The 2012 red meat-mortality study (Arch Intern Med): The data suggests that red meat is protective

I am not a big fan of using arguments such as “food questionnaires are unreliable” and “observational studies are worthless” to completely dismiss a study. There are many reasons for this. One of them is that, when people misreport certain diet and lifestyle patterns, but do that consistently (i.e., everybody underreports food intake), the biasing effect on coefficients of association is minor. Measurement errors may remain for this or other reasons, but regression methods (linear and nonlinear) assume the existence of such errors, and are designed to yield robust coefficients in their presence. Besides, for me to use these types of arguments would be hypocritical, since I myself have done several analyses on the China Study data (), and built what I think are valid arguments based on those analyses.

My approach is: Let us look at the data, any data, carefully, using appropriate analysis tools, and see what it tells us; maybe we will find evidence of measurement errors distorting the results and leading to mistaken conclusions, or maybe not. With this in mind, let us take a look at the top part of Table 3 of the most recent (published online in March 2012) study looking at the relationship between red meat consumption and mortality, authored by Pan et al. (Frank B. Hu is the senior author) and published in the prestigious Archives of Internal Medicine (). This is a prominent journal, with an average of over 270 citations per article according to Google Scholar. The study has received much media attention recently.

Take a look at the area highlighted in red, focusing on data from the Health Professionals sample. That is the multivariate-adjusted cardiovascular mortality rate, listed as a normalized percentage, in the highest quintile (Q5) of red meat consumption from the Health Professionals sample. The non-adjusted percentages are 1.4  percent mortality in Q5 and 1.13 in Q1 (from Table 1 of the same article); so the multivariate adjustment-normalization changed the values of the percentages somewhat, but not much. The highlighted 1.35 number suggests that for each group of 100 people who consumed a lot of red meat (Q5), when compared with a group of 100 people who consumed little red meat (Q1), there were on average 0.35  more deaths over the same period of time (more than 20 years).

The heavy red meat eaters in Q5 consumed 972.37 percent more red meat than those in Q1. This is calculated with data from Table 1 of the same article, as: (2.36-0.22)/0.22. In Q5, the 2.36 number refers to the number of servings of red meat per day, with each serving being approximately 84 g. So the heavy red meat eaters ate approximately 198 g per day (a bit less than 0.5 lb), while the light red meat eaters ate about 18 g per day. In other words, the heavy red meat eaters ate 9.7237 times more, or 972.37 percent more, red meat.

So, just to be clear, even though the folks in Q5 consumed 972.37 percent more red meat than the folks in Q1, in each matched group of 100 you would not find a single additional death over the same time period. If you looked at matched groups of 1,000 individuals, you would find 3 more deaths among the heavy red meat eaters. The same general pattern, of a minute difference, repeats itself throughout Table 3. As you can see, all of the reported mortality ratios are 1-point-something. In fact, this same pattern repeats itself in all mortality tables (all-cause, cardiovascular, cancer). This is all based on a multivariate analysis that according to the authors controlled for a large number of variables, including baseline history of diabetes.

Interestingly, looking at data from the same sample (Health Professionals), the incidence of diabetes is 75 percent higher in Q5 than in Q1. The same is true for the second sample (Nurses Health), where the Q5-Q1 difference in incidence of diabetes is even greater - 81 percent. This caught my eye, being diabetes such a prototypical “disease of affluence”. So I entered the whole data reported in the article into HCE () and WarpPLS (), and conducted some analyses. The graphs below are from HCE. The data includes both samples – Health Professionals and Nurses Health.

HCE calculates bivariate correlations, and so does WarpPLS. But WarpPLS stores numbers with a higher level of precision, so I used WarpPLS for calculating coefficients of association, including correlations. I also double-checked the numbers with other software, just in case (e.g., SPSS and MATLAB). Here are the correlations calculated by WarpPLS, which refer to the graphs above: 0.030 for red meat intake and mortality; 0.607 for diabetes and mortality; and 0.910 for food intake and diabetes. Yes, you read it right, the correlation between red meat intake and mortality is a very low and non-significant 0.030 in this dataset. Not a big surprise when you look at the related HCE graph, with the line going up and down almost at random. Note that I included the quintiles data from both the Health Professionals and Nurses Health samples in one dataset.

Those folks in Q5 had a much higher incidence of diabetes, and yet the increase in mortality for them was significantly lower, in percentage terms. A key difference between Q5 and Q1 being what? The Q5 folks ate a lot more red meat. This looks suspiciously suggestive of a finding that I came across before, based on an analysis of the China Study II data (). The finding was that animal food consumption (and red meat is an animal food) was protective, actually reducing the negative effect of wheat flour consumption on mortality. That analysis actually suggested that wheat flour consumption may not be so bad if you eat 221 g or more of animal food daily.

So, I built the model below in WarpPLS, where red meat intake (RedMeat) is hypothesized to moderate the relationship between diabetes incidence (Diabetes) and mortality (Mort). Below I am also including the graphs for the direct and moderating effects; the data is standardized, which reduces estimation error, particularly in moderating effects estimation. I used a standard linear algorithm for the calculation of the path coefficients (betas next to the arrows) and jackknifing for the calculation of the P values (confidence = 1 – P value). Jackknifing is a resampling technique that does not require multivariate normality and that tends to work well with small samples; as is the case with nonparametric techniques in general.

The direct effect of diabetes on mortality is positive (0.68) and almost statistically significant at the P < 0.05 level (confidence of 94 percent), which is noteworthy because the sample size here is so small – only 10 data points, 5 quintiles from the Health Professionals sample and 5 from the Nurses Health sample. The moderating effect is negative (-0.11), but not statistically significant (confidence of 61 percent). In the moderating effect graphs (shown side-by-side), this negative moderation is indicated by a slightly less steep inclination of the regression line for the graph on the right, which refers to high red meat intake. A less steep inclination means a less strong relationship between diabetes and mortality – among the folks who ate the most red meat.

