;t quite stack up uh because caloric restriction is so well validated it might be giving us the wrong information especially when the newer generation clocks trained on biological signals like grimage and denan Pace show the right directionality um even though grimage didn&amp;#39;t meet significance um and and so uh so this was finally proved in that calorie study earlier this year published in nature um with the dedan pace um however we also recently published a study looking at the epigenetic uh effects of distintive and cortin treatment so disad and cortin as a combo has been one of the things which has been shown to increase Mouse lifespan um and so we were expecting to see some type of epigenetic effect here but unfortunately what we saw with these first generation clocks is that cytic actually increase them much like we would have seen with these clocks with chloric restriction um and so again we&amp;#39;re getting some conflicting information here these epigenetic first generation clocks are telling us they&amp;#39;re going up where we&amp;#39;re seeing you know studies with chloric restriction and and patinum and Corin showing in animal studies that they&amp;#39;re increasing lifespan so this doesn&amp;#39;t quite make sense right um and really it&amp;#39;s because these first generation clocks are not providing us the right information they&amp;#39;re capturing signals which are correlated to chronological AG not cpg methylation signals which are correlated to biological phenotypes and outcomes and so when we actually looked at un paac and grimage in the same cohort what we actually saw was that that on average they they did have some reversals um but it wasn&amp;#39;t close to significance so while the jury was still out on calics for these second and third generation clocks what we saw in these first generation clocks is that they went up and that&amp;#39;s probably not right which leads us to be very suspicious of any clock which is a first generation clock right because it&amp;#39;s probably not picking out the signals which are most predictive or most well quantifying our biological aging um and so um so so that&amp;#39;s a big part but we also want to come back to this idea of accuracy um and how we measure accuracy for biological clocks and generally the best way to predict accuracy is by seeing how well these clocks capture risk of disease and we do that by Hazard ratios um by looking at at things like um your likelihood of of getting a disease with accelerated aging or your likelihood of dying early with accelerated aging and what we see here is that our omic age outperforms every other clock um in in estimation of disease there&amp;#39;s only one exception which is COPD um where grimage actually outperformed but in every other Disease Association omic age uh was significantly better um in the case of even 10year survival prediction we can actually predict your your uh rate of uh I should say risk of death over the the next 10 years with a 92% accuracy so um compared to chronological age which is around 75% accuracy this is a big Improvement because we&amp;#39;re we&amp;#39;re we&amp;#39;re getting much closer to predicting um ultimate longevity and by capturing all of these different omic signals and all of the biology that goes into how we age um and again we can do it significantly better than most of the other clocks as you can see in that that testing cohort for their haard ratios and odds ratios so again our accuracy beats every other clock outside of grimage for one individual category of COPD and that&amp;#39;s most likely because Grim&amp;#39;s uh pack your smoking prediction is excellent um and generally outperformed RS in our cohort um and so that&amp;#39;s the reason that COPD is probably a little bit better but um at the end of all of this uh you know we&amp;#39;ve created a better clock but that might not change how you interact with it and so if there there&amp;#39;s probably the biggest benefit of this omic age algorithm is that now we can give personalized guidance to why you as an individual are agent so previously if you had done our test we might have told you you were accelerated aging or you were decelerated in aging but we couldn&amp;#39;t really tell you why um and this could happen for a lot of reasons some people might have had you know a lot of uh insulin resistance and visceral adiposity which is causing their aging other people might have high inflammatory burdens which is causing their aging um but we had no way of differentiating between the two or even telling you which was driving that process um so now this is different and the way this is different is going back to those epigenetic biomarker proxies um and and so on our report now you will see um all all these epigenetic biomar proxies sort of categorized by organ system um but you can also see their relative impact on your score um and so you&amp;#39;ll see it a little bit like here at the box at the bottom where you can see factors which are associated with better omic aging and those that are associated with worse omeg aging and we can actually tell you individually which effect