Written by Christopher Kelly
Aug. 14, 2018
[0:00:00]
Tommy: Hello and welcome to the Nourish Balance Thrive podcast. My name is Tommy Wood, and today I am joined by my great friend, Alex Ferretti. Hi, Alex.
Alex: Hello, hello.
Tommy: Thanks for joining me. We've been planning to take on a podcast for some time, so I'm really glad that I managed to make that happen. Anybody who exists in sports science field, maybe the low carb or keto field has probably heard your name before. You've actually made some pretty big-name friends in the kind of world that we exist in.
I know Robb Wolf loves you work, so does Ben Lynch. You've been collaborating with some great sports science researchers recently like Dan Plews and Paul Laursen, who I've had on the podcast previously. So maybe you could start by just giving us a little bit about your background but then I'm really interested in hearing about how you developed relationships with those guys through your work.
Alex: Sure, sure. First of all, I just wanted to really thank you guys for having me on the podcast. I really do hope I'm worthy of it because your podcast is great. It's the one that I keep going back to. I took a little time off now for personal reasons, on work, but it's the one that I look forward to, and great stuff, really, really love the stuff that you guys do.
When you mentioned all of the names that I've been doing some work or researching with, it's quite interesting because I don't ever stop and think about those things. I think things happened quite organically, so we got in each other's world most of the time by social media or lectures and conferences.
Benjamin Lynch, for example, was in the UK doing a conference. I got there late, he had to stand, and we started to chat. It was really interesting because I said I was studying HRV, and with my accent and his ear, he thought HIV. The problem is the conversation kind of got into ten minutes before he inquired, "Sorry, what are you researching again?" Then things made quite a lot of sense.
So, I managed to get close to these people and look in their work primarily because of the type of work I was doing. I started to be interested in metabolism and environment, how the environment affects our physiology. I love looking at how things happen. I have still an engineer kind of brain, and I love the test and retest, if I do this, what will happen and why did that happen? Especially when it doesn't work, when you think it's going to work and it doesn't, this is when I get excited, not with patience.
So, that is what prompted me in trying to research metabolism because as I mentioned a few times in podcasts, I had a fasting glucose of 5.5 which is your 100, 102, 104, on organic food, no junk, exercising very actively as I've always done, pretty great lifestyle and eating little and often, as I was taught, and things didn't quite work that way for me. So I started to do this research and allowed me to get working with these great people really. This is pretty much how it happened.
The area that I naturally developed was mainly metabolism, which I studied very little at college as far as this specific side of metabolism. We studied the nervous system and endocrine system but not really fueling efficiency or metabolic flexibility, that kind of stuff. Then I started to experiment on people, guinea pig, the three Fs, as we call it here in England, the friends, fools and family, and then extending it to a proper, couple of three cohorts that I worked with and on, more than with. This is pretty much how it happened.
Tommy: You mentioned metabolic flexibility there. I've just interviewed Mike T. Nelson who, he talks about this a lot, I'm sure you know. Maybe this is a good chance to start talking about how you track metabolic flexibility or how you start to investigate whether somebody is metabolically flexible.
Alex: That's a brilliant question which links into one of the exchanges that you, Weikko and I had some time ago. For people that don't know, Weikko is a great guy that I wanted to work with because he had no previous training in nutrition, so completely free from any dogma. How I got to work with Weikko is because I gave him a bunch of data and told him, "What's going on in here? Can you tell me what's happening in here?" He started to see patterns and pivot charts and all of that, and that confirmed what was happening.
[0:05:06]
So, as far as metabolic flexibility, this is, if I may finesse that, it's a jargon term that has been used in different ways. I may tell you what I mean by that. Mike's work is brilliant, by the way. I love his work, and I think he means something similar. What I looked at as far as metabolic flexibility is the body's ability to efficiency fueling energy demand. Despite the type of substrate, obviously there is a little question mark on protein being so thermogenic and preferentially, perhaps spared from energy supply unless strictly needed, so people could have a good energy supply with a minimum amount of substrate available coming from carbs or fats or a combination of both.
In essence, also the lack of chronic inflammatory response, what I've noticed is that in people that have active inflammatory response there is a skew towards carbohydrate metabolism which naturally seems to happen. I researched and found some brilliant papers, which I'd be very happy to share with you guys, of course, on certain size of immune system in certain cells, they only work by carbohydrate metabolism.
You can imagine some interesting scenarios in the keto community when people were trying to push that regimen and yet having inflammatory responses which were needed that were potentially inhibited by ketogenic diet, where they reduced, where they stopped, so that is what I tend to consider for metabolic flexibility, if that makes any sense.
Tommy: I guess there are a couple of parts to that. One thing, you may be saying that there are times when there's a carbohydrate demand in the body particularly during an inflammatory process that may be interfered with by going on a ketogenic diet that might be why some people don't always see a benefit from a ketogenic diet. Is that what you're saying?
