Thursday, April 29, 2010

Tender cuts of meat for the grill: Filet mignon and bison

Filet mignon is one of the tenderest cuts of beef. It is also one of my favorites. Filet mignon comes from the tenderloin area (see this picture), which is not a weight-bearing area and thus is very tender. The bison cuts I get here in South Texas are close in terms of tenderness, but not as tender, probably because they are from the round area.

One steak of either filet mignon or bison will yield about 100 g of cooked meat, with 30 g of protein and 10 g of fat. About half of that fat will be saturated and half monounsaturated (as in olive oil). It will provide you with plenty of vitamins (particular B vitamins) and minerals. Good amounts of selenium, phosphorus, zinc and potassium.

On the photo below (click on it to enlarge), the bison steak is at the top. The other pieces are all filet mignon cuts. They are all medium-cooked. I cooked two plates of these, for 6 people. All ate to satisfaction, with a side salad. The leftovers are delicious for breakfast in small amounts.

For the filet mignon, I think you really have to go to a specialty meats store (butcher) and make sure that they cut the smaller tail end of the tenderloin muscle. You will be paying a lot for it, so it makes sense to be choosy. Experience butchers will cut it right in front of you and won’t mind your choosiness.

Below is a simple recipe; simple like most of the recipes on this blog. I like my meals quick and delicious.

- Prepare some dry seasoning powder by mixing sea salt, garlic power, chili powder, and a small amount of cayenne pepper.
- Season the steaks at least 2 hours prior to placing them on the grill.
- Grill with the lid on, checking the meat every 10 minutes or so. (I use charcoal, one layer only to avoid burning the surface of the meat.) Turn it frequently, always putting the lid back on.
- If you like it rare, 20 minutes (or a bit less) may be enough.

These are as tender as any piece of beef can possibly get. No need for any tenderizer juices during seasoning. If you are doing both filet mignon and bison together, either eat only bison or bison first. Because once you taste the filet mignon, the bison cut may taste a bit hard!

For me the filet mignon is a 10-dollar per pound treat for special occasions. The price of the bison cut is about the same, at least here in Laredo, Texas, where I get it shipped from Dakota via my local supermarket. You can also get it online.

By the way, some folks like to say that bison is the “salmon of the prairie”. This is in reference to bison’s omega-3 content. Well, here is the polyunsaturated fatty acid composition of 100 g of bison steak: 29 mg of omega-3, and 197 mg of omega-6. For salmon it is 1424 mg of omega-3, and 113 mg of omega-6.

Salmon of the prairie or not, I love it!

Monday, April 26, 2010

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

Friday, April 23, 2010

There are more geniuses among men than among women, and more idiots too

Deary and colleagues (2007) conducted an interesting study on differences in intelligence scores among men and women. In the context of this blog, this study highlights yet one more counterintuitive and intriguing aspect of Darwinian evolution, adding to points previously made in other posts (see here, and here). Evolution may look simple at first glance, but that is a bit of a mirage. In my opinion, to really understand it one has to understand the mathematics underlying it, a lot of which comes from the field of population genetics.

What makes the study by Deary and colleagues (2007) particularly interesting is that its participants were opposite-sex siblings. This helped control for the influence of environmental factors. The downside is that the effect sizes might have been decreased, because of the high gene correlation among siblings, so we could expect larger differences between unrelated groups of men women. The differences, as you will see, are not in overall scores, but in score dispersion.

Let us get straight to the point made by the study. On average, men and women seem to score equally well on intelligence tests. The main difference is that there is more variation in the scores achieved by men than by women, which leads to an interesting effect: there are more geniuses and more idiots among men than among women.

This does NOT mean that a man’s genius is of a higher order; just that there is a tendency for more men to be geniuses (and idiots) than women in any random population sample. The women who are geniuses can be super geniuses, like two-time Nobel Prize winner Marie Curie, the first PERSON to receive such an honor. Albert Einstein is said that have greatly admired her intelligence.

As an illustration of this score dispersion effect, Deary and colleagues (2007) note that: “… for example, in terms of indices of scientific achievement, men were awarded 545 out of the 557 Nobel prizes awarded for science.” On the “idiot” end of the scale: there are a lot more men than women in prison, and one common denominator of prison inmates is that they tend to score very low on intelligence tests. (This is not to say that all criminals have low intelligence; perhaps mostly the ones that get caught do.)

Having said that, it is important to acknowledge that there are multiple types of intelligence, and even multi-indicator intelligence coefficients are usually poor approximations of an overall measure of intelligence (if there is one). This does not invalidate the main point of this post, which is related to score variability.