Not too surprisingly, at least to me, the results above suggest that red meat per se may well be protective. Although we should consider a least two other possibilities. One is that red meat intake is a marker for consumption of some other things, possibly present in animal foods, that are protective - e.g., choline and vitamin K2. The other possibility is that red meat is protective in part by displacing other less healthy foods. Perhaps what we are seeing here is a combination of these.

Whatever the reason may be, red meat consumption seems to actually lessen the effect of diabetes on mortality in this sample. That is, according to this data, the more red meat is consumed, the fewer people die from diabetes. The protective effect might have been stronger if the participants had eaten more red meat, or more animal foods containing the protective factors; recall that the threshold for protection in the China Study II data was consumption of 221 g or more of animal food daily (). Having said that, it is also important to note that, if you eat excess calories to the point of becoming obese, from red meat or any other sources, your risk of developing diabetes will go up – as the earlier HCE graph relating food intake and diabetes implies.

Please keep in mind that this post is the result of a quick analysis of secondary data reported in a journal article, and its conclusions may be wrong, even though I did my best not to make any mistake (e.g., mistyping data from the article). The authors likely spent months, if not more, in their study; and have the support of one of the premier research universities in the world. Still, this post raises serious questions. I say this respectfully, as the authors did seem to try their best to control for all possible confounders.

I should also say that the moderating effect I uncovered is admittedly a fairly weak effect on this small sample and not statistically significant. But its magnitude is apparently greater than the reported effects of red meat on mortality, which are not only minute but may well be statistical artifacts. The Cox proportional hazards analysis employed in the study, which is commonly used in epidemiology, is nothing more than a sophisticated ANCOVA; it is a semi-parametric version of a special case of the broader analysis method automated by WarpPLS.

Finally, I could not control for confounders because, given the small sample, inclusion of confounders (e.g., smoking) leads to massive collinearity. WarpPLS calculates collinearity estimates automatically, and is particularly thorough at doing that (calculating them at multiple levels), so there is no way to ignore them. Collinearity can severely distort results, as pointed out in a YouTube video on WarpPLS (). Collinearity can even lead to changes in the signs of coefficients of association, in the context of multivariate analyses - e.g., a positive association appears to be negative. The authors have the original data – a much, much larger sample - which makes it much easier to deal with collinearity.

Moderating effects analyses () – we need more of that in epidemiological research eh?

Gaining muscle and losing fat at the same time: A more customized approach based on strength training and calorie intake variation

In the two last posts I discussed the idea of gaining muscle and losing fat at the same time () (). This post outlines one approach to make that happen, based on my own experience and that of several HCE () users. This approach may well be the most natural from an evolutionary perspective.

But first let us address one important question: Why would anyone want to reach a certain body weight and keep it constant, resorting to the more difficult and slow strategy of “turning fat into muscle”, so to speak? One could simply keep on losing fat, without losing or gaining muscle, until he or she reaches a very low body fat percentage (e.g., a single-digit body fat percentage, for men). Then he or she could go up from there, slowly putting on muscle.

The reason why it is advisable to reach a certain body weight and keep it constant is that, below a certain weight, one is likely to run into nutrient deficiencies. Non-exercise energy expenditure is proportional to body weight. As you keep on losing body weight, calorie intake may become too low to allow you to have a nutrient intake that is the minimum for your body structure. Unfortunately eating highly nutritious vegetables or consuming copious amounts of vitamin and mineral supplements will not work very well, because the nutritional needs of your body include both micro- and macro-nutrients that need co-factors to be properly absorbed and/or metabolized. One example is dietary fat, which is necessary for the absorption of fat-soluble vitamins.

If you place yourself into a state of nutrient deficiency, your body will compensate by mounting a multipronged defense, resorting to psychological and physiological mechanisms. Your body will do that because it is hardwired for self-preservation; as noted below, being in a state of nutrient deficiency for too long is very dangerous for one's health. Most people cannot oppose this body reaction by willpower alone. That is where binge-eating often starts. This is one of the key reasons why looking for a common denominator of most diets leads to the conclusion that all succeed at first, and eventually fail ().

If you are one of the few who can oppose the body’s reaction, and maintain a very low calorie intake even in the face of nutrient deficiencies, chances are you will become much more vulnerable to diseases caused by pathogens. Individually you will be placing yourself in a state that is similar to that of populations that have faced famine in the past. Historically speaking, famines are associated with decreases in degenerative diseases, and increases in diseases caused by pathogens. Pandemics, like the Black Death (), have historically been preceded by periods of food scarcity.

The approach to gaining muscle and losing fat at the same time, outlined here, relies mainly on the following elements: (a) regularly conducting strength training; (b) varying calorie intake based on exercise; and (c) eating protein regularly. To that, I would add becoming more active, which does not necessarily mean exercising but does mean doing things that involve physical motion of some kind (e.g., walking, climbing stairs, moving things around), to the tune of 1 hour or more every day. These increase calorie expenditure, enabling a slightly higher calorie intake while maintaining the same weight, and thus more nutrients on a diet of unprocessed foods. In fact, even things like fidgeting count (). These activities should not cause muscle damage to the point of preventing recovery from strength training.

As far as strength training goes, the main idea, as discussed in the previous post, is to regularly hit the supercompensation window, with progressive overload, and maintain your current body weight. In fact, over time, as muscle gain progresses, you will probably want to increase your calorie intake to increase your body weight, but very slowly to keep any fat gain from happening. This way your body fat percentage will go down, even as your weight goes up slowly. The first element, regularly hitting the supercompensation window, was discussed in a previous post ().

Varying calorie intake based on exercise. Here one approach that seems to work well is to eat more in the hours after a strength training session, and less in the hours preceding the next strength training session, keeping the calorie intake at maintenance over a week. Individual customization here is very important. Many people will respond quite well to a calorie surplus window of 8 – 24 h after exercise, and a calorie deficit in the following 40 – 24 h. This assumes that strength training sessions take place every other day. The weekend break in routine is a good one, as well as other random variations (e.g., random fasts), as the body tends to adapt to anything over time ().