Alex: That is my assumption from the observation I made, in the sense that -- the reason why I'm hesitating in stating that is because we cannot say, A happens, B happens, therefore A equals C, purely because the research is pretty clear in stating that in certain situations of immune system activation and challenge, there is a glycolytic pathway that is preferred in some cells of the immune system, only work through substrate, as far as carbohydrate. Now, this does not mean that someone on a ketogenic diet cannot produce enough carbohydrate through breakdown of glycerol from fatty acids or by other means.
What I'm interested in and I was interested in, could this be a limiting factor for an inflammation to actually take place? Could a potential anti-inflammatory effect of a ketogenic diet, of some aspect of the ketogenic diet interfere with recovery in athletic performance? This seems, and I'm saying seems because my cohort was very, very small, that could potentially be the case.
Tommy: This makes sense to me too, and I'll admit that my bias has been that there are some processes in the body that are dependent on carbohydrate metabolism or glycolysis or the pentose phosphate pathway. There are certainly some people, particularly certain types of autoimmune disease where people just don't respond well to a ketogenic diet. My thought was always that that was part of the process.
Equally, if you take it through athletic performance, which is something that you and I are both interested in, when somebody goes on a very strict ketogenic diet, there's often a confusion between the number of carbohydrates that you can eat whilst training in a very high-volume or high-intensity and still be fat-deducting or ketogenic or whatever you want to call it.
You can actually consume quite a lot of carbohydrates and still achieve that, depending on your volume and intensity. Actually, over-restriction or overly restricting them because you think it's going to be beneficial in the sense of ketone production, may well be detrimental. It sounds like that's the kind of thing that you've been starting to see.
Alex: That is correct. What is really interesting is that when you mention ketogenic diet or a ketogenic state, that's the point that I always question, in the sense that, what is a ketogenic approach? The way how I see it, and please stop me if I'm going completely off my trolley here, but ketogenic diet, it's a diet, but that does not necessarily always reflect to a metabolic state.
[0:10:19]
Some people with certain [0:10:22] [Indiscernible] have beta-hydroxybutyrate in blood, some others have way more acetone measured from acetoacetate, so the ratio is different. This is when people start to involve and point the fingers to electrolyte balance or Niacin riboside and et cetera, et cetera. However, what I've noticed is that in the blood, these hydroxybutyrate, the more flexible is the individual, especially going towards elite athletes, the lower it is even when complying strictly to a ketogenic diet ratio.
Tommy: So, you're saying, in elite level athletes, those who are the most metabolically healthy or metabolically flexible, it tends to be lower. Is that a different balance in terms of production, utilization and wasting through the breath or the urine, with acetone or acetoacetate? Or is it reflecting, say, the redox state of the liver which changes the balance between beta-hydroxybutyrate and C2 acetate? Do you have some idea where those changes are coming from?
Alex: Yeah, this is where I'm not fully sure. The liver hypothesis has been quite interesting because it's something that I hadn't thought at the time. That was brilliant because it gave me a different insight. I suspected that the blood ketones at high levels were a buffer. Under normal physiology, our body will have ketones in a state of fasting, so anything that would mimic fasting will normally be considered by the body as a danger.
It would make a great deal of sense, although we have no proof, that the body will bump up the blood ketones in order to have a buffer availability, a certain reservoir that can quickly be shifted where the energy is needed. This is when someone throws in the liver shift in production in order to feed the energy demand. In both cases, what is really interesting is that when people start --
We did a study that was not published, out of six individuals, long-distance events or high-volume events, these were elite athletes though, and their adaptation to a ketogenic diet was very, very quick. This is when we started to research that the more efficient, potentially, mitochondria might be, the quicker is the adaptation.
Some people that were pre-diabetic or type 2 diabetic, I'm talking about, it took 6, 8, 10 weeks at times, for them to start to feel great. I can't say it was the ketogenic diet, it may have been other things, but as soon as we reintroduced carbs, the people felt dramatically better, pretty much straight away. I did this especially with a few individuals, three times, so, off-on, off-on and off-on.
Whereas with athletes, in our study, the one that is not published, it took, literally, less than a week, and that was quite impressive. They had virtually no symptoms. That's the reason why it was skewing a bit towards that way. Is that making any sense?
Tommy: That makes sense. So then, potentially, adaptation to a ketogenic or low carbohydrate diet -- and by adaptation, you're largely going off subjective feelings, just like whether somebody feels able to perform or feels like --
Alex: Yeah, there were a few parameters, RP, so, how they felt, their motivation to training, usual things like muscle soreness, breath ketones, blood ketones and, at the time, also had a continuous glucose monitor sensor which was, the reliability on the trend, my assumption is that is good, but the reliability on the exact data, not so sure. Sometimes it was one millimolar, so, 18 milligrams per deciliter difference. However, the trends were what were interested in and then we spot-checked at key points, if that makes any sense.
