Does tallness cause heart disease? No, but sex does

Popular beliefs about medical issues are sometimes motivated by a statistical phenomenon known as “spurious relationship”, among other names. Two variables X and Y are influenced by a third variable C, which leads to X and Y being correlated and thus the impression that X and Y are causally associated.

Take a look at the table below, which I blogged about in a previous post (). This table shows that there is a strong unadjusted correlation between height and arterial stiffness, a marker of heart disease. The likelihood that the correlation is due to chance is lower than one tenth of a percentage point (P<.001).

Interestingly, the authors of the study even use height as a control variable to narrow down the “true” causes of arterial stiffness (column with adjusted results), assuming that height did indeed influence arterial stiffness and what they found to be a key predictor of arterial stiffness, 2-hour postprandial glucose.

But there is no convincing evidence that height causes heart disease, with exception of pathological extremes – e.g., acromegaly. Extremes tend to influence statistical results somewhat, leading to conflicting conclusions that end up being disseminated by the popular media (). This is one of the sources of popular beliefs about medical issues.

Another, more important, source are real confounders. And this takes us back to the issue of height being associated with heart disease. In fact, height will typically be significantly associated with heart disease in almost any study that includes men and women and does not control for biological sex.

One of the reasons is that women overall tend to have a significantly lower incident of heart disease than men. The other is that height is significantly lower among women than men, on average, even though there are several women who are taller than the average man.

The table above was from a study including both sexes. Therefore, the strong association between height and arterial stiffness is a “reflection” of the strong association between being male and increased arterial stiffness. If one were to add a variable coded as 0 for male and 1 for female, and use it in a multivariate analysis of predictor of arterial stiffness, together with height, the effect of height would probably “disappear”.

Biological sex is the control variable, the “confounder”, that the authors should have used to narrow down the “true” causes of arterial stiffness (second column in the table). In the absence of biological sex, controlling for height accomplished something similar, but in a “wobbly” way, leaving many readers scratching their heads in confusion.

Being glucose intolerant may make you live only to be 96, if you would otherwise live to be 100

This comes also from the widely cited Brunner and colleagues study, published in Diabetes Care in 2006. They defined a person as glucose intolerant if he or she had a blood glucose level of 5.3-11 mmol/l after a 2-h post–50-g oral glucose tolerance test. For those using the other measurement system, like us here in the USA, that is a blood glucose level of approximately 95-198 mg/dl.

Quite a range, eh!? This covers the high end of normoglycemia, as well as pre- to full-blown type 2 diabetes.

In this investigation, called the Whitehall Study, 18,403 nonindustrial London-based male civil servants aged 40 to 64 years were examined between September 1967 and January 1970. These folks were then followed for over 30 years, based on the National Health Service Central Registry; essentially to find out whether they had died, and of what. During this period, there were 11,426 deaths from all causes; with 5,497 due to cardiovascular disease (48.1%) and 3,240 due to cancer (28.4%).

The graph below shows the age-adjusted survival rates against time after diagnosis. Presumably the N values refer to the individuals in the glucose intolerant (GI) and type 2 diabetic (T2DM) groups that were alive at the end of the monitoring period. This does not apply to the normoglycemic N value; this value seems to refer to the number of normoglycemic folks alive after the divergence point (5-10 years from diagnosis).

Note by the authors: “Survival by baseline glucose tolerance status diverged after 5-10 years of follow-up. Median survival differed by 4 years between the normoglycemic and glucose intolerant groups and was 10 years less in the diabetic compared with the glucose intolerant group.”

That is, it took between 5 and 10 years of high blood glucose levels for any effect on mortality to be noticed. One would expect at least some of the diagnosed folks to have done something about their blood glucose levels; a confounder that was not properly controlled for in this study, as far as I can tell. The glucose intolerant folks ended up living 4 years less than the normoglycemics, and 10 years more than the diabetics.

One implication of this article is that perhaps you should not worry too much if you experience a temporary increase in blood glucose levels due to compensatory adaptation to healthy changes in diet and lifestyle, such as elevated growth hormone levels. It seems unlikely that such temporary increase in blood glucose levels, even if lasting as much as 1 year, will lead to permanent damage to cells involved in glucose metabolism like the beta cells in the pancreas.

Another implication is that being diagnosed as pre-diabetic or diabetic is not a death sentence, as some people seem to take such diagnoses at first. Many of the folks in this study who decided to do something about their health following an adverse diagnosis probably followed the traditional advice for the treatment of pre-diabetes and diabetes, which likely made their health worse. (See Jeff O’Connell’s book Sugar Nation for a detailed discussion of what that advice entails.) And still, not everyone progressed from pre-diabetes to full-blow diabetes. Probably fewer refined foods available helped, but this does not fully explain the lack of progression to full-blow diabetes.

It is important to note that this study was conducted in the late 1960s. Biosynthetic insulin was developed in the 1970s using recombinant DNA techniques, and was thus largely unavailable to the participants of this study. Other treatment options were also largely unavailable. Arguably the most influential book on low carbohydrate dieting, by Dr. Atkins, was published in the early 1970s. The targeted use of low carbohydrate dieting for blood glucose control in diabetics was not widely promoted until the 1980s, and even today it is not adopted by mainstream diabetes doctors. To this I should add that, at least anecdotally and from living in an area where diabetes is an epidemic (South Texas), those people who carefully control their blood sugars after type 2 diabetes diagnoses, in many cases with the help of drugs, seem to see marked and sustained health improvements.

Finally, an interesting implication of this study is that glucose intolerance, as defined in the article, would probably not do much to change an outside observer’s perception of a long-living population. That is, if you take a population whose individuals are predisposed to live long lives, with many naturally becoming centenarians, they will likely still be living long lives even if glucose intolerance is rampant. Without carefully conducted glucose tolerance tests, an outside observer may conclude that a damaging diet is actually healthy by still finding many long-living individuals in a population consuming that diet.

Fasting blood glucose of 83 mg/dl and heart disease: Fact and fiction

If you are interested in the connection between blood glucose control and heart disease, you have probably done your homework. This is a scary connection, and sometimes the information on the Internetz make people even more scared. You have probably seen something to this effect mentioned:

Heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl.

In fact, I have seen this many times, including on some very respectable blogs. I suspect it started with one blogger, and then got repeated over and over again by others; sometimes things become “true” through repetition. Frequently the reference cited is a study by Brunner and colleagues, published in Diabetes Care in 2006. I doubt very much the bloggers in question actually read this article. Sometimes a study by Coutinho and colleagues is also cited, but this latter study is actually a meta-analysis.

So I decided to take a look at the Brunner and colleagues study. It covers, among other things, the relationship between cardiovascular disease (they use the acronym CHD for this), and 2-hour blood glucose levels after a 50-g oral glucose tolerance test (OGTT). They tested thousands of men at one point in time, and then followed them for over 30 years, which is really impressive. The graph below shows the relationship between CHD and blood glucose in mmol/l. Here is a calculator to convert the values to mg/dl.

The authors note in the limitations section that: “Fasting glucose was not measured.” So these results have nothing to do with fasting glucose, as we are led to believe when we see this study cited on the web. Also, on the abstract, the authors say that there is “no evidence of nonlinearity”, but in the results section they say that the data provides “evidence of a nonlinear relationship”. The relationship sure looks nonlinear to me. I tried to approximate it manually below.

Note that CHD mortality really goes up more clearly after a glucose level of 5.5 mmol/l (100 mg/dl). But it also varies significantly more widely after that level; the magnitudes of the error bars reflect that. Also, you can see that at around 6.7 mmol/l (121 mg/dl), CHD mortality is on average about the same as at 5.5 mmol/l (100 mg/dl) and 3.5 mmol/l (63 mg/dl). This last level suggests an abnormally high insulin response, bringing blood glucose levels down too much at the 2-hour mark – i.e., reactive hypoglycemia, which the study completely ignores.

These findings are consistent with the somewhat chaotic nature of blood glucose variations in normoglycemic individuals, and also with evidence suggesting that average blood glucose levels go up with age in a J-curve fashion even in long-lived individuals.

