Will The Polypill Prevent Your Heart Attack?

Giving the polypill to everybody above the age of 55 kills two birds with one stone: cardiovascular risk and preventive medicine. That's what the proponents of the polypill say. The medical establishment is in uproar. Here is why you should be, too. But for a different reason. [tweet this].

   

We are typically sold on the notion, that heart disease and stroke have become today's major killer, for one simple reason: We live far longer than our ancestors of a hundred years ago, whose major cause of death were infectious diseases. Their eradication has brought upon us the blessings of longer lives, and with it the detriments of aging related cardiovascular disease. It's root cause is elevated cholesterol, a theory enshrined in the so-called lipid hypothesis. Questioning it is to the medical establishment what Galileo's theories were to the catholic church: plain heresy. After all, cholesterol lowering drugs, the statins, are a blessing to mankind and substantial reducer of cardiovascular death. 

    

This is what nearly everyone believes.
The Chinese Tao has a quote for such situations. It goes something like this: "when everyone knows something is good, this is bad already." You might reject my suggestion that such ancient wisdom could possibly apply to modern medicine.  So, let's get cracking at those facts which everyone knows. 

Claim 1: Heart disease, stroke and cancer are today's major killers 

Undeniably.  Cardiovascular disease accounts for roughly one in three deaths (30%), followed by cancer, which kills another one in four (23%) [1]. Which means your chance of dying of any one of those two clusters is fifty-fifty. By the way, these data, and the ones which follow, are drawn from U.S. statistics. Unfortunately they are typical for the rest of the developed world and pretty close to what the developing nations experience, too. 

Claim 2: One hundred years ago, Infectious diseases were the main killers

Yes, indeed. In 1900, one third of all deaths were due to tuberculosis and influenza alone. 

Claim 3: Since we eliminated those infectious diseases we have a longer life expectancy and therefore we simply die of aging related diseases.
This is where it starts to get hairy. First, you must NOT confuse life expectancy with life span. Life expectancy is typically quoted as life expectancy at birth. It is an average value of all the years lived divided by the number of those born alive. You can imagine how this number is very sensitive to the rate of infant deaths and of deaths during the early adult years. Particularly when one third of all newborns die within the first 12 months. Which was a typical infant death rate, not only in ancient Rome but throughout most of modern history until the 17th century. While this infant mortality rate made Roman's have an average life expectancy at birth of a little less than 30 years, a considerable part of the population lived to their sixties and seventies. In fact, very few people will have died at age 30, most either having done so way earlier or much later. Back to 1900. 

In 1900, U.S. females had a life expectancy at birth of 51 years, whereas those who reached 50 had a remaining life expectancy of another 22 years, to reach 72. Today these numbers stand at 80 years life expectancy at birth and 82 years at the age of 50. Which means two things: First, while life expectancy at birth has increased dramatically by more than 30 years over the past 100 years, life span hasn't increased that much. Second, life expectancies at birth and at age 50 have become virtually the same. The reason is a substantial reduction in infectious diseases, which killed considerable numbers of infants, of women giving birth, and of young adults. Which brings us to ...

Claim 4: Cardiovascular disease and cancer are diseases of old age, which is why they are more prominent today than 100 years ago. 
When we compare today's death rates with those of the past, we need to keep in mind that the age distribution in 1900 was substantially different to what it is today. In 1900 there were a lot less people of age 65 and older than there are today. So, we need to answer the question, what would the CVD mortality have been in 1900 if the population had had the same age distribution as ours has today. Thankfully, the U.S. CDC provides us with a standardization tool, which allows us to answer this question. They simply use the U.S. population at the year 2000 as the standard to which all other population data can be standardized. The process is called "adjustment for age" and, when applied to mortality rates, they become truly comparable as  so-called age-adjusted mortality rates. So, in the future, when you read something about mortality rates or disease rates, make sure to check which rates he uses for comparison. If he doesn't say which is which, you need to be very skeptical about his interpretation. 

Now here comes the surprise: The mortality rate for cardiovascular disease in 1900 was 22% vs. today's 31%. At first blush, this doesn't sound that much different. But think about it: If CVD is merely the disease of old age, why should there be a difference at all? And if there is a difference, why should we be dying of this disease at a 50% higher rate when we have all the medical technology, and the statins, which our grand parents didn't have.  

The entire issue becomes even weirder when you look at the development of the CVD mortality rate over the 11 decades from 1900 to today (Figure 1). CVD rose to a 60% prominence in 1960 before steeply falling to today's level. You can see that in the 1950s and 1960s people died of "age-related" heart attacks and strokes at a 50% higher rate than 50 years earlier. Another 60 years later we die at a quarter the rate of the 1960s. Which begs the question: What happened?

Figure 1

Actually, there are two parts to this question: If heart disease is age-related, why was there such a dramatic rise in age-adjusted mortality over the first half of the past century, when there should have been none. I have my theories, but I will keep them for one of my next posts.

Far more pertinent to this post's subject is the second part of the question: What did happen in the 1960s and thereafter? If you think the answer is "statins happened, stupid", then you are in for a surprise. The first statin to hit the market was Merck's Lovastatin. In 1987! Its the red vertical line in the chart of figure 1. Almost 30 years after CVD mortality rate began its steep descent. A descent, which did not accelerate with the introduction of statins to the market.  

