Written by Christopher Kelly
Jan. 6, 2018
Christopher: Hello and welcome to the Nourish Balance Thrive podcast. My name is Christopher Kelly and today I'm joined once again by my Chief Scientific Officer, Dr. Tommy Wood. Say hello, Tommy.
Tommy: Hello, Tommy.
Christopher: Oh, Tom, I should have about that more carefully before I ask you a question. Every time it gets me. Well, Tommy, I'm delighted to have you here. Thank you so much for being here.
Tommy: I'm excited to be here, too, as always.
Christopher: On a recent podcast I interviewed Bryan Walsh, who we both know. We're a huge fan of Bryan and we tease people a little bit of the idea of extracting more information out of a basic blood chemistry and I think I made it pretty obvious in that interview that we were working on some software. And so recently Bryan and Megan and you have been doing some pretty intensive research on the reference ranges for our reference ranges which is a bit confusing but it makes sense when you think about it, reference ranges for the references ranges, right? So in the interview with Bryan we talked about how, for the most part, people like LabCorp and Quest and whoever do the test that just using two standard deviations either side of the mean, so the reference range is going to include 95% of the people who do the test which is not terribly useful when Bryan went into the history of the idea of a functional reference range, so one that's different from a standard reference range. But, of course, there's lots of evidence out there we can look at to try and decide on our reference range, so you've been doing that. Can you tell us about some of the work that you've been doing?
Tommy: Yeah, definitely. A lot of this is based on some work that Bryan has done on a number of markers. And one of the things that I, as well as everybody else, has been digging into particularly recently is lipid ranges. So standard blood lipids that you could get for your doctor or from LabCorp or Quest for almost no money, LDL, HDL, triglycerides. The interesting thing is that the more time you spend looking at that the more you realize how backwards our current approach to lipids is and you can also find some really interesting data in terms of how these lipids then correlate with all-cause mortality. Part of the reason why I've always been a little bit reticent to talk about lipids particularly is because it's a [0:02:19] [Indiscernible] topic. It's like talking about vaccinations in kids and I've made that mistake before. And as soon as you start talking about lipids and how maybe they're not the be all-end all for cardiovascular disease, we give the reasons why somebody shouldn't take a statin or something like that and obviously discuss this with your cardiologist, as soon as you start doing that people get really angry and I think we've taken kind of a step away from that. We can and we'll talk about some aspects of the heart disease risk.
But what I'm really interested in, we're trying to create what we would call an optimal reference range. You have to figure out how are you going to find something this optimal and I think one of the best things that we can look at is all-cause mortality. And obviously there's health span versus life span, that's really important, too, and that's kind of built in to a lot of these papers, as well. But I think all-cause mortality is a much more interesting marker than say, cardiovascular disease because you can muddle things by saying "Oh, but if you drop your LDL these many points you reduce the risk of cardiovascular disease." Yeah, but that increases my risk of cancer or frailty or dying of pneumonia, particularly in the elderly. I don't really give a shit about my cardiovascular disease is if I'm still more likely to die of something else. So all-cause mortality is a really nice marker, and there's some great data from the insurance literature, and that's where we spent a lot of time.
There were smaller papers as well, but there's this one great paper from I think this is the Journal of Medical Insurance and they have 1.5 million applicants for medical insurance and that followed over an average of 12 years, but this blows every other epidemiological study out of the water, like nobody follows that many people for that long because it's expensive. But the medical insurance, they have this data and it's in their interest to figure out what kills these people because they're going to be the ones paying out. So the medical insurance, they have real skin in the game so they're going to really care about what actually matters.
And so we can go through optimal ranges for each of the different things that you can measure but it's just interesting that LDL particularly doesn't really make very much difference in their models. They kind of talk about it a little bit in this paper and then it kind of drops off and they kind of ignore it from there. And the things that they do actually focus on total cholesterol and HDL, that's the sort of things that they focus on and for both of them it's actually a U-shaped curve, not very much is good and high isn't very good as well and that changes particularly based on age. So what we see is that total cholesterol, and it's the same with LDL cholesterol, maybe the risk is higher in people who are younger, so the cut-off on this paper is on the 60-some people use under 65 think the risk of higher cholesterol levels also decrease even more once you're into like the 75-plus, over that point is definitely protective actually. So age is definitely important.
And one of the interesting things that again comes out of this is the fact that higher LDL isn't necessarily better. There are all people who, again, we spent a lot of time hanging out in the low-carb space, people are kind of really happy if their LDL or their HDL is over a hundred but--
Christopher: So you said LDL there just for a second but you meant HDL.
Tommy: Yeah, I meant HDL. So we can go back to LDL and yes, that's important, that's what people call bad cholesterol. It's definitely not bad cholesterol. When you're talking about this particular study that I was talking about here, 1.5 million people, LDL really didn't make much difference in terms of predicting all-cause mortality but HDL did, as did total cholesterol. But for HDL higher isn't necessarily better, there's not many people talking about that and this is sort of one of the reasons why we want to dig into this just trying to find optimal ranges because you can say, "Oh, great, my LDL--" God, I keep on doing it! My HDL, I'm conditioned by the medical literature so I always talk about LDL as being bad.
Christopher: You're a medical doctor, I'm not surprised.
Tommy: I'm a medical doctor, that's what they taught me. Bad cholesterol. So your good cholesterol, your HDL, if that's over 70, 80, 90, that is actually associated with an increased risk of all-cause mortality and we definitely know it can become dysfunctional as the levels get higher associated with things like inflammation or oxidative stress, it can become oxidizing itself. So this is just one of the really interesting insights that we've gotten by looking into sort of big, big studies is that where we might think one thing, HDL is good, as always almost, more, just because some is good doesn't mean more is better.
Christopher: I'm going to slow you down a little bit and have you explain some of these terms because I'm sure there are some people listening that may have never heard that before. So can you explain carefully the idea of all-cause mortality?