The table below (from: Deary and colleagues, 2007; click on it to enlarge; full reference at the end of this post) shows scores obtained by men and women (1,292 pairs of opposite-sex siblings) in various subtests of the Armed Services Vocational Aptitude Battery (ASVAB) test.

Note that nearly all of the differences between means (i.e., averages) are significant, but the direction of the differences (captured by the signs of the Cohen’s d coefficients, which are measures of effect size) varies a lot. That is, on several subtests (e.g., “Arithmetic”) men score higher, but in others (e.g., “Numerical operations”) women score higher. It all comes down to men and women scoring equally well overall.

Now look at the columns showing the standard deviations (“SD”) for men and women. In all subtests but two (“Coding speed” and “Numerical operations”) the standard deviation is higher for men; in many cases significantly higher (e.g., 44 percent higher for “Mechanical comprehension”). The standard deviations are about the same for “Coding speed” and “Numerical operations”. What this means is that variability in scores is nearly always higher, often significantly higher, among men than among women. I prepared the schematic figure below to illustrate the effect that this has on the numbers of individuals at the extremes.

The figure above shows two (badly drawn) quasi-normal distributions of scores. (This post shows a better illustration of a normal distribution.) The red curve refers to a distribution with a lower standard deviation than the blue curve; the latter is flatter. Each point on a curve reflects the number of individuals obtaining a particular score, which would be indicated on the horizontal axis. The number of individuals with that score is on the vertical axis. As you can see, the numbers of individuals scoring very high and low (geniuses and idiots, if the scores reflected intelligence) are greater for the blue curve, which is the curve with the higher standard deviation (higher dispersion of scores). The farther one goes to the left or right (the extremes), the bigger this difference becomes.

What does this have to do with evolution?

Well, there are a few possibilities, two of which appear to be particularly compelling. Maybe this effect is due to a combination of these two.

One is that ancestral women, like women today, selected mating partners based on a wide range of traits. Ancestral men on the other hand, like modern men, focused on a much smaller set of traits (Buss, 1995). The end result is more variation in traits, generally speaking, among men than among women. This refers to traits in general, not only intelligence. For example, there seems to be more variation in height among men than among women.

The other possible explanation is that, in our ancestral past, staying out of the extremes of intelligence was associated with higher survival success in both sexes. It seems that the incidence of certain types of mental disease (e.g., schizophrenia) is quite high among geniuses. This leads to more deaths due to related issues – suicide, depression leading to the metabolic syndrome, etc. And this is today, where geniuses can find many opportunities to “shine” in our complex urban societies. In our ancestral past the cognitive demands would have been much lower, and so would the practical value of being a genius.

If staying out of the extremes has indeed enhanced survival success in our evolutionary past, then it is reasonable to expect more women to fit that pattern than men. As with almost any “thing” that enhances survival success, women (especially pre-menopausal) naturally have more of that “thing” than men (e.g., HDL cholesterol).

The reason is that women are more important for the survival of any population than men; today and 1 million years ago. A population of 99 women and 1 man can potentially generate 99 children every few years. Here inbreeding in subsequent generations will be a problem, but that is better than extinction. A population with 99 women and 99 men (or even 1,000 men) will not generate significantly more children.


Buss, D.M. (2003). The evolution of desire: Strategies of human mating. New York, NY: Basic Books.

Deary, I.J., Irwing, P., Der, G., & Bates, T.C. (2007). Brother–sister differences in the g factor in intelligence: Analysis of full, opposite-sex siblings from the NLSY1979. Intelligence, 35(5), 451-456.

Wednesday, April 21, 2010

Interesting links


Health Data Analysis with HCE

Managing Multiple Health Variables with HCE

Ned Kock interviewed on Episode 477 of The Livin’ La Vida Low-Carb Show with Jimmy Moore

Reality check

Many people fall prey to scams that are presented as valid alternative medicine options for diseases that are psychosomatic in nature. The sites below can be helpful in avoiding these scams.


Somatic symptom disorder

Various health sites

Below is a list of links to web sites that deal with health issues in general. These have been moved from the previous “favorite links” area to this post so that some space could be saved on the main page of the blog. Some of these sites are excellent sources of research-based and reliable information. Others are somewhat light in content, but still interesting.

Alan Aragon

Animal Pharm

Ancestralize Me!