Now, don't get me wrong, I'm not saying statins do not reduce the risk of dying from CVD, or the risk of experiencing a non-fatal heart attack or stroke. There is quite some evidence to their benefits. My point is that, whatever statins do, they do not show up on our mortality radar as the grand reducer of CVD death. Not within the current medical practice of risk estimation and subsequent risk-based treatment. 

Enter the proponents of the polypill, which contains a statin, a blood pressure lowering medication, and an aspirin. Are these proponents right to say, give a statin to everyone, who has hit the age of 55? Well, they have a point. Wald and colleagues ran a computer simulation to compare the most simple of all screenings, age, vs. the UK's National Institute of Health guidelines, which recommend screening everybody from age 40 at five-yearly intervals until people reach the risk threshold of a 20% chance of a cardiovascular event in the next 10 years [2]. That's the cut-off for treatment. Astonishingly, the benefits are virtually the same. What this screening routine buys at the costs for doctor visits and blood tests, we get free of charge with the age threshold.  

This paper was so counterintuitive to the established way of medical thinking, that the authors' paper, first submitted to the British Medical Journal in 2009, went through a 2-years Odyssey of being rejected by 4 Journals and 24 reviewers, before finally being published in PLoS One in 2011. 

But costs from a societal perspective are not the costs which interest you. You might be more interested to know, that even at an elevated risk of CVD, 25 people would have to swallow a statin for 5 years to prevent just 1 heart attack. How much larger will this number be, the number needed to treat (NNT), as we call it, if you are simply 55 but with no other CVD risk factor? You won't get an answer anytime soon. Big Pharma is not interested to finance a study, which could deliver the answer. They don't earn much money from polypills which only use generic statins, those whose patent protection has expired. 

To me the NNT is definitely too high. I won't take the polypill, though I just crossed that age threshold a few days back. I pursue another path to health and longevity. And I believe, you might want to look at my reasoning for that path. I will introduce it progressively over the next few posts. Not that I evangelize it, not to worry. I simply believe there is a third alternative to the risk-oriented practice of preventive medicine and to the kitchen-sink approach of its polypill wielding opponents. This third alternative is heresy to both. But with heresy I'm in good company. Dr. Ignaz Semmelweis was a heretic when he suggested in the mid 1800s that the high rate of deadly childbed fever was due to physicians not washing their hands between dissecting dead bodies and helping women deliver their children. It took about 50 years for his ideas to become medical mainstream. 

That's because new ideas become accepted in medicine not upon proof of being better than the old ones, but upon the old professors, who have built their careers on the old ideas, dying out. So, let's try to survive them. 

1. Kochanek, K.D., et al., Deaths: Preliminary Data for 2009, in National Vital Statistics Reports 2011, U.S. Department of Health And Human Services.

2. Wald, N.J., M. Simmonds, and J.K. Morris, Screening for future cardiovascular disease using age alone compared with multiple risk factors and age. PLoS ONE, 2011. 6(5): p. e18742.

Wald NJ, Simmonds M, & Morris JK (2011). Screening for future cardiovascular disease using age alone compared with multiple risk factors and age. PloS one, 6 (5) PMID: 21573224

Why Risk Screening For Heart Disease Is As Good As Crystal Ball Gazing

If weather forecasts were as reliable as cardiovascular risk prediction tools, meteorologists would miss two thirds of all hurricanes, expect rain for 8 out of 10 sunny days, and fail to see the parallels to fortune telling.    

When you are older than 35 and visit your doctor, there is a good chance he will evaluate your risk of suffering a heart attack or stroke over the next 10 years. The motivation behind this risk scoring is to prevent such an event while you still can. After all, these cardiovascular diseases are the number one causes of disability and death. In Europe alone 1.8 Million people die from it every year. In fact, they die prematurely, which means at an age younger than 75. [tweet this].


That's why, at first blush, it sounds reasonable to develop risk prediction scores to help doctors identify the high-risk patient whose asymptomatic state makes him blissfully unaware of being a walking time bomb. Forewarned is forearmed, or something like that the reasoning goes. But what if the forewarning part is as reliable as a six weeks weather forecast and the forearming as effective as the wish for world peace?

As with any medical technology, risk prediction tools should be judged by their ability to improve YOUR health outcome before they are used on YOU. While the latest publication about the UK QRISK score is an upbeat evaluation of its improved performance, it fails to convince me that using these tools actually makes sense [1]. 

Let's look at the data first: 

The QRISK score was developed for the UK population, because the grand dame of risk prediction scores, the Framingham Risk Score (FRS), doesn't do so hot in northern European people. FRS was seen to over-predict the risk in the UK population by up to 50%. In an effort to do better than that, QRISK was developed. It packs a lot more variables into its score than FRS. In its latest version, QRISK includes the risk factors age, smoking status (with a 5-level differentiation), ethnicity, blood pressure, cholesterol, BMI, family history, socioeconomic status, and various disease diagnoses. An algorithm calculates your risk, expressed as a %-chance to suffer a heart attack or stroke over the next 10 years. 

In clinical practice a 20% risk is defined as the critical threshold that separates the high-risk person from those in the low-to-moderate risk categories. 20% is an entirely arbitrary number, selected simply for convenience's sake and economic reasons. Set it too high, and you identify too few at-risk people, set it too low and you have to deal with too many false positives, that is, people who you would treat for elevated risk but who will not suffer an event even if you didn't treat them. The latter is clearly a strain on limited health budgets.

Now, let's see how QRISK at a threshold of 20% risk would work for you, provided you are between 30 and 84 years old, which is the age range to which QRISK is applicable. Let's also assume you are female.  

