The amounts of water, carbohydrates, fat, and protein lost during a 30-day fast

When it comes to losing fat and maintaining muscle, at the same time, there are no shortcuts. The process generally has to be slow to be healthy. When one loses a lot of weight in a few days, most of what is being lost is water, followed by carbohydrates. (Carbohydrates are stored as liver and muscle glycogen.) Smaller amounts of fat and protein are also lost. The figure below (see reference at the end of post) shows the weights in grams of stored water, carbohydrates (glycogen), fat, and protein lost during a 30-day water fast.

On the first few days of the fast a massive amount of water is lost, even though drinking water is allowed in this type of fast. A significant amount of glycogen is lost as well. This is no surprise. About 2.6 g of water are lost for each 1 g of glycogen lost. That is, water is stored by the body proportionally to the amount of glycogen stored. People who do strength training on a regular basis tend to store more glycogen, particular in muscle tissue; this is a compensatory adaptation. Those folks also tend to store more water.

Not many people will try a 30-day fast. Still, the figure above has implications for almost everybody.

One implication is that if you use a bioimpedance scale to measure your body fat, you can bet that it will give you fairly misleading results if your glycogen stores are depleted. Your body fat percentage will be overestimated, because water and glycogen are lean body mass. This will happen with low carbohydrate dieters who regularly engage in intense physical exercise, aerobic or anaerobic. The physical exercise will deplete glycogen stores, which will typically not be fully replenished due to the low intake of carbohydrates.

Light endurance exercise (e.g., walking) is normally easier to maintain with a depleted “glycogen tank” than strength training, because light endurance exercise relies heavily on fat oxidation. It uses glycogen, but more slowly. Strength training, on the other hand, relies much more heavily on glycogen while it is being conducted (significant fat oxidation occurs after the exercise session), and is difficult to do effectively with a depleted “glycogen tank”.

Strength training practitioners often will feel fatigued, and will probably be unable to generate supercompensation, if their “glycogen tank” is constantly depleted. Still, compensatory adaptation can work its “magic” if one persists, and lead to long term adaptations that make athletes rely much more heavily on fat than the average person as a fuel for strength training and other types of anaerobic exercise. Some people seem to be naturally more likely to achieve this type of compensatory adaptation; others may never do so, no matter how hard they try.

Another implication is that you should not worry about short-term weight variations if your focus is on losing body fat. Losing stored water and glycogen may give you an illusion of body fat loss, but it will be only that – an illusion. You may recall this post, where body fat loss coupled with muscle gain led to some weight gain and yet to a much improved body composition. That is, the participants ended up leaner, even though they also weighed more.

The figure above also gives us some hints as to what happens with very low carbohydrate dieting (i.e., daily consumption of less than 20 grams of carbohydrates); at least at the beginning, before long term compensatory adaptation. This type of dieting mimics fasting as far as glycogen depletion is concerned, especially if protein intake is low, and has many positive short term health benefits. The depletion is not as quick as in a fast because a high fat and/or protein diet promotes higher rates of fat/protein oxidation and ketosis than fasting, which spare glycogen. (Yes, dietary fat spares glycogen. It also spares muscle tissue.) Still, the related loss of stored water is analogous to that of fasting, over a slightly longer period. The result is a marked weight loss at the beginning of the diet. This is an illusion as far as body fat loss is concerned.

Dietary protein cannot be used directly for glycogenesis; i.e., for replenishing glycogen stores. Dietary protein must first be used to generate glucose, through a process called gluconeogenesis. The glucose is then used for liver and muscle glycogenesis, among other things. This process is less efficient than glycogenesis based on carbohydrate sources (particularly carbohydrate sources that combine fructose and glucose), which is why for quite a few people (but not all) it is difficult to replenish glycogen stores and stimulate muscle growth on very low carbohydrate diets.

Glycogen depletion appears to be very healthy, but most of the empirical evidence seems to suggest that it is the depletion that creates a hormonal mix that is particularly health-promoting, not being permanently in the depleted state. In this sense, the extent of the glycogen depletion that is happening should be positively associated with the health benefits. And significant glycogen depletion can only happen if glycogen stores are at least half full to start with.

Reference

Wilmore, J.H., Costill, D.L., & Kenney, W.L. (2007). Physiology of sport and exercise. Champaign, IL: Human Kinetics. [Note: the figure may be found in a different edition.]

Insulin responses to foods rich in carbohydrates and protein

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

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

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

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

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

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

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

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

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

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

Reference:

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

The impressive nutrition value of whole dried small fish

When I visited Japan several years ago I noticed a variety of dried small fish for sale in grocery stores and supermarkets. They came in what seemed to be vacuum-packed flat plastic bags, often dried. The packing was a bit like that of beef jerky in the USA. Since I could not read the labels, I could not tell if preservatives or things like sugar were added. Beef jerky often has sugar added to it; at least the popular brands.

I have since incorporated dried or almost dried small fish, eaten whole, into my diet. My family eats it, but they don’t seem to like it as much as I do. The easiest small fish to find for sale where I live are smelts. A previous post has a recipe (). I can easily eat 200 g of smelts, about twice as much as on the plate below; not quite dried, but almost so. The veggies are a mix of lettuce and cabbage.

As you can see from the macronutrient composition below (from Nutritiondata.com, for a 100 g portion), 200 g of smelts have about 112 g of protein, and 36 g of fat. No carbohydrates; or a very small amount of them.

Unless you misguidedly think that they will “give you cholesterol”, the macronutrient to calorie ratio of a plate with 200 g of dried (or almost dried) smelts is very good. Let us take a look at the fat content, below (from Nutritiondata.com as well), which is for 100 g of dried smelts.

The “net” omega-3 content of 200 g of dried smelts, after subtracting the omega-6 content, is approximately 4.4 g. The concept of “net” omega-3 content was discussed in a previous post ().

So, the net omega-3 content of 200 g of dried smelts is the equivalent to the net omega-3 content of about 20 fish oil softgels. (Yes, you read it right!) And you would get a lot more omega-6 from the softgels.

Not to mention the fact that isolated omega-3 and omega-6 fats tend to become oxidized much more easily than when they come in “nature’s package”.

Below is the mineral content (also from Nutritiondata.com) of a 100 g portion. Dried smelts are clearly a very good source of selenium. The significant amount of calcium comes mostly from the bones, as with many varieties of small fish that are eaten whole. Combined with the above, we could say that, overall, the nutrient content is high up there next to beef liver as a super food; a natural multivitamin, if you will.

