There has been yet another wave of “too much” exercise stories in the media based on a recent study of 1 million women from the UK. The idea is that moderate levels of physical activity most days with occasional bouts of strenuous activity can cause big drops in both cardiovascular and all-cause mortality. However, doing a lot of hard training is not as beneficial.
This topic has been recycling for the last couple of years. Alex Hutchinson (who has a Ph.D. in physics) has done an excellent numerical/statistical breakdown on one of the key studies that “supports” the too much exercise hypothesis. Put simply there are many limitations to the whole argument. I have done a couple of posts on what both the epidemiology and physiology tell us on the topic. The first was in 2012 and another one with Brad Stulberg in 2014. I too am a skeptic.
I am in the camp that 30-60 minutes of physical activity most days is the sweet spot for general health and that more is not better, but it is not worse either. Those who really push it most days are also likely motivated by things other than return on investment thinking about their health. Perhaps they want to compete in races or are into pushing themselves to meet more hard core physical goals.
The Swedish Skiers
Whenever this topic comes up I also bring up a paper that followed about 50,000 male finishers of the 90km (~55 miles) Vasaloppet cross country ski race in Sweden. This study used the Swedish medical records system to look at mortality in the race finishers. In preparing for an upcoming talk on exercise and health, I asked my colleague Andy Miller to generate some figures from the skiing study. The one below shows that mortality is about 50% or less than predicted for race finishers compared to the expected rate gleaned from Swedish population records. It also shows that finishing more races was not associated with an uptick in mortality, if anything it was associated with a down tick. Who knows exactly what these folks were doing, but those who finished a number of races certainly had to be doing a lot of strenuous training over many years.
I have repeatedly asked those in the “too much” much camp to rebut this paper and point out any major flaws in it. The bottom line is that it is at least as strong or stronger than the studies “supporting” the too much exercise hypothesis. Until data comes along that clearly refutes the data in the chart above I will remain a skeptic.
There is a fascinating recent study from Finland on pairs of identical twins with very different exercise habits. This is unusual because widely divergent behavior patterns between identical twins are uncommon. There were some pretty striking differences in thinks like exercise capacity, metabolism and even brain structure in the active vs. inactive twins showing that even when the “genes are the same” behavior can really make a difference. The details of the paper were beautifully summarized by Gretchen Reynolds in the New York Times with some excellent insights from the authors of the paper included in her article. Some additional thoughts about what this all means are available in an excellent commentary by Alex Hutchinson in Runner’s World.
This study and the outstanding pieces by Gretchen and Alex reminded me of a paper from the early 1980s on the different physiological adaptations to strength and endurance training. The paper included the pictures below of identical twin brothers. One was an endurance runner, the other a weight lifter.
The picture speaks for itself. The lifter was 16kg (35 lbs) heavier than the runner, but the runner’s heart was about 25% larger and his maximal oxygen uptake more than 50% greater than his brother’s. Of note, the height of the brothers and things like their hair patterns are strikingly similar. For those who want to know more about the strengths and limitations of twin studies and what can be inferred from them here is an informative link.
That such big differences in physiology can be seen in people who have “identical” genes is pretty convincing evidence that for many things our genes are not our destiny.
One of the ideas riding the wave of enthusiasm for precision medicine is that with enough big data it should be possible to make increasingly accurate “forecasts” about who gets what disease and how it might be prevented, treated or even “cured”. An analogy to precision weather forecasting and climatology is frequently drawn. Cynicism aside about just how good weather forecasting is and how much it has improved, there are a couple of basic intellectual issues with the comparison that are typically glossed over by advocates of the analogy.
Problem 1: The Nature of the Data
Weather data includes things like continuously monitored surface temperature and wind patterns over essentially most of the world. Some of the data is very granular with high spatial and temporal resolution. Things like pressure measurements, above ground temperatures, below ground temperatures, satellite photos, and information on things like humidity are available. There is also a vast store of historical records dating back 100 or more years in many places. This type of multilevel, highly accurate data with essentially continuous time resolution is simply not available even in the most monitored humans living in the real world even with the best monitors. The accuracy of various wearable devices, the granularity of the data, and the historical information they provide pales in comparison to the available weather related data. As someone who has been making some the most detailed possible measurements of human physiology since the late 1970s, things have been miniaturized and made portable, but the quality of the data has not improved and in many ways has gotten sloppier or at least harder to calibrate.
