Supplements: Nutrition Science Or Nutrition Crap?

Nutritionists claim they are doing science, consumers buy it, and the supplements industry makes a healthy living from it. Only you probably won't. Here is why: 

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One of the enduring diet questions is whether supplements are a good tool to (a) improve health, and (b) compensate for nutritional deficits of an enjoyable but less than healthy dietary habit.  

To most people, the answer seems to be a resounding "Yes". In the U.S. more than 65% of the population are regular supplement users. They spend north of 28 Billion US$ annually on their pills and potions. To put this into perspective:  28 Billion is more than the gross domestic product of Cyprus - the latest EU country in need of being bailed out. While Cyprus circles the drain, the supplement industry doesn't. In fact it is growing by 10% annually. A growth, which, in 2008, Dr. Daniel Fabricant, then vice president of the Natural Products Association (NPA), had correctly predicted. He knew the drivers of that growth: "...the products that grow are the ones with science behind them. When there’s good science like there is behind ... vitamin D and omega 3s, that’s really where the dollar is going to be spent.” So, let's have a look at how good that science really is.  

Remember the time when Vitamin E and beta Carotene - the thing in veggies and fruits, which your body turns into Vitamin A - were found to be associated with decreased risk of lung cancer. The year was 1981 and the knowledge of that time had been summarized in the journal Nature [1]. You must keep in mind: if it's in Nature, it's like God's gospel.  Also keep in mind, that those studies were observational by design, that is, they observed an association between increased beta-carotene intake and lower incidence of lung cancers. Such observations do not allow us to say that one causes the other, even though the media types are typically quick in doing just that.  So, the natural conclusion from these association studies was: give smokers, those people who have the highest risk of getting lung cancer, a Vitamin supplement to reduce their risk. 

Then, in 1985, a group of Finnish researchers (The Alpha-Tocopherol, Beta Carotene Cancer Prevention Group, ATBC) did the one and only thing, which can establish a cause-effect relationship: a study in which male smokers, the people at highest risk for lung cancer, were given the supplement and another group wasn't [2]. In fact, the 29,000 participants had been randomized into one of 4 equal-sized groups, with group A receiving Vitamin E, group B receiving Vitamin A , group C receiving both Vitamins and group D getting simply a placebo. In 1994 the results came out. Certainly not in favor of the supplement. The guys on beta-carotene had an 18% higher rate of developing lung cancer than their peers who did not get this Vitamin. Actually, this rate was seen accelerating over time.

Another large trial, the beta-carotene and retinol efficacy trial (CARET) did essentially the same thing. It investigated the effect of beta-carotene on lung cancer risk in more than 18,000 participants at elevated risk due to their being smokers or having been exposed to asbestos. CARET was done in the U.S., and it delivered more sobering results: A 28% increase in lung cancer risk among those who had been randomized to receive the beta-carotene supplement [3]. The trial was halted, and follow-up observations showed a gradual reversal of elevated risk. That's a clear indication that the increased risk of lung cancer was attributable to the supplementation with beta-carotene and vitamin E. 

While these results certainly put a damper on the enthusiasm for vitamin A & E, the truly interesting finding is often overlooked and underreported: For the placebo guys in the ATCB study, there was a clear inverse relationship between intake of FOODS high in Vitamin E & A and the risk of lung cancer. The group with the lowest intake of those veggies and fruits, which deliver Vitamin E & A, had a 50% higher risk of developing lung cancer compared to those guys with the highest intake of fruits and veggies. 

These observation have been confirmed in the EPIC study which investigated the effects of diet on cancer. Also here, a high intake of fruit and vegetables, not supplements, was found to reduce smokers' risk of lung cancer considerably [4]. 

With these facts about nutrition science, and how the supplement industry uses it, I simply wanted to set the mood. Now, let's look at how this science is doing in the vitamin D and omega-3 department as emphasized by Dr. Fabricant.