Anthony Colpo

Arthur De Vany

At Darwin's Table

Athletics by Nature

Barefoot Ted's Adventures

Beef and Whiskey

Big Muscles Fast

Blaine's Low Carb Kitchen

Blood Sugar 101

Body by Science

Body Recomposition


Brad Pilon's Blog

Canibais e Reis

Cholesterol and Health

Colorado State University's Physiologic Effects of Insulin

Conditioning Research

Cooling Inflammation

Cut the Carb

David Mendosa

Diabetes Update

Diet Doctor

Discover Magazine Online

Dr. Bernstein's Diabetes Solution

Dr. Gabe Mirkin

Dr. Michael R. Eades

Dr. Nemechek's Integrative Medicine

Dr. Ron Rosedale

Entropy Production

Ernestine Shepherd

Evolution for Everyone

Evolutionary Psychiatry

Evolving Thoughts by John Wilkins

Exercise Prescription on the Net

Experiments in Lifestyle Design by Tim Ferriss

Fat Head

Fit 2 Fat 2 Fit

Free the Animal

Grassroots Health

Girl Gone Primal

Gnolls by J. Stanton

Health News Review

Healthcare Epistemocrat


Homo Consumericus

Hunt, Gather, Love

Hunter Gatherer


ItsTheWooo's The Scribble Pad

John Hawks Weblog

Julianne's Paleo & Zone Nutrition Blog

Lean Gains

Low-Carb for You

Lucas Tafur

Mark's Daily Apple

Matt Metzgar's Blog

Maxwell Murphy

Metabolism Society

Michael Barker's Type 2 Ketosis Prone Diabetes

Muscle and the City


My Carb Sane-Asylum

My Carb Sane Chronicles

Natural Messiah


Nigee's Diet & Nutrition Blog

Nourishing by Heart

Nutrition and Physical Regeneration

Nutrition, Health & Heart Disease

Omega-6 Fat News Commentary

Paleo Clinic

Paleo Diet

Paleo Hacks


Patrick Ward's Optimum Sports Performance

Pay Now Live Later

Philosophy of Weight Management

Prague Stepchild

Primal Montain

Primal Wisdom

Principle Into Practice

Protein Power


Rambling Outside the Box

Ramblings of a Carnivore

Raw Food SOS

Ray Peat

Robb Wolf

Ron Brown's The Myth of Loose Skin

Sandwalk by Laurence Moran

Scooby's Home Bodybuilding Workouts

Seth Roberts's Blog

Skyler Tanner

Sock Doc - Natural Injury Treatment & Prevention

Son of Grok

Spark of Reason

Stella Style

Survivorman - Discovery

That Paleo Guy

The Carnivore Health Weblog

The Daily Lipid

The Evolution & Medicine Review

The Heart Scan

The Healthy Skeptic

The Livin' La Vida Low-Carb Show

The Paleo Diet

The Paleo Diet Blog

The Weston A. Price Foundation

Theory to Practice

Vitamin D Council

Vitamin D Wiki


Whole Health Source

Wikipedia - Strength Training

Wildly Fluctuating

Zero Currency, Moneyless World - By Daniel Suelo

Zeroing in on Recovery

Zoe Harcombe

180 Degree Health

Sunday, April 18, 2010

Ketones and Ketosis: Physiological and pathological forms

Ketones are compounds that have a specific chemical structure. The figure below (from: Wikipedia) shows the chemical structure of various types of ketones. As you can see, all ketones share a carbonyl group; that is the “O=” part of their chemical structure. A carbonyl group is an oxygen atom double-bonded to a carbon atom.

Technically speaking, many substances can be classified as ketones. Not all of these are involved in the same metabolic processes in humans. For example, fructose is technically a ketone, but it is not one of the three main ketones produced by humans from dietary macronutrients (discussed below), and is not metabolized in the same way as are those three main ketones.

Humans, as well as most other vertebrates, produce three main ketones (also known as ketone bodies) from dietary macronutrients. These are acetone, acetoacetate and beta-hydroxybutyrate. Low carbohydrate diets tend to promote glycogen depletion, which in turn leads to increased production of these ketones. Glycogen is stored in the liver and muscles. Liver glycogen is used by the body to maintain blood glucose levels within a narrow range in the fasted state. Examples of diets that tend to promote glycogen depletion are the Atkins Diet and Kwaśniewski’s Optimal Diet.

A search for articles on ketosis in scientific databases usually returns a large number of articles dealing with ketosis in cows. Why? The reason is that ketosis reduces milk production, by both reducing the amount of fat and glucose available for milk synthesis. In fact, ketosis is referred to as a “disease” in cows.