Smelts, like many small non-predatory fish, are not a significant source of toxic metals. Many people avoid seafood because of concerns about toxic metal contamination, particularly mercury. The infamous incident that led to a major scare in that respect – in Minamata, Japan – did involve consumption of small marine animals. But it also involved years of direct and indirect exposure to very high levels of methylmercury from untreated industrial waste.

Other cases have been reported among populations consuming large amounts of whale, shark, dogfish and other relatively large marine animals with tissues compromised via biomagnification. Generally speaking, large predatory fish and predatory aquatic mammals are best avoided as food. If they are consumed, they should be consumed very sporadically.

Many people would say that a plate like the one above, with smelts and veggies, is not very appetizing. But I can really devour it quickly and go for seconds. How come? I use a special spice that enhances the natural flavor or almost any combination of “natural” foods – foods that are not engineered by humans – making them taste delicious.

This special spice is “hunger”. This spice can be your best friend, or your worst enemy.

Subcutaneous versus visceral fat: How to tell the difference?

The photos below, from Wikipedia, show two patterns of abdominal fat deposition. The one on the left is predominantly of subcutaneous abdominal fat deposition. The one on the right is an example of visceral abdominal fat deposition, around internal organs, together with a significant amount of subcutaneous fat deposition as well.

Body fat is not an inert mass used only to store energy. Body fat can be seen as a “distributed organ”, as it secretes a number of hormones into the bloodstream. For example, it secretes leptin, which regulates hunger. It secretes adiponectin, which has many health-promoting properties. It also secretes tumor necrosis factor-alpha (more recently referred to as simply “tumor necrosis factor” in the medical literature), which promotes inflammation. Inflammation is necessary to repair damaged tissue and deal with pathogens, but too much of it does more harm than good.

How does one differentiate subcutaneous from visceral abdominal fat?

Subcutaneous abdominal fat shifts position more easily as one’s body moves. When one is standing, subcutaneous fat often tends to fold around the navel, creating a “mouth” shape. Subcutaneous fat is easier to hold in one’s hand, as shown on the left photo above. Because subcutaneous fat tends to “shift” more easily as one changes the position of the body, if you measure your waist circumference lying down and standing up, and the difference is large (a one-inch difference can be considered large), you probably have a significant amount of subcutaneous fat.

Waist circumference is a variable that reflects individual changes in body fat percentage fairly well. This is especially true as one becomes lean (e.g., around 14-17 percent or less of body fat for men, and 21-24 for women), because as that happens abdominal fat contributes to an increasingly higher proportion of total body fat. For people who are lean, a 1-inch reduction in waist circumference will frequently translate into a 2-3 percent reduction in body fat percentage. Having said that, waist circumference comparisons between individuals are often misleading. Waist-to-fat ratios tend to vary a lot among different individuals (like almost any trait). This means that someone with a 34-inch waist (measured at the navel) may have a lower body fat percentage than someone with a 33-inch waist.

Subcutaneous abdominal fat is hard to mobilize; that is, it is hard to burn through diet and exercise. This is why it is often called the “stubborn” abdominal fat. One reason for the difficulty in mobilizing subcutaneous abdominal fat is that the network of blood vessels is not as dense in the area where this type of fat occurs, as it is with visceral fat. Another reason, which is related to degree of vascularization, is that subcutaneous fat is farther away from the portal vein than visceral fat. As such, it has to travel a longer distance to reach the main “highway” that will take it to other tissues (e.g., muscle) for use as energy.

In terms of health, excess subcutaneous fat is not nearly as detrimental as excess visceral fat. Excess visceral fat typically happens together with excess subcutaneous fat; but not necessarily the other way around. For instance, sumo wrestlers frequently have excess subcutaneous fat, but little or no visceral fat. The more health-detrimental effect of excess visceral fat is probably related to its proximity to the portal vein, which amplifies the negative health effects of excessive pro-inflammatory hormone secretion. Those hormones reach a major transport “highway” rather quickly.

Even though excess subcutaneous body fat is more benign than excess visceral fat, excess body fat of any kind is unlikely to be health-promoting. From an evolutionary perspective, excess body fat impaired agile movement and decreased circulating adiponectin levels; the latter leading to a host of negative health effects. In modern humans, negative health effects may be much less pronounced with subcutaneous than visceral fat, but they will still occur.

Based on studies of isolated hunger-gatherers, it is reasonable to estimate “natural” body fat levels among our Stone Age ancestors, and thus optimal body fat levels in modern humans, to be around 6-13 percent in men and 14–20 percent in women.

If you think that being overweight probably protected some of our Stone Age ancestors during times of famine, here is one interesting factoid to consider. It will take over a month for a man weighing 150 lbs and with 10 percent body fat to die from starvation, and death will not be typically caused by too little body fat being left for use as a source of energy. In starvation, normally death will be caused by heart failure, as the body slowly breaks down muscle tissue (including heart muscle) to maintain blood glucose levels.

References:

Arner, P. (2005). Site differences in human subcutaneous adipose tissue metabolism in obesity. Aesthetic Plastic Surgery, 8(1), 13-17.

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

Fleck, S.J., & Kraemer, W.J. (2004). Designing resistance training programs. Champaign, IL: Human Kinetics.

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

Book review: The Eclipse of a Mind

The Eclipse of a Mind () is a 722-page book published in 1942 that describes the life of Alonzo Graves. Alonzo is also listed as the author of the book, even though the narrative is not that of a typical autobiography.

The book is an in-depth study of manic depression. Alonzo is the sufferer. He is a very intelligent college dropout journalist who narrates his lifelong struggle for mental balance. We are taken through World War I, the great Great Depression (iconic photo below: ), the various treatments of bacterial diseases prior to antibiotics, among a variety of other topics; all through Alonzo’s eyes.

This book is rather “dense”, and not very easy to read in a linear fashion – i.e., from beginning to end. Since it is annotated, with comments by various psychiatrists and medical doctors who examined and treated Alonzo, the book is fairly repetitive at points. Nevertheless, it is a fascinating read.

Alonzo delves into important historical events that many today are likely unaware of, such as the Bonus Expeditionary Force movement in the 1930s. World War I veterans had been issued paper money that they could not exchange for real money until 1945. Out-of-work veterans revolted during the Great Depression, demanding early payment. Alonzo was right in the middle of this movement, acting as a journalist and taking the side of the veterans. The ensuing stress caused a manic episode that eventually led to Alonzo's hospitalization.