Tommy: Basically all-cause mortality is just dying from any cause essentially.
Christopher: It's that simple.
Tommy: It's literally that simple. And the reason why I really like this as the marker and this is the thing that I've really focused on when trying to find optimal ranges and sometimes we need to look at other things. For instance, if you look at all-cause mortality or any other marker in any study, there's no lower limit for triglycerides. So triglycerides is another thing that you could measure on your basic lipids panel. We know it's elevated in fatty liver, type 2 diabetes, high levels of triglycerides associated with poor metabolic health. But the lower end of the range, nobody really knows what that should be and even in the all-cause mortality cardiovascular disease that show there's no real low end of the range.
But Bryan found this really nice paper about how lower triglycerides, or say below 50, are associated with some autoimmune diseases and we've also seen low triglycerides in people who are under-eating, who have too much of a calorie deficit and that's very common in the athletes that we work with. So the reason why I went off on that track was just to kind of say that there's not everything that we can look at that correlates all-cause mortality. But the reason why I want to look at all-cause mortality is because you have to die of something, so it's very for somebody who cares about your heart, so your cardiologist, can throw loads of data at you about what is associated with cardiovascular disease, lipids-wise, and that happens very often. This goes up or down or we could medicate this up or down or reduce the risk of cardiovascular disease, but it's not necessarily in their interest or in their knowledge base to talk about the other things that might kill you or the other things you might be at increased risk of if you take that medication to reduce risk of cardiovascular disease. Does that make sense?
Christopher: It does make sense. So what we're saying is cardiovascular disease and all-cause mortality are not necessarily the same thing.
Tommy: No, absolutely not, and you can definitely reduce your risk of cardiovascular disease but you might be increasing your risk of all-cause mortality because you've increased your risk of dying of something else. So we filter out that noise as much as we can we focus on all-cause mortality because that's a definite thing. So you have to die of something so we want to reduce your risk of dying full stop and I think that's a good place to start.
Christopher: Can you describe some of the charts that you've seen in that insurance paper, for example? So you're plotting all-cause mortality against what?
Tommy: So you have mortality ratios as just your risk of mortality on the Y-axis which is the up and down, and on the X-axis which is across they have percentiles of total cholesterol, HDL, and total cholesterol to HDL ratio which is they ended up focusing on because that's what seem to matter the most. They have a table which says there "At this percentile," so in males under 60, the 75th percentile of total cholesterol was 231, so even though on the graph they have the percentiles which isn't the absolute numbers, you can then go and reference what those numbers are elsewhere in the paper.
Christopher: I get it. So I could look at that chart and I could say where do I want to be on this chart and I can literally stick a pin in it and then wherever I stick the pin I can then go and look up to see what that absolute level of whatever it is I'm measuring and then I can say, that's kind of where I would like my blood levels of said thing to be.
Tommy: Yeah, exactly. And what's really nice about this approach, you can only do it when you have large enough numbers, but what's nice about this approach you can plot pretty much a continuous line. And yes, it's not necessarily always a smooth straight line or a smooth U-shaped curve but you can plot a line as cholesterol increases by this much, your mortality ratio changes by this much. So they can plot that because they've got one-and-a-half million data points, they've got huge amount of data they can put into this graph. What other studies tend to do and again, if we're trying to measure cardiovascular disease risk or it could be all-cause mortality, when they have fewer numbers of people they create an arbitrary cutoff. They'll say "We'll lump these people together, they have an LDL cholesterol more than 130 and we'll compare it to people whose LDL cholesterol is less than 130," and one group may or may not have higher risks than the other. But you created an arbitrary cutoff based on whatever you think is optimal, it doesn't give you a continuous true picture of what might happen at a given level because if you're above 130 you could be anywhere from 131 up to 200 or 300, and if you're below 130 you could be anywhere from 129 all the way down to 0. So some people are trying to get LDL down below 50 because they think that that will magically reverse cardiovascular disease. But the problem with those arbitrary cut-offs is you don't get that kind of full picture.
Christopher: Why do they do that, why create an arbitrary cutoff?
Tommy: Because you have to create groups so that you can then compare them statistically, it's just much easier to do that. So if you go back to the insurance paper they didn't do any statistics, they just like "At this level this is your mortality ratio," and they can plot it over the whole range.
Christopher: I like that, I like not having to be a statistician to find out what you're trying to say.
Tommy: Yeah, exactly. Over time we've tried to use statistics more and more to make our point. I always go back to physiology, back 100 or 200 years ago. They used to just do something and then tell you what happens as a result of that. There was no statistically this or that, no statistically significant increase. And yes, statistics are important but it makes it easier to sort of muddy the waters, whereas if they just show you, here is the full range of total cholesterol, here is the mortality risk at every single level then you can make your own decisions from that.
The way that this is really entrenched into the medical literature was it came up in this nice study. It was the InChianti study which is an Italian study of healthy aging and longevity. They're doing a lot of studies into centenarians and supercentenarians in certain areas of Italy because they have some of the pockets, some of the blue zones. So they were looking at LDL and HDL in an elderly population and they decided that an optimal or near optimal LDL was less than 130, then normal HDL was above 40 or 50. And even though optimal LDL was there under 30 the people who had the highest LDL, so more than 130, and normal HDL actually had the best survival, but they still called it optimal LDL being below 130 even though those people didn't have the best survival. And the more you read about this, the more papers you sift through, you just realize it's so completely backwards. There's another paper that we relied on fairly heavily for some of our reference ranges and again, because it was I think it was about 50,000 or 60,000 people, it was the ESCARVAL risk study. So it's in a high-risk population but they judged somebody being high-risk by having high cholesterol, high LDL, yet in this study having high cholesterol was actually protective for all-cause mortality.