In humans, most articles on ketosis refer to pathological ketosis (a.k.a. ketoacidosis), especially in the context of uncontrolled diabetes. One notable exception is an article by Williamson (2005), from which the table below was taken. The table shows ketone concentrations in the blood under various circumstances, in mmol/l.

As you can see, relatively high concentrations of ketones occur in newborn babies (neonate), in adults post-exercise, and in adults fed a high fat diet. Generally speaking, a high fat diet is a low carbohydrate diet, and a high carbohydrate diet is a low fat diet. (One occasionally sees diets that are high in both carbohydrates and fat; which seem excellent at increasing body fat and thus reducing life span. This diet is apparently popular among sumo wrestlers, where genetics and vigorous exercise usually counter the negative diet effects.)

Situations in which ketosis occurs in newborn babies (neonate), in adults post-exercise, and in adults fed a high fat diet are all examples of physiological, or benign, ketosis. Ketones are also found in low concentrations in adults fed a standard American diet.

Ketones are found in very high concentrations in adults with untreated diabetes. This is an example of pathological ketosis, even though ketones are produced as part of a protective compensatory mechanism to spare glucose for the brain and red blood cells (which need glucose to function properly). Pathological ketosis leads to serum ketone levels that can be as much as 80 times (or more) those found in physiological ketosis.

Serum ketone concentrations increase proportionally to decreases in stored glycogen and, when glycogen is low or absent, correlate strongly (and inversely) with blood glucose levels. In some individuals glycogen is practically absent due to a genetic condition that leads to hepatic glycogen synthase deficiency. This is a deficiency of the enzyme that promotes glycogen synthesis by the liver. The figure below (also from Williamson, 2005) shows the variations in glucose and ketone levels in a child with glycogen synthase deficiency.

What happened with this child? Williamson answers this question: “It is of interest that this particular child suffered no ill effects from the daily exposure to high concentrations of ketone bodies, underlining their role as normal substrates for the brain when available.”

Unlike glucose and lipoprotein-bound fats (in VLDL, for example), unused ketones cannot be converted back to substances that can be stored by the body. Thus excess ketones are eliminated in the urine; leading to their detection by various tests, e.g., Ketostix tests. This elimination of unused ketones in the urine is one of the reasons why low carbohydrate diets are believed to lead to enhanced body fat loss.

In summary, ketones are present in the blood most of the time, in most people, whether they are on a ketogenic diet or not. If they do not show up in the urine, it does not mean that they are not present in the blood; although it usually means that their concentration in the blood is not that high. Like glucose, ketones are soluble in water, and thus circulate in the blood without the need for carriers (e.g., albumin, which is needed for the transport of free fatty acids; and VLDL, needed for the transport of triglycerides). Like glucose, they are used as sources of energy by the brain and by muscle tissues.

It has been speculated that ketosis leads to accelerated aging, through the formation of advanced glycation endproducts (AGEs), a speculation that seems to be largely unfounded (see this post). It is difficult to believe that a metabolic process that is universally found in babies and adults post-exercise would have been favored by evolution if it led to accelerated aging.


Williamson, D.H. (2005). Ketosis. Encyclopedia of Human Nutrition, 91-98.

Tuesday, April 13, 2010

Long-term adherence to Dr. Kwaśniewski’s Optimal Diet: Healthy with high LDL cholesterol

This is a study (Grieb, P. et al., 2008; full reference at the end of this post) that I read a few years ago, right after it came out, and at the time I recall thinking about the apparent contradiction between the positive effects of the Optimal Diet and the very elevated LDL cholesterol levels among the participants. I say “contradiction” because of the established and misguided dogma among medical doctors, particularly general practitioners, that decreasing LDL cholesterol levels is the best strategy to avoid cardiovascular disease.

The Optimal Diet is one of the best examples of a healthy diet where LDL cholesterol levels are generally high, in fact much higher than most people are willing to accept as healthy today. (In this study, LDL cholesterol levels were calculated based on the Friedewald equation.)

It is not uncommon to see people concerned about their high LDL cholesterol levels after adopting a low carbohydrate diet. (A low carbohydrate diet is, generally speaking, a high fat diet.) This study shows that this is a rather common thing, and also that it is not something that those who experience it should be too concerned about. To be convinced of this, one can always do a VAP test (see this post for a link to a sample VAP test report) and check his or her LDL particle pattern.

The study presents the Optimal Diet as the Polish equivalent to the Atkins diet. It states that the Diet’s main characteristic is maintaining the proportion of proteins:fat:carbs. in the range of 1:2.5-3.5:0.5, with no restriction on the amount of food consumed. In fact, as you will see in this post, more than 70 percent of the calories consumed by the study participants came from fat.