One example would be someone following a two-day cycle where on the first day he or she would do strength training, and eat the following to satisfaction: muscle meats, fatty seafood (e.g., salmon), cheese, eggs, fruits, and starchy tubers (e.g., sweet potato). On the second day, a rest day, the person would eat the following, to near satisfaction, limiting portions a bit to offset the calorie surplus of the previous day: organ meats (e.g., heart and liver), lean seafood (e.g., shrimp and mussels), and non-starchy nutritious vegetables (e.g., spinach and cabbage). This would lead to periodic glycogen depletion, and also to unsettling water-weight variations; these can softened a bit, if they are bothering, by adding a small amount of fruit and/or starchy foods on rest days.

Organ meats, lean seafood, and non-starchy nutritious vegetables are all low-calorie foods. So restricting calories with them is relatively easy, without the need to reduce the volume of food eaten that much. If maintenance is achieved at around 2,000 calories per day, a possible calorie intake pattern would be 3,000 calories on one day, mostly after strength training, and 1,000 calories the next. This of course would depend on a number of factors including body size and nonexercise thermogenesis. A few calories could be added or removed here and there to make up for a different calorie intake during the weekend.

Some people believe that, if you vary your calorie intake in this way, the calorie deficit period will lead to muscle loss. This is the rationale behind the multiple balanced meals a day approach; which also works, and is successfully used by many bodybuilders, such as Doug Miller () and Scooby (). However, it seems that the positive nitrogen balance stimulus caused by strength training leads to a variation in nitrogen balance that is nonlinear and also different from the stimulus to muscle gain. Being in positive or neutral nitrogen balance is not the same as gaining muscle mass, although the two should be very highly correlated. While the muscle gain window may close relatively quickly after the strength training session, the window in which nitrogen balance is positive or neutral may remain open for much longer, even in the face of a calorie deficit during part of it. This difference in nonlinear response is illustrated through the schematic graph below.

Eating protein regularly. Here what seems to be the most advisable approach is to eat protein throughout, in amounts that make you feel good. (Yes, you should rely on sense of well being as a measure as well.) There is no need for overconsumption of protein, as one does not need much to be in nitrogen balance when doing strength training. For someone weighing 200 lbs (91 kg) about 109 g/d of high-quality protein would be an overestimation () because strength training itself pushes one’s nitrogen balance into positive territory (). The amount of carbohydrate needed depends on the amount of glycogen depleted through exercise and the amount of protein consumed. The two chief sources for glycogen replenishment, in muscle and liver, are protein and carbohydrate – with the latter being much more efficient if you are not insulin resistant.

How much dietary protein can you store in muscle? About 15 g/d if you are a gifted bodybuilder (). Still, consumption of protein stimulates muscle growth through complex processes. And protein does not usually become fat if one is in calorie deficit, particularly if consumption of carbohydrates is limited ().

The above is probably much easier to understand than to implement in practice, because it requires a lot of customization. It seems natural because our Paleolithic ancestors probably consumed more calories after hunting-gathering activities (i.e., exercise), and fewer calories before those activities. Our body seems to respond quite well to alternate day calorie restriction (). Moreover, the break in routine every other day, and the delayed but certain satisfaction provided by the higher calorie intake on exercise days, can serve as powerful motivators.

The temptation to set rigid rules, or a generic formula, always exists. But each person is unique (). For some people, adopting various windows of fasting (usually in the 8 – 24 h range) seems to be a very good strategy to achieve calorie deficits while maintaining a positive or neutral nitrogen balance.

For others, fasting has the opposite effect, perhaps due to an abnormal increase in cortisol levels. This is particularly true for fasting windows of 12 – 24 h or more. If regularly fasting within this range stresses you out, as opposed to “liberating” you (), you may be in the category that does better with more frequently meals.

Gaining muscle and losing fat at the same time: Various issues and two key requirements

In my previous post (), I mentioned that the idea of gaining muscle and losing fat at the same time seems impossible to most people because of three widely held misconceptions: (a) to gain muscle you need a calorie surplus; (b) to lose fat you need a calorie deficit; and (c) you cannot achieve a calorie surplus and deficit at the same time.

The scenario used to illustrate what I see as a non-traumatic move from obese or seriously overweight to lean is one in which weight loss and fat loss go hand in hand until a relatively lean level is reached, beyond which weight is maintained constant (as illustrated in the schematic graph below). If you are departing from an obese or seriously overweight level, it may be advisable to lose weight until you reach a body fat level of around 21-24 percent for women or 14-17 percent for men. Once you reach that level, it may be best to stop losing weight, and instead slowly gain muscle and lose fat, in equal amounts. I will discuss the rationale for this in more detail in my next post; this post will focus on addressing the misconceptions above.

Before I address the misconceptions, let me first clarify that, when I say “gaining muscle” I do not mean only increasing the amount of protein stored in muscle tissue. Muscle tissue is mostly water, by far. An important component of muscle tissue is muscle glycogen, which increases dramatically with strength training, and also tends to increase the amount of water stored in muscle. So, when you gain muscle, you gain a significant amount of water.

Now let us take a look at the misconceptions. The first misconception, that to gain muscle you need a calorie surplus, was dispelled in a previous post featuring a study by Ballor and colleagues (). In that study, obese subjects combined strength training with a mild calorie deficit, and gained muscle. They also lost fat, but ended up a bit heavier than at the beginning of the intervention. Another study along the same lines was linked by Clint (thanks) in the comments section under the last post ().

The second misconception, that to lose fat you need a calorie deficit; is related to the third, that you cannot achieve a calorie surplus and deficit at the same time. In part these misconceptions are about semantics, as most people understand “calorie deficit” to mean “constant calorie deficit”. One can easily vary calorie intake every other day, generating various calorie deficits and surpluses over a week, but with no overall calorie deficit or surplus for the entire week. This is why I say that one can achieve a calorie surplus and deficit “at the same time”. But let us make a point very clear, most of the evidence that I have seen so far suggests that you do not need a calorie deficit to lose fat, but you do need a calorie deficit to lose structural weight (i.e., non-water weight). With a few exceptions, not many people will want to lose structural weight by shedding anything other than body fat. One exception would be professional athletes who are already very lean and yet are very big for the weight class in which they compete, being unable to "make weight" through dehydration.