So this weird circular thinking about high cholesterol puts you at high risk but then in that same study, high cholesterol is actually protective for all-cause mortality. The more we think about it the more it just doesn't make any sense. So the way you have to sift through this is find the studies where you can is where over the whole range they say this is the level of cholesterol, be it HDL, LDL, total cholesterol, this is the risk of mortality, and then you can sort of remove a lot of the biases there because you're not having to go based on arbitrary cut-offs or one particular disease or anything like that. So that's what we've tried to do as we've tried to figure out what something that looks like an optimal range of some of these lipids might be.
Christopher: Okay. I'll just point out at this point that if you are interested at looking at any of the papers that Tommy is mentioning here, I will of course link those in the show notes for this episode so that you can find them. I think that's very important. I always go and have a look at the papers when someone mentions it, be it someone on my team or someone else. So yeah, do come and find the show notes and find these citations.
Tommy, you just made me think of something as you were talking there and I suspect you're doing this a lot when you read papers, is you're looking at the data but you're pretty much ignoring what scientists say about the data? Is that something you do often? Because it seems like you'd have to do that to draw this conclusion in this case.
Tommy: Yeah, it's definitely something that comes with more papers that you read. I think we have talked about this at some point in the past, trying to assess data or assess scientific research. But in reality as long as you're fairly well-oriented in the field that you're looking at, you have some kind of rough idea of what means what, what word means what, then the results section is pretty much the important thing that you need to look at. Because the introduction and the conclusion, they're largely going to be based on how the authors want to set up the data and then how they want to interpret their own conclusion but it doesn't necessarily mean that that's really what the study is telling you. So again, if I go back to that study I mentioned before where these elderly people who had high, in the words of the paper, high LDL and normal HDL, they were the ones with the best survival curve in terms of all-cause mortality. Even though that's what the paper shows, and you can see it's a very nice graph, in the abstract and in the conclusions, they say that this paper supports having an optimal LDL which is less than 130. That's not what the data shows you, that's really not what it shows, but because that's how they want to interpret it that's what ends up coming out.
So I always start with the result and the simpler the graph the simpler the results are to interpret, the more you're able to rely on it because if you have this then you'll see it particularly they do this in nutritional epidemiology studies but they also do it in some studies like this. They basically create this huge table of complex numbers where they did some kind of statistical magic to find some kind of result. And if this table is just so mind-bogglingly complicated you can't understand it, in reality they've probably done so much nonsense and they've picked some arbitrary cut-offs and they've statistically tortured the data to kind of give the result that they want, at that point I just let my eyes sideways. Even if this was good data and a good conclusion, I don't want to read it. Whereas, people who really understand their data and are not trying to do anything fancy with it, they can often produce some very intuitive graphs or tables or something. So if the data is easy to understand, in my mind, and this is kind of how I've ended up figuring it out over time, it's almost easier to trust. If you look at the graph, okay, I understand that, that probably hasn't taken a lot of manipulation to get to that point. Of course there's going to be exceptions either way but that sort of seems to be the best way to navigate it.
Christopher: Let's take it from the top then with total cholesterol, I want to work though these quite carefully, it has. I mean, that was one of the main things that motivated me to a, get you and Megan and Bryan to do the research to give me the optimal reference range; and then b, create the software to report it is because I'm so sick and tired of explaining to people that I don't think they're going to have a heart attack just because their total cholesterol is above 200 and maybe that's not what their doctor said, but nevertheless I think it's true. So let's talk about the optimal reference range for total cholesterol and I know that you have to stratify that perhaps both by gender and age?
Tommy: Yeah, and that's what we've done. Again, going back to the biggest epidemiological data that we have, all-cause mortality, total cholesterol, we've split it an age of 60, so above or below 60, and then split it by gender as well. Like I said, depending on the study, depending on how you want to do it, again, they've used arbitrary cut-off, so this paper used an arbitrary cut-off of 60. They could have made it 65 or 70, but we have to pick somewhere based on the data that we have.
If you're below 60 for both males and females, we have an optimal reference range. You can definitely go outside of this and not be a huge amount of risk but this means you need to look at everything else. So none of these markers are in isolation, that's an important thing to remember, that you can't just look at one and suddenly panic. So we've said 120 to 140 for total cholesterol being optimal and again, this is looking at under 60s, all-cause mortality, and then how that changes based on total cholesterol level. I will make a brief side note which is again, we have a lot of people in this low-carb space who listen to this and they all immediately say all this data is collected from carb-eaters so that doesn't mean anything to us. And yes, that's an absolute possibility, but again, what do we know about cholesterol and heart disease risk in people who are keto are low-carb? We know pretty much nothing at all, we just have to work with what we've got.
Then if we're going above 60 years old then for men we've said the optimal reference range looks like 170 to 270, and then it's actually higher in women and women seem to do better with more total cholesterol particularly when they're older so then it's 200 to 300.
Christopher: Wait, I want to stop you right there, let's repeat that. So if I'm older than 60 and I'm a woman then my optimal cholesterol range is 200 to 300. So the bottom of the range starts at the top of the standard reference range, is that what we're saying?
Tommy: That's what we're saying and every epidemiological study that you look at agrees with that. If you're looking at all-cause mortality the older you get the more cholesterol seems to be protective, both total and LDL cholesterol, and the more that seems to be the case in women.
Again, we go to that insurance study, there's another nice study, it's the HUNT2 epidemiological study in Norway, where basically they found no upper limit for total cholesterol for women. They only went up as high as 7 millimoles, so up until the top around 300, but at that point actually those women were at the lowest risk of all-cause mortality, they couldn't find an upper limit. Obviously there are going to be some people who are like 400, 500, even higher. Again, that's sort of outside of the norms that we can really tell you anything about but at least within that range women seem to be protected or live longer as they get older with high levels of total cholesterol.
