Monday, July 28, 2014
This post is in response to an inquiry by Ivor (sorry for the delayed response). It refers to a recent study by Rantakömi and colleagues on the effect of alcohol consumption frequency on mortality from stroke (). The study followed men who consumed alcohol to different degrees, including no consumption at all, over a period of a little more than 20 years.
The study purportedly controlled for systolic blood pressure, smoking, body mass index, diabetes, socioeconomic status, and total amount of alcohol consumption. That is, its results are presented as holding regardless of those factors.
The main results were reported in terms of “relative risk” (RR) ratios. Here they are, quoted from the abstract:
“0.71 (95% CI, 0.30–1.68; P = 0.437) for men with alcohol consumption <0.5 times per week and 1.16 (95% CI, 0.54–2.50; P = 0.704) among men who consumed alcohol 0.5–2.5 times per week. Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 (95% CI, 1.19–7.72; P = 0.020).”
Note the P values reported within parentheses. They are the probabilities that the results are due to chance and thus “not real”, or not due to actual effects. By convention, P values equal to or lower than 0.05 are considered statistically significant. In consequence, P values greater than 0.05 are seen as referring to effects that cannot be unequivocally considered real.
This means that, of the results reported, only one seems to be due to a real effect, and that is the one that: “Among men who consumed alcohol >2.5 times per week compared with nondrinkers, RR was 3.03 …”
Why the authors report the statistically non-significant results as if they were noteworthy is unclear to me.
Before we go any further, let us look at what “relative risk” (RR) means. RR is given by the following ratio:
(Probability of an event when exposed) / (Probability of an event when not exposed)
In the study by Rantakömi and colleagues, the event is death from stroke. The exposure refers to alcohol consumption at a certain level, compared to no alcohol consumption (no exposure).
Now, let us go back to the result regarding consumption of alcohol more than 2.5 times per week. That result sounds ominous. It is helpful to keep in mind that the study by Rantakömi and colleagues followed a total of 2609 men with no history of stroke, of whom only 66 died from stroke.
Consider the following scenario. Let us say that 1 person in a group of 1,000 people who consumed no alcohol died from stroke. Let us also say that 3 people in a group of 1,000 people who consumed alcohol more than 2.5 times per week died from stroke. Given this, the RR would be: (3/1,000) / (1/1,000) = 3.
One could say, based on this, that: “Consuming alcohol more than 2.5 times per week increases the risk of dying from stroke by 200%”. Based on the RR, this is technically correct. It is rather misleading nevertheless.
If you think that increasing sample size may help ameliorate the problem, think again. The RR would be the same if it was 3 people versus 1 person in 1,000,000 (one million). With these numbers, the RR would be even less credible, in my view.
This makes the findings by Rantakömi and colleagues look a lot less ominous, don’t you think? This post is not really about the study by Rantakömi and colleagues. It is about the following question, which is in the title of this post: What is “relative risk” (RR)?
Quite frankly, given what one sees in RR-based studies, the answer is arguably not far from this:
RR is a ratio used in statistical analysis that makes minute effects look enormous; the effects in question would not normally be noticed by anyone in real life, and may be due to chance after all.
The reason I say that the effects “may be due to chance after all” is that when effects are such that 1 event in 1,000 would make a big difference, a researcher would have to control for practically everything in order to rule out confounders.
If one single individual with a genetic predisposition toward death from stroke falls into the group that consumes more alcohol, falling in that group entirely by chance (or due to group allocation bias), the RR-based results would be seriously distorted.
This highlights one main problem with epidemiological studies in general, where RR is a favorite ratio to be reported. The problem is that epidemiological studies in general refer to effects that are tiny.
One way to put results in context and present them more “honestly” would be to provide more information to readers, such as graphs showing data points and unstandardized scales, like the one below. This graph is from a previous post on latitude and cancer rates in the USA (), and has been generated with the software WarpPLS ().
This graph clearly shows that, while there seems to be an association between latitude and cancer rates in the USA, the total variation in cancer rates in the sample is only of around 3 in 1,000. This graph also shows outliers (e.g., Alaska), which call for additional explanations.
As for the issue of alcohol consumption frequency and mortality, I leave you with the results of a 2008 study by Breslow and Graubard, with more citations and published in a more targeted journal ():
“Average volume obscured effects of quantity alone and frequency alone, particularly for cardiovascular disease in men where quantity and frequency trended in opposite directions.”
In other words, alcohol consumption in terms of volume (quantity multiplied by frequency) appears to matter much more than quantity or frequency alone. We can state this even more simply: drinking two bottles of whiskey in one sitting, but only once every two weeks, is not going to be good for you.
