Is Meat Unhealthy? Part III

When we consider the health impacts of eating meat, cardiovascular disease is the first thing that comes to mind.  Popular diet advocates often hold diametrically opposed views on the role of meat in cardiovascular disease.  Even among researchers and public health officials, opinions vary.  In this post, I'll do my best to sort through the literature and determine what the weight of the evidence suggests.

Ancel Keys and the Seven Countries Study

Ancel Keys was one of the first researchers to contribute substantially to the study of the link between diet and cardiovascular disease.  Sadly, there is a lot of low-quality information circulating about Ancel Keys and his research (1).  The truth is that Keys was a pioneering researcher who conducted some of the most impressive nutritional science of his time.  The military "K ration" was designed by Keys, much of what we know about the physiology of starvation comes from his detailed studies during World War II, and he was the original Mediterranean Diet researcher.  Science marches on, and not all discoveries are buttressed by additional research, but Keys' work was among the best of his day and must be taken seriously.

One of Keys' earliest contributions to the study of diet and cardiovascular disease appeared in an obscure 1953 paper titled "Atherosclerosis: A Problem in Newer Public Health" (2).  This paper is worth reading if you get a chance (freely available online if you poke around a bit).  He presents a number of different arguments and supporting data, most of which are widely accepted today, but one graph in particular has remained controversial.  This graph shows the association between total fat intake and heart disease mortality in six countries.  Keys collected the data from publicly available databases on global health and diet:

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What is “relative risk” (RR)? The case of alcohol frequency and its impact on mortality from stroke

This post is in response to an inquiry by Ivor (sorry for the delayed response). It refers to a recent study by Rantakömi and colleagues on the effect of alcohol consumption frequency on mortality from stroke (). The study followed men who consumed alcohol to different degrees, including no consumption at all, over a period of a little more than 20 years.

The study purportedly controlled for systolic blood pressure, smoking, body mass index, diabetes, socioeconomic status, and total amount of alcohol consumption. That is, its results are presented as holding regardless of those factors.

The main results were reported in terms of “relative risk” (RR) ratios. Here they are, quoted from the abstract:

“0.71 (95% CI, 0.30–1.68; P = 0.437) for men with alcohol consumption <0.5 times per week and 1.16 (95% CI, 0.54–2.50; P = 0.704) among men who consumed alcohol 0.5–2.5 times per week. Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 (95% CI, 1.19–7.72; P = 0.020).”

Note the P values reported within parentheses. They are the probabilities that the results are due to chance and thus “not real”, or not due to actual effects. By convention, P values equal to or lower than 0.05 are considered statistically significant. In consequence, P values greater than 0.05 are seen as referring to effects that cannot be unequivocally considered real.

This means that, of the results reported, only one seems to be due to a real effect, and that is the one that: “Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 …”

Why the authors report the statistically non-significant results as if they were noteworthy is unclear to me.

Before we go any further, let us look at what “relative risk” (RR) means. RR is given by the following ratio:

(Probability of an event when exposed) / (Probability of an event when not exposed)

In the study by Rantakömi and colleagues, the event is death from stroke. The exposure refers to alcohol consumption at a certain level, compared to no alcohol consumption (no exposure).

Now, let us go back to the result regarding consumption of alcohol more than 2.5 times per week. That result sounds ominous. It is helpful to keep in mind that the study by Rantakömi and colleagues followed a total of 2609 men with no history of stroke, of whom only 66 died from stroke.

Consider the following scenario. Let us say that 1 person in a group of 1,000 people who consumed no alcohol died from stroke. Let us also say that 3 people in a group of 1,000 people who consumed alcohol more than 2.5 times per week died from stroke. Given this, the RR would be: (3/1,000) / (1/1,000) = 3.

One could say, based on this, that: “Consuming alcohol more than 2.5 times per week increases the risk of dying from stroke by 200%”. Based on the RR, this is technically correct. It is rather misleading nevertheless.

If you think that increasing sample size may help ameliorate the problem, think again. The RR would be the same if it was 3 people versus 1 person in 1,000,000 (one million). With these numbers, the RR would be even less credible, in my view.

This makes the findings by Rantakömi and colleagues look a lot less ominous, don’t you think? This post is not really about the study by Rantakömi and colleagues. It is about the following question, which is in the title of this post: What is “relative risk” (RR)?

Quite frankly, given what one sees in RR-based studies, the answer is arguably not far from this:

RR is a ratio used in statistical analysis that makes minute effects look enormous; the effects in question would not normally be noticed by anyone in real life, and may be due to chance after all.

The reason I say that the effects “may be due to chance after all” is that when effects are such that 1 event in 1,000 would make a big difference, a researcher would have to control for practically everything in order to rule out confounders.

If one single individual with a genetic predisposition toward death from stroke falls into the group that consumes more alcohol, falling in that group entirely by chance (or due to group allocation bias), the RR-based results would be seriously distorted.

This highlights one main problem with epidemiological studies in general, where RR is a favorite ratio to be reported. The problem is that epidemiological studies in general refer to effects that are tiny.

One way to put results in context and present them more “honestly” would be to provide more information to readers, such as graphs showing data points and unstandardized scales, like the one below. This graph is from a previous post on latitude and cancer rates in the USA (), and has been generated with the software WarpPLS ().

This graph clearly shows that, while there seems to be an association between latitude and cancer rates in the USA, the total variation in cancer rates in the sample is only of around 3 in 1,000. This graph also shows outliers (e.g., Alaska), which call for additional explanations.

As for the issue of alcohol consumption frequency and mortality, I leave you with the results of a 2008 study by Breslow and Graubard, with more citations and published in a more targeted journal ():

“Average volume obscured effects of quantity alone and frequency alone, particularly for cardiovascular disease in men where quantity and frequency trended in opposite directions.”

In other words, alcohol consumption in terms of volume (quantity multiplied by frequency) appears to matter much more than quantity or frequency alone. We can state this even more simply: drinking two bottles of whiskey in one sitting, but only once every two weeks, is not going to be good for you.

In the end, providing more information to readers so that they can place the results in context is a matter of scientific honesty.

Corrections to the New Review Paper on Dietary Fat and Cardiovascular Risk

The meta-analysis by Chowdhury et al. raised quite a furor from certain segments of researchers and the popular media.  I find this reaction interesting.  I usually write about obesity, which is a topic of great interest to people, but my post about the review paper received more than twice my usual traffic.  People whose findings or opinions are questioned by the paper are aggressively denouncing it in the media, even calling for retraction (1).  This resembles what happens every time a high-profile review paper is published that doesn't support the conventional stance on fatty acids and health (e.g., Siri-Tarino et al. [2], which despite much gnashing of teeth is still standing*).  I'm not sure why this issue in particular arouses such excitement, but I find it amusing and disturbing at the same time.  This kind of reaction would be totally out of place in most other fields of science, where aggressive public media outbursts by researchers are usually frowned upon.

