Should you drink your coffee filtered?

Coffee is one of the most widely consumed beverages in the world. Arguably a key reason for this is that coffee has psychoactive properties that we may be hardwired to value, even if subconsciously. For example, it increases alertness; possibly a fitness-enhancing effect in our evolutionary past. Here the term “fitness” in “fitness-enhancing effect” means “reproductive success”, and does not mean having great athletic ability or having shredded abs.

The two most common sources of coffee beans, which are roasted and ground prior to brewing, are the widely favored Coffea arabica, and the "robusta" form Coffea canephora. The arabica form accounts for 80 percent or so of world consumption. The graph below, from a study by Bonita and colleagues (), shows the per capita consumption of coffee in various countries. As you can see, Scandinavian countries are big consumers.



Most people probably drink filtered coffee. However, there are many unfiltered coffee preparation methods that are also widely used. Greek coffee, Turkish coffee, coffee prepared with a French press, and “cowboy coffee” are all unfiltered.

In the photo below (from: Goldenstate.wordpress.com), illustrating cowboy coffee, note that the coffee pot is placed near but not over the fire.



What is “cowboy coffee”? This method of preparation has many variations. A simple one involves mixing ground coffee with hot water, and then keeping the coffee simmering on very low fire for a while. It is called cowboy coffee due to its association with coffee drank by cowboys around a campfire.

After brewed, coffee tends to rise and spill out of the pot if heated at a high temperature. To avoid this, one should turn off the fire just prior to the coffee boiling, heat the coffee in a pot on very low fire, or heat the coffee by placing the pot near but not too close to a campfire. The same is generally true for tea.

With cowboy coffee you need significantly less coffee per measure of water, and the coffee ends up with a stronger flavor – if prepared properly. You also keep two key oily components of the coffee, namely the diterpenes known as kahweol and cafestol; its polyphenols, most notably chlorogenic acid; and some of the coffee particles.

Both kahweol and cafestol seem to be associated with reduction in certain types of cancer in humans, and show strong anti-cancer effects in rats (). The same seems to be generally true for chlorogenic acid (). The coffee particles, if ingested, would probably be treated as indigestible fiber and promote colon health. This is usually the fate of indigestible and partially digestible plant matter.

Why is filtered coffee often recommended? Well, unfiltered coffee is believed to promote heart disease. But that is not primarily due to any strong association having been found between unfiltered coffee consumption and heart disease. In fact, the absence of evidence in favor of this hypothesis in long-term studies is rather conspicuous ().

The belief that unfiltered coffee can promote heart disease is due to evidence showing that consumption of 4 cups per day of unfiltered coffee raises total cholesterol by up to 10 mg/dl ().

Only diehard proponents of the lipid hypothesis would look at total cholesterol increase as a marker of heart disease, in part because total cholesterol may increase due to an increase in HDL cholesterol – a much more reliable marker, but of protection against heart disease, particularly within certain ranges. And yes, unfiltered coffee consumption is associated with an increase in HDL cholesterol ().

Moreover, some of the metabolites of caffeine, 1-methyxanthine and 1-methyluric acid, appear to help prevent LDL oxidation; caffeine metabolites also seem to have potent anti-inflammatory properties ().

Some research provides evidence of the importance of moderation in coffee consumption as an important factor in its relationship with health. In this respect, coffee is like almost anything that can be ingested, including water – the dose makes the poison. In a study of 40,000 post-menopausal women in the US reviewed by Bonita and colleagues (), the hazard ratio of death attributed to heart disease was 0.76 for consumption of 1–3 cups/day, 0.81 for 4–5 cups/day, and 0.87 for ≥6 cups/day. Interestingly, the same study reported that the hazard ratio for death from other inflammatory diseases was 0.72 for consumption of 1–3 cups/day, 0.67 for 4–5 cups/day, and 0.68 for ≥6 cups/day.

Frequently you hear about the possible connection between coffee consumption and gastritis. The most widely cited study I could find that looked into this link found no association between coffee consumption and reflux-associated gastritis ().

By the way, if you have gastritis, you should consider getting tested for Helicobacter pylori (), especially if you like eating raw fish.

Stress and coffee consumption may have similar effects in those who test positive for Helicobacter pylori (see, e.g., ). In those individuals, past research has found a link between: (a) stress, coffee consumption, and other purported “stomach irritants”; and (b) exacerbation of gastritis symptoms, stomach ulcers, and stomach cancer.

This discussion on gastritis is largely unrelated to the issue of drinking unfiltered coffee. It is unclear based on the past studies that I reviewed whether coffee filtration has anything to do with any possible connection between coffee consumption and exacerbation of gastritis symptoms caused by other factors.

As a side note, it is important to keep in mind that the acidity of coffee is nowhere near the acidic of gastric acid, which the stomach is uniquely designed to handle.

I may be wrong, but from what I can see, if you drink coffee regularly and it causes no problems for you, drinking unfiltered coffee is not a bad idea at all.

Sudden cholesterol increase? It may be psychological

There are many published studies with evidence that cholesterol levels are positively associated with heart disease. In multivariate analyses the effects are usually small, but they are still there. On the other hand, there is also plenty of evidence that cholesterol is beneficial in terms of health. Here of course I am referring to the health of humans, not of the many parasites that benefit from disease.

For example, there is evidence () that cholesterol levels are negatively associated with mortality (i.e., higher cholesterol leading to lower mortality), and are positively associated with vitamin D production from skin exposure to sunlight ().

Most of the debris accumulated in atheromas are made up of macrophages, which are specialized cells that “eat” cell debris (ironically) and some pathogens. The drug market is still hot for cholesterol-lowering drugs, often presented in TV and Internet ads as effective tools to prevent formation of atheromas.

But what about macrophages? What about calcium, another big component of atheromas? If drugs were to target macrophages for atheroma prevention, drug users may experience major muscle wasting and problems with adaptive immunity, as macrophages play a key role in muscle repair and antibody formation. If drugs were to target calcium, users may experience osteoporosis.

So cholesterol is the target, because there is a “link” between cholesterol and atheroma formation. There is also a link between the number of house fires in a city and the amount of firefighting activity in the city, but we don’t see mayors announcing initiatives to reduce the number of firefighters in their cities to prevent house fires.

When we talk about variations in cholesterol, we usually mean variations in cholesterol carried by LDL particles. That is because LDL cholesterol seems to be very “sensitive” to a number of factors, including diet and disease, presenting quite a lot of sudden variation in response to changes in those factors.