Perhaps the most surprising to some people is that, based on my own experience and that of several HCE () users, you don’t even need to vary your calorie intake that much to gain muscle and lose fat at the same time. You can achieve that by eating enough to maintain your body weight. In fact, you can even slowly increase your calorie intake over time, as muscle growth progresses beyond the body fat lost. And here I mean increasing your calorie intake very slowly, proportionally to the amount of muscle you gain; which also means that the incremental increase in calorie intake will vary from person to person. If you are already relatively lean, at around 21-24 percent of body fat for women and 14-17 percent for men, gaining muscle and losing fat in equal amounts will lead to a visible change in body composition over time () ().

Two key requirements seem to be common denominators for most people. You must eat protein regularly; not because muscle tissue is mostly protein, but because protein seems to act as a hormone, signaling to muscle tissue that it should repair itself. (Many hormones are proteins, actually peptides, and also bind to receptor proteins.) And you also must conduct strength training to the point that you are regularly hitting the supercompensation window (). This takes a lot of individual customization (). You can achieve that with body weight exercises, although free weights and machines seem to be generally more effective. Keep in mind that individual customization will allow you to reach your "sweet spots", but that still results will vary across individuals, in some cases dramatically.

If you regularly hit the supercompensation window, you will be progressively spending slightly more energy in each exercise session, chiefly in the form of muscle glycogen, as you progress with your strength training program. You will also be creating a hormonal mix that will increase the body’s reliance on fat as a source of energy during recovery. As a compensatory adaptation (), your body will gradually increase the size of its glycogen stores, raising insulin sensitivity and making it progressively more difficult for glucose to become body fat.

Since you will be progressively spending slightly more energy over time due to regularly hitting the supercompensation window, that is another reason why you will need to increase your calorie intake. Again, very slowly, proportionally to your muscle gain. If you do not do that, you will provide a strong stimulus for autophagy () to occur, which I think is healthy and would even recommend from time to time. In fact, one of the most powerful stimuli to autophagy is doing strength training and fasting afterwards. If you do that only occasionally (e.g., once every few months), you will probably not experience muscle loss or gain, but you may experience health improvements as a result of autophagy.

The human body is very adaptable, so there are many variations of the general strategy above. In my next post, I will talk a bit more about a variation that seems to work well for many people. It involves a combination of strength training and calorie intake variation that may well be the most natural from an evolutionary perspective.

10,000 hours vs training debate: No scientific limits making it impossible for any individual to become an elite athlete with practice?

Dear Anders Ericsson...a request on behalf of sports science to stop telling people that the world is flat

The 10,000 hours vs genetic debate, and correcting Prof Ericsson's mistruths 

So last night, I was (un)fortunate enough to be involved in a radio debate with Prof Anders Ericsson on the concept of talent vs training.  For those who don't know, Ericsson is the father of the 10,000 hour concept, where he prescribes that ANY individual can become an elite athlete if they engage in the required hours of deliberate practice.  He sets that number at 10,000 hours, which is really more marketing than it is science, and I had the chance to "debate" this on air last night.

Unfortunately, the debate ended before I was able to adequately respond to some of Ericsson's claims, and so this is a post to do just that - respond, put the sports science side of the debate across.  I address the article to Ericsson somewhat tongue-in-cheek, and I don't mean to appoint myself on behalf of sports science, but the truth is that someone has to point out that the books, the popular media, and Ericsson are misrepresenting the evidence (either deliberately or ignorantly).  And besides, Ericsson did ask in the radio interview (see below).

The debate was a glorious seven minutes long (I was told it would be much longer), and it involved two opportunities for Ericsson to state his case, and two for me to try to explain the physiology of elite athletes.  Going in, I was under the impression we would debate the points, but that never really happened, mostly because I didn't think it was going to be cut short at 7 minutes.

You can listen to the podcast here.  Just click "Listen Now"  The interview portion starts at 9:00, as the section before is an interview with Chrissie Wellington (this provides some context for some of my comments in my first response).

A stunned reaction

I was, throughout the interview, stunned at what I was hearing.  And it's not as though I'm new to this particular debate - I've recently written two review articles on this topic with a colleague of mine (a geneticist, because unlike Anders Ericsson, I don't like the idea of commenting about a field that I'm not an expert in - he's a psychologist, but he was throwing physiology around with abandon, as you'll hear and read later).  These articles will be published in peer reviewed journals later this year, I'll let you know when.  There are also the two articles (PART I and PART II) that I wrote here on The Science of Sport last year, and then I presented on this at the UK Sports and Exercise Medicine conference in London last November.

I have also read the books - Bounce, Outliers, so in theory, I've heard it before.  But I was just absolutely stunned that Ericsson was saying some of the things he did - you can hear this in my reaction in the podcast as I start my response to both questions!  What he says is just ludicrous, empty and baseless, and I can only think he's misinformed, or has some other agenda to push.  Maybe he is writing a book...

Truth is, you don't even need research, you just need common sense and a tiny bit of experience with elite athletes in training groups.  For example, if any of you have ever run with a training group, you have seen and felt the reality of "individual responses" to training - you know that 1,000 hours of identical training will not produce an identical result in ten different people.  There are examples all over the place that show that practice is not sufficient for elite performance, and there are as many examples of athletes who have succeeded on far, far less than this (there are even cases in chess, where, dare I say it, performance is a little less complex because there's no risk of overtraining, injury, etc)

Also, Ericsson's theory that it is the training done during the adolescent years that matters is not only wrong (look how many talented young athletes fail at senior level despite accumulating far more hours than their peers by the age of 18, and how many endurance athletes only take up the sport in their 20s and become world class in a few years despite zero training when adolescents), it's also very irresponsible, because it compels parents, teachers and coaches to start training young athletes too soon and that's detrimental to the person (see Cote et al for review).