Manic episodes are characterized by euphoric states and increased levels of activity. The episodes are often triggered by stress. Some people become creative and highly productive during manic states, whereas others become irritable and prone to engaging in risky behavior. Frequently manic episodes are followed by debilitating depression ().

Alonzo’s falls into manic states usually started with benign increases in work-related activity. However, as that high-energy state was maintained for various consecutive days, causing periods of very poor sleep, it often led to psychotic or near-psychotic episodes. This produced a total of five hospitalizations, all of which are described in detail in the book. The book ends with Alonzo moving to Russia, whose government ideology he admired, and never being heard of again.

One of the most interesting aspects of this book is Alonzo’s insights into other people’s mental illnesses, some of whom were manic depressive, combined with his inability to recognize the signs of his own illness. Notably, Alonzo was unable to recognize early signs, or “prodomes” (), which made it difficult for him to avoid entering manic states.

As noted earlier, this book is not an easy read. And it is an old book, copies of which are probably difficult to find today. Nevertheless, it is unique in its tell-it-all style, with detailed narratives from both the patient and doctors about a mental illness that is widespread today. Manic depression is an eminently treatable condition that tends to be highly correlated with creating intelligence ().

A frequently unrecognized reality is put forth by this book. Manic depression is not a “new” condition, even though it may be a “disease of civilization”. The levels of sustained stress found in urban societies are probably much higher than those experienced by our ancestors during most of our evolutionary history, and stress is a trigger of manic depression symptoms. The Eclipse of a Mind is a goldmine of insights into this condition.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder

Let us say you are one of the gifted few who are able to put on 1 lb of pure muscle per month, or 12 lbs per year, by combining strength training with a reasonable protein intake. Let us go even further and assume that the 1 lb of muscle that we are talking about is due to muscle protein gain, not glycogen or water. This is very uncommon; one has to really be genetically gifted to achieve that.

And you do that by eating a measly 80 g of protein per day. That is little more than 0.5 g of protein per lb of body weight if you weigh 155 lbs; or 0.4 per lb if you weigh 200 lbs. At the end of the year you are much more muscular. People even think that you’ve been taking steroids; but that just came naturally. The figure below shows what happened with the 80 g of protein you consumed every day. About 15 g became muscle (that is 1 lb divided by 30) … and 65 g “disappeared”!

Is that an amazing feat? Yes, it is an amazing feat of waste, if you think that the primary role of protein is to build muscle. More than 80 percent of the protein consumed was used for something else, notably to keep your metabolic engine running.

A significant proportion of dietary protein also goes into the synthesis of albumin, to which free fatty acids bind in the blood. (Albumin is necessary for the proper use of fat as fuel.) Dietary protein is also used in the synthesis of various body tissues and hormones.

Dietary protein does not normally become body fat, but can be used in place of fat as fuel and thus allow more dietary fat to be stored. It leads to an insulin response, which causes less body fat to be released. In this sense, dietary protein has a fat-sparing effect, preventing it from being used to supply the energy needs of the body.

Nevertheless, the fat-sparing effect of protein is lower than that of another "macronutrient" – alcohol. That is, alcohol takes precedence over protein and carbohydrates for use as fuel. Protein takes precedence over carbohydrates. Neither alcohol nor protein typically becomes body fat. Carbohydrates can become body fat, but only when glycogen stores are full.

What does this mean?

As it turns out, a reasonably high protein intake seems to be quite healthy, and there is nothing wrong with the body using protein to feed its metabolism.

Having said that, one does not need enormous amounts of protein to keep or even build muscle if one is getting enough calories from other sources.

Wheat flour, rice and vascular diseases in the China Study II data: Article on Cliodynamics

My article on volume 6, number 2, of the journal Cliodynamics has recently been published; it is titled “Wheat flour versus rice consumption and vascular diseases: Evidence from the China Study II data” (). While this is an academic article, I think that the main body of the article is fairly easy to read. More technical readers may want to check under “Supporting material”, which is one of the links on the left, where they will find a detailed description of the data used and the results of some specialized statistical tests.

In the past I have discussed in this blog the associations with vascular diseases, in the China Study dataset, of wheat flour and rice consumption. The interest in the possible effects of wheat flour AND rice consumption comes from the fact that these foods are similar in some important respects – e.g., they tend to raise insulin levels in similar ways. But as you will see in the article, their associations with vascular diseases are clearly different, particularly when we conduct nonlinear analyses.

While I do not think that wheat flour consumption per se is particularly healthy, the results of the analysis go somewhat against the idea that wheat flour intake is the primary culprit with respect to vascular diseases. The results also go somewhat against the “insulin theory of obesity”, at least in a narrow sense, and call for a broader explanation that includes cultural elements. These points are further elaborated in the article. There is speculation in the article, and also a discussion of possible limitations.

Enjoy!

Do prominent health gurus live longer?

Many years ago, when I started blogging about health issues, I noticed a couple of interesting patterns. The first pattern is that prominent health “gurus” often talk about having had serious health problems in their past, which they describe as having motivated them to do research on health issues – and thus become health gurus. Frequently these problems pop up before 45 years of age; this is a threshold beyond which there is a clearly noticeable increase in severity of health problems.

In fact, I remember being somewhat surprised by one such “guru” (I will not name him), who would regularly write posts saying something to the effect that “… finally, my health is now on the right track …” In other words, every few months or so this person had to deal with serious health problems, always coming up with reasonable knowledge-based solutions. The knowledge seemed to be of good quality, but this guy’s health was poor to say the least.

The second pattern, related to the above, is that prominent health gurus seem to have a below average life expectancy. The life expectancy for the general population is around 79 years of age in the USA at the time of this writing, according to the World Health Organization (). Anthony Colpo has written an interesting post about this below average life expectancy pattern among health gurus ().

To better understand and illustrate this situation to our blog’s readers, I created a dataset with 100 records, corresponding to 100 health gurus, with various variables interacting in ways that reflect the above observations. The observations are summarized as assumptions, listed later. The following variables are on a scale from 1 to 7; in real life they would have been measured retrospectively, looking back at a guru’s entire life:

- The guru's health before age 45 (BEF45).

- The guru's knowledge about health issues (KNOWL).

- The guru's health after age 45 (AFT45).

- The guru's prominence (GPROM).