We also know that traits vary along a bell curve for any population of individuals. Research results are often reported as averages, but the average individual does not exist. The average individual is an abstraction, and you are not it. Glucose metabolism is a complex trait, which is influenced by many factors. This is why there is so much variation in mortality for different glucose levels, as indicated by the magnitudes of the error bars.

In any event, these findings are clearly inconsistent with the statement that "heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl". The authors even state early in the article that another study based on the same dataset, to which theirs was a follow-up, suggested that:

…. [CHD was associated with levels above] a postload glucose of 5.3 mmol/l [95 mg/dl], but below this level the degree of glycemia was not associated with coronary risk.

Now, exaggerating the facts, to the point of creating fictitious results, may have a positive effect. It may scare people enough that they will actually check their blood glucose levels. Perhaps people will remove certain foods like doughnuts and jelly beans from their diets, or at least reduce their consumption dramatically. However, many people may find themselves with higher fasting blood glucose levels, even after removing those foods from their diets, as their bodies try to adapt to lower circulating insulin levels. Some may see higher levels for doing other things that are likely to improve their health in the long term. Others may see higher levels as they get older.

Many of the complications from diabetes, including heart disease, stem from poor glucose control. But it seems increasingly clear that blood glucose control does not have to be perfect to keep those complications at bay. For most people, blood glucose levels can be maintained within a certain range with the proper diet and lifestyle. You may be looking at a long life if you catch the problem early, even if your blood glucose is not always at 83 mg/dl (4.6 mmol/l). More on this on my next post.

Book review: Sugar Nation

Jeff O’Connell is the Editor-in-Chief for Bodybuilding.com, a former executive writer for Men’s Health, and former Editor-in-Chief of Muscle & Fitness. He is also the author of a few bestselling books on fitness.

(Source: Bodybuilding.com)

It is obvious that Jeff is someone who can write, and this comes across very clearly in his new book, Sugar Nation.

Now, with a title like this, Sugar Nation, I was expecting a book discussing trends of sugar consumption in the USA, and the related trends in various degenerative diseases. So when I started reading the book I was slightly put off by what seemed to be a book about a very personal journey, written in the first person by the author.

Yet, after reading it for a while I was hooked, and literally could not put the book down. Jeff has managed to write something of a page-turner, combining a harrowing personal account with carefully researched scientific information, about a relatively rare form of type 2 diabetes.

Jeff has a genetic propensity to insulin resistance, just like his father did. What makes Jeff’s case a little unusual is that Jeff is thin, and apparently has difficulty gaining weight. The most common type of diabetes is type 2, and most of those who develop type 2 diabetes do so via the metabolic syndrome. Typically this involves becoming obese or overweight before getting diagnosed as a diabetic.

In fact, in a thin person who is insulin resistant it seems that body fat cells become resistant to the normal actions of insulin much sooner than in the obese. This essentially means that they start rejecting fat. This is a problem, because fat should either be stored in fat cells (adipocytes) or used for energy; as opposed to being deposited in other tissues or remaining in circulation. Apparently this makes it even more difficult for them to control glucose levels once insulin resistance sets in; there is no “cushion”, so to speak.

Still, Jeff appears to believe that his case was that of a skinny-fat person, where body fat percentage is a lot higher than expected based on a low body mass index, and where excess visceral fat is a main culprit. In fact, Jeff seems to think that most cases of thin folks who developed type 2 diabetes are like this, as they follow the metabolic syndrome progression pattern. Fasting triglycerides go up and HDL cholesterol goes down, among other things, but in a skinny-fat body.

Somewhat predictably, what Jeff found out is that, in his case, adopting a low carbohydrate diet made an enormous difference. In fact, it made the difference between having a fairly normal life versus constantly suffering through hypoglycemic episodes. And, at the stage in which Jeff caught the problem, he did not have to avoid all natural carbohydrate-rich foods, not even things like apples. (He had to control portions though.) It is the refined carbohydrate-rich foods that were the problem for him.

I must say that I disagree with a few of the statements in the book. For example, the author seems to believe that excess saturated fat and salt may be quite unhealthy. I think that foods rich in refined carbohydrates and sugars are much more of a problem; cut them out and often excess saturated fat and salt either cease to be a problem, or become healthy. Jeff doesn’t seem to think that excess omega-6 fats can also cause diabetes; I believe the opposite to be true, via a pro-inflammatory path.

Still, this is a great book on so many levels. Jeff meticulously records his experience dealing with doctors, most of whom seem to be clueless as to what to do to prevent the damage that is caused by abnormally high glucose levels. This happens even though diabetes is those doctors’ main area of expertise. He talks about himself with complete abandon, and manages to mix that up with quite a lot of relevant research on diabetes. He gives us an insider’s view of the professional bodybuilding culture, including its use of insulin injections. His description of the Amish is very interesting and somewhat surprising.

For these reasons and a few others, I think this is a great book, and highly recommend it!

There is no doubt that abnormally elevated insulin is associated with body fat accumulation

For as long as diets existed there have been influential proponents, or believers, who at some point had what they thought were epiphanies. From that point forward, they disavowed the diets that they formally endorsed. Low carbohydrate dieting seems to be in this situation now. Among other things, it has been recently “discovered” that the idea that insulin drives fat into body fat cells is “wrong”.

Based on some of the comments I have been receiving lately, apparently a few readers think that I am one of those “enlightened”. If you are interested in what I have been eating, for quite some time now, just click on the link at the top of this blog that refers to my transformation. It is essentially high in all macronutrients on days that I exercise, and low in carbohydrates and calories on days that I don’t. It is a cyclic approach that works for me; calorie surpluses on some days and calorie deficits on other days.

But let me set the record straight regarding what I think: there is no doubt that insulin is associated with body fat accumulation. I was told that an influential health blogger (whom I respect a lot) denied this recently, going to the extreme of saying that no professional metabolism or endocrinology researcher believes in it, but I couldn’t find any evidence of that statement. It is not hard at all to find professional metabolism and endocrinology researchers who have asserted that insulin is associated with body fat accumulation, based on very reliable evidence. Actually, this is Biochemistry 101.

What I think is truly unclear is whether insulin spikes associated with carbohydrate-rich foods in general are the cause of obesity. This idea is, indeed, probably wrong given the evidence we have from various human populations whose members consume plenty of non-industrialized carbohydrate-rich foods. On a related note, I particularly disagree with the notion that the pancreas gets tired over time due to having to secrete insulin in bursts, which seems to also be one of the foundations on which many low carbohydrate diet varieties rest.

As with almost everything related to health, the role of insulin in body fat gain is complex, and part of that complexity is due to the nonlinear relationship between body fat gain and postprandial insulin release. Industrial carbohydrate-rich foods have a much higher glycemic load than natural carbohydrate-rich foods, even though their glycemic index may be the same in some cases. In other words, the quantity of easily digestible carbohydrates per gram is much higher in industrial carbohydrate-rich foods.

In normoglycemic folks, this leads to an abnormally elevated insulin response, among other hormonal responses. For example, circulating growth hormone, which promotes body fat loss, is inversely correlated with circulating insulin. Insulin drives fat, typically from dietary sources of fat, into adipocytes. That fat may also come from excess carbohydrates, packaged into VLDL particles.

Under normal circumstances, that would be fine, since our body is designed to store fat and release it as needed. But the abnormal insulin response elicited by industrial carbohydrate-rich foods, together with other hormonal responses, leads to a little more body fat accumulation, and for longer, than it should. And I’m talking here about people without any metabolic damage. Saturated and monounsaturated fats are healthy when eaten, but when they are stored as excess body fat, they become pro-inflammatory.

Body fat is like an organ, secreting many hormones into the bloodstream, several of which are pro-inflammatory. One of those pro-inflammatory hormones, which I believe is closely linked with many diseases of civilization, is tumor necrosis factor. (The acronym is now TNF. Apparently the “-alpha” after its name and acronym has been dropped recently.) Dietary fat, particularly saturated fat, seems to be anti-inflammatory. In other words, body fat accumulation is the problem. You only need 30 g/d of excess body fat accumulation to gain around 24 lbs of fat per year. Over three years, that will add up to over 70 lbs of body fat.