As it turns out, the critics have a point this time.  Significant errors were uncovered in the original version of the meta-analysis, which have been corrected in the current version (3).  These include the following two errors, one of which alters the conclusion somewhat:

• The outcome of one observational study on omega-3 fatty acids was reported as slightly negative, when it was actually strongly positive.  This changes the conclusion of the meta-analysis, making it somewhat more favorable to omega-3 consumption for cardiovascular protection.
• The authors left out two studies on omega-6 fatty acids.  These didn't change the overall conclusions on omega-6.

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New Review Paper on Dietary Fat and Heart Disease Risk

A new review paper on dietary fatty acids and heart disease risk was just published by Dr. Rajiv Chowdhury and colleagues in the Annals of Internal Medicine-- one of the top medical journals (1).  The goal of the paper is to comprehensively review the studies evaluating the effect of dietary fatty acids on heart (coronary) disease.  The review covers observational and intervention studies pertaining to saturated, monounsaturated, trans, omega-6 polyunsaturated, and omega-3 polyunsaturated fats.  The paper is notable for its comprehensiveness (inclusion criteria were very lax).

Here is a summary of the results:

• In observational studies that measured diet, only trans fat was related to cardiovascular risk.  Saturated, monounsaturated, and polyunsaturated fats were unrelated to risk.
• In observational studies that measured circulating concentrations of fatty acids, long-chain polyunsaturated fatty acids (DHA, DPA, EPA, AA) were associated with lower risk.  The dairy-fat-derived margaric acid (17:0) was also associated with lower risk.  No other fatty acids were related to risk, including trans fatty acids.
• In controlled trials, supplementation with omega-3 or omega-6 fatty acids did not alter risk.

The authors conclude:

In conclusion, the pattern of findings from this analysis did not yield clearly supportive evidence for current cardiovascular guidelines that encourage high consumption of polyunsaturated fatty acids and low consumption of saturated fats.  Nutritional guidelines on fatty acids and cardiovascular guidelines may require reappraisal to reflect the current evidence.

My view

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Does "Metabolically Healthy Obesity" Exist?

Obesity is strongly associated with metabolic alterations and negative health outcomes including diabetes, cardiovascular disease, and some types of cancer (1, 2, 3, 4).  Excess body fat is one of the primary causes of preventable health problems and mortality in the United States and many other affluent nations, ranking in importance with cigarette smoking and physical inactivity.  Obesity is thought to contribute to disease via the metabolic disturbances it causes, including excess glucose and lipids in the circulation, dysregulated hormone activity including insulin and leptin, and inflammatory effects.  This immediately raises two questions:

1. Does metabolically healthy obesity exist?
2. If so, are metabolically healthy obese people at an elevated risk of disease and death?

Does metabolically healthy obesity exist?

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Neuronal Control of Appetite, Metabolism and Weight

Last week, I attended a Keystone conference, "Neuronal Control of Appetite, Metabolism and Weight", in Banff.  Keystone conferences are small, focused meetings that tend to attract high quality science.  This particular conference centered around my own professional research interests, and it was incredibly informative.  This post is a summary of some of the most salient points.

Rapid Pace of Scientific Progress

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Atherosclerosis in Ancient Mummies Revisited

Many of you are already aware of the recent study that examined atherosclerosis in 137 ancient mummies from four different cultures (1).  Investigators used computed tomography (CT; a form of X-ray) to examine artery calcification in mummies from ancient Egypt, Peru, Puebloans, and arctic Unangan hunter-gatherers.  Artery calcification is the accumulation of calcium in the vessel wall, and it is a marker of severe atherosclerosis.  Where there is calcification, the artery wall is thickened and extensively damaged.  Not surprisingly, this is a risk factor for heart attack.  Pockets of calcification are typical as people age.

I'm not going to re-hash the paper in detail because that has been done elsewhere.  However, I do want to make a few key points about the study and its interpretation.  First, all groups had atherosclerosis to a similar degree, and it increased with advancing age.  This suggests that atherosclerosis may be part of the human condition, and not a modern disease.  Although it's interesting to have this confirmed in ancient mummies, we already knew this from cardiac autopsy data in a variety of non-industrial cultures (2, 3, 4, 5).

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Does the Mediterranean Diet Reduce Cardiovascular Risk?

By now, most of you have probably heard about the recent study on the "Mediterranean diet" (1), a diet that was designed by diet-heart researchers and is based loosely on the traditional diet of Crete and certain other Mediterranean regions.  The popular press has been enthusiastically reporting this trial as long-awaited proof that the Mediterranean diet reduces the risk of cardiovascular events-- by a full 30 percent over a 4.8-year period.  I wish I could share their enthusiasm for the study.

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Body Fatness and Cardiovascular Risk Factors

I recently revisited a really cool paper published in the Lancet in 2009 on body fatness, biomarkers, health, and mortality (1). It's a meta-analysis that compiled body mass index (BMI) data from nearly 900,000 individual people, and related it to circulating lipids and various health outcomes.  This is one of the most authoritative papers on the subject.

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The 2012 Atherosclerosis egg study: More smoking is associated with more plaque, unless you eat more eggs

I blogged before about the study by David Spence and colleagues, published online in July 2012 in the journal Atherosclerosis (). This study attracted a lot of media attention (e.g., ). The article is titled: “Egg yolk consumption and carotid plaque”. 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.

I used the numbers in Table 2 of the article (only 5 rows of data, one per quintile; i.e., N=5) to conduct a type of analysis that is rarely if ever conducted in health studies – a moderating effects analysis. A previous blog post summarizes the results of one such analysis using WarpPLS (). It looked into the effect of the number of eggs consumed per week on the association between blood LDL cholesterol and plaque (carotid plaque). The conclusion, which is admittedly tentative due to the small sample (N=5), was that plaque decreased as LDL cholesterol increased with consumption of 2.3 eggs per week or more ().

Recently I ran an analysis on the moderating effect of number of eggs consumed per week on the association between cumulative smoking (measured in “pack years”) and plaque. As it turns out, if you fit a 3D surface to the five data points that you get for these three variables from Table 2 of the article, you end up with a relatively smooth surface. Below is a 3D plot of the 5 data points, followed by a best-fitting 3D surface (developed using an experimental algorithm).

Based on this best-fitting surface you could then generate a contour graph, shown below. The “lines” are called “isolines”. Each isoline refers to plaque values that are constant for a set of eggs per week and cumulative smoking combinations. Next to the isolines are the corresponding plaque values. The first impression is indeed that both egg consumption and smoking are causing plaque buildup, as plaque clearly increases as one moves toward the top-right corner of the graph.

But focus your attention on each individual isoline, one at a time. It is clear that plaque remains constant for increases in cumulative smoking, as long as egg consumption increases. Take for example the isoline that refers to 120 mm2 of plaque area. An increase in cumulative smoking from about 14.5 to 16 pack years leads to no increase in plaque if egg consumption goes up from about 2 to 2.3 eggs per week.