size is is causing you to age faster um and so we do that with effect sizes but we actually also read out your individual scores so on this new reporting we will actually read out your alkaline phosphatases we&amp;#39;ll read out your fasting glucose your HB andc your blood UA nitrogen um and uh we&amp;#39;ll do that with 34 other individual biomarkers um and so uh you can actually now use this to quantify these biomarkers just with a single drop of blood um but again this is the waiting and I and I I want to give a couple examples of the waiting and some of the things that we found because as I mentioned we didn&amp;#39;t select these ourselves they were selected by the algorithm to be the most predictive of these outcomes um and and so here you can see for instance some of the metabolites that were selected and and I want to go into a couple examples of these um so the first one that I want to give an example of is urine uh so urine plays a role in in cellular function and energy metabolism um it is also used in in nucleotide synthesis so making the things that make up our DNA um and uh and it&amp;#39;s been linked to as a biomarker for a lot of different things um it&amp;#39;s mostly present in the cere spinal fluid and people do this as a supplement particularly for neurolog iCal impact um but if you look at the literature we see that urine has lots of connections to aging um so for instance you can see this in the study we reference here where we see uh intestinal aging is alleviated by urine um it&amp;#39;s also been selected as one of the most potent metabolites to help with regeneration um even in other species like salamanders um who are regrowing their tails um and as you can see in this plot we see our mrss score for urine decrease as we get older um tends to be a little bit higher in men um and a little bit lower in women um but uh but but this is one of the factors which was positively associated with better omage meaning that the higher your levels uh estimated levels of urine um generally the better you performed on biological aging um and so uh and and this is we&amp;#39;re not the only studies to have selected this as an important factor multiple Studies have shown a significant Association of this with all cause mortality and one study in the world the Women&amp;#39;s Health Initiative um found that this along with lysine and isocitrate um were associated with two to three times um higher odds of attaining longevity than those who didn&amp;#39;t have high values of these these metabolites um and so uh this is not a new finding but but it is interesting that it was selected as one of the most important um and so some people might be able to supplement with this to improve their biological aging especially if they&amp;#39;re low um and we can actually tell you if you&amp;#39;re low based on our methylation analysis uh of yourself and our cohort um another good example is keratin dial so another name for ker keratin 33 dial is ludian um this is uh uh but but generally K keratinoid are found in you know your leafy green vegetables they&amp;#39;re found in spinach kale but also most notably yellow carrots um and and ludian in particular has used a supplement for years to help improve cognitive function and Eye Health um and uh multiple studies big large metaanalyses of over 38 ,000 people have shown that higher intake of ludian is usually correlated with less coronary heart disease stroke and metabolic syndrome and lower levels of inflammation but our algorithm selected this as an important uh factor and in fact this is not the first algorithm to show us that carotenoids are really important for biological age in fact grimage also trained till time until death um had showed in association a significant Association um with coronoid levels um showing again the higher the carotenoid the lower the bi biological aging um and so again this is one thing that was selected but makes sense and shows us um sort of uh correlations to other epigenetic age algorithms which have been selected to predict time until death um and so uh again you could supplement with things like coronoids or or ludian in particular and probably improve your overall omag age especially if you&amp;#39;re low um and so uh so that was certainly helpful um although this says metabolomic uh these are also a list of all the proteins that we included in our analysis um so so there are a couple proteins some of the most positively Associated proteins are carboxypeptidase B2 um and then serum peroxin aerol esterase one which we call paon one um and so we&amp;#39;re to give you another example we&amp;#39;ll look at Pon One so um Pon One is an enzyme which is encoded by the paon one gene um and it&amp;#39;s found in almost every tissue but specifically secreted within the liver and it is the anti-os scoic component of HDL that good cholesterol um and so uh this Gene is activated by PPR gamma which uh generally is increased by things like exercise um and it is inversely Associated or correlated with things like leftin um hscrp and il6 which are all these inflammatory biomarkers and so uh what we want to see is high