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.

Long distance running causes heart disease, unless it doesn’t

Regardless of type of exercise, disease markers are generally associated with intensity of exertion over time. This association follows a J-curve pattern. Do too little of it, and you have more disease; do too much, and incidence of disease goes up. There is always an optimal point, for each type of exercise and marker. A J curve is actually a U curve, with a shortened left end. The reason for the shortened left end is that, when measurements are taken, usually more measures fall on the right side of the curve than on the left.

The figure below (click to enlarge) shows a schematic representation that illustrates this type of relationship. (I am not very good at drawing.) Different individuals have different curves. If the vertical axis was a measure of health, as opposed to disease, then the curve would have the shape of an inverted J.

The idea that long distance running causes heart disease has been around for a while. Is it correct?

If it is, then one would expect to see certain things. For example, let’s say you take a group of long distance runners who have been doing that for a while, ideally runners above age 50. That is when heart disease becomes more frequent. This would also capture more experienced runners, with enough running experience to cause some serious damage. Let us say you measured markers of heart disease before and after a grueling long distance race. What would you see?

If long distance running causes heart disease, you would see a significant proportion with elevated makers of heart disease among the runners at baseline (i.e., before the race). After all, running is causing a cumulative problem. The levels of those markers would be correlated with practice, or participation in previous races, since the races are causing the damage. Also, you would see a uniformly bad increase in the markers after the race, as the running is messing up everybody more or less equally.

Sahlén and colleagues (2009), a group of Swedish researchers, studied males and females aged 55 or older who participated in a 30-km (about 19-mile) cross-country race. The full reference to the article is at the end of this post. The researchers included only runners who had no diagnosed medical disorders in their study. They collected data on the patterns of exercise prior to the race, and participation in previous races. Blood was taken before and after the race, and several measurements were obtained, including measurements of two possible heart disease markers: N-terminal pro-brain natriuretic peptide (NT-proBNP), and troponin T (TnT). The table below (click to enlarge) shows several of those measurements before and after the race.

We can see that NT-proBNP and TnT increased significantly after the race. So did creatinine, a byproduct of breakdown in muscle tissue of creatine phosphate; something that you would expect after such a grueling race. Yep, long distance running increases NT-proBNP and TnT, so it leads to heart disease, right?

Wait, not so fast!

NT-proBNP and TnT levels usually increase after endurance exercise, something that is noted by the authors in their literature review. But those levels do not stay elevated for too long after the race. Being permanently elevated, that is a sign of a problem. Also, excessive elevation during the race is also a sign of a potential problem.

Now, here is something interesting. Look at the table below, showing the variations grouped by past participation in races.

The increases in NT-proBNP and TnT are generally lower in those individuals that participated in 3 to 13 races in the past. They are higher for the inexperienced runners, and, in the case of NT-proBNP, particularly for those with 14 or more races under their belt (the last group on the right). The baseline NT-proBNP is also significantly higher for that group. They were older too, but not by much.

Can you see a possible J-curve pattern?

Now look at this table below, which shows the results of a multiple regression analysis on its right side. Look at the last column on the right, the beta coefficients. They are all significant, but the first is .81, which is quite high for a standardized partial regression coefficient. It refers to an almost perfect relationship between the log of NT-proBNP increase and the log of baseline NT-proBNP. (The log transformations reflect the nonlinear relationships between NT-proBNP, a fairly sensitive health marker, and the other variables.)

In a multiple regression analysis, the effect of each independent variable (i.e., each predictor) on the dependent variable (the log of NT-proBNP increase) is calculated controlling for the effects of all the other independent variables on the dependent variable. Thus, what the table above is telling us is that baseline NT-proBNP predicts NT-proBNP increase almost perfectly, even when we control for age, creatinine increase, and race duration (i.e., amount of time a person takes to complete the race).

Again, even when we control for: AGE, creatinine increase, and RACE DURATION.

In order words, baseline NT-proBNP is what really matters; not even age makes that much of a difference. But baseline NT-proBNP is NEGATIVELY correlated with number of previous races. The only exception is the group that participated in 14 or more previous races. Maybe that was too much for them.

Okay, one more table. This one, included below, shows regression analyses between a few predictors and the main dependent variable, which in this case is TnT elevation. No surprises here based on the discussion so far. Look at the left part, the column labeled as “B”. Those are correlation coefficients, varying from -1 to 1. Which is the predictor with the highest absolute correlation with TnT elevation? It is number of previous races, but the correlation is, again, NEGATIVE.

In follow-up tests after the race, 9 out of the 185 participants (4.9 percent) showed more decisive evidence of heart disease. One of those died while training a few months after the race. An autopsy was conducted showing abnormal left ventricular hypertrophy with myocardial fibrosis, coronary artery narrowing, and an old myocardial scar.

Who were the 9 lucky ones? You guessed it. Those were the ones who had the largest increases in NT-proBNP during the race. And large increases in NT-proBNP were more common among the runners who were too inexperienced or too experienced. The ones at the extremes.

So, here is a summary of what this study is telling us:

- The 30-km cross-country race studied is no doubt a strenuous activity. So if you have not exercised in years, perhaps you should not start with this kind of race.

- By and large, individuals who had elevated markers of heart disease prior to the race also had the highest elevations of those markers after the race.

- Participation in past races was generally protective, likely due to compensatory body adaptations, with the exception of those who did too much of that.

- Prevalence of heart disease among the runners was measured at 4.9 percent. This does not beat even the mildly westernized Inuit, but certainly does not look so bad considering that the general prevalence of ischemic heart disease in the US and Sweden is about 6.8 percent.

It seems reasonable to conclude that long distance running may be healthy, unless one does too much of it. The ubiquitous J-curve pattern again.

How much is too much? It certainly depends on each person’s particular health condition, but the bar seems to be somewhat high on average: participation in 14 or more previous 30-km races.

As for the 4.9 percent prevalence of heart disease among runners, maybe it is caused by something else, and endurance running may actually be protective, as long as it is not taken to extremes. Maybe that something else is a diet rich in refined carbohydrates and sugars, or psychological stress caused by modern life, or a combination of both.

Just for the record, I don’t do endurance running. I like walking, sprinting, moderate resistance training, and also a variety of light aerobic activities that involve some play. This is just a personal choice; nothing against endurance running.

Mark Sisson was an accomplished endurance runner; now he does not like it very much. (Click here to check his excellent book The Primal Blueprint). Arthur De Vany is not a big fan of endurance running either.

Still, maybe the Tarahumara and hunter-gatherer groups who practice persistence hunting are not such huge exceptions among humans after all.

Reference:

Sahlén, A., Gustafsson, T.P., Svensson, J.E., Marklund, T., Winter, R., Linde, C., & Braunschweig, F. (2009). Predisposing Factors and Consequences of Elevated Biomarker Levels in Long-Distance Runners Aged >55 Years. The American Journal of Cardiology, 104(10), 1434–1440.