LDL particles seem to be intimately involved with disease, but do not be so quick to conclude that they cause disease. Something so widespread and with so many functions in the human body could not be primarily an agent of disease that needs to be countered with statins. That makes no sense.

Looking at the totally of evidence linking cholesterol with health, it seems that cholesterol is extremely important for the human body, particularly when it is under attack. So the increases in LDL cholesterol associated with various diseases, notably heart disease, may not be because cholesterol is causing disease, but rather because cholesterol is being used to cope with disease.

LDL particles, and their content (including cholesterol), may be used by the body to cope with conditions that themselves cause heart disease, and end up being blamed in the process. The lipid hypothesis may be a classic case of reverse causation. A case in point is that of cholesterol responses to stress, particularly mental stress.

Grundy and Griffin () studied the effects of academic final examinations on serum cholesterol levels in 2 groups of medical students in the winter and spring semesters (see table below). During control periods, average cholesterol levels in the two groups were approximately 213 and 216 mg/dl. During the final examination periods, average cholesterol levels were 248 and 240 mg/dl. These measures were for winter and spring, respectively.



One could say that even the bigger increase from 213 to 248 is not that impressive in percentage terms, approximately 16 percent. However, HDL cholesterol does not go up significantly response to sustained (e.g., multi-day) stress, it actually goes down, so the increases reported can be safely assumed to be chiefly due to LDL cholesterol. For most people, LDL particles are the main carriers of cholesterol in the human body. Thus, in percentage terms, the increases in LDL cholesterol are about twice those reported for total cholesterol.

A 32-percent increase (16 x 2) in LDL cholesterol would not go unnoticed today. If one’s LDL cholesterol were to be normally 140 mg/dl, it would jump to 185 mg/dl with a 32-percent increase. It looks like the standard deviations were more than 30 in the study. (This is based on the standard errors reported, and assuming that the standard deviation equals the standard error multiplied by the square root of the sample size.) So we can guess that several people might go from 140 to 215 or more (this is LDL cholesterol, in mg/dl) in response to the stress from exams.

And the effects above were observed with young medical students, in response to the stress from exams. What about a middle-aged man or woman trying to cope with chronic mental stress for months or years, due to losing his or her job, while still having to provide for a family? Or someone who has just been promoted, and finds himself or herself overwhelmed with the new responsibilities?

Keep in mind that sustained dieting can be a major stressor for some people, particular when one gets to that point in the dieting process where he or she gets regularly into negative nitrogen balance (muscle loss). So you may have heard from people saying that, after months or years of successful dieting, their cholesterol levels are inexplicably going up. Well, this post provides one of many possible explanations for that.

The finding that cholesterol goes up with stress has been replicated many times. It has been known for a long time, with studies dating back to the 1950s. Wertlake and colleagues () observed an increase in average cholesterol levels from 214 to 238 (in mg/dl); also among medical students, in response to the mental and emotional stress of an examination week. A similar study to the one above.

Those enamored with the idea of standing up the whole day, thinking that this will make them healthy, should know that performing cognitively demanding tasks while standing up is a known stressor. It is often used in research where stress must be induced to create an experimental condition. Muldoon and colleagues () found that people performing a mental task while standing experienced an increase in serum cholesterol of approximately 22 points (in mg/dl).

What we are not adapted for is sitting down for long hours in very comfortable furniture (, ). But our anatomy clearly suggests adaptations for sitting down, particularly when engaging in activities that resemble tool-making, a hallmark of the human species. Among modern hunter-gatherers, tool-making is part of daily life, and typically it is much easier to accomplish sitting down than standing up.

Modern urbanites could be seen as engaging in activities that resemble tool-making when they produce things at work for internal or external customers, whether those things are tangible or intangible.

So, stress is associated with cholesterol levels, and particularly with LDL cholesterol levels. Diehard lipid hypothesis proponents may argue that this is how stress is associated with heart disease: stress increases cholesterol which increases heart disease. Others may argue that one of the reasons why LDL cholesterol levels are sometimes found to be associated with heart disease-related conditions, such as chronic stress, and other health conditions is that the body is using LDL cholesterol to cope with those conditions.

Specifically regarding mental stress, a third argument has been put forth by Patterson and colleagues, who claimed that stress-mediated variations in blood lipid concentrations are a secondary result of decreased plasma volume. The cause, in their interpretation, was unspecified – “vascular fluid shifts”. However, when you look at the numbers reported in their study, you still see a marked increase in LDL cholesterol, even controlling for plasma volume. And this is all in response to “10 minutes of mental arithmetic with harassment” ().

I tend to think that the view that cholesterol increases with stress because cholesterol is used by the body to cope with stress is the closest to the truth. Among other things, stress increases the body’s overall protein demand, and cholesterol is used in the synthesis of many proteins. This includes proteins used for signaling, also known as hormones.

Cholesterol also seems to be a diet marker, tending to go up in high fat diets. This is easier to explain. High fat diets increase the demand for bile production, as bile is used in the digestion of fat. Most of the cholesterol produced by the human body is used to make bile.

The anatomy of a VAP test report

The vertical auto profile (VAP) test is an enhanced lipid profile test. It has been proposed, chiefly by the company Atherotech (), as a more complete test that relies on direct measurement of previously calculated lipid measures. The VAP test is particularly known for providing direct measurements of LDL cholesterol, instead of calculating them through equations ().

At the time of this writing, a typical VAP test report would provide direct measures of the cholesterol content of LDL, Lp(a), IDL, HDL, and VLDL particles. It would also provide additional measures referred to as secondary risk factors, notably particle density patterns and apolipoprotein concentrations. Finally, it would provide a customized risk summary and some basic recommendations for treatment. Below is the top part of a typical VAP test report (from Atherotech), showing measures of the cholesterol content of various particles. LDL cholesterol is combined for four particle subtypes, the small-dense subtypes 4 and 3, and the large-buoyant subtypes 2 and 1. A breakdown by LDL particle subtype is provided later in the VAP report.

In the table above, HDL cholesterol is categorized in two subtypes, the small-dense subtype 2, and the large-buoyant subtype 3. Interestingly, most of the HDL cholesterol in the table is supposedly of the least protective subtype, which seems to be a common finding in the general population. VLDL cholesterol is categorized in a similar way. IDL stands for intermediate-density lipoprotein; this is essentially a VLDL particle that has given off some of its content, particularly its triglyceride (or fat) cargo, but still remains in circulation.