In the end, providing more information to readers so that they can place the results in context is a matter of scientific honesty.
Monday, June 30, 2014
Salivary stones are the most common type of salivary gland disease. Having said that, they are very rare – less than 1 in 200 people will develop a symptomatic salivary stone. Usually they occur on one side of the mouth only. They seem to be more common in men than in women. Most of the evidence suggests that they are not strongly correlated with kidney stones, although some factors can increase both (e.g., dehydration).
Singh and Singh () discuss a case of a 55-year-old man who went to the Udaipur Dental Clinic with mild fever, pain, and swelling in the floor of the mouth. External examination, visually and through palpation, found no swelling or abnormal mass. The man’s oral hygiene was rather poor. The figures below show the extracted salivary stone, the stone perforating the base of the mouth prior to extraction, and an X-ray image of the stone.
I am not a big fan of X-ray tests in dental clinics, as they are usually done to convince patients to have dental decay treated in the conventional way – drilling and filling. Almost ten years ago, based on X-ray tests, I was told that I needed to treat some cavities urgently. I refused and instead completely changed my diet. Those cavities either reversed or never progressed. As the years passed, my dentist eventually became convinced that I had done the right thing, but told me that my case was very rare; unique in fact. Well, I know of a few cases like mine already. I believe that the main factors in my case were the elimination of unnatural foods (e.g., wheat-based foods), and consumption of a lot of raw-milk cheese.
However, as the case described here suggests, an X-ray test may be useful when a salivary stone is suspected.
Monday, June 2, 2014
Sun exposure leads to the production in the human body of a number of compounds that are believed to be health-promoting. One of these is known as “vitamin D” – an important hormone precursor ().
About 10,000 IU is considered to be a healthy level of vitamin D production per day. This is usually the maximum recommended daily supplementation dose, for those who have low vitamin D levels.
How much sun exposure, when the sun is at its peak (around noon), does it take to reach this level? Approximately 10 minutes.
We produce about 1,000 IU per minute of sun exposure, but seem to be limited to 10,000 IU per day. This assumes a level of skin exposure comparable to that of someone wearing a bathing suit.
Contrary to popular belief, this does not significantly decrease with aging. Among those aged 65 and older, pre-sunburn full-body exposure to sunlight leads to 87 percent of the peak vitamin D production seen in young subjects ().
Evolution seems to have led to a design that favors chronic (every day or so) but relatively brief sun exposure. Most of the sun rays are of the UVA type. However it is the UVB rays, which peak when the sun is high, that stimulate vitamin D production the most. The UVA rays in fact deplete vitamin D. Therefore, after 10 minutes of sun exposure per day when the sun is high, we would be mostly depleting vitamin D by sunbathing when the sun is low.
There is a lot of research that suggests that extended sun exposure also causes skin damage, even exposure below skin cancer levels. Also, anecdotally there are many reports of odd things happening with people who sunbathe for extended periods of time at the pool. Examples are moles appearing in odd places like the bottom of the feet, cases of actinic keratosis, and even temporary partial blindness.
There is something inherently unnatural about sunbathing at the pool, and exponentially more so in tan booths. Hunter-gatherers enjoy much sun exposure by generally avoiding the sun; particularly from the front, as this impairs the vision.
Pools often have reflective surfaces around them, so that people will not burn their feet. They cause glare, and over time likely contribute to the development of cataracts.
When you go to the pool, put your hands perpendicular to your face below you nose so that much of the light coming from those reflective surfaces does not hit your eyes directly. If you do this, you’ll probably notice that the main source of glare is what is coming from below, not from above.
In the African savannas, where our species emerged, this type of reflective surface has no commonly found analog. You don't have to go to the pool to find all kinds of sources of unnatural glare in urban environments.
Snow is comparable. Hunter-gatherers who live in areas permanently or semi-permanently covered with snow, such as the traditional Inuit, have a much higher incidence of cataracts than those who don’t.
So, what would be some of the characteristics of sensible sun exposure during the summer, particular at pools? Considering all that is said above, I’d argue that these should be in the list:
- Standing and moving while sunbathing, as opposed to sitting or lying down.
- Sunbathing for about 10 minutes, when the sun is high, staying mostly in the shade after 10 minutes or so of exposure.
- Wearing eye protection, such as polarized sunglasses.
- Avoiding the sun hitting you directly in the face, even with eye protection, as the facial skin is unlikely to have the same level of resistance to sun damage as other parts that have been more regularly exposed in our evolutionary past (e.g., shoulders).
- Covering those areas that get sunlight perpendicularly while sunbathing when the sun is high, such as the top part of the shoulders if standing in the sun.