These within-isoline trends, which are fairly stable across isolines (they are all slanted to the right), clearly contradict the idea that eggs cause plaque buildup. So, why does plaque buildup seem to clearly increase with egg consumption? Here is a good reason: egg consumption is very strongly correlated with age, and plaque increases with age. The correlation is a whopping 0.916. And I am not talking about cumulative egg consumption, which the authors also measure, through a variable called “egg-yolk years”. No, I am talking about eggs per week. In this dataset, older folks were eating more eggs, period.

The correlation between plaque and age is even higher: 0.977. Given this, it makes sense to look at individual isolines. This would be analogous to what biostatisticians often call “adjusting for age”, or analyzing the effect of egg consumption on plaque buildup “keeping age constant”. A different technique is to “control for age”; this technique would be preferable had the correlations been lower (say, lower than 0.7), as collinearity levels might have been below acceptable thresholds.

The underlying logic of the “keeping age constant” technique is fairly sound in the face of such a high correlation, which would make “controlling for age” very difficult due to collinearity. When we “keep age constant”, the results point at egg consumption being protective among smokers.

But diehard fans of the idea that eggs are unhealthy could explain the results differently. Maybe egg consumption causes plaque to go up, but smoking has a protective effect. Again taking the isoline that refers to 120 mm2 of plaque area, these diehard fans could say that an increase in egg consumption from 2 to 2.3 eggs per week leads to no increase in plaque if cumulative smoking goes up from about 14.5 to 16 pack years.

Not too long ago I also blogged about a medical case study of a man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious The New England Journal of Medicine, and was in surprisingly good health (). This man was not a smoker.

Perhaps if this man smoked 25 cigarettes per day, and ate no eggs, he would be in even better health eh!?

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.

New Review Paper by Yours Truly: High-Fat Dairy, Obesity, Metabolic Health and Cardiovascular Disease

My colleagues Drs. Mario Kratz, Ton Baars, and I just published a paper in the European Journal of Nutrition titled "The Relationship Between High-Fat Dairy Consumption and Obesity, Cardiovascular, and Metabolic Disease".  Mario is a nutrition researcher at the Fred Hutchinson Cancer Research Center here in Seattle, and friend of mine.  He's doing some very interesting research on nutrition and health (with an interest in ancestral diets), and I'm confident that we'll be getting some major insights from his research group in the near future.  Mario specializes in tightly controlled human feeding trials.  Ton is an agricultural scientist at the University of Kassel in Germany, who specializes in the effect of animal husbandry practices (e.g., grass vs. grain feeding) on the nutritional composition of dairy.  None of us have any connection to the dairy industry or any other conflicts of interest.

The paper is organized into three sections:

1. A comprehensive review of the observational studies that have examined the relationship between high-fat dairy and/or dairy fat consumption and obesity, metabolic health, diabetes, and cardiovascular disease.
2. A discussion of the possible mechanisms that could underlie the observational findings.
3. Differences between pasture-fed and conventional dairy, and the potential health implications of these differences.

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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.

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.

Polyphenols, Hormesis and Disease: Part I

What are Polyphenols?
Polyphenols are a diverse class of molecules containing multiple phenol rings. They are synthesized in large amounts by plants, certain fungi and a few animals, and serve many purposes, including defense against predators/infections, defense against sunlight damage and chemical oxidation, and coloration. The color of many fruits and vegetables, such as blueberries, eggplants, red potatoes and apples comes from polyphenols. Some familiar classes of polyphenols in the diet-health literature are flavonoids, isoflavonoids, anthocyanidins, and lignins.

The Case Against Polyphenols

Many diet-health authorities seem pretty well convinced that dietary polyphenols are an important part of good health, due to their supposed antioxidant properties. In the past, I've been critical of the hypothesis. There are several reasons for it:

1. Polyphenols are often, but not always, defensive compounds that interfere with digestive processes, which is why they often taste bitter and/or astringent. Plant-eating animals including humans have evolved defensive strategies against polyphenol-rich foods, such as polyphenol-binding proteins in saliva (1).
2. Ingested polyphenols are poorly absorbed (2). The concentration in blood is low, and the concentration inside cells is probably considerably lower*. In contrast, essential antioxidant nutrients such as vitamins E and C are efficiently absorbed and retained rather than excluded from the circulation.
3. Polyphenols that manage to cross the gut barrier are rapidly degraded by the liver, just like a variety of other foreign molecules, again suggesting that the body doesn't want them hanging around (2).
4. The most visible hypothesis of how polyphenols influence health is the idea that they are antioxidants, protecting against the ravages of reactive oxygen species. While many polyphenols are effective antioxidants at high concentrations in a test tube, I don't find it very plausible that the low and transient blood concentration of polyphenols achieved by eating polyphenol-rich foods makes a meaningful contribution to that person's overall antioxidant status, when compared to the relatively high concentrations of other antioxidants in blood* (uric acid; vitamins C, E; ubiquinone) and particularly inside cells (SOD1/2, catalase, glutathione reductase, thioredoxin reductase, paraoxonase 1, etc.).
5. There are a number of studies showing that the antioxidant capacity of the blood increases after eating polyphenol-rich foods. These are often confounded by the fact that fructose (in fruit and some vegetables) and caffeine (in tea and coffee) can increase the blood level of uric acid, the blood's main water-soluble antioxidant. Drinking sugar water has the same effect (2).
6. Rodent studies showing that polyphenols improve health typically use massive doses that exceed what a person could consume eating food, and do not account for the possibility that the rodents may have been calorie restricted because their food tastes awful.

The main point is that the body does not seem to "want" polyphenols in the circulation at any appreciable level, and therefore it gets rid of them pronto. Why? I think it's because the diversity and chemical structure of polyphenols makes them potentially bioactive-- they have a high probability of altering signaling pathways and enzyme activity, in the same manner as pharmaceutical drugs. It would not be a very smart evolutionary strategy to let plants (that often don't want you eating them) take the reins on your biochemistry. Also, at high enough concentrations polyphenols can be pro-oxidants, promoting excess production of free radicals, although the biological relevance of that may be questionable due to the concentrations required.