The statements - no scientific evidence showing that genes or physiology limit performance?

In his second response in the podcast, Ericsson makes the following statement in response to my argument that the scientific evidence suggests without doubt that elite athletes and champions are BORN AND MADE:

"I would argue here, and reading all the reviews, and we've had reviews where every scientist from the exercise physiology field and sports psychology.  And I find it kind of remarkable that Ross is making these claims because I've never seen them made in print in any peer-reviewed publication" - 13:55 in the podcast

He goes on to say the following:

"I have to say that I'd be very interested to see Ross finding any scientific studies that support the kind of claims that he was making at the beginning of the programme" - 15:01 in the podcast

Outside the scope of knowledge - don't tread where you shouldn't unless you have a guide

Before I continue, just have to mention that I have provided a list of peer reviewed publications as references at the end of this post - they are both review articles (Ericsson made the claim that he's read ALL the reviews - clearly he's missed these ones), and they are original research studies that show the importance of genetic factors and physiological variability to training.  Clearly, he's never read these either.  I'd excuse this on the basis that Anders Ericsson is a psychologist, so one would not expect him to have a firm grasp of sports science, performance,  physiology and the genetic literature, but the fact of the matter is that he's making claims in those fields, so therefore it's fair-game to challenge his knowledge and understanding of the literature and the sports science performance fields.

And I must just make this point - I don't for a second think we should create intellectual "silos" where you can ONLY comment on your field.  I think that would be foolish because it's the process of thinking, the scientific approach to a question that matters more than the actual content.  And I'd like to think that the biggest advances in our understanding often come from thinking outside the "constraints" of what we know, and by integrating research from different fields by different experts.

In other words, someone may be trained as a physiologist, but it's their application of the scientific approach, allied to some small physiological understanding, that may allow them to contribute to the field of biomechanics of barefoot running, for example.  Key to this are two things:  a)  you must be dilligent about doing your research, and b) find someone who IS an expert to assist.  That's why when in writing review articles on talent vs training and elite performance, I partnered with a geneticist (Malcolm Collins) who does understand the field at the depth that is required to put scientific statements out there.  Similarly, I'm now doing barefoot running research, where my interest is the physiology, but we have a team that includes an engineer and biomechanist, so that I don't have to tread where I'm not capable.

Responding to Ericsson - one example of "limited" physiological response to training

In any event, during the debate, I tried to respond to Ericsson, and there are four things that I think are essential to understand here:

1.  It is true that genetic "proof" has yet to be provided.  But elite sporting performance is too complex, and genetic factors too varied to ever "prove" the link Ericsson seems to require.  Consider this:  height is a pretty straightforward characteristic, and it's known to be highly heritable (tall parents = tall children).  In fact, 80% of the variance in height is known to be genetic.  However, studies have found that it takes an astonishing 300,000 genetic variants to account for only 45% of this variance.  That's just height - how much more then would it take to explain something as complex as sports performance? 
   
   The reality is that the field of genetics is young, and with time, more evidence will emerge.  But there's a massive difference between something being "proven" (where I agree with Ericsson) and saying that it is absent (which is what he implies).  Genetic evidence is not absent - most physiological factors that are known to limit performance have been associated with genes (including injury risk, aerobic capacity, muscle fiber type), and others can be easily related to heritable factors (think height for basketball, limb proportion, bone mass etc).  When Ericsson suggests there is no evidence, it is because he is ignorant of the evidence.
2.  Ericsson's own work disproves his theory - his studies have tried to explain performance level as a function of training, yet research he has been involved in shows that only a very small part of performance can be explained by practice.  Only 28% of the variance darts performance is explained by the number of hours practiced!  That's astonishingly low, and it means that time spent in practice is a very poor predictor for performance.  The question you should be asking is what accounts for the other 72%, and could some of it be innate?  It's definitely enough to throw out the deliberate practice, 10,000 hour theory, because Ericsson is clearly predicting that most (or all, in some of his articles) of performance is explained by training.
3. You cannot prove that practice is necessary AND sufficient to produce champions or elite performers based on retrospective studies.  They're weak, because there are so many other ways to explain the findings.  For example, Ericsson's famous violin study showed that the expert performers did the most practice, and he concluded that the practice turned them into experts.  However, it's equally possible, in this study design anyway, that the children with the innate violin ability were encouraged by others and their own success to practice more.  Retrospective studies are poor ways to show that practice makes perfect.  You have to do prospective studies.
4. Prospective studies have been done.  Most notably, Bouchard published a study in 2011 (reference below) in which he found that the response in VO2max (a measure of aerobic capacity and adaptation to training, and ultimately performance) of a large cross-section of the population to a standardized training programme was enormously varied.  Some individuals improved by less than 5%, others improve by 30%.  And here's the key point - it is possible, using genetic techniques, to identify which genetic polymorphisms (think of them as variants of genes) are responsible for this huge difference. 
   
   It turns out that Bouchard's work has provided some pretty important findings:
• About 50% of an individual's starting VO2max and 50% of the "trainability" in VO2max is heritable
• 21 Genetic polymorphisms have been associated with 50% of the training response to VO2max
• If a person carries NINE OR FEWER of these genetic variants, then they are low responders and improve VO2max by only 200 ml/min.
• If a person carries NINETEEN OR MORE of these variants, then they are high responders and improve VO2max by over 600 ml/min

In other words, genetic factors very clearly impact on what we start with, they impact on how we adapt to training, and therefore, by extension, they impact on where we get to.  Our physiology has a ceiling, and it is in part, genetically determined.  This is clear for VO2max, and it's clear for other factors that are known to affect performance.  Skeletal muscle, for example, is known to be BOTH heritable and trainable.  Running economy differs enormously between individuals as a function of factors such as height, limb proportions, muscle mass, and other factors that are known to be genetically determined.  And so unless you have the right skeletal structure, height, limb proportions, you cannot have the running economy required to run a 2:08 marathon.  It's physiology, limiting performance, and related to factors that we are born with.  To deny this is to say that the world is flat when you are staring out of a spaceship window at the globe.

