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

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

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

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

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

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

What is “relative risk” (RR)? The case of alcohol frequency and its impact on mortality from stroke

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.

How much alcohol is optimal? Maybe less than you think

I have been regularly recommending to users of the software HCE () to include a column in their health data reflecting their alcohol consumption. Why? Because I suspect that alcohol consumption is behind many of what we call the “diseases of affluence”.

A while ago I recall watching an interview with a centenarian, a very lucid woman. When asked about her “secret” to live a long life, she said that she added a little bit of whiskey to her coffee every morning. It was something like a tablespoon of whiskey, or about 15 g, which amounted to approximately 6 g of ethanol every single day.

Well, she might have been drinking very close to the optimal amount of alcohol per day for the average person, if the study reviewed in this post is correct.

Studies of the effect of alcohol consumption on health generally show results in terms of averages within fixed ranges of consumption. For example, they will show average mortality risks for people consuming 1, 2, 3 etc. drinks per day. These studies suggest that there is a J-curve relationship between alcohol consumption and health (). That is, drinking a little is better than not drinking; and drinking a lot is worse than drinking a little.

However, using “rough” ranges of 1, 2, 3 etc. drinks per day prevents those studies from getting to a more fine-grained picture of the beneficial effects of alcohol consumption.

Contrary to popular belief, the positive health effects of moderate alcohol consumption have little, if anything, to do with polyphenols such as resveratrol. Resveratrol, once believed to be the fountain of youth, is found in the skin of red grapes.

It is in fact the alcohol content that has positive effects, apparently reducing the incidence of coronary heart disease, diabetes, hypertension, congestive heart failure, stroke, dementia, Raynaud’s phenomenon, and all-cause mortality. Raynaud's phenomenon is associated with poor circulation in the extremities (e.g., toes, fingers), which in some cases can progress to gangrene.

In most studies of the effects of alcohol consumption on health, the J-curves emerge from visual inspection of the plots of averages across ranges of consumption. Rarely you find studies where nonlinear relationships are “discovered” by software tools such as WarpPLS (), with effects being adjusted according.

You do find, however, some studies that fit reasonably justified functions to the data. Di Castelnuovo and colleagues’ study, published in JAMA Internal Medicine in 2006 (), is probably the most widely cited among these studies. This study is a meta-analysis; i.e., a study that builds on various other empirical studies.

I think that the journal in which this study appeared was formerly known as Archives of Internal Medicine, a fairly selective and prestigious journal, even though this did not seem to be reflected in its Wikipedia article at the time of this writing ().

What Di Castelnuovo and colleagues found is interesting. They fitted a bunch of nonlinear functions to the data, all with J-curve shapes. The results suggest a lot of variation in the maximum amount one can drink before mortality becomes higher than not drinking at all; that maximum amount ranges from about 4 to 6 drinks per day.

But there is little variation in one respect. The optimal amount of alcohol is somewhere around 5 and 7 g/d, which translates into about the following every day: half a can of beer, half a glass of wine, or half a “shot” of spirit. This is clearly a common trait of all of the nonlinear functions that they generated. This is illustrated in the figure below, from the article.

As you can seen from the curves above, a little bit of alcohol every day seems to have an acute effect on mortality reduction. And it seems that taking little doses every day is much better than taking the equivalent dose over a larger period of time; for instance, the equivalent per week, taken once a week. This is suggested by other studies as well ().

The curves above do not clearly reflect a couple of problems with alcohol consumption. One is that alcohol seems to be treated by the body as a toxin, which causes some harm and some good at the same time, the good being often ascribed to hormesis (). Someone who is more sensitive to alcohol’s harmful effects, on the liver for example, may not benefit as much from its positive effects.

The curves are averages that pass through points, after which the points are forgotten; even though they are real people.

The other problem with alcohol is that most people who are introduced to it in highly urbanized areas (where most people live) tend to drink it because of its mood-altering effects. This leads to a major danger of addiction and abuse. And drinking a lot of alcohol is much worse than not drinking at all.

Interestingly, in traditional Mediterranean Cultures where wine is consumed regularly, people tend to generally frown upon drunkenness ().

The 2012 Atherosclerosis egg study: More smoking is associated with more plaque, unless you eat more eggs

I blogged before about the study by David Spence and colleagues, published online in July 2012 in the journal Atherosclerosis (). This study attracted a lot of media attention (e.g., ). The article is titled: “Egg yolk consumption and carotid plaque”. The study argues that “regular consumption of egg yolk should be avoided by persons at risk of cardiovascular disease”. It hints at egg yolks being unhealthy in general, possibly even more so than cigarettes.

