Do blood sugar spikes cause heart disease? [Study 287-294 Study Analysis]

Hi, welcome to a Physionic detailed study analysis. Today, we're going to be going over an incredibly interesting topic that I had never looked into previously, but we'll hopefully get to some answers. It's a lot more complicated than a lot of people make it out to be, and that topic is: do glucose spikes—so when your blood sugar suddenly increases—have a link or cause cardiovascular disease? That's what we're going to answer from all kinds of different angles. I've pulled together something like eight studies, something in that realm, to find the answer to that question.

That said, if you're not familiar with who I am, my name is Nicholas Verhoven. I'm a PhD candidate in molecular medicine. This is what I do: I translate studies and open them up for you so you can look at the data and understand my rationale for how I got to the conclusions that I do. If you're not interested in the actual data analysis part and actually seeing my work, then just jump over to the conclusion, and you'll get a shortened summary. You'll be missing out on a lot of the context as well as the mechanisms of action, but you know, some people just want a quick answer, which I absolutely respect.

That said, let's go ahead and jump into this.

Okay, so here are the covered topics we're going to go over. As usual, the mechanisms of action, then we'll answer the actual question itself: do glucose spikes increase cardiovascular disease? Then we'll go over two factors that might increase risk related to this topic. If you're a Physionic Insider, we'll be going into far more depth in the extended version of this video, where we'll be going over a specific nutrient that may actually reduce risk, as well as a supplement that may reduce risk that some people actually already take, along with some other strategies for reducing glucose burden. Really intriguingly, we'll discuss HbA1c as an imperfect marker in relation to this topic. So it's really fascinating. If you're interested in the full version of this video, as well as all my other study analyses and many other benefits, then you're certainly welcome to join the Physionic Insiders. There's a link for you. If you just want the answer to the overall topic, then let's move on.

All right, so let's go over the mechanism of action. Now, there are multiple mechanisms of action that have been proposed. Does this necessarily mean that this immediately translates to the actual clinical outcomes? Well, no, the answer is no. But we're still going to be going over the mechanisms of action before we get to the clinical outcomes just so we can understand this a little bit more in depth.

We're going to start out by opening up Study 289. Study 289 is nonprofit funding. If you're interested in more on the funding sources and conflicts of interest, I'll have everything aggregated together into a document for you, so that will be linked in the description box as well. You can download that; it's going to have all the studies, including all the conflicts of interest and more information on what the actual funding sources are.

All right, so let's walk you through this real quick. Imagine you're a human being. I imagine that's not going to be too difficult unless you're an alien, in which case this may or may not apply to you. I know a lot more about human physiology than I do alien physiology, but let's just assume that you're a human being like me—let's hope.

Anyway, so you consume carbohydrates. This is a generalized image of carbohydrates, and then you masticate, meaning that you chew it up. It ends up as a bolus in your stomach, gets broken up further, and gets into your intestines. Usually, it'll end up as disaccharides—so two sugar molecules stuck together. It doesn't necessarily have to be glucose, but in this situation, we're going to focus on glucose.

So these disaccharides then get cleaved so that you get these single monosaccharides—D being two, mono being one, and saccharide being sugar. Okay, so now you have these monosaccharides that get taken up by the epithelial cells of your small intestine. As they go through the epithelial cells, they'll go through these different transporters called GLUT transporters. There are other types of transporters as well, but the most common one is the GLUT transporters, and then they'll end up in the bloodstream.

Okay, so if you consume carbohydrates, then they're going to eventually end up in the bloodstream, which of course then means that you would have an increase in blood sugar, which is what we see here. This is in a fasted state over here, just as a preliminary example. Then you consume some amount of carbohydrates or some food that can be converted into carbohydrates. There are certainly some physiological processes that can do that as well, like for example in your liver, known as a process called gluconeogenesis, but we're not as interested in that for the time being. What we're interested in is when you consume carbohydrates, especially simple carbohydrates—talking like sugar, for example—then you see this rise as it goes from the intestines across.

So when it's in the intestines, you're not seeing a rise in blood sugar because it's not in the blood. But then once it crosses this epithelial layer and gets ejected out of the back end, which is called the basolateral membrane of the epithelial cells, that gets ejected and then put into the bloodstream. Then from there, as blood levels increase, you can see a measurable increase. If you were to take a blood test or do one of those glucose monitors that a lot of people like to use, then you see the rise in blood sugar. Eventually, you have the secretion of insulin, which will then allow the blood sugar to enter into the cells, which then removes it back out of the bloodstream, and you go back to a more normal or normalized level of blood sugar. It can be a little bit higher, and usually, it'll taper off over time until it gets right around to fasting levels again.

Now, one of the proposed mechanisms by this scientific review that I'm getting this information from, again that's Study 289, is that you can have this glycation of these different proteins. Now, it doesn't necessarily have to be the proteins that are in the bloodstream themselves, and usually, the proteins are actually much smaller than the red blood cells. But in this image, just to accentuate things, you can see you have this linking of these glucose molecules—these sugar molecules—with the proteins. What that does is it can lead to a dysfunctional protein.

These different proteins in your body are folded in such a manner that they have a particular shape, and that shape confers an ability. So that means that it's a functional protein. Think of like an enzyme, for example. So that enzyme has a specific section of that protein that will interact with a specific molecule and will somehow convert that molecule to something else, and that serves a function for the body.

So if you have glycation—which means it's a non-enzymatic reaction—for the biochemistry nerds out there, because I do like to get a little bit more technical here, you have glycation and you have glycosylation. Those are two different processes. Glycosylation, from what I remember, is an enzymatic reaction, as in it's done on purpose. You have an enzyme that interacts with the sugar and adds it to a protein or fat or something—a membrane, a part of the cell—and that serves a function because sugars are actually also used as signaling molecules and can be part of the structure of a cell membrane.

However, glycation is a non-enzymatic reaction where you have high levels of sugar that interact with the proteins and actually chemically interact with the proteins, which then can lead to this dysfunctional protein. That can happen to the proteins that are outside of the cells, but it can also happen to the proteins within the cells. For example, here, we're talking about the glycation of hemoglobin, which is a protein that, as I briefly mentioned in the covered topics, HbA1c is a metric of the glycation of hemoglobin. If you have high levels of glycation, then that means you have a lot of these sugar molecules that are bound to the hemoglobin protein that is found inside of the red blood cell.

Now, hemoglobin, just as a little bit of an aside, its function—and the reason why it becomes less functional when it's glycated—is that its function is to carry oxygen and CO2 across your body. Now, there are other mechanisms by which we carry oxygen as well, like there's free oxygen, but oxygen gets bound to that hemoglobin protein, and then the red blood cells carry the oxygen with the hemoglobin attached to it. Or I guess I should say the other way: oxygen is bound to hemoglobin throughout the body and then releases that oxygen to be taken up by the different tissues, like your muscle cells, brain cells, etc., to then use that oxygen for all kinds of different cellular processes.

I'll stop it there; I won't go into more detail on that. But the point is, if you have over-glycation, then the idea here is that you have some level of dysfunction that can occur to the proteins and molecules that are glycated.

Okay, now, as I mentioned, you can have glucose or sugar molecules that will enter into the cell. When you have an overabundant level of glucose—remember, you have the high glucose in the bloodstream, but it gets taken up into the cells, therefore reducing the blood sugar back down—give you real quick right here: high levels, then it gets reduced back down. So you have low levels in the blood, but where it doesn't just disappear, it ends up inside the cell.

So we've got the outside of the cell here—this would be the blood and the interstitial fluid or the extracellular fluid, which would be the combination of the two. You have blood sugar here, and then it enters through a transporter called the GLUT protein. I briefly mentioned that before when I talked about the epithelial cells in the intestines. So the blood sugar molecule will enter, and then from there, if you have this high abundance of sugar, glucose, then it can—technically, this arrow alone—there's so much more complexity that goes from this to this. But I again don't want to get too bogged down in the biochemistry.

Typically, it goes through a process called glycolysis, which will then convert these glucose molecules into another type of molecule, eventually turning into something like pyruvate. That pyruvate will then be in the mitochondria, and then the mitochondria, if it's overburdened with nutrient supply, will produce reactive oxygen species, which is what we're talking about here.