[0:15:00]
The error was quite constant, either a calibration error or electrolyte influencing that, subcutaneously, and what we've noticed is that after the first three to four days, so, the first day was fine, second day, most of them felt unease, third and fourth day were the worst, from the fifth day to the seventh day, then people got better and better and better and then after that, people felt okay, as they normally would feel. Interestingly, a couple of them mentioned that their perception of the length of their recovery from the sessions was better, as in, shorter; two, unchanged; and three, slightly longer. It's a kind of 50-50 chance so, yeah, I can't make any inferences on that.
Tommy: I think the data in the published literature would agree with that. I'm thinking of one study which they did in New Zealand, looking at the ketogenic diet in some elite cyclists. If you looked at, in terms of their performance metrics, some went up, some went down, and it was very different from person to person. I think there's always going to be some personal factor that we can't predict yet, in terms of how people are going to respond in these kinds of interventions.
Alex: What I've done is starting to collect genetic data out of -- this is going to sound ridiculous, as in, eight snps out of what percentage has that been, so it's very limited -- these are involving carbohydrate metabolism and tolerance, like NL3, CP1, ADBF2, ADBF3 and some of the others, and also fat metabolism. I have not had the means to check CPT1 and 2 and CAT, specifically, but that could potentially be a factor.
So, some people would do better given a certain genetic predisposition. Some other people would do better on another substrate, given a different genetic predisposition. This seems to be confirmed in the N37. They are done in my personal research project. I also had seven individuals that had this test, the DNAFit test. It seems to correlate quite well, moderate correlation, so, matched all carbohydrate or fat sensitivity. This is what the test calls it.
I did not have enough 3C1, and I did not have CAT and CPT1 and 2, so these could potentially be quite important, as far as utilizing long-chain fatty acid for energy. So anything longer than 12 carbon, as far as length of the fatty acid, has to use CPT1 and 2 and CAT and carnitine, basically. You can't use transferase shuttle system.
Could there be a limiting factor there? Potentially, that seems to potentially provide us some ideas on future research as far as application, on a more targeted application of a ketogenic type of approach.
Tommy: Do you have any other thoughts on what environmental factors might be important? We know that the eventual phenotype is an interaction between genotype and the environment, and the environment often plays a dominant role.
I'm thinking about the DIETFITS study that just came out which basically showed that, whether people went on a low-carb or low-fat diet, at least with the current analysis that they published so far, there didn't really seem to be a genetic interaction when looking at people who might prefer a fat-dominant genotype or have the type, versus carbohydrate like the ones you're talking about. So, what kind of environmental factors might be influencing that response?
Alex: Not specifically, I looked at more general environmental factors that made me realize a few things about ketogenic applications in relation -- so, for example, sometimes it's not what people eat, but it's when and how much of it and how they eat it that could potentially skew the result of a certain diet.
For example, I'll give you a quick example. These are the typical people, so, people who are on a typical standard American diet or standard European diet. Then they decide to go into this trendy keto intermittent fasting, cold immersion type of approach, just to name one, and they obtain results which many times, I have to say, are great.
The problem with that is that when I then started to look, for example, when someone starts to skip breakfast and tried to go on a ketogenic diet, there are obviously benefits associated with the ketogenic diet or a low-carbohydrate type of approach, but many times, people were eating less because it's a more satiating diet and they were also reducing, naturally, their eating window.
[0:20:20]
So, we have a few confounders which often had, in my observation, I can't speak for the whole world, in my observation in the people that I looked into, seemingly there was a hypoenergetic approach in concomittance with a low-carbohydrate approach and other changes within lifestyle factors from the environment that could have contributed to an in amelioration of their metabolic state. In a euenergetic type of approach, things didn't really change that much.
So, what I've done, I took four individuals and put them on a really high carbohydrate diet, very low fat, a kind of Mediterranean type of approach but made them follow the slightly restricted window. What was really interesting is that the compliance was slightly hardest with, seemingly, the higher carbohydrate and lower fat were not as satiating but despite the compliance, potential for failure for people, the results were mutually identical.
So, we were taught pretty much only on the what. I think from the environment interacting, leading to our phenotype, we also have to consider the when, the how and obviously the how much. Now the question is, if we go on a ketogenic diet, could the satiating factor be a winning factor for some people? I think that holds some value in that.
For athletes, definitely something to consider in long-distance and high-volume events because many times, I'm not sure if these people are eating enough, across the board, and they are normally typical high carb diet consumers, but there could be some advantages in perhaps using any kind of training low, competing high type of approach, one of the advantages that could bring. So, this is the lifestyle, environmental factor that I looked into.
Another one would be sleep. Often, people seem to report a worsening of the sleep on a ketogenic diet but mainly when people are skipping breakfast and, if I may say so, over-compensating with dinner. So I took other individuals, not many, and I said, "Okay, try to consume the same amount of calories but on a high carb, low fat diet," and guess what, disruptions were the same. So, seemingly, the worsening of sleep on a ketogenic diet seems to be related to the fact that many people skip breakfast.
Tommy: Then consuming a large amount of food just before potentially going to sleep or within an hour or two going to sleep so then you're trying to digest at the same time that you want to be switching things off and recovering which doesn't necessarily line up.