activities of things like Pawn One um and uh and generally that means lower levels of inflammation um and uh and generally lower odds of apos sclerosis um or cardiovascular complications and so again this was selected as our by our algorithm as being very very important till time until death which just so happens though there are also multiple dietary supplements which can help increase Pawn One activity so for instance uh there are things like pomegranate juice or um chokeberry which is this aronia micara extract which is probably the most promising for increasing Pawn One activity um and so again once if we see this as being elevated or or I should say low on your omic age we can then make recommendations specifically for you to increase these things and we only went through three examples with the carotenoids with um paon one and with iDine but we have 38 of these to make really specific recommendations and and plans to help improve that biological aging process so with all that being said I hope that you now can see that this algorithm does a couple things it&amp;#39;s the most accurate and precise it is the most consistent with your clinical picture it is the most predictive of negative diseases a and aging outcomes um and it also answers the question as to why are we aging and provides resolution on an individualized basis so let&amp;#39;s get into the reporting let&amp;#39;s ask about how we should use this um and so uh as we get into this I I just want to mention that after this presentation you will have multiple resources available to you um one of the resources will actually be a sample uh uh om AG report which is annotated with uh information on every single part of the report which helps you understand or or know how to interpret these results um in addition to that we will give you an entire PowerPoint uh which introduces all of these epigenetic biomarker proxies so it&amp;#39;ll tell you all about the proteins uh that you might not know about or the metabolites you might not know about and how it impacts sort of clinical treatment or it might impact your aging we also will give um links to uh Dr Jessica lais su&amp;#39;s introduction to om AG um and her presentation slides as well as uh frequently asked questions and uh a sort of a a Layman&amp;#39;s example for in in media um and so these will all be available after uh the discussion um and uh and uh I think will hopefully be very very helpful as you start to look at these results so let&amp;#39;s sort of dive into it and see what we found um and so uh again we have two different cohorts here we have our 30,000 plus cohort in the true diagnostic uh but we also had that 5,000 cohort from Harvard which we used for training um and so uh these are some of the trends that we found um and so this is how the the actual biological age and omic age will be reported if you decide to do this report um we&amp;#39;ll look at your calendar versus your biological age we&amp;#39;ll show you that age acceleration and we will track it longitudinally so if you look at some of the trends here we sort of the the largest acceleration we had in our cohort was 29.4 years um so someone was 29.4 years older than their chronologic age it actually belonged to a young male at 18 um and uh conversely the largest deceleration that we had was almost negative 20 years and it belonged to someone uh a female who was aged 97.8 and so this brings up a good point which is that if you&amp;#39;re younger you&amp;#39;re more likely to see age accelerations and if you&amp;#39;re older you&amp;#39;re more likely to see age decelerations however um we we take sort of the guesswork out of this because we tell you where you are at in the population so what percentage percentile you are in um and we calculate your risk um so even if you&amp;#39;re accelerated at younger ages so if you&amp;#39;re in your 20s for instance um and you see age acceleration you still might have extremely low risk of developing these negative outcomes um and so this is just uh something we oftentimes see in the algorithm but if you&amp;#39;re in your your low 30s if you&amp;#39;re in your 20s you might see age acceleration but you also might see that your risk is significantly negative um and vice versa if if you&amp;#39;re older you might see that age Gap start to shrink um and you might be younger even than than your your your chronological age but you still might see increased risk because the algorithm takes all that into account um and so uh so I just wanted to mention that if you&amp;#39;re younger you might see age acceleration if you&amp;#39;re you know particularly older you might see age deceleration but it it still is going to be calculating your risk uh uh all the same and what you really want to try to do is to perform the best out of our cohort so to to really try and score in the lowest percentiles um of of your rankings um and again this is where we actually give these rankings um one of the new features we also do do here is we actually separate it by sex so you can see where you uh relate to both men or women in the cohort and this is important because uh at age 50 men