Problem 2: Predicting What?
The other thing to remember is that with weather prediction the goal is to predict what it is going to be like “outside” in a given place on a given day at a given time. Precision weather forecasting does not tell us anything about the temperature and humidity inside “your house” much less inside a given room inside the house. To make that sort of estimate all sorts of additional information is needed about the size of the house, the surface area exposed to the outside world, the heating and cooling system, how insulated the house is, how good the thermostat is etc., etc, etc… Then there is always the possibility that a window is open or that on a cold day you choose to wear a sweater and reduce the temperature “set-point” on the thermostat. The same issues also apply to a given room the house.
The point here is that for human disease, except perhaps for some elements of dermatology, we are generally interested in what is happening inside a specific room inside the body like the “heart” room, or the “liver” room or the “kidney” room. For things like diabetes or high blood pressure that affect multiple rooms, we are interested in the overall house. Also many diseases of specific rooms also frequently do collateral damage to “the rest of the house”. In many of these diseases the ultimate problem that “brings people to the Dr.” has something to do with a complex feedback control system that has gone haywire. That is certainly the case for diabetes, heart failure, and high blood pressure. In heart failure shortness of breath and exercise intolerance is usually the problem patients complain about vs. a weak heart.
So the weather is an outside condition we are trying to predict based on outside data. Medical conditions are generally inside conditions and predicting them from outside data of questionable quality with limited time resolution and historical tracking is clearly an area where the precision medicine vs. precision weather analogy breaks down. Things like biopsies, images and blood tests are inside samples but they are small snap shots and not the sort of continuous measures available to the weather forecasters
Problem 3: What About Inside-out Prediction?
The flip side of the weather analogy is the idea that if you know enough about the building blocks (the cells for example) that make up the house you can predict what is going on inside the house as a whole. Of course the outside world influences what is happening inside and those who favor an inside-out paradigm tend to ignore or discount that problem. Another issue is that unlike static structures humans can move around and change their behavior depending on the conditions outside. When I lived in Arizona I went outside mostly during the cooler parts of the day. In Minnesota where I now live, most of the year, I go outside during the warmer parts of the day. A cell based approach to modeling what is going on inside the body can miss this key but obvious point.
Then there is the problem of the cells as building materials. Imagine decorative concrete blocks like the ones used in the wall shown below.
Depending on the orientation of such blocks, a wall made from them can have very different properties. Flip them on their side and a solid vs. porous wall “emerges”. Thus, the temperature inside a structure made from such concrete blocks could vary widely depending on their orientation. However, subject the blocks from a wall of any design to chemical analysis and the “basic” properties of the wall are the same. Things of course get even more complicated if you add a heating and cooling system with a thermostat or other design features that influence the temperature in your concrete block building. This sort of inside-out modeling would be less problematic if the DNA in our cells was a better blueprint for what the “whole building”, but it turns out that DNA is a pretty sloppy and much more adaptive blue print than was once thought.
I would be curious to see just how much better or accurate weather forecasting has gotten over the years. If there is data on this topic perhaps someone will post a source in the comments section. In the meantime, I hope the concepts noted above make you question the precision medicine, precision weather analogy.
Two recent scientific papers make it time for a quick update on the topic of fitness and mortality.
1. Fitness vs. Cancer Mortality
The first paper is meta-analysis that summarizes the results of a number of studies on the relationship between fitness and cancer mortality. The results were pretty striking:
“Six prospective studies with an overall number of 71 654 individuals and 2002 cases of total cancer mortality were included. The median follow-up time in the studies was 16.4 years. Cardiorespiratory fitness showed a strong, graded, inverse association with total cancer mortality. Using low cardiorespiratory fitness as the reference group, intermediate and high levels of cardiorespiratory fitness were related to statistically significant decreased summary relative risks (RRs) of total cancer mortality of 0.80 [95% confidence interval (CI) 0.67-0.97] and 0.55 (95% CI 0.47-0.65), respectively. Studies that adjusted for adiposity yielded similar results to those that did not adjust for adiposity.”