So these reactive oxygen species are damaging molecules or can be damaging molecules, and they can also be signaling molecules, and that's what we're talking about here. So what's proposed is that these reactive oxygen species can interact with different signaling proteins. Now, I'm not entirely certain which signaling proteins they were talking about specifically. Usually, we're talking about signaling proteins like NF-kappa B, which is a common one, or we're talking about gene expression of pro-inflammatory molecules, which I'll get to in just a second.

But ultimately, they interact with these different signaling proteins, which then triggers the signaling proteins to enter the nucleus. Some of these signaling proteins are called transcription factors, which means that they go from the outside of the cell—or outside of the nucleus, excuse me. So we are inside of the cell at this point; we're in the cytosol—and then they get activated by interaction with these reactive oxygen species. Usually, they interact with one another as well, but ultimately, they end up translocating to—so moving from the cytosol to the nucleus.

Once they're in the nucleus, then they can bind to the specific genes that they are responsible for binding. Once they bind, then you can get the recruitment of other proteins, of other transcription factors, which will then lead to the generation of new proteins. So they enact their role by binding to these different genes and either suppressing them—reducing their gene expression—or expressing them—increasing their gene expression. So then the gene gets read or transcribed, and then from transcription, it gets turned into a protein through translation. There are a lot of different systems that are involved in that, so we're definitely glossing over a lot of molecular biology. But I'm giving you kind of a high summary of this because we're talking about glucose spikes, and we're not talking about the whole process of transcription and translation and all that.

So anyway, two of the genes that the signaling proteins can affect are interleukin 6, which is a pro-inflammatory cytokine, otherwise stated as a pro-inflammatory molecule, which will recruit more immune cells to a region. Another one is called ICAM. ICAM is a transmembrane protein, which means that it embeds itself into the membrane, and when immune cells get near it, it can bind to those immune cells and essentially capture them or hold them in place, we'll put it that way.

So one is going to recruit more immune cells, and one is going to essentially allow those immune cells to aggregate and stay in a particular location. So we see that here. We've got IL-6 that's released by what's known as the endothelium. So if we were to—let me quickly walk you through here—this is a really generalized view of a blood vessel. We've got the different sections of the blood vessel, but on the very inner part of the blood vessel, where the blood is actually rushing through, you have these cells called endothelial cells. You have a single line of endothelial cells, and that's what we're talking about right here—this endothelium. These are the single endothelial cells, and they regulate blood pressure; they regulate your vasculature as a whole.

When we have these blood sugar spikes, what's been proposed—and keep in mind I'm using these words very specifically, talking about mechanisms—I'm not proving anything here. What I'm saying is that this is what researchers believe based on mechanistic data that we have these blood sugar spikes that ultimately lead to the release of IL-6 or pro-inflammatory cytokines as a whole, which then allows these immune cells to get attracted to this high concentration of IL-6.

So IL-6 will bind to the immune cell and essentially attract it to these areas of high secretion of IL-6. Okay, so why do we care about that? Well, we care about that because the next step is that we also have more ICAM. If more ICAM and VCAM and all these different adhesion proteins essentially are expressed on the cell surface, then more of these immune cells can bind. Okay, well, why is that problematic? The reason why is because you have this process called extravasation, or you can have the invasion essentially of immune cells to the subendothelium—so under the endothelial cells—which can then lead to the pro-inflammatory stage of atherosclerosis or plaque buildup.

So it is one of the critical stages of plaque buildup in your arteries, which hopefully I don't need to go too much more into that. But if you build up plaque in your arteries, then you are at higher and higher risk, as it progresses, of cardiovascular disease and serious events like actual heart attacks, strokes, peripheral artery disease, and things of that nature.

Now, another thing that's been proposed is that the high levels of glucose—because of this increase in the reactive oxygen species—let me go back real quick—because of the high levels of reactive oxygen species that get produced from the glucose spikes can also lead to the oxidation of a vasodilatory molecule called nitric oxide. So let me back up and explain what that is.

Okay, so nitric oxide is a molecule that's also produced by your endothelium, and nitric oxide can open your blood vessels. It essentially lowers blood pressure. In a systematic fashion, it would lower blood pressure, but regionally what it does is it interacts with smooth muscle cells. Those smooth muscle cells, which are behind the endothelium—so they would be a little bit deeper into the subendothelium—are the ones that are responsible for the contracting, so the closing together of your blood vessels or the opening of your blood vessels. The opening of your blood vessels is controlled largely—not solely, but largely—by this molecule called nitric oxide.

Now think about it: if you have the same amount of volume—so the same amount of blood—but you have more space, what happens to pressure? It decreases. And that's the idea here: that if you have more nitric oxide, you are able to dilate, and therefore you have a lowering of blood pressure. So that's another aspect of cardiovascular disease because high blood pressure is a risk factor, just like plaque buildup is a risk factor. High blood pressure is also a risk factor for cardiovascular disease.

Now, the problem here is that if you have the oxidation of this nitric oxide—again, you're damaging the molecule to potentially be nonfunctional—so even though you're producing nitric oxide and you're probably producing less of it in general, what is present—even if it were normal levels—the quality of that molecule is probably reduced, and therefore you can't reduce blood pressure nearly as much. So that's another proposed mechanism.

Another pathway has been through the RAGE pathway, which I think is such a cool name for a pathway to call it the RAGE pathway. It might be my favorite. Anyway, you have these proteins that get glycated—remember we talked about that, right? The non-enzymatic binding of glucose to proteins—and those proteins with their glycans can then bind to receptors on the cells.

So if we zoom into that, there are actually RAGE receptors themselves that are specific to—or maybe I should, well, yeah, we'll call it that—they're specific to these glycated proteins. These glycated proteins bind to the extracellular section of this receptor, and then the receptor has an intracellular section here, which can then lead to a cascade or protein cascade, a signaling cascade, which has an effect on NADPH oxidase. This NADPH oxidase is one of a few sites within your cells that produces reactive oxygen species. We saw the other one is the mitochondria, so the mitochondria generate reactive oxygen species or free radicals, and NADPH oxidase also produces this.

So this is another way that we get this high degree of reactive oxygen species and generally greater levels of oxidants. Now, RAGE also has additional effects, and certainly, we're not going to be going over all of them in this presentation, but some of the others are through the activation of the—generally, the general term for this is called the MAPK pathway. Because, well, I won't go into why; there are a number of different types of MAPK pathways.

So again, the interaction of these different proteins with one another. If this RAGE pathway, this signaling receptor, is activated—meaning that you have a conformational change, meaning that you have a shift in the internal component of the receptor—that then attracts these different proteins, these signaling proteins that then start to have this signal, as in they start binding to one another. So RRA, for example, will bind and activate MEK, and then MEK will activate ERK, and then ERK will then translocate to the nucleus, like we talked about earlier, and will then activate different pro-inflammatory genes, produce different cytokines like IL-6, TNF-alpha, chemokine IL-8.

There's a lot of different pro-inflammatory—I mean, we would be here for hours if we were to talk about all the different sub-effects of each one of these cytokines, so I'm being pretty general here. But that's an example of a few of the cytokines that would be then more expressed, and then with greater expression, you get greater production of these different proteins, which then get released, or they may affect the actual cell itself—the endothelial cell itself.

Okay, so that's what I've got for you in terms of the mechanisms. Now let's actually go over the clinical evidence or the research evidence. I don't want to say it's all clinical, but some of it is clinical evidence looking at the direct effect or association of glucose spikes actually causing cardiovascular disease.

The first study we're going to be looking at is Study 291, and I could not figure out the funding source, so you'll have to excuse me. Unfortunately, I couldn't find it. To give you a bit of background on the study design, we've got 49 participants—men and women. They were overweight, roughly the age of 50, and there's a combination of diabetics and healthy individuals. So some people—they had essentially these different subgroups; some were in this diabetic subgroup, some people were in the healthy subgroup.

We don't have a placebo group; we don't have blinding; it is non-randomized, so there are definitely some methodological weaknesses with this overall study. However, it's also an acute study, so they're just trying to figure out if we give sugar, what happens to the cells? So this still doesn't—I just want to be clear here because people sometimes get this information and just run with it. Please don't do that. This is telling us a very finite amount of information. It's telling us: do we see some of the things that we described earlier with the mechanisms? Do we see that in human beings when we directly stimulate with blood sugar? When blood sugar spikes up, what happens to the cells and what happens to the body in a very finite instance?

