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

Why You Should Arm Your Bullshit Alarm Before Reading Diet News.

In the fight over best diet for health and weight loss, it's protein lovers vs. vegetarian zealots. So far, a clear winner has not emerged. Only one loser: you, the victim of biased research. Here is an example of why you should keep your bullshit alarm on high alert when reading about weight loss diets.  

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Ellen M. Evans and colleagues wanted to know whether overweight men and women differ in their body composition responses to different weight loss diets [1]. So they enrolled 58 men and 72 women with a BMI greater than 26, and randomized them into two diet groups.
One group was instructed to follow a high-protein low-carbohydrate diet, which delivered 1.6 g of protein per kg bodyweight per day. The high-carb group  received only half that amount of protein, and both groups' fat intake was capped at 30% of total energy intake. Both diets contained the same amount of fiber. Women received a daily total of 1700 calories, men 1900 calories. The intervention lasted for 4 months, followed by an 8-months weight maintenance period. Fast forward to the 12-months results:

Both diet groups and both genders lost about 10% of their body weight. But expressing weight loss in kilos of body weight can be a deceptive thing. Ideally we want that loss to be fat loss rather than loss of lean mass, that is, muscle mass. In the study at hand, for men on the high-carb diet, a little over one third of their weight loss came from lean body mass. Meaning, of the 14 kilos, which they lost on average, 5 Kilos came from a reduction in muscle tissue. The high-protein guys maintained their muscle mass to a greater extent: only 20% of their weight loss came from wasted muscle. For the women the picture looked almost identical: muscle mass contributed 37% to the weight loss of the high-carb women, compared to 23% in the high-protein group. 

You would be forgiven if you now agreed with the authors' statement that the high-protein diet "...was more effective in reducing percent body fat...". Or in other words, a high-protein diet is superior to a high-carb alternative, as losing lean mass isn't a good thing in weight loss. I'll get to that point shortly in a little more detail. 

Before we go there, let me state, that, being a firm supporter of the high-protein low-carb dietary philosophy, I loved to read this study. But I'm an equally firm supporter of proper scientific methods. And they have been prostituted in this case, which is why I love this study a lot less than its results. 

Here is why: When I read the tables in which the authors present the results, I was impressed by the fact that both groups not only managed to rescue the 4-months weight loss to the 12-months finish line, but even increased this weight loss a little. When you have read literally hundreds of studies on weight loss interventions, as I have done, you'll find this observation to be in stark contrast to what we typically see: a reversal of weight loss. That is, at least a partial post-intervention regain of the weight lost during the dietary period. 

We find the explanation for this miraculous exception in the number of participants. Or rather in the number of disappearing participants. Of the 66 participants who started in the high-carb group, only 30 made it to the finish line 12 months later. That's a drop-out rate of more than 50%!  And of the 64 participants in the high-protein group 23, or 36%, had dropped out by month 12. 

High drop-out rates are nothing unusual in weight loss trials, but it is good practice for researchers to tell their readers, how they accounted for these drop outs in the statistics, with which they interpret the data. Nothing of that in this paper. So, we don't know whether the drop-outs simply did not show up for their measurements, or whether the researchers did not consider the data of those participants, who failed to achieve some arbitrary weight loss threshold. The latter is an absolute no-no. It enables researchers to skew the results every which way they want. And the former is reason to investigate whether the drop-outs differed in some way significantly from the adherent participants. Such differences often affect the interpretation of the results. 

One interpretation emerges right away, when checking the differences of relative fat loss while considering the drop-out rates:  the smaller relative loss of muscle mass in the high-protein diet is not significantly different from the loss observed in the high-carb group. That does not mean, there is no difference between these two diet types. It only means, the study was underpowered to detect such difference, if there was any. And if it was underpowered to detect the difference between diet groups, it was certainly underpowered to differentiate between men and women in this respect. 

If you still want the final verdict on high-carb vs. high-protein, I'm afraid I can't give it to you, even though I'm heavily leaning in favor of the high-protein version. I base my judgment on a 2009 systematic review of all randomized controlled trials, which were performed between 2000 and 2007, and which had pitted high-carb vs. high-protein strategies [2]. This review demonstrated that high-protein diets are more effective with respect to weight loss and probably with respect to cardiovascular risk factors than high-carb diets. At least over observation periods of 6 to 12 months. 

Only long-term observations, comparing hard endpoints, can decide which diet may be better. Those studies are a long way off. To complicate matters, we might find that different people react differently to the same type of dietary strategy. Until we know better, we need to go with what we know: 

The preservation of lean body mass certainly is a key aspect. Muscle tissue is an important endocrine organ, which, when exercised, produces potent anti-inflammatory substrates and hormones. These are the key elements of physical activity's protection against the initiating step of heart disease: atherosclerosis. Muscle tissue is also the body's primary site to store dietary carbohydrate in the form of glucose. The other site being the liver. With a high-carb diet, these storage sites are easily overwhelmed, which leads to conversion of carbs to fat. When, ironically, a high-carb diet nibbles away at the body's carb storage sites, you can imagine what this means to the body's relative fat content. Another aspect is that muscle tissue consumes energy, even at rest. The loss of this "burner" during weight loss makes weight rebound more likely.

So, if all these matters are known and understood, why perform a study, which is underpowered and fraud with questionable interpretations? Why produce the food equivalent of a scientology propaganda piece?  

Beats me. Maybe because part of the study's funding came from the National Cattlemen's Beef Association and The Beef Board. Both of which are, of course, entirely neutral to the outcome of research funded by them, and unbiased to its interpretation. 

It also beats me, why a respected journal and its peer reviewers facilitate the publication of such a study. Maybe because its senior author, Professor DK Layman, is a leading researcher in nutrition science, and... 

...the Egg Nutrition Center's director of research. 

As much as my dietary preferences place me in the protein camp of this contest, my bullshit alarm is set to high-sensitivity. And so should yours be. 

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1. Evans, E., et al., Effects of protein intake and gender on body composition changes: a randomized clinical weight loss trial. Nutrition and Metabolism, 2012. 9(1): p. 55.

2. Hession, M., et al., Systematic review of randomized controlled trials of low-carbohydrate vs. low-fat/low-calorie diets in the management of obesity and its comorbidities. Obesity Reviews, 2009. 10(1): p. 36-50.

Evans, Ellen, Mojtahedi, Mina, Thorpe, Matthew, Valentine, Rudy, Kris-Etherton, Penny, & Layman, Donald (2012). Effects of protein intake and gender on body composition changes: a randomized clinical weight loss trial Nutrition and Metabolism : doi:10.1186/1743-7075-9-55

Hession, M., Rolland, C., Kulkarni, U., Wise, A., & Broom, J. (2009). Systematic review of randomized controlled trials of low-carbohydrate vs. low-fat/low-calorie diets in the management of obesity and its comorbidities Obesity Reviews, 10 (1), 36-50 DOI: 10.1111/j.1467-789X.2008.00518.x