Vitamin D supplements are believed to improve or maintain bone health in older adults, particularly in women. Indeed, what comes out of science labs seems to support this notion. Dr. Bischoff-Ferrari and her colleagues evaluated 11 randomized controlled trials to answer the question whether vitamin D supplementation reduces fracture risk in women aged 65 and older. It does. But only in those with the highest daily intake, more than 800IU. Good news for the supplement industry? You bet. But is it good news for you, too? Maybe not. Vitamin D needs to be taken with calcium to be effective. But high calcium intake by way of supplements appears to increase the risk for heart attacks, whereas dietary calcium intake, say from milk and cheese, does not [5]. 

In view of all this evidence the United States Preventive Services Task Force (USPSTF) recently issued its draft recommendation, which says that there is insufficient evidence to "...to assess the balance of the benefits and harms of combined vitamin D and calcium supplementation...". But rest assured, the supplement industry has all the evidence and science, which the USPSTF has not. Or so they want you to believe. 

Let's move over to the famous fish oils and their Omega-3s.

The Omega-3 fatty acids are often praised as the constituents of fish oil, which protect against heart disease. At least that's what the supplement industry says. Science says something else. A double-blind prospective study of 2500 men and women aged 45 to 80, who had experienced a heart attack or stroke, investigated whether omega3- supplementation would prevent further cardiovascular events [6]. It didn't. 

You might ask, why this study looked only at people who had already cardiovascular disease. Maybe they are so far down the drain, that fish oil can't do its trick any more. Wouldn't it be nice to know whether omega-3 is protective in people who do not have cardiovascular disease? Yeah, it would. It would also be nice for you to tell me how to run such a study. Realistically. You would have to enroll thousands of healthy people, randomize them into those who MUST NOT EVER get their hands on omega-3 supplements and those who MUST take it every day for many years. Go find those people. Then, after many years, you would have to compare the outcome between the two groups. And you also would have to rule out those outcomes to be affected by such factors as physical activity and all the different food habits those thousands of people have. Of course, you would need funding for this type of research. Only, who will give you the funds? Certainly not the pharmaceutical industry. It pumps billions into research, yes, but only for proprietary chemicals. There is nothing proprietary about a vitamin, which every Tom, Dick and Harry can put into a pill. Which is why even the supplements industry won't give you a single dollar for your research. Now you know why such studies are not being performed. And why nutrition science is so fickle with its results. 

What's the taking home point: When it comes to nutrient-health interactions, it is obviously not as simple as boiling down the effects of food to an individual vitamin or other nutrient. Neither is it as simple as stuffing this nutrient into a pill and shoving it down your throat. In the words of Einstein: "Make things as simple as possible, but not simpler." Reducing the effects of food to individual vitamins or other nutrients is obviously an oversimplification. When, as a result of oversimplification, nutrition science makes you jump from one supplement to the next, what does the supplement industry do? They are laughing their way to the bank. And, as we have seen, Mr Fabricant knows why. He is no more with the NPA, though. He has switched sides to work now for the FDA as director of its Dietary Supplement Programs division. Let's hope the FDA's view on nutrition science remains as skeptical as it ought to be. In the interest of your health. [tweet this].

1. Peto R, Doll R, Buckley JD, Sporn MB: Can dietary beta-carotene materially reduce human cancer rates? Nature 1981, 290(5803):201-208.

2. The effect of vitamin E and beta carotene on the incidence of lung cancer and other cancers in male smokers. The Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group. N Engl J Med 1994, 330(15):1029-1035.

3. Goodman GE, Thornquist MD, Balmes J, Cullen MR, Meyskens FL, Jr., Omenn GS, Valanis B, Williams JH, Jr.: The Beta-Carotene and Retinol Efficacy Trial: incidence of lung cancer and cardiovascular disease mortality during 6-year follow-up after stopping beta-carotene and retinol supplements. J Natl Cancer Inst 2004, 96(23):1743-1750.

4. Gonzalez CA, Riboli E: Diet and cancer prevention: Contributions from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Eur J Cancer 2010, 46(14):2555-2562.