For every 1000 women, 40 will suffer a first heart attack or stroke over the next 10 years. Of these 40 obviously high-risk, women, QRISK identifies 17 correctly. Which means the remaining 23, or 60% of all those who will suffer a heart attack or stroke, fly below the QRISK radar. But that's not the intriguing part. We get to that by looking at the group of women who are identified as high-risk. 

If the 20% risk score threshold predicts correctly, then about 20 of every 100 women identified as high-risk will suffer a first event over the next 10 years. After all, that's what a 20% risk means: Of a hundred women having the same profile, 20 will eventually suffer a first heart attack or stroke over the next 10 years. Which brings us to the really juicy part: In the population from which QRISK was developed, 16% of the high-risk women actually did suffer that predicted heart attack or stroke. 

You are forgiven if you don't immediately see, why I call this the juicy part. But think about it this way: The QRISK numbers were not plugged from an observational study, which simply observes and follows women for 10 years, without doing anything to or with them. These numbers represent women who were identified to be at high risk by the very health care system, which claims to do the risk scoring to protect them from such events in the first place. So, what happened to actually preventing those events? 16% vs. 20% doesn't sound like a terrific preventive job. 

By the way, for men the figures are very much the same. The reason why I chose women is because there is an inconsistency in the study's published tables which compare the events in two age groups - the 35-74 year old men, and the 30-80 year old men. The number of heart attacks and strokes is given as 54 and 50 for the first and second group respectively. But it can't be that there are less events in the 30-80 year range than in the 35-74 year range. Since there is no such detectable inconsistency in the numbers for women, I chose them as the example.  

Back to the risk score and a summary of its performance. First, the score misses 60% of all cases right off the bat. Second, among the correctly identified future sufferers of heart attacks and strokes, the subsequent treatment only prevents a small minority of events, which amounts to about 4% of all cases happening over the 10-year period.  If our preventive interventions were worth their salt, we should see no, or only a few, cases happening in the high-risk group. Because this is the group, which is supposed to benefit from intensive treatment and intervention. 

This public health strategy of targeting the high-risk part of the population with an intervention is appropriately called the high-risk strategy. As we have seen, it makes public health miss the majority of disease events, which it set out to prevent in the first place. So what is the alternative? It's called the population strategy. And, yes, it means targeting the entire population in an effort to reduce all people's exposure to whatever are the causes of the disease. That entails necessarily a one-size-fits-all approach to health. Which you encounter in the form of those exercise and diet recommendations preached to us from every public health pulpit. 

In theory, this strategy could potentially have a large effect on the health of the entire population, materializing as a substantial reduction in the number of heart attacks and strokes. But when you look at it from YOUR point of view, you have to invest the sizeable effort of changing your eating and exercising habits, while you'll find the benefits hardly perceivable. After all, health is when you don't feel it. A prevented disease is never perceived as such. In public health, this situation, where an individual's large perceived sacrifice yields only an imperceptibly small personal benefit, is called the prevention paradox. It's a more academic way of saying it doesn't work either.
    

The data are certainly there to prove my case. In my previous post I highlighted how little change in health behaviors has happened over the past 20 years. And the little change, that did happen, went mostly into the wrong direction. 

Which is why we will continue to see most of us dying, ironically, from preventable diseases: heart disease, stroke, diabetes, many cancers. Which is why I'm questioning the current clinical practice of risk scoring. After all, it costs money and time.

It's this question which has lead some researchers to suggest giving everybody above the age of 50 a so-called polypill. A pill which reduces blood pressure and cholesterol, and which delivers a low dose of aspirin. It aims at killing three birds with one stone: hypertension, hypercholesterolemia and thrombotic events, all of which are causally related to heart attack and stroke. But to me, the polypill is preventive medicine's declaration of bankruptcy.

In my next post, I will talk about this, about how preventive medicine may really work, and, most importantly, what it means to you. Practically and presently. Because we already have the tools to help you prevent your heart attack or stroke. And those tools don't go by the name of any known risk score. if you are still keen on scoring your risk, we have a tool on our website for you to do that. It also shows you, how your risk would be if all risk factors were in the green zone, or how your risk will be if you maintain your current status over the next ten years. You can play around with it here, and make a couple of other tests, too. But don't get fooled by numbers. Your greatest risk is to take those risk scores too seriously. 

Reference:

1. Collins, G.S. and D.G. Altman, Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ, 2012. 344.

Collins GS, & Altman DG (2012). Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ (Clinical research ed.), 344 PMID: 22723603

Are You A Unique Medical Case?

Research says yes, public health doesn't listen, and you suffer the consequences: too little benefits from generic interventions. And it could be so simple.

Different people always react differently to the same type of treatment. In my previous post I showed you the wide range of blood pressure changes in over 700 participants of the HERITAGE study's 20-weeks endurance exercise program (Figure 1). Unfortunately, most studies do not present their results in a way, which would allow us to construct such charts as in figure 1. But when they do, the charts look virtually the same. Figure 2 shows you how 30 obese men changed their bodyweight and fat weight as a consequence of a 12-weeks supervised exercise program [1]. As you can see, the mean change of 3.7 kg for both values (the horizontal red line) doesn't tell you anything about how these 30 men reacted INDIVIDUALLY to the program.

Figure 1

When your doctor tells you what exercise to do, what diet to follow or what drug to take, she refers to studies, which report their outcomes in terms of mean values for groups of participants. But as you know now, these values don't answer your question: What would my outcome have been, had I participated in this study? Which is the same as asking, what your results will be if you follow your doctor's advice. 