I used the numbers in Table 2 of the article (only 5 rows of data, one per quintile; i.e., N=5) to conduct a type of analysis that is rarely if ever conducted in health studies – a moderating effects analysis. A previous blog post summarizes the results of one such analysis using WarpPLS (). It looked into the effect of the number of eggs consumed per week on the association between blood LDL cholesterol and plaque (carotid plaque). The conclusion, which is admittedly tentative due to the small sample (N=5), was that plaque decreased as LDL cholesterol increased with consumption of 2.3 eggs per week or more ().

Recently I ran an analysis on the moderating effect of number of eggs consumed per week on the association between cumulative smoking (measured in “pack years”) and plaque. As it turns out, if you fit a 3D surface to the five data points that you get for these three variables from Table 2 of the article, you end up with a relatively smooth surface. Below is a 3D plot of the 5 data points, followed by a best-fitting 3D surface (developed using an experimental algorithm).

Based on this best-fitting surface you could then generate a contour graph, shown below. The “lines” are called “isolines”. Each isoline refers to plaque values that are constant for a set of eggs per week and cumulative smoking combinations. Next to the isolines are the corresponding plaque values. The first impression is indeed that both egg consumption and smoking are causing plaque buildup, as plaque clearly increases as one moves toward the top-right corner of the graph.

But focus your attention on each individual isoline, one at a time. It is clear that plaque remains constant for increases in cumulative smoking, as long as egg consumption increases. Take for example the isoline that refers to 120 mm2 of plaque area. An increase in cumulative smoking from about 14.5 to 16 pack years leads to no increase in plaque if egg consumption goes up from about 2 to 2.3 eggs per week.

These within-isoline trends, which are fairly stable across isolines (they are all slanted to the right), clearly contradict the idea that eggs cause plaque buildup. So, why does plaque buildup seem to clearly increase with egg consumption? Here is a good reason: egg consumption is very strongly correlated with age, and plaque increases with age. The correlation is a whopping 0.916. And I am not talking about cumulative egg consumption, which the authors also measure, through a variable called “egg-yolk years”. No, I am talking about eggs per week. In this dataset, older folks were eating more eggs, period.

The correlation between plaque and age is even higher: 0.977. Given this, it makes sense to look at individual isolines. This would be analogous to what biostatisticians often call “adjusting for age”, or analyzing the effect of egg consumption on plaque buildup “keeping age constant”. A different technique is to “control for age”; this technique would be preferable had the correlations been lower (say, lower than 0.7), as collinearity levels might have been below acceptable thresholds.

The underlying logic of the “keeping age constant” technique is fairly sound in the face of such a high correlation, which would make “controlling for age” very difficult due to collinearity. When we “keep age constant”, the results point at egg consumption being protective among smokers.

But diehard fans of the idea that eggs are unhealthy could explain the results differently. Maybe egg consumption causes plaque to go up, but smoking has a protective effect. Again taking the isoline that refers to 120 mm2 of plaque area, these diehard fans could say that an increase in egg consumption from 2 to 2.3 eggs per week leads to no increase in plaque if cumulative smoking goes up from about 14.5 to 16 pack years.

Not too long ago I also blogged about a medical case study of a man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious The New England Journal of Medicine, and was in surprisingly good health (). This man was not a smoker.

Perhaps if this man smoked 25 cigarettes per day, and ate no eggs, he would be in even better health eh!?

Does tallness cause heart disease? No, but sex does

Popular beliefs about medical issues are sometimes motivated by a statistical phenomenon known as “spurious relationship”, among other names. Two variables X and Y are influenced by a third variable C, which leads to X and Y being correlated and thus the impression that X and Y are causally associated.

Take a look at the table below, which I blogged about in a previous post (). This table shows that there is a strong unadjusted correlation between height and arterial stiffness, a marker of heart disease. The likelihood that the correlation is due to chance is lower than one tenth of a percentage point (P<.001).

Interestingly, the authors of the study even use height as a control variable to narrow down the “true” causes of arterial stiffness (column with adjusted results), assuming that height did indeed influence arterial stiffness and what they found to be a key predictor of arterial stiffness, 2-hour postprandial glucose.

But there is no convincing evidence that height causes heart disease, with exception of pathological extremes – e.g., acromegaly. Extremes tend to influence statistical results somewhat, leading to conflicting conclusions that end up being disseminated by the popular media (). This is one of the sources of popular beliefs about medical issues.

Another, more important, source are real confounders. And this takes us back to the issue of height being associated with heart disease. In fact, height will typically be significantly associated with heart disease in almost any study that includes men and women and does not control for biological sex.