In my view, ultimately it is excess inflammation (which is, in essence, a vascular response) that is at the source of most of the diseases of civilization.

That is where the nonlinearity comes in. Insulin is healthy up to a point. Beyond that, it starts causing health problems, over time. And one of the main mechanisms by which it does so is via excessive body fat accumulation, with different damage threshold levels for different people. Insulin may decrease appetite as it goes up, but it increases it if goes down too much. If it goes up abnormally, typically it will go down too much. As it reaches a trough it induces hypoglycemia, even if mildly.

Take a look at the graph below, from this post showing the glucose variations in normoglycemic individuals. There is a lot of variation among different individuals, but it is clear that the magnitude of the hypoglycemic dips is inversely correlated with the magnitude of the glucose spikes. That inverse correlation is due primarily to the effect of insulin. Under normal circumstances, a decrease in circulating insulin would promote an increase in free fatty acids in circulation, which would normally have a suppressing effect on hunger in the hours after a meal. But industrial carbohydrate-rich foods lead to increases and decreases in glucose and insulin that are too steep, causing the opposite effect.

You may ask: why do you keep talking about industrial carbohydrate-rich foods? Why not talk about industrial protein- or fat-rich foods as well? The reason is that the food industry has not been very successful at producing industrial protein- or fat-rich foods that are palatable without adding a lot of carbohydrate to them.

More often than not they need enough carbohydrate added in the form of sugar to become truly addictive.

Strength training plus fasting regularly, and becoming diabetic!? No, it is just compensatory adaptation at work

One common outcome of doing glycogen-depleting exercise (e.g., strength training, sprinting) in combination with intermittent fasting is an increase in growth hormone (GH) levels. See this post for a graph showing the acute effect on GH levels of glycogen-depleting exercise. This effect applies to both men and women, and is generally healthy, leading to improvements in mood and many health markers.

It is a bit like GH therapy, with GH being “administered” to you by your own body. Both glycogen-depleting exercise and intermittent fasting increase GH levels; apparently they have an additive effect when done together.

Still, a complaint that one sees a lot from people who have been doing glycogen-depleting exercise and intermittent fasting for a while is that their fasting blood glucose levels go up. This is particularly true for obese folks (after they lose body fat), as obesity tends to be associated with low GH levels, although it is not restricted to the obese. In fact, many people decide to stop what they were doing because they think that they are becoming insulin resistant and on their way to developing type 2 diabetes. And, surely enough, when they stop, their blood glucose levels go down.

Guess what? If your blood glucose levels are going up quite a bit in response to glycogen-depleting exercise and intermittent fasting, maybe you are one of the lucky folks who are very effective at increasing their GH levels. The blood glucose increase effect is temporary, although it can last months, and is indeed caused by insulin resistance. An HbA1c test should also show an increase in hemoglobin glycation.

Over time, however, you will very likely become more insulin sensitive. What is happening is compensatory adaptation, with different short-term and long-term responses. In the short term, your body is trying to become a more efficient fat-burning machine, and GH is involved in this adaptation. But in the short term, GH leads to insulin resistance, probably via actions on muscle and fat cells. This gradually improves in the long term, possibly through a concomitant increase in liver insulin sensitivity and glycogen storage capacity.

This is somewhat similar to the response to GH therapy.

The figure below is from Johannsson et al. (1997). It shows what happened in terms of glucose metabolism when a group of obese men were administered recombinant GH for 9 months. The participants were aged 48–66, and were given in daily doses the equivalent to what would be needed to bring their GH levels to approximately what they were at age 20. For glucose, 5 mmol is about 90 mg, 5.5 is about 99, and 6 is about 108. GDR is glucose disposal rate; a measure of how quickly glucose is cleared from the blood.

As you can see, insulin sensitivity initially goes down for the GH group, and fasting blood glucose goes up quite a lot. But after 9 months the GH group has better insulin sensitivity. Their GDR is the same as in the placebo group, but with lower circulating insulin. The folks in the GH group also have significantly less body fat, and have better health markers, than those who took the placebo.

There is such a thing as sudden-onset type 2-like diabetes, but it is very rare (see Michael’s blog). Usually type 2 diabetes “telegraphs” its arrival through gradually increasing fasting blood glucose and HbA1c. However, those normally come together with other things, notably a decrease in HDL cholesterol and an increase in fasting triglycerides. Folks who do glycogen-depleting exercise and intermittent fasting tend to see the opposite – an increase in HDL cholesterol and a decrease in triglycerides.

So, if you are doing things that have the potential to increase your GH levels, a standard lipid panel can help you try to figure out whether insulin resistance is benign or not, if it happens.

By the way, GH and cortisol levels are correlated, which is often why some associate responses to glycogen-depleting exercise and intermittent fasting with esoteric nonsense that has no basis in scientific research like “adrenal fatigue”. Cortisol levels are meant to go up and down, but they should not go up and stay up while you are sitting down.

Avoid chronic stress, and keep on doing glycogen-depleting exercise and intermittent fasting; there is overwhelming scientific evidence that these things are good for you.

Do you lose muscle if you lift weights after a 24-hour fast? Probably not if you do that regularly

Compensatory adaptation (CA) is an idea that is useful in the understanding of how the body reacts to inputs like dietary intake of macronutrients and exercise. CA is a complex process, because it involves feedback loops, but it leads to adaptations that are fairly general, applying to a large cross-section of the population.

A joke among software developers is that the computer does exactly what you tell it to do, but not necessarily what you want it to do. Similarly, through CA your body responds exactly to the inputs you give it, but not necessarily in the way you would like it to respond. For example, a moderate caloric deficit may lead to slow body fat loss, while a very high caloric deficit may bring body fat loss to a halt.

Strength training seems to lead to various adaptations, which can be understood through the lens provided by CA. One of them is a dramatic increase in the ability of the body to store glycogen, in both liver and muscle. Glycogen is the main fuel used by muscle during anaerobic exercise. Regular strength training causes, over time, glycogen stores to more than double. And about 2.6 the amount of glycogen is also stored as water.

When one looks bigger and becomes stronger as a result of strength training, that is in no small part due to increases in glycogen and water stored. More glycogen stored in muscle leads to more strength, which is essentially a measure of one’s ability to move a certain amount of weight around. More muscle protein is also associated with more strength.

Thinking in terms of CA, the increase in the body’s ability to store glycogen is to be expected, as long as glycogen stores are depleted and replenished on a regular basis. By doing strength training regularly, you are telling your body that you need a lot of glycogen on a regular basis, and the body responds. But if you do not replenish your glycogen stores on a regular basis, you are also sending your body a conflicting message, which is that dietary sources of the substances used to make glycogen are not readily available. Among the substances that are used to make glycogen, the best seems to be the combination of fructose and glucose that one finds in fruits.

Let us assume a 160-lbs untrained person, John, who stored about 100 g of glycogen in his liver, and about 500 g in his muscle cells, before starting a strength training program. Let us assume, conservatively, that after 6 months of training he increased the size of his liver glycogen tank to 150 g. Muscle glycogen storage was also increased, but that is less relevant for the discussion in this post.

Then John fasted for 24 hours before a strength training session, just to see what would happen. While fasting he went about his business, doing light activities, which led to a caloric expenditure of about 100 calories per hour (equivalent to 2400 per day). About 20 percent of that, or 20 calories per hour, came from a combination of blood glucose and ketones. Contrary to popular belief, ketones can always be found in circulation. If only glucose were used, 5 g of glucose per hour would be needed to supply those 20 calories.

During the fast, John’s glucose needs, driven primarily by his brain’s needs, were met by conversion of liver glycogen to blood glucose. His muscle glycogen was pretty much “locked” during the fast; because he was doing only light activities, which rely primarily on fat as fuel. Muscle glycogen is “unlocked” through anaerobic exercise, of which strength training is an instance.