Christopher: And then the men are not much lower, right, so men older than 60… So if you're listening to this, you're a man and you're older than 60, we're saying that your optimal reference range is 170 to 270 milligrams per deciliter. So if I was to go and walk into my primary care practitioner's office with the total cholesterol of 250, what do you think is going to be recommended to me?
Tommy: I think they wouldn't look at your LDL but they're also likely going to tell you to take a statin. The interesting thing about it is this is what got me kind of worked up about this is if you're looking at the epidemiological data the people who are at risk of cardiovascular disease or all-cause mortality associated with high levels of cholesterol are people who are young. So like I said, we have a lower optimal reference range for people who are below 60 versus above 60 and the older you get the less cholesterol correlates with all-cause mortality and actually it seems end up becoming protective, so the higher cholesterol the longer you live. There's a really nice story which goes along with this which mirrors what happens in familial hypercholesterolemia which is something that people I'm very worried about in terms of cardiovascular disease risk. In those people they have a mutation or one of many potential mutations of the LDL receptor, so they clear less LDL out of the blood and they have high levels of LDL.
In those people, again, they've shown this very nicely, that in the young people with familial hypercholesterolemia, and this is usually just one mutation because people who are homozygous for these LDL receptor mutations is very rare. So you have one mutation on your LDL receptor, you have high levels of LDL, and those people with familial hypercholesterolemia are only at increased risk of cardiovascular disease when they're in their 20s and 40s. When they then get above 60, regardless of what their LDL is, they have normal risk of cardiovascular disease. So it's very similar to what we see in terms of just the general population that high cholesterol only seems to be associated with something bad when you're younger. And when they've looked at the familial hypercholesterolemia literature they've seen something very similar which is if you go back to the 19th century, people in families which have these mutations they seem to be protected, they actually live longer, probably because LDL is really important as part of the immune system and maybe they were protected against certain infections and then they became at increased risk when we started to smoke and we have picked up the Western diet in the 20th century, it's very much dependent on the environment.
So again, in that literature, they say that environment is much more important than cholesterol and again, if you look at people with familial hypercholesterolemia, and it's very similar in general population, too, people who have heart attacks, their LDL cholesterol is pretty much exactly the same as people who don't have heart attacks. So other stuff is important like insulin resistance and any kind of other associated problems with that. If you're looking at these people who are at high risk, they both in familial hypercholesterolemia literature and in the general population, they're at high risk of cardiovascular disease, so particularly we're going back to that because that's what people are worried about in young people with high cholesterol, they appear to have some kind of insulin resistance and that could be high levels of insulin, elevated HbA1c, they could be smokers. So anything in that kind of arena, that seems to be what's causing the risk, and then the cholesterol maybe makes that worse but it's sort of along for the ride, that's not what's causing the problem.
So the more I looked at this the more I realized that we have it flipped around. We should be maybe focusing on people who are young who have high cholesterol and then figuring out why that cholesterol is high. Again, if we're talking about LDL, you might have a thyroid problem or you might be insulin-resistant and that is where you need to focus because insulin resistance changes how you produce and clear the LDL receptor, the thyroid is also really important for that thyroid hormone function. So if that's what you're worried about we should be looking at this in young people and figuring out what the problem is, is it insulin resistance, is it problems with thyroid, I think those are going to be the two most common, we should be focusing on that. Then when you get older, when you get into your 60s or 70s, actually cholesterol doesn't really matter that much.
Interestingly, if you're looking at risk calculators for cardiovascular disease, which then decides whether you get put on a statin, the age is a continuous variable, and the older and older you get the more likely you are to be considered a high-risk and then prescribed a statin, whereas actually that's completely opposite of what we should be doing. Does that make sense?
Christopher: It does make sense.
Tommy: So it doesn't make any sense, the approach doesn't make any sense based on the data that's out there.
Christopher: Well, it makes sense if there's a drug to lower it, then it starts to make sense. And I don't want to be a conspiracy theorist but it can only make sense. I mean, not that they're not caring about insulin in the same way as a biomarker, and that's because there's no drug to lower it. We have a diet that lowers it but I can't make money selling the diet. I mean, that's my only suggestion as to why this is the case. And it's bonkers, right? So we're saying, so for me as a guy aged 41 and you're a bit younger than me, aren't you, Tommy?
Tommy: Yeah, I'm 33.
Christopher: 33, you're quite a lot younger than me. So yeah, we're saying that our total cholesterol upper reference range is 240. Well, that's a crazy difference! The standard reference range is 125 to 200, so nearly every single person that we see, thousands now, people that we’ve done a blood chemistry on, they're above 200, and that kind of makes sense given your optimal reference range that you just told me.
Tommy: Yeah, it's probably a good thing.
Christopher: I just don't know how the situation can continue where you've got literally everybody outside of the standard reference range.
Tommy: You just look at the normative data. And Ivor Cummins has actually done a really nice graph comparing this. So there's a great study that came out a few years ago, they measure the LDL of everybody coming in to the hospital with a heart attack. The LDL cholesterol, just like the normative graph, looked almost exactly like the graph you just get from a normal population distribution. It was just under 50% of people had optimal LDL which they considered at that time to be under 100. So that means that those 50% of people, despite having optimal LDL, were still coming into the hospital with a heart attack. So that means that there just must be something else going on and then particularly as you get older you see increased risk of frailty, increased risk of mortality in the hospital, increased risk of some cancers in people who have low total cholesterol or low LDL cholesterol, it then becomes protective, we're just doing it wrong. I can't think of any other way to summarize it.
Christopher: Well, let's move on and talk about LDL then. I know you've mentioned that quite a bit already but let's talk about these optimal reference ranges. For men under the age of 60 like us, you're saying that the optimal reference range is 80 to 170. 170, that's pretty high.