Doing these things could potentially maximize the benefits of sun exposure, while at the same time minimizing its possible negative consequences.
Tuesday, May 6, 2014
There have been many academic articles in the past linking red meat intake with increased mortality, and there will be more in the future. I discussed one such article before here (, ). The findings in this article, which received an enormous amount of media attention, are the basis for my discussion in this post. I am interested in answering the question: Why red meat consumption may appear unhealthy in scientific studies?
This question leads to other questions, which are also addressed in this post. Can red meat intake be associated with increases and decreases in mortality, in the same study? Can red meat intake possibly cause increased mortality, at least for a percentage of the population?
All of the analyses discussed below have been conducted with the software WarpPLS (). This software supports multivariate analyses where relationships can be modeled as linear or nonlinear, with or without moderating effects included.
The ubiquitous J curve
The graph below shows how mortality varies with red meat intake. As you can see, the relationship is overall flat, meaning that red meat intake is overall unrelated with mortality. However, when we look at the two sets of points above and below the relationship line, for males and females, we see a different pattern. It appears that red meat intake and mortality are indeed significantly associated with one another, but in a J-curve pattern. That is, red meat intake is associated with increases and decreases in mortality, in the same study.
Each serving of red meat corresponds to approximately 84 g. Therefore, we could say, based on the graph above, that mortality would be minimized with consumption of a little less than 67 g/d of red meat (0.80*84) for males, and a little more than 115 g/d (1.37*84) for females. Not zero consumption, simply not a lot.
Now, one may say that this is very reasonable: a little bit of red meat is fine, but not too much. Generally females lose blood periodically, so they need a bit more than males. However, based on a number of other studies, it seems that the optimal intake amounts that we are seeing here are unusually low. If this is the case, what could be biasing the results?
Multivariate associations can distort results quite a lot. Such associations arise from correlations among multiple variables; correlations that should not per se be taken as strong indications of causality. Below are the correlations between “Red meat intake (servings/d)” and other relevant variables in the dataset taken from the study being considered here.
- Physical activity (MET-h/wk): -0.696. That is, increases in red meat intake are very strongly associated with decreases in physical activity in this study. One MET unit is equal to the energy produced per unit surface area of an average person seated at rest.
- Diabetes (%): 0.781. Increases in red meat intake are very strongly associated with increases in the percentages of individuals with diabetes.
- Food intake (cal/d): 0.604. Increases in red meat intake are strongly associated with increases in food intake in general.
- Current smoker (%): 0.519. Increases in red meat intake are strongly associated with increases in the percentages of smokers.
Let us take the physical activity variable, for example. It is inversely correlated with red meat intake, with a strong correlation coefficient, and it is unlikely that this correlation is due to direct causation - one way or the other. Below is the same graph as above, but now with labels indicating physical activity levels.
You can see that physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories. Here you can see that physical activity is associated with mortality in a pattern that is pretty much the reverse of red meat intake. The reason for this is the strong inverse correlation between physical activity and red meat intake.
The highest mortality is associated with the lowest physical activity at the highest red meat intake. Interestingly, mortality goes up as one reaches the point at which physical activity is the highest at the lowest red meat intake.
Now take a look at the two graphs below. Both show the relationship between diabetes incidence and mortality. The first has biological sex indicated through legends. The second has physical activity levels indicated through labels.
One way to untangle the messy nature of the relationships above is to try to look for possible moderating effects, based on reasonable causal assumptions. One such assumption is that physical activity moderates the relationship between red meat intake and mortality.
The moderating effect of physical activity
The two graphs below show the relationships between red meat intake and mortality with (first graph) and without (second graph) the moderating effect of physical activity. Basically and with minimum statistical jargon, the numbers next to the arrows indicate the strengths of the associations (betas) and the probabilities that the associations are not real (Ps). By convention, a P value lower than 0.05 is normally seen as an indication that the association is strong enough to be considered real – i.e., not due to chance.
What the graphs above suggest is that increases in physical activity tend to make the relationship between red meat intake and mortality go from flat (or nonexistent) to negative. This is the meaning of the negative moderating coefficient next to the dashed arrow. In other words, as physical activity levels go up, more red meat intake is associated with less mortality.
The role of genetics
While being male or female means having different genetic profiles, with a full chromosome difference, the effect of biological sex on mortality appears to be confounded by the effect of physical activity. That is, physical activity, as measured in this study (using METs), is strongly correlated with biological sex, and also with mortality. As noted earlier, physical activity levels tend to be lower among females, which is in part due to them being on average smaller than males and thus burning fewer calories.