I will give an example here to contextualize this with exercise. You sometimes have increases in blood pressure, but as I briefly mentioned, with increases in blood pressure, you think, "Okay, more cardiovascular disease," because blood pressure is related to cardiovascular disease. However, we're talking about acute changes, short-term changes. Now we need more data, and we're going to go into some of that more data—the long-term data—which pairs with this data along with long-term data will give us a better indication of whether there is a risk. But just looking at this study is insufficient data. No matter what it says, it is insufficient to make any definitive conclusions; it's just going to point us in a direction.

Okay, I just want to be very clear about that because I said people really run with this stuff sometimes. So it's a one-day study; they literally just give them sugar, a set amount of sugar, spike their blood glucose, and then see what happens to different measurements. This is a parallel design, meaning that they have separate individuals in each group and are being compared against one another.

All right, to give you a bit on the baseline characteristics, we've got the control subjects here—those are the healthy individuals—and then we've got the diabetic individuals here. We've got 27 people and 22; you can see it's roughly an equal split. You can look at some of the other metrics. Like I said, they're 50 years of age, measured by BMI. You're usually going to use BMI; I realize it's an imperfect metric. You don't need to tell me in the comments; I get told that all the time. I'm well aware. But it's okay for just general information, even if it doesn't give you body composition information.

Obviously, because these are diabetics, we're talking about much higher fasting blood glucose levels, HbA1c, etc. So there you go; there's a few of the metrics. You usually want them to be as similar as possible, but it's kind of difficult when you're talking about specifically people that already have a pathology versus people that don't have a pathology. There are going to be differences.

All right, so the first thing is to look at glycemia. So this is the amount of blood sugar that's present. Then we have FMD, so that's flow-mediated dilation, and then the last one is nitrotyrosine, which is an indicator of cell damage as it interacts with reactive nitrogen species. So we talked about reactive oxygen species, and in this case, we're measuring reactive nitrogen species.

Now, to give you a little bit of background on what each one of these points is, we've got the open circles—so they are the open diamonds, I guess I should call them. Actually, I kind of mis-saw them initially, but now that I'm looking at them, they're actually open diamonds. The open diamonds are glucose spikes to 15 molar, so they're spiking their glucose and then allowing it to go back down. Okay, so that's the condition that we're most interested in.

The black triangles, which is this one right here, is the glucose maintained at 10 millimolar, so it's elevated, but it's then maintained at a lower level. The black circles, which is this up here, are the glucose maintained at 15 millimolar. That's why we see here—that's why we see millimolar 15 up here. We see the spikes go up, and then they come back down. Then they spike back up, and then they come back down. You can see that this is done over, you know, two days, so they're continuously measuring over that time.

Now, this doesn't tell us any information on the actual outcomes; it just tells us when we say this is at 10 m, and we maintain blood levels at 10 m, are they actually at 10 m? They're essentially proving that what they say is true, and we can see that it's 10 m here. You say it's 15 m here; we can see that these are spikes back and forth.

Now, the FMD is one of the flow-mediated dilation. So this is a short-term measure of the—this is looking at the arteries. So are they dilating? So you want the ability to dilate; it's a benefit. So if you see reductions in flow-mediated dilation, that's typically seen as a negative. Now, again, we have to contextualize that long-term; we don't have that context yet, so we're just building context here. But generally, if it decreases, that's bad.

What we see is that as blood sugar rises—even if it's maintained at 10—if we focus on this right here, we can see that flow-mediated dilation decreases. Okay, so that's some evidence that high blood sugar decreases flow-mediated dilation. Probably not a shock, but at least we have some evidence here.

Then when we look at the spikes, or let's look at the continuous at 15, we can see that it further decreases flow-mediated dilation. So the more blood sugar you have for longer periods of time, the greater the effect on flow-mediated dilation. Additionally, looking at the spikes, now we see that flow-mediated dilation moves with blood sugar. As blood sugar goes up at the six-hour time point, flow-mediated dilation goes down. As blood sugar goes down at the 12-hour timeline, it goes back to kind of normal-ish. It's trying to get back there, but maybe it doesn't quite accomplish that, so it kind of moves in sync with what the glucose spikes are doing, which isn't a huge shock considering that even raised blood sugar in general lowers or worsens flow-mediated dilation.

Now, I'd like to point out here something that the researchers point out. They say that—well, actually, let me first tie this off with nitrotyrosine. So the more nitrotyrosine, the more damage or more interaction of these reactive nitrogen species. So this one, if it goes up, that's worse, just to explain that. And there's always nuance to that as well, but as you can see, largely it's about the same. So if blood sugar increases, then you have more nitrotyrosine.

On the other hand, glucose spikes also go up, and then it goes down. It goes up, and then it tries to go down. So again, we're seeing the same pattern as we are with FMD, just in the reverse direction. Now, the researchers specifically point out that endothelial dysfunction was the same between the 15 m constant glucose in the first oscillation of glucose. So we're talking about this condition—so jumping up and at—or the condition that stayed at 15 m. It stayed; this was roughly equally detrimental.

However, then they say after that—so over time, the spikes were more dangerous. They showed a worse effect according to the researchers. I should push back here, however, that they actually don't report what's known as an area under the curve. The reason why that's important is because while we're looking at these values kind of in isolation—so we're looking at 6 hours, yes, the spike is at 15; at 12 hours, yes, the spike is at 5, we'll say—the problem is that an area under the curve will take all this data together and put it all together to show you the overall amount of blood sugar exposure that the endothelial cells have experienced, and they don't actually report that, which I think is a weakness of this data.

So the researchers are indicating that spikes are worse, but my counterargument is that that's a possibility, but they still didn't report on all the data that I would like to see. They end up concluding that oscillating glucose is more damaging than the constant glucose, but again, they didn't measure AUC because if AUC—area under the curve—is potentially higher in the glucose spikes, which seems unlikely, I'll be the first to admit that, but it seems unlikely. But still, it's possible that if the AUC is higher in the glucose spikes condition, then it's possible that that's the actual reason—that it's just generally more exposure to blood sugar that leads to that problem, not the actual spikes themselves. But I fully admit that that's an unlikely scenario, I'll put it that way.

Okay, and I guess I should say real quick that—let me walk back—all of this data that we just went over was in healthy individuals. Okay, the next right here, we are now talking about diabetic individuals. So in diabetic individuals, we largely see the same pattern, so it's not going to change anything. I'm not going to walk you through each thing again; it's the same exact thing just in diabetic individuals.

Okay, the next study is Study 290. Again, I couldn't find the funding information just because it wasn't listed, and we are looking again at another short-term study. This is again an acute intervention, so we're talking about a day or two, just like the previous study. This is in 32 participants, again including men and women. Their age range is roughly about 36, normal weight individuals, and we're looking at healthy and insulin-resistant individuals. So we've got impaired glucose tolerance, so the insulin-resistant individuals, and then we've got the healthy individuals.

This is again, unfortunately, non-randomized, non-placebo driven, and no blinding, so those are weaknesses of the study. It's again a parallel design, so we're comparing this group versus this group as opposed to this group at one point versus the same group at another point. Here are the baseline values. Again, we're talking about glucose intolerant individuals, so people that already have some level of pathology, so they are going to have higher levels of glucose, higher levels of HbA1c, and things like that.

In this case, however, what we're interested in is the effect that we're going to see from these glucose spikes on inflammation. So we looked at the effect that it has on flow-mediated dilation in the arteries; we looked at the effect that it has from these reactive nitrogen species, and now we're looking to look at some of these cytokines that I had mentioned earlier.

Okay, so we've got two examples here. We've got the infusion, so they are artificially infusing a high amount of glucose, and in that experiment, they're increasing it up to roughly 15 m, just like in the last experiment, or they have a condition where they spike it. Again, they're essentially proving what happens when you spike glucose, or they're essentially proving that their methods are actually what they say they are.