Tommy: Yeah. I have no skin in this game, I don't want to be going against standard recommendations, this is just what the data tells us. If you're somebody under 60 and you just want to reduce your risk of all-cause mortality, then the upper risk range seems to be 170. And again, this is the group where having low LDL could potentially be protective. Again, if we're going to go in and figure out why it's high, we see people surviving with LDL down to 80 or so and then risk starts to maybe increase again with lower LDL. So the range is 80 to 170 for men and women age under 60. And then we actually keep that upper limit the same, again, this is sort of based on the data we have. We increase the lower end of the range so then the optimal range becomes 120 to 170 and again, that's balancing all-cause mortality. So all the things you could possibly die from, if you want to have an LDL that's associated with the lowest risk of all-cause mortality because I can't say that having the LDL that's saving you but the LDL that's associated with the lowest risk of all-cause mortality in the early 60s is in that range, 120 to 170, roughly. It's going to change from paper to paper but sort of overall from the evidence we've looked at that seems to be kind of the right range.
Christopher: The standard reference range I'm looking at right now for LDL, the lower ends, because it's bad cholesterol, it's zero. How can you have too little of a bad thing? It's zero, obviously.
Tommy: No, no, you can't survive without it. [0:28:50] [Indiscernible] come to zero, it doesn't make any sense. So we've seen other people who we've suggested optimal reference ranges for things like insulin, yes, too much insulin is definitely bad for you, but the lower end of the optimal reference range for insulin was zero, that means you have type 1 diabetes, that is definitely not a good thing. So even if something is bad when it's high and that is still potentially possible with certain cholesterol molecules, that doesn't mean that zero is good for you.
Christopher: And it's the same thing with HDL, that's the good cholesterol, therefore there's no upper end to how much I should have, right?
Tommy: Yeah, and we talked about this earlier, that's definitely not the case. So if you look, again, you go back to the study and we'll include it in the show notes and these brilliant graphs. You're looking at HDL in every age group that when it increases and you're into kind of the 90th, 95th percentile of people, so you're talking HDL into their 80s and 90s, that's associated with an increased risk of all-cause mortality. We know from some of the mechanistic data that HDL can become they call it the dysfunctional, it can be oxidizing thus associated with increased risk of oxidative stress or issues with oxidative stress or issues with your antioxidant system, all that kind of stuff. HDL has been traditionally considered the good cholesterol but the more HDL you have and you go well above what we might consider the optimal reference range and is associated with increased risk of all-cause mortality. So I can't tell you it's the HDL that's the problem but I can tell you that it's associated with an increased risk.
Christopher: Right, so it's an engine check light.
Tommy: Yeah, exactly. And for some people that could be great, that's where they function, that's where they're optimal, that's fine. But it should be something that makes you think, "Oh, hang on a second, why is it so high?" And it could be so we know that alcohol increase HDL and so maybe all these people are boozers and then they're dying of liver disease, but if that's not you that's fine. So I can't figure out exactly why these people had a high HDL, but it's just something that if you're outside the range, you need to then look at everything else and make sure that that all looks good, and then you can sort of figure out whether you're happy about your risk or not.
Christopher: We used to have a saying at the hedge fund which was we don't like mysteries here, like if something goes wrong then we don't like to just shrug our shoulders and say, "No, it would probably be okay tomorrow. Let's just wait until the market opens and we'll see how it goes." And I think the same is true here, if the engine check light is on, then I really want to know why, I don't like mysteries.
Tommy: Yeah, exactly. And there may be a simpler answer, it could be the vast amount of exercise they're doing or it could be the bottle of wine you're drinking every night and then maybe have a good idea that that's something you should be fixing.
Christopher: Right, but the main point is that we know what's causing the problem, or what's causing the change is perhaps a better way of putting that. So you didn't find any need to stratify by, you've got multiple references that's stratified by age and gender here but the range is still the same for HDL in milligrams per deciliter and that's 40 to 70 for both men and women above and below 60 years of age.
Tommy: Yeah, that's pretty much what's shaken out. If you look at some studies they don't seem to have an upper risk range of HDL so it's not consistent whether everybody says the higher HDL is worse. But if you look at the lowest risk of all-cause mortality it seems to fit into that kind of range, so the 40 to 70 range, and then it starts to increase a little bit after that. Again, we go back to that insurance study, so many people, again, it's sort of going above 70, it's like that engine check light, you need to figure out why if everything else looks really good and that's the only thing that's a bit high and you're happy with everything else, then fine. But you need to know when it's worth checking what's causing these issues and we would argue that what you get in your normal reference range from your doctor is not what's going to tell you when you should look and then hopefully these ranges are going to be much closer to things that are going to trigger you to go and dig into your health or your life or whatever might be affecting that and then trying to improve that as much as possible.
Christopher: Okay. Well, let's talk about triglycerides, you already mentioned this a little bit and the fact that lower may not be better. So the optimal reference range that you've got here is 50 to 90 milligrams per deciliter. I can tell you that almost no one that we test is above 50. You know our population very well that mostly endurance athletes, not all of them, everybody is very active, possibly under-fueling is one of the most common problems that we see. I will stop asking the question after I've realized I'm leading you where I want you to go. So can I get you to speculate on why we may never see triglycerides above 50?
Tommy: I think it's exactly because our population defines the two things that I think are associated with low triglycerides. So the first one is autoimmunity and we do see a lot of people with autoimmune disease, so one flavor or another, and again, based on that paper that worked that Bryan found. And then the other one is just not eating enough. I think we're going to talk a little bit about hyper responders and people whose LDL total cholesterol goes up really high on a ketogenic diet. What we're seeing even from those guys and then from our own people is that as your energy deficit increases then your triglycerides seem to drop because your body is just hoovering up those triglycerides and using them for energy.
Christopher: Oh, that makes sense, I like that analogy.