Lp(a) is a special subtype of the LDL particle that is purported to be associated with markedly atherogenic factors. Mainstream medicine generally considers Lp(a) particles themselves to be atherogenic, which is highly debatable. Among other things, cardiovascular disease (CVD) risk and Lp(a) concentration follow a J-curve pattern, and Lp(a)’s range of variation in humans is very large. A blog post by Peter (Hyperlipid) has a figure right at the top that illustrates the former J-curve assertion (). The latter fact, related to range of variation, generally leads to a rather wide normal distribution of Lp(a) concentrations in most populations; meaning that a large number of individuals tend to fall outside Lp(a)’s optimal range and still have a low risk of developing CVD.

Below is the middle part of a typical VAP report, showing secondary risk factors, such as particle density patterns and apolipoprotein concentrations. LDL particle pattern A is considered to be the most protective, supposedly because large-buoyant LDL particles are less likely to penetrate the endothelial gaps, which are about 25 nm in diameter. Apolipoproteins are proteins that bind to fats for their transport in lipoproteins, to be used by various tissues for energy; free fatty acids also need to bind to proteins, notably albumin, to be transported to tissues for use as energy. Redundant particles and processes are everywhere in the human body!

Below is the bottom part of a typical VAP report, providing a risk summary and some basic recommendations. One of the recommendations is “to lower” the LDL target from 130mg/dL to 100mg/dL due to the presence of the checked emerging risk factors on the right, under “Considerations”. What that usually means in practice is a recommendation to take drugs, especially statins, to reduce LDL cholesterol levels. A recent post here and the discussion under it suggest that this would be a highly questionable recommendation in the vast majority of cases ().

What do I think about VAP tests? I think that they are useful in that they provide a lot more information about one’s lipids than standard lipid profiles, and more information is better than less. On the other hand, I think that people should be very careful about what they do with that information. There are even more direct tests that I would recommend before a decision to take drugs is made (, ), if that decision is ever made at all.

Familial hypercholesteromia: Why rely on cholesterol levels when more direct measures are available?

There are two forms of familial hypercholesteromia (FH), namely heterozygous and homozygous FH. In heterozygous FH only one copy of the gene that causes it is present, inherited either from the father or the mother. In homozygous FH, which is the most lethal form, two copies of the gene are present. FH is associated with early-onset cardiovascular disease (CVD).

Homozygous FH may happen if both the father and mother have heterozygous or homozygous FH. If both the father and mother have heterozygous FH, the likelihood that at least one in four children will have homozygous FH will be high. If both parents have homozygous FH the likelihood that all children will have homozygous FH will be high.

In fact, in the latter case, homozygous FH in the children is almost certain. One case in which it won’t occur is if the combining FH gene from the father or mother mutates into a non-FH gene before it is used in the assembly of the genome of the child. A gene mutation in a specific locus, only for the father or mother, is an unlikely event, and would lead to heterozygous FH. Two gene mutations at once in the same locus, for the father and mother, is a very unlikely event.

By the way, despite what many are led to believe based on fictional characters in movies and series like the X-Men and Hulk, mutations in functional genes usually lead to harmful traits. In our evolutionary past, those traits would have been largely removed from the gene pool by selection, making them rare or nonexistent in modern humans. Today we have modern medicine; a double-edged sword.

Mutations leading to super-human traits are very, very unlikely. The myostatin gene, for example, suppresses muscle growth. And yet the mutations that lead to little or no secretion of the related myostatin protein are very uncommon. Obviously they have not been favored by selection, even though their holders are very muscular – e.g., Germany’s “Incredible Hulky” ().

Okay, back to FH. Xanthelasmas are relatively common among those who suffer from FH (see photo below, from Globalskinatlas.com). They are skin deposits of cholesterol, have a genetic basis, and are NOT always associated with FH. This is important – several people have xanthelasmas but not FH.

FH is a fairly rare disease, even in its heterozygous form, with an overall incidence of approximately 0.2 percent. That is, about 1 in 500 people in the general population will have it. Genetically related groups will see a much higher or lower rate of incidence, as the disease is strongly influenced by a genetic mutation. This genetic mutation is apparently in the LDL receptor gene, located on the short arm of chromosome 19.

The table below, from a study by Miltiadous and colleagues (), paints a broad picture of the differences one would typically see between heterozygous FH sufferers and non-FH controls.

The main difference is in total cholesterol and in the relatively large contribution of LDL to total cholesterol. A large difference is also seen in Apolipoprotein B (indicated as "Apo B"), which acts as a LDL transporter (not to be confused with a LDL receptor). The LDL cholesterol shown on the table is calculated through the Friedewald equation, which is notoriously imprecise at low triglyceride levels ().

Looking at the total cholesterol row on the table, and assuming that the numbers after the plus/minus signs are standard deviations, we can conclude that: (a) a little more than two-thirds of the heterozygous FH sufferers had total cholesterol levels falling in between 280 and 446; and (b) a little more than two-thirds of the non-FH controls had total cholesterol levels falling in between 135 and 225.

Keep in mind that about 13.5 percent {calculated as: (95-68)/2} of the non-FH controls had total cholesterol levels between 225 and 270. This is a nontrivial percentage; i.e., these may be a minority but are not rare individuals. Heterozygous FH sufferers are rare, at 0.2 percent of the general population. Moreover, about 2 percent of the non-FH controls had non-pathological total cholesterol levels between 270 and 315. That is not so rare either, amounting to an “incidence” 10 times higher than heterozygous FH.

What would happen if people with heterozygous FH were to replace refined carbohydrates and sugars with saturated fat and cholesterol in their diets? Very likely their already high total cholesterol would go up higher, in part because their HDL cholesterol would go up (). Still, how could they be sure that CVD progression would accelerate if they did that?

According to some studies, the higher HDL cholesterol would either be generally protective or associated with protective factors, even among those with FH (). One of those protective factors may be a more nutrient-dense diet, as many foods rich in cholesterol are very nutrient-dense – e.g., eggs, organ meats, and seafood.

This brings me to my main point in this post. It is mainstream practice to diagnose people with FH based on total and/or LDL cholesterol levels. But the main problem with FH is that it leads to early onset of CVD, which can be measured more directly through simple tests, such as intima-media thickness and related ultrasound plaque tests (). These are noninvasive tests, done in 5 minutes or so, and often covered by insurance.

Even if simple direct tests are not perfect, it seems utterly nonsensical to rely on cholesterol measures to diagnose and treat FH, given the possible overlap between pathological and non-pathological high total cholesterol levels.

The 2012 Atherosclerosis egg study: Plaque decreased as LDL increased with consumption of 2.3 eggs per week or more

A new study by David Spence and colleagues, published online in July 2012 in the journal Atherosclerosis (), has been gaining increasing media attention (e.g., ). The article is titled: “Egg yolk consumption and carotid plaque”. As the title implies, the study focuses on egg yolk consumption and its association with carotid artery plaque buildup.