This means that folks with high cardiorespiratory fitness have about half the risk of death from cancer as people in the low fit group. Usually, in these types of population studies “high fit” is defined as the ability to do “10 mets” of exercise. For example a person with a 10 met exercise capacity can typically run 6 miles (10 km) per hour for few minutes at the end of a maximum exercise test. This is also an exercise capacity that many if not most middle aged people can attain if they watch their weight and workout regularly. So while physical activity and cardiorespiratory fitness are not the same thing, many active middle aged people can get to 10 mets. The flip side of this relationship is that most highly fit people are also pretty active and they do at least some higher intensity exercise training which makes it likely they have a 10 met exercise capacity.
2. Fitness vs. Cardiovascular mortality
It has been known for a long time that increased fitness is associated with both lower all-cause mortality and lower cardiovascular mortality. A remaining question is whether this relationship flattens out at about 10 mets. In other words do people who can do more than 10 mets have even lower mortality? The graph below comes from a short report on this topic in almost 70,000 people (64% men) followed for about 12 years including about 38,000 with an exercise capacity of greater than 10 mets. The top panel shows that very high fitness was associated with lower mortality in all age groups. The bottom panel shows the dose response relationships between fitness for the entire study population. So greater fitness equals lower mortality.
Evidence for the protective effects of fitness and its close relative physical activity keeps piling up. The cancer data is especially heartening, and that fact that things don’t flatten out for fitness vs. mortality at very high levels of fitness is perhaps another piece of objective evidence against the too much exercise “hypothesis”.
It has been a while since I did a post with a hardcore “human limits” angle, but some impressive middle and long distance running results over the last couple of weeks make the time ripe for a few observations. So, here goes!
Is 45 the new 35?
In mid-February the 40 year old and seemingly ageless Bernard Lagat went 3:54.91 for the mile at the Millrose game in New York City. The previous best time for a 40+ was 3:58.15 by Eamonn Coghlan – the “Chairman of the Boards” set in 1994. Lagat went 3:51:38 at age 36 and his best mile time is 3:47:28 at age 26. Who knows just how fast he could go if he focused on the mile and ran five or ten highly competitive races in the next several years. My bet is that he could run at least a couple of seconds faster.
I also bet that if Lagat continues to train for the next five years, he might be able break 4 minutes in his middle 40s. If top competitors can avoid injury and keep training hard, it is possible for them to maintain their fitness at a level near the peak values typically seen in their 20s. However, even the most dedicated and injury free trainers start to lose something in their 40s. The minimal rate of loss for maximum oxygen uptake is probably about 4% from age 40 to 50, and this is a key determinant of mile time.
So if Lagat lost only 2% in the next 5 years that would slow his mile time by about 5 seconds and his projected age group record at 45 would be 3:58 to 3:59. The old timers reading this will remember that in the early 1970s four-time Olympian George Young broke 4 minutes in his middle 30s. At the time that seemed both inconceivable and unbelievable.
I wonder if and when we will see a sub 4 minute mile by a 50 year old?
The world record for the women’s marathon is 2:15:25, set by Paula Radcliffe who also owns the two next fastest times and no one else has broken 2:18. Her marks are true outliers and a number of people wonder if and when the field will “catch up”. On February 16, Florence Kiplagat ran the half-marathon in 1:05:09 which converts to an estimated marathon time of just less than 2:16. A few days later Genzebe Dibaba went 14:18:86 for 5,000m. That time is equal to about a 2:17 marathon.
The chart below is from an analysis that Sandra Hunter, Andy Jones, and I did on the 2-hour marathon “equivalent” for women. It shows the 100 top men and women’s times as a % of the world record with and without Radcliffe’s times. Based on these recent times at shorter distances, perhaps the men’s and women’s curves might start to look similar as more women run times between 2:15 and 2:18 in the next few years.
That having been said, it will be interesting to see if Paula Radcliffe holds the three fastest marathon times for women in five years. My bet is no.
Last week Tim Kruse who worked in my lab prior to starting med-school at the University of Washington sent me an e-mail pointing out that about 20 collegiate male runners broke 4 minutes for the mile in February. In response I asked where these guys came from and he drilled down on it a bit and said that 13 came from the U.S. and several more from Canada.
The question then became why? Better training? A bigger talent pool? Better tracks? Doping? I bounced these ideas off David Epstein, Alex Hutchinson, Amby Burfoot and Rickey Carter. The general consensus was that doping was not a major issue. Better tracks and better competitive opportunities likely played a role. However, several people commented on the role internet and dedicated track and distance running sites. These sites (like LetsRun) promote the sport in general and are also a great forum to share information about training and also inspirational stories. My guess is that they have been catalytic.