Alex: The main thought, I think we may have discussed this a little while ago, Tommy, was the sleep cycles are disrupted. What was really interesting is that imagine these people's pre-retiring glucose, and it was lower than the fasting glucose the following morning. It cannot be because the body has not processed the meal, if that makes sense.
Tommy: Yeah.
Alex: Basically the meal seems to affect the sleep quality, which the sleep quality seems to affect the actual fasting glucose the following day. If people eat late enough, it will also affect heart rate variability snapshot reading in the morning.
Tommy: I think, particularly in people who have some kind of metabolic disease or impaired fasting glucose or type 2 diabetes, that sounds to be the case that when you have most of your calories late in the day or most of your carbohydrates late in the day, you then seem to get some carryover and worse metabolic effects the next day which are affecting sleep. All of that stuff will have a knock-on effect.
If we go back and take a big picture, maybe you can tell us how you would assess a client, and we could potentially split these between people who have some kind of metabolic disease or metabolic derangement versus an athlete, but how you're looking at their food intake, macronutrient balance.
You've gone to the point where you said a lot of things that I would agree with. The timing is very important. The partitioning of macronutrients could mean maybe you, if you're trying to improve metabolic healthy, you could go remove most of the carbohydrates or you could remove most of the fats. If you get everything else the same, you actually might see a very similar benefit. How are you using all the information that you've collected to try and figure out what the best dietary approach might be for the person in front of you?
[0:25:14]
Alex: That's a great question. Interestingly, I'm writing a book on five main points, so the usual stuff, life load, lifestyle basically, so, stress, what people call it. I call it life load because sometimes people don't consider that if they love doing something, that something is still a load, so, doing my work, surely and clearly, you guys do the same, yet it's still a load. You are still investing energy, thought processes, cognitive ability and et cetera, et cetera.
Sleep, chronobiology, physical activity, that is a good one. It was really interesting monitoring my data. As long as you move, people actually get benefit. We can stay there [0:26:00] [Indiscernible] HIIT versus CrossFit versus endurance versus all sorts of different exercises, but as long as they move and are physically active, this is when we have a big jump in the correlation and then a drop-off after that. These are the main -- and diet, of course, being a nutritionist, I should put that one, shall I.
So, the first thing I look, nowadays, in a person is actually a combination of chronobiology, sleep, stress and diet. I want to have a snapshot of their typical day, what they do, what they eat, when they eat it and how they eat it. So many times, people, can you give me some digestive enzymes? Dude, you're eating, walking and on the phone. It cannot work like that. It might but, clearly, it is not one of the best ways for the body to digest food. Everyone would know that.
What I'm starting to see is a very clear picture that people are delaying breakfast and over-compensating later on in the day, especially people that are very challenged during the day. They have a reasonably high life load. This could be involved by dopamine reward systems and all sorts of other things that can influence the amount of food and the type of food that people consume in the evening.
There are two studies, one of them really elegant study, beautiful study on food preferences in relation to stress response and sleep deprivation. So, keeping everything pretty much the same, just by affecting the sleep quality and length, people would make different choices as far as, not only in the night, but also during the day.
So, my first approach is trying to assess, to have a snapshot of the life of the person then obviously work with the one that I would think would require the immediate attention, this, obviously, related to the symptoms. I ask them to gather a couple of weeks of continuous data. I have apps that will give me information, and I can check remotely, these people. I don't have to have constant calls or Excel or pages, sheets sent through, and if they can measure their glucose on top of their heart rate variability.
Inevitably, in that specific subset of population and often in athletes, I find that the glucose is elevated. So, for an athlete to have 100 or 5.5 millimolar, so 100 milligram per deciliter and 5.5 millimolar fasting glucose, that's not great really. This is when I started again on what potentially could be a chronic inflammatory response. If it's present, does it happen in the rest day? Do they have a rebound in a rest day?
Often, we call it rebound. In the rest day, you see the glucose shooting up around six, so, 120, 118, those kinds of levels. If that's the case then they're pushing, if they're aware that they're pushing the boundaries, fine, but if it's unjustified then -- so if that happens in a tapering phase, for example, we've got a problem because clearly the body is not recovering.
In the other subset of population, that could be slightly more stable but still constantly elevated. These are the first things that I commonly do which is very, very different from what I was trying to do.
Tommy: You mentioned apps that you're using. I know people listening to this would probably be very interested on what apps you're using and what it is that you're asking your clients to track and how you might be able to follow that data remotely. Can you tell us about that?
[0:30:00]
Alex: I tend to use two HRV apps. If you're sport-orientated, I use HRV4Training from Marco Altini, Alessandra Saviotti. The other one I use more for general population is the Elite HRV from Jason Moore. Both, they have coach versions, and people can measure their HRV, takes between 60 and 120 seconds, first thing in the morning.
With one, you may need a sensor. With Elite HRV, you need either a strap or a Zoom HRV or whatever to take the reading. With HRV4Training, you can do it on a camera as long as you're keeping the hand still, no jiggling around, which you should do anyhow when taking heart rate variability readings as a snapshot. There are studies on the validity of ultra short which are very consistent.