Further, as one of you commented on Facebook yesterday, biomechanical factors such as the muscle's moment arm exert huge effects on performance, and so characteristics that we are born with determine the level of performance that we can attain.  I would point out the most obvious example of this is basketball, but there are countless others.

A pointless polarization of the debate

Ultimately, however, the idea that elite sporting performance can be explained by one factor is foolish.  That's why when Ericsson makes the claims he does, in the field of physiology, it's so absurd, and potentially damaging because people believe it at face value, and they implement sports systems and strategies that buy into this flawed concept.   It's quite clear, from what we observe in athletes, what we study in laboratories, what we know from geneticists, that there is a significant contribution of all kinds of factors to performance.  Physiology matters, but so does practice.  Psychological factors are crucial, but so too are financial and economic considerations.

The dominance of Kenyan runners, for example, will never be found to be due to ONE factor.  Those who are looking solely at genes are doomed to failure, but so are those who want to say that it's purely an altitude, diet, socio-economic, lifestyle, or incentive-driven phenomenon.  All these factors contribute, and the environment interacts with the genes to produce a champion.  I've said this before, but training should be defined as the realization of genetic potential.

Every single person improves as a result of training - some, as Bouchard has shown, improve by very little in a variable like aerobic capacity.  Perhaps they are better suited to skill-based sports.  Some improve enormously, and those who do are more suited to endurance sport.  Then there is injury - this is vital and completely overlooked.  We know that certain genes are associated with different performance characteristics, and there are genes that are associated with injury.  Some people will never even reach 10,000 hours because they are susceptible to injury at five hours per week of training and cannot do more - they'd need 40 years to get good enough if that's all it took.

So the point I'd like to conclude with, before I list some more points for Anders Ericsson to consider, is that we should not polarize the debate.  We should recognize that there are many paths to elite performance, and that a one-size or one-number fits all approach is foolish.  We should learn what we can from those who succeed, including that they are dedicated and practice a lot, which is obvious.  And we should learn why people fail.  And we should avoid generalizations and simplifications that help us sell books to motivate people.

Training is the realization of genetic potential - practically, that means that every single one of you reading this, discussing this, can improve through training.  That's the motivation. But will we all become Olympic caliber athletes in any sport we choose?  Keep dreaming.  The world is not flat, Prof Ericsson.  Please stop telling people it is...

Ross

By way of an "Appendix", here is a little more on Ericsson's views, because I don't want to take him out of context in a 7 minute radio interview...

Ericsson's first entry into this field was his work looking at skill acquisition in activities such as music - his seminal study of violinists showed that expert performers engaged in at least 10,000 hours of training whereas those violinists judged merely as "good" or "average" did about 8,000 and 5,000 hours respectively.

But then Ericsson moved beyond education and skill acquisition and began to tackle sport.  He wrote a review article in the New York Academy of Sciences Journal in 2009, in which he states the following:

"the distinctive characteristics of exceptional performers are the result of adaptations to extended and intense practice activities that selectively activate dormant genes that are contained within all healthy individuals’ DNA." - Ericsson et al, NYAS, 1172: 199-217, 2009

So in other words, he is now going to tackle genetics.  He is saying (and I want to be careful here about taking this out of context), that exceptional performers become exceptional because they practice, and this training activates dormant genes, and these genes are present in ALL healthy individuals' DNA - his word, my emphasis.

Right, so this is fine, if he sticks to "performance" in skill-based activities.  I would disagree with him - studies on chess show clearly that some people get good very quickly, others never improve to Master level no matter what they do.  The same is true of darts, tennis, golf, any activity.  But nevertheless, let's assume that he is referring to his study on musicians and things like mathematical ability.

But he doesn't stop there. He then tackles physiology, and writes the following in the same paper:

"From this evidence it would appear that VO2max/kg (aerobic capacity) would not be a good candidate for a factor that was constrained by heredity"

This comes from a section in the paper where Ericsson, a psychologist, tackles the PHYSIOLOGY of elite performance and comes to this incredible conclusion that there is no evidence that aerobic capacity is constrained by genetic factors.  If you read the study, you will discover that Ericsson arrives at this conclusion based on THREE studies - one review, and two other studies, one of which actually finds the opposite to what he concludes. 

I have explained above the recent work that shows clearly that genetic factors influence VO2max, and admittedly, this review precedes that series of studies.  But there were still others that had found a) huge inter-individual differences between people in response to the same training and b) accounted for large parts of VO2 as being heritable.

Ericsson's approach to the physiology side of this argument is simply not good enough when physiologists can cite dozens of physiological systems or factors that are known to affect performance, and when geneticists can show associations between these systems and our genes.

And yes, I agree that this area is not yet developed - it's so "young" a field that it will take time to understand the genetic complexity.  But even here, there's a difference between something being absent and something being proven.  Neither side will "prove" their argument, but I think it's pretty clear that evidence shows conclusively that BOTH genes and training make champions.

And finally, here are some references that Ericsson may have missed:

Duffy L, Baluch B. Dart performance as a function of facets of practice amongst professional and amateur men ana women players. Int J Sport Psychol. 2004;35:232-245.

Vaeyens R, Güllich A, Warr CR et al. Talent identification and promotion programmes of Olympic athletes. J Sports Sci. 2009;27:1367-80.

Elferink-Gemser MT, Jordet G, Coelho-E-Silva MJ et al. The marvels of elite sports: how to get there? Br J Sports Med. 2011;45:683-4.

Phillips E, Davids K, Renshaw I et al. Expert performance in sport and the dynamics of talent development. Sports Med. 2010;40:271-83.

Huijgen BC, Elferink-Gemser MT, Post WJ et al. Soccer skill development in professionals. Int J Sports Med. 2009;30:585-91.

Gobet F, Campitelli G. The role of domain-specific practice, handedness, and starting age in chess. Dev Psychol. 2007;43:159-72.