One of the reasons is that women overall tend to have a significantly lower incident of heart disease than men. The other is that height is significantly lower among women than men, on average, even though there are several women who are taller than the average man.

The table above was from a study including both sexes. Therefore, the strong association between height and arterial stiffness is a “reflection” of the strong association between being male and increased arterial stiffness. If one were to add a variable coded as 0 for male and 1 for female, and use it in a multivariate analysis of predictor of arterial stiffness, together with height, the effect of height would probably “disappear”.

Biological sex is the control variable, the “confounder”, that the authors should have used to narrow down the “true” causes of arterial stiffness (second column in the table). In the absence of biological sex, controlling for height accomplished something similar, but in a “wobbly” way, leaving many readers scratching their heads in confusion.

Fasting blood glucose of 83 mg/dl and heart disease: Fact and fiction

If you are interested in the connection between blood glucose control and heart disease, you have probably done your homework. This is a scary connection, and sometimes the information on the Internetz make people even more scared. You have probably seen something to this effect mentioned:

Heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl.

In fact, I have seen this many times, including on some very respectable blogs. I suspect it started with one blogger, and then got repeated over and over again by others; sometimes things become “true” through repetition. Frequently the reference cited is a study by Brunner and colleagues, published in Diabetes Care in 2006. I doubt very much the bloggers in question actually read this article. Sometimes a study by Coutinho and colleagues is also cited, but this latter study is actually a meta-analysis.

So I decided to take a look at the Brunner and colleagues study. It covers, among other things, the relationship between cardiovascular disease (they use the acronym CHD for this), and 2-hour blood glucose levels after a 50-g oral glucose tolerance test (OGTT). They tested thousands of men at one point in time, and then followed them for over 30 years, which is really impressive. The graph below shows the relationship between CHD and blood glucose in mmol/l. Here is a calculator to convert the values to mg/dl.

The authors note in the limitations section that: “Fasting glucose was not measured.” So these results have nothing to do with fasting glucose, as we are led to believe when we see this study cited on the web. Also, on the abstract, the authors say that there is “no evidence of nonlinearity”, but in the results section they say that the data provides “evidence of a nonlinear relationship”. The relationship sure looks nonlinear to me. I tried to approximate it manually below.

Note that CHD mortality really goes up more clearly after a glucose level of 5.5 mmol/l (100 mg/dl). But it also varies significantly more widely after that level; the magnitudes of the error bars reflect that. Also, you can see that at around 6.7 mmol/l (121 mg/dl), CHD mortality is on average about the same as at 5.5 mmol/l (100 mg/dl) and 3.5 mmol/l (63 mg/dl). This last level suggests an abnormally high insulin response, bringing blood glucose levels down too much at the 2-hour mark – i.e., reactive hypoglycemia, which the study completely ignores.

These findings are consistent with the somewhat chaotic nature of blood glucose variations in normoglycemic individuals, and also with evidence suggesting that average blood glucose levels go up with age in a J-curve fashion even in long-lived individuals.

We also know that traits vary along a bell curve for any population of individuals. Research results are often reported as averages, but the average individual does not exist. The average individual is an abstraction, and you are not it. Glucose metabolism is a complex trait, which is influenced by many factors. This is why there is so much variation in mortality for different glucose levels, as indicated by the magnitudes of the error bars.

In any event, these findings are clearly inconsistent with the statement that "heart disease risk increases in a linear fashion as fasting blood glucose rises beyond 83 mg/dl". The authors even state early in the article that another study based on the same dataset, to which theirs was a follow-up, suggested that:

…. [CHD was associated with levels above] a postload glucose of 5.3 mmol/l [95 mg/dl], but below this level the degree of glycemia was not associated with coronary risk.

Now, exaggerating the facts, to the point of creating fictitious results, may have a positive effect. It may scare people enough that they will actually check their blood glucose levels. Perhaps people will remove certain foods like doughnuts and jelly beans from their diets, or at least reduce their consumption dramatically. However, many people may find themselves with higher fasting blood glucose levels, even after removing those foods from their diets, as their bodies try to adapt to lower circulating insulin levels. Some may see higher levels for doing other things that are likely to improve their health in the long term. Others may see higher levels as they get older.

Many of the complications from diabetes, including heart disease, stem from poor glucose control. But it seems increasingly clear that blood glucose control does not have to be perfect to keep those complications at bay. For most people, blood glucose levels can be maintained within a certain range with the proper diet and lifestyle. You may be looking at a long life if you catch the problem early, even if your blood glucose is not always at 83 mg/dl (4.6 mmol/l). More on this on my next post.