Amby pointed to an article he wrote a few years ago about the “turning point” in U.S. distance running being marked by the 2000 Foot Locker High-School Cross-Country Championships which featured Alan Webb, Dathan Ritzenhein and Ryan Hall. Evidently these guys had been more or less stalking each other for some time via an early internet site devoted to high school running. Amby’s article is a great read and really nails the reasons behind improved middle and long distance running in the U.S. in specific and N. America in general.
That having been said, the depth at the collegiate level indicates the North American talent pipeline for middle and perhaps long distance running is as full as it has been in many years. It will be interesting to see if this ultimately translates into things like more international top 10 rankings, victories at major competitions and even Olympic medals.
My bet is that the East Africans might not seem so invincible in 10 years.
A Good Month
It was a good month for middle and long distance running that makes me as optimistic about the future of “my” sport as I have been for a long time. The growth of mass participation distance running by people committed to fitness has been the big story over the last 10 or 20 years. The re-emergence of a sizable number of elites in North America has the potential to be the frosting on this wonderful cake.
Yesterday I was fortunate enough to land a featured op-ed in the New York Times about precision medicine in specific and the general topic of moonshots in medicine like the war on cancer. For those interested in learning more about these topics here is link to a classic paper by Comroe and Dripps on medical innovation and goal directed progress via “big science” vs. blind luck and marginal gains leading to progress. On a related note Bill Gates recently reviewed the 10 year results of his billion dollars of spending to improve health in the developing world. His conclusion was that not much had changed……
The graphic below is about the hype cycle. The question is always when and how long does it take to hit the plateau of productivity and just how high is it compared to the original expectations.
It has been said that “demography is destiny” suggesting that at some level basic facts – like the distribution of the population in different age groups – are key determinants of what happens to a given culture, country or even the world. That having been said, it is well known that the “population is aging” and some have argued that the impact of aging on the world will ultimately be as big as the development of agriculture or the Industrial Revolution. I agree with this assessment and would add that aging is happening much faster and that we can see it coming in comparison the rise of agriculture and industrialization.
Countries age for two basic reasons: 1) fewer children being born per woman, and 2) people living longer. These two factors also operate to determine the total population of a given country and the world as well. The chart below shows world population pyramids starting in 1950 with projects to 2100.
In 1950 the relative number of young people was far greater than older people. The so-called pyramid then gets progressively “squared” by 2010 and beyond. This chart also projects total world population to be in the 9-10 billion range in 2010, it is currently about 7 billion.
China’s 1 Child Cliff
In the 1970s China took some pretty drastic population control measures including a strictly enforced 1 child policy. The rationale was to avoid famines. The policy has “worked” but now China’s population is rapidly aging and in fact the population of China is also projected to decline and population experts in China believe the 1 child policy needs to be reversed. The chart below shows the projections for China. The solid red line is the best estimate for what will happen this century with various high and low projections bracketing it. Subsequent charts for other countries follow the same format.
The impact of both an older population with fewer workers for every retiree and less total population is uncharted territory especially for a developing country like China. However, several more advanced countries like Japan and to a lesser extent Germany (and much of the rest of Western Europe) are already headed in this direction or at least headed to stagnating populations with a higher average age and fewer workers per old person.
In terms of both total population and the ratio of old people to workers, the US is relatively well off compared to China, Japan and most other developed countries. The reasons for this are more babies per woman and immigration.
Population is a topic that leads to all sorts of discussion and debates:
- What are the ecological costs of increased population and what is sustainable?
- How aggressive should specific countries and the world in general be in promoting birth control and lower population growth?
- If there are fewer workers per old person, how will old age pension systems and medical care be funded? This is a topic in the US, but it is also a topic essentially everywhere. The relative good news for the US also points out one advantage of liberal immigration policies – fewer young people to pay the bills.
- Should the retirement age be raised?
- What will the psychology of an older world be like? Young people take chances and roll the dice on all sorts of things while older people tend to be more risk averse and seek “certainty”. Will the aging population lead to less economic growth as a smaller faction of the world decides to “go for it”?
Who knows how all of this will play out, but if the phrase “demography is destiny” is a truism, it is likely to be especially true over the next 50 or so years. And, we can see at least some potential demographic cliffs coming.