We share an ancestor who probably lived no more than 640 years ago

We all evolved from one single-celled organism that lived billions of years ago. I don’t see why this is so hard for some people to believe, given that all of us also developed from a single fertilized cell in just 9 months.

However, our most recent common ancestor is not that first single-celled organism, nor is it the first Homo sapiens, or even the first Cro-Magnon.

The majority of the people who read this blog probably share a common ancestor who lived no more than 640 years ago. Genealogical records often reveal interesting connections - the figure below has been cropped from a larger one from Pinterest.

You and I, whoever you are, have each two parents. Each of our parents have (or had) two parents, who themselves had two parents. And so on.

If we keep going back in time, and assume that you and I do not share a common ancestor, there will be a point where the theoretical world population would have to be impossibly large.

Assuming a new generation coming up every 20 years, and going backwards in time, we get a theoretical population chart like the one below. The theoretical population grows in an exponential, or geometric, fashion.

As we move back in time the bars go up in size. Beyond a certain point their sizes go up so fast that you have to segment the chart. Otherwise the bars on the left side of the chart disappear in comparison to the ones on the right side (as several did on the chart above). Below is the section of the chart going back to the year 1371.

The year 1371 is a little more than 640 years ago. (This post is revised from another dated a few years ago, hence the number 640.) And what is the theoretical population in that year if we assume that you and I have no common ancestors? The answer is: more than 8.5 billion people. We know that is not true.

Admittedly this is a somewhat simplistic view of this phenomenon, used here primarily to make a point. For example, it is possible that a population of humans became isolated 15 thousand years ago, remained isolated to the present day, and that one of their descendants just happened to be around reading this blog today.

Perhaps the most widely cited article discussing this idea is this one by Joseph T. Chang, published in the journal Advances in Applied Probability. For a more accessible introduction to the idea, see this article by Joe Kissell.

Estimates vary based on the portion of the population considered. There are also assumptions that have to be made based on migration and mating patterns, as well as the time for each generation to emerge and the stability of that number over time.

Still, most people alive today share a common ancestor who lived a lot more recently than they think. In most cases that common ancestor probably lived less than 640 years ago.

And who was that common ancestor? That person was probably a man who, due to a high perceived social status, had many consorts, who gave birth to many children. Someone like Genghis Khan.

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.

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 TG = 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).

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We need more analyses!


Notes:

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

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

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

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

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

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

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

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

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

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

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

Could grain-fed beef liver be particularly nutritious?

There is a pervasive belief today that grain-fed beef is unhealthy, a belief that I addressed before in this blog () and that I think is exaggerated. This general belief seems to also apply to a related meat, one that is widely acknowledged as a major micronutrient “powerhouse”, namely grain-fed beef liver.

Regarding grain-fed beef liver, the idea is that cattle that are grain-fed tend to develop a mild form of fatty liver disease. This I am inclined to agree with.

However, I am not convinced that this is such a bad thing for those who eat grain-fed beef liver.

In most animals, including Homo sapiens, fatty liver disease seems to be associated with extra load being put on the liver. Possible reasons for this are accelerated growth, abnormally high levels of body fat, and ingestion of toxins beyond a certain hormetic threshold (e.g., alcohol).

In these cases, what would one expect to see as a body response? The extra load is associated with high oxidative stress and rate of metabolic work. In response, the body should shuttle more antioxidants and metabolism catalysts to the organ being overloaded. Fat-soluble vitamins can act as antioxidants and catalysts in various metabolic processes, among other important functions. They require fat to be stored, and can then be released over time, which is a major advantage over water-soluble vitamins; fat-soluble vitamins are longer-acting.

So you would expect an overloaded liver to have more fat in it, and also a greater concentration of fat-soluble vitamins. This would include vitamin A, which would give the liver an unnatural color, toward the orange-yellow range of the spectrum.

Grain-fed beef liver, like the muscle meat of grain-fed cattle, tends to have more fat than that of grass-fed animals. One function of this extra fat could be to store fat-soluble vitamins. This extra fat appears to have a higher omega-6 fat content as well. Still, beef liver is a fairly lean meat; with about 5 g of fat per 100 g of weight, and only 20 mg or so of omega-6 fat. Clearly consumption of beef liver in moderation is unlikely to lead to a significant increase in omega-6 fat content in one’s diet (). By consumption in moderation I mean approximately once a week.

The photo below, from Wikipedia, is of a dish prepared with foie gras. That is essentially the liver of a duck or goose that has been fattened through force-feeding, until the animal develops fatty liver disease. This “diseased” liver is particularly rich in fat-soluble vitamins; e.g., it is the best known source of the all-important vitamin K2.



Could the same happen, although to a lesser extent, with grain-fed beef liver? I don’t think it is unreasonable to speculate that it could.

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.

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.]

Is heavy physical activity a major trigger of death by sudden cardiac arrest? Not in Oregon

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”.

Good cardiology textbooks, such as the Mayo Clinic Cardiology, tend to give us a more complex and complete picture. So do medical research articles that report on studies of heart attacks based on comprehensive surveys.

Reddy and colleagues (2009) studied sudden cardiac arrest events followed by death from 2002 to 2005 in Multnomah County in Oregon. This study was part of the ongoing Oregon Sudden Unexpected Death Study. Multnomah County has an area of 435 square miles, and had a population of over 677 thousand at the time of the study. The full reference to the article and a link to a full-text version are at the end of this post.