Tommy: Yeah. Because the triglycerides, they are the there if your body is burning. They are standard fat. And if your body is crying out for fuel because you're just not eating enough, you're exercising too much, you're not getting that balance right, then you're just going to be continuously taking those up out of the blood to use them as energy, so I think that's where that's coming from. So if you look at the overall literature, because most people have high triglycerides, the triglycerides in the average population is going to be well above 100, into the 200s, 300s, particularly if they have some kind of metabolic disease, we're kind of talking about a very small part of the population. But if you're regularly seeing triglycerides below 50 my guess is that you have some kind of autoimmune disease or you're just not eating enough. And for most of our athletes it's because they're not eating enough. So if you're listening to this and you work with us, go eat a banana right now.
Christopher: Wait, don't bananas have carbohydrates in them?
Tommy: Yes, and the banana is going to give you diabetes, but at least the triglycerides will be higher. I'm being sarcastic. God, I have to say that, or else somebody is going to take me seriously.
Christopher: Okay. Let's get into some of these ratios. I'm familiar with the triglyceride-HDL ratio because our old software reports it. Can you talk about that one first?
Tommy: Yes. So the triglyceride to HDL ratio is really nice because it seems to be one of our best indicators of insulin resistance and also LDL particle rather than LDL cholesterol and those two things seem to be our best predictors or cardiovascular disease overall. This is something that you can measure a basic blood test. So it's basically your triglycerides divided by your HDL. The one caveat is that the range that we're going to give which is an optimal range of 1 to 2 and again, from a broad spectrum of studies this is what it seems to play out. And between 2 and 3.5 or 4, you're at increased risk but it's not like super high, but as soon as your triglycerides to HDL is above like 3.5 or 4 you're in dangerous territory. But 1 to 2 is the range that we aim to have people at but this is only if you're measuring in milligrams per deciliter. It is slightly different in millimoles and then you're looking at I think is like 0.88 to 1.6 or 1.7, something like that.
Christopher: When you say dangerous territory, you're talking about all-cause mortality or are you talking about cardiovascular disease?
Tommy: Both. That seems to hold true regardless of populations in terms of risk of cardiovascular disease or all-cause mortality. If we're talking about correlating with measures of insulin resistance and insulin sensitivity, so like a HOMA-IR which is based on your fasting blood glucose and insulin, or an insulinemic clamp where they basically see how much glucose they can force into your body by holding your levels of glucose and insulin with infusions. That's kind of the gold standard for insulin sensitivity. And the correlation between triglycerides and HDL and that kind of insulin sensitivity only seems to hold really nicely in Caucasians, but even in people of other ethnicities the triglycerides to HDL ratio does have some utility in terms of overall prediction of list.
Christopher: Talk about total cholesterol divided by HDL, that's a ratio I've seen less of.
Tommy: So all-cause mortality, also cardiovascular disease risk, people look at this. The ratios seem to always end up being more powerful than individual markers. So total cholesterol to HDL, people have probably have heard of the apo A-apo B ratio which is the ratio of particles of HDL and particles of LDL and that's a very strong predictor of risk, as well. But it requires a more complex test, whereas this is some pretty powerful stuff we can do based on just the real basics that anybody could get a hold of. And this is a bit trickier, we've looked at a lot of papers trying to figure this out and it seems like in everybody the optimal range of the ratio of total cholesterol to HDL is 3 to 4, but then what we might consider increased risk but not super high risk. So when we have our calculators, we have like the green area and we have like the yellow area and we have the red area, then the yellow is slightly different from males to females. So even though 3 to 4 seems to be about right for everybody in terms of optimal, men seem to be able to tolerate a slightly higher total cholesterol to HDL ratio with the same overall risk. Then our expanded range might look like 2.5 to 5.5 in men and 2 to 4.5 in women and that holds across a few studies. For some reason men with a total cholesterol to HDL ratio is slightly higher seem to do just as well or better than women who need a slightly lower ratio to get an optimal risk.
Christopher: Do you have a drug that I can take to raise LDL?
Tommy: To raise LDL?
Tommy: Yeah, butter.
Christopher: That was my little joke. I knew you were going to say that. At least I find it funny. But I do want to talk about how changes in my diet might affect what I see on a lipid panel. So let's take for example I start eating a ketogenic diet, what would the typical response be of my lipids and will I still stay inside of these optimal ranges that you've just been talking about?
Tommy: It actually seems to depend. We know that insulin can activate HMG-CoA reductase which is actually the enzyme that is inhibited by statins. If you have higher insulin you're eating more carbohydrates, you could have higher LDL, higher cholesterol levels because you're driving that pathway with a higher insulin. So if you take on a ketogenic diet you may want to expect to see your LDL or your total cholesterol come down. There's another side of that which is the hyper responder which is people who go on a ketogenic diet particularly if they're eating a lot of saturated fat and their LDL, their total cholesterol, seems to really go up and their LDL particle may go up with that, too. What increases your risk of being a hyper responder? I think some people who are reading into this think that you might be somebody with familial hypercholesterolemia, you might be somebody who--
Christopher: Let me interrupt you there and yes, the FH diagnosis, is that something I can figure out from 23andMe or do I need to go to a doctor or lipidologist to find that out?
Tommy: You need to go to a lipidologist. So there are so many potential mutations that could that issue that I think a lot of people become diagnosed with exclusion rather than a definitive diagnosis. Oh, the other cause of the hyper responder is thought to be having an APOE4, either being heterozygous or homozygous for APOE4 may increase the likelihood of being a hyper responder.
Christopher: And that you can definitely find out from doing the 23andMe test.
Tommy: Yes, that you can definitely find out.
Christopher: Okay. So then what do you do then? You're a hyper responder, you just cut back on the saturated fat?
