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