Figure 2

The honest answer is: nobody knows.  Augmented by: in all likelihood you will see some benefit; if you are very lucky you'll see an extremely large benefit. Or you might be unlucky and see no benefit at all. Call this the uncertainty principle of medicine. 

You won't hear your doctor talking about it. Particularly not when he recommends lifestyle change as your first line of defense against heart attack, stroke or diabetes. For two reasons: First, public health is not concerned with your point of view. I'll get to this in a moment. Second, doctors know that lifestyle change is hard to sell as it is. So, why make it even harder by telling you the truth about the uncertainty of  benefits. Think about it, we all like to enjoy now and pay later, if at all. That's certainly the case when it comes to cigarettes, salt, sugar and a sedentary lifestyle. To forgo these pleasures in favor of health benefits, which may or may not materialize decades from now, is simply not how we are wired. 

But public health does not seem to get it. Even the American Heart Association's (AHA) latest invention, the seven health metrics, is nothing but the same song and dance, which has not had any impact on the health of the population. Let's look at it in a little more detail: 

   

The AHA has defined 7 metrics to help you navigate your way to chronic health. 4 of those metrics are behavioral - smoking, physical activity, BMI and diet. The remaining 3 are biomarkers: blood pressure, fasting glucose and total cholesterol. 

Have all 7 in the green zone and you should do well with health. Exactly how well, that was the question Dr. Yang and colleagues had asked in a study which investigated (a) how many U.S. residents meet how many of those metrics and (b) how much of the U.S. population's death burden can be attributed to these risk factors [2]. Fast forward to the results. More than half of the population, 52.2%, meet only 3 or less of those 7 metrics. That's a 4 % increase compared to 20 years ago. Another 25% meet just 4 metrics. At the same time the percentage of people who meet at least 6 of the 7 metrics has gone down from 10.3% to 8.7%. The percentage of obese people has increased by 50%, and the rate of physical inactivity (that is, people who do not exercise at all!) has doubled from 15.6% to 31.9%. Compared with people who meet no more than 1 metric, those who meet at least 6 reduce their risk of dying by 50%. 

When you look at these correlations, you'll certainly agree with the researchers' statement that "the presence of a greater number of cardiovascular health metrics was associated with a graded and significantly lower risk of total and CVD mortality". That's nice to know, but you are probably not so much interested in the number of deaths in the population, which are attributable to whatever health metric score is the flavor of the day. You are interested to know the answer to three questions:  (a) what does it mean to you, if you don't meet those metrics, (b) how does your effort of getting these metrics into the green zone reduce your risk, and (c) which strategy should you use to lower your risk most effectively.

Fortunately, with a little bit of digging into published numbers, we can get fairly good answers to these questions. So, let's start with the first one: 

Dong and colleagues had done a fairly similar investigation asking how the number of AHA health metrics correlated with cardiovascular events (heart attack and stroke) in the Northern Manhattan Study Cohort [3]. The study's almost 3000 persons were on average 69 years old when they entered the study, and they were followed up for 11 years. Of those who had met at least 4 health metrics, 28% suffered a cardiovascular event during that time, vs. 32% of those who only met 3 or less metrics. 

That's a 4% improvement. 

I don't know, how you feel about it, but my experience with our health lab's clients is that a 4% risk reduction doesn't make them go nuts about exercise and health food. I sympathize, because life is not all about self-flagellation with veggie burgers, tofu swill and weekly marathons. Which is why it is justified to go for the biggest possible health benefit that is achievable with the smallest possible effort. The answer hinges around the question of what is the most critical health metric. Back to Yang's investigation. 

He had asked the question, which of the seven metrics, if met, would yield the largest reduction in deaths? 

If your bet was on smoking and obesity, you might be surprised to hear that blood pressure turned out to be a far more effective executioner, being responsible for 30% of the deaths in this cohort. With 24%, smoking took 2nd place, and obesity didn't show up as a killer at all. Which does not mean obesity doesn't cause death. You have to keep in mind that the average age of the Yang study cohort was 45 years, and the median observation period was 14 years.  

Again, what does all that mean for you? Principally you decide for yourself. I can only tell you what I practice with our clients in our health lab. For each case we define a benchmark biomarker depending on the individual's health profile. In many cases that's blood pressure or, better still, a biomarker of arterial function (I'll talk about the amazing role of arterial function in one of my next posts). We then agree on a certain exercise and dietary strategy, the effect of which we carefully measure in terms of change of the chosen biomarker. If that change does happen, and if it goes into the right direction, that's fine. If the client turns out to be one of the fringe cases, we need to adjust the strategy. We do that until we get it right. That's individualized prevention. While it does not eliminate the uncertainty principle of medicine, it makes prevention efforts far more effective and much more rewarding. It certainly beats following some generic advice drawn from studies, whose mean effect values conceal a wide range of possible effects. 

Let's see when public health will finally see the light. Fortunately you don't need to wait for that to happen. Arm yourself with one of those home measurement devices, and actively measure and chart your progress against your chosen lifestyle change strategy. You'll see very soon, how unique you are as a medical case. 

1. King, N.A., et al., Individual variability following 12 weeks of supervised exercise: identification and characterization of compensation for exercise-induced weight loss. Int J Obes (Lond), 2007.

2. Yang, Q., et al., Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults. JAMA: The Journal of the American Medical Association, 2012.