Easily digestible carbohydrate-rich foods are not part of the Optimal Diet. More specifically, the following foods were listed as not being allowed in the Optimal Diet: sucrose, sweets, honey, jam, white rice, bread, starches in general, beans, potatoes (only in small amounts), and sweetened drinks. Also, the Optimal Diet is definitely a low carbohydrate diet, but not what is often referred to as a "very low carbohydrate diet". In this study, the typical carbohydrate intake per day was around 60 g.

Thirty-one healthy people participated in the study, 17 women and 14 men. The average age was 51.7 (standard deviation: 16.6). They had self-reportedly adhered to the Optimal Diet for at least 1 year prior to the study; the average period of adherence was 4.1 years (standard deviation: 1.9). So, the vast majority had been on the diet for more than 2.2 years, about half for 4.1 years or more, and about one-sixth for more than 6 years. (Check this post if you want to know how these figures can be calculated based on the average period of adherence and the standard deviation.)

The table below (click on it to enlarge) shows anthropometric and physiologic characteristics of the participants. Note that longer adherence to the Optimal Diet (right end of the table) was associated with lower systolic and diastolic blood pressure, as well as lower body mass index (BMI). (It was also associated with lower height and BMR, so I am guessing that more women tended to be long-term adherers than men.) Most of the participants had BMIs in the normal range, with only one in the obese category. That was a 43-year-old man who followed the diet for 1.5 years; he had a BMI of 34.1.

The macronutrient distribution of the Optimal Diet is shown on the table below (click on it to enlarge), as followed by the participants. As you can see, protein intake was not that high; about 53.9 g per day on average for men, a bit less for women. Note the percentage of calories from fat: more than 77 percent for men and 72 percent for women. Given the BMIs just discussed, one can safely say based on this that eating a lot of fat did not make the participants fat.

The table below (click on it to enlarge) has some interesting health markers. Note that free fatty acids (FFAs) were elevated. This is to be expected, as these folks were burning fat for energy most of the time, and not as much glucose. The FFAs are not really “free”, but bound to a protein called albumin, which is abundant in human blood. FFAs yield large quantities of adenosine triphosphate (ATP), the main energy “currency” used by the body.

These levels of FFAs are also usually associated with mild ketosis, where ketones are produced by the body and used for energy. Unlike albumin-bound FFAs, ketones are soluble in water, and thus circulate freely through the blood. The mild ketosis experienced by the participants was possibly to the point where ketones showed in the urine. The article mentions this, and provides a measure of beta-HB (beta-hydroxybutyrate, a ketone body), which is elevated as expected, but does not provide urine or other blood ketone measures (e.g., blood acetone levels). Also note the fairly healthy fasting glucose levels, slightly higher in men than in women, but fairly low overall. Fairly healthy insulin levels as well; at the high end of what Stephan at Whole Health Source would recommend, but still significantly lower than the average insulin level in the U.S. at the time of the article's publication.

Finally, the table below (click on it to enlarge) shows lipids and a few other measures. Total cholesterol was on average a bit more than 278 mg/dL. LDL cholesterol was a bit higher than 188 mg/dL on average; high enough to make most doctors cringe today. Based on the means and standard deviations provided, we can estimate that about 16 percent of the participants had LDL cholesterol levels higher than 228.1 mg/dL. About 2.5 percent of the participants had LDL cholesterol levels higher than 268 mg/dL. And this is all after adhering to the diet for a relatively long period of time; even higher LDL cholesterol levels might have occurred right after adoption.

Yet average HDL cholesterol was a very high and protective 71.6 mg/dL. This high HDL and the relatively low triglycerides suggest a large-buoyant non-atherogenic LDL particle pattern.

Average HOMA(IR), a measure of insulin resistance, was a low 1.35 mU/mmol; strongly indicating, together with the relatively low fasting glucose levels, that the participants were far from being pre-diabetic, let alone diabetic.

Diabetes is a strong risk factor for cardiovascular disease, and many other health complications; much more so than elevated LDL cholesterol.

The Optimal Diet does not seem to be a diet for bodybuilders, but I would say that, overall, Peter at Hyperlipid has chosen a diet that makes some sense.


Grieb, P. et al. (2008). Long-term consumption of a carbohydrate-restricted diet does not induce deleterious metabolic effects. Nutrition Research, 28(12), 825-833.

Sunday, April 11, 2010

The Friedewald and Iranian equations: Fasting triglycerides can seriously distort calculated LDL

This post has been revised and re-published. The original comments are preserved below. Typically this is done with posts that attract many visits at the time they are published, and whose topics become particularly relevant or need to be re-addressed at a later date.