Finally, the variable below is on a continuous scale of years, with an average of 79 and a standard deviation of 10. As mentioned earlier, 79 is the life expectancy for someone living in the USA at the time of this writing. The standard deviation of 10, which approximates that figure in the USA, means that approximately 68 percent of the individuals in the simulated dataset will have a life expectancy between 69 and 89. That is 79-10 and 79+10, respectively.

- The guru's age at the time of death (GAGED).

This experimental exercise with simulated data can be seen as a simulation “game”, where various variables interact to generate results that are not obvious. A widely used process to create data is known as the Monte Carlo method (), which is what we used here. I also made the following assumptions in the data creation process:

- That the poorer is the guru's health before age 45 (BEF45), the greater is the guru's knowledge about health issues (KNOWL). The reason for this is that poor health compels the person to study about health issues.

- That the poorer is the guru's health before age 45 (BEF45), the poorer is the guru's health after age 45 (AFT45). This assumes that the person has an underlying condition that causes the poor health in the first place, and that can be exacerbated by a poor diet and lifestyle.

- That the greater is the guru's knowledge about health issues (KNOWL), the better is the guru's health after age 45 (AFT45). This counteracts the effect above, and assumes that the knowledge is put to good use and contributes to improving the person’s health.

- That the greater is the guru's knowledge about health issues (KNOWL), the greater is also the guru's prominence (GPROM). In other words, a guru’s status among followers is enhanced by the guru’s knowledge.

- That the better is the guru's health after age 45 (AFT45), the higher is the guru's age at the time of death (GAGED).

A final assumption made is that the causal relationships laid out above have a small effect size (more technically, that they are associated with f-squared coefficients slightly below 0.1), meaning that random influences are not only present but also play a big role in what happens in the simulation. The causality links are summarized in the graph below, created with WarpPLS (). We also used this software to analyze the data.

Note that in our simulated data the guru's prominence (GPROM) does not directly influence the guru's age at the time of death (GAGED). Stated differently, there is no causality link between GPROM and GAGED, one way or the other, even though these two variables are likely to be correlated due to the network of causality links in which they exist. Nevertheless, it is by looking at the relationship between these two variables, GPROM and GAGED, that we can answer the question in the title of this post: Do prominent health gurus live longer?

And the answer appears to be “no” in our simulation. The plot below shows the relationship between a guru's prominence (GPROM), on the horizontal axis, and the guru's age at the time of death (GAGED), on the vertical axis. Each data point refers to a guru. On average, the greater a guru's prominence, the lower seems to be the guru’s life expectancy. Each one-point increase in prominence is associated, on average, with approximately a one-year decrease in life expectancy.

Note that there is one very prominent guru whose age at the time of death was around 95; the data point at the top-right corner (GPROM=7, GAGED~95). This happened largely by chance in our data. Nevertheless, assuming that our data somewhat reflects what could happen in real life, the followers of the guru would probably point at that longevity as being caused by the guru’s knowledge about health issues. They would likely be wrong.

Our dataset also allows us to estimate the probability that a fairly prominent guru (GPROM greater than 4, on a 1-7 scale) would have a below average life expectancy (GAGED lower than 79). That conditional probability would be approximately 60 percent.

You can eat a lot during the Holiday Season and gain no body fat, as long as you also eat little

The evolutionary pressures placed by periods of famine shaped the physiology of most animals, including humans, toward a design that favors asymmetric food consumption. That is, most animals are “designed” to alternate between eating little and then a lot.

Often when people hear this argument they point out the obvious. There is no evidence that our ancestors were constantly starving. This is correct, but what these folks seem to forget is that evolution responds to events that alter reproductive success rates (), even if those events are rare.

If an event causes a significant amount of death but occurs only once every year, a population will still evolve traits in response to the event. Food scarcity is one such type of event.

Since evolution is blind to complexity, adaptations to food scarcity can take all shapes and forms, including counterintuitive ones. Complicating this picture is the fact that food does not only provide us with fuel, but also with the sources of important structural components, signaling elements (e.g., hormones), and process catalysts (e.g., enzymes).

In other words, we may have traits that are health-promoting under conditions of food scarcity, but those traits are only likely to benefit our health as long as food scarcity is relatively short-term. Not eating anything for 40 days would be lethal for most people.

By "eating little" I don’t mean necessarily fasting. Given the amounts of mucus and dead cells (from normal cell turnover) passing through the digestive tract, it is very likely that we’ll be always digesting something. So eating very little within a period of 10 hours sends the body a message that is similar to the message sent by eating nothing within the same period of 10 hours.

Most of the empirical research that I've reviewed suggests that eating very little within a period of, say, 10-20 hours and then eating to satisfaction in one single meal will elicit the following responses. Protein phosphorylation underlies many of them.

- Your body will hold on to its most important nutrient reserves when you eat little, using selective autophagy to generate energy (, ). This may have powerful health-promoting properties, including the effect of triggering anti-cancer mechanisms.

- Food will taste fantastic when you feast, to such an extent that this effect will be much stronger than that associated with any spice ().

- Nutrients will be allocated more effectively when you feast, leading to a lower net gain of body fat ().

- The caloric value of food will be decreased, with a 14 percent decrease being commonly found in the literature ().

- The feast will prevent your body from down-regulating your metabolism via subclinical hypothyroidism (), which often happens when the period in which one eats little extends beyond a certain threshold (e.g., more than one week).

- Your mood will be very cheerful when you feast, potentially improving social relationships. That is, if you don’t become too grouchy during the period in which you eat little.

I recall once participating in a meeting that went from early morning to late afternoon. We had the option of taking a lunch break, or working through lunch and ending the meeting earlier. Not only was I the only person to even consider the second option, some people thought that the idea of skipping lunch was outrageous, with a few implying that they would have headaches and other problems.

When I said that I had had nothing for breakfast, a few thought that I was pushing my luck. One of my colleagues warned me that I might be damaging my health irreparably by doing those things. Well, maybe they were right on both grounds, who knows?

It is my belief that the vast majority of humans will do quite fine if they eat little or nothing for a period of 20 hours. The problem is that they need to be convinced first that they have nothing to worry about. Otherwise they may end up with a headache or worse, entirely due to psychological mechanisms ().

There is no need to eat beyond satiety when you feast. I’d recommend that you just eat to satiety, and don’t force yourself to eat more than that. If you avoid industrialized foods when you feast, that will be even better, because satiety will be achieved faster. One of the main characteristics of industrialized foods is that they promote unnatural overeating; congrats food engineers on a job well done!