So we can see that there's a spike, and then after an hour, it decreases, and then after, you know, at the 2-hour mark, they spike it again. They keep doing that three times over. Additionally, this is a question that probably will pop up, so let me go ahead and address it. The individuals were all given a drug that blocks insulin release, so the effects presumably are insulin-independent. This is very important. So the effects that we should be seeing here are not due to glucose rising and then a mass release of insulin, which of course insulin has its own problems because if you have chronically elevated insulin, that can cause—that supposedly causes issues to your cells as well.

So they are controlling for that by keeping insulin low and just exposing the body in a very acute sense—because they can't do this forever; otherwise, it becomes really unethical—in a very acute sense, just a few hours to just a day or two. The effect is supposed to be just glucose-oriented.

Okay, so let me walk you through this real quick. The control individuals, the healthy individuals, are the black dots, excuse me. These are the people that are not insulin resistant, and the white dots are the ones that are insulin resistant. We can see that there's a little bit of a difference between them. So we're looking at IL-6; this is that pro-inflammatory cytokine I was talking about earlier, TNF-alpha, which is another one of the pro-inflammatory cytokines that we talked about before, and another one which is called interleukin 18. It's definitely not nearly as well studied as these two; these two are extremely common.

We're looking at time, so over 5 hours, what happens to the levels of these different cytokines in the body? Okay, so in the data that we're looking at here, this is after a long-term, so sustained high blood sugar. To be clear, this is not looking at glucose spikes yet, but we see that in general, there's a slight rise, at least at first, in interleukin 6. We also see increases in TNF-alpha, and they mentioned that there are also increases. You don't see any statistical asterisk or anything like that, but just by what the researchers point out for the statistics, they indicate that all of them rose.

So all of them increased related to baseline. So baseline would be zero for each one of these, and then you see that, you know, in the next metric, there's an increase. That said, they also said that interleukin 6 and TNF-alpha—only these two increased more in the insulin-resistant individuals. The line here is the standard error of the mean or standard deviation; I can't remember which one it was—standard deviation. So it's looking at the overall variance of the data. Some people had higher; some people had lower; that's the idea of the overall variance.

So we do not see statistically significant for IL-18. You know, again, with these kinds of standard deviations, they're kind of all over the place. It's possible that they just didn't have enough individuals to detect an effect, but overall, the general conclusion here is that with high blood sugar, you're going to have increases in these pro-inflammatory cytokines.

Now here, we're looking at the spikes themselves. So we're seeing in this situation, we're looking at not the continuous infusion, but we're looking at—let me go back here real quick—this condition right here where they're spiking glucose and then they're making their measurements. Now, what's striking here for me, and what the researchers point out, is that, okay, so here we've got our baseline, right? And again, the open circles are the ones that are insulin resistant; the black circles are the ones that are the control conditions, so the healthy individuals.

We're looking at the same conditions, and what you'll notice is that, yes, with the first spike, you see this increase, and then you see a decrease, and then you see an increase again, and then you see a decrease, and then you see an increase. But the overall trend is that with each glucose spike, there's increasing levels. The overall trend is you go from, I don't know, let's say 2.5 or 2.8 all the way up to over 4. So even though—let me go back here—even though you have this decline and then popping back up and then decline, you would expect it to just go up and then down and then up and then down.

We see that to a degree; it goes up, and then it goes down, and it goes up, and it goes down. But the overall trend is that you have more and more of this interleukin 6 as you have greater and greater spikes. So the spikes remain constant, but there's like an additive effect essentially, which is somewhat in line with the previous acute study indicating that there may be this especially detrimental effect that comes from the glucose spikes that we don't see when we have the continuous exposure to glucose.

So is diabetes healthy? No, I'm kidding. Having high blood sugar is still unhealthy, but this indicates that potentially glucose spikes have an additional added risk that come along with it. Again, in the context of these acute studies, we still need to look at long-term studies.

All right, now we're going to get into more long-term studies, but we are looking at associative studies. The reason why we're looking at associative studies as well is because sometimes we simply can't do randomized control trials for years and years and years, so we have to look. We are going to be looking at some control trials, but I'd like to also include some of the additional studies that have been done associatively that can offer some more information on long-term effects.

So that's Study 293. There's no funding for this because it's a meta-analysis of many studies. They go through and find, you know, maybe close to 575 studies. I guess it says it right here—576 studies—and they end up excluding 503 studies because they had a particular inclusion criteria, meaning that they're only going to keep certain studies that meet certain criteria that the researchers define.

So the endpoints, for example, had to be cardiovascular disease—so mortality due to coronary heart disease, stroke, etc.—had to be in people without diabetes. So this analysis is purely in people without diabetes, but I have something to say about that. It had to report blood glucose levels, post-meal glucose, and HbA1c. So those are the three main inclusion criteria, and if these studies didn't have these three different criteria, then they were excluded, which makes sense because you don't want to be including studies that look at cancer risk, for example, when you're looking at cardiovascular disease, and then so on and so forth.

They looked at a number of things, ended up removing a lot, and ended up with, I believe, 38 prospective associative studies in total. Yeah, 38 right here. Now, looking at the analysis—the actual meta-analysis itself—if you're interested in learning how to read this kind of stuff for yourself, I actually have a course that I just released where I teach you how to read meta-analyses and teach you how to read randomized control trials.

I know that people jump to AI, but honestly, AI is not going to tell you this kind of level of analysis. It's just going to read the summary and give you what the summary says. But often, as you'll see with this study, actually, if you know what to look for, sometimes you can discount studies because they're not done exactly as they should be. So if you're interested in learning how to actually analyze studies for yourself, then certainly hop on the course, and I'd love to have you learn how to read these studies in general for yourself, which then you can bring to your physician or your doctor and actually have a more educated discussion after you actually know how to read studies—maybe even better than your physician does.

And the best of all, actually, the course only takes about five hours to go from complete beginner to being a lot more knowledgeable, and then I have some additional content after that. So it's linked for you in the description if you're interested.

Anyway, let me give you a little bit of an overview here on how to read this specifically, and there's certainly a lot more detail in the course.

Okay, so here we've got all the different studies on the left side. This is in studies that include men and women, and here we've got just men, so separating those out. On this line here, we've got one. So one represents neutral risk. This is the baseline risk that glucose spikes have on cardiovascular disease. If it moves to the right—so each one of these lines, which looks a little hectic—but let's focus on this one, the Casiglia et al. study, 1996. This black box is the best estimate of the effect of everyone included in that study.

So they took a bunch of data from every single individual and then aggregated it all together to create an average. So just like if you were to get the average of like five, four, and six—so 5 + 4 + 6, and then divide that by three, you get the average, which I believe is five. So that's just a single number to represent 5, 4, and 6—the average—and that's what we have here.

Then the certainty of the evidence is based on this line. So the true effect, the true result, may actually fall down here; it may fall up here. It gives you the certainty of where the results actually may lie. This is just the best estimate. So if it falls to the right, if the totality of this falls to the right of this 1.0 line, it indicates there's increased risk. You can do that for each one of these studies, right?

So this one essentially shows no risk. You could say that this study—even though it doesn't even have a black dot—that's probably because it's such a small study that it just didn't get like an average. Well, it didn't get an average, but the study was so small that they didn't represent it with like an average result on this analysis. You can see that that one is further to the left, so it indicates that there's no effect, and some of these other studies are to the right.

So anyway, the point is that you have studies all over the place, right? So it's difficult to then say, "Oh, okay, well, there's a risk," or "there's no risk," even though I guess you could argue most of these studies lean, you know, to the right.

So another way to analyze this is by looking at the overall effects diamond. So all these studies are then—which are averages themselves—are then averaged into the main effects diamond. That main effects diamond is an easy representation of the overall effect of all these studies combined into one. So it's not that you should only look at that, but it's just an easy representation.

So in the mixed-sex studies, the overall effect diamond clearly moves to the right, and they don't report any statistics here, but if you read the study itself, they mention that there is increased risk. So there's increased risk for the mixed sex, and then they have another one right here that represents the cohorts just for the men, and we can see that it also moves to the right.

Then they combine the men only as well as the mixed-sex studies, so this result plus this result, and come to the combined result, and that does also indicate that there's increased risk because it's definitely to the right of the 1.0 mark, therefore indicating that postprandial—or post-consumption of anything that's going to increase blood sugar, typically carbohydrates, especially simple carbohydrates like sugar—those are linked to increased cardiovascular disease risk.