But another genetic factor that may influence the results and that is not included in this analysis is HFE hereditary haemochromatosis, a hereditary disease that leads to excessive intestinal absorption of dietary iron, resulting in iron overload. This genetic condition is relatively common in northern Europeans and their descendants, with a prevalence of 1 in 200 in this group. Factoid: it is quite common in Australia.
This level of prevalence matters when you are looking at mortality levels that vary along only approximately 8 in 1,000, as in this study. That translates to 0.4 in 200; much less than the prevalence of HFE hereditary haemochromatosis in northern Europeans and their descendants. That is, HFE hereditary haemochromatosis may be a major confounder in our analyses above, one that has not been controlled for. The study included 37,698 men from the Health Professionals Follow-up Study (1986-2008) and 83,644 women from the Nurses' Health Study (1980-2008). There must have been many individuals with HFE hereditary haemochromatosis in the sample.
In summary …
Based on all of the above, I think it is quite possible that for those who suffer from HFE hereditary haemochromatosis, both biological sex and physical activity affect the relationship between red meat intake and mortality.
Past menopause, women who suffer from HFE hereditary haemochromatosis should consider reducing their red meat intake, as well as intake of iron from other sources (particularly from pills). The same goes for men with the condition. Male and post-menopausal female sufferers should consider regularly donating blood.
Both men and women who suffer from HFE hereditary haemochromatosis should consider significantly increasing their level of physical activity to reduce the likelihood of iron overload. (This would be good for anyone.)
Why physical activity? Because iron is used to transport oxygen and in biological redox reactions, both of which are significantly increased during and after physical activity. In those who tend to accumulate iron in tissues, physical activity creates an increase in demand for iron that can balance the increased supply from iron-rich sources.
Our bodies evolved in the context of physical activity, often intense physical activity, and are thus maladapted for sedentary behavior.
Monday, April 21, 2014
Gut flora is found in many areas of our digestive tract, particularly in the colon. Whenever we eat anything we feed the microbes that make up our gut flora and/or add new microbes. Much of this flora is made up of bacteria. Not all of it is made up of bacteria though. The much talked about Candida albicans (a.k.a. “the American parasite”) is a fungus that is found predominantly in our digestive tract and mouths.
Candida’s recent fame is more a testament to the power of well-orchestrated Internet campaigns to sell products than to the actual importance of the fungus in determining the health of non-immunodepressed individuals. Claims about Candida, including dubious ones, have been made many times in the past ().
The relationship between the human gut flora and health was a topic of much interest to Élie Metchnikoff (photo below from Wikipedia), who received the Nobel Prize in Medicine in 1908 for his research on phagocytosis (). Metchnikoff was also a pioneer in the study of aging.
Gut flora discussions often refer to foods and supplements that fall into one of two main categories: probiotics and prebiotics (). Probiotics are generally defined as foods and supplements that include health-promoting live microbes. Prebiotics are non-digestible foods and supplements that feed health-promoting microbes living primarily in the human colon.
Food fermentation, under the appropriate conditions, leads to the formation of natural probiotics. This applies to both animal foods (e.g., cheese, cured meats) and plant foods (e.g., sauerkraut, pickles). Prebiotics occur naturally in many raw plant foods as fiber and resistant starch, and can also be produced through starch retrogradation ().
Again, whenever we eat anything we feed our gut flora. This gut flora is reportedly made up of 10 to the power of 14 cells of bacteria, 10 times more cells than the human body (), plus other types of microbes (e.g., fungi). Different species of microbes in our gut have genomes that are markedly different from ours. Thus we carry in our gut significantly more genes than our own; and genes are selfish.
Genes are selfish in the sense that they seek to propagate themselves. From the perspective of our gut microbes, this can be achieved by inducing the secretion of chemicals that will make us crave foods that will also feed the microbes, whether this will lead to an improvement in our health or not. Even unhealthy human hosts can live long enough to sustain a large number of generations of microbes.
Killing the host human organism may seem like a suicidal strategy for gut microbes, but not if the host organism passes the microbes to other host organisms before the microbes themselves die. Microbes can pass from one human to another through many mechanisms.
So how can we improve our gut flora?
Supplementation or transplantation of microbes have been attempted with mixed but generally positive results ().
Few approaches combine the effectiveness and simplicity of avoiding highly processed industrialized foods. The emphasis here is on inhibiting the growth of unnatural gut flora; flora that has not been carried regularly by our Paleolithic ancestors.
Having done that for a while, which can be difficult due to cravings induced by unnatural gut flora, your own body may become very effective at telling you what is good for you and what is not.