Gibbons T, Hill R, McConnell, A. et al. The path to excellence: A comprehensive view of development of U.S. Olympians who competed from 1984-1998 United States Olympic Committee. 2002.

Baker J, Côté J, Deakin J. Expertise in Ultra-Endurance Triathletes Early Sport Involvement, Training Structure, and the Theory of Deliberate Practice. J Appl Sport Psychol. 2005;17:64-78.

Oldenziel K, Gagne F. Factors affecting the rate of athlete development from novice to senior elite: How applicable is the 10-year rule. Athens 2004: Pre-olympic Congress Sport Science Through the Ages: Challenges in the New Millennium. Athens. 2004.

Hodges NJ, Starkes JL. Wrestling with the nature of expertise: A sport specific test of Ericsson, Krampe and Tesch-Römer’s (1993) theory of “deliberate practice”. Int J Sport Psychol. 1996;27:400-24.

Helsen WF, Starkes JL, Hodges NJ. Team sports and the theory of deliberate practice. J Sport Exerc Psychol. 1998;20:12-34.

Bullock N, Gulbin JP, Martin DT et al. Talent identification and deliberate programming in skeleton: ice novice to Winter Olympian in 14 months. J Sports Sci. 2009;27:397-404.

Roescher CR, Elferink-Gemser MT, Huijgen BC et al. Soccer endurance development in professionals. Int J Sports Med. 2010;31:174-9.

Vaeyens R, Lenoir M, Williams AM et al. Talent identification and development programmes in sport : current models and future directions. Sports Med. 2008;38:703-14.

Tucker R, Collins M. Athletic performance and risk of injury - Can genes explain all? Dialog Cardiovasc Med. In Press.

Collins M, Raleigh SM. Genetic risk factors for musculoskeletal soft tissue injuries. Med Sport Sci. 2009;54:136-49.

Gaining muscle and losing fat at the same time: If I can do it, anyone can

The idea of gaining muscle and losing fat at the same time seems impossible because of three widely held misconceptions: (a) to gain muscle you need a calorie surplus; (b) to lose fat you need a calorie deficit; and (c) you cannot achieve a calorie surplus and deficit at the same time.

Not too long ago, unfortunately I was in the right position to do some self-experiments in order to try to gain muscle and concurrently lose fat, without steroids, keeping my weight essentially constant (within a range of a few lbs). This was because I was obese, and then reached a point in the fat loss stage where I could stop losing weight while attempting to lose fat. This is indeed difficult and slow, as muscle gain itself is slow, and it apparently becomes slower as one tries to restrict fat gain. Compounding that is the fact that self-experimentation invariably leads to some mistakes.

The photos below show how I looked toward the end of my transformation from obese to relatively lean (right), and then about 1.5 years after that (left). During this time I gained muscle and lost fat, in equal amounts. How do I know that? It is because my weight is the same in both photos, even though on the left my body fat percentage is approximately 5 points lower. I estimate it to be slightly over 12 percent (on the left). This translates into a difference of about 7.5 lbs, of “fat turning into muscle”, so to speak.

A previous post on my transformation from obese to relatively lean has more measurement details (). Interestingly, I am very close to being overweight, technically speaking, in both photos above! That is, in both photos I have a body mass index that is close to 25. In fact, after putting on even a small amount of muscle, like I did, it is very easy for someone to reach a body mass index of 25. See the table below, from the body mass index article on Wikipedia ().

As someone gains more muscle and remains lean, approaching his or her maximum natural muscular potential, that person will approach the limit between the overweight and obese areas on the figure above. This will happen even though the person may be fairly lean, say with a body fat percentage in the single digits for men and around 14-18 percent for women. This applies primarily to the 5’7’’ – 5’11’’ range; things get somewhat distorted toward the extremes.

Contrast this with true obesity, as in the photo below. This photo was taken when I was obese, at the beach. If I recall it properly, it was taken on the Atlantic City seashore, or a beach nearby. I was holding a bottle of regular soda, which is emblematic of the situation in which many people find themselves in today’s urban societies. It reminds me of a passage in Gary Taubes’s book “Good Calories, Bad Calories” (), where someone who had recently discovered the deliciousness of water sweetened with sugar wondered why anyone “of means” would drink plain water ever again.

Now, you may rightfully say that a body composition change of about 7.5 lbs in 1.5 years is pitiful. Indeed, there are some people, typically young men, who will achieve this in a few months without steroids. But they are relatively rare; Scooby has a good summary of muscle gain expectations (). As for me, I am almost 50 years old, an age where muscle gain is not supposed to happen at all. I tend to gain fat very easily, but not muscle. And I was obese not too long ago. My results should be at the very low end of the scale of accomplishment for most people doing the right things.

By the way, the idea that muscle gain cannot happen after 40 years of age or so is another misconception; even though aging seems to promote muscle loss and fat gain, in part due to natural hormonal changes. There is evidence that many men may experience of low point (i.e., a trough) in their growth hormone and testosterone levels in their mid-40s, possibly due to a combination of modern diet and lifestyle factors. Still, many men in their 50s and 60s have higher levels ().

And what are the right things to do if one wants to gain muscle and lose fat at the same time? In my next post I will discuss the misconceptions mentioned at the beginning of this post, and a simple approach for concurrently gaining muscle and losing fat. The discussion will be based on my own experience and that of several HCE () users. The approach relies heavily on individual customization; so it will probably be easier to understand than to implement. Strength training is part of this simple strategy.

One puzzling aspect of strength training, from an evolutionary perspective, is that people tend to be able to do a lot more of it than is optimal for them. And, when they do even a bit more than they should, muscle gain stalls or even regresses. The minimalists frequently have the best results.

The “pork paradox”? National pork consumption and obesity

In my previous post () I discussed some country data linking pork consumption and health, analyzed with WarpPLS (). One of the datasets used, the most complete, contained data from Nationmaster.com () for the following countries: Australia, Brazil, Canada, China, Denmark, France, Germany, Hong Kong, Hungary, Japan, Mexico, Poland, Russia, Singapore, Spain, Sweden, United Kingdom, and United States. That previous post also addressed a study by Bridges (), based on country-level data, suggesting that pork consumption may cause liver disease.

