Tuesday, January 26, 2016

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!

Saturday, January 2, 2016

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.

Wednesday, December 23, 2015

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.

Tuesday, November 24, 2015

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

Monday, October 26, 2015

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.

Sunday, September 27, 2015

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.

Sunday, August 23, 2015

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.