One of the roles of ketones is to spare liver glycogen, delaying the use of muscle protein to make glucose down the road, so the percentage of ketones in circulation in John’s body increased in a way that was inversely proportional to stored liver glycogen. According to this study, after 72 hours fasting about 25 percent of the body’s glucose needs are met by ketones. (This may be an underestimation.)

If we assume a linear increase in ketone concentration, this leads to a 0.69 percent increase in circulating ketones for every 2-hour period. (This is a simplification, as the increase is very likely nonlinear.) So, when we look at John’s liver glycogen tank, it probably went down in a way similar to that depicted on the figure below. The blue bars show liver glycogen at the end of each 2-hour period. The red bars show the approximate amount of glucose consumed during each 2-hour period. Glucose consumed goes down as liver glycogen decreases, because of the increase in blood ketones.

As you can see, after a 24-hour fast, John had about 35 g of glycogen left, which is enough for a few extra hours of fasting. At the 24-hour mark the body had no need to be using muscle protein to generate glucose. Maybe some of that happened, but probably not much if John was relaxed during the fast. (If he was stressed out, stress hormones would have increased blood glucose release significantly.) From the body’s perspective, muscle is “expensive”, whereas body fat is “cheap”. And body fat, converted to free fatty acids, is what is used to produce ketones during a fast.

Blood ketone concentration does not go up dramatically during a 24-hour fast, but it does after a 48-hour fast, when it becomes about 10 times higher. This major increase occurs primarily to spare muscle, including heart muscle. If the increase is much smaller during a 24-hour fast, one can reasonably assume that the body is not going to be using muscle during the fast. It can still rely on liver glycogen, together with a relatively small amount of ketones.

Then John did his strength training, after the 24-hour fast. When he did that, the muscles he used in the exercise session converted locally stored glycogen into lactate. A flood of lactate was secreted into the bloodstream, which was used by his liver to produce glucose and also to replenish liver glycogen a bit. Again, at this stage there was no need for John’s body to use muscle protein to generate glucose.

Counterintuitive as this may sound, the more different muscles John used, the more lactate was made available. If John did 20 sets of isolated bicep curls, for example, his body would not have released enough lactate to meet its glucose needs or replenish liver glycogen. As a result, stress hormones would go up a lot, and his body would send him some alarm signals. One of those signals is a feeling of “pins and needles”, which is sometimes confused with the symptoms of a heart attack.

John worked out various muscle groups for 30 minutes or so, and he did not even feel fatigued. He felt energetic, in part because his blood glucose went up a lot, peaking at 150 mg/dl, to meet muscle needs. This elevated blood glucose was caused by his liver producing blood glucose based on lactate and releasing it into his blood. Muscle glycogen was depleted as a result of that.

Do you lose any muscle if you lift weights after a 24-hour fast?

I don’t think so, if you deplete your glycogen stores by doing strength training on a regular basis, and also replenish them on a regular basis. In fact, your liver glycogen tank will increase in size, and you may find yourself being able to fast for many hours without feeling hungry.

You will feel hungry after the strength training session following the fast though; probably ravenous.

References

Brooks, G.A., Fahey, T.D., & Baldwin, K.M. (2005). Exercise physiology: Human bioenergetics and its applications. Boston, MA: McGraw-Hill.

Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics.

Blood glucose levels in birds are high yet HbA1c levels are low: Can vitamin C have anything to do with this?

Blood glucose levels in birds are often 2-4 times higher than those in mammals of comparable size. Yet birds often live 3 times longer than mammals of comparable size. This is paradoxical. High glucose levels are generally associated with accelerated senescence, but birds seem to age much slower than mammals. Several explanations have been proposed for this, one of which is related to the formation of advanced glycation endproducts (AGEs).

Glycation is a process whereby sugar molecules “stick” to protein or fat molecules, impairing their function. Glycation leads to the formation of AGEs, which seem to be associated with a host of diseases, including diabetes, and to be implicated in accelerated aging (or “ageing”, with British spelling).

The graphs below, from Beuchat & Chong (1998), show the glucose levels (at rest and prior to feeding) and HbA1c levels (percentage of glycated hemoglobin) in birds and mammals. HbA1c is a measure of the degree of glycation of hemoglobin, a protein found in red blood cells. As such HbA1c (given in percentages) is a good indicator of the rate of AGE formation within an animal’s body.

The glucose levels are measured in mmol/l; they should be multiplied by 18 to obtain the respective measures in mg/dl. For example, the 18 mmol/l glucose level for the Anna’s (a hummingbird species) is equivalent to 324 mg/dl. Even at that high level, well above the level of a diabetic human, the Anna’s hummingbird species has an HbA1c of less than 5, which is lower than that for most insulin sensitive humans.

How can that be?

There are a few possible reasons. Birds seem to have evolved better mechanisms to control cell permeability to glucose, allowing glucose to enter cells very selectively. Birds also seem to have a higher turnover of cells where glycation and thus AGE formation results. The lifespan of red blood cells in birds, for example, is only 50 to 70 percent that of mammals.

But one of the most interesting mechanisms is vitamin C synthesis. Not only is vitamin C a powerful antioxidant, but it also has the ability to reversibly bind to proteins at the sites where glycation would occur. That is, vitamin C has the potential to significantly reduce glycation. The vast majority of birds and mammals can synthesize vitamin C. Humans are an exception. They have to get it from their diet.

This may be one of the many reasons why isolated human groups with traditional diets high in fruits and starchy tubers, which lead to temporary blood glucose elevations, tend to have good health. Fruits and starchy tubers in general are good sources of vitamin C.

Grains and seeds are not.

References

Beuchat, C.A., & Chong, C.R. (1998). Hyperglycemia in hummingbirds and its consequences for hemoglobin glycation. Comparative Biochemistry and Physiology Part A, 120(3), 409–416.

Holmes D.J., Flückiger, R., & Austad, S.N. (2001). Comparative biology of aging in birds: An update. Experimental Gerontology, 36(4), 869-883.

Our body’s priority is preventing hypoglycemia, not hyperglycemia

An adult human has about 5 l of blood in circulation. Considering a blood glucose concentration of 100 mg/dl, this translates into a total amount of glucose in the blood of about 5 g (5 l x 0.1 g / 0.1 l). That is approximately a teaspoon of glucose. If a person’s blood glucose goes down to about half of that, the person will enter a state of hypoglycemia. Severe and/or prolonged hypoglycemia can cause seizures, comma, and death.

In other words, the disappearance of about 2.5 g of glucose from the blood will lead to hypoglycemia. Since 2.5 g of glucose yields about 10 calories, it should be easy to see that it does not take much to make someone hypoglycemic in the absence of compensatory mechanisms. An adult will consume on average 6 to 9 times as many calories just sitting quietly, and a proportion of those calories will come from glucose.

While hypoglycemia has severe negative health effects in the short term, including the most severe of all - death, hyperglycemia has primarily long-term negative health effects. Given this, it is no surprise that our body’s priority is to prevent hypoglycemia, not hyperglycemia.

The figure below, from the outstanding book by Brooks and colleagues (2005), shows two graphs. The graph at the top shows the variation of arterial glucose in response to exercise. The graph at the bottom shows the variation of whole-body and muscle glucose uptake, plus hepatic glucose production, in response to exercise. The full reference to the Brooks and colleagues book is at the end of this post.

Note how blood glucose increases dramatically as the intensity of the exercise session increases, which means that muscle tissue consumption of glucose is also increasing. This is particularly noticeable as arm exercise is added to leg exercise, bringing the exercise intensity to 82 percent of maximal capacity. This blood glucose elevation is similar to the elevation one would normally see in response to all-out sprinting and weight training within the anaerobic range (with enough weight to allow only 6 to 12 repetitions, or a time under tension of about 30 to 70 seconds).