In this post we continue that analysis, but with a much more complex model containing the following country variables: wealth (PPP-adjusted GNP/person), pork consumption (lbs/person/year), alcohol consumption (liters/person/year), obesity (% of population), and life expectancy (years). The model and results, generated by WarpPLS, are shown on the figure below. (See notes at the end of this post.) These results are only for direct effects.

WarpPLS also calculates total effects, which are the effects of each variable on any other variable to which it is linked directly and/or indirectly. Two variables may be linked indirectly, through various paths, even if they are not linked directly (i.e., have an arrow directly connecting them). Another set of outputs generated by the software are effect sizes, which are calculated as Cohen’s f-squared coefficients. The figure below shows the total effects table. The values underlined in red are for total effects that are both statistically significant and also above the effect size threshold recommended by Cohen to be considered relevant (f-squared > 0.02).

As I predicted in my previous post, wealth is positively associated with pork consumption. So is alcohol consumption, and more strongly than wealth; which is consistent with a study by Jeanneret and colleagues showing a strong association between alcohol consumption and protein rich diets (). The inclusion of wealth in the model, compared with the model without wealth in the previous post, renders the direct and total effects of alcohol and pork consumption on life expectancy statistically indistinguishable from zero. (This often happens when a confounder is added to a model.)

Pork consumption is negatively associated with obesity, which is interesting. So is alcohol consumption, but much less strongly than pork consumption. This does not mean that if you eat 20 doughnuts every day, together with 1 lb of pork, you are not going to become obese. What this does suggest is that maybe countries where pork is consumed more heavily are somewhat more resistant to obesity. Here it should be noted that pork is very popular in Asian countries, which are becoming increasingly wealthy, but without the widespread obesity that we see in the USA.

But it is not the inclusion of Asian countries in the dataset that paints such a positive picture for pork consumption vis-à-vis obesity, and even weakens the association between wealth and obesity so much as to make it statistically non-significant. Denmark is a wealthy country that has very low levels of obesity. And it happens to have the highest level of pork consumption in the whole dataset: 142.6 lbs/person/year. So we are not talking about an “Asian paradox” here.

More like a “pork paradox”.

Finally, as far as life expectancy is concerned, the key factors seem to be wealth and obesity. Wealth has a major positive effect on life expectancy, while obesity has a much weaker negative effect. Well, access to sanitation, medical services, and other amenities of civilization, still trumps obesity in terms of prolonging life; however miserable life may turn out to be. The competing effects of these two variables (i.e., wealth and obesity) were taken into consideration, or controlled for, in the calculation of total effects and effect sizes.

The fact that pork consumption is negatively associated with obesity goes somewhat against the idea that pork is inherently unhealthy; even though pork certainly can cause disease if not properly prepared and/or cooked, which is true for many other plant and animal foods. The possible connection with liver problems, alluded to in the previous post, is particularly suspicious in light of these results. Liver diseases often impair that organ’s ability to make glycogen based on carbohydrates and protein; that is, liver diseases frequently lead to liver insulin resistance. And obesity frequently follows from liver insulin resistance.

Given that pork consumption appears to be negatively associated with obesity, it would be surprising if it was causing widespread liver disease, unless its relationship with liver disease was found to be nonlinear. (Alcohol consumption seems to be nonlinearly associated with liver disease.) Still, most studies that suggest the existence of a causal link between pork consumption and liver disease, like Bridges’s (), hint at a linear and dose-dependent relationship.

Notes

- Country-level data is inherently problematic, particularly when simple models are used (e.g., a model with only two variables). There are just too many possible confounders that may lead to the appearance of causal associations.

- More complex models ameliorate the above situation somewhat, but bump into another problem associated with country-level data – small sample sizes. We used data from 18 countries in this analysis, which is more than in the Bridges study. Still, the effective sample size here (N=18) is awfully small.

- There were some missing values in this dataset, which were handled by WarpPLS employing the most widely used approach in these cases – i.e., by replacing the missing values with the mean of each column. The percentages of missing values per variable (i.e., column) were: alcohol consumption: 27.78%; life expectancy: 5.56%; and obesity: 33.33%.

Doping in cycling: Science, the law, and PR, and insights on Pistorius

The complexity of science vs law vs PR: Implications for anti-doping and Oscar Pistorius

I've finally emerged from the "bubbles" that were the trip up Kilimanjaro, which was followed almost immediately by a trip to the USA where I spent a week with the SA Sevens team for the IRB Series tournament in Las Vegas.  It was another harsh reminder that the best preparation and hardest work can sometimes fail because on the day, things don't work and other teams are better...competitive sports is a ruthless world!

In any event, those tournament weeks are always something of a "bubble", inside which I miss many interesting sports stories.  The jet lag and 9 hour time difference don't help, but the bubble has finally burst and I thought I'd share one or two links, and some short insights on stories that have broken since late January.

Cycling and doping: Three big stories

To begin with, three big stories in the world of cycling and doping.  In no particular time order, Jan Ullrich, Tour de France champion and many time runner-up was sanctioned by the Court of Arbitration for Sport, and all his results since 2005 annulled.  He of course retired years ago, so the two year ban is more symbolic than practical, but it ends one of cycling's more high-profile chapters.  Ullrich, for his part, responded on his website, apologizing for his dealings with Fuentes, but not entirely accepting the court's opinion.  You can read his full statement here.

This decision was preceded by perhaps an even more significant one - the US Attorney Andre Birotte Jr announced that the federal investigation into Lance Armstrong would be ending.  The announcement was strategically timed to garner as little media coverage (in the USA) as possible, coming the Friday before the Superbowl.  There has however been some reaction to it, mostly dealing with the timing of the decision (the investigation had been a 2-year long struggle up to this point), and perhaps more importantly, the legal vs ethical issues surrounding doping.

Those who have kept up with the case will be well aware that doping in sport is not a federal crime.  As a result, the investigation was not about whether Armstrong doped or