5. Li K, Kaaks R, Linseisen J, Rohrmann S: Associations of dietary calcium intake and calcium supplementation with myocardial infarction and stroke risk and overall cardiovascular mortality in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition study (EPIC-Heidelberg). Heart 2012, 98(12):920-925.

6. Galan P, Kesse-Guyot E, Czernichow Sb, Briancon S, Blacher J, Hercberg S: Effects of B vitamins and omega 3 fatty acids on cardiovascular diseases: a randomised placebo controlled trial. BMJ, 341.

Peto R, Doll R, Buckley JD, & Sporn MB (1981). Can dietary beta-carotene materially reduce human cancer rates? Nature, 290 (5803), 201-8 PMID: 7010181

The Alpha-Tocopherol Beta Carotene Cancer Prevention Study Group (1994). The effect of vitamin E and beta carotene on the incidence of lung cancer and other cancers in male smokers. The Alpha-Tocopherol, Beta Carotene Cancer Prevention Study Group. The New England journal of medicine, 330 (15), 1029-35 PMID: 8127329

Goodman GE, Thornquist MD, Balmes J, Cullen MR, Meyskens FL Jr, Omenn GS, Valanis B, & Williams JH Jr (2004). The Beta-Carotene and Retinol Efficacy Trial: incidence of lung cancer and cardiovascular disease mortality during 6-year follow-up after stopping beta-carotene and retinol supplements. Journal of the National Cancer Institute, 96 (23), 1743-50 PMID: 15572756

Gonzalez CA, & Riboli E (2010). Diet and cancer prevention: Contributions from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. European journal of cancer (Oxford, England : 1990), 46 (14), 2555-62 PMID: 20843485

Li K, Kaaks R, Linseisen J, & Rohrmann S (2012). Associations of dietary calcium intake and calcium supplementation with myocardial infarction and stroke risk and overall cardiovascular mortality in the Heidelberg cohort of the European Prospective Investigation into Cancer and Nutrition study (EPIC-Hei Heart (British Cardiac Society), 98 (12), 920-5 PMID: 22626900

Galan P, Kesse-Guyot E, Czernichow S, Briancon S, Blacher J, Hercberg S, & SU.FOL.OM3 Collaborative Group (2010). Effects of B vitamins and omega 3 fatty acids on cardiovascular diseases: a randomised placebo controlled trial. BMJ (Clinical research ed.), 341 PMID: 21115589

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

10 Good Reasons Not To Exercise?

Exercise may actually be bad for you! A professor says he stumbled upon this "potentially explosive" insight. The New York Times has been quick to peddle it. And couch potatoes descend on it like vultures on road kill. But professors can get it wrong, too. 

Before we judge the verity of the "exercise may be bad" claim, let's first look at how the media present it to us. We shall use the recent article in The New York Times, headlined "For Some, Exercise May Increase Heart Risk". The first paragraph confronts us with a journalist's preferred procedure for feeding us contentious scientific claims: presenting an authoritative author with stellar academic credentials and a publication list longer than your arm. While that is certainly better than having, say, Paris Hilton as the source of scientific insights, it is a far cry from actually investigating such claims. Which is what we want to do now.

The basis of the exercise-may-be-bad claim is a study which investigated the question "whether there are people who experience adverse changes in cardiovascular risk factors" in response to exercise [1]. The chosen risk factors in question were some of the usual suspects: systolic blood pressure, HDL-cholesterol, triglycerides and insulin. The research question: Are there people whose risk factors actually get worse when they change from sedentary to more active lifestyles? 

Sounds simple enough to investigate. Put a group of couch potatoes on a work-out program for a couple of weeks and see how their risk factors change. Only it is not that simple. In the realm of biomedicine, every measurement of every biomarker is subject to (a) errors in measurement and (b) other sources of variability. This makes it virtually impossible for you to see exactly the same results on your lab report for, say, blood pressure, cholesterol, glucose or any other parameter, when you get them measured two or more days in a row. Even if you were to eat exactly the same food every day and to perform exactly the same activities.  