3. Dong, C., et al., Ideal Cardiovascular Health Predicts Lower Risks of Myocardial Infarction, Stroke, and Vascular Death across Whites, Blacks and Hispanics: the Northern Manhattan Study. Circulation, 2012.

References


King NA, Hopkins M, Caudwell P, Stubbs RJ, & Blundell JE (2008). Individual variability following 12 weeks of supervised exercise: identification and characterization of compensation for exercise-induced weight loss. International journal of obesity (2005), 32 (1), 177-84 PMID: 17848941

Yang, Q., Cogswell, M. E., Flanders, W. D., Hong, Y., Zhang, Z., Loustalot, F., Gillespie, C., Merritt, R., & Hu, F. B. (2012). Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults JAMA : the journal of the American Medical Association DOI: 10.1001/jama.2012.339

Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, & Sacco RL (2012). Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan study. Circulation, 125 (24), 2975-84 PMID: 22619283

Individualized Medicine, Ignorant Medics And An Invitation To Lose Weight.

In my previous post I promised to talk about your individualized way to achieving optimal health. If that made you think about personalized medicine, you were right. Almost. Because personalized medicine is still light-years away from us. That's the bad news. The good news, personalized prevention is an emerging reality. At least in my lab. Which is why I would like to invite you to become a part of it. No strings attached. But before we get to this let's first get on the same page about the personalization of medicine.

Two questions we need to ask ourselves: What is personalized medicine and why would we want it?

Professor Jeremy K Nicholson of the Imperial College, London, defined personalized medicine as "effective therapies that are tailored to the exact biology or biological state of an individual" [1]. Such tailoring of a treatment, say for your high blood pressure, would require your doctor to evaluate your biochemical and metabolic profile in order to prescribe you the most effective drug or treatment at the most effective dose, with the least possibility of side effects.

Now, why would we want this?
Simply because we don't have it. Because our current drugs do not work optimally in most people [2]. But don't just take my word for it. Take that of Dr. Allen D. Roses, head of the Drug Discovery Institute at Duke University School of Medicine. In an interview he told a UK newspaper, The Independent, that more than 90% of modern drugs work, at best, in 30-50% of the people. He said that in 2003. At the time, Roses was also senior vice president for genetics research and pharmacogenetics at GlaxoSmithKline. 

Contrary to what you might think, Roses did not reveal any nasty industry secret. What he said is plainly visible for everyone who can read the results of clinical trials through the lens of statistics. I simply quote Roses for effect. After all, he knows what he is talking about. Contrary to many medical doctors, who have an amusingly limited grasp of the basic statistics used to interpret and present the results of clinical trials. Just how limited, that has been recently demonstrated for the case of cancer screening in a mock-up trial investigating the understanding of practicing physicians [3].

Before I tell you the results of this trial, let me make you understand what it was about. One big question in cancer screening is whether screening helps to reduce the number of people dying from cancer. Let's take a hypothetical example, and here I reuse the one which the study's authors used to explain statistical outcomes. Let's say, cancer was detected in a group of people at age 67. All of them died of their cancer at age 70. The 5-year survival rate from diagnosis would stand at 0% (they all died before 5 years were over). Now imagine that all those cancer cases would have been detected at age 60 with a screening test. And also imagine that all of them still died at age 70. In this case the 5-year survival rate would have been 100% (they were all still alive at 65). You see the issue: the survival rate was better with screening, but the rate of dying remained the same. In epidemiology we call this sort of thing lead-time bias. That is, simply detecting a disease earlier might lead to an improved survival rate which has, in fact, nothing to do with improved survival. Such lead time bias is rarely an all-or-nothing thing as in this hypothetical case. Most of the time it comes in degrees. But in any case, it would help you as a patient, if your doctor was able to see through the reporting, and to question the clinical relevance of the results so presented. Your doctor should look for the mortality rate, the rate of dying, not the survival rate.

Back to the results of the mock-up trial about physicians' interpretive skills of clinical research publications. If the results of this mock-up trial are representative of the population of your doctors, then you should be worried. Of the over 200 practicing physicians enrolled in this trial, fully 76% would recommend you this useless screening test. They considered an improved 5-year survival rate as prove for the test's efficacy! These were not undereducated physicians of a third world country, mind you. They were randomly selected from the Harris Interactive Physician Panel, which is representative of the general U.S. physician population.

OK, you may say that this was a test related to cancer screening. What has it got to do with understanding the efficiency of a drug, which your doctor prescribes you? Well, maybe your doctor aces the statistics test on drug trials after he has flunked the one on cancer screening. If you believe that, you probably also believe in the tooth fairy and in Santa Claus.  But you may have another question: Can trial results be presented in such misleading ways? Aren't researchers supposed to report their results honestly and correctly? And what use is the peer-review process which every published paper has to go through?

With 70% of all medical research being financed by the private sector, data are a commodity. So, whether you develop a screening test, a drug or a treatment, you will want to dress it up as a magic bullet. Because when you have the magic bullet for, say high blood pressure or high cholesterol, it will make it into every physician's armory. That's where the money is. It's certainly not in personalized medicine, which may find your competitors' drugs as more suitable solutions for a variety of cases. 

Which brings us back to personalized medicine. I have told you in my previous posthow much it costs to develop a drug. Which is why Big Pharma would love to concentrate its research on the areas where the probability of success is high and the potential risk of failure is low. That's the area of follow-up drugs, drugs of the same class as established drugs, but with incremental improvements over the older version. Ironically, our health care system discourages this type of pharmacological research. Incrementally improved drugs are typically reimbursed at the same rate as older drugs. Not much profit potential there. Particularly when competition is fierce.   