The dashed line at the bottom graph represents whole-body glucose uptake, including what would be necessary for the body to function in the absence of exercise. This is why whole-body glucose uptake is higher than muscle glucose uptake induced by exercise; the latter was measured through a glucose tracing method. The top of the error bars above the points on the dashed line represent hepatic glucose production, which is always ahead of whole-body glucose uptake. This is our body doing what it needs to do to prevent hypoglycemia.

One point that is important to make here is that at the beginning of an anaerobic exercise session muscle uses up primarily local glycogen stores (not liver glycogen stores), and can completely deplete them in a very localized fashion. Muscle glycogen stores add up to 500 g, but intense exercise depletes glycogen stores locally, only within the muscles being used. Still, muscle glycogen use generates lactate as a byproduct, which is then used by the liver to produce glucose (gluconeogenesis) to prevent hypoglycemia. The liver also makes some glycogen (glycogenesis) during this time. This means that it is not only pre-exercise liver glycogen that is being used to maintain blood glucose levels above whole-body glucose uptake. This makes sense, since the liver stores only about 100 g of glycogen.

The need to prevent hypoglycemia at all costs is the main reason why there are several hormones that increase blood glucose, while apparently there is only one that decreases blood glucose. Examples of hormones that increase blood glucose are cortisol, adrenaline, noradrenaline, growth hormone, and, notably, glucagon. The only hormone that decreases blood glucose levels in a significant way is insulin. These hormones do not increase or decrease blood glucose directly; they signal to various tissues to either secrete or absorb glucose.

Evolution typically prioritizes processes that have a higher impact on reproductive success, and one must be alive to successfully reproduce. Hypoglycemia causes death. Often those processes that have a significant effect on reproductive success rely on redundant mechanisms. So our evolved mechanisms to deal with hypoglycemia are redundant. Evolution is not an engineer; it is a tinkerer!

What about hyperglycemia – doesn’t it cause death? Well, not in the short term, so related selection pressures were fairly small compared to those associated with hypoglycemia. Besides, there were no foods rich in refined carbohydrates and sugars in the Paleolithic - e.g., white bread, bagels, doughnuts, pasta, cereals, fruit juices, regular sodas, table sugar. Those are the foods that contribute the most to hyperglycemia.

Reference:

Brooks, G.A., Fahey, T.D., & Baldwin, K.M. (2005). Exercise physiology: Human bioenergetics and its applications. Boston, MA: McGraw-Hill.

Exercise and blood glucose levels: Insulin and glucose responses to exercise

The notion that exercise reduces blood glucose levels is widespread. That notion is largely incorrect. Exercise appears to have a positive effect on insulin sensitivity in the long term, but also increases blood glucose levels in the short term. That is, exercise, while it is happening, leads to an increase in circulating blood glucose. In normoglycemic individuals, that increase is fairly small compared to the increase caused by consumption of carbohydrate-rich foods, particularly foods rich in refined carbohydrates and sugars.

The figure below, from the excellent book by Wilmore and colleagues (2007), shows the variation of blood insulin and glucose in response to an endurance exercise session. The exercise session’s intensity was at 65 to 70 percent of the individuals’ maximal capacity (i.e., their VO2 max). The session lasted 180 minutes, or 3 hours. The full reference to the book by Wilmore and colleagues is at the end of this post.

As you can see, blood insulin levels decreased markedly in response to the exercise bout, in an exponential decay fashion. Blood glucose increased quickly, from about 5.1 mmol/l (91.8 mg/dl) to 5.4 mmol/l (97.2 mg/dl), before dropping again. Note that blood glucose levels remained somewhat elevated throughout the exercise session. But, still, the elevation was fairly small in the participants, which were all normoglycemic. A couple of bagels would easily induce a rise to 160 mg/dl in about 45 minutes in those individuals, and a much larger “area under the curve” glucose response than exercise.

So what is going on here? Shouldn’t glucose levels go down, since muscle is using glucose for energy?

No, because the human body is much more “concerned” with keeping blood glucose levels high enough to support those cells that absolutely need glucose, such as brain and red blood cells. During exercise, the brain will derive part of its energy from ketones, but will still need glucose to function properly. In fact, that need is critical for survival, and may be seen as a bit of an evolutionary flaw. Hypoglycemia, if maintained for too long, will lead to seizures, coma, and death.

Muscle tissue will increase its uptake of free fatty acids and ketones during exercise, to spare glucose for the brain. And muscle tissue will also consume glucose, in part for glycogenesis; that is, for making muscle glycogen, which is being depleted by exercise. In this sense, we can say that muscle tissue is becoming somewhat insulin resistant, because it is using more free fatty acids and ketones for energy, and thus less glucose. Another way of looking at this, however, which is favored by Wilmore and colleagues (2007), is that muscle tissue is becoming more insulin sensitive, because it is still taking up glucose, even though insulin levels are dropping.

Truth be told, the discussion in the paragraph above is mostly academic, because muscle tissue can take up glucose without insulin. Insulin is a hormone that allows the pancreas, its secreting organ, to communicate with two main organs – the liver and body fat. (Yes, body fat can be seen as an “organ”, since it has a number of endocrine functions.) Insulin signals to the liver that it is time to take up blood glucose and either make glycogen (to be stored in the liver) or fat with it (secreting that fat in VLDL particles). Insulin signals to body fat that it is time to take up blood glucose and fat (e.g., packaged in chylomicrons) and make more body fat with it. Low insulin levels, during exercise, will do the opposite, leading to low glucose uptake by the liver and an increase in body fat catabolism.

Resistance exercise (e.g., weight training) induces much higher glucose levels than endurance exercise; and this happens even when one has fasted for 20 hours before the exercise session. The reason is that resistance exercise leads to the conversion of muscle glycogen into energy, releasing lactate in the process. Lactate is in turn used by muscle tissues as a source of energy, helping spare glycogen. It is also used by the liver for production of glucose through gluconeogenesis, which significantly elevates blood glucose levels. That hepatic glucose is then used by muscle tissues to replenish their depleted glycogen stores. This is known as the Cori cycle.

Exercise seems to lead, in the long term, to insulin sensitivity; but through a fairly complex and longitudinal process that involves the interaction of many hormones. One of the mechanisms may be an overall reduction in insulin levels, leading to increased insulin sensitivity as a compensatory adaptation. In the short term, particularly while it is being conducted, exercise nearly always increases blood glucose levels. Even in the first few months after the beginning of an exercise program, blood glucose levels may increase. If a person who was on a low carbohydrate diet started a 3-month exercise program, it is quite possible that the person’s average blood glucose would go up a bit. If low carbohydrate dieting began together with the exercise program, then average blood glucose might drop significantly, because of the acute effect of this type of dieting on average blood glucose.

Still exercise is health-promoting. The combination of the long- and short-term effects of exercise appears to lead to an overall slowing down of the progression of insulin resistance with age. This is a good thing.

Reference:

Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics.

Fructose in fruits may be good for you, especially if you are low in glycogen

Excessive dietary fructose has been shown to cause an unhealthy elevation in serum triglycerides. This and other related factors are hypothesized to have a causative effect on the onset of the metabolic syndrome. Since fructose is found in fruits (see table below, from Wikipedia; click to enlarge), there has been some concern that eating fruit may cause the metabolic syndrome.

Vegetables also have fructose. Sweet onions, for example, have more free fructose than peaches, on a gram-adjusted basis. Sweet potatoes have more sucrose than grapes (but much less overall sugar), and sucrose is a disaccharide derived from glucose and fructose. Sucrose is broken down to fructose and glucose in the human digestive tract.

Dr. Robert Lustig has given a presentation indicting fructose as the main cause of the metabolic syndrome, obesity, and related diseases. Yet, even he pointed out that the fructose in fruits is pretty harmless. This is backed up by empirical research.

The problem is over-consumption of fructose in sodas, juices, table sugar, and other industrial foods with added sugar. Table sugar is a concentrated form of sucrose. In these foods the fructose content is unnaturally high; and it comes in an easily digestible form, without any fiber or health-promoting micronutrients (vitamins and minerals).

