 Thank you all for coming today. I have a special guest joining me, and I'm just gonna set everything up for him And he's gonna deliver the actual money presentation here that this is Sean mark. He is a nutrition PhD in nutrition epidemiology, but don't hate him He's the good kind of nutrition epidemiologist he looks at the problem of nutrition and nutrition information from systems perspective which is the inspiration for the talk that we're gonna talk about so he's he's taking that wider view So it's not just about telling people what to eat But looking at how information interacts with a health care system and has created the problem Problems that we're dealing with so a lot of us here including me I spent a lot of time wondering how we can fix the dietary guidelines. We know things aren't working We know that people continue to experience health To experience poor health in relation to all of the operations that are related to and reinforce the dietary guidelines institutions practices programs textbooks lectures education Websites labels experts on the internet. I could go on and on and this is exactly the point What I have failed to appreciate up until recently and what we'll be talking about today is the extent to which the dietary guidelines are far More than a policy that needs changing in fact They are and function as a capital s system Approaching the dietary guidelines from a systems perspective doesn't make them Unfortunately any easier to deal with systems are notoriously Obstraparous it does however give us the opportunity to think outside of the system because one of the things that systems are really good at are Keeping us from doing that So because it's central to any system I'm going to start by addressing why the communication and information production that motivates the dietary guidelines is central to the problem that we're looking at and Then I'll look at Ways that we might try to fix the system But that probably aren't going to work and this is why we need to suck it up and learn to live with the dietary guidelines And then finally Sean's going to come up here and he's going to present a way to jump out of this system by Disrupting rather than trying to fix it and show how this approach Benefits three of the major stakeholders in the current system the individuals who are affected the health care providers and clinicians who work with them And then the nutrition researchers who actually benefit a great deal from this system right here If we're going to look at the dietary guidelines of the system We're going to start at the beginning the fundamental systems theorem is that new systems mean new problems This can be read two ways first of all a new system implies that there's a new problem That we need to deal with with the system human systems are typically created in order to address what is perceived as a new problem Of course new systems means new problems can be can also mean that once there's a new system Then there are new problems created by the system, which is exactly what seems to have happened with the dietary guidelines So if we're This is what seems to have happened with the dietary guidelines. This is a Google engram if you haven't played around with Google engram So lots of fun What this does is it gives a rough picture of the use of a phrase in popular and scientific literature So in other words it gives us a sense of how much people are talking about something over time So this is how we were talking about the phrase obesity epidemic over time And you'll notice this little bump in the middle where in 1975 we started talking about it a lot And then we stopped talking about it. Well, apparently what happened is we interpreted this slight rise in the obesity as a Problem that needed fixing and let me just point out in And relate this to the conversation that happened earlier about the state of paleo is this problem was fixated specifically in a problem population women were women rates of obesity and women were increasing faster than rates of obesity and Men men weren't actually had much obesity in their demographic section black women in particular were a problem population and that's Part of what stimulated this Which is the first government dietary guidance for the prevention of obesity and chronic disease? Now you all know what happens next right well first of all Let me point out to you that we thought we fixed the problem. Why because we stopped talking about it So you see this drop-off in discourse where we stopped talking about it until about 1993-94 where we noticed rises in rates of obesity so in true true to form we fixed our efforts to fix the obesity epidemic resulted in Creating a new problem in the second sense of the word which is the true obesity epidemic We caused the obesity epidemic by attempting to prevent it from a systems perspective However, this is actually to be expected because the thing that systems are best at doing is not doing What they're supposed to do? This is the natural state of a complex human system. You're familiar with the acronym snafu, right? Situation normal all fudged up right normal. That's the normal state of a system So central to the malfunctioning of the system and I'm gonna have to go back here because he's a little out of order is The communication information that takes place early systems theory actually proposed that all systems failure Stem from failures of communication information a system is no better than its sensory organs and the sensory organs of dietary guidance Have been in question from the start So this is how we collect information nutrition epidemiology has long relied on this process of extracting information from individuals Using tools like the Harvard FFQ or food fantasy