The study argues that “regular consumption of egg yolk should be avoided by persons at risk of cardiovascular disease”. It hints at egg yolks being unhealthy in general, possibly even more so than cigarettes. Solid critiques have already been posted on blogs by Mark Sisson, Chris Masterjohn, and Zoe Harcombe (, , ), among others.

These critiques present valid arguments for why the key findings of the study cannot be accepted, especially the finding that eggs are more dangerous to one’s health than cigarettes. This post is a bit different. It uses the data reported in the study to show that it (the data) suggests that egg consumption is actually health-promoting.

I used the numbers in Table 2 of the article to conduct a test that is rarely if ever conducted in health studies – a moderating effect test. I left out the “egg-yolk years” variable used by the authors, and focused on weekly egg consumption (see Chris’s critique). My analysis, using WarpPLS (), had to be done only visually, because using values from Table 2 meant that I had access only to data on a few variables organized in quintiles. That is, my analysis here using aggregate data is an N=5 analysis; a small sample indeed. The full-text article is not available publicly; Zoe was kind enough to include the data from Table 2 in her critique post.

Below is the model that I used for the moderating effect test. It allowed me to look into the effect that the variable EggsWk (number of eggs consumed per week) had on the association between LDL (LDL cholesterol) and Plaque (carotid plaque). This type of effect, namely a moderating effect, is confusing to many people, because it is essentially the effect that a variable has on the effect of another variable on a third. Still, being confusing does not mean being less important. I should note that this type of effect is similar to a type of conditional association tested via Bayesian statistics – if one eats more eggs, what is the association between having a high LDL cholesterol and plaque buildup?

You can see what is happening visually on the graph below. The plot on the left side is for low weekly egg consumption. In it, the association between LDL cholesterol and plaque is positive – eating fewer eggs, plaque and LDL increase together. The plot on the right side is for high weekly egg consumption. In this second plot, the association between LDL cholesterol and plaque is negative – eating more eggs, plaque decreases as LDL increases. And what is the turning point? It is about 2.3 eggs per week.

So the “evil” particle, the LDL, is playing tricks with us; but thankfully the wonderful eggs come to the rescue, right? Well, it looks a bit like it, but maybe other foods would have a similar effect. In part because of the moderating effect discussed above, the multivariate association between LDL cholesterol and plaque was overall negative. This multivariate association was estimated controlling for the moderating effect of weekly egg consumption. You can see this on the plot below.

The highest amount of plaque is at the far left of the plot. It is associated with the lowest LDL cholesterol quintile. (So much for eggs causing plaque via LDL cholesterol eh!?) What is happening here? Maybe egg consumption above a certain level shifts the size of the LDL particles from small to large, making the potentially atherogenic ones harmless. (Saturated fat consumption, in the context of a nutritious diet in lean individuals, seems to have a similar effect.) Maybe eggs contain nutrients that promote overall health, leading LDL particles to "behave" and do what they are supposed to do. Maybe it is a combination of these and other effects.

Triglycerides, VLDL, and industrial carbohydrate-rich foods

Below are the coefficients of association calculated by HealthCorrelator for Excel (HCE) for user John Doe. The coefficients of association are calculated as linear correlations in HCE (). The focus here is on the associations between fasting triglycerides and various other variables. Take a look at the coefficient of association at the top, with VLDL cholesterol, indicated with a red arrow. It is a very high 0.999.

Whoa! What is this – 0.999! Is John Doe a unique case? No, this strong association between fasting triglycerides and VLDL cholesterol is a very common pattern among HCE users. The reason is simple. VLDL cholesterol is not normally measured directly, but typically calculated based on fasting triglycerides, by dividing the fasting triglycerides measurement by 5. And there is an underlying reason for that - fasting triglycerides and VLDL cholesterol are actually very highly correlated, based on direct measurements of these two variables.

But if VLDL cholesterol is calculated based on fasting triglycerides (VLDL cholesterol  = fasting triglycerides / 5), how come the correlation is 0.999, and not a perfect 1? The reason is the rounding error in the measurements. Whenever you see a correlation this high (i.e., 0.999), it is reasonable to suspect that the source is an underlying linear relationship disturbed by rounding error.

Fasting triglycerides are probably the most useful measures on standard lipid panels. For example, fasting triglycerides below 70 mg/dl suggest a pattern of LDL particles that is predominantly of large and buoyant particles. This pattern is associated with a low incidence of cardiovascular disease (). Also, chronically high fasting triglycerides are a well known marker of the metabolic syndrome, and a harbinger of type 2 diabetes.

Where do large and buoyant LDL particles come from? They frequently start as "big" (relatively speaking) blobs of fat, which are actually VLDL particles. The photo is from the excellent book by Elliott & Elliott (); it shows, on the same scale: (a) VLDL particles, (b) chylomicrons, (c) LDL particles, and (d) HDL particles. The dark bar at the bottom of each shot is 1000 A in length, or 100 nm (A = angstrom; nm = nanometer; 1 nm = 10 A).

If you consume an excessive amount of carbohydrates, my theory is that your liver will produce an abnormally large number of small VLDL particles (also shown on the photo above), a proportion of which will end up as small and dense LDL particles. The liver will do that relatively quickly, probably as a short-term compensatory mechanism to avoid glucose toxicity. It will essentially turn excess glucose, from excess carbohydrates, into fat. The VLDL particles carrying that fat in the form of triglycerides will be small because the liver will be in a hurry to clear the excess glucose in circulation, and will have no time to produce large particles, which take longer to produce individually.

This will end up leading to excess triglycerides hanging around in circulation, long after they should have been used as sources of energy. High fasting triglycerides will be a reflection of that. The graphs below, also generated by HCE for John Doe, show how fasting triglycerides and VLDL cholesterol vary in relation to refined carbohydrate consumption. Again, the graphs are not identical in shape because of rounding error; the shapes are almost identical.

Small and dense LDL particles, in the presence of other factors such as systemic inflammation, will contribute to the formation of atherosclerotic plaques. Again, the main source of these particles would be an excessive amount of carbohydrates. What is an excessive amount of carbohydrates? Generally speaking, it is an amount beyond your liver’s capacity to convert the resulting digestion byproducts, fructose and glucose, into liver glycogen. This may come from spaced consumption throughout the day, or acute consumption in an unnatural form (a can of regular coke), or both.

Liver glycogen is sugar stored in the liver. This is the main source of sugar for your brain. If your blood sugar levels become too low, your brain will get angry. Eventually it will go from angry to dead, and you will finally find out what awaits you in the afterlife.