If people have the Oura ring, I can use that, especially the data, as far as chronobiology. I ask permission from them on accessing their cloud data and then I can have a snapshot of that. I have checked the Oura data on HRV, overnight, but that gives me a slightly different information from snapshot in the morning. That tells me how hard the body has worked to get to the snapshot reading in the morning, if that makes sense.
So, these are the ones that I use the most. If they use HRV4Training, they can have their personal tags which are numerical, so they can put their fasting glucose and name it as such and then I actually get to see that. The app will naturally work out correlations between the two which is pretty great.
Tommy: Yeah, so this is a good point to talk about the Ferretti Index, as I call it, which is a combination of fasting blood glucose and resting heart rate variability in the morning. Maybe you can tell us about that, how it came to be and how you're using it.
Alex: Sure, sure. It sounds very ominous too, to have my name attached to an index. I was researching the correlation between HRV and blood glucose, and the data I had, I found a correlation of 0.78, which is a decent one. I thought, okay, great. Tying in what we were mentioning earlier, I got really interested on what happens when there isn't a correlation, why there isn't a correlation.
HRV tells us the lack of parasympathetic status or presence, or reduced or whatever that may be. It's a measure of our central nervous system, SNS versus PNS, and heart health and et cetera, et cetera. Glucose is sensitive to inflammation. I thought, okay, when something happens, these two normally, would actually move so there is a sympathetic activation. Often there is an inflammatory response present as well.
What I've noticed especially in athletes is that athletes have naturally higher heart rate variability, and their heart rate variability changes, given their training, given their metabolism, are harder to detect if we look at population data. They seem to be sensitive to glucose. So I started to do some digging and looked at the literature that was available.
HRV seems to pick up the sympathetic nervous system dominance over PNS, parasympathetic nervous system. Glucose seems to pick up beta inflammation, especially the chronic one because it is enough to raise glucose, mainly thought to affect insulin sensitivity in cells. But these two, at times, may not go hand-in-hand. That's the reason why I put together that factor, that index because I wanted to give more weight to the glucose because this was my purpose.
I put the glucose at the denominator and squared it, so it's something readable, in a sense. That has been, in my view, for our use, very insightful especially with athletes. When changes of chronic inflammatory response, if justified, great, but if not justified and detected, can potentially save, especially as far as injury prevention or perhaps other state in which then the athlete would underperform.
[0:35:23]
We also have population data for both -- I remember Chris mentioning about the work of Dr. Walsh, mentioning that the all-cause mortality, started to increase above 4.6, 4.7 millimolar equivalent of fasting glucose, and we have population data for heart rate variability. So, we can still use the same index in order to actually check, although heart rate variability has a bigger standard deviation than glucose, as far as optimal health.
Some people have a naturally, slightly lower heart rate variability. That does not necessarily mean that their health is affected negatively. So, that's the reason why I started to combine these two, to give a very quick -- and also it's very simple. It's just a simple -- you can do it on any phone calculator app very, very quickly and gives you an idea that if the index is dropping but the HRV is staying the same, there is something underlying there that can be detected, 3, 4 days prior a substantial drop in HRV. That is the value in it.
Tommy: So, just to reiterate, you're taking the RMSSD and dividing it by the square of the blood glucose in millimolar. So if somebody is in the US and they're measuring in milligrams per deciliter, they divide that number by 18 and then square it and then divide their RMSSD by that. Can you tell us what a good number for an athlete? You're able to do that?
This is probably something where, like most things, the absolute number tells you less than tracking the number, over time, but then maybe if you see the number changing over time -- you said you could pick up stuff before you might see a significant decrease in HRV so then how might you intervene with that, with diet, with training, with other things?
Alex: The absolute number is so variable that anything I'm going to say is going to freak people out if they're on that bracket. What I can definitely tell you is -- this is the methodology that I ask people. So, take, for example, in a general population, take their average rest day, their average typical day and measure it. I prefer two weeks of data but with one week could be okay. Also, some researchers have shown that if you take a reading every other day, it still actually works. It still gives you a decent data set point that you can build a trend reasonably well, reasonably precise, reasonably correlating with what's actually happening.
The square of the glucose, I use the square of the glucose because of the millimolar. People can use that system in whichever way they want. At the end of the day, it's the concept. In fact, there is a paper that reasonably recently came out that they had done the same but with CRP. The problem is that with CRP, you need a lab, and to detect the trend with a lab is not that easy or not that practical more than easy, can be easy, but phlebotomy and et cetera, et cetera.
That's the reason why I took the fasting glucose because it's still affected by inflammatory response. What I tend to observe is, if something is justified, so, a person following a heavy session at the gym, the following day, HRV is reduced, okay, justified, carry on, we need to check tomorrow what that is like. Has he recovered? Has he not?