Friday, April 9, 2010

The huge gap between glycemic loads of refined and unrefined carbohydrate-rich foods

I often refer to foods rich in refined carbohydrates in this blog as among the most disease-promoting agents of modern diets. Yet, when one looks at the glycemic indices of foods rich in refined and unrefined carbohydrates, they are not all that different.

The glycemic index of a carbohydrate-rich food reflects how quickly the food is digested and generate a blood glucose response. Technically, it is measured as the area under a two-hour blood glucose response curve following the consumption of a portion of the food with a fixed amount of carbohydrates.

A measure that reflects much better the underlying difference between foods rich in refined and unrefined carbohydrates is the glycemic load, which is the product of the glycemic index of a food by the carbohydrate content in a 100 g portion of the food.

The glycemic load is also the reason for one known fact among diabetics. If a diabetic person eats a very small amount of a high glycemic index food, he or she will have a relatively small increase in blood sugar. If that person consumes a large amount of the same food, the increase in blood sugar will be dramatic.

The table below (click on it to enlarge) shows the remarkable differences between the glycemic loads of foods rich in refined and unrefined carbohydrates. It was taken from an article co-authored by Loren Cordain, Michael R. Eades, and Mary D. Eades (full reference at the end of this post).

At the time of this post’s writing, the article from which the table above was taken had a solid number of citations to it; a total of 74 citations on Google Scholar’s database. It is an excellent article, which I highly recommend reading in full (the link to the online full text is at the end of this post).

What is the reason for the differences in glycemic loads?

The answer is that foods rich in unrefined carbohydrates, even those with a high glycemic index (such as potatoes), are also packed with a number of other things – e.g., micronutrients, fiber, water, and even some protein. An Irish (white) potato is 75 percent water. By comparison, cereal, without milk added, is about 1 percent water. You have to add a lot of whole milk to it to make it a bit healthier. And even unsweetened whole milk is about 5 percent sugar.

There was nothing even remotely similar to modern foods rich in refined carbohydrates in the diet of our Paleolithic ancestors. In fact, the types of food rich in refined carbohydrates shown on the table above are very recent, typically dating back to less than a hundred years ago. That is, they are so recent that it is unlikely that any of us have genetic adaptations to those types of food.

Once one’s glucose metabolism is seriously impaired, which seems to be associated with consumption over many years of refined carbohydrates and sugars (as well as some genetic predisposition, which may have evolved among some of our ancestors), then even the foods with high glycemic index and low glycemic load (e.g., potato) will lead to highly elevated glucose levels if eaten in more than very small amounts.

Insulin resistant individuals should avoid even foods with high glycemic index and low glycemic load, as well as any food that significantly increases their blood glucose levels after a meal, because highly elevated glucose levels are toxic to various tissues in the body. The longer those highly elevated serum glucose levels are maintained, the more damage is done; e.g., 2 hours as opposed to 30 minutes at 180 mg/dl. One reason why they are toxic is because they lead to high levels of protein glycation; this is a process whereby sugar binds to protein and “warps” it, impairing its functions.

Generally speaking, the more glycation is going on in our body, the more accelerated is the aging process.


Loren Cordain, Michael R. Eades, Mary D. Eades (2003). Hyperinsulinemic diseases of civilization: More than just Syndrome X. Comparative Biochemistry and Physiology: Part A, 136, 95–112.

Tuesday, April 6, 2010

Low fasting triglycerides: A marker for large-buoyant LDL particles

Small-dense LDL particles are particles that are significantly smaller than the gaps in the endothelium. The endothelium is a thin layer of cells that line the interior of arteries. Those gaps are about 25-26 nanometers (nm) in diameter. Small-dense LDL particles can contribute a lot more to the formation of atheromas (atherosclerotic plaques) in predisposed individuals than large-buoyant LDL particles.

Note that typically LDL particles are about 23-25 nm in diameter in most people, and yet not everybody develops atheromas. It is illogical to believe that evolution made LDL particles within those ranges of size to harm us, given the size of the gaps in the endothelium, unless you believe in something like this joke theory. There are underlying factors that make individuals much more prone to the development of atheromas than others.

One of those factors is chronic inflammation, which is caused by: chronic stress, excessive exercise (aerobic or anaerobic), and a diet rich in refined carbohydrates (e.g., white bread, pasta) and refined sugars (e.g., high fructose corn syrup, table sugar).

Can a standard lipid profile report tell me anything about my LDL particle pattern?