Should you be a healthy athlete who severely depletes liver glycogen stores on a regular basis, you will probably have an above average liver glycogen storage and production capacity. That will be a result of long-term compensatory adaptation to glycogen depleting exercise (). As such, you may be able to consume large amounts of carbohydrates, and you will still not have high fasting triglycerides. You will not carry a lot of body fat either, because the carbohydrates will not be converted to fat and sent into circulation in VLDL particles. They will be used to make liver glycogen.

In fact, if you are a healthy athlete who severely depletes liver glycogen stores on a regular basis, excess calories will be just about the only thing that will contribute to body fat gain. Your threshold for “excess” carbohydrates will be so high that you will feel like the whole low carbohydrate community is not only misguided but also part of a conspiracy against people like you. If you are also an aggressive blog writer, you may feel compelled to tell the world something like this: “Here, I can eat 300 g of carbohydrates per day and maintain single-digit body fat levels! Take that you low carbohydrate idiots!”

Let us say you do not consume an excessive amount of carbohydrates; again, what is excessive or not varies, probably dramatically, from individual to individual. In this case your liver will produce a relatively small number of fat VLDL particles, which will end up as large and buoyant LDL particles. The fat in these large VLDL particles will likely not come primarily from conversion of glucose and/or fructose into fat (i.e., de novo lipogenesis), but from dietary sources of fat.

How do you avoid consuming excess carbohydrates? A good way of achieving that is to avoid man-made carbohydrate-rich foods. Another is adopting a low carbohydrate diet. Yet another is to become a healthy athlete who severely depletes liver glycogen stores on a regular basis; then you can eat a lot of bread, pasta, doughnuts and so on, and keep your fingers crossed for the future.

Either way, fasting triglycerides will be strongly correlated with VLDL cholesterol, because VLDL particles contain both triglycerides (“encapsulated” fat, not to be confused with “free” fatty acids) and cholesterol. If a large number of VLDL particles are produced by one’s liver, the person’s fasting triglycerides reading will be high. If a small number of VLDL particles are produced, even if they are fat particles, the fasting triglycerides reading will be relatively low. Neither VLDL cholesterol nor fasting triglycerides will be zero though.

Now, you may be wondering, how come a small number of fat VLDL particles will eventually lead to low fasting triglycerides? After all, they are fat particles, even though they occur in fewer numbers. My hypothesis is that having a large number of small-dense VLDL particles in circulation is an abnormal, unnatural state, and that our body is not well designed to deal with that state. Use of lipoprotein-bound fat as a source of energy in this state becomes somewhat less efficient, leading to high triglycerides in circulation; and also to hunger, as our mitochondria like fat.

This hypothesis, and the theory outlined above, fit well with the numbers I have been seeing for quite some time from HCE users. Note that it is a bit different from the more popular theory, particularly among low carbohydrate writers, that fat is force-stored in adipocytes (fat cells) by insulin and not released for use as energy, also leading to hunger. What I am saying here, which is compatible with this more popular theory, is that lipoproteins, like adipocytes, also end up holding more fat than they should if you consume excess carbohydrates, and for longer.

Want to improve your health? Consider replacing things like bread and cereal with butter and eggs in your diet (). And also go see you doctor (); if he disagrees with this recommendation, ask him to read this post and explain why he disagrees.

Alcohol intake increases LDL cholesterol, in some people

Occasionally I get emails from people experiencing odd fluctuations in health markers, and trying to figure out what is causing those fluctuations. Spikes in LDL cholesterol without any change in diet seem to be a common occurrence, especially in men.

LDL cholesterol is a reflection of many things. It is one of the least useful measures in standard lipid profiles, as a predictor of future health problems. Nevertheless, if one’s diet is not changing, whether it is high or low in fat, significant fluctuations in LDL cholesterol may signal a change in inflammatory status. Generally speaking, the more systemic inflammation, the higher is the measured LDL cholesterol.

Corella and colleagues (2001) looked into alcohol consumption and its effect on LDL cholesterol, as part of the Framingham Offspring Study. They split the data into three genotypes, which are allele combinations. Alleles are genes variations; that is, they are variations in the sections of DNA that have been identified as coding for observable traits. The table below summarizes what they have found. Take a look at the last two columns on the right.

As you can see, for men with the E2 genotype, alcohol consumption significantly decreases LDL cholesterol. For men with the E4 genotype, alcohol consumption significantly increases LDL cholesterol. No significant effects were observed in women. The figure below illustrates the magnitude of the effects observed in men.

On average, alcohol consumption was moderate, around 15 g per day, and did not vary significantly based on genotype. This is important. Otherwise one could argue that a particular genotype predisposed individuals to drink more, which would be a major confounder in this study. Other confounders were also ruled out through multivariate controls - e.g., fat and calorie intake, and smoking.

Alcohol consumption in moderation seems, on average, to be beneficial. But for some individuals, particularly men with a certain genotype, it may be advisable to completely abstain from alcohol consumption. Who are those folks? They are the ones for whom LDL cholesterol goes up significantly following moderate alcohol consumption.

Interview with Jimmy Moore, and basics of intima-media thickness and plaque tests

Let me start this post by telling you that my interview with Jimmy Moore is coming up in about a week. Jimmy and I talk about evolution, statistics, and health – the main themes of this blog. We talk also about other things, and probably do not agree on everything. The interview was actually done a while ago, so I don’t remember exactly what we discussed.

From what I remember from mine and other interviews (I listen to Jimmy's podcasts regularly), I think I am the guest who has mentioned the most people during an interview – Gary Taubes, Chris Masterjohn, Carbsane, Petro (a.k.a., Peter “the Hyperlipid”), T. Colin Campbell, Denise Minger, Kurt Harris, Stephan Guyenet, Art De Vany, and a few others. What was I thinking?

In case you listen and wonder, my accent is a mix of Brazilian Portuguese, New Zealand English (where I am called “Need”), American English, and the dialect spoken in the “country” of Texas. The strongest influences are probably American English and Brazilian Portuguese.

Anyway, when medical doctors (MDs) look at someone’s lipid panel, one single number tends to draw their attention: the LDL cholesterol. That is essentially the amount of cholesterol in LDL particles.

One’s LDL cholesterol is a reflection of many factors, including: diet, amount of cholesterol produced by the liver, amount of cholesterol actually used by your body, amount of cholesterol recycled by the liver, and level of systemic inflammation. This number is usually calculated, and often very different from the number you get through a VAP test.