Now imagine, if you conducted an intervention study on your couch-potato subjects and you found their risk factors changed after a couple of weeks of doing exercise, you could theoretically be seeing nothing else but a random variation caused by the error inherent in such measurement. 

To avoid falsely interpreting such a variation as a change into one or the other direction, it makes good sense to know the bandwidth of these errors for each biomarker, before you embark on interpreting the results of your study. Which is what the authors of this particular study did. They took 60 people and measured their risk factors three times over three weeks. From these measurements they were able to calculate the margin of error. Actually, they didn't do this for this particular paper, they had done this measurement as an ancillary study in the HERITAGE study performed earlier. The HERITAGE study had investigated the effects of a 20-weeks endurance training program on various risk factors in previously sedentary adults. Whether heritability plays a role in this response was a key question. That's why this study recruited entire families, that is, parents up to the age of 65, together with their adult children. 

I mention this because the paper, which we are deciphering now, is a re-hash of the HERITAGE study's results, to which the authors added the data of another 5 exercise studies. That's what is called a meta-analysis. In this case it covers more than 1600 people, with the HERITAGE study delivering almost half of them. 

Fast forward to answering the question of how many of those participants had experienced a worsening of at least 1 risk factor. Close to 10%. That is, about 10% of the participants had an adverse change of a risk factor in excess of the margin of error, which I mentioned earlier. I'm going to demonstrate the results, using systolic blood pressure and the Heritage study as the example. I do this exemplification for three reasons: First, blood pressure is the more serious of the investigated risk factors. Secondly, the HERITAGE study delivers most of the participants, and thirdly, the effects seen and discussed with respect to blood pressure and HERITAGE apply similarly to the other 5 studies and risk factors. 

But before we go there I need to familiarize you with a basic concept of statistics. It is called the "normal distribution of data". It is an amazing observation of how data are distributed when you take many measurements. Let's take blood pressure as an example. 

If you were to measure the blood pressure values for every individual living in your village, city or country, you could easily calculate the average blood pressure for this group of people. You could put all those data into a chart such as the one in figure 1. 

Figure 1

On the x-axis, the horizontal axis, you write down the blood pressure values, and on the y-axis (the vertical axis) you write down the number of observations, that is, how often a particular blood pressure reading has been observed. You will find that most people have a blood pressure value pretty close to the average. Fewer people will have values, which lie further away from this average, and very few people will have extreme deviations from the average. 

It so turns out, that when you map almost any naturally occurring value, be it blood pressure, IQ or the number of hangovers over the past 12 months, the curve, which you get from connecting all the data points in your graph, will look very similar in shape. Some curves are a bit flatter and broader, while others are a bit steeper and narrower. But the underlying shape is called the "normal distribution", and it means just that: It's how data are normally distributed over a range of possible values. The curve's shape being reminiscent of a bell, has lead to this curve being called the "bell curve". 

In statistics, especially when we use them to interpret study data, we always go through quite some effort to ensure that the data we measure are normally distributed. That's because many statistic tools don't give us reliable answers if the distribution is not normal.

Back to our famous study. What you see in figure 2 is how the authors present their results for the blood pressure response of the HERITAGE participants. 

Figure 2

For each individual (x-axis) they drew a thin bar representing the height of that person's change in blood pressure after 20 weeks of exercise. Bars extending below the x-axis represent reduced blood pressure, and those extending above the x-axis represent increased blood pressure. The bars in red are those of the people whose blood pressure increase was in excess of the error margin of about 8mmHg. 

Now, Claude Bouchard, the lead author of the paper, is being quoted in the NYT as saying that the counterintuitive observation of exercise causing systolic blood pressure to worsen "is bizarre". 

Here is why it is neither counterintuitive nor bizarre: When we accept the blood pressure values of our study population to be distributed normally, we have every reason to expect the change in blood pressure to be distributed normally, too. Specifically, since all participants went through the same type of intervention. 