I started to get a little bit more concerned when I see trends that are unjustified especially when the glucose starts to raise because normally that tends to correlate with the low-grade, chronic type of inflammatory response which is much harder to see. If someone has a very heavy gym session, they are achy, they know they worked hard, the muscles are sore, the motivation is a bit lower, et cetera. Fine, that's detectable. That's tangible, in a way.
Unfortunately, the type of inflammatory response that seems to be more affecting so that the glucose is more sensitive to, is the one that is silent, that we don't actually see or feel. That's the reason why I wanted to use glucose as a proxy because that will give me an idea. So, when I see the trend not justified, this is when I start to observe what numbers in specific but taking in consideration the trend, so the number in relation to the trend, not the absolute number.
[0:40:16]
Tommy: When you start to see this happen, what's your thought process for trying to figure out the potential causes?
Alex: The first question I ask myself is, what is changing these proxies? So, could be dietary, for example, could be, often, alcohol too close to bed, in general population, not athletes, sleep disruptions going from newborn baby to work load to starting a new routine at the gym; these are okay. That can be still reasonably justified but obviously, there has to be an intervention following the monitoring.
I try to consider the 80-20 rule, if that makes sense. What is the 20% investment that will give me the 80% of the return? If someone has lots of vegetables, I'm not going to say, "Okay, from six pieces of broccoli, go to nine." That's not going to be the 80-20 rule. It may have improvements, but I'm trying to see what is the biggest shortfall that a person might have.
This is when I would look perhaps, not just the macro ratio, but the whole diet in general, so, the produce, the amount of vegetables, the nutrient density, the whole thing, and also when they eat it, to me, is quite important because when people say, "I eat at 8:00," and then I ask, "Okay, what time do you retire, and what size of the meal?" It's a large meal because I skipped breakfast, and I retire at 10. Well, two hours is nowhere near enough.
When I took that cohort of 37 people, what I worked out is that, what I have observed is that if people retired, on average, at 11:00, between 6 and 8, I have to transform the data in categorical data because time is normally not, so I pick two-hour slots, so, 12, 14, 16, 18 and 2000 hours. Anyone that ate around 8:00, not only had the highest fasting glucose the following day, the highest chances of sleep disruption, and it increased in heart rate variability.
Whereas people that finished between 6 and 8 had the sweet spot, if that makes sense. These were the people, their HRV -- glucose was still not the lowest out of the whole of them, but it was still okay. The only person that didn't seem to make any difference is one that the glucose is constantly elevated anyhow. I would call that outlier. That would skew the data but not for a good reason.
So, chronobiology, as mentioned, I looked at the sleep quality. Stress response was really interesting, is that if I have HRV data on continuous monitoring data, continued HRV data, like my first beat, for example, the body got two equipment, movie sense, for example, even with repetitive snapshots throughout the day at specific intervals, any specific situation mentioned, if I generate an average of that, then we can actually see the effect of the stress response which may not always correlate well with their perception which is rather interesting.
These are the things that I look at first. Obviously, ameliorating the diet in the usual sense, look at nutrients, produce, is it fresh, is it good quality, organic and whatever? How much in one sitting is the person eating? Are they eating little and often? Because it's great for some people, but definitely not great for others.
I took a bunch of people, made them eat exactly the same quantity of food but divided in six or three. I wanted to really accent and enhance that difference. Out of that cohort, which were people with a tendency to type 2 diabetes, the people that ate smaller, more numerous meals were the ones that had higher average glucose. Maybe, perhaps, in that sense, it's not great. For athletes, it makes no difference whatsoever.
Tommy: I'm wondering if I can get you to speculate on some stuff that is talked about a lot in the keto community, maybe those who have a tendency to eschew eating all plants. It's fairly common that fasting blood glucose, first thing in the morning, will be pretty high, maybe even over 120. We're into the six and seven-plus millimolar, but that will be the highest blood glucose of the day. It will actually continue to drop during the day.
[0:45:08]
Do you see this as beneficial, pathological, part of the adaptive response of somebody who is not eating any appreciable carbohydrate at all? If you saw that in somebody, are there things that you try to investigate to see whether there's something that could be improved there?
Alex: It's another great point you're making, as always, Tommy. If it's part of the dawn phenomenon and I have heart rate data for the period between 2 and 4 in the morning then I can trace back either through an andrenergic response that they're trying to maintain, the glucose or cortisol response. Either cases, the sleep is not great.
So, when someone says that their sleep isn't great and we find that their fasting glucose is elevated normally by, in my cases, I cannot speak for everyone, tidying up their sleep, tidying up their eating habits, tidying up perhaps the macro, tweaking the macro a little, that, so far, has been fine. They lost that. If it persists, I don't feel comfortable in saying, "It's fine." That does not mean that it's detrimental. I just don't think that, if it's going on and on and on, is a good adaptation process.
If it happens prior exercise, just even the thought of exercising, the preparation, that will start to get the neurotransmitters and hormones kicking left and right, and we see that. We measure that. I measure that quite a few times in athletes, in myself, in other people, so when they get excited about something, we see variations in that. What's really interesting is that we see variation in HRV, between 25 and 30 minutes prior to that.