Yes, check you fasting triglycerides. If they are below 70 mg/dL, it is very likely that you have a predominance of large-buoyant LDL particles in your blood. That is, your LDL particle pattern is most likely Pattern A (see figure below, from:, the least atherogenic of the patterns identified by a Vertical Auto Profile (VAP) test. This test is more sophisticated than a standard lipid profile test, where the LDL cholesterol is typically calculated. For a discussion of a sample VAP test report, see this post.

So, you can get a rough idea about your LDL pattern type only by checking your fasting triglyceride levels on a standard lipid profile test report, if you cannot or do not want to have a VAP test done. The higher your fasting triglyceride levels are, above 70, the more likely it is that your LDL particle pattern is Pattern B, which is the most potentially atherogenic pattern.

Large-buoyant LDL particles often lead to high measured LDL cholesterol levels. This situation is analogous to that of water-filled balloons. If you have 10 balloons, each holding 0.5 L of water, then your total water amount is 5 L. If the same balloons are filled with 1 L of water each, then your total water amount is 10 L. That is, even though the number of LDL particles (analogous to the number of balloons) may be the same as that of a person with low LDL cholesterol, large-buoyant LDL particles have more cholesterol (water content in each balloon) in them, and lead to higher measured LDL cholesterol (total amount of water in the balloons) levels.

This leads to the counterintuitive situation where your LDL cholesterol levels go up, and your risk of developing cardiovascular disease actually goes down.

Also worth keeping in mind is that fasting triglyceride levels are strongly and negatively correlated with HDL cholesterol levels. The higher your fasting triglyceride levels are, usually the lower are your HDL cholesterol levels. The latter are also provided in standard lipid profile reports.

How do you decrease your fasting triglycerides?

A good way to start is to do some of the things that increase your HDL cholesterol.


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

Lemanski, P.E. (2004). Beyond routine cholesterol testing: The role of LDL particle size assessment. CDPHP Medical Messenger, May 2004.

Sunday, April 4, 2010

Genetic clustering of metabolic disorders: Meet your relatives

As noted in this post, it is possible for a food-related trait to evolve to fixation in an entire population in as little as 396 years; not the millions of years that some believe are necessary for mutations to spread.

Moreover, evolution through fixation can occur in the absence of any selective pressure. That is, traits that are neutral with respect to fitness may evolve by chance, particularly in small populations. (A group of 100 individuals who made it to the Americas after a long and grueling trek would fit the bill.) This rather counterintuitive phenomenon is known as genetic drift (Hartl & Clark, 2007; Maynard Smith, 1998).

Fast evolution of traits certainly applies to polygenic traits, such as traits associated with nutrient metabolism. Polygenic traits are traits that are influenced by multiple genes, with those genes acting together to influence the expression of the trait.

Moreover, a mutation in one single pleiotropic gene (a gene that influences various traits) can lead to dramatic changes in interconnected phenotypic traits. This includes traits associated with complex processes involving multiple body tissues, such as glucose and fat metabolism.

Some disagree, arguing that complex traits need much longer to evolve. I wish I could be convinced of that; it would make our understanding of health issues and related predictions a lot easier. For example, we could zero in on Homo erectus as our target for an ideal Paleolithic diet.

Unfortunately, when you look around, you see people with food allergies, metabolic disorders, and other food- and lifestyle-related complications; and those problems cluster among people who seem to share recent common ancestors. Interestingly, in many cases those people do not look alike, in spite of sharing common ancestors.

For example, here in South Texas, it is clear that people from Amerindian ancestry (like me, although mine is from South America) are a lot more predisposed to diabetes than others. There are exceptions, of course; we are talking about probabilities here. Especially common here in South Texas are people with South and Central American Indian ancestry; less common but also represented are descendants of North American Indian tribes such as the Kickapoos.

Very recent food inventions, such as refined carbohydrates, refined sugars, omega-6-rich vegetable oils, and hydrogenated fats are too new to have influenced the genetic makeup of anybody living today. So, chances are, they are bad for the vast majority of us. Sure, a small percentage of the population may not develop any hint of diseases of civilization after consuming them for years, but chances are they are not going to be as healthy as they could be.

Other not so recent food inventions, such as olive oil, certain types of bread, certain types of dairy etc. may be better, in terms of overall health effects, for some people than for others. In fact, they may be particularly health-promoting for certain groups of individuals. The reason may be found in inherited metabolic traits. Learning about your ancestors could be helpful in this respect. The problem is that many people's ancestry is quite mixed; again, I give myself as an example - South American Indian, German, Italian, Portuguese ... and who knows what else.