Tommy: Yes. So this gets really interesting and there are kind of two schools of thought. If you're talking more traditional lipidology, so the person who's probably the best, most respected in this field is Thomas Dayspring, people might have heard of him. He is a lipidologist who I think would agree with all the stuff we're talking about. His risk levels for things like LDL and total cholesterol are definitely what we would say then what the traditional lipidologist might say. He's helped create a panel with a company called the True Health Diagnostics, they have a very comprehensive panel of advanced lipid markers which people can do if they're interested. And he would say in this person that reducing saturated fat, increasing monounsaturated or polyunsaturated fats can bring down LDL and total cholesterol and he would also say look at LDL particle rather than LDL itself. So LDL cholesterol is the total amount or it's the estimated, because it's usually calculated rather than directly measured, it's the estimated amount of cholesterol in LDL particles whereas LDL particles is the total number of particles that carry that cholesterol. Does that make sense?
Christopher: Yes, it does, yeah.
Tommy: So the numbers aren't the same. They used to be this theory that it was important the size of your LDL particles if they were large and fluffy, we used to call them so why you had particles that had a lot of cholesterol, so you maybe had higher cholesterol but all your particles, you had fewer particles and they were just bigger and stuffed with more cholesterol, that was lower risk. Whereas if you had small dense particles and they had less cholesterol in each one but because they're smaller that they're the high-risk, that puts you in a high-risk group. But it seems that if you actually normalize the total number of LDL particles that seems to cancel out. Some people still think that it's the size of your LDL particles that matter, whereas I think people are going more towards the saying the total number of LDL particles is your biggest risk factor. So this is still a bit debated, but that's where most people are going.
Think Thomas Dayspring and people in that camp. Peter Attia has done some very extensive blog posts describing the process of LDL particles in atherosclerosis. They seem to think that it's just like an LDL particle so the more particles you have and then the more time you have those particles that is what causes atherosclerosis. And I think that's probably a bit simplified but that's what they would say and to reduce that less saturated fat, maybe less fat overall, maybe you're somebody who does better on a higher carbohydrate diet and that doesn't mean you need to eat bread and pasta and things like that, it just means you eat more whole foods source of carbohydrates.
The other side of that argument goes back to some really interesting work that's being done by Dave Feldman who people may have heard of. His website is cholesterolcode.com, and he's basically been working with people who are hyper responders and his main theory is that the LDL network is an energy distribution network. What he's shown particularly in the hyper responders that he's worked that the more fat that you eat in three to five days for LDL particle, the more fat you eat the lower your cholesterol the lower your LDL particle. So the idea is that if you're getting lots of fat energy in from the diet then your liver needs to make less LDL to send energy out to the body because it's coming in from your mouth. So what he's done is he could show that if you eat a few hundred grams of fat a day before your test, then your LDL drops pretty impressively. And it's not like you just crash it completely, but it seems like the baseline stays the same but the top like one-third to 50% is super dynamic. The interesting thing is that most traditional people would tell you that your LDL is a fixed thing, you need to take a drug or do a dietary change for weeks or months to see a change but data's showing quite the opposite, that is you can something for three days and you can decrease your LDL cholesterol by 30%, 40%, 50% which is pretty impressive.
Christopher: Haven't heard Dave talk about many caveats here, though, do you think there are any caveats to this idea? We know that LDL is part of the immune system, it's kind of a big deal, isn't it?
Tommy: So there are a couple of things that just give me some pause and he may have answers to this that I haven't seen. I've met him in person, I've discussed this with him in person, watched a couple of his talks, read most of the stuff that's on his blog, but there are things that I just wonder about. I don't have a good answer, I just wonder about them. The theory is that the LDL system is there to deliver energy and you deliver that energy in the form of triglycerides. However, LDL cholesterol is what correlates most tightly with fat intake but not triglycerides. If you were trying to increase energy being sent out to the body, that energy is in the form of triglycerides so then why is it not the triglycerides that most tightly correlated to fat intake, why is it the cholesterol? Because the cholesterol isn't an energy substrate, it's used for membranes and some synthetic stuff but it's not the main energy substrate. So that just makes me question in. So the triglycerides do correlate with fat intake but just not as close as LDL cholesterol.
And then there's a little of a breakdown in the system so he did an experiment where he was doing endurance exercise including a marathon and when he did long periods of endurance exercise that's when he saw gap in the prediction. He had lower cholesterol than he would expect based on his fat intake and the theory is that cholesterol is being taken up into cells and used for repair which makes sense, like I would definitely agree with that. But if the network is so tightly regulated, wouldn't the body be able to predict that and then just up regulate production, why would you see a drop? Also in support of that theory is the fact that his lowest triglycerides were after long periods of endurance racing, which goes back to us talking about under-fueling, right? And that's what he says. I'm using all these triglycerides for energy so therefore they've been cleared out of my blood. That makes perfect sense.
So I think there were certain parts that make sense, there were certain parts that I just have questions about. The big thing that I wonder about and I've talked to Gudmundur Johannsson who people would have heard about, if you hang out in groups like Lower Insulin, Optimising Nutrition, he's always there talking about gut health and some really cool things to do with insulin and insulin signaling and incretins in the gut and stuff. He's the CEO of the Icelandic Health Symposium, he and I talk fairly frequently. Both of us thought the same thing about the LDL crash protocol that Dave produced which is basically we eat loads of fat for the few days before your blood test and then your LDL drops. What that looks like is somebody with sepsis coming into the hospital, and when you get septic your LDL crashes because it's part of the immune system, you're using that LDL to bind endotoxins who should then cleared by the LDL receptor. Again I've talked a little bit about this earlier, if you look at elderly people who come into hospital with sepsis they do worse basically the lower the LDL is because then they've kind of run out of that buffering system.
The alternative view could be and I'm not saying this is definitely correct, this may just be a small part of it, is that if you eat a load of fat, like hundreds and hundreds of grams, you're then creating the perfect shuttle for a load of endotoxins that come across the gut and then all your LDL is going to be taken up trying to shuttle those endotoxins into the liver to be cleared binding them to try and clear them. So maybe the reason why a high fat intake in a hyper responder is causing massive drops in LDL is because you're actually using up all your LDL to then clear the endotoxin that the fat is bringing across the gut wall.