A Reappraisal

After reading more about polyphenols, and coming to understand that the prevailing hypothesis of why they work makes no sense, I decided that the whole thing is probably bunk: at best, specific polyphenols are protective in rodents at unnaturally high doses due to some drug-like effect. But-- I kept my finger on the pulse of the field just in case, and I began to notice that more sophisticated studies were emerging almost weekly that seemed to confirm that realistic amounts of certain polyphenol-rich foods (not just massive quantities of polyphenol extract) have protective effects against a variety of health problems. There are many such studies, and I won't attempt to review them comprehensively, but here are a few I've come across:

• Dr. David Grassi and colleagues showed that polyphenol-rich chocolate lowers blood pressure, improves insulin sensitivity and lowers LDL cholesterol in hypertensive and insulin resistant volunteers when compared with white chocolate (3). Although dark chocolate is also probably richer in magnesium, copper and other nutrients than white chocolate, the study is still intriguing.
• Dr. Christine Morand and colleagues showed that drinking orange juice every day lowers blood pressure and increases vascular reactivity in overweight volunteers, an effect that they were able to specifically attribute to the polyphenol hesperidin (4).
• Dr. F. Natella and colleagues showed that red wine prevents the increase in oxidized blood lipids (fats) that occurs after consuming a meal high in oxidized and potentially oxidizable fats (5).
• Several studies have shown that hibiscus tea lowers blood pressure in people with hypertension when consumed regularly (6, 7, 8). It also happens to be delicious.
• Dr. Arpita Basu and colleagues showed that blueberries lower blood pressure and oxidized LDL in men and women with metabolic syndrome (9).
• Animal studies have generally shown similar results. Dr. Xianli Wu and colleagues showed that whole blueberries potently inhibit atherosclerosis (hardening and thickening of the arteries that can lead to a heart attack) in a susceptible strain of mice (10). This effect was associated with a higher expression level of antioxidant enzymes in the vessel walls and other tissues.

Wait a minute... let's rewind. Eating blueberries causes mice to increase the expression level of their own antioxidant enzymes?? Why would that happen if blueberry polyphenols were protecting against oxidative stress? One would expect the opposite reaction if they were. What's going on here?

In the face of this accumulating evidence, I've had to reconsider my position on polyphenols. In the process, and through conversations with knowledgeable researchers in the polyphenol field, I encountered a different hypothesis that puts the puzzle pieces together nicely.  I'll discuss that in the next post.


* Serum levels of polyphenols briefly enter the mid nM to low uM range, depending on the food (2). Compare that with the main serum antioxidants: ~200 uM for uric acid, ~100 uM for vitamin C, ~30 uM for vitamin E.

Does Dietary Saturated Fat Increase Blood Cholesterol? An Informal Review of Observational Studies

The diet-heart hypothesis states three things:

1. Dietary saturated fat increases blood cholesterol
2. Elevated blood cholesterol increases the risk of having a heart attack
3. Therefore, dietary saturated fat increases the risk of having a heart attack

To evaluate the second contention, investigators have examined the relationship between blood cholesterol and heart attack risk. Many studies including MRFIT have shown that the two are related (1):

The relationship becomes much more complex when you consider lipoprotein subtypes, density and oxidation level, among other factors, but at the very least there is an association between habitual blood cholesterol level and heart attack risk. This is what you would want to see if your hypothesis states that high blood cholesterol causes heart attacks.

Now let's turn to the first contention, the hypothesis that dietary saturated fat increases serum cholesterol. This idea is so deeply ingrained in the scientific literature that many authors don't even bother providing references for it anymore. When references are provided, they nearly always point to the same type of study: short-term controlled diet trials, in which volunteers are fed different fats for 2-13 weeks and their blood cholesterol measured (2)*. These studies show that saturated fat increases both LDL cholesterol ("bad cholesterol") and HDL cholesterol ("good cholesterol"), but typically the former more than the latter.  These are the studies on which the diet-heart hypothesis was built.

But now we have a problem. Nearly every high-quality (prospective) observational study ever conducted found that saturated fat intake is not associated with heart attack risk (3). So if saturated fat increases blood cholesterol, and higher blood cholesterol is associated with an increased risk of having a heart attack, then why don't people who eat more saturated fat have more heart attacks?

I'll begin to answer that question with another question: why do researchers almost never cite observational studies to support the idea that dietary saturated fat increases blood cholesterol? Surely if the hypothesis is correct, then people who habitually eat a lot of saturated fat should have high cholesterol, right? One reason may be that in most instances, when researchers have looked for a relationship between habitual saturated fat intake and blood cholesterol, it has been very small or nonexistent. Those findings are rarely cited, but let's have a look...

The Studies

It's difficult to do a complete accounting of these studies, but I've done my best to round them up. I can't claim this post is comprehensive, but I doubt I missed very many, and I certainly didn't exclude any that I came across. If you know of any I missed, please add them to the comments.  [UPDATE 4-2012: I did miss several studies, although they're basically consistent with the conclusion I came to here.  I plan to update this post with the new references at some point.]

The earliest and perhaps most interesting study I found was published in the British Medical Journal in 1963 and is titled "Diet and Plasma Cholesterol in 99 Bank Men" (4). Investigators asked volunteers to weigh all food consumed at home for 1-2 weeks, and describe in detail all food consumed away from home. Compliance was good. This dietary accounting method is much more accurate than in most observational studies today**. Animal fat intake ranged from 55 to 173 grams per day, and blood cholesterol ranged from 154 to 324 mg/dL, yet there was no relationship whatsoever between the two. I'm looking at a graph of animal fat intake vs. blood cholesterol as I write this, and it looks like someone shot it with a shotgun at 50 yards. They analyzed the data every which way, but were never able to squeeze even a hint of an association out of it:

Making the most out of the data in other ways- for example, by analysis of the men very stable in their diets, or in whom weighing of food intake was maximal, or where blood was taken close to the diet [measurement]- did not increase the correlation. Because the correlation coefficient is almost as often negative as positive, moreover, what is being discussed mostly is the absence of association, not merely association that is unexpectedly small.

The next study to discuss is the 1976 Tecumseh study (5). This was a large cardiovascular observational study conducted in Tecumseh, Michigan, which is often used as the basis for comparison for other cardiovascular studies in the literature. Using the 24 hour dietary recall method, including an analysis of saturated fat, the investigators found that:

Cholesterol and triglyceride levels were unrelated to quality, quantity, or proportions of fat, carbohydrate or protein consumed in the 24-hr recall period.

They also noted that the result was consistent with what had been reported in other previously published studies, including the Evans county study (6), the massive Israel Ischemic Heart Disease Study (7) and the Framingham study. One of the longest-running, most comprehensive and most highly cited observational studies, the Framingham study was organized by Harvard investigators and continues to this day. When investigators analyzed the relationship between saturated fat intake, serum cholesterol and heart attack risk, they were so disappointed that they never formally published the results. We know from multiple sources that they found no significant relationship between saturated fat intake and blood cholesterol or heart attack risk***.

The next study is the Bogalusa Heart Study, published in 1978, which studied the diet and health of 10 year old American children (8). This study found an association by one statistical method, and none by a second method****. They found that the dietary factors they analyzed explained no more than 4% of the variation in blood cholesterol. Overall, I think this study lends very little support to the hypothesis.

Next is the Western Electric study, published in 1981 (9). This study found an association between saturated fat intake and blood cholesterol in middle-aged men in Chicago. However, the correlation was small, and there was no association between saturated fat intake and heart attack deaths. They cited two other studies that found an association between dietary saturated fat and blood cholesterol (and did not cite any of the numerous studies that found no association). One was a very small study conducted in young men doing research in Antarctica, which did not measure saturated fat but found an association between total fat intake and blood cholesterol (10). The other studied Japanese (Nagasaki and Hiroshima) and Japanese Americans in Japan, Hawai'i and California respectively (11).