Dr. Lustig’s presentation is available from this post by Alan Aragon. At the time of this writing, there were over 450 comments in response to Aragon’s post. If you read the comments you will notice that they are somewhat argumentative, as if Lustig and Aragon were in deep disagreement with one other. The reality is that they agree on a number of issues, including that the fructose found in fruits is generally healthy.

Fruits are among the very few natural plant foods that have been evolved to be eaten by animals, to facilitate the dispersion of the plants’ seeds. Generally and metaphorically speaking, plants do not “want” animals to eat their leaves, seeds, or roots. But they “want” animals to eat their fruits. They do not “want” one single animal to eat all of their fruits, which would compromise seed dispersion and is probably why fruits are not as addictive as doughnuts.

From an evolutionary standpoint, the idea that fruits can be unhealthy is somewhat counterintuitive. Given that fruits are made to be eaten, and that dead animals do not eat, it is reasonable to expect that fruits must be good for something in animals, at least in one important health-related process. If yes, what is it?

Well, it turns out that fructose, combined with glucose, is a better fuel for glycogen replenishment than glucose alone; in the liver and possibly in muscle, at least according to a study by Parniak and Kalant (1988). A downside of this study is that it was conduced with isolated rat liver tissue; this is a downside in terms of the findings’ generalization to humans, but helped the researchers unveil some interesting effects. The full reference and a link to the full-text version are at the end of this post.

The Parniak and Kalant (1988) study also suggests that glycogen synthesis based on fructose takes precedence over triglyceride formation. Glycogen synthesis occurs when glycogen reserves are depleted. The liver of an adult human stores about 100 g of glycogen, and muscles store about 500 g. An intense 30-minute weight training session may use up about 63 g of glycogen, not much but enough to cause some of the responses associated with glycogen depletion, such as an acute increase in adrenaline and growth hormone secretion.

Liver glycogen is replenished in a few hours. Muscle glycogen takes days. Glycogen synthesis is discussed at some length in this excellent book by Jack H. Wilmore, David L. Costill, and W. Larry Kenney. That discussion generally assumes no blood sugar metabolism impairment (e.g., diabetes), as does this post.

If one’s liver glycogen tank is close to empty, eating a couple of apples will have little to no effect on body fat formation. This will be so even though two apples have close to 30 g of carbohydrates, more than 20 g of which being from sugars. The liver will grab everything for itself, to replenish its 100 g glycogen tank.

In the Parniak and Kalant (1988) study, when glucose and fructose were administered simultaneously, glycogen synthesis based on glucose was increased by more than 200 percent. Glycogen synthesis based on fructose was increased by about 50 percent. In fruits, fructose and glucose come together. Again, this was an in vitro study, with liver cells obtained after glycogen depletion (the rats were fasting).

What leads to glycogen depletion in humans? Exercise does, both aerobic and anaerobic. So does intermittent fasting.

What happens when we consume excessive fructose from sodas, juices, and table sugar? The extra fructose, not used for glycogen replenishment, is converted into fat by the liver. That fat is packaged in the form of triglycerides, which are then quickly secreted by the liver as small VLDL particles. The VLDL particles deliver their content to muscle and body fat tissue, contributing to body fat accumulation. After delivering their cargo, small VLDL particles eventually become small-dense LDL particles; the ones that can potentially cause atherosclerosis.

Reference:

Parniak, M.A. and Kalant, N. (1988). Enhancement of glycogen concentrations in primary cultures of rat hepatocytes exposed to glucose and fructose. Biochemical Journal, 251(3), 795–802.

Postprandial glucose levels, HbA1c, and arterial stiffness: Compared to glucose, lipids are not even on the radar screen

Postprandial glucose levels are the levels of blood glucose after meals. In Western urban environments, the main contributors to elevated postprandial glucose are foods rich in refined carbohydrates and sugars. While postprandial glucose levels may vary somewhat erratically, they are particularly elevated in the morning after breakfast. The main reason for this is that breakfast, in Western urban environments, is typically very high in refined carbohydrates and sugars.

HbA1c, or glycated hemoglobin, is a measure of average blood glucose over a period of a few months. Blood glucose glycates (i.e., sticks to) hemoglobin, a protein found in red blood cells. Red blood cells are relatively long-lived, lasting approximately 3 months. Thus HbA1c (given in percentages) is a good indicator of average blood glucose levels, if you don’t suffer from anemia or a few other blood abnormalities.

Based on HbA1c, one can then estimate his or her average blood glucose level for the previous 3 months or so before the test, using one of the following equations, depending on whether the measurement is in mg/dl or mmol/l.

Average blood glucose (mg/dl) = 28.7 × HbA1c − 46.7
Average blood glucose (mmol/l) = 1.59 × HbA1c − 2.59

Elevated blood glucose levels cause damage in the body primarily through glycation, which leads to the formation of advanced glycation endproducts (AGEs). Given this, HbA1c can be seen as a proxy for the level of damage done by elevated blood glucose levels to various body tissues. This damage occurs over time; often after many years of high blood glucose levels. It includes kidney damage, neurological damage, cardiovascular damage, and damage to the retina.

Most regular blood exams focus on fasting blood glucose as a measure of glucose metabolism status. Many medical practitioners have as a target a fasting blood glucose level of 125 mg/dl (7 mmol/l) or less, and largely disregard postprandial glucose levels or HbA1c in their management of glucose metabolism. Leiter and colleagues (2005; full reference at the end of this post) showed that this focus on fasting blood glucose is a mistake. They are not alone; many others made this point, including some very knowledgeable bloggers who focus on diabetes (see “Interesting links” section of this blog). Leiter and colleagues (2005) also provided some interesting graphs and figures, including eye-opening correlations between various variables and arterial stiffness. The figure below (click to enlarge) shows the contribution of postprandial glucose to HbA1c.

Note that the lower the HbA1c is in the figure (horizontal axis), the higher is the postprandial glucose contribution to HbA1c. And, the lower the HbA1c, the closer the individuals are to what one could consider having a perfectly normal HbA1c level (around 5 percent). That is, only for individuals whose HbA1c levels are very high, fasting blood glucose levels are relatively reliable measures of the tissue damage done be elevated blood glucose levels.

The table below (click to enlarge) shows P values associated with the impact of various variables (listed on the leftmost column) on arterial stiffness. This measure, arterial stiffness, is strongly associated with an increased risk of cardiovascular events. Look at the middle column showing P values adjusted for age and height. The lower the P value, the more a variable affects arterial stiffness. The variable with the lowest P value by far is 2-hour postprandial blood glucose; the blood glucose levels measured 2 hours after meals.

Fasting glucose levels were reported to be statistically insignificant because of the P = 0.049, in terms of their effect on arterial stiffness, but this P value is actually significant, although barely, at the 0.05 level (95 percent confidence). Interestingly, the following measures are not even on the radar screen, as far as arterial stiffness is concerned: systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting insulin levels.

What about the lipid hypothesis, and the “bad” LDL cholesterol!? This study is telling us that these are not very relevant for arterial stiffness when we control for the effect of blood glucose measures. Not even fasting insulin levels matters much! Wait, not even HDL!!! A high HDL has been definitely shown to be protective, but when we look at the relative magnitude of various effects, the story is a bit different. A high HDL’s protective effect exists, but it is dwarfed by the negative effect of high blood glucose levels, especially after meals, in the context of cardiovascular disease.

What all this points at is what we could call a postprandial glucose hypothesis: Lower your postprandial glucose levels, and live a longer, healthier life! And, by the way, if your postprandial glucose levels are under control, lipids do not matter much! Or maybe your lipids will fall into place, without any need for statin drugs, after your postprandial glucose levels are under control. One way or another, the outcome will be a positive one. That is what the data from this study is telling us.

How do you lower your postprandial glucose levels?

A good way to start is to remove foods rich in refined carbohydrates and sugars from your diet. Almost all of these are foods engineered by humans with the goal of being addictive; they usually come in boxes and brightly colored plastic wraps. They are not hard to miss. They are typically in the central aisles of supermarkets. The sooner you remove them from your diet, the better. The more completely you do this, the better.