If you are relatively lean, satiety will normally be achieved with less food than if you are not. Hunger intensity and duration tends to be generally associated with body weight. Except for dedicated bodybuilders and a few other athletes, body weight gain is much more strongly influenced by body fat gain than by muscle gain.

PLS Applications Symposium; 13 - 15 April 2016; Laredo, Texas

PLS Applications Symposium; 13 - 15 April 2016; Laredo, Texas
(Abstract submissions accepted until 19 February 2015)

*** Health researchers ***

The research techniques discussed in this Symposium are finding growing use among health researchers. This is in part due to steady growth in the use of the software WarpPLS (visit: http://warppls.com) among those researchers. For those interested in learning more, a full-day workshop will be conducted (see below).

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

http://plsas.net

*** Workshop on PLS-SEM ***

On 13 April 2015 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock, using the software WarpPLS. This workshop will be hands-on and interactive. To participate in the workshop, please indicate your interest when making your registration for the Symposium.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Conducting a Multi-group Analysis with Range Restriction - Viewing Nonlinear Relationships - Conducting a Factor-Based PLS-SEM Analysis - Viewing and Changing Missing Data Imputation Settings - Isolating Mediating Effects - Identifying and Dealing with Outliers - Solving Indicator Problems - Solving Collinearity Problems.

*** Proceedings of the Symposium ***

Accepted submissions will be published in the online proceedings of the Symposium, subject to the following registration requirements. At least one of the authors listed for a presentation must register for the Symposium. Panels must have 3-5 participants, all of whom must register for the Symposium. Abstracts must have 150-500 words. Below is an example of submission.

------------ Example of submission ------------

Using PLS in medical technology studies: What if I have only one group and one condition?

Type of submission: Presentation

John Doe
Professor of Medicine
Division of General Internal Medicine
ABC University
1234 University Boulevard
University City, Texas, USA
Tel: +1-956-333-1234
Fax: +1-956-333-4321
Email: johndoe@abcu.edu
Web site: http://www.abcu.edu/johndoe

Jane Doe
Professor of Medicine
Division of General Internal Medicine
ABC University
1234 University Boulevard
University City, Texas, USA
Tel: +1-956-333-2345
Fax: +1-956-333-5432
Email: janedoe@abcu.edu
Web site: http://www.abcu.edu/janedoe

Abstract
What if a researcher obtains empirical data by asking questions to gauge the effect of a medical technology on task performance, but does not obtain data on the extent to which the medical technology is used? This characterizes what is referred to here as a scenario with one group and one condition, where the researcher is essentially left with only one column of data to be analyzed. When this happens, often researchers do not know how to analyze the data, or analyze the data making incorrect assumptions and using unsuitable techniques. Some of the PLS method’s features make it particularly useful in this type of scenario, such as its support for small samples and the use of data that does not meet parametric assumptions. The main goal of this presentation is to help medical technology researchers use the PLS method to analyze data in this type of scenario, where only one group and one condition are available. Two other scenarios are also discussed – a typical scenario, and a scenario with one group and two before-after technology introduction conditions. While the focus here is on medical technology use, the recommendations apply to many other fields.

Keywords: Multivariate Statistics, Partial Least Squares, Structural Equation Modeling, Field Research, Action Research, Medical Technology

-----------------------------------------------------------
Ned Kock
Symposium Chair
http://plsas.net

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

Standard lipid profiles provide LDL cholesterol measures based on equations that usually have the following as their inputs (or independent variables): total cholesterol, HDL cholesterol, and triglycerides.

Yes, LDL cholesterol is not measured directly in standard lipid profile tests! This is indeed surprising, since cholesterol-lowering drugs with negative side effects are usually prescribed based on estimated (or "fictitious") LDL cholesterol levels.

The most common of these equations is the Friedewald equation. Through the Friedewald equation, LDL cholesterol is calculated as follows (where TC = total cholesterol, and TG = triglycerides). The inputs and result are in mg/dl.

    LDL = TC – HDL – TG / 5

Here is one of the problems with the Friedewald equation. Let us assume that an individual has the following lipid profile numbers: TC = 200, HDL = 50, and trigs. = 150. The calculated LDL will be 120. Let us assume that this same individual reduces triglycerides to 50, from the previous 150, keeping all of the other measures constant with except of HDL, which goes up a bit to compensate for the small loss in total cholesterol associated with the decrease in triglycerides (there is always some loss, because the main carrier of triglycerides, VLDL, also carries some cholesterol). This would normally be seen as an improvement. However, the calculated LDL will now be 140, and a doctor will tell this person to consider taking statins!

There is evidence that, for individuals with low fasting triglycerides, a more precise equation is one that has come to be known as the “Iranian equation”. The equation has been proposed by Iranian researchers in an article published in the Archives of Iranian Medicine (Ahmadi et al., 2008), hence its nickname. Through the Iranian equation, LDL is calculated as follows. Again, the inputs and result are in mg/dl.

    LDL = TC / 1.19 + TG / 1.9 – HDL / 1.1 – 38

The Iranian equation is based on linear regression modeling, which is a good sign, although I would have liked it even better if it was based on nonlinear regression modeling. The reason is that relationships between variables describing health-related phenomena are often nonlinear, leading to biased linear estimations. With a good nonlinear analysis algorithm, a linear relationship will also be captured; that is, the “curve” that describes the relationship will default to a line if the relationship is truly linear (see: warppls.com).

Anyway, an online calculator that implements both equations (Friedewald and Iranian) is linked here; it was the top Google hit on a search for “Iranian equation LDL” at the time of this post’s writing.

As you will see if you try it, the online calculator linked above is useful in showing the difference in calculated LDL cholesterol, using both equations, when fasting triglycerides are very low (e.g., below 50).

The Iranian equation yields high values of LDL cholesterol when triglycerides are high; much higher than those generated by the Friedewald equation. If those are not overestimations (and there is some evidence that, if they are, it is not by much), they describe an alarming metabolic pattern, because high triglycerides are associated with small-dense LDL particles. These particles are the most potentially atherogenic of the LDL particles, in the presence of other factors such as chronic inflammation.

In other words, the Iranian equation gives a clearer idea than the Friedewald equation about the negative health effects of high triglycerides. You need a large number of small-dense LDL particles to carry a high amount of LDL cholesterol.