Another, easier and perhaps more effective, way to figure out what particular foods, and in what quantities, may be healthy for you is to keep in touch with close and distant biological relatives; e.g., grandparents, parents, siblings, cousins (family gathering photo below from: It is likely that you share genes with them. If several of them developed a particular disease, and they consumed a lot of a certain type of food prior to that, then maybe that food should not be part of your diet.

This may also help you avoid making serious mistakes regarding health issues by acting too fast in response to laboratory test results. Relatives may share some quirky metabolic responses, which could be indicative of a disease at first glance and actually have no negative long term effects, and perhaps some positive ones.

For example, let us assume that a person, let us call her Mary, is in her early 50s and has been consuming a diet rich in refined carbohydrates and sugars for her entire life. Her fasting blood glucose looks pretty good at around 82 mg/dL.

Mary then adopts a diet that includes only vegetables and animal fat and protein. This new diet induces mild ketosis. She then notices that her fasting blood sugar is now 113 mg/dL, much higher than the previous 82 mg/dL. Mary’s doctor tells her that she may be pre-diabetic.

Mary knows that the change in diet was associated with the increase in fasting blood sugar, and reverts back to her diet rich in refined carbohydrates and sugars. Her fasting blood sugar goes down to 82 mg/dL, and she is happy. Her doctor congratulates her. However, she becomes obese and develops the metabolic syndrome in her late 50s, and several related diseases soon after.

Let us now look at a different scenario. After getting the 113 mg/dL fasting blood sugar reading on a mildly ketogenic diet, Mary talks to as many of her living relatives as she can. She asks many questions and finds out that a few of them were big meat and veggie eaters and had the same metabolic response. They are in their 60s and 70s and have no hint of diabetes. In fact, they are relatively lean and fairly healthy. She then sticks to her diet of only vegetables and animal fat and protein for life, and never develops the metabolic syndrome.

This fictitious case is based on the idea that low carbohydrate diets that induce mild ketosis may also induce physiological (not pathological) insulin resistance, leading to a version of the much talked about dawn phenomenon. This phenomenon, in this context, seems to be related to our good friend, but much maligned, palmitic acid. Several bloggers discussed it in excellent posts. Peter at Hyperlipid blogged about it here and here; Stephan at Whole Health Source blogged about it here.

Now, going back to keeping in touch with close and distant relatives. It is important to check your relatives’ lifestyle patterns as well, because diet is not everything, even though it is a major contributor to health outcomes. By lifestyle patterns I mean things like level and type of physical activity, sunlight exposure (which strongly influences vitamin D levels), and frequency and quality of social interactions (which reduce stress).

Regarding social interactions, it is worth noting that humans are highly social beings, and social isolation is almost universally detrimental to both mental and physical health.


Hartl, D.L., & Clark, A.G. (2007). Principles of population genetics. Sunderland, MA: Sinauer Associates.

Maynard Smith, J. (1998). Evolutionary genetics. New York, NY: Oxford University Press.

Thursday, April 1, 2010

Body mass index and cancer deaths in various US states

Ancel Keys is often heavily criticized for allegedly originating the fat phobia that we see today in the US and other countries, perhaps with good reason. But he has also made many important contributions to the health sciences.

One of them was the index known as body mass index (BMI), calculated based on a person's weight and height. Unlike other measures, such as body fat percentage and body fat mass, BMI is very easy to calculate; divide your weight (kg) by your height (m) squared.

BMI is strongly correlated with body fat percentage, and body fat mass. Very muscular people are exceptions; they may have a high BMI and yet reduced body fat.

Excessive body fat mass leads to chronic inflammation, due in part to elevated circulating levels of pro-inflammatory hormones such as tumor necrosis factor-alpha (cute name eh?).

Chronic inflammation, in turn, leads to increased incidence of cancer.

Thus it should be no surprise that having a BMI above 30 (obesity level) is strongly correlated with cancer death rates; see graph below (click on it to enlarge), from: Florida, 2009 (full reference at the end of this post).

The correlation for the graph above is a high 0.702, calculated as the square-root of the R-squared value shown at the bottom-right. The R-squared is the percentage of explained variance for cancer deaths, meaning that nearly 50 percent of the cancer deaths are "explained", or caused, by the BMI percentages.

One more reason to bring body fat down to healthy levels.

How do you do that? A good way to start is to replace refined carbohydrates and sugars with natural sources of protein and fat in your diet; eggs included, no need to worry about dietary cholesterol.


Florida, R. (2009). The geography of obesity. Creative Class, Nov. 25.