This study requires some discussion. Published in 1973, it found a correlation between saturated fat intake and blood cholesterol in Japan, Hawai'i but not in California. The strongest association was in Japan, where going from 5 to 75 g/day of saturated fat (a 15-fold change!) was associated with an increase in blood cholesterol from about 175 to 200 mg/dL. However, I don't think this study offers much support to the hypothesis upon closer examination. Food intake in Japan was collected by 24-hour recall in 1965-1967, when the diet was roughly 3/4 white rice by calories. The lower limit of saturated fat intake in Japan was 5g/day, 1/12th what was typically eaten in Hawai'i and California, and the Japanese average was 16g, with most people falling below 10g. That is an extraordinarily low saturated fat intake. I think a significant portion of the Japanese in this study, living in the war-ravaged cities of Nagasaki and Hiroshima, were over-reliant on white rice and had a very peculiar and perhaps deficient diet.  Also, what is the difference between a diet with 5 and 75 grams of saturated fat per day?  Those diets are probably very different, in many other ways than their saturated fat content.

In Japanese-Americans living in Hawai'i, over a range of saturated fat intakes between 5 and 110 g/day, cholesterol went from 210 to 220 mg/dL. That was statistically significant but it's not exactly knocking my socks off, considering it's a 22-fold difference in saturated fat intake. In California, going from 15 to 110 g/day of saturated fat (7.3-fold change) was not associated with a change in blood cholesterol. Blood cholesterol was 20-30 mg/dL lower in Japan than in Hawai'i or California at any given level of saturated fat intake (e.g., Japanese eating 30g per day vs. Hawai'ians eating 30g per day). I think it's probable that saturated fat is not the relevant factor here, or at least it's much less influential than other factors. An equally plausible explanation is that people in the very low range of saturated fat intake are the rural poor who eat a  diet that differs in many ways from the diets at the upper end of the range, and other aspects of lifestyle such as physical activity also differ.

The most recent study was the Health Professional Follow-up study, published in 1996 (12). This was a massive, well funded study that found no relationship between saturated fat intake and blood cholesterol.

Conclusion

Of all the studies I came across, only the Western Electric study found a clear association between habitual saturated fat intake and blood cholesterol, and even that association was weak. The Bogalusa Heart study and the Japanese study provided inconsistent evidence for a weak association. The other studies I cited, including the bank workers' study, the Tecumseh study, the Evans county study, the Israel Ischemic Heart study, the Framingham study and the Health Professionals Follow-up study, found no association between the two factors.

Overall, the literature does not offer much support for the idea that long term saturated fat intake has a significant effect on the concentration of blood cholesterol in humans. If it's a factor at all, it must be rather weak. It may be that the diet-heart hypothesis rests in part on an over-reliance on the results of short-term controlled feeding studies.  It would be nice to see this discussed more often (or at all) in the scientific literature.  It is worth pointing out that the method used to collect diet information in most of these studies, the food frequency questionnaire, is not particularly accurate, so it's possible that there is a lot of variability inherent to the measurement that is partially masking an association.  In any case, these controlled studies have typically shown that saturated fat increases both LDL and HDL, so even if saturated fat did have a modest long-term effect on blood cholesterol, as hinted at by some of the observational studies, its effect on heart attack risk would still be difficult to predict.

The Diet-heart Hypothesis: Stuck at the Starting Gate
Animal Models of Atherosclerosis: LDL


* As a side note, many of these studies were of poor quality, and were designed in ways that artificially inflated the effects of saturated fat on blood lipids. For example, using a run-in period high in linoleic acid, or comparing a saturated fat-rich diet to a linoleic acid-rich diet, and attributing the differences in blood cholesterol to the saturated fat. Some of them used hydrogenated seed oils as the saturated fat. Although not always consistent, I do think that overall these studies support the idea that saturated fat does have a modest ability to increase blood cholesterol in the short term.

** Although I would love to hear comments from anyone who has done controlled diet trials. I'm sure this method had flaws, as it was applied in the 1960s.

*** Reference cited in the Tecumseh paper: Kannel, W et al. The Framingham Study. An epidemiological Investigation of Cardiovascular Diseases. Section 24: The Framingham Diet Study: Diet and the Regulation of Serum Cholesterol. US Government Printing Office, 1970.

**** Table 5 shows that the Pearson correlation coefficient for saturated fat intake vs. blood cholesterol is not significant; table 6 shows that children in the two highest tertiles of blood cholesterol have a significantly higher intake of saturated fat, unsaturated fat, total fat and sodium than the lowest tertile. The relationship between saturated fat and blood cholesterol shows no evidence of dose-dependence (cholesterol tertiles= 15.6g, 18.4g, 18.5g saturated fat). The investigators did not attempt to adjust for confounding factors.

Potato Diet Interpretation

If you read my post on December 16th, you know that Chris Voigt saw remarkable fat loss and improvements in health markers as a result of two months of eating almost nothing but potatoes. This has left many people scratching their heads, because potatoes are not generally viewed as a healthy food. This is partially due to the fact that potatoes are very rich in carbohydrate, which also happens to be a quickly digested type, resulting in a high glycemic index. The glycemic index refers to the degree to which a particular food increases blood glucose when it's eaten, and I've questioned the relevance of this concept to health outcomes in the past (1, 2, 3). I think Mr. Voigt's results once again argue against the importance of the glycemic index as a diet-health concept.

It's often pointed out that potatoes are low in vitamins and minerals compared to vegetables on a per-calorie basis, but I think it's a misleading comparison because potatoes are much more calorie-dense than most vegetables. Potatoes compare favorably to other starchy staples such as bread, rice and taro.

Over the course of two months, Mr. Voigt lost 21 pounds. No one knows exactly how much of that weight came out of fat and how much out of lean mass, but the fact that he reported a decrease in waist and neck circumference indicates that most of it probably came out of fat. Previous long-term potato feeding experiments have indicated that it's possible to maintain an athletic muscle mass on the amount of protein in whole potatoes alone (4). So yes, Mr. Voigt lost fat on a very high-carbohydrate diet (75-80% carbohydrate, up to 440g per day).

On to the most interesting question: why did he lose fat? Losing fat requires that energy leaving the body exceed energy entering the body. But of course, that's obvious but it doesn't get us anywhere. In the first three weeks of his diet, Mr. Voigt estimates that he was only eating 1,600 calories per day. Aha! That's why he lost weight! Well, yes. But let's look into this more deeply. Mr. Voigt was not deliberately restricting his calorie intake at all, and he did not intend this as a weight loss diet. In my interview, I asked him if he was hungry during the diet. He said that he was not hungry, and that he ate to appetite during this period, realizing only after three weeks that he was not eating nearly enough calories to maintain his weight*. I also asked him how his energy level was, and he said repeatedly that it was very good, perhaps even better than usual. Those were not idle questions.