When they have an ongoing measuring HRV system or device, I saw a drop in HRV and an increase in glucose, offset by, let's say, half an hour to get a round figure. That was really interesting. We see that in exercise, for example, but the most common scenario is eating too late. You see the variations in heartbeat between 1:30 and 4, mainly around 2 and 3. There is a big variation, but that is when it seems to happen.
With an Australian scientist, we speculated, well, if it is andrenergic then beta blockers prior to that should actually work by minimizing that effect. If it is then cortisol then, normally associated with slightly different symptoms, so they wake up and there's disruption but they don't tend to wake up in a fearful or anxious state like in an andrenergic response.
So, if it's cortisol then we may able to pick it up with a DUTCH test, for example. Other tests, they are able to pick up a type of an incorrect daily patterns. The DUTCH test also provides us some little information about melatonin which is also useful in this case because we can see the relationship that there is between the two. An offset cortisol, to us, has always shown a higher fasting glucose the following day, so that makes perfect sense.
That's the reason why my speculation is that if it happens occasionally, it's fine, but if it happens regularly, it could be an adaptation. If it's an adaptation, should the person be on that? That's my speculation.
Tommy: Everything that you said makes a huge amount of sense. The first part is a bit of the precautionary principle. So, knowing what we do about elevated fasting blood glucose or elevated HbA1C and long-term health, seeing those things in a given individual makes you at least wonder about what might be causing that, and there's a lot of data that you could generate in order to figure out whether that's potentially normal, physiological or detrimental things.
So, asking the questions, gathering the data that you need rather than just essentially convincing yourself that it's not necessarily a bad thing, which is what most people are currently doing. They're saying, "Well, my diet is great because I don't eat any carbs. Therefore, having an HbA1C of seven or something is probably going to be fine." That is a bit of a jump there. There's more data that you can collect, and being cautious about that is probably good.
Alex: Yeah, and the other thing I would want to do in that specific case is also measure homocysteine. If the glucose is elevated despite the HRV and homocysteine is sub-optimal, this is when I would not feel comfortable, definitely would not feel comfortable. Homocysteine can be part of a cardiovascular panel, can be done by the doctor in just as a single test. This is when I would be a lot more keen to intervene.
[0:50:12]
Now, one thing that I would want to mention is that when we are introduced a slightly higher amount of carbs, still considered pretty low carbs compared to average population, in most of these individuals, the glucose dropped. So, that makes me think, is it a natural process that the body is doing in trying to preempt a dangerous situation that the body perceives in the environment? Is it normal to have a slightly higher buffer?
We go back to my idea, thinking that ketones could be seen as a buffer in the blood? It's trying to preempt a situation. It's one of the biggest learning algorithm machines we ever found in our life is our body. It's constantly trying to predict things and trying to compensate. As soon as we have a routine, we adapt to the routine. So there are a few things to take in consideration.
My fasting glucose. I've been increasing my carbs due to the inflammatory response, and my ops and the knee stuff and rehab, I've been doing, and it has not shifted, 0.1 millimolar, so, from 4.3, went up to 4.5. Yesterday I did a heavier session. It's still 4.6, and I had, yesterday, nearly 200 grams of carbs, as an estimate, but it was quite a lot more than what I normally would have. That will go back to the index I used that given the lack of inflammatory response, it doesn't seem that the macro ratio is as important, if that makes any sense.
Tommy: It makes perfect sense to me. I think the notion that carbohydrates in themselves are going to damage metabolic health in the absence of a whole host of other factors in quality and movement and in environment and all that kind of stuff, I completely agree with your approach, and I think that makes perfect sense.
We're running out of time. There are so many different questions I had to ask you, but the first thing, I know that you have relationships with some sports scientists. You're doing some more formal research. Are you planning to, or you may even already be doing it, to publish the effects of tracking the Ferretti Index or whatever you end up calling it so that it's a formal thing that people can reference and use?
Alex: Yes. To put it simply, we slowed down that side of things, and I'm looking to do that with Dr. Daniel Plews and Weikko, obviously, purely because, especially in athletic performance, you can imagine if an athlete can have a two to three-day warning prior HRV drops, on the setting of chronic inflammatory response, given that 70 to 75%, 78%, I remember it now, of the athletes data set we have are in the pre-diabetic range, that can tell you a lot about it.
So, yes, Dan is structuring the study, and I'm going to fill it in with my thoughts and ideas. I started to research more in-depth, Interleukin-6, the classic and trans-signaling pathway when anti-inflammatory becomes pro-inflammatory. I looked at insulin being an anti-inflammatory hormone, when it has an anti-inflammatory effect. These are all the things that I want to put in this paper in order to provide ideas to other researchers to perhaps take it further or better in whichever way. So, yes, as soon as I finish my current project, it's something that I really want to get stuck into.
Tommy: Great. It sounds like it's going to be fairly comprehensive and covering a lot of aspects that I find very interesting, like the, again, insulin isn't necessarily a bad thing. It's often useful in its increase for a beneficial adaptation or in trying to resolve some kind of issue that's ongoing.