Which is why Big Pharma looks for new grounds, that is new therapeutic classes, for which, of course, there need to exist a large market [4]. Again, individualization is certainly not desirable, as it would fragment any market. There is another draw-back: when you break new grounds, it takes a lot longer to get off that ground with some new product. Which is what we see in the FDA's records of drug approvals over the past 10-15 years [5]. Ten years ago the FDA approved on average 90-100 new drugs every year. For the past few years this number has dwindled to 20-30 drugs per year, with the average development period for a drug increasing from 10 years to 14 years. Seven of those years are locked up in the clinical trials required by the FDA. Faced with these risks and costs, how eager, do you think, is Big Pharma to develop niche products for individualized medicine?

Even if we didn't have all those economic issues, individualizing medicine is not as easy as making some genetic test and reading the right drug combo and dosage for your ailment from it. True, genetic testing has become possible and prices are coming down. But to know your organism's blueprint doesn't mean to know what your organism does with this blueprint. In my earlier post I have explained about epigenetics, and how environmental and behavioral factors have a great influence on how your genes play out in the final version of "you". I'm afraid, without this knowledge we can't get individualized medicine off the ground. Not to the extent it exists in most people's fantasy.       

How about personalized prevention? What's the big difference to personalized medicine? Well, for one, we don't need to develop a drug. When I talk about prevention, I talk about preventing what kills most of us today: heart disease, stroke, cancer, and diabetes. Actually, diabetes per se does not kill us, it's those cardiovascular diseases which ride on it. Anyway, to prevent them and diabetes and many cancers takes only some modifications to your lifestyle, chiefly not smoking, not being overweight, being physically active and eating a healthy diet. Any of these comes without undesirable side effects. And for all of them an incredibly large number of studies has investigated their effects under virtually all possible combinations of risk factors, biomarkers and population characteristics.

What doesn't exist is the knowledge of what will work best for you. For two reasons: First, most of this research has been correlated with our classical risk factors. In an earlier post I suggested why these risk factors really suck when it comes to predicting your risk for disease or your health career. Second, there is no knowing how you will react to any intervention even if a research paper tells you that this-and-this exercise routine has cut blood pressure in the participants from 140 to 120 mmHg. Each participant will have experienced a different effect on his blood pressure, ranging from a lot more to no effect at all. The 20-mmHg reduction is merely an average value. We would need to know how similar you are to which participant to tell you exactly what you might expect.

These are the two issues which we work on in my lab: getting away from inconclusive risk factors to what really predicts health, disease and longevity. And making this trial-and-error approach a systematic one. Instead of working with risk factors we have identified key organic functions which predict health and disease much more accurately than risk factors do. And instead of dishing out the generic "spend-150-minutes-per-week-on-exercise" advice we are building a database of biomedical knowledge which will match your profile with the most promising exercise and dietary interventions to help you achieve your personal goals with the least possible effort. And to monitor the effects of your efforts on your organic functions, we are developing tools for you to precisely measure them. For convenience's sake, preferably at home, or at least in your fitness center, at your office or your doctor's practice. 

We do walk entirely new ways to achieve all this, but we never stray from the scientific method. I will, in the twice-weekly postings of this blog, report occasionally on the progress we make. I can't hold my tongue, simply because this work is so fascinating and exciting, at least to me. Of course, I do know that most people are obviously not interested in their health. Judging by the fact that less than 2% of Americans achieve ideal health metrics [6]. But for those who really want to achieve chronic health and functional longevity, we will have something to offer. In fact, I have something right now:

With overweight being one of the biggest issues, we have developed a little tool with which you train what we call a 6th sense for your calorie balance. We have tested this tool in a successful proof-of-concept study. Which is why I would like to invite you to use it. Free of charge, no strings attached. Except for the following three:

First, bear with us for the design of this web-based tool. It can't compete with what you are used to from the design gods of Apple. Second, give me your feedback and suggestions. And third, use it as it is intended to be used: daily. You'll see what I mean when you get there. 

You can find it on facebook. Just type the name "adiphea" into the search bar and click on the app. Or call it up directly from here. It doesn't cost anything, and there is no advertisement other than what facebook puts on all our pages. The tool itself is described in all details on its app-page on facebook. Most of the explanations come in the form of short videos. Which is why I'm not going into details right here. Only one thing I need to mention: Ideally, you should have a body-fat scale instead of the regular bathroom scale. Body fat scales calculate your body water, too.  And the app works best when you enter body water together with your weight daily.

We have set aside a limited contingent for users who are truly interested to work on their health and on their weight in an entirely new way. For those who are determined enough to use our tool properly and thereby help us to perfect it, it will remain accessible free of charge. For all others, utilization will be terminated after one month.

If you are a coach, operate a fitness center, run a company or a medical practice, and you want the app for a group of your clients, staff or patients, talk to me. You'll find my email on my lab's website (www.adiphea.com) . I will arrange for you to get administrative functions, so that you can manage your clients. And not to worry, the tool is built on top of an electronic patient data file, which meets the strictest data security and privacy requirements.We also do not use your email address for anything else than responding to your inquiry.

Let's see whether we can make personalized prevention fly. Big Pharma certainly wouldn't like it. They can't make money from chronically healthy people. But you could be on your way to NOT become one of the 50-70% of people in whom Big Pharma's drugs don't work so well. Now, is that an inducement or what ?