were around 4.35 years younger or sorry older in our Harvard cohort while women were um around 3.4 years younger um and so this is something we always see what we&amp;#39;ve seen even classically with even the older algorithms is that men age a little bit worse than women but this makes sense because women live approximately 3 to four years longer on average average than men um so again I think that this makes a little bit of intuitive sense um but the other thing to mention here in this population level cohort that we report on our reports is that this is made up of our population this means that uh generally uh people are very healthy uh you&amp;#39;re sort of competing against the best of the best because there everyone here is you know taking this test because they&amp;#39;re interested in preventative medicine and in aging so if you&amp;#39;re doing well on this cohort you&amp;#39;re going to be doing well in almost any cohort um and so uh and again this is where we see that the men were 4.35 years younger than Harvard cohort while the women um were around 3.4 years younger so uh there is a big difference between a disease data set like Harvard and our internal data set um again we do report out how your risk is increased or decreased um and how you can actually interpret these for for your relative risk of multiple diseases um again we we&amp;#39;re using this based on assumptions made in the Harvard cohort so often times you might see that uh your risk uh is maybe a little bit more elevated um because we&amp;#39;re using the assumptions of a diseased population um but again this is uh hopefully a way to to gamify this process and try and get your risk as low as possible um so this is again what I think is probably one of the biggest parts of this report and these are the clinical factors or those clinical epigenetic biomarker proxies which are associated with Better or Worse aging so in this particular person we can see that you know the biggest negative impacts on their biological aging are red cell distribution withd and hba1c um and vice versa probably the most protective is albumin and we know albumin is protective against mortality and decreases as we age so for this uh we probably would want to focus on um hb1c and red cell distribution withth as ways to improve this person&amp;#39;s omic age um and so this is how it&amp;#39;s going to be read out but we&amp;#39;ll do this for clinical we&amp;#39;ll do this for metabolomic and we&amp;#39;ll do this for proteomic so all three categories will be listed on your reports each with their relative effect sizes so you can see what&amp;#39;s impacting you in the best or worst way but we also report out all of these things individually and and sort of plot where you are at on the population as well um and so here it&amp;#39;s important to mention that some of these epigenic biomarker proxy weights might be a little bit misleading and that&amp;#39;s because some of these things are weighted more important than others so for instance Serum alumin is very very highly weighted meaning that it&amp;#39;s almost always going to be the most predictive um or I should say the most protective epigenetic biomarker proxy in your clinical values vice versa Red Cell distribution withd is weighted um very highly as well so it&amp;#39;s almost always going to be the worst of your performing markers and so um if you look at this you might see overall how they&amp;#39;re impacting but it might not tell you the biggest impact you can make on your score and so for this we recommend these different types of values and what you really want to look at here is your percentile ranking if you are uh you know in the bottom 10% or in the top 10 percent it&amp;#39;s probably worth looking into this a little bit more so some things like fast and glucose we would want to be in the bottom 10% um whereas other things like albumin we would want to be in the top you know 90% um because it&amp;#39;s weighted positively so depending on how this is weighted you you either want to be you know really good or really bad and where you are in the extremes are probably where you can impact your score the most um and so um so for instance I want to use this as an example so this is H actually someone who did our test and and collected their data from Lab Core at the exact same time so many of you might be thinking how accurate is this and so here we can actually look at an example um so you can see for instance High correlation to some things so for instance albumin uh we we predicted albumin to be 4.2 the actual clinical albumin was 4.1 um you know you look at uh things like fast and glucose we estimated that this would be a 94 um and in fact it was 9 won so we were really close even with blood URI nitrogen um we thought this would be 11.4 and it read is 11 on a lab core test um and so those are really consistent we see some that are less consistent so for instance with red cell distribution width this person read at 13.8 well in fact they were at 16.5 um and then uh you know even things like chatrit and and hemoglobin are relatively close um so this tends to be pretty accurate