Note that the evidence discussed in this post is in connection with blood glucose levels, not glucose metabolism per se. If you have impaired glucose metabolism (e.g., diabetes type 2), you can still avoid a lot of problems if you effectively control your blood glucose levels. You may have to be a bit more aggressive, adding low carbohydrate dieting (as in the Atkins or Optimal diets) to the removal of refined carbohydrates and sugars from your diet; the latter is in many ways similar to adopting a Paleolithic diet. You may have to take some drugs, such as Metformin (a.k.a. Glucophage). But you are certainly not doomed if you are diabetic.

Reference:

Leiter, L.A., Ceriello, A., Davidson, J.A., Hanefeld, M., Monnier, L., Owens, D.R., Tajima, N., & Tuomilehto, J. (2005). Postprandial glucose regulation: New data and new implications. Clinical Therapeutics, 27(2), S42-S56.

Blood glucose variations in normal individuals: A chaotic mess

I love statistics. But statistics is the science that will tell you that each person in a group of 20 people ate half a chicken per week over six months, until you realize that 10 died because they ate nothing while the other 10 ate a full chicken every week.

Statistics is the science that will tell you that there is an “association” between these two variables: my weight from 1 to 20 years of age, and the price of gasoline during that period. These two variables are indeed highly correlated, by neither has influenced the other in any way.

This is why I often like to see the underlying numbers when I am told that such and such health measure on average is this or that, or that this or that disease is associated with elevated consumption of whatever. Statistical results must be interpreted carefully. Lying with statistics is very easy.

A case in point is that of blood glucose variations among normal individuals. Try plotting them on graphs. What do you see? A chaotic mess, even when the individuals are pre-screened to exclude anybody with blood glucose abnormalities that would even hint at pre-diabetes. You see wild fluctuations that, while not going up to levels like 200 mg/dl, are much less predictable than many people are told they should be.

Blood glucose levels are influenced by so many factors (Elliott & Elliott, 2009) that I would be surprised if they were as smooth as those in graphs that are frequently used to show how blood glucose is supposed to vary in healthy individuals. Often we see a flat line up until the time of a meal, when the line curves up rapidly and then goes down quickly. It usually peaks at around 140 mg/dl, dropping well below 120 mg/dl after 2 hours.

Those smooth graphs are usually obtained through algorithms that have statistical methods at their core. The algorithms are designed to generate a smooth representations of scattered or disorganized data points. A little bit like the algorithms in software tools that plot best-fit regression curves passing through scattered points (e.g., warppls.com).

The picture below (click on it to enlarge) is from a 2006 symposium presentation by Prof. J.S. Christiansen, who is a widely cited diabetes researcher. The whole presentation is available from: www.diabetes-symposium.org. It shows the blood glucose variations of 21 young and normal individuals, based on data collected over a period of 2 days. Each individual is represented by a different color. The points on each curve are actually averages of two blood glucose measurements; the original measurements themselves vary even more chaotically.

As you can see from the picture above, each individual has a unique set of responses to main meals, which are represented by the three main blood glucose peaks. Overall, blood glucose levels vary from about 50 to 170 mg/dl, and in several cases remain above 120 mg/dl after 2 hours since a large meal. They vary somewhat chaotically during the night as well, often getting up to around 110 mg/dl.

And these are only 21 individuals, not 100 or 1000. Again, these individuals were all normal (i.e., normoglycemic, in medical research parlance), with an average glycated hemoglobin (HbA1c) of 5 percent, and a range of variation of HbA1c of 4.3 to 5.4 percent.

We can safely assume that these individuals were not on a low carbohydrate diet. The spikes in blood glucose after meals suggest that they were eating foods loaded with refined carbohydrates and/or sugars, particularly for breakfast. So, we can also safely assume that they were somewhat "desensitized" (in terms of glucose response) to those types of foods. Someone who had been on a low carbohydrate diet for a while, and who would thus be more sensitive, would have had even wilder blood glucose variations in response to the same meals.

Many people measure their glucose levels throughout the day with portable glucometers, and quite a few are likely to self-diagnose as pre-diabetics when they see something that they think is a “red flag”. Examples are a blood glucose level peaking at 165 mg/dl, or remaining above 120 mg/dl after 2 hours passed since a meal. Another example is a level of 110 mg/dl when they wake up very early to go to work, after several hours of fasting.

As you can see from the picture above, these “red flag” events do occur in young normoglycemic individuals.

If seeing “red flags” helps people remove refined carbohydrates and sugars from their diet, then fine.

But it may also cause them unnecessary chronic stress, and stress can kill.

Reference:

Elliott, W.H., & Elliott, D.C. (2009). Biochemistry and molecular biology. 4th Edition. New York: NY: Oxford University Press.

Blood glucose control before age 55 may increase your chances of living beyond 90

I have recently read an interesting study by Yashin and colleagues (2009) at Duke University’s Center for Population Health and Aging. (The full reference to the article, and a link, are at the end of this post.) This study is a gem with some rough edges, and some interesting implications.

The study uses data from the Framingham Heart Study (FHS). The FHS, which started in the late 1940s, recruited 5209 healthy participants (2336 males and 2873 females), aged 28 to 62, in the town of Framingham, Massachusetts. At the time of Yashin and colleagues’ article publication, there were 993 surviving participants.

I rearranged figure 2 from the Yashin and colleagues article so that the two graphs (for females and males) appeared one beside the other. The result is shown below (click on it to enlarge); the caption at the bottom-right corner refers to both graphs. The figure shows the age-related trajectory of blood glucose levels, grouped by lifespan (LS), starting at age 40.

As you can see from the figure above, blood glucose levels increase with age, even for long-lived individuals (LS > 90). The increases follow a U-curve (a.k.a. J-curve) pattern; the beginning of the right side of a U curve, to be more precise. The main difference in the trajectories of the blood glucose levels is that as lifespan increases, so does the width of the U curve. In other words, in long-lived people, blood glucose increases slowly with age; particularly up to 55 years of age, when it starts increasing more rapidly.

Now, here is one of the rough edges of this study. The authors do not provide standard deviations. You can ignore the error bars around the points on the graph; they are not standard deviations. They are standard errors, which are much lower than the corresponding standard deviations. Standard errors are calculated by dividing the standard deviations by the square root of the sample sizes for each trajectory point (which the authors do not provide either), so they go up with age since progressively smaller numbers of individuals reach advanced ages.

So, no need to worry if your blood glucose levels are higher than those shown on the vertical axes of the graphs. (I will comment more on those numbers below.) Not everybody who lived beyond 90 had a blood glucose of around 80 mg/dl at age 40. I wouldn't be surprised if about 2/3 of the long-lived participants had blood glucose levels in the range of 65 to 95 at that age.

Here is another rough edge. It is pretty clear that the authors’ main independent variable (i.e., health predictor) in this study is average blood glucose, which they refer to simply as “blood glucose”. However, the measure of blood glucose in the FHS is a very rough estimation of average blood glucose, because they measured blood glucose levels at random times during the day. These measurements, when averaged, are closer to fasting blood glucose levels than to average blood glucose levels.

A more reliable measure of average blood glucose levels is that of glycated hemoglobin (HbA1c). Blood glucose glycates (i.e., sticks to, like most sugary substances) hemoglobin, a protein found in red blood cells. Since red blood cells are relatively long-lived, with a turnover of about 3 months, HbA1c (given in percentages) is a good indicator of average blood glucose levels (if you don’t suffer from anemia or a few other blood abnormalities). Based on HbA1c, one can then estimate his or her average blood glucose level for the previous 3 months before the test, using one of the following equations, depending on whether the measurement is in mg/dl or mmol/l.