Calorie restriction causes a predictable physiological response in humans that includes hunger and decreased energy. It's the starvation response, and it's powerful in both lean and overweight people, as anyone knows who has tried to lose fat by decreasing calorie intake alone. The fact that he didn't experience hunger or fatigue implies that his body did not think it was starving. Why would that be?

I believe Mr. Voigt's diet lowered his fat mass 'setpoint'. In other words, for whatever reason, the diet made his body 'want' to be leaner that it already was. His body began releasing stored fat that it considered excess, and therefore he had to eat less food to complete his energy needs. You see this same phenomenon very clearly in rodent feeding studies. Changes in diet composition/quality can cause dramatic shifts in the fat mass setpoint (5, 6). Mr. Voigt's appetite would eventually have returned to normal once he had stabilized at a lower body fat mass, just as rodents do.

Rodent studies have made it clear that diet composition has a massive effect on the level of fat mass that the body will 'defend' against changes in calorie intake (5, 6). Human studies have shown similar effects from changes in diet composition/quality. For example, in controlled diet trials, low-carbohydrate dieters spontaneously reduce their calorie intake quite significantly and lose body fat, without being asked to restrict calories (7). In Dr. Staffan Lindeberg's Paleolithic diet trials, participants lost a remarkable amount of fat, yet a recent publication from his group shows that the satiety (fullness) level of the Paleolithic group was not different from a non-Paleolithic comparison group despite a considerably lower calorie intake over 12 weeks (8, 9). I'll discuss this important new paper soon. Together, this suggests that diet composition/quality can have a dominant impact on the fat mass setpoint.

One possibility is that cutting the wheat, sugar, most vegetable oil and other processed food out of Mr. Voigt's diet was responsible for the fat loss.  Many people find, for example, that they lose fat simply by eliminating wheat from their diet.

Another possibility that I've been exploring recently is that changes in palatability (pleasantness of flavor) influence the fat mass setpoint. There is evidence in rodents that it does, although it's not entirely consistent. For example, rats will become massively obese if you provide them with chocolate flavored Ensure (a meal replacement drink), but not with vanilla or strawberry Ensure (10). They will defend their elevated fat mass against calorie restriction (i.e. they show a physiological starvation response when you try to bring them down to a lower weight by feeding them less chocolate Ensure) while they're eating chocolate Ensure, but as soon as you put them back on unpurified rodent pellets, they will lose fat and defend the lower fat mass. Giving them food in liquid or paste form often causes obesity, while the same food in solid pellet form will not. Eating nothing but potatoes is obviously a diet with a low overall palatability.

So I think that both a change in diet composition/quality and a decrease in palatability probably contributed to a decrease in Mr. Voigt's fat mass setpoint, which allowed him to lose fat mass without triggering a starvation response (hunger, fatigue).

The rest of his improvements in health markers were partially due to the fat loss, including his decreased fasting glucose, decreased triglycerides, and presumably increased insulin sensitivity. They may also have been partially due to a lack of industrial food and increased intake of certain micronutrients such as magnesium.

One of the most striking changes was in his calculated LDL cholesterol ("bad" cholesterol), which decreased by 41%, putting him in a range that's more typical of healthy non-industrial cultures including hunter-gatherers. Yet hunter-gatherers didn't eat nothing but potatoes, often didn't eat much starch, and in some cases had a high intake of fat and saturated fat, so what gives? It's possible that a reduced saturated fat intake had an impact on his LDL, given the relatively short timescale of the diet. But I think there's something mysterious about this setpoint mechanism that has a much broader impact on metabolism than is generally appreciated. For example, calorie restriction in humans has a massive impact on LDL, much larger than the impact of saturated fat (11). And in any case, the latter appears to be a short-term phenomenon (12). It's just beginning to be appreciated that energy balance control systems in the brain influence cholesterol metabolism.

Mr. Voigt's digestion appeared to be just fine on his potato diet, even though he generally ate the skins. This makes me even more skeptical of the idea that potato glycoalkaloids in common potato varieties are a health concern, especially if you were to eliminate most of the glycoalkaloids by peeling.

I asked Mr. Voigt about what foods he was craving during the diet to get an idea of whether he was experiencing any major deficiencies. The fact that Mr. Voigt did not mention craving meat or other high-protein foods reinforces the fact that potatoes are a reasonable source of complete protein. The only thing he craved was crunchy/juicy food, which I'm not sure how to interpret.

He also stopped snoring during the diet, and began again immediately upon resuming his normal diet, perhaps indicating that his potato diet reduced airway inflammation. This could be due to avoiding food allergies and irritants (wheat anyone?) and also fat loss.

Overall, a very informative experiment! Enjoy your potatoes.


*Until the last 5.5 weeks, when he deliberately stuffed himself beyond his appetite because his rapid weight loss worried him. Yet, even with deliberate overfeeding up to his estimated calorie requirement of 2,200 calories per day, he continued to lose weight. He probably was not quite reaching his calorie goal, or his requirement is higher than he thought.

Diet-Heart Controlled Trials: a New Literature Review

Many controlled studies have measured the cardiovascular effects of replacing animal ("saturated") fats with seed oils (predominantly the omega-6 polyunsaturated fat linoleic acid) in humans. A number of these studies recorded heart attacks and total mortality during the following 1-8 years. Several investigators have done meta-analyses (literature reviews) to try to tease out overall conclusions from these studies.

I'm pleased to point out a new meta-analysis of these controlled trials by Dr. Christopher Ramsden and colleagues (1). This paper finally cleans up the mess that previous meta-analyses have made of the diet-heart literature. One recent paper in particular by Dr. Dariush Mozaffarian and colleagues concluded that overall, the controlled trials show that replacing animal fat with linoleic acid (LA)-rich seed oils reduces heart attack risk (2). I disagreed strongly with their conclusion because I felt their methods were faulty (3).

Dr. Ramsden and colleagues pointed out several fundamental flaws in the review paper by Dr. Mozaffarian and colleagues, as well as in the prevailing interpretation of these studies in the scientific literature in general. These overlap with the concerns that I voiced in my post (4):

1. Omission of unfavorable studies, including the Rose corn oil trial and the Sydney diet-heart trial.
2. Inclusion of weak trials with major confounding variables, such as the Finnish mental hospital trial.
3. Failure to distinguish between omega-6 and omega-3 fatty acids.
4. Failure to acknowledge that seed oils often replaced large quantities of industrial trans fats in addition to animal fat in these trials.
   

Dr. Ramsden and colleagues accounted for all of these factors in their analysis, which has never been done before. They chose inclusion criteria* that made sense, and stuck with them. In addition, they did an impressive amount of historical work, digging up old unpublished data from these trials to determine the exact composition of the control and experimental diets. The paper is published in the British Journal of Nutrition, an excellent journal, and overall is written in a scientific and professional manner.

What did they find?

• Interventions that replaced animal and trans fat with seed oils that were rich in LA but low in omega-3 caused a non-significant trend toward increased heart attacks (13% increase) and overall mortality.
  
• Interventions that replaced animal and trans fat with a combination of LA and omega-3 fats significantly reduced heart attacks (by 22%). The numbers for total mortality followed a similar trend.