As a side note, just because a food is fermented one cannot assume that it is health-promoting. Bread is a fermented food.
Over the years I have noticed that I prefer eating certain meat dishes cold, and several days after they have been prepared. I wonder if this has anything to do with a small amount of fermentation bringing to life probiotic microbes.
Monday, March 31, 2014
A few years ago I wrote about a meatza made with lean ground beef and bison (). This post is about another kind of meatza, one that takes a lot less time to prepare. In fact, this one is very quick, and still very nutritious.
The recipe below is for a meal that feeds 3-6 people. If you are preparing this for an opinionated family, and you do not want to be accused of preparing “grilled ham and cheese” for them, you can always add some sautéed vegetables to the ham.
- Place 2 to 3 lbs of folded ham into a sheet pan. There is no need to coat the pan, as some of the water and fat in the ham will seep out and prevent sticking.
- Add some dry seasoning and butter. For the dry seasoning, I suggest a mix of garlic powder and cayenne pepper.
- Add a layer of genoa salami, and another layer of swiss cheese.
- Preheat oven to 375 degrees Fahrenheit.
- Bake the meatza for about 15 minutes.
The photo montage above shows the different stages of preparation and the final product. Since ham cuts tend to be very lean, the amount of fat in the entire meatza will normally depend heavily on the amount of added butter, salami, and cheese.
In this kind of meatza, the protein-to-fat ratio will normally be greater than 1. I think a ratio closer to 2 is ideal for those semi-sedentary office workers who do moderate exercise. The reason is that fat is the most calorie-dense macronutrient. Protein is the least calorie-dense macronutrient.
You do lose something with this dish, as you do with hot dishes in general. You lose the probiotic bacteria that would normally be found in significant amounts in the ham, salami, and cheese. These are all fermented foods that are better consumed raw.
Tuesday, March 18, 2014
Doing resistance exercise to failure is normally recommended for those who want to maximize strength and muscle mass gains from the exercise. Yet, going to failure tends to significantly increase the chances of injury, after which the ability to do resistance exercise is impaired – also impairing gains, in the long term.
From an evolutionary perspective, getting injured is clearly maladaptive. Prey animals that show signs of injury, for example, tend to be targeted by predators. There is also functional loss, which would be reflected in impaired hunting and gathering ability.
So, assuming that going to failure is at least somewhat unnatural, because of a higher likelihood of subsequent injuries, how can it be advisable in the context of resistance exercise?
The graph below is from a study by Izquierdo and colleagues (). They randomly assigned several athletes to two exercise conditions, namely resistance training to failure (RF) and not to failure (NRF). A control group of athletes did not do any resistance exercise. The athletes were tested at four points in time: before the initiation of training (T0), after 6 wk of training (T1), after 11 wk of training (T2), and after 16 wk of training (T3).
The graph above shows the gains in terms of weight lifted in two exercises, the bench press and squat. It is similar to other graphs from the study in that it clearly shows: (a) improvements in the amount of weight lifted over time for both the RF and NRF groups, which reflect gains in strength; and (b) no significant differences in the improvements for the RF and NRF groups.
When you look at the results of the study as a whole, it seems that RF and NRF are associated with slightly greater or lesser gains, depending on the type of exercise and the measure of gains employed. The differences are small, and one can reasonably conclude that no significant difference in overall gains exists between RF and NRF.
It is clear that going to failure leads to increased metabolic stress, and that increased metabolic stress is associated with greater secretion of anabolic hormones (). How can this be reconciled with the lack of a significant difference in gains in the RF and NRF groups?
The graph below provides a hint as to the answer to this question. It shows resting serum cortisol concentrations in the participants. As you can see, after 16 wk of training (T3) cortisol levels are higher in the RF group, which is particularly interesting because the NRF group had higher cortisol levels at baseline (T0). Cortisol is a catabolic hormone, which may in this case counter the effects of the anabolic hormones, even though going to failure is expected to lead to greater anabolic hormone secretion.
It seems that cortisol levels tend to go up over time for those who go to failure, and go down for those who do not. I am not sure if this is a strictly metabolic effect. There may be a psychological component to it, as strength and power gains over time tend to be increasingly more difficult to achieve (see schematic graph below); perhaps leading to some added mental stress as well, as one tries to continue increasing resistance (or weight) while regularly going to failure.
And, of course, it is also possible that the results of the study reviewed here are a statistical “mirage”. The authors explain how they controlled for various possible confounders by adjusting the actual measures. This approach is generally less advisable than controlling for the effects of confounders by including the confounders in a multivariate analysis model ().
Nevertheless, in light of the above I am not so sure that regularly doing resistant exercise to failure is such a good idea.