Okay, so now we're looking at this table. The reason why we're looking at this, which actually is the same data, but it's being represented with some certain advantages. So in the previous data here, this was an unadjusted analysis, meaning that they're just looking: is there risk, is there not risk? In this situation, what we're looking at is we're breaking things up based on different subcategories, and on top of that, they also do adjustments for other CVD risk factors—so cardiovascular disease risk factors like blood pressure, total cholesterol, HDL, BMI, smoking, and physical activity.

However, how they included studies into this analysis that they—there are some weaknesses here. They only included the studies that had these analyses. That's why in the total number of studies, we've dropped down to 29 from what, 38 or whatever it was. So that's fine because they didn't include certain studies that didn't have the adjustments. However, the criteria for inclusion into this analysis here was that the study had to adjust for at least two of the factors that I just went over: blood pressure, total cholesterol, HDL, BMI, smoking, and physical activity.

So they only had to control for two of them; they did not have to control for all of them, which is a huge weakness of this analysis. I want to be abundantly clear about that because it's very important. Additionally, even if they had—let's assume all 29 studies accounted or, yeah, accounted for all of those factors that we just went over—there are still other factors that have not been accounted for, like lipoproteins, as just one example, Apo levels, non-HDL. If you want to say insulin, that's another factor; diabetes status—those things are not adjusted in any of these.

Okay, so I just want to point that out because it's very important, and it really changes how we look at this. Additionally, they did the analysis based on categorical or continuous. Continuous is a more numerical metric, and it's more sensitive as opposed to just putting people into categories and then doing these adjustments. So I'm going to focus on the continuous because it's more sensitive, although it does lower the number of studies that are included in the analysis.

So in total, all the studies together, we're looking at 17 studies, and we see that the risk is slightly elevated still, so it's 1.04, but it's only very slight. Additionally, let me add one more thing that when they say—when we're doing a comparison here, what they're actually looking at is not glucose spikes on a daily basis. What they're looking at is the post-challenge glucose studies. Post-challenge means a post-glucose test, not a post-meal test, meaning that they are giving a set amount of glucose and seeing the glucose spike, which is going to happen to everyone because you're giving a certain amount of glucose or sugar, and then relating the characteristics of that glucose spike.

So for example, did it go up to 200 mg per deciliter? Did it go up to 140 mg per deciliter? So the one is obviously much higher than the other one, and then they relate that to risk. That, to me, is not a great illustration of a day-to-day glucose spike. So to me, this is a flawed analysis. I'm still including it here because, one, I wanted to show you the data because it is still relatively related, but the other aspect is because I want you to be aware that not every study—just because it says something doesn't mean that it always is representing everything perfectly.

I'm not saying researchers are doing things, you know, nefariously or anything like that. I'm just saying that there are nuances when you open up a study in actual study analysis that require your attention beyond just reading an abstract. I could make an argument that a person's glucose spike in a single test is not necessarily going to be representative of every single time they consume carbohydrates or how often they consume carbohydrates. Am I right? Like a person could be consuming carbohydrates three times a day and have a lower glucose spike than somebody who consumes carbohydrates never. But then does that mean that they are exposed—the person that never consumes carbohydrates—are they then exposed to more glucose spikes? No, they're not.

So there's not quite that parallel, so it's important to point that kind of stuff out. Additionally, one more thing I found really weird in the statistics section that I found very important as well is that they set non-diabetic glucose at a cutoff of 140 mg per deciliter. So—and that's fasting. I literally read this multiple times because I was just so—I was just incredulous. Let me read it straight off here: "We then divided the range of non-diabetic blood glucose levels under pre-1997 criteria." This is a little bit older analysis: "55 to 140 mg per deciliter of fasting blood glucose level."

Okay, so—and then go on: "55 to 200 mg per deciliter of post-challenge glucose level." So I understand that some people are going to get to this point and be like, "Well, why would you cover it, Nick?" Because this isn't just about finding answers; it's also about just learning that there's a lot of nuance in study analysis, and I want you to see studies that aren't necessarily bad. Sometimes they really are bad, but you have to read it; you have to read the data and understand what the data says.

It's something I'm continuously working on, but I just want to point these caveats out because I would probably put this study—although it includes 38 studies—I would put this much lower on the priority list, not because it's an associative study, but because it has several major weaknesses.

Okay, that said, let's continue here. We have the relative risk under continuous pulled for all the studies is 1.04, as we indicated, and the confidence interval—so where the true value actually lies—is 1.02 to 1.06. The number of studies is 17 that are included in this analysis. The adjusted analysis, which, as I said, is kind of semi-adjusted, and here the p-value for heterogeneity is not talking about the p-value of is this statistically significant; it's indicating the p-value of is there a lot of difference between these studies. That's what's called heterogeneity. The answer is yes; there's a lot of studies that show one effect; there's a lot of studies that show another effect.

So this is important because it could indicate that if we were to really dive into that data, that maybe there are some certain qualifying features about certain studies that other studies don't have, and that's why you have these divergent effects. I'm not going to go into it any more than that, and then you can break these up accordingly. You can read them for yourself, but overall, across the board—except for maybe this situation—but you can see the confidence interval is all over the place. So middle-aged individuals that are fasting, they only have two studies; that's probably why the confidence interval is all over the place.

But overall, for all these conditions, there seems to be an increased risk of cardiovascular disease, or if you adjust for the cardiovascular disease risk factors—yes, you adjust for them. Again, it's the limited number that we talked about. There's still an increased risk across those 17 studies, but again, there were some problems with that, as I mentioned, and we're not looking at necessarily—we're looking at a single test that's a glucose spike that still doesn't represent all glucose spikes for the entirety of a person's nutrition.

But don't get mad at me; we're going to get to a few studies that are going to try to tease that out a little bit. Next, my friend, we go to this study—Study 294, public funding. If you want more information on the public funding source, then just download the document that I'll have in the description box, or you know it's in my free community. But you can download the document with all the information on each study.

Okay, so here we're looking again at associative studies. I'm not going to go over every aspect of this analysis, but we've got different groups. So we've got Group 1, which is again—we're basing things off of a 2-hour post-challenge. We're not looking at individual glucose spikes. I'm throwing this in here because technically we're trying to relate the glucose spikes and the effect that they have on the 2-hour post-challenge, which is what we're actually using here.

So Group 1, if you have a 2-hour post-challenge, in addition—let me add this real quick—the one advantage of this is that you can actually directly test this on yourself. If you were to go to a lab and actually have a 2-hour glucose test, you could test where you fall in these studies and assess your own risk, which I think is far more beneficial than saying, "Oh, I've had four glucose spikes today." So therefore, like, there's no studies that indicate amount of glucose spikes causes this amount of damage. We can just relate: do glucose spikes cause damage, or do they not, at least based off the data that we have so far?

Okay, so Group 1, which is right here, we've got men and women. We've got Group 1, and that's a post-challenge as equal or lower than their fasting blood sugar, as in they can have this spike in glucose, and it very quickly decreases. On the other hand, their Group 2 is higher than their fasting blood glucose levels, and you can see the number of individuals is in the thousands of individuals.

You can see that for the most part, roughly everything is about the same. Maybe fasting insulin is slightly different for Group 1 compared to Group 2. What I've got here is that fasting insulin was slightly lower in women for Group 1. So Group 1, this group right here, BMI was lower in women. For both of these groups, oh no, for just Group 1, slightly more women were smokers.

So if we look at smoking, current percentage, 23.9% were smokers, and slightly fewer women had high blood pressure in Group 1. So those are kinds of considerations that you want to keep in mind as you go forward looking at the data itself because there are some baseline differences between the two—between, well, I guess four groups.

All right, here we're looking at this. We've got different models, so these are statistical adjustments I'm about to go into, and we're looking at cardiovascular disease death, non-cardiovascular disease death, or all causes combined. We're looking at the overall risk, so anything above one—I shouldn't say everything, but typically if it goes above one, that indicates there's increased risk.