1.           Nicholson, J.K., Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol, 2006. 2: p. 52.

2.           Nicholson, J.K. and E. Holmes, Global systems biology and personalized healthcare solutions. Discov Med, 2006. 6(32): p. 63-70.

3.           Wegwarth, O., et al., Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Annals of Internal Medicine, 2012. 156(5): p. 340-9.

4.           Pammolli, F., L. Magazzini, and M. Riccaboni, The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov, 2011. 10(6): p. 428-38.

5.           Loscalzo, J., Personalized Cardiovascular Medicine and Drug Development. Circulation, 2012. 125(4): p. 638-645.

6.           Yang, Q., et al., Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults. JAMA: The Journal of the American Medical Association, 2012.

Nicholson, J. (2006). Global systems biology, personalized medicine and molecular epidemiology Molecular Systems Biology, 2 DOI: 10.1038/msb4100095

Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, & Gigerenzer G (2012). Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Annals of internal medicine, 156 (5), 340-9 PMID: 22393129

Pammolli, F., Magazzini, L., & Riccaboni, M. (2011). The productivity crisis in pharmaceutical R&D Nature Reviews Drug Discovery, 10 (6), 428-438 DOI: 10.1038/nrd3405

Loscalzo, J. (2012). Personalized Cardiovascular Medicine and Drug Development: Time for a New Paradigm Circulation, 125 (4), 638-645 DOI: 10.1161/CIRCULATIONAHA.111.089243

Yang, Q. (2012). Trends in Cardiovascular Health Metrics and Associations With All-Cause and CVD Mortality Among US Adults JAMA: The Journal of the American Medical Association, 307 (12) DOI: 10.1001/jama.2012.339

Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, & Hu FB (2012). Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA : the journal of the American Medical Association, 307 (12), 1273-83 PMID: 22427615

The one way to make you slim, fit and healthy?

That your fattening lifestyle drives health insurance costs up is nothing but a fat lie. That much I have told you in the previous post. With Marlboro Man and Ronald McDonald doing better for your health insurer's balance sheet than Healthy Living, you might think that public health should look beyond economics as an argument for health.  In this post I will tell you why they shouldn't. 

 And why economics may well turn out to be the one and only way to getting you to exercise and reduce your weight. And, no, with economics I don't mean punishing you with penalty premiums on your health insurance and punitive taxes on your fast food. Let's leave such uninspired nonsense to the politicians. We can do better than that. Before I get to that point, let's pick up the thread from where we left it in the previous post. 

There I introduced you to the fact that the amazing arithmetic of sicker-equals-cheaper has been introduced by economists working in the employment of public health agencies. They are interested in the financial health of their government, not of a health insurance company. From that point of view, convincing smokers to quit and obese people to slim down doesn't seem to make much sense either. Here is why:

When smokers quit, their near-term health care costs may go down, but in the long run they will be offset by higher medical bills for causes unrelated to smoking but related to a longer life [1]. This longer life hurts the government twice. First, when smokers stop lighting up they also stop paying tobacco taxes to the government. Second, with longer lives come longer pension payments. In fact, if all smokers would quit today, we would have very unhappy finance ministers. Ours, here in Germany, would have his tax revenues reduced by € 14.5 Billion per annum. 

What goes for smoking goes for obesity, too. So, how sincere are our politicians with their professed concerns for our health? Is this a pretext for soon taxing your consumption of sugar and fast food? Well, they certainly have the backing of the World Health Organization. The WHO recommended the introduction of punitive taxes in their 2010 Global status report on noncommunicable diseases. What our politicians apparently don't have is the ingenuity to come up with a more innovative solution, for once. Which is why we have to find it. By looking a little closer at the economics of health.  

So, I'm asking you: aside from you personally, who benefits from your health so much, that promoting it makes economic sense? Your employer, for instance. Not only is a healthy employee less often absent from work, he is also more productive while he is at work. The costs related to work absence have been appropriately termed absenteeism, which makes you immediately understand what is meant with its twin, presenteeism. It describes the costs of being less productive while at work. 

As it turns out, presenteeism clobbers companies' profits much more than absenteeism. In fact, for cardiovascular disease and diabetes, the costs of reduced productivity, while at work, exceed those of absenteeism by a factor of 10 [2]. Admittedly, the calculation of presenteeism is not an exact science. But all available evidence points to a substantial return on employers' investments into preventing those chronic diseases, which produce chronically less productive workers. Across companies and nations, the overall cost:benefit ratio has been found to be in the region of 1:2.2 [3]. Which means, for every dollar spent on corporate health promotion, 2.2 dollars are gained. Not bad. But it could be a lot better if you really did prevent those chronic diseases.

Only, you don't. How do I know? By looking at the trends for the 7 metrics used by the American Heart Association (AHA) as the Strategic Impact Goals for improving cardiovascular health. By 2020 cardiovascular health shall be improved by 20%. That doesn't sound very ambitious. But in all likelihood it is way too ambitious. Here is why: Let's look at obesity, which the IOM has just branded a "catastrophic" problem in the U.S.
Instead of falling, the percentage of obese people has been on the rise, again, over the past 10 years, with now 34% of women and 32% of men being obese [4]. Physical activity levels have not improved significantly, neither did dietary habits. Blood sugar control has actually worsened, and blood pressure control has only slightly improved in men. Based on these data the improvements of cardiovascular health in 2020 will be around 6%, not 20%.