The researchers grouped deaths by sudden cardiac arrests (SCAs) according to the main type of activity being performed before the event. Below is how the authors defined the activities, quoted verbatim from the article. MET is a measure of the amount of energy spent in the activity; one MET is the amount of energy spent by a person sitting quietly.

- Sleep (MET 0.9): subjects who were sleeping when they sustained SCA.
- Light activity (MET 1.0–3.4): included bathing, dressing, cooking, cleaning, feeding, household walking and driving.
- Moderate activity (MET 3.5–5.9): included walking for exercise, mowing lawn, gardening, working in the yard, dancing.
- Heavy activity (MET score ≥6): included sports such as tennis, running, jogging, treadmill, skiing, biking.
- Sexual activity (MET score 1.3): included acts of sexual intercourse.

What did they find? Not what many people would expect.

The vast majority of the people dying of sudden cardiac arrest were doing things that fit the “light activity” group above prior to their death. This applies to both genders. The figure below (click to enlarge) shows the percentages of men and women who died from sudden cardiac arrest, grouped by activity type.

Sudden cardiac arrests were also categorized as witnessed or un-witnessed. For witnessed, someone saw them happening. For un-witnessed, the person was seen alive, and within 24 hours had died. So the data for witnessed sudden cardiac arrests is a bit more reliable. The table below displays the distribution of mean age, gender and known coronary artery disease (CAD) in those with witnessed sudden cardiac arrest.

Look at the bottom row, showing those with known coronary artery disease. Again, light activity is the main trigger. Sleep comes second. The numbers within parentheses refer to percentages within each activity group. Those percentages are not very helpful in the identification of the most important triggers, although they do suggest that coronary artery disease is a major risk factor. For example, among those who died from sudden cardiac arrest while having sex, 57 percent had known coronary artery disease. For light activity, 36 percent had known coronary artery disease.

As a caveat, it is worth noting that heavy activity appears to be more of a trigger in younger individuals than in older ones. This may simply reflect the patterns of activities at different ages. However, this does not seem to properly account for the large differences observed in triggers; the standard deviation for age in the heavy activity group was large enough to include plenty of seniors. Still, it would have been nice to see a multivariate analysis controlling for various effects, including age.

So what is going on here?

The authors give us a hint. The real culprit may be bottled up emotional stress and sleep disorders; the latter may be caused by stress, as well as by obesity and other related problems. They have some data that points in those directions. That makes some sense.

We humans have evolved “fight-or-flight” mechanisms that involve large hormonal discharges in response to stressors. Our ancestors needed those. For example, they needed those to either fight or run for their lives in response to animal attacks.

Modern humans experience too many stressors while sitting down, as in stressful car commutes and nasty online interactions. The stresses cause “fight-or-flight” hormonal discharges, but are followed by neither “fight” nor “flight” in most cases. This cannot be very good for us.

Death by running!? More like death by not running!

Reference:

Reddy, P.R., Reinier, K., Singh, T., Mariani, R., Gunson, K., Jui, J., & Chugh, S.S. (2009). Physical activity as a trigger of sudden cardiac arrest: The Oregon Sudden Unexpected Death Study. International Journal of Cardiology, 131(3), 345–349.

What is a good low carbohydrate diet? It is a low calorie one

What is a good low carbohydrate diet?

For me, and many people I know, the answer is: a low calorie one. What this means, in simple terms, is that a good low carbohydrate diet is one with plenty of seafood and organ meats in it, and also plenty of veggies. These are low carbohydrate foods that are also naturally low in calories. Conversely, a low carbohydrate diet of mostly beef and eggs would be a high calorie one.

Seafood and organ meats provide essential fatty acids and are typically packed with nutrients. Because of that, they tend to be satiating. In fact, certain organ meats, such as beef liver, are so packed with nutrients that it is a good idea to limit their consumption. I suggest eating beef liver once or twice a week only. As for seafood, it seems like a good idea to me to get half of one’s protein from them.

Does this mean that the calories-in-calories-out idea is correct? No, and there is no need to resort to complicated and somewhat questionable feedback-loop arguments to prove that calories-in-calories-out is wrong. Just consider this hypothetical scenario; a thought experiment. Take two men, one 25 years of age and the other 65, both with the same weight. Put them on the same exact diet, on the same exact weight training regime, and keep everything else the same.

What will happen? Typically the 65-year-old will put on more body fat than the 25-year-old, and the latter will put on more lean body mass. This will happen in spite of the same exact calories-in-calories-out profile. Why? Because their hormonal mixes are different. The 65-year-old will typically have lower levels of circulating growth hormone and testosterone, both of which significantly affect body composition.

As you can see, it is not all about insulin, as has been argued many times before. In fact, average and/or fasting insulin may be the same for the 65- and 25-year-old men. And, still, the 65-year-old will have trouble keeping his body fat low and gaining muscle. There are other hormones involved, such as leptin and adiponectin, and probably several that we don’t know about yet.

A low carbohydrate diet appears to be ideal for many people, whether that is due to a particular health condition (e.g., diabetes) or simply due to a genetic makeup that favors this type of diet. By adopting a low carbohydrate diet with plenty of seafood, organ meats, and veggies, you will make it a low calorie diet. If that leads to a calorie deficit that is too large, you can always add a bit more of fat to it. For example, by cooking fish with butter and adding bacon to beef liver.

One scenario where I don’t see the above working well is if you are a competitive athlete who depletes a significant amount of muscle glycogen on a daily basis – e.g., 250 g or more. In this case, it will be very difficult to replenish glycogen only with protein, so the person will need more carbohydrates. He or she would need a protein intake in excess of 500 g per day for replenishing 250 g of glycogen only with protein.