Now, of course, that's highly dependent on the confidence intervals as well, but I'll tell you what's considered statistically significant. Now, in terms of the adjustments, Model 1—so everything in this category here—we've got it split men and women. Everything in this category here, Model 1, is considered just an age adjustment. So everything else is not adjusted for; just age is adjusted. Model 2 adjusts for age, fasting glucose, BMI, total cholesterol, smoking, and high blood pressure. Model 3 adjusts for all of that, including not just fasting glucose but fasting insulin.

We can see that largely there's increased risk across the board for each of these conditions. So there's increased risk of cardiovascular disease in Group 2. Yeah, there's increased risk of cardiovascular disease in Group 2 because the standard is considered Group 1, so the people that have really well-controlled blood sugar versus the people that are Group 2 that do not have well-controlled blood sugar spikes or have this pro-high postprandial effect.

So when they consume sugar, they have a high blood sugar spike. The comparison between the two—so if it's at one, that means that Group 2 and Group 1 are essentially the same. If it's below one, then that means that Group 2 is outperforming Group 1. So the people with high blood sugar spikes are outperforming the people with very low blood sugar spikes. If it's anything above one, that indicates there's increased risk for Group 2.

What we find is that largely there's increased risk of cardiovascular disease in Group 2 versus Group 1 in both men and women across the board. There's a little bit of uncertainty when it comes to non-cardiovascular disease because the confidence intervals are so wide. But when you're looking at cardiovascular disease, which is our main metric that we're interested in, we do see that there's a pretty reliable increase in risk.

Now again, you're still making some limited adjustments here. You're not measuring for—you're measuring for total cholesterol, but you're not measuring for ApoB, specific containing lipoproteins. You're not adjusting for diet, diabetes specifically. You're not adjusting for medication use, for example. There's a lot of adjustments that they've left out, mainly probably because they're limited on the amount of data that they actually have.

But this data indicates there's increased risk when you have the higher blood sugar spike from one of these glucose tests that people can apply to themselves, and it's something that you can do for yourself. This indicates the exact same results.

So I just want to walk through this real quick. Everything in A here is the men; everything in B here is the women. Let me make sure I'm not obscuring the screen here. We can see that the overall survival—the closer you are to one, the more survival you have—looks to me like women generally have an advantage over men. But overall, what we're actually comparing is not this versus this, but we're comparing the dotted line.

So the dotted line is Group 2, so that's the individuals with the higher blood sugar spike when they're exposed to a set amount of glucose versus the continuous line, or the black line, whatever you want to call it—the non-dotted line—is the individuals that have the lower blood glucose spikes. You can see that it starts to diverge.

So after years of follow-up—decades of follow-up—the people that have the impaired glucose or have the higher glucose tend to die more than the people that don't have that impaired glucose. Now, the question, though, is: is it due to the glucose spikes, or is it due to diabetes, for example? Unfortunately, this analysis doesn't really answer that, even though it adjusts for certain things.

All right, finally, we go to some of the controlled trials. Now we're going to get a little bit more direct answers from this kind of research. The associative trials, the ones that we looked over, had a lot of problems with them—not that you can't get anything out of them, but you just really have to contextualize them a lot more. These trials are semi-controlled trials; they're intervention trials.

So we're actually going to try to get some idea of like a cause and effect because we're actually prospectively giving certain people a drug that affects their glucose spikes and another group of people a drug that does not affect their glucose spikes, and we're going to see if there's any sort of effect on atherosclerosis—that plaque buildup.

Now, this study is publicly funded; however, there are still some negatives of this study. Unfortunately, this specific topic of getting glucose spikes and relating that to heart disease is way more complex than one might imagine. But the two studies that we're going to go over—this one and the next one—actually give us some pretty good details, some more information than what we've been able to gather so far.

Okay, so we've got men and women included; they're overweight, age 52, 161 participants. These people are type 2 diabetic; they are hyperlipidemic, and they're hypertensive, meaning they have high blood pressure. This was a randomized trial, so that's great; however, it was non-placebo controlled. There's no placebo group, and it's single-blind in that the participants knew which condition they were in, but the researchers did not. So not ideal, but also not that horrible.

They end up taking these different drugs. I'm going to call this Repa and Gly, or Gly, mainly because I can have trouble pronouncing these for myself for 12 months, and it's a parallel design. So we're comparing the Repa versus the Gly.

Okay, so what do the drugs do? That's probably pretty important. Repa is a drug that is insulinotropic, so it increases the production of insulin. Repa has an effect on reducing blood sugar by increasing insulin. Gly is a drug that is long-acting; it also stimulates insulin release, but over a long period of time.

So the Repa one is the most effective at reducing postprandial blood glucose. So this one is the more targeted one that's going to reduce blood sugar spikes, whereas Gly does not have as much of an effect on blood sugar spikes, but it does overall help with blood sugar because these people are diabetic. So they can't say, "Hey, for the next 12 months, we're going to give you a drug that's going to do nothing, and you're basically just going to worsen your condition."

Maybe, I suppose, maybe they could have done that with a placebo, but I don't know how it would have worked out with the ethics. Anyway, we're comparing the Repa, which is the drug that's most effective at reducing postprandial blood glucose, versus the one that has the lower effect.

How they did this: they started out with 210 individuals; they ended up randomizing 175, ended up with 88 in the Repa condition, 87 in the Gly condition, and it looks like they had a few people that dropped out. But they ended up doing a specific type of analysis, which is called an intention-to-treat analysis, if I remember correctly, which means that instead of eliminating these particular data points, they ended up including them up to the point that they had the data.

So if people ended up coming in continuously for 5 months and then they ended up deciding to opt out at the 5-month mark, they would continue to use their data up to the 5-month mark, and then they just wouldn't have data for them continuing onwards, obviously, because those people didn't show up anymore.

In terms of the baseline results here, we're looking at diabetic patients, and this is just a comparison between the two. This is not the actual— they're not actually doing Repa in control subjects and Gly in control subjects versus diabetic individuals. They're just indicating these people truly are diabetic, and we can see that because the plasma glucose levels are much higher and their HbA1c is much higher. So yes, these are actually diabetic individuals—type 2 diabetes.

Okay, what we're actually interested in is in that diabetic group. We've got the Repa and the Gly. Now we've split them up, and this is the analysis that we're actually interested in. What we want to know is: are there any differences between the two conditions at the beginning of the study? The answer is no; these are all statistically nonsignificant. If the p-value is below 0.05, it is statistically significant, indicating that there's a likely effect. In this case, there is no likely effect, at least that could be argued.

So across all these different metrics, you know, smoking, glucose levels, HbA1c, insulin levels, lipid levels, blood pressure, and all these different cytokines—the pro-inflammatory cytokines like IL-6 and IL-18, CRP, or C-reactive protein—and this is a measure of atherosclerosis. They're looking at the actual media thickness. So under the endothelium that we talked about earlier, the overall thickness—if that thickens more and more, then that means you have more plaque deposition. So they want to know at the beginning, are there any differences between these two?

All right, so here we've got the Repa condition; here we've got the Gly condition, and they're doing these different tests. So let me start out down here. We've got the before, which they've indicated with these kind of like lightning bolt arrows—interesting way to show it. So you've got the before and the after, taking Repa for the last 12 months and the same with the Gly condition.

You can see that the Repa condition does flatten out this blood sugar curve. I wish that they had actually compared these against one another because that's probably the proper comparison between the two, as opposed to doing what's known as within-treatment comparisons. If you take my course, you'll understand what I'm talking about. They're essentially comparing the Repa before versus Repa after, so before they were put on the drug Repa and after, and the Gly before they were put on Gly and then after.

But they're not comparing the actual before and before and after versus after. That's the true comparison that we should really have here, unfortunately. But I think it's pretty clear to say that at the beginning, they were roughly the same in their glucose effect that would happen when they consumed glucose.

When they took the drug, this one, which is specifically designed to have more of a postprandial reduction, so it makes sure that your blood sugar spike is dampened—just compare it here versus here. You can see that there's a much greater rise in the Gly condition than there is in the Repa condition. That's the main point that we're trying to get across here.

The postprandial peak certainly decreases afterwards as well for the Gly condition, but it seems to be greater—in the Repa condition. Okay, now we're looking at CIMT progression. So this is the atherosclerosis. If we see greater progression, then that means that you have more plaque that's in the arteries, and they're doing before versus after.