Christopher: But to go back to what you said earlier, why wouldn't the body just make more to compensate?
Tommy: Yeah, absolutely, and I honestly don't know the answer, so I couldn't tell you who's right. It's just that knowing how important LDL is in other things, I can't say that one answer is definitely correct. And one of the reasons why I've been really reticent to talk about LDL in the past is just there's just more questions than answers, we just don't really know. It's really nice if go on Dave's blog, he talks about that a lot. It's like I read all these stuff, I don't know what theory is right, and he probably knows the literature as well as anybody and he still couldn't tell you exactly what's going on. So he has his theory, other people have other theories and you just acknowledge that you don't know what the right answer is. Again, when we go back to risks, so let's say cardiovascular is a risk, that's what people are really interested in, if you're low-carb, you're in ketosis and your blood sugar is really nice and tightly regulated, you're highly insulin sensitive, you have no inflammation but you have high LDL particle or high LDL, does that mean anything? And we honestly don't know the answer. There are some people in the keto world who are kind of hedging their bets and they do some stuff to try and bring their LDL or their LDL particles down, just in case, but other people will say, actually everything else looks good but I have a total cholesterol of 500 but I'm okay with that. And what you believe is probably going to be part of what ends up big, big, right? So if you believe that LDL is bad for you, then guess what, it probably is. But if you believe that LDL is fine, is good for you, then if you have high LDL that's probably going to be good for you. At this point that's probably going to be part of it. The answer is for a lot of this stuff we just don't know.
Christopher: Let me ask you one final howler of a question and I asked Jeff Gerber this same question and I think he did a really nice job on it. The question is, are there any circumstances under which you think a statin are justified?
Tommy: Yes. To the best of my knowledge it is a male who's had a heart attack who refuses to make any other changes to their diet or lifestyle. So it's secondary prevention in somebody who's high-risk, so say they're still smoking, they're insulin-resistant, they're any other stuff going on I think on those people is probably worth taking. They're not going to do any exercise so the fat and the statin might reduce their ability to adapt to exercise although that affect seems to be bigger in females than in makes. But if they're not going to do anything else that's going to improve their risk and they've already had a heart attack then I think in that group a statin is probably worth trying.
Christopher: And is there any group where it's never justified?
Tommy: Women who haven't had a heart attack, pretty much. The benefit of statins, it's primary benefits before a heart attack in women is pretty much non-existent and then you see increased risk of type 2 diabetes. Actually, I made a mistake, I misremembered. In men, there seems to be a greater effect of statins reducing response to exercise, less in women. Sorry about that so if people are confused about what I said earlier, it seems to be a big effect in men than in women. So in women taking statins increase risk of type 2 diabetes and doesn't seem to reduce risk of cardiovascular disease or all-cause mortality and again, you go back to the epidemiological studies. Women with higher cholesterol levels seem to do better particularly as they get older. There doesn't seem to be an upper limit. So women who haven't had heart attacks, unless there's something else going on I'm not predicting but in terms of a group in general I don't really see any benefit of a statin in them.
Christopher: Okay, thank you for that.
Tommy: I confused it by misquoting some research in the middle, so I hope people can still parse that out.
Christopher: No, that makes sense. So what we really need now then, as I mentioned on the last podcast with Bryan, is Tommy in a box, is this idea that one, is Tommy inside of a kiosk, so I'm going to take the results of my blood test, it's got a standard lipid panel on it, not fancy particle counts, LP that lay any of that stuff, just the basic lipid panel that everybody has done at some point in their life. And then I'm going to take that piece of paper and I'm going to feed it into the front of the box and then Tommy's going to be inside the box and he's going to write down what he sees, he's going to give you his evidence-based optimal reference ranges and then he's going to tell you what you think you should think about next and feed that piece of paper back out the box so that you then know, you've got your roadmap, what do I need to worry about, what should I do next. The problem is that you can't really have Tommy in a box, so what do you think about that, Tommy?
Tommy: No, you can't because I need to sleep at some point. But if you have Chris in a box then Chris would be able to do some predictions for you. So I know that based on some of the machine learning models you've been putting together, you can predict say a high-risk LDL particle from some really basic blood test so you don't have to go to the fancy one.
Christopher: That's really good. So I'll stop teasing people. We've been working on some software and in particular we've been doing some machine learning experiments and so some of the fancy markers, like LDL particle count, is very easy to predict using a whole bunch of markers, but the LDL cholesterol is very predictive of the particle count. So I have some really, really good models.
Tommy: That fits very well with what you see in the physio.
Christopher: In the physiology.
Tommy: Yeah, yeah, absolutely.
Christopher: Absolutely, yes. So by the time you hear this, I think we're going to be ready for you to send us your blood chemistry so that we can give you some evidence-based optimal ranges and then also make some predictions about what we see from a basic blood chemistry. So if you come to the show notes for this episode, nourishbalancethrive.com/podcast, drill down to an individual podcast and then an episode, I will link to a tool that you can use to submit your blood chemistry. So if you're international, then you're going to have to have already done the blood chemistry and everybody in the world should have done this, we're not asking for fancy markers here. If you're in the US, I'm going to be able to sell you a blood chemistry. You can go to Quest Laboratories in the US to get some blood drawn and then we can use those results as input for our software. So yeah, come to the show notes, and you'll find the link there. We'll do another podcast, Tommy, and we'll talk a little bit more about how the software works and what people can expect to learn from using it. How does that sound?
Tommy: Sounds great.
Christopher: Awesome. Well, thank you so much for your time. I really appreciate it.
Tommy: Thank you.
[0:54:08] End of Audio