That's how I know that you aren't following your employer's corporate health program. Why would you when you don't follow public health's promotions and recommendations in the first place? Unless, of course, your employer makes you an offer you can't refuse. What would you do if your employer rewarded your participation in his health promotion program with hard cash, additional leave, or a tangible good you desire? What if he tied those benefits to your effort (e.g. your participation rate), or your measurable outcome (e.g. kgs of weight lost, or weight maintenance), or any mixture of effort and result? Would that entice you to pick up healthier habits?

As I have pointed out before, the argument that people who live healthy generate less health care costs than their unhealthily living peers is unsubstantiated. But that should not make us eliminate economics as a metric when it comes to promoting health. On the contrary. By making health an economic good we bring to the table what motivates people most: tangible rewards. The question is, would it get you to pick up exercise, if you didn't do it already, and would it get you to lose weight, if you needed to?

The reason why I'm asking you is, because as a public health scientist, I'm utterly disillusioned with the success rate of our preventive efforts. On one hand, we have this wonderfully simple and enormously effective preventive tool called exercise and weight loss. And on the other hand we have 4 out of 5 people not using this tool. On one hand, we have the new guidelines for the treatment of diabetes [5] and for the prevention of cardiovascular disease  [6], both of which have been released over the past few weeks. Both guidelines acknowledge lifestyle change as the first line of defense against those diseases. But on the other hand we have less than 2% of the population achieving the 7 simple health metrics of the AHA. Guidelines won't change that. So, how can we make the remaining 98% of the population achieve the 7 metrics? Obviously not with the same song and dance that didn't get the job done in the past.

Which is why we need to explore new ways. Taxing your consumption of the foods you enjoy isn't new. Making health an investment good, that's new. But without attracting those people who we haven't reached in the past, it won't work either. Now what do you think?

Will tangible rewards make employees exercise and lose weight?

1.          Temple, N.J., Why prevention can increase health-care spending. The European Journal of Public Health, 2011.

2.          Collins, J.J., et al., The assessment of chronic health conditions on work performance, absence, and total economic impact for employers. J Occup Environ Med, 2005. 47(6): p. 547-57.

3.          Dietmar Bräunig and T. Kohstall, The return on prevention: Calculating the costs and benefits of investments in occupational safety and health in companies, 2012, ISSA: Geneva, Switzerland.

4.          Huffman, M.D., et al., Cardiovascular Health Behavior and Health Factor Changes (1988-2008) and Projections to 2020: Results from the National Health and Nutrition Examination Surveys (NHANES).Circulation, 2012.

5.           Inzucchi, S.E., et al., Management of hyperglycaemia in type 2 diabetes: a patient-centered approach. Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diab tologia, 2012.

6.           Perk, J., et al., European Guidelines on cardiovascular disease prevention in clinical practice (version 2012): The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts) * Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). European Heart Journal, 2012.

Temple, N. (2011). Why prevention can increase health-care spending The European Journal of Public Health DOI: 10.1093/eurpub/ckr139
 
Collins, J., Baase, C., Sharda, C., Ozminkowski, R., Nicholson, S., Billotti, G., Turpin, R., Olson, M., & Berger, M. (2005). The Assessment of Chronic Health Conditions on Work Performance, Absence, and Total Economic Impact for Employers Journal of Occupational and Environmental Medicine, 47 (6), 547-557 DOI: 10.1097/01.jom.0000166864.58664.29
 
Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, & Lloyd-Jones DM (2012). Cardiovascular Health Behavior and Health Factor Changes (1988-2008) and Projections to 2020: Results from the National Health and Nutrition Examination Surveys (NHANES). Circulation PMID: 22547667
Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, Peters AL, Tsapas A, Wender R, & Matthews DR (2012). Management of hyperglycaemia in type 2 diabetes: a patient-centered approach. Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia, 55 (6), 1577-96 PMID: 22526604
 
Authors/Task Force Members:, Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren M, Albus C, Benlian P, Boysen G, Cifkova R, Deaton C, Ebrahim S, Fisher M, Germano G, Hobbs R, Hoes A, Karadeniz S, Mezzani A, Prescott E, Ryden L, Scherer M, Syvänne M, Scholte Op Reimer WJ, Vrints C, Wood D, Zamorano JL, Zannad F, Other experts who contributed to parts of the guidelines:, Cooney MT, ESC Committee for Practice Guidelines (CPG):, Bax J, Baumgartner H, Ceconi C, Dean V, Deaton C, Fagard R, Funck-Brentano C, Hasdai D, Hoes A, Kirchhof P, Knuuti J, Kolh P, McDonagh T, Moulin C, Popescu BA, Reiner Z, Sechtem U, Sirnes PA, Tendera M, Torbicki A, Vahanian A, Windecker S, Document Reviewers:, Funck-Brentano C, Sirnes PA, Aboyans V, Ezquerra EA, Baigent C, Brotons C, Burell G, Ceriello A, De Sutter J, Deckers J, Del Prato S, Diener HC, Fitzsimons D, Fras Z, Hambrecht R, Jankowski P, Keil U, Kirby M, Larsen ML, Mancia G, Manolis AJ, McMurray J, Pajak A, Parkhomenko A, Rallidis L, Rigo F, Rocha E, Ruilope LM, van der Velde E, Vanuzzo D, Viigimaa M, Volpe M, Wiklund O, & Wolpert C (2012). European Guidelines on cardiovascular disease prevention in clinical practice (version 2012): The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by re European heart journal PMID: 22555213