In other words, LA-rich seed oils do not prevent heart attacks (and may actually promote them), but correcting an omega-3 deficiency and reducing industrial trans fat intake may be protective. This is similar to what I've been saying for a while now, based on my own interpretation of the same studies and others. However, Dr. Ramsden and colleagues have taken the idea to a new level by their thorough and sophisticated detective work and analysis. For example, I didn't realize that in virtually all of these controlled trials, the intervention group reduced its trans fat intake substantially in addition to reducing animal fat. From the paper:

...experimental diets replaced common ‘hard’ margarines, industrial shortenings and other sources of [trans fat] in all seven of the [controlled trials] included in the meta-analysis by Mozaffarian et al. The mean estimated [trans fat] content of the seven control diets was 3·0 [% of calories] (range 1·5–9·6 [%]).
...the displacement of [trans fat], rather than the substitution of mixed n-3/n-6 [polyunsaturated fat] for [saturated fat], may account for some or all of the 22% reduction in non-fatal [heart attacks and heart attack] death in our meta-analysis. By contrast, the increased [heart attack] risks from n-6 specific [polyunsaturated fat] diets in our meta-analysis may be underestimated as n-6 [polyunsaturated fat] also replaced substantial quantities of [trans fat] (Table 3). The consistent trends towards increased [heart attack] risk of n-6 specific [polyunsaturated fat] diets may have become significant if the n-6 [polyunsaturated fat] replaced only [saturated fat], instead of a combination of [saturated fat] and [trans fat].

In other words, it looks like trans fat is probably the issue, not animal fat, but these trials replaced both simultaneously so we can't know for sure. I will note here that trans fat does not generally promote atherosclerosis (thickening and hardening of arteries) in animal models, so if it does truly increase heart attack risk as many studies suggest, it's probably through a mechanism that is independent of atherosclerosis.

The article also contains an excellent discussion of the Finnish mental hospital trial (5, 6) and why it was excluded from the meta-analysis, in which Dr. Ramsden and colleagues point out major design flaws, some of which I was not aware of. For example, trans fat intake was on average 13 times higher in the control groups than in the experimental groups. In addition, one of the control groups received more than twice as much of the antipsychotic drug thioridazine, which is known to be highly toxic to the heart, as any other group. Ouch. I'm glad to see this study finally discussed in an open and honest manner. I discussed my own problems with the Finnish trial in an earlier post (7).

I was also glad to see an open discussion of the Oslo Diet-heart study (8), in which diet changes led to a reduction in heart attack risk over five years. Dr. Mozaffarian and colleagues included it in their analysis as if it were a controlled trial in which animal fat was replaced by seed oils only. In reality, the investigators changed many variables at once, which I had also pointed out in my critique of Dr. Mozaffarian's meta-analysis (9). Here's what Dr. Ramsden and colleagues had to say about it:

First, experimental dieters were instructed to substitute fish, shellfish and ‘whale beef’ for meats and eggs, and were actually supplied with ‘considerable quantities of Norwegian sardines canned in cod liver oil, which proved to be popular as a bread spread’(32)... Second, the experimental group consumed massive amounts of soybean oil, which provided large quantities of both LA (15·6 en %) and ALA (2·7 en %). ALA consumption was about 4·5 times average US intake(42), or about twelve typical flax oil pills (1 g pill ¼ 560 mg ALA) per d. In addition, the fish and cod liver oil consumption provided Oslo (598N latitude) dieters with 610 IU (15·25 mg) of daily vitamin D3, recently linked to lower blood pressure, plaque stabilisation, and reduced [heart attack risk] (64). Furthermore, experimental dieters were encouraged to eat more nuts, fruits, and vegetables; to limit animal fats; and to restrict their intake of refined grains and sugar.

trans fat intake was also reduced substantially by excluding margarine in the experimental group. Other review papers have used this trial as a justification to replace animal fat with seed oils. Hmm... The only reason they get away with this is because the trial was published in 1966 and almost no one today has actually read it.

One criticism I have of Dr. Ramsden's paper is that they used the Oslo trial in their analysis, despite the major limitation described above. However, they were extremely open about it and discussed the problem in detail. Furthermore, the overall result would have been essentially the same even if they had excluded the Oslo trial from the analysis.

Overall, the paper is an excellent addition to the literature, and I hope it will bring a new level of sophistication to the dialogue on dietary prevention of cardiovascular disease. In the meantime, brace yourselves for an avalanche of criticism from the seed oil brigade.


* Guidelines that determine which studies to include in the analysis. For example, you want to exclude any study that wasn't randomized, because it will not be interpretable from a statistical standpoint. You also want to exclude trials where major variables differ between groups besides the specific variable you're trying to test. The Finnish mental hospital trial fails by both criteria.

Income, obesity, and heart disease in US states

The figure below combines data on median income by state (bottom-left and top-right), as well as a plot of heart disease death rates against percentage of population with body mass index (BMI) greater than 30 percent. The data are recent, and have been provided by CNN.com and creativeclass.com, respectively.

Heart disease deaths and obesity are strongly associated with each other, and both are inversely associated with median income. US states with lower median income tend to have generally higher rates of obesity and heart disease deaths.

The reasons are probably many, complex, and closely interconnected. Low income is usually associated with high rates of stress, depression, smoking, alcoholism, and poor nutrition. Compounding the problem, these are normally associated with consumption of cheap, addictive, highly refined foods.

Interestingly, this is primarily an urban phenomenon. If you were to use hunter-gatherers as your data sources, you would probably see the opposite relationship. For example, non-westernized hunter-gatherers have no income (at least not in the “normal” sense), but typically have a lower incidence of obesity and heart disease than mildly westernized ones. The latter have some income.

Tragically, the first few generations of fully westernized hunter-gatherers usually find themselves in the worst possible spot.

The China Study II: Wheat flour, rice, and cardiovascular disease

In my last post on the China Study II, I analyzed the effect of total and HDL cholesterol on mortality from all cardiovascular diseases. The main conclusion was that total and HDL cholesterol were protective. Total and HDL cholesterol usually increase with intake of animal foods, and particularly of animal fat. The lowest mortality from all cardiovascular diseases was 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 was quite large; the mortality in the lowest range was approximately 3.3 times higher than in the highest.

This post focuses on the intake of two main plant foods, namely wheat flour and rice intake, and their relationships with mortality from all cardiovascular diseases. After many exploratory multivariate analyses, wheat flour and rice emerged as the plant foods with the strongest associations with mortality from all cardiovascular diseases. Moreover, wheat flour and rice have a strong and inverse relationship with each other, which suggests a “consumption divide”. Since the data is from China in the late 1980s, it is likely that consumption of wheat flour is even higher now. As you’ll see, this picture is alarming.

The main model and results

All of the results reported here are from analyses conducted using WarpPLS. Below is the model with the main results of the analyses. (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; MVASC = mortality from all cardiovascular diseases (ages 35-69); TKCAL = total calorie intake per day; WHTFLOUR = wheat flour intake (g/day); and RICE = and rice intake (g/day).