Figure 3

If we now run a computer simulation, using the same number of people, the same mean change in blood pressure, and the same error values, then we can construct a curve for this group, too. Which is what you see in figure 3. Eerily similar to the one in figure 2, isn't' it? 

That's because we are looking at a normal distribution of the biomarker called 'blood pressure change'. It is an inevitable fact of nature that a few of our participants will change "for the worse". And I'm putting this in inverted comma because we don't really know whether this change is for the worse. 

After all, we are talking risk factors, not actual disease events. In the context of this study you need to keep in mind, that all participants had normal blood pressure values to begin with. The average was about 120mmHg. The mean change was reported as 0.2 mmHg. That's not only clinically insignificant, that's way below the measurement capability of clinical devices. 

When I started to dig deeper into this study, I found quite a number of inconsistencies with earlier publications. For example, in the latest paper, the one discussed in the NYT, the number of HERITAGE participants was stated as 723. In a 2001 paper, which investigated participants' blood pressure change at a 50-Watt work rate, the number was stated as 503 [2].  In the same year Bouchard had published a paper putting this number at 723 [3]. Anyway, the observation that the blood pressure change during exercise was significantly larger (about -8 mmHg) than the marginal change of resting blood pressure indicates that there probably was some effect of exercise. 

So, what's the take-home point of all this? With the "normal distribution" being a natural phenomenon that underlies so many biomarkers, it is neither bizarre nor in any other way astonishing to find "adverse" reactions in everything from pharmaceutical to behavioral interventions and treatments.  Whether such reactions are truly adverse can't be answered by a study like the one, which is now bandied about in the media. That's because risk factors are not disease endpoints. They are actually very poor predictors of the latter, as I have explained in my post "Why Risk Factors For Heart Attack Really Suck". 

So, keep in mind, that there is no treatment or intervention, which has the same effect on everybody. Pharmaceutical research uses this knowledge, for example, when determining the toxicity of a substance. This toxicity is often defined as the LD50 value, that is, the lethal dose, which kills 50% of the experimental animals.  Meaning, the same dose which kills half the animals, leaves the other half alive and kicking. 

And correspondingly, the same dose of exercise, which cures your neighbor from hypertension, may have no effect on you. Because you belong to those 10% who react differently. But are these 10 good reasons not to exercise? How to deal with this question will be the subject of my next post. Until then, stay skeptical. 

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1. Bouchard, C., et al., Adverse Metabolic Response to Regular Exercise: Is It a Rare or Common Occurrence? PLoS ONE, 2012. 7(5): p. e37887.

2. Wilmore, J.H., et al., Heart rate and blood pressure changes with endurance training: the HERITAGE Family Study. Medicine and Science in Sports and Exercise, 2001. 33(1): p. 107-16.

3. BOUCHARD, C. and T. RANKINEN, Individual differences in response to regular physical activity. Medicine and Science in Sports and Exercise, 2001. 33(6): p. S446-S451.

Bouchard C, Blair SN, Church TS, Earnest CP, Hagberg JM, Häkkinen K, Jenkins NT, Karavirta L, Kraus WE, Leon AS, Rao DC, Sarzynski MA, Skinner JS, Slentz CA, & Rankinen T (2012). Adverse metabolic response to regular exercise: is it a rare or common occurrence? PloS one, 7 (5) PMID: 22666405

Wilmore, J. H., Stanforth, P. R., Gagnon, J., Rice, T., Mandel, S., Leon, A. S., Rao, D. C., Skinner, J. S., & Bouchard, C. (2001). Heart rate and blood pressure changes with endurance training: the HERITAGE family study. Medicine and Science in Sports and Exercise DOI: 10.1097/00005768-200101000-00017

Bouchard, C., & Rankinen, T. (2001). Individual differences in response to regular physical activity Med Sci Sports Exerc DOI: 10.1097/00005768-200106001-00013