An even more precise measure of LDL particle configuration is the VAP test; this post has a discussion of a sample VAP test report.

Reference:

Ahmadi SA, Boroumand MA, Gohari-Moghaddam K, Tajik P, Dibaj SM. (2008). The impact of low serum triglyceride on LDL-cholesterol estimation. Archives of Iranian Medicine, 11(3), 318-21.

Should you drink your coffee filtered?

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

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



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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hypervitaminosis A and sweet potatoes

Can consumption of sweet potatoes cause hypervitaminosis A? The answer is “no”, even if you eat ten or more sweet potatoes per day. Sweet potatoes do have high vitamin A content, more than almost any other food. However, most of it is in the form of β-carotene, which is used by the body to produce the active form of vitamin A, retinal (yes, with an “a”), only if the body’s vitamin A status is low.

The graph below shows the vitamin A content of different foods, together with the recommended daily allowance. It was prepared with information from Nutritiondata.com (), with the horizontal axis in international units (). The graph also takes into consideration some key research findings related to the bioavailability of vitamin A. For example, the sweet potato is assumed to be taken with some fat to facilitate the absorption of vitamin A.

Primarily, vitamin A is available either as retinol, from animal foods; or β-carotene, from plant foods. There are other carotenes available from plant foods, but their vitamin A contribution is relatively small compared with β-carotene. High β-carotene content is “advertised” by plant foods to animals via a characteristic orange color. The main sources of β-carotene throughout human evolution have probably been fruits, which plants “want” animals to eat so that the plants’ seeds are dispersed.

Retinol also needs to be converted by the body to retinal, and when consumed in excess it tends to be stored in body fat reserves – hence lean individuals tend to store less retinol than fat ones. It seems that intake of retinol from sources like beef liver is naturally controlled via satiety. In the case of plant sources, like sweet potatoes, a key control mechanism is limited internal production of retinal. My impression is that most people, if given the chance, would prefer to eat a lot of sweet potato than a lot of beef liver.

Like all of the fat-soluble vitamins, the bioavailability of vitamin A from foods is dependent on whether they are consumed together with fat. For example, a lot more vitamin A will be absorbed from a sweet potato if it is eaten with butter than if it is eaten by itself (again, if the body’s vitamin A status is low). I should note that butter is itself a good source of vitamin A, in addition to providing the fat needed for absorption. Beef liver is low in fat, which means that the vitamin A content in the graph above may be an overestimation.

Hypervitaminosis is a fat-soluble vitamin phenomenon, and it is usually associated with consumption of supplements (e.g., cod liver oil). Generally speaking, one does not develop noticeable hypervitaminosis symptoms from consumption of natural food sources. This is probably due to a combination of satiety and internal regulation of the production of the active forms of the vitamins.

The PCSK9 enzyme, LDL cholesterol, and cardiovascular diseases

Cardiovascular diseases are currently the leading cause of death in most developed countries. They are particularly common among seniors; i.e., those aged 65 and older. Part of the reason for this is that infectious diseases do not kill as many people as they used to.

Given the trend toward population aging, with seniors making up an increasingly larger percentage of the population, the market for drugs against cardiovascular diseases is growing. A new class of such drugs is making the news lately; they target the PCSK9 enzyme ().

Enzymes are (usually) proteins that speed up chemical reactions, and are needed in virtually all metabolic processes that occur in cells. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an enzyme that degrades LDL cholesterol receptors on the surface of liver cells. Fewer LDL cholesterol receptors mean reduced uptake of the particles that carry LDL cholesterol, and thus more LDL particles in circulation. This may be problematic if these are small-dense LDL particles ().

Small-dense LDL particles include 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.

There is evidence of the natural occurrence of low LDL cholesterol in individuals of African descent due to genetic mutations influencing PCSK9 levels (). This leads us to a very important question. By reducing PCSK9 in circulation, can we also reduce the incidence of cardiovascular disease?

The answer to this question depends on whether LDL cholesterol is a causative factor in cardiovascular disease. If it is, then reducing PCSK9 in circulation can indeed reduce the incidence of cardiovascular disease. The problem is that, most of the evidence so far suggests that LDL cholesterol is NOT a causative factor in cardiovascular disease.

Yes, there are studies that show that LDL cholesterol is correlated with cardiovascular disease, but the problem is that LDL cholesterol is a marker of other factors that are better candidates for causes of cardiovascular disease – hence the correlation. For example, LDL cholesterol goes up with mental stress (), and chronic mental stress seems to be a good candidate for a cause of cardiovascular disease.

LDL cholesterol is also a marker of a diet with more saturated fat in it (). In many contexts, a diet with more saturated fat in it is a more nutritious diet, which leads to a negative association between LDL cholesterol and mortality.

The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC), based on an analysis of the China Study II dataset (). LDL cholesterol is the main component of total cholesterol in most people. The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The best-fitting curve was obtained with the software WarpPLS ().

In fact, when we combine the totality of the evidence linking LDL cholesterol and cardiovascular diseases, LDL cholesterol seems to come out as a marker of protective factors. A reflection of this is a widely cited study by Weverling-Rijnsburger and colleagues, of LDL and HDL cholesterol as factors in cardiovascular diseases among people aged 85 and older (). The conclusions of the study were that:

- There was no association between LDL cholesterol level and risk of fatal cardiovascular disease.

- A low HDL cholesterol level was associated with a two-fold higher risk of fatal cardiovascular disease.

- Both low LDL cholesterol and low HDL cholesterol levels were associated with an increased mortality risk from infections.

The results above are particularly interesting because the study participants, given their ages, were at a high risk of mortality from cardiovascular diseases. It seems that the best scenario for these folks would have been a concomitant increase in both LDL and HDL cholesterol levels, which seems to be exactly what happens when one increases his or her intake of foods rich in saturated fat and dietary cholesterol ()!

Should you take a drug that targets the PCSK9 enzyme, to reduce your LDL cholesterol? Maybe you should ask Peter ().

Ischemic heart disease among Greenland Inuit: Data from 1962 to 1964

The traditional Inuit diet is very high in animal protein and fat. It also includes plant matter. Typically it is made up primarily of the following: fish, walrus, seal, whale, berries, and fireweed (of which syrups and jellies can be made).

Kjærgaard and colleagues (see under References, at the end of this post) examined data from an Inuit population in Greenland from 1962 to 1964, prior to the heavy westernization of their diet that is seen today. They investigated 96.9% of the whole population in three areas, including Ammassalik in East Greenland (n = 1,851).