The variables to the left of MVASC are the main predictors of interest in the model. The one to the right is a control variable – SexM1F2. 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, the model above seems to be telling us that:

- As rice intake increases, wheat flour intake decreases significantly (beta=-0.84; P<0.01). This relationship would be the same if the arrow pointed in the opposite direction. It suggests that there is a sharp divide between rice-consuming and wheat flour-consuming regions.

- As wheat flour intake increases, mortality from all cardiovascular diseases increases significantly (beta=0.32; P<0.01). This is after controlling for the effects of rice and total calorie intake. That is, wheat flour seems to have some inherent properties that make it bad for one’s health, even if one doesn’t consume that many calories.

- As rice intake increases, mortality from all cardiovascular diseases decreases significantly (beta=-0.24; P<0.01). This is after controlling for the effects of wheat flour and total calorie intake. That is, this effect is not entirely due to rice being consumed in place of wheat flour. Still, as you’ll see later in this post, this relationship is nonlinear. Excessive rice intake does not seem to be very good for one’s health either.

- Increases in wheat flour and rice intake are significantly associated with increases in total calorie intake (betas=0.25, 0.33; P<0.01). This may be due to wheat flour and rice intake: (a) being themselves, in terms of their own caloric content, main contributors to the total calorie intake; or (b) causing an increase in calorie intake from other sources. The former is more likely, given the effect below.

- The effect of total calorie intake on mortality from all cardiovascular diseases is insignificant when we control for the effects of rice and wheat flour intakes (beta=0.08; P=0.35). This suggests that neither wheat flour nor rice exerts an effect on mortality from all cardiovascular diseases by increasing total calorie intake from other food sources.

- Being female is significantly associated with a reduction in mortality from all cardiovascular diseases (beta=-0.24; P=0.01). This is to be expected. In other words, men are women with a few design flaws, so to speak. (This situation reverses itself a bit after menopause.)

Wheat flour displaces rice

The graph below shows the shape of the association between wheat flour intake (WHTFLOUR) and rice intake (RICE). The values are provided in standardized format; e.g., 0 is the mean (a.k.a. average), 1 is one standard deviation above the mean, and so on. The curve is the best-fitting U curve obtained by the software. It actually has the shape of an exponential decay curve, which can be seen as a section of a U curve. This suggests that wheat flour consumption has strongly displaced rice consumption in several regions in China, and also that wherever rice consumption is high wheat flour consumption tends to be low.

As wheat flour intake goes up, so does cardiovascular disease mortality

The graphs below show the shapes of the association between wheat flour intake (WHTFLOUR) and mortality from all cardiovascular diseases (MVASC). In the first graph, the values are provided in standardized format; e.g., 0 is the mean (or average), 1 is one standard deviation above the mean, and so on. In the second graph, the values are provided in unstandardized format and organized in terciles (each of three equal intervals).

The curve in the first graph is the best-fitting U curve obtained by the software. It is a quasi-linear relationship. The higher the consumption of wheat flour in a county, the higher seems to be the mortality from all cardiovascular diseases. The second graph suggests that mortality in the third tercile, which represents a consumption of wheat flour of 501 to 751 g/day (a lot!), is 69 percent higher than mortality in the first tercile (0 to 251 g/day).

Rice seems to be protective, as long as intake is not too high

The graphs below show the shapes of the association between rice intake (RICE) and mortality from all cardiovascular diseases (MVASC). In the first graph, the values are provided in standardized format. In the second graph, the values are provided in unstandardized format and organized in terciles.

Here the relationship is more complex. The lowest mortality is clearly in the second tercile (206 to 412 g/day). There is a lot of variation in the first tercile, as suggested by the first graph with the U curve. (Remember, as rice intake goes down, wheat flour intake tends to go up.) The U curve here looks similar to the exponential decay curve shown earlier in the post, for the relationship between rice and wheat flour intake.

In fact, the shape of the association between rice intake and mortality from all cardiovascular diseases looks a bit like an “echo” of the shape of the relationship between rice and wheat flour intake. Here is what is creepy. This echo looks somewhat like the first curve (between rice and wheat flour intake), but with wheat flour intake replaced by “death” (i.e., mortality from all cardiovascular diseases).

What does this all mean?

- Wheat flour displacing rice does not look like a good thing. Wheat flour intake seems to have strongly displaced rice intake in the counties where it is heavily consumed. Generally speaking, that does not seem to have been a good thing. It looks like this is generally associated with increased mortality from all cardiovascular diseases.

- High glycemic index food consumption does not seem to be the problem here. Wheat flour and rice have very similar glycemic indices (but generally not glycemic loads; see below). Both lead to blood glucose and insulin spikes. Yet, rice consumption seems protective when it is not excessive. This is true in part (but not entirely) because it largely displaces wheat flour. Moreover, neither rice nor wheat flour consumption seems to be significantly associated with cardiovascular disease via an increase in total calorie consumption. This is a bit of a blow to the theory that high glycemic carbohydrates necessarily cause obesity, diabetes, and eventually cardiovascular disease.

- The problem with wheat flour is … hard to pinpoint, based on the results summarized here. Maybe it is the fact that it is an ultra-refined carbohydrate-rich food; less refined forms of wheat could be healthier. In fact, the glycemic loads of less refined carbohydrate-rich foods tend to be much lower than those of more refined ones. (Also, boiled brown rice has a glycemic load that is about three times lower than that of whole wheat bread; whereas the glycemic indices are about the same.) Maybe the problem is wheat flour's  gluten content. Maybe it is a combination of various factors, including these.

Reference

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

Acknowledgment and notes

- Many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. The data is from this site, created by those researchers to disseminate their work in connection with 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 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). Whenever nonlinear relationships were modeled, the path coefficients were automatically corrected by the software to account for nonlinearity.

- The software used here identifies non-cyclical and mono-cyclical relationships such as logarithmic, exponential, and hyperbolic decay relationships. Once a relationship is identified, data values are corrected and coefficients calculated. This is not the same as log-transforming data prior to analysis, which is widely used but only works if the underlying relationship is logarithmic. Otherwise, log-transforming data may distort the relationship even more than assuming that it is linear, which is what is done by most statistical software tools.

- 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).

- Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance, 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 assumptions 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.

- Since all the data was collected around the same time (late 1980s), this analysis assumes a somewhat static pattern of consumption of rice and wheat flour. In other words, let us assume that variations in consumption of a particular food do lead to variations in mortality. Still, that effect will typically take years to manifest itself. This is a major limitation of this dataset and any related analyses.

- Mortality from schistosomiasis infection (MSCHIST) does not confound the results presented here. Only counties where no deaths from schistosomiasis infection were reported have been included in this analysis. Mortality from all cardiovascular diseases (MVASC) was measured using the variable M059 ALLVASCc (ages 35-69). See this post for other notes that apply here as well.