It is not uncommon for a high saturated fat diet to lead to a benign increase in LDL cholesterol. In this case the LDL particles will be large, which will also be reflected in a low “fasting triglycerides number” (lower than 70 mg/dl). While I say "benign" here, which implies a neutral effect on health, an increase in LDL cholesterol in this context may actually be health promoting.

Large LDL particles are less likely to cross the gaps in the endothelium, the thin layer of cells that lines the interior surface of blood vessels, and form atheromatous plaques.

Still, when an MD sees an LDL cholesterol higher than 100 mg/dl, more often than not he or she will tell you that it is bad news. Whether that is bad news or not is really speculation, even for high LDL numbers. A more reliable approach is to check one’s arteries directly. Interestingly, atheromatous plaques only form in arteries, not in veins.

The figure below (from: Novogen.com) shows a photomicrograph of carotid arteries from rabbits, which are very similar, qualitatively speaking, to those of humans. The meanings of the letters are: L = lumen; I = intima; M = media; and A = adventitia. The one on the right has significantly lower intima-media (I-M) thickness than the one on the left.

Atherosclerosis in humans tends to lead to an increase in I-M thickness; the I-M area being normally where atheromatous plaques grow. Aging also leads to an increase in I-M thickness. Typically one’s risk of premature death from cardiovascular complications correlates with one’s I-M thickness’ “distance” from that of low-risk individuals in the same sex and age group.

This notion has led to the coining of the term “vascular age”. For example, someone may be 30 years old, but have a vascular age of 80, meaning that his or her I-M thickness is that of an average 80-year-old. Conversely, someone may be 80 and have a vascular age of 30.

Nearly everybody’s I-M thickness goes up with age, even people who live to be 100 or more. Incidentally, this is true for average blood glucose levels as well. In long-living people they both go up slowly.

I-M thickness tests are noninvasive, based on external ultrasound, and often covered by health insurance. They take only a few minutes to conduct. Their reports provide information about one’s I-M thickness and its relative position in the same sex and age group, as well as the amount of deposited plaque. The latter is frequently provided as a bonus, since it can also be inferred with reasonable precision from the computer images generated via ultrasound.

Below is the top part of a typical I-M thickness test report (from: Sonosite.com). It shows a person’s average (or mean) I-M thickness; the red dot on the graph. The letter notations (A … E) are for reference groups. For the majority of the folks doing this test, the most important on this report are the thick and thin lines indicated as E, which are based on Aminbakhsh and Mancini’s (1999) study.

The reason why the thick and thin lines indicated as E are the most important for the majority of folks taking this test is that they are based on a study that provides one of the best reference ranges for people who are 45 and older, who are usually the ones getting their I-M thickness tested. Roughly speaking, if your red dot is above the thin line, you are at increased risk of cardiovascular disease.

Most people will fall in between the thick and thin lines. Those below the thick line (with the little blue triangles) are at very low risk, especially if they have little to no plaque. The person for whom this test was made is at very low risk. His red dot is below the thick line, when that line is extended to the little triangle indicated as D.

Below is the bottom part of the I-M thickness test report. The max I-M thickness score shown here tends to add little in terms of diagnosis to the mean score shown earlier. Here the most important part is the summary, under “Comments”. It says that the person has no plaque, and is at a lower risk of heart attack. If you do an I-M thickness test, your doctor will probably be able to tell you more about these results.

I like numbers, so I had an I-M thickness test done recently on me. When the doctor saw the results, which we discussed, he told me that he could guarantee two things: (1) I would die; and (2) but not of heart disease. MDs have an interesting sense of humor; just hang out with a group of them during a “happy hour” and you’ll see.

My red dot was below the thick line, and I had a plaque measurement of zero. I am 47 years old, eat about 1 lb of meat per day, and around 20 eggs per week - with the yolk. About half of the meat I eat comes from animal organs (mostly liver) and seafood. I eat organ meats about once a week, and seafood three times a week. This is an enormous amount of dietary cholesterol, by American diet standards. My saturated fat intake is also high by the same standards.

You can check the post on my transformation to see what I have been doing for years now, and some of the results in terms of levels of energy, disease, and body fat levels. Keep in mind that mine are essentially the results of a single-individual experiment; results that clearly contradict the lipid hypothesis. Still, they are also consistent with a lot of fairly reliable empirical research.

The China Study II: Cholesterol seems to protect against cardiovascular disease

First of all, many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. This data is from this site, created by those researchers to disseminate the data from a study often referred to as the “China Study II”. It has already been analyzed by other bloggers. Notable analyses have been conducted by Ricardo at Canibais e Reis, Stan at Heretic, and Denise at Raw Food SOS.

The analyses in this post differ from those other analyses in various aspects. One of them is that data for males and females were used separately for each county, instead of the totals per county. Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance (for more details, see “Notes” at the end of the post), which is desirable since the dataset is relatively small. This also allowed for the test of commonsense assumptions (e.g., the protective effects of being female), which is always a good idea in a complex analysis because violation of commonsense assumption may suggest data collection or analysis error. On the other hand, it required the inclusion of a sex variable as a control variable in the analysis, which is no big deal.

The analysis was conducted using WarpPLS. Below is the model with the main results of the analysis. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore associations between variables, which are shown within ovals. The meaning of each variable is the following: SexM1F2 = sex, with 1 assigned to males and 2 to females; HDLCHOL = HDL cholesterol; TOTCHOL = total cholesterol; MSCHIST = mortality from schistosomiasis infection; and MVASC = mortality from all cardiovascular diseases.

The variables to the left of MVASC are the main predictors of interest in the model – HDLCHOL and TOTCHOL. The ones to the right are control variables – SexM1F2 and MSCHIST. The path coefficients (indicated as beta coefficients) reflect the strength of the relationships. A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to. The P values indicate the statistical significance of the relationship; a P lower than 0.05 generally means a significant relationship (95 percent or higher likelihood that the relationship is “real”).

In summary, this is what the model above is telling us:

- As HDL cholesterol increases, total cholesterol increases significantly (beta=0.48; P<0.01). This is to be expected, as HDL is a main component of total cholesterol, together with VLDL and LDL cholesterol.

- As total cholesterol increases, mortality from all cardiovascular diseases decreases significantly (beta=-0.25; P<0.01). This is to be expected if we assume that total cholesterol is in part an intervening variable between HDL cholesterol and mortality from all cardiovascular diseases. This assumption can be tested through a separate model (more below). Also, there is more to this story, as noted below.