Of those, only 181 adults, or 9.7 percent, had anything that looked like an abnormality that could suggest ischemia. This included ventricular hypertrophy (an enlargement of the heart chambers), leading to an overestimation because benign ventricular hypertrophy is induced by continuous physical exertion. These 181 adults were then selected for further screening.

Benign ventricular hypertrophy is also known as athlete's heart, because it is common among athletes. A prevalence of ventricular hypertrophy at a relatively young age, and declining with age, would suggest benign hypertrophy. The opposite would suggest pathological hypertrophy, which is normally induced by chronic hypertension.

As you can see from the figure below, from Kjærgaard et al. (2009), the pattern observed among the Inuit was of benign hypertrophy, suggestive of strong physical exertion at a young age.

A pattern of benign hypertrophy induced by robust physical activity is also consistent with reports by Stefansson (1958) about the life of the Eskimos in Northern Alaska. It is reasonable to assume that these Eskimos had a diet and lifestyle similar to the Greenland Inuit.

Back to Kjærgaard et al.’s (2009) study. The 181 adults selected for further screening then had a 12-lead ECG performed (this is a widely used test to check for heart abnormalities). The results suggested that only two men, aged 62 and 63 years, had ischemic heart disease. All in all, this suggests a prevalence of ischemic heart disease of 0.11 percent, which is very low.

(The authors of the article estimated the prevalence of ischemic heart disease at 1.1 percent, because they used the n = 181, as opposed to the original n = 1,851, in their calculation. The latter is the correct baseline sample size, in my opinion. Still, the authors present the 1.1 percent number as quite low as well, which it is.)

The prevalence of ischemic heart disease in the US of approximately 6.8 percent. That is, the prevalence in the US is 63 times higher than among the Inuit studied (using the 0.11 percent as the basis for comparison). And, it should be noted that there are many countries with a higher prevalence of ischemic heart disease than modern US.

It is possible that the low prevalence of ischemic heart disease among the Inuit was partly due to a higher mortality of those with the disease than in modern US, where medical intervention can prolong one's life in the presence of almost any disease. That is, perhaps many of those Inuit with ischemia would die quickly, and thus would not be captured by a study like this.

It is doubtful, however, that this would explain a difference as large as the one observed. Moreover, if many Inuit were dying due to ischemia, there would probably be plenty of evidence suggesting that. (I would imagine that the mysterious deaths associated with chest pain, and other related symptoms, would be a constant topic of conversation.) Reports from early explorers, however, suggest the opposite (e.g., Stefansson, 1958), and are consistent with the study described here.

In conclusion, this study suggests that the diet and lifestyle of the Greenland Inuit prior to the 1960’s (i.e., not their traditional diet and lifestyle, but approaching it) could be seen today as heart-healthy (at least for them), even though the Greenland Inuit ate a lot of animal protein and fat.

References:

Kjærgaard, M., Andersen, S., Holten, M., Mulvad, G., Kjærgaard, J.J. (2009). Low occurrence of ischemic heart disease among Inuit around 1963 suggested from ECG among 1851 East Greenland Inuit. Atherosclerosis, 203(2), 599-603.

Stefansson, V. (1958). Eskimo longevity in Northern Alaska. Science, 127(3288), 16-19.

Large LDL and small HDL particles: The best combination

High-density lipoprotein (HDL) is one of the five main types of lipoproteins found in circulation, together with very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and chylomicrons.

After a fatty meal, the blood is filled with chylomicrons, which carry triglycerides (TGAs). The TGAs are transferred to cells from chylomicrons via the activity of enzymes, in the form of free fatty acids (FFAs), which are used by those cells as sources of energy.

After delivering FFAs to the cells, the chylomicrons progressively lose their TGA content and “shrink”, eventually being absorbed and recycled by the liver. The liver exports part of the TGAs that it gets from chylomicrons back to cells for use as energy as well, now in the form of VLDL. As VLDL particles deliver TGAs to the cells they shrink in size, similarly to chylomicrons. As they shrink, VLDL particles first become IDL and then LDL particles.

The figure below (click on it to enlarge), from Elliott & Elliott (2009; reference at the end of this post), 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).

As you can see from the figure, most of the LDL particles shown are about 1/4 of the length of the dark bar in diameter, often slightly more, or about 25-27 nm in size. They come in different sizes, with sizes in this range  being the most common. The smaller and denser they are, the more likely they are to contribute to the formation of atherosclerotic plaque in the presence of other factors, such as chronic inflammation. The larger they become, which usually happens in diets high in saturated fat, the less likely they are to form plaque.

Note that the HDL particles are rather small compared to the LDL particles. Shouldn’t they cause plaque then? Not really. Apparently they have to be small, compared to LDL particles, to do their job effectively.

HDL is a completely different animal from VLDL, IDL and LDL. HDL particles are produced by the liver as dense disk-like particles, known as nascent HDL particles. These nascent HDL particles progressively pick up cholesterol from cells, as well as performing a number of other functions, and “fatten up” with cholesterol in the process.

This process also involves HDL particles picking up cholesterol from plaque in the artery walls, which is one of the reasons why HDL cholesterol is informally called “good” cholesterol. In fact, neither HDL nor LDL are really cholesterol; HDL and LDL are particles that carry cholesterol, protein and fat.

As far as particle size is concerned, LDL and HDL are opposites. Large LDL particles are the least likely to cause plaque formation, because LDL particles have to be approximately 25 nm in diameter or smaller to penetrate the artery walls. With HDL the opposite seems to be true, as HDL particles need to be small (compared with LDL particles) to easily penetrate the artery walls in order to pick up cholesterol, leave the artery walls with their cargo, and have it returned back to the liver.

Interestingly, some research suggests HDL particles that are larger in size, when compared with other HDL particles (not with LDL particles), seem to do a better job than very small HDL particles in terms of reducing risk of cardiovascular disease. It is also possible that a high number of larger HDL particles in the blood is indicative of elevated levels of "effective" HDL particles; i.e., particles that are effective at picking up cholesterol from the artery walls in the first place.

Another interesting aspect of this cycle is that the return to the liver of cholesterol picked up by HDL appears to be done largely via IDL and LDL particles (Elliott & Elliott, 2009), which get the cholesterol directly from HDL particles! Life is not that simple.

Reference:

William H. Elliott & Daphne C. Elliott (2009). Biochemistry and Molecular Biology. 4th Edition. New York: NY: Oxford University Press.