and I think this is hopefully a very good example um this is just one of the first ones that we pulled uh but I also want to highlight that these epigenetic biomarker proxies are not always going to be perfect or consistent with traditional lab measurements because they&amp;#39;re measuring technically something different um and oftentimes these are longer averages than the individual biomarker themselves um and so that&amp;#39;s important to mention if you see inconsistencies that&amp;#39;s probably um something that that is noteworthy um but generally most of them are going to be very very close together if with the traditional blood markers that you would get um and so here&amp;#39;s another example of the same individual here we can see their clinical biomarker um uh proxies um and what we can see jump out of this immediately is the most protective is albumin as we thought because it&amp;#39;s weighted relatively well and probably the worst impact of their omic age is actually going to be that red cell distribution with um but if we look beyond that we see that the other two biggest factors are going to be fast and glucose and hba1c and so what jumps out to us immediately is that there&amp;#39;s probably an insulin resistance type of issue um because we have the the some the two uh I would say outside of Red Cell distribution with the two highest categories have to do with uh uh insulin resistance and and and glucose um and so this gives us something to Target we probably want to do what we can to drive down this person&amp;#39;s uh insulin resistance by seeing what we can do to improve glucose in hba1c um and and so that can be very very helpful um I should also mention that that red cell distribution with is almost always going to be highly weighted um and so you&amp;#39;re going to see this is the most important aging factor in several different clinical analyses um and in in in an analysis of this we actually see that this is not surprising so this particular study found that uh uh High Red Cell distribution with um is linked to all cause mortality and then sort of asked the question as to why and so the findings were that things like Baseline values of VFR um igf-1 binding protein 2 temp one uh uh saw to some of these inflammatory genes were all involved but particularly igf1 binding protein 2 and and compliment another inflammatory Factor um were also associated with changes in Red Cell distribution with and so their interpretation in this paper was that cellular inessence might contribute to Red Cell distribution with and mortality um or it&amp;#39;s mediated by inflammation so um and we know this because multiple Studies have shown us that excess oxidative stress inflammation and cells inessence um have been proposed as conditions which cancre Red Cell distribution with so this is something uh Red Cell distribution with in this particular output is probably a surrogate marker for just general inflammation um and uh and something that probably everyone is going to struggle with um and and something we definitely want to see people get lower um and so that&amp;#39;s why you always see Red Cell Distribution withd on here um but that&amp;#39;s also why you want to look at the population level graphs to see where someone is uh compared to others of of their same age and sex um and uh and again this is just another uh good example of all the different um uh I would say associations and while uh why this is highly linked to inflammation and mortality but uh but going back to it this is also why you want to look at this population level graph so this again is for the same patient we discussed earlier with those lab core results um and so uh what we really want is low percentages for things which are associated with lower omic age um uh and or I should say higher omic Age and and higher percentages for those ebps which are associated with uh with better aging so for instance we know urine is a positive metric higher rates are associated with improved omic age um and so uh so we want this to be high and in this patient it&amp;#39;s actually really really high it&amp;#39;s this patient is in the 97.6 percentile so for this patient we&amp;#39;re probably not going to recommend supplementation with iDine because they&amp;#39;re already so High um and uh vice versa we can look at um you know this plasminogen uh this uh uh sterile adrenal GPC um and we see that that this particular patient um is actually pretty low compared to people of their age group um and so with this being higher rates associated with improved doac AG we might want to supplement with a plasminogen for instance um if we and vice versa we look at Red Cell distribution withd again this is uh lower rates or associated with better omic age um and we see this patient at 91.22 per. so we really want to tackle inflammation to try and dive that down um versus we also have cysteine um which is uh you know lower weights are improved with omic age and this is really really low in this patient so their cysteine is actually doing very very well um and so again by looking at these population level graphs you can get an idea of