What we see is that there is basically no change in the Gly condition, which is good, right? I mean, no change. The individual dots, by the way, are the individual participants and their data, and then repeat it again on this side. Then we've got the box, so kind of focus on this middle line here and compare that to the previous line. So this line versus this line—there's roughly no change; that's good.

But it's also only over 12 months, so you could argue that there might not be a change. On the other hand, you could argue that there should be a change because in the Repa condition, the before versus after—the before condition is higher than the after condition, indicating that there is a regression of atherosclerosis in the after condition with the use of Repa.

Okay, now we're looking at the other things that may have changed. So we just looked at like the blood sugar levels and the CIMT, which we see repeated down here, by the way. And we're looking at the comparison between the two groups now. And again, if this is below 0.05, there's a statistically significant difference between the change from baseline in the Gly condition versus 12 months later.

So they're just doing the comparison of those two and putting that number here. So before they were put on Gly, they had a fasting—I'm just taking one example—a fasting blood sugar level that was 32 points higher than it is afterwards. So 12 months later, they saw a reduction of 32 milligrams per deciliter, and then in the Repa condition, they see a drop of minus 24 of fasting blood sugar levels.

The peak was reduced more in the Repa condition than it was in the Gly condition. You can do this all the way down. I'm going to focus on the blood sugar levels because there's an important distinction here. So one weight did not change between the two conditions, so that's actually great to see because that would be a huge confounding variable if their weight ended up changing because of the use of this drug.

However, while the fasting levels did decrease in the Repa condition—most likely because we actually don't have statistics comparing the—actually showing that, but that's beside the point—most likely we see a drop in the fasting condition, and most likely we see a drop in the Gly condition for the fasting. It is statistically significant, indicating that there's a greater drop in the Gly condition.

Okay, the peak is a greater drop in the Repa condition, and even though there is a drop in the Gly condition, it's not as severe as in the Repa condition. Now, what's important here, however, is that the area under the curve—so the overall change after—in a 2-hour experiment, let me go back here—in a 2-hour experiment like this, once they give sugar to these individuals, a standard amount, and they measure the total amount of sugar in the blood over two hours, we see that there's a greater reduction in the Repa condition versus the Gly condition.

You'll notice that there are a few other differences, like CRP is one that changed, and interleukin 6 was decreased more in the Repa conditions as well. So this offers some evidence that if you block the postprandial—specifically the postprandial—that you were able to then positively affect this CIMT number here, indicating that there's an effect at reducing atherosclerosis in the individuals that block that or reduce that overall spike in blood sugar.

However, I should note that in this situation, we also don't have a placebo group, and I think that there are certain experimental designs that would be much more difficult but would probably give a more clear indication of the overall progression. The problem here that I see is that you have reduced inflammation in these conditions. Is that because necessarily of this particular drug? Does it have a specific effect on anti-inflammation, or is it because of the actual reduction in the postprandial glucose spikes that's leading to this reduced level of inflammation?

Now, we do have some evidence from the acute studies that if you have these spikes, that you have potentially more of these inflammatory signals or these inflammatory cytokines. So there's still a few outstanding questions. I don't think that this study design is perfect, but it at least leans us in the direction of, yeah, maybe these spikes may have an additive effect—that they may have an especially detrimental effect in that regard.

Now, another thing that I would really like to see is an area under the curve that is over the full 24 hours as opposed to just the first two hours because if it's over the first 24 hours and this Gly condition may have higher blood sugar levels just generally, then that may be a confounding variable as well. So there are a few things that I would like to see to really hammer this point home.

Finally, let's go over Study 292. Unfortunately, I could not figure out the funding source for this, but we're looking at another drug study. In this example, we're looking at 70 participants. We don't know their age, as far as I can remember. This is men and women. Again, this is in normal weight individuals, but they are type 2 diabetic. This is a randomized trial; however, it is open-label, so the participants and the researchers know what the actual intervention is. There's obviously no blinding if it's open-label.

It is 12 months in duration; it's parallel design. We're comparing a control group, so no placebo, so they essentially get no treatment whatsoever. So I guess going back to my earlier point, I guess it is ethical within a 12-month period to just not give people anything. Maybe they just have standard treatment of talking to their doctor, getting regular advice that even the intervention group is also receiving—that's a possibility.

But the intervention group is, in addition, at least getting this other drug called Nate Glinide. I'm going to call it Nate just because, again, I'm going to have trouble saying the name—all kinds of weird names when it comes to drugs. So Nate or Nate Glinide is a drug that is insulin-promoting, so it—and it's very short duration and action. So it increases insulin after a meal, reducing therefore the postprandial or post-food consumption glucose spike.

So we're comparing this condition versus the control as a parallel design. Here we have 105 individuals; we ended up with 78. It was randomized. We have 40 individuals in the non-treated, so that's the control, and the 38 individuals in the Nate condition. Then after 12 months, they measured IMT, so again looking at atherosclerosis.

Here we've got the baseline values before the study begins, and we can see that there are some distinctions. So for some of these, like it's, you know, maybe approaching statistical significance. The diabetic individuals are normal weight by BMI, but there might be slightly different looking, obviously, at like insulin levels, fasting glucose levels. I mean, if they're diabetic, of course, they're going to be different from control subjects that are actually healthy, that don't have type 2 diabetes.

Serum triglycerides—they're going to have high levels of that. You know, a lot of different differences, and of course, the intimate media thickness is the measure that we're using for atherosclerosis, and that is higher for the diabetic individuals.

Okay, now we're looking at the baseline characteristics of just the diabetic individuals, like we did in one of those other studies as well that just tried to quick compare against control healthy individuals and then diabetic individuals—just looking at diabetic individuals, and that's what we're doing here. So we got the non-treated group; this is the control that we're actually interested in versus the Nate group, so the ones that are given the drug.

What we want to know is, again, this is before they're given the drug. Are there any baseline differences? The answer is no; there are no baseline differences, which is good at the onset because then we can—whatever differences occur are due to the actual drug.

Okay, finally, looking at the actual results themselves. So these are all the deltas, meaning again they're just taking the baseline results and subtracting the actual results at the end of the 12 months, so the overall difference over that time, and they're comparing those differences in the non-treated group, the control group, versus the Nate group.

What we see is that largely there aren't that many differences except for the HbA1c was one difference. So there was a reduced HbA1c in the Nate group, and there was, I would probably characterize it as more of an increase in VCAM. Remember, that's like ICAM; it's like these adhesion molecules that allow immune cells to bind to the endothelium and then to potentially invade past the endothelium, which can be part of this atherosclerosis process, the inflammatory process.

We see that for the VCAM, there's an increase in a likely increase in VCAM, but we don't see that increase with the Nate group. So essentially, it kind of puts a cap on it. Finally, looking at the intimate media thickness, we can see that there's a decrease in that intimate media thickness for the Nate group, and there's an increase in the non-treated group.

So maybe if you were to compare baseline versus 12 months later in just one of these groups, it may not be statistically significant, but since one is going up and another one is going down, the difference between those two endpoints is greater, and therefore you get statistical significance.

So this would indicate that Nate—the true pure definition is that this Nate Glinide drug does help protect against heart disease. Now, the mechanism by which that drug does that is supposedly through an insulin promotion that's short duration that only activates after a person eats a meal, and therefore reducing their blood sugar levels.

Now, here's one potential confounder: is it actually because of the insulin spike, the continuous reduction in insulin spike, or is it because of the HbA1c? I guess functionally, it probably doesn't matter to most people because you're most likely going to be improving both at the same time anyway. But if you want to get real technical of like which one was it, they probably should have had another group in here that improved HbA1c without improving the glucose spike to tease out those differences. Unfortunately, we don't have that.

Okay, another study is Study 288, and this is again another intervention trial. This is industry-funded, so just bear that in mind. If you want to discount this completely, feel free. I don't do that, but I'm still going to show you the data. What's beautiful about this study is that, one, the length, and two, look at those green: randomized, placebo-controlled, double-blind—it's a prospective study. This is fantastic to see.