- The effect of HDL cholesterol on mortality from all cardiovascular diseases is insignificant when we control for the effect of total cholesterol (beta=-0.08; P=0.26). This suggests that HDL’s protective role is subsumed by the variable total cholesterol, and also that it is possible that there is something else associated with total cholesterol that makes it protective. Otherwise the effect of total cholesterol might have been insignificant, and the effect of HDL cholesterol significant (the reverse of what we see here).

- Being female is significantly associated with a reduction in mortality from all cardiovascular diseases (beta=-0.16; P=0.01). This is to be expected. In other words, men are women with a few design flaws. (This situation reverses itself a bit after menopause.)

- Mortality from schistosomiasis infection is significantly and inversely associated with mortality from all cardiovascular diseases (beta=-0.28; P<0.01). This is probably due to those dying from schistosomiasis infection not being entered in the dataset as dying from cardiovascular diseases, and vice-versa.

Two other main components of total cholesterol, in addition to HDL cholesterol, are VLDL and LDL cholesterol. These are carried in particles, known as lipoproteins. VLDL cholesterol is usually represented as a fraction of triglycerides in cholesterol equations (e.g., the Friedewald and Iranian equations). It usually correlates inversely with HDL; that is, as HDL cholesterol increases, usually VLDL cholesterol decreases. Given this and the associations discussed above, it seems that LDL cholesterol is a good candidate for the possible “something else associated with total cholesterol that makes it protective”. But waidaminet! Is it possible that the demon particle, the LDL, serves any purpose other than giving us heart attacks?

The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC). The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The curve is the best-fitting S curve obtained by the software (an S curve is a slightly more complex curve than a U curve).

The graph below shows some of the data in unstandardized format, and organized differently. The data is grouped here in ranges of total cholesterol, which are shown on the horizontal axis. The lowest and highest ranges in the dataset are shown, to highlight the magnitude of the apparently protective effect. Here the two variables used to calculate mortality from all cardiovascular diseases (MVASC; see “Notes” at the end of this post) were added. Clearly the lowest mortality from all cardiovascular diseases is in the highest total cholesterol range, 172.5 to 180; and the highest mortality in the lowest total cholesterol range, 120 to 127.5. The difference is quite large; the mortality in the lowest range is approximately 3.3 times higher than in the highest.

The shape of the S-curve graph above suggests that there are other variables that are confounding the results a bit. Mortality from all cardiovascular diseases does seem to generally go down with increases in total cholesterol, but the smooth inflection point at the middle of the S-curve graph suggests a more complex variation pattern that may be influenced by other variables (e.g., smoking, dietary patterns, or even schistosomiasis infection; see “Notes” at the end of this post).

As mentioned before, total cholesterol is strongly influenced by HDL cholesterol, so below is the model with only HDL cholesterol (HDLCHOL) pointing at mortality from all cardiovascular diseases (MVASC), and the control variable sex (SexM1F2).

The graph above confirms the assumption that HDL’s protective role is subsumed by the variable total cholesterol. When the variable total cholesterol is removed from the model, as it was done above, the protective effect of HDL cholesterol becomes significant (beta=-0.27; P<0.01). The control variable sex (SexM1F2) was retained even in this targeted HDL effect model because of the expected confounding effect of sex; females generally tend to have higher HDL cholesterol and less cardiovascular disease than males.

Below, in the “Notes” section (after the “Reference”) are several notes, some of which are quite technical. Providing them separately hopefully has made the discussion above a bit easier to follow. The notes also point at some limitations of the analysis. This data needs to be analyzed from different angles, using multiple models, so that firmer conclusions can be reached. Still, the overall picture that seems to be emerging is at odds with previous beliefs based on the same dataset.

What could be increasing the apparently protective HDL and total cholesterol in this dataset? High consumption of animal foods, particularly foods rich in saturated fat and cholesterol, are strong candidates. Low consumption of vegetable oils rich in linoleic acid, and of foods rich in refined carbohydrates, are also good candidates. Maybe it is a combination of these.

We need more analyses!

Reference:

Kock, N. (2010). WarpPLS 1.0 User Manual. Laredo, Texas: ScriptWarp Systems.


Notes:

- The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable).

- The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. In complex and multi-factorial phenomena such as health-related phenomena, many would consider an R-squared of 0.20 as acceptable. Still, such an R-squared would mean that 80 percent of the variance for a particularly variable is unexplained by the data.

- The P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This and other related techniques also tend to yield more reliable results for small samples, and samples with outliers (as long as the outliers are “good” data, and are not the result of measurement error).

- Colinearity is an important consideration in models that analyze the effect of multiple predictors on one single variable. This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the “winners”. This often backfires, as colinearity can severely distort the results. Some multiple regression techniques, such as automated stepwise regression with backward elimination, are particularly vulnerable to this problem. Colinearity is not the same as correlation, and thus is defined and measured differently. Two predictor variables may be significantly correlated and still have low colinearity. A reasonably reliable measure of colinearity is the variance inflation factor. Colinearity was tested in this model, and was found to be low.

- An effort was made here to avoid multiple data points per county (even though this was available for some variables), because this could artificially reduce the variance for each variable, and potentially bias the results. The reason for this is that multiple answers from a single county would normally be somewhat correlated; a higher degree of intra-county correlation than inter-county correlation. The resulting bias would be difficult to control for, via one or more control variables. With only two data points per county, one for males and the other for females, one can control for intra-country correlation by adding a “dummy” sex variable to the analysis, as a control variable. This was done here.

- Mortality from schistosomiasis infection (MSCHIST) is a variable that tends to affect the results in a way that makes it more difficult to make sense of them. Generally this is true for any infectious diseases that significantly affect a population under study. The problem with infection is that people with otherwise good health or habits may get the infection, and people with bad health and habits may not. Since cholesterol is used by the human body to fight disease, it may go up, giving the impression that it is going up for some other reason. Perhaps instead of controlling for its effect, as done here, it would have been better to remove from the analysis those counties with deaths from schistosomiasis infection. (See also this post, and this one.)

- Different parts of the data were collected at different times. It seems that the mortality data is for the period 1986-88, and the rest of the data is for 1989. This may have biased the results somewhat, even though the time lag is not that long, especially if there were changes in certain health trends from one period to the other. For example, major migrations from one county to another could have significantly affected the results.

- The following measures were used, from this online dataset like the other measures. P002 HDLCHOL, for HDLCHOL; P001 TOTCHOL, for TOTCHOL; and M021 SCHISTOc, for MSCHIST.

- SexM1F2 is a “dummy” variable that was coded with 1 assigned to males and 2 to females. As such, it essentially measures the “degree of femaleness” of the respondents. Being female is generally protective against cardiovascular disease, a situation that reverts itself a bit after menopause.