    Average blood glucose (mg/dl) = 28.7 × HbA1c − 46.7

    Average blood glucose (mmol/l) = 1.59 × HbA1c − 2.59

The table below, from Wikipedia, shows average blood glucose levels corresponding to various HbA1c values. As you can see, they are generally higher than the corresponding fasting blood glucose levels would normally be (the latter is what the values on the vertical axes of the graphs above from Yashin and colleagues’ study roughly measure). This is to be expected, because blood glucose levels vary a lot during the day, and are often transitorily high in response to food intake and fluctuations in various hormones. Growth hormone, cortisol and noradrenaline are examples of hormones that increase blood glucose. Only one hormone effectively decreases blood glucose levels, insulin, by stimulating glucose uptake and storage as glycogen and fat.

Nevertheless, one can reasonably expect fasting blood glucose levels to have been highly correlated with average blood glucose levels in the sample. So, in my opinion, the graphs above showing age-related blood glucose trajectories are still valid, in terms of their overall shape, but the values on the vertical axes should have been measured differently, perhaps using the formulas above.

Ironically, those who achieve low average blood glucose levels (measured based on HbA1c) by adopting a low carbohydrate diet (one of the most effective ways) frequently have somewhat high fasting blood glucose levels because of physiological (or benign) insulin resistance. Their body is primed to burn fat for energy, not glucose. Thus when growth hormone levels spike in the morning, so do blood glucose levels, as muscle cells are in glucose rejection mode. This is a benign version of the dawn effect (a.k.a. dawn phenomenon), which happens with quite a few low carbohydrate dieters, particularly with those who are deep in ketosis at dawn.

Yashin and colleagues also modeled relative risk of death based on blood glucose levels, using a fairly sophisticated mathematical model that takes into consideration U-curve relationships. What they found is intuitively appealing, and is illustrated by the two graphs at the bottom of the figure below. The graphs show how the relative risks (e.g., 1.05, on the topmost dashed line on the both graphs) associated with various ranges of blood glucose levels vary with age, for both females and males.

What the graphs above are telling us is that once you reach old age, controlling for blood sugar levels is not as effective as doing it earlier, because you are more likely to die from what the authors refer to as “other causes”. For example, at the age of 90, having a blood glucose of 150 mg/dl (corrected for the measurement problem noted earlier, this would be perhaps 165 mg/dl, from HbA1c values) is likely to increase your risk of death by only 5 percent. The graphs account for the facts that: (a) blood glucose levels naturally increase with age, and (b) fewer people survive as age progresses. So having that level of blood glucose at age 60 would significantly increase relative risk of death at that age; this is not shown on the graph, but can be inferred.

Here is a final rough edge of this study. From what I could gather from the underlying equations, the relative risks shown above do not account for the effect of high blood glucose levels earlier in life on relative risk of death later in life. This is a problem, even though it does not completely invalidate the conclusion above. As noted by several people (including Gary Taubes in his book Good Calories, Bad Calories), many of the diseases associated with high blood sugar levels (e.g., cancer) often take as much as 20 years of high blood sugar levels to develop. So the relative risks shown above underestimate the effect of high blood glucose levels earlier in life.

Do the long-lived participants have some natural protection against accelerated increases in blood sugar levels, or was it their diet and lifestyle that protected them? This question cannot be answered based on the study.

Assuming that their diet and lifestyle protected them, it is reasonable to argue that: (a) if you start controlling your average blood sugar levels well before you reach the age of 55, you may significantly increase your chances of living beyond the age of 90; (b) it is likely that your blood glucose levels will go up with age, but if you can manage to slow down that progression, you will increase your chances of living a longer and healthier life; (c) you should focus your control on reliable measures of average blood glucose levels, such as HbA1c, not fasting blood glucose levels (postprandial glucose levels are also a good option, because they contribute a lot to HbA1c increases); and (d) it is never too late to start controlling your blood glucose levels, but the more you wait, the bigger is the risk.

References:

Taubes, G. (2007). Good calories, bad calories: Challenging the conventional wisdom on diet, weight control, and disease. New York, NY: Alfred A. Knopf.

Yashin, A.I., Ukraintseva, S.V., Arbeev, K.G., Akushevich, I., Arbeeva, L.S., & Kulminski, A.M. (2009). Maintaining physiological state for exceptional survival: What is the normal level of blood glucose and does it change with age? Mechanisms of Ageing and Development, 130(9), 611-618.

Insulin responses to foods rich in carbohydrates and protein

Insulin is often presented as a hormone that is at the core of the diseases of civilization, particularly because of the insulin response elicited by foods rich in refined carbohydrates and sugars. What is often not mentioned is that protein also elicits an insulin response and so do foods where carbohydrates are mixed with fat. Sometimes the insulin responses are way more than one would expect based on the macronutrient compositions of the foods.

Holt et al. (1997; full reference at the end of this post) conducted a classic study of insulin responses. This study has been widely cited, and paints an interesting picture of differences in insulin responses to various foods. But you have to be careful where you look. There has been some confusion about the results because of the way they are often reported in places like Wikipedia and on various Internet sites that refer to the study.

The key thing to bear in mind when reviewing this study is that the amounts of food used were designed to have the same calorie content: 1000 kJ or 240 kcal (i.e., 240 calories). This led to wild variations in the size of the portions that are compared and their weight in grams. Also, some of the food portions are probably not what people usually eat in one sitting.

In Holt et al.’s (1997) study the participants were 41 lean and healthy university students. They were fed 1000 kJ (240 kcal) portions of the test foods on separate mornings after a 10-hour fast overnight. Blood insulin levels were measured at different times within a 120-minute period after each meal. An insulin score was then calculated from the area under the insulin response curve for each food; white bread was used as the reference food.

Part of Table 2 on page 1267 is shown below (the full text version of the paper is linked at the end of this post), just to illustrate the types and amounts of food served, and the macronutrient breakdown for each food. I hope you can see what I meant when I said that some of the food portions are probably not what people usually eat in one sitting. I don’t think it would be hard to find someone who would eat 158 g of beef steak in one sitting, but 333 g of fish is a little more difficult. Fish has a higher proportion of protein than beef steak, and thus is more satiating. The same goes for 625 g of orange, about 6 oranges. Foods that have more fat have more calories per gram; hence the smaller portions served for high-fat foods.

Table 4 of the article is a bit long, so I am providing it in two parts below. AUC stands for “area under the curve”. As you can see, for isocaloric portions of different foods (i.e., with the same amount of calories), there is a huge variation in insulin response. The insulin AUCs are shown on the second numeric column from the left. Also note that the insulin responses (AUC) for white bread varied in different meals. This complicates things a bit, but at least provides a more realistic view of the responses since each participant served as his or her own control.

Look at the third column from the right, which shows the insulin responses per gram of each food, compared with the response to white bread, always shown at the top for each group of related foods (e.g., protein-rich foods). The gram-adjusted response for whole-meal bread is rather high, and so is the glucose response. The gram-adjusted insulin response to potatoes is less than one-third of the response to white bread, even though the non-gram-adjusted glucose response is higher. The insulin response to beef is also less than one-third of the response to white bread, gram-for-gram. Even cheese leads to a gram-adjusted response that is about half the one for white bread, and I don’t think many people will eat the same amount of cheese in one sitting as they would do with white bread.

In summary, insulin responses to protein-rich foods are often 50 to 70 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods. Also, insulin responses to unrefined carbohydrate-rich foods (e.g., potato, fruits) are often 70 to 90 percent lower than responses to equivalent amounts of refined carbohydrate-rich foods.

Why do insulin levels go up in response to dietary protein?

One of the reasons is that insulin is needed for tissue protein synthesis. That is, increased circulating protein (as amino acids) and insulin have a net anabolic effect, promoting muscle growth and inhibiting muscle breakdown. (Muscle protein synthesis and breakdown happen all the time; the net effect defines whether muscle grows or shrinks.) In this respect, insulin acts in conjunction with other hormones, such as growth hormone and insulin-like growth factor 1.

Reference:

Holt, S.H., Miller, J.C., & Petocz, P. (1997). An insulin index of foods: The insulin demand generated by 1000-kJ portions of common foods. American Journal of Clinical Nutrition, 66, 1264-1276.