1,368 individuals—men and women, 54 years of age—and they were overweight, but otherwise, I didn't have too much more information on their overall health, as far as I could tell. Well, yeah, as far as I could tell. Oh, they were overweight, and they were maybe slightly—not diabetic, but pre-diabetic. So not overly super healthy individuals, but it doesn't matter because we're comparing—well, it matters for them, but for our purposes, from a data perspective, I don't want to be insensitive.

The point is we've got this true comparison of a placebo versus acarbose, which is this drug that we're going to get into. I guess actually real quick before we get into that, what does acarbose do? This one is slightly different from the other drugs that we looked at. So acarbose is a drug that inhibits an enzyme called alpha-glucosidase. So it's an enzyme in the pancreas that cleaves the complex carbohydrate chain.

So we talked about how they're disaccharides, while they're also like polysaccharides or disaccharides that then get converted to monosaccharides—that's what I should say. And there are these different enzymes that allow for that cleavage. Well, this drug inhibits some of those cleavages, which thereby reduces the absorption of glucose and therefore at the intestinal level—not after the glucose molecules are absorbed and then an increase in insulin like the other drugs.

This has a specific effect in that it stops some of the glucose molecules from entering the bloodstream in the first place, so it's a completely different mechanism of action. So really interesting. Here we've got the number of individuals; we can see that those are actually included. The analysis are 682 and 686. They start out with 1,429; they assigned them, and then they had several dropouts.

Baseline characteristics across these—so we can ignore the overall, but we're actually interested in the comparison of acarbose versus the placebo here. And as I mentioned, these people are slightly overweight. We've got their BMI over here; they're roughly, you know, 54 years of age. This is where I was getting that point that they may be pre-diabetic because their fasting levels are above 100 but technically below like 120-129.

So a few differences, a few aspects that indicate that they're not necessarily in the healthiest state, but they're also not necessarily full-blown diabetic either. And you can

Of whatever you're looking at, so as an example here, the treatment group—if you were in the carbos condition or the placebo condition—did that make a difference? Did that relate to risk? The answer is yes, it is statistically significant, and yes, it is because it's reduced. Presumably, they're talking about the treatment group, so they're talking about a carbos condition. If you were in the carbos condition, you had a reduced risk of cardiovascular disease, which is just a repetition of all the other data that we already looked at.

In addition, looking at fasting blood sugar levels, we see that fasting blood sugar levels do relate as well. If you have increasing fasting blood sugar levels, you see an increased risk, and that is statistically significant. However, the 2-hour test did not show that effect. Looking at insulin levels, I mean, you can go all the way down and keep in mind that for some of these, it's just not statistically significant because they need more participants. There have been other analyses that have looked at insulin levels and shown that there's risk attributed to the overproduction of insulin. Now, that could be just because people are diabetic. I haven't done those individual analyses where you account for diabetes and then see if insulin is still a problem, but at least based on this analysis, it does not seem to be.

As for hemoglobin, the glycated hemoglobin is not technically statistically significant, but it seems like it's leaning that way, and there are plenty of other analyses that show that HbA1c has a statistically significant effect. Another one that people are often concerned with is cholesterol, for example. We see that the most prominent one that people look at, like LDL or low-density lipoprotein, is not statistically significant in this example, but you can also look at the confidence intervals; it's kind of all over the place—some show low, some show high. So I think, again, you just don't have the power for that analysis.

Regarding total triglycerides, if you have high triglycerides, that did reach statistical significance, indicating that there's increased risk. Blood pressure—this one was really tight. Although the actual risk wasn't astronomically much higher, there was increased risk with blood pressure. You can again go through each one of these. The number of medications—I don't think that's a huge shock. If you're more sick, then you're going to be on more medications, and therefore that's going to relate to increased cardiovascular disease risk.

Keep in mind these are univariate analyses, so we're just looking at one thing and seeing if that relates; we're not accounting for anything else. What we can do then is actually perform a multivariate analysis. Let me tell you what they ended up adjusting for here: they adjusted for baseline fasting postprandial blood glucose (after consumption of the set amount of glucose), insulin, HbA1c, total HDL, LDL cholesterol, triglyceride levels, blood pressure, heart rate, body weight, and medications, except for hypertensive medications and smoking. Then they just retested these three, as we saw up here. We can find that all three of them were still statistically significant, indicating that, like fasting glucose, for example, also has a relationship with cardiovascular disease independent of all of the adjustments that we just talked about.

Finally, I wanted to quickly discuss the two primary factors that lead to increased risk, at least based on what these researchers point out. This is a scientific review of all the literature on the topic. In study 289, again this nonprofit funding, we've actually been over this study when we looked at the mechanisms. There were two things that they pointed out as the detrimental aspects: one is the height of the glucose spike, the actual amount of glucose increase—that's risk factor number one. The second thing is the length of the hypoglycemia. If you have a high glucose spike but you're able to reduce it very quickly back down, that may be less detrimental than if you have a high one that ends up carrying out for a while, which really comes down to insulin sensitivity. So that's one factor—another factor, I should say.

You can get this kind of test done for yourself; it's probably a bit cumbersome and it's going to take some actual dedication, but it can give you an idea of whether you are really insulin sensitive and able to handle these glucose spikes or if you're not able to.

Okay, we covered a ton of stuff, and a lot of it was very confusing. I totally understand that, mainly because I did want to show some of the data that isn't that convincing, and I still think that there's a lot of work that needs to be done on this. But let's go ahead and dive into what I think where we are currently.

So glucose spikes are proposed to cause cardiovascular disease by increasing activity of the RAGE pathway, which we went over, including increasing oxidative stress, reducing endothelial function, and several other mechanisms. Intervention and long-term associative trials indicate that there's some, although weak, evidence of glucose spikes raising cardiovascular disease risk. However, while the evidence suggests that glucose spikes are detrimental, the methodology of studies is generally poor and doesn't generally answer the question. So that's what I mentioned—there are still gaps in the research that need to be addressed.

Better controlled studies—I talked about a few examples of that. If glucose spikes are to be considered a CBD risk, reducing the height of the glucose spike and the length of the glucose spike are the most effective ways of reducing glucose burden. I would like to quickly point out here, as well, just real quick, if you're interested in continuing this analysis, I'm going to go into even more specifics on all this—just join the Physionic Insiders.

But let me quickly talk to you a little bit about some of this stuff. Unfortunately, what's going to happen with this is that some people are going to watch this and then they're going to think, "Well, then a low-carb diet is for sure 100% better than any other diet." I'm not here to dissuade you that a low-carb diet doesn't have profound benefits. However, just because we have taken one intervention or one problem, one metric, and one measurement and looked at an outcome—a single outcome—and that outcome is extremely important, cardiovascular disease, the problem here is that people are then going to assume that blood sugar and glucose spikes are the only thing to pay attention to for cardiovascular disease.

That is the incorrect conclusion here. The correct conclusion is that, assuming this data pans out and is absolutely true, glucose spikes are one factor, just like anything else—like atherosclerosis, right? It's ApoB levels, it's blood pressure levels, it's glucose levels—even high glucose levels that are not related to these fluctuations and glucose spikes. It could be glucose spikes, it could be insulin levels; there are a bunch of factors. So to say that just looking at glucose spikes is going to be the answer to eliminating cardiovascular disease is probably not the correct way to go about it.

Additionally, if you address the glucose spikes, you're probably addressing several other factors, like inflammation, potentially blood pressure, and a lot of other things. So I just want to be very clear about that. Additionally, if you do decide to do, let's say, a low-carb diet because you really want to reduce the glucose spikes, that's fine, but there are certain ways of going about a low-carb diet that can also introduce risk. So you just have to be very careful, and there's certainly plenty of evidence that eating, for example, complex carbohydrates can lead to a small glucose spike that may not be detrimental. We have long-term data looking at that kind of stuff, and that's something I'll go into more in the future.

But the direct effect of glucose spikes, in my estimation, I'm leaning that way, but I'm not necessarily completely convinced. I think we need more data to be absolutely sure. I'm just saying that I'll just leave it at that—just don't be super focused only on glucose spikes and think that there's only one way to go about this particular problem.

Anyway, that's what I've got for you. Again, I realize this was a lengthy one, but hopefully, you got some information out of it, and I hope to speak with you in the near future. Have a great one! See you.