- MVASC is a composite measure of the two following variables, provided as component measures of mortality from all cardiovascular diseases: M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). A couple of obvious problems: (a) they does not include data on people older than 69; and (b) they seem to capture a lot of diseases, including some that do not seem like typical cardiovascular diseases. A factor analysis was conducted, and the loadings and cross-loadings suggested good validity. Composite reliability was also good. So essentially MVASC is measured here as a “latent variable” with two “indicators”. Why do this? The reason is that it reduces the biasing effects of incomplete data and measurement error (e.g., exclusion of folks older than 69). By the way, there is always some measurement error in any dataset.

- This note is related to measurement error in connection with the indicators for MVASC. There is something odd about the variables M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). According to the dataset, mortality from cardiovascular diseases for ages 0-34 is typically higher than for 35-69, for many counties. Given the good validity and reliability for MVASC as a latent variable, it is possible that the values for these two indicator variables were simply swapped by mistake.

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.

Niacin and its effects on growth hormone, glucagon, cortisol, blood lipids, mental disorders, and fasting glucose levels

Niacin is a very interesting vitamin. It is also known as vitamin B3, or nicotinic acid. It is an essential vitamin whose deficiency leads to a dreadful disease known as pellagra. In large doses of 1 to 3 g per day it has several effects on blood lipids, including these: it increases HDL cholesterol, decreases triglycerides, and decreases Lp(a). Given that this is essentially a reversal of the metabolic syndrome, for those who are on their way to developing it, niacin must really do something good for our body. Niacin is also a powerful antioxidant.

The lipid modification effects of niacin are so consistent across a broad spectrum of the population that some companies that commercialize niacin-based products guarantee some measure of those effects. The graphs below (click to enlarge) are from Arizona Pharmaceuticals, a company that commercializes an instant-release niacin formulation called Nialor (see: arizonapharmaceuticals.com). The graphs show the peak effects on HDL cholesterol and triglycerides at the recommended dose, which is 1.5 g per day. The company guarantees effects; not the peak effects shown, but effects that are large enough to have clinical significance.

Niacin also has been used in the treatment of various mental disorders, including schizophrenia. Its effectiveness in this domain (mental disease) is still under debate. Yet many people, including reputable mental health researchers, swear by it. Empirical research suggests beyond much doubt that niacin helps in the treatment of depression and bipolar disorder.

Abram Hoffer, a Canadian psychiatrist who died in 2009, at the age of 91, has discussed at length the many beneficial health effects of niacin. He was also a niacin user. He argued that it can even make people live longer, and be generally healthier and more active. The effect on longevity may sound far-fetched, but there is empirical data supporting this hypothesis as well. (For more, see this book.)

By the way, moderate niacin supplementation seems to increase the milk output of cows, without any effect on milk composition.

Most people dislike the sensation that is caused by niacin, the “niacin flush”. This is a temporary sensation similar to that of sunburn covering one’s full torso and face. It goes away after a few minutes. This is niacin’s main undesirable side effect at doses up to 3 g per day. Higher doses are not recommended, and can be toxic to the liver.

Nobody seems to understand very well how niacin works. This leads to some confusion. Many people think that niacin inhibits the production of VLDL, free fatty acids, and ketones; preventing the use of fat as an energy source. And it does!

So it makes you fat, right?

No, because these effects are temporary, and are followed, often after 3 to 5 hours, by a large increase in circulating growth hormone, cortisol and glucagon. These hormones are associated with (maybe they cause, maybe are caused by) a large increase in free fatty acids and ketones in circulation, but not with an increase in VLDL secretion by the liver. So ketosis is at first inhibited by niacin, and then comes in full force after a few hours.

The decreased VLDL secretion is no surprise, because VLDL is not really needed in large quantities if muscle tissues (including the heart) are being fed what they really like: free fatty acids and ketones. When VLDL particles are secreted by the liver in small numbers, they tend to be large. As they shrink in size after delivering their lipid content to muscle tissues, they become large LDL particles; too large to cross the endothelial gaps and cause plaque formation.

It is as if niacin held you back for a few hours, in terms of fat burning, and then released you with a strong push.

Since niacin does not seem to suppress the secretion of chylomicrons by the intestines, it should be taken with meals. The meals do not necessarily have to have any carbohydrates in them. If you take niacin while fasting, you may feel “funny” and somewhat weak, because of the decrease in VLDL, free fatty acids, and ketones in circulation. These, particularly the free fatty acids and ketones, are important sources of energy in the fasted state.

Given niacin’s delayed effects, it does not seem to make much sense to take slow release niacin of any kind. In fact, the form of niacin that seems to work best is the instant-release one, the one that gives you the flush. It may be a good idea to wait until 3 to 5 hours after you take it to do heavy exercise. You may feel a surge of energy 3 to 5 hours after taking it, when the delayed effects kick in.

The delayed effects of niacin on growth hormone, cortisol and glucagon are probably the reasons why people taking niacin frequently see a small increase in fasting glucose levels. This increase is usually of a few percentage points, but can be a bit higher in some people. Growth hormone, cortisol and particularly glucagon increase blood glucose levels; and the blood levels of these hormones naturally rise in the morning to get you ready for the day ahead. Niacin seems to boost that. Hence the increase in fasting blood glucose levels. This appears to be a benign effect, easily counterbalanced by niacin’s many benefits.

In spite of a possible increase in fasting glucose levels, there is no evidence that niacin increases average blood glucose levels. If it did, that would not be a good thing. In fact, it has been argued that niacin intake can be part of an effective approach to treating diabetes; Robert C. Atkins discussed this in his Vita-Nutrient Solution book.

Niacin’s effects on lipids are somewhat similar to those of low carbohydrate dieting. For example, both lead to a decrease in fasting triglycerides and an increase in HDL cholesterol. But the mechanisms by which those effects are achieved appear to be rather different.

References:

Quabbe, H.J., Trompke, M., & Luyckx, A.S. (1983). Influence of ketone body infusion on plasma growth hormone and glucagon in man. J. Clin Endocrinol Metab., 57(3):613-8.

Quabbe, H.J., Luyckx, A.S., L'age M., & Schwarz, C. (1983). Growth hormone, cortisol, and glucagon concentrations during plasma free fatty acid depression: different effects of nicotinic acid and an adenosine derivative (BM 11.189). J. Clin Endocrinol Metab., 57(2):410-4.

Schade, D.S., Woodside, W., & Eaton, R.P. (1979). The role of glucagon in the regulation of plasma lipids. Metabolism, 28(8):874-86.