how much you can impact each individual value um and uh the other great thing is this can actually help us identify causes of Aging uh again using these epigenic biomar proxies and aging studies might let us know mechanisms of action might let us know which which genes are are most important to predict certain features um and so here I I want to talk about some conflicting information um so uh so we know that lower rates of igf-1 binding protein 2 um are generally uh associated with better omic ages um however we also know that as your insulin sensitivity um improves your igf-1 binding protein tends to go down um and so this is a little bit paradoxical um we even know that igf1 binding protein 2 uh mediates some of the early metabolic improvements caused by bariatric surgery so is it good or is it bad um sometimes we don&amp;#39;t know the answer to this because the data conflicts but if we look a little bit deeper we can see that what we&amp;#39;re probably measuring here with igf-1 binding protein 2 is again probably related to inflammatory status rather than insulin sensitivity um and so ig1 binding protein 2 has been a biomarker for several different conditions here are just a few examples uh so we can see it as a marker for idiopathic pulmonary fibrosis which is characterized by a lot of ccent cells in the lungs um we can see that uh it&amp;#39;s it predicts deterioration and renal function we see that it&amp;#39;s a candidate for uh heart failure uh and we also know that it activates this this nfca beta pathway which is a highly inflammatory pathway so again what we&amp;#39;re probably measuring with aone binding protein 2 is theic inflammation rather than this as a surrogate marker itself so again I think what if this just adds to this analysis is that um these biomarker proxies can give us really really good information um about how our biological system is behaving and how that&amp;#39;s impacting our aging and then can give us specific recommendations to try and drive this process down so it really is for the first time ever um a a epigenetic age algorithm which can help explain the why um and we want to really help you understand this so we&amp;#39;ve created lots of assets which explain each of these individual uh proteins each of these individual uh metabolites and I imagine most of the clinical biomarkers uh most of you will already be familiar with but we really want to give you a step-by-step approach um and so at the end of all this I think we&amp;#39;re sort of at the end of our presentation um but this is um again an algorithm we are so excited to have on our platform um I will also mention that we do expect to have another algorithm on our platform in January which will actually give you the age of 11 different organ systems um and and so we&amp;#39;re really excited to be debut that as well and eventually we hope to combine um this uh both of those algorithms into an amazing single algorithm uh and continue to make these things better across time um as Hannah mentioned earlier we are having to charge for this particular algorithm and Analysis uh the reason for that is because we have to pay a license fee from Harvard who we co-developed it with um if we can ever add new reporting to your report um we usually will do it free of charge so tomorrow um or even as of now you can upgrade this report in your portal uh but you&amp;#39;ll also find three additional reports new reports in your portal which we&amp;#39;ve provided Absol absolute for free even if you decide not to upgrade um your analysis for this omage algorithm um so that will include our inflammatory marker report which looks at il6 the CRP it&amp;#39;ll include our fitness age report which will give you a fitness age but also then tell you um your grip strength prediction your your V2 Max and fpv1 prediction um and your walking speed prediction um and then we will also do an advanced immune offering which includes all 12 of those immune cell subsets which we just recently published with the Chinese Academy of Sciences Hopkins in Harvard um and found some interesting associations with immune cell subsets and different types of behaviors um so all those reports will be for free um but this report will be an additional charge um and uh and it&amp;#39;s only 1999 for one report and 2999 for every report if you had multiple different types of tests um and again we&amp;#39;ll offer this as a as an addition incorporated into the price in the future um as well so if anyone has any questions about this algorithm please feel free to reach out to me or to anyone on our team at support atre diagnostic.com or to me directly at Ryan atre diagnostic.com uh as I mentioned we will be giving um uh many resources you can use to interpret this report if anyone has questions or wants to know how to review these results with their patients directly or wondering how this might affect you personally um again feel free to reach out and we will try and put some time on the calendar to help walk you through um all the insights that you can generate from this report um and so um so with that I know that I miss quite a few answers