Heavy physical activity may significantly reduce heart disease deaths, especially after age 45

The idea that heavy physical activity is a main trigger of heart attacks is widespread. Often endurance running and cardio-type activities are singled out. Some people refer to this as “death by running”. Others think that strength training has a higher lethal potential. We know based on the Oregon Sudden Unexpected Death Study that this is a myth ().

Here is some evidence that heavy physical activity in fact has a significant protective effect. The graph below shows the number of deaths from coronary heart disease, organized by age group, in longshoremen (dock workers). The shaded bars represent those whose level of activity at work was considered heavy. The unshaded bars represent those whose level of activity at work was considered moderate or light (essentially below the “heavy” level).

The data is based on an old and classic study of 6351 men, aged 35 to 74 years, who were followed either for 22 years, or to death, or to the age of 75. It shows a significant protective effect of heavy activity, especially after age 45 () . The numbers atop the unshaded bars reflect the relative risk of death from coronary heart disease in each age group. For example, in the age group 65-74, the risk among those not in the heavy activity group is 110 percent higher (2.1 times higher) than in the heavy activity group.

It should be noted that this is a cumulative effect, of years of heavy activity. Based on the description of the types of activities performed, and the calories spent, I estimate that the heavy activity group performed the equivalent of a few hours of strength training per week, plus a lot of walking and other light physical activities. The authors of the study concluded that “… repeated bursts of high energy output established a plateau of protection against coronary mortality.”

Heavy physical activity may not make you lose much weight, but has the potential to make you live longer.

A viral cure for cancer only a few years away?

Adopting an evolutionarily sound lifestyle may reduce the probability that one will develop cancer, but there will be those who will nevertheless have cancer. As we live longer lives, cancer diagnoses are likely to become more and more common.

There are viruses that cause the formation and growth of cancer tumors: oncoviruses. However, and quite interestingly, there are also viruses that seek and kill cancer cells: oncolytic viruses. The video below discusses emerging treatments based on oncolytic viruses.



This Penn Medicine YouTube video is about 6 minutes in length. (A previous HBO video was about 40 minutes in length, and it was worth watching in full. However it became unavailable soon after I linked it here. Its title on YouTube was "Vice Special Report: Killing Cancer".)

Cancer treatment via oncolytic viruses had a promising start in the mid-1990s. However, due to technical complications it has been sidelined for years. Interest has been picking up dramatically in recent years. Could it be foundation for the long promised cure for cancer, as the video implies?

Only time and research will tell …

What is the probability that you are NOT diabetic if your fasting blood glucose is 110-126 mg/dl?

Often I hear from readers who have changed their diets and lifestyles toward a more evolutionarily sound direction () that their fasting blood glucose (FBG) readings have gone up. Frequently numbers in the range 110-126 mg/dl (6.1-7 mmol/l) are mentioned.

If you have a FBG reading of 110-126 mg/dl (6.1-7 mmol/l) very likely your doctor will tell you that you are either diabetic or well on your way be becoming diabetic.

Diabetes is a condition that in humans is most frequently associated with damage to the beta cells in the pancreas, significantly impairing insulin secretion. With limited insulin, glucose levels tend to go up, leading to high FBG levels and high glucose peaks after consumption of carbohydrates. The latter, high glucose peaks, appear to be particularly damaging when happening regularly over time.

What is the probability that you are NOT diabetic with this FBG reading?

I put together the table below, based on data from a widely cited meta-analysis () conducted by the research group called The Emerging Risk Factors Collaboration. It shows the distribution of FBG levels in urban settings among individuals who do not have diabetes.

The numbers in this table are fairly consistent with those from various other surveys of large numbers of individuals in urban settings.

The study mentioned above also tells us that the incidence of diabetes in urban populations is in the neighborhood of 6.8 percent. This may not sound like much, but as disease incidences goes, it is very high – approximately 1 in every randomly selected group of 15 people has diabetes.

The vast majority of those diagnosed will have diabetes mellitus type 2, which tends to develop over time and be associated with the metabolic syndrome ().

We know from Bayes' theorem, which is a fundamental element of the increasingly popular Bayesian statistics, that the probability of an event A given that an event B has occurred [denoted P(A|B)] is given by:

P(A|B)=P(B|A)*P(A)/P(B).

In the equation above, P(B|A) is the probability of event B given A, P(A) is the probability of event A, and P(B) is the probability of event B.

To answer the question posed in the title of this blog post, we need to calculate the probability that a person will have no diabetes given that he or she has a fasting blood glucose of 110-126 mg/dl.

Replacing A and B in the equation above with “NoDiabetes” (short for not having diabetes) and “FBG=110-126 mg/dl” respectively, we arrive at the formula to calculate the probability that answers the question:

P(NoDiabetes|FBG=110-126 mg/dl)=P(FBG=110-126 mg/dl|NoDiabetes)*P(NoDiabetes)/P(FBG=110-126 mg/dl).

From the table above we know that P(FBG=110-126 mg/dl|NoDiabetes)=7 percent. From our previous discussion, we know that P(NoDiabetes)=(100-6.8)/100 =93.2 percent.

Finally, the study tells us that P(FBG=110-126 mg/dl) is 9.1 percent. This includes individuals with diabetes (2.1 percent) and without diabetes (7 percent).

With these numbers, we can calculate the probability that a person will have no diabetes given that he or she has a FBG of 110-126 mg/dl:

P(NoDiabetes|FBG=110-126 mg/dl)=0.07*(1-0.068)/0.091=0.72.

That is, if your fasting blood glucose is in the 110-126 mg/dl range (6.1-7 mmol/l) then the probability that you DO NOT have diabetes is 72 percent. It would be much safer to bet that you do not have diabetes than that you do, even at that relatively high range.

Surprising eh!?

The above discussion not only highlights the lack of reliability of fasting blood glucose levels for diabetes diagnoses in the 110-126 mg/dl range (6.1-7 mmol/l), but also begs the question – what could cause high fasting blood glucose levels in healthy individuals?

Some of the folks I heard from have gone through insulin sensitivity tests (see, e.g., ), and were found to be insulin sensitive (in at least one case, highly sensitive), even though their baseline glucose levels are generally high. This goes against the possible speculation that they are prediabetics well on their way to becoming diabetic.

One possibility has been discussed in a previous post, which also mentions what could happen with HbA1c levels ().