Archive for the ‘Research and Health’ Category
It is finally here! Our data packed and evidence based book on major issues affecting the health of the U.S. population, including smoking, diet, physical activity, and the policy options to move us in the right direction is now available. You can download a no cost PDF version of this book (and other books from the Roadmap series) from the website of the Arizona State University’s Healthcare Delivery and Policy Program. A paperback version is also available from Amazon (no profits to us). We hope that this book will be useful to a wide range of people interested in the topics of population health, physical activity, exercise and diet. We have focused on basic data related to these topics and what policies might be used to promote healthier lifestyles for both individuals and society as a whole.
Over the last few months I have run across a couple of ideas — really catchy phrases — that are influencing the way I think about trends and hopefully progress (or lack of it) in medicine. The phrases are idea bubbles, biological plausibility, and bio-babble.
Idea Bubbles & Alzheimer’s Disease
I ran across the phrase idea bubbles when I was doing web search on the amyloid hypothesis for Alzheimer’s Disease. The idea that first emerged in the 1990s is that a buildup of amyloid proteins in the brain is central to the development of Alzheimer’s. This has led to the development of animal models that generate excess amyloid in their brains and also drugs that either slow the buildup or help clear it. It has also led to a number of promising early stage human drug trials in patients with Alzheimer’s that have ultimately failed in larger trials. At this time there are no effective approved anti-amyloid therapies on the market in spite of this vast effort.
All of this was reviewed in a great blog post on Forbes by David Grainger and he discusses why the hypothesis lives on to fight another day and why drug companies, investors and the scientific community is continuing to make “large bets” on the amyloid hypothesis:
— There are a few important lessons from this sorry tale, that extend well beyond Alzheimer’s Disease. It highlights the danger of what I previously called “idea bubbles” – that a hypothesis gains so much credibility over a long period of time that even when the data tells you otherwise, adherents (acolytes may be a better word) question everything but the hypothesis.–
As I drilled down I found the definition of an idea bubble and how it relates to better known bubbles like stock market bubbles.
— An “ideas bubble” occurs when, over a long period of time, positive social feedback disconnects the perceived validity (of the idea) from the real underlying validity – in the same way price and value dissociate in a stock market bubble.” –
Bubbles are sustained by gold rush mentalities, optimism, the fact that careers have been invested in one thing or another, and the general problem of sunk costs.
Bubbles similar to Alzheimer’s and amyloid have occurred for all sorts of cancer therapies (surgery, radiation, chemo) over the last hundred years and I wonder if the next big bubble is going to be the precision medicine bubble and as the noted public health expert David Hunter recently pointed out:
— “In searching for a cure for cancer, we have repeatedly climbed on various bandwagons. They include the radical mastectomy for breast cancer, high-dose chemotherapy, immunotherapy, and — more recently — molecularly “targeted” therapies. In each case, it took someone with courage to point out the limitations or futility of the approaches.
Hope is critical to cancer patients and those treating them, but hope that is not rooted in the facts risks becoming an illusion. As Mikkael Sekeres of the Cleveland Clinic has commented, we should not delude ourselves into believing targeted therapies will be a panacea for cancer treatment.”
Bubbles & Biological Plausibility
One of the things required for a bubble (or perhaps a bio-bubble) to take off is the need for a narrative that makes biological sense and can then underpin a big idea. In a great editorial in the British Medical Journal, David Healy traces how the serotonin hypothesis emerged as an explanation for depression and led to the generation of whole new classes of drugs with marginal efficacy that have been vastly over-used. Another good example is the idea that “free-radicals” cause cancer, aging, and heart disease and that taking anti-oxidants can make people healthier. In fact big clinical trials show the opposite, anti-oxidants can be associated with worse instead of better outcomes. However, the theory lives on as do the pitches. Each of the cancer therapies mentioned above had at the time of their adoption a tight and biologically plausible back story.
The biology of most hard to treat or cure diseases is complicated and usually defies a simple linear story. However, we persist in seeking them. One example that hits home for me as an anesthesiologist and physiologist is what has been described as the “Cult of the Swan-Ganz Catheter”. In the early 1970s it became possible to routinely put big catheters into the hearts of most patients in intensive care units. The idea was that by carefully and precisely measuring the pressure, oxygen levels, and blood flow various places in the heart “goal directed” therapy could be used to give just the right amount of fluid and drugs to patients. This would then improve outcomes for hard to treat diseases like heart failure or severe infections.
Sounds like a good idea, but it has not worked. What is also interesting is that the general narrative appeared in the early 70s, was questioned in the middle 80s, was not really evaluated objectively on a large scale until the 90s, and a firm consensus about the limitations of these catheters only really emerged in the 2000s with an “obituary” written in 2013.
As an aside, when I was a resident in the late 1980s, the placement of these catheters in the ICU was almost like a religious ceremony or sacrament. The more senior Drs. served as high priests while the younger interns and medical student acolytes watched on and waited for ordination and their chance at the altar.
Ideas that are perhaps too good to be true die hard. This is true in medicine and health in so many ways. That the some of the “smartest people around” continue to fall into the same cognitive traps over and over again should make us all think twice before jumping on any bandwagons that are “sure” to cure anything.
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”.
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.
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