You mentioned Weikko again. The final thing I was going to ask you about was the tool that you guys have put together, the Mitokinetics website, the tool. It allows you to put in your macros and your activity levels and then it gives you some output based on what your expected ratios might be or your respiratory quotient and how you might want to juggle your macros. Can you just briefly explain what the tool is then also how you might suggest somebody would use it?
[0:55:02]
Alex: First of all, thank you for mentioning it. I'm a sucker to self-promote things. First of all, it's free. It's on the web. We are testing now a mobile app that will be the usual, something not a lot, 99 kind of thing. What we are aiming for is practitioners, so it's not like we want to make a million bucks on it, but we want to provide practitioners with a tool that, through refining the ratio of the macros in relation to the patient's condition or status, they can optimize their metabolic output, in a nutshell.
We found some pretty strong correlation between the respiratory quotient, the breathing rate, believe it or not, and the macro ratio, inflammatory response. We put it all into one. Weikko had a good discussion with Dr. Kevin Hall, out of all people. Very kindly he pointed out that there might have been an error which now is being rectified, within the calculation in relation to the potential output.
What we discovered is that when people are on a ketogenic diet or on a low carbohydrate diet and they become, let's say, in jargon, more efficient in extracting energy from carbons, they may require less total energetic intake, but this is also true for people that fast a lot. This is the reason why, earlier, I mentioned --
Do you remember the time, Tommy, when you mentioned to us, "Are you using the constrained model or the additive model in order to calculate?" That was the biggest thing you could have ever done for us, is in our understanding of metabolism because that never really fitted with me and some of the patients I was following.
So, we decided to design a tool that takes in consideration, let's say, the number of carbons and how the body efficiently would extract energy from it, rather than just quantity of calories which can be skewed if people go on dietary regimen like the ketogenic diet. I'm not sure if this makes any sense at all.
Tommy: It does make sense. One of the main things that I remember from conversations with you guys about it, was that point which is the, if you are trying to stay eucaloric or in caloric balance, if you're on a ketogenic or very low carbohydrate diet, that may require fewer calories than if you are eating a mixed or carbohydrate-based diet, just due to the adaptations that occur.
I think that that's definitely something that needs to be taken into account when we're trying to compare diets, calorie for calorie, because in reality that's such a very unfair thing to do. Kevin Hall is certainly involved in that process. It's more complicated than I'm stating it right now, but that's certainly something that we need to take into consideration.
Alex: I still have people coming to me quoting, "I'm consuming 2,532 calories. Should I take 1,550?" I say, "Dude, the error in caloric calculation through indirect calorimetry is in the double digit figure region, so, you making a change in the double digit figure region, I'm hoping, for you, that that is not going to be a relevant change because, otherwise, metabolically, you are in trouble in the sense that if such subtle changes can bring you change, I don't think the change is in relation to a double digit figure change within the caloric input."
We're trying to measure something in a more relevant manner that can fit more diets. That's the reason why we created that triangle where, given your goal, if it's losing body fat or increasing lean mass or maintenance, then people can start to skew their ratio accordingly to their goal and fix a caloric goal which has been found to be not as precise. Because, otherwise, some athletes, especially long-distance athletes, you could not explain how they're alive given the amount of calories they do not have. That gives you an idea on that. Yeah, that's the reason why we designed that tool.
Tommy: I think it's a great idea. I also think it's going to take some time to be able to wrap their heads around it just because you're essentially flipping the way that we should be thinking about these things, on its head. Just takes some time for people to wrap their minds around that, but I really hope that people go and try it out and play with it. I know you guys always appreciate feedback from users so --
[1:00:03]
Alex: Absolutely.
Tommy: -- we'll put a link to that in the show notes. We could continue for hours, but I'm worried that we'll just overwhelm everybody, so maybe we can stop there and to be continued in the future. In the meantime, can you tell us where people might go to find the things that you're talking about to find out more about your work?
Alex: I have a very modest website. It's just my full name dot co dot UK, so, alessandroferretti.co.uk. I haven't updated anything for a couple of months now at least, but this is where I keep people updated on some of the podcasts and some of the stuff I'm researching and some of the observation I made.
There is a video, for example - calories, are we measuring the right thing - where I don't demean the calories, they’re great, but I just explain some of the errors that people may need to take into consideration going into different types of dietary approaches.
So the people can search on my website. There is a link to mitokinetics as well, with the app already in there. Yeah, that will be probably -- I keep active, reasonably active on social medias, on sharing some of the more interesting studies or some work from other people. This is where I normally am.
Tommy: Okay, great. Well, I certainly enjoyed this a lot. I'm sure everybody listening to this enjoyed it too. Thank you so much for your time.
Alex: Very, very welcome, still very honored to be here, so, thank you, guys, for the opportunity to share my stuff.
Tommy: You're very welcome. Thank you.
Alex: Thank you.
[1:01:35] End of Audio
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