question and answers as I was going um but uh would be absolutely happy to go through these uh one by one so if anyone has any other questions please let me know um but I&amp;#39;m I will quickly look through um some of these as Hannah uh uh starts this raffle to give away the three kits um and then we&amp;#39;ll answer any other questions which come up in the meantime uh Hannah feel free to uh to start the process if you&amp;#39;d like awesome thanks so much for the wonderful presentation I just want to address one question um before before I announce um these winners um Curtis asked a good question if we can upgrade true age Pace kits so unfortunately not true AG Pace kits are on a smaller array that only measure 10,000 cpgs so you can only upgrade the complete kits that&amp;#39;s a great question and um one that we should have addressed earlier so uh thanks so much Curtis but without further Ado the three winners are William Stanford Nita Jane and Rick Cohen so you three um wanton a complete kit uh go ahead and email me your shipping address please um don&amp;#39;t put it in the Q&amp;amp;A um and we&amp;#39;ll ship you those kits out uh free of charge tomorrow so Rick Cohen Nita Jane and William Stanford perfect thanks Hannah um and again if you have any uh issues uh uh you know with with some of this please let us know um we&amp;#39;re happy happy to to help you through any of these different reports especially because it&amp;#39;s so much new data um I do see a question out there which asks uh about the treatments which are most highly associated with improving biological aging um and I and and I want to address this because unfortunately it it has a a good answer and a bad answer the I think the good answer is that now that we have this tool we&amp;#39;re seeing results that that are more expected so for instance you know with choric restriction we&amp;#39;re seeing decreases in omag gauge where we didn&amp;#39;t see it with some of those other CL um and so the answer is that we&amp;#39;re really going to be doing this analysis on almost every published study that we can um so we&amp;#39;ll be taking the systems age clock that was developed at Yale the omic age clock um and some other new algorithms and uh and really reviewing all the previous data to see if we can put together a full list of all of the Interventional things we know to change this result um in our actual report there&amp;#39;s actually one thing we didn&amp;#39;t mention which is that we do sort of a meta analysis on things which are positively or negatively associated um and again this isn&amp;#39;t too groundbreaking but we see associations we&amp;#39;ve typically seen before so again the more uh Fitness Behavior you do the better your omic age um if you do recreational drugs that&amp;#39;s obviously negative for your omic age uh so we SE a lot of those those typical assumptions but we&amp;#39;re going to start to provide a lot of data on things like Hyperbaric stem cells exosomes um uh vegan versus vegetarian diets uh senolytics um uh you know Mediterranean versus um uh other diets and so we&amp;#39;re going to be in all of this data again um and uh and and hope to continue to update you so that we can give specific recommendations but we&amp;#39;re also doing novel things like I mentioned urine and and carotenoid supplementation um and looking at how some of these other things fix it so we will continue to update our information on longitudinal interventions um to be able to make sure that you can make the right decision um and so um so I hope that that helps um you know I will say for anybody who is a researcher and interested in in getting this done we will make the code public uh to be used on on for any researcher who wants to investigate their cohort but if you ever need us to run samples for you we do so at a research price um to try and get this uh data out there and to learn more so we usually give substantially discounted pricing you can always reach us out to us directly at uh Ryan at true Diagnostic and we&amp;#39;re happy to coordinate that we do have really cool studies coming out and we love working with researchers to certainly let us know um and uh and so I think that that&amp;#39;s it for questions thank you all much again for attending um I hope it was worth it I know that the devil is in the details here so we are always going to be here to answer any questions um and help you use this um feel free to to share this webinar um if if you get the time and and tell people I think about these big breakthroughs and just how much has happened um and not every biological age algorithm or biological age test is created equal we always encourage you to look at the Publications and data look at the validation data because otherwise it&amp;#39;s like going to a fortune teller you can choose to believe them but it&amp;#39;s better to have proof uh so with that I think we&amp;#39;ll try and end this call um always feel free to reach out to us and thank you so much for again for attending

Transcript for:Major Report Update and Omic Age Algorithm

Transcript for:
Major Report Update and Omic Age Algorithm