questionnaires. I like to call it or the what we eat in America dietary refall Recall this information is then taken and sequestered away in these proprietary data sets. Just try Getting information out of the Harvard data set even though it's collected using some public funds The information belongs to institutions and experts not the individuals from whence it came However after it's filtered through obscure modeling and statistical methods and distilled into dietary guidance by experts Burdened with ideological and institutional allegiances It's projected back on these individuals and they're treated as if everyone is is a statistical average of everyone else This has been called the platonic backhand after Plato's ideal forms This is where simplified abstractions or ideal forms are extracted from a complex in very world There's not a this is not a problem in and of itself because this is what we also call inductive thinking It's a fundamental scientific approach Where we derive theories from patterns that we see in nature the problem comes when we begin to assume that the abstraction that figure in the middle Is the reality? We treat that as as what is rather than acknowledging the original source of the information When this happens these ideal forms become the real reality and complex and variety and individuals appear as failed Outcomes of the system because they don't match that ideal form The more information the system collects the worse the problem that they were supposed to fix by collecting the information gets or As my husband likes to put it nutrition epidemiology studies are strongly associated with a rise in obesity The system creates its own epidemic of information to match the rising rates of obesity Because the real world is whatever is communicated to the system What the system has determined is bad for you turns out to be Hello bad for you We can explain this phenomenon through a simple principle from physics the act of observing a system Changes it our dietary guidance treats information as if it's collected in a vacuum Disregarding the fact that the information that the system generates affects the behavior of the people Invested in the system again part of the conversation that we were having next door the result is a self-perpetuation of Consensus of findings people with the social and cultural capital to be concerned about dietary health are the ones that eat a healthy diet a healthy diet is the ones that those people eat for example in the United States a Consumption which is bad for you Is associated with an increased risk of diabetes in other countries and the countries in this analysis so on the Right side of the screen Spain Japan Finland and France where eggs are not considered bad for you There is no association between eggs and diabetes How did that happen so currently right now and I'm sure that you all this has come through your Facebook feed the US mainstream nutrition is dealing with the pure study because they seem to contradict the US dietary guidance Because eating meat is bad for you in America if you live in America It's bad for you But if you live elsewhere in the world where meat is a valuable source of nutrition guess what it's not actually bad for you so in order to deal with these anomalies between the reality of real people and the reality of the system the system has identified a new Set of problems because new system equals new problems right in terms of information Gathering we have a problem that didn't exist before called misreporting This is when the information collected by the system shows that people who follow dietary guidance But don't get the outcomes that the system expects to see must be of course lying about it The reverse of this problem, which also didn't exist before is called non-compliance This is when the people who don't appear to follow the dietary guidance that is generated from the information That the ostensibly misreported in the first place do get the results that the system gets But they get them because they didn't follow the guidelines So here's the system in its awful horrible Simplicity it's a closed loop of winners and losers the winners are the policymakers and the bureaucrats They keep their jobs by appearing to do something about the problem that they help create while placing Responsibility for the problem on people that they don't know exist the health care industry wins Because the system is set up to generate the problem It was meant to fix ensuring that there will be plenty of customers who need to be treated for a problem They didn't have 40 years ago and the nutrition academic research industry really wins Mark Hegsted who was one of the folks who helped develop the dietary guidelines in the first place suggested way back in 1990 that nutrition research was rescued his term by obesity and that certainly seems to be the case last year the NIH Alone spent nearly a billion dollars on obesity research and the losers are the individuals who should be the central focus of the system They're shut out of it Providers are the on the other hand are locked into the system forced to treat their patients as statistics instead of individuals One of the problems with systems is that it's difficult to see past them as we've heard from conversations already here Systems become givens and even thinking that opposes a system Nevertheless opposes it on the terms that the system has set up One of the terms set up by the dietary guidelines is that there needs to be an overarching nutrition policy that directs the eating habits of Americans Those of us who understand how misguided The specifics of this policy are still buy into the idea sometimes that such a policy is needed We just need to fix it and give Americans better dietary guidelines, but Systems theory thinking again a complex system cannot be made to work In fact pushing on the system makes things worse We already know that systems don't work as they should we saw this with the low fat low carb dietary guidance So this data right here is from what we eat in America a different and it shows that when we gave people low low low fat guidance Fat level stayed the same and carbon take went up There's a different set from the what we eat in America that shows the same pattern except that it shows that fat intake actually increased We have no way of knowing Really which one is right and what the reality really is and it hardly matters because the system doesn't deal with reality It justifies this grass by suggesting that Americans have misreported what's going on and it would justify a different graph By pointing to non-compliance So this means that changing the guidance would not necessarily produce the outcomes that we would predict or expect or hope for Why because complicated systems produce complicated outcomes? So if low-fat dietary guidance did not really reduce our intake of dietary fat consumption and May have even produced according to whatever dataset you look at an increase if Higher fat lower cop carb guidance produces the same pattern Then we'd have the same problem But only more so fat is energy dense and the impact on calories would more than double And I know that there's a lot of but but but satiety, but but we don't know what's gonna happen with the system when our new Improved in system fails to deliver. What are we gonna blame it on misreporting or non-compliance? I've been a fan of the anarchist approach get rid of the system But as I've come to understand the dietary guidance I've come to realization that this may be possibly impossible once established a system serves its own goals No matter how external circumstances change whether the pony is tied to a tree or a plastic chair The system continues to operate and the most fundamental goal of any system is to continue to exist as a system So what are we going to do? And this is what my co-host Sean is Here to explain and hopefully provide us with some insights that don't leave us in this closely Thank you very much everyone for having me here. I do have a conflict of interest I am a founder of a for-profit organization Looking at developing a system to disrupt the current guidelines system and I'll get into that in a little bit here So an alternative instead of You know, I think we're now at a stage with technology and information technology and in an awareness We see the way social media operates through societies Where we don't if we don't like a particular system we can develop an alternative system And I think I use the word system in full Recognition that what Adele just mentioned new systems will create new problems. So even though We know the previous problems with the dietary guidelines system We believe there is a better way of doing what the dietary guidelines have Set out to do So this is really a nested problem you have the issue of science down there at R2-D2 and within or you have the issue of the guidelines and then like Adele was mentioning you have the issue of the science and The then you have issues of accessing datasets that Adele referenced if you try to go access say the women's health initiative data Well, you better have the right pedigree. You better have the right questions And even if you get into that data set and you find something that goes against the dogma good luck publishing that and and that's kind of Kind of cloaked knowledge from within the nutrition field we just Getting access to some of this data can be really really difficult and you know when we get up to towards Chewbacca This system You know the guidelines system runs into economic Interests and so obviously keeping people unwell. There's a lot more money in that keeping people Medicated there's a lot more money and then trying to keep people healthy So at its very basic level we have individuals and their experiences with diet and or with food and health and each of those blocks of Lego are more or less an individuals experience on Any particular diet as we've talked about a bit before everyone's Experience can be different either physiologically individuals can have diabetes or pre diabetes or There can be ethnic traditions Cultures and whatnot where people have certain food preferences Again when we look at how data is organized in the health system a lot of the time A lot of the data sets that could really help us move things forward to produce better guidelines are kept in closely guarded data Repositories now this situation exists Some of it obviously is important that we have Protection around data for for privacy purposes and whatnot, but beyond that there is data is is a Lot of the time is about Empire building and that's been our experience So that is situation exists both in the research realm and it also exists If we look at health organizations as well in the health system data tends to be quite closely guarded to build careers So a top level view Another way of looking at Data for an individual on diet and health is a very you know a lot of a big mess of different pieces The data is all very unstructured and and it's difficult to make heads or tails from it if you can find a way to Put those pieces together in a certain configuration You could build a death star and blast the current dietary guidelines system or if you're having a good day, you could build a mermaid castle and You're feeling all rainbow and unicorns so I make this kind of jokingly a little bit, but I think the potential to You know gather data and and organize it. These are not expensive technologies anymore cloud computing is is really Very affordable now and so what we've been busy building up in Western Canada is a data platform and you'll have to excuse the Very kind of extractive nature of this photograph, but when I was looking for a platform photograph I didn't have too many choices, but it illustrates the fact that platforms tend to be difficult to build and That goes the same with data platforms as well The difficulty with data platforms is not the technology It's more just getting people to kind of trust a particular platform and to buy into it So current platforms that we're all familiar with obviously Google search is a platform Obviously very extractive platform Amazon is another platform obviously very extractive as well and We're getting into these Chewbacca level issues when we talk about platforms and that data is kind of sucked up through the internet and Is used in ways to sell you more things and kind of program you to be a good consumer So that's kind of the ecosystem for data platforms in this day and age One last thing about platforms platforms are Incredible learning machines. So if we can actually create a platform the platform we're looking to create The capacity for learning on nutrition and health will be unprecedented Both in terms of velocity of data the flow of data through systems It'll be just absolutely Incredible, but they're difficult to build Okay, so Importance of being diagnostic. I think something I've been in the nutrition realm for Good chunk of time. I had more hair when I started my studies in nutrition Science and I've been kind of more affiliate with a low carb high fat movement and what I've observed over the years is just Nutrition science unfortunately is is really kind of a battle royal And I think it's a huge disservice to the to the discipline and I you know, frankly, I find it very sad So whatever we build with our platform, we have to operate in a diet agnostic fashion So which is to say if there's low fat vegans out there and they're having great health outcomes with their diets Then we're happy to work with that population to show the health benefits for that subset of individuals The diet agnostic piece comes largely inspired by this piece of work in rural Canada Northern British Columbia and it was the experience of working with some practitioners That began using lifestyle interventions in their practices with pre-diabetics and diabetics And these folks that were the practitioners in this context were Not very savvy when it came to nutrition. In fact, they had little to no nutrition training Excuse me, but the thing that was fascinating was they started using small quality improvement trials small non-randomized trials in their practices And the kind of stuff they were figuring out was just fascinating with absolutely little to no infrastructure so this is really a model of grassroots science that was very powerful and they This model for lifestyle interventions went on to be replicated without any resources for at five other practice sites in different parts of the province What was really interesting about this is when we tried to talk to health administrators in Canada We have a publicly funded health system We were saying hey look look at these health improvements. These people are shedding all sorts of medications They feel amazing and this this model is replicating all over the province and I literally spent years Trying to get the attention of health system administrators with no luck and I also co-wrote a million dollar grant to look at the kind of health economic implications of this lifestyle intervention and because of the data Empires in in Canada in that area We the project went off the rails so what the take-home message is that grassroots science is powerful and That we can't rely on bureaucracies for either funding or for sourcing data this is What we've built so far or this is kind of our second attempt our first attempt at building an app kind of crashed and burned miserably and so we kind of came came around to working with about 1,700 Physicians in Canada who are unhappy with the current dietary guidelines and they came to us or you know We kind of got in conversation and they're saying well, we'd like to find a way to aggregate our data and so That was something that we had been kind of thinking about and working on and we said okay We'll take that problem on and so we kind of looked into or have been looking into How do we pull data from a lot of different places like for? Physician context you have electronic medical records And whatnot so we've really been Specializing and are really looking into how do you pull data together and this is what we've come up with Some other of all wellness databases and it's really darn simple. It's not too complicated But this is just kind of phase one. It's Phase one will be with Canadian physicians will be piloting a diet agnostic data collection tool and then we're in the process developing an individualized nutrition app that will also be diet agnostic and Deploy that with hopefully with the physician community and actually save them time in their clinical encounters This is what we're all about Universal and individualized nutrition guidance the beauty of today's technology tools They do not take too much money to operate And we're a kind of a startup on a on a boots or on a shoestring budget bootstrapping But already we've come very far in in towards this Obviously scaling will require funding and we're in the process of looking at different options and I'll end with this slide Throwing rocks at the Google bus. We really have to question the dominant economic paradigm here We don't want to be one of those death star platforms where we extract data from individuals through app use or through your search histories and whatnot and Use that data to program you to become a better consumer Thank you very much for your attention. Happy to take any questions There's a microphone over there if anyone wants any questions. I can hear you What is wrong with extracting the data from the consumer? if you can If the consumer knows that they're Uploading that data to the cloud and they're There they're there's an exchange of data and in exchange for their data being compared against Hundreds and thousands of other people and being able to get something back from it Is that better than their health practitioner owning their data and Them not having access to it when they need it And that's a great question. I think what you're speaking about is individuals owning their own data and having custodianship of it There's a lot of frustration in kind of the data realm about you know lack of access to your own health records It also needs to be said, you know Google health or Microsoft vaults or health excuse me health vaults have Tried to run data patient driven data repositories, and they more or less failed So patient entered or patient data is is a huge It's kind of like a new frontier, but the big money in that area hasn't necessarily succeeded Well, I used to work with health Canada the organization that produces the guidelines and I came a little bit radicalized and and to this day that carbohydrate expert in Health Canada is just drives me crazy like not often do people drive me crazy But he kind of drives me crazy in a bad way and and so a lot of this is you know, there's a You know these bureaucracies are not going to solve this problem This is really a problem that we need to solve kind of at the grassroots level and and build a system That will be imperfect it will have flaws But hopefully the more open nature of that system will create a better way of giving guidelines that's individualized So I am thinking from my own prejudice and perspective of certain industries That might have been marginalized by the system up until now who might be interested in Having their own members Take advantage of something like this is this to a point where this is now Deployable to oh, I don't know say the beef industry Well, you've seen our data collection tool that's kind of where we're at it's a pretty basic thing for capturing data, I think we do have to be careful with this the said beef industry I love eating beef, but I Think the diagnostic piece needs to be something we operationalize So if if we go beef industry or partner with them, then we should partner with the almond industry or the tofu industry or you know So it the diagnostic piece is not trivial and and so that would be my concern concern with that I think within a few months are our second crack at the app should be coming online and I think will be past kind of this stage of Yeah, we should have an individualized nutrition app in a few months. I Eagerly, sorry. Yes. Yes. I eagerly await that I tried through some formal organization to get some traction on this and Basically because of their degree of funding to as you say address the problem But based on their perspective of how that solution would look They weren't interested in collecting Diet agnostic data from individuals that would mesh with health outcome to see what works and what doesn't There their their attitude is already we know what works So don't confuse us with trying to find out what works. So keep at it looking forward to it. Thanks, Peter Thank you very much. If there's no more questions any questions for Adele And I ask you a question John can I do that? So can you can you tell a nice crowd what you told me about? About near neighbor populations and about about clinicians using this Well as we've talked about this he's explained this to me in a lot of different ways because I'm a little thick when it comes to Technology But I remember you telling me about nearest neighbors and a way of understanding how to Guide people through nutrition and lifestyle changes using that principle Sure. Well, I think You know, I do have a secret agenda here. I do want to see nutritional epidemiology rise from the ashes So I really do believe in the science of nutrition and it really does sadden me that it's such a mess right now At first will be limited by sample size So we'll be looking at say pre minute like more course kind of Individualization say pre menopausal perimenopausal post menopausal women and the kind of data We're looking to collect is particularly around individuals who are Metabolically unwell and who are initiating a therapeutic diet with their health practitioner So if we were just to collect a lot of information say on Individuals like yourselves that are generally healthy and eating a paleo an ancestral health diet Where a diet consistent and ancestral health principles, we would just have an observational data set. So we're looking to kind of aggregate some of the intervention data and Like Adele says start doing subgroups looking at subgroups and see if Say pre menopausal women respond differently to different diets than say post menopausal women And whatnot so at first it'll be quite these smaller divisions and the more data we get again This comes to the point about the platform The bigger it gets and the more established the more you can accelerate the learning on it so the more data we have we can start looking at smaller and smaller subgroups and you know from a method logic point of view there's a lot of That that's a bit kind of something that big pharma does to to pull out results from their trials looking at subgroups So there are methodologic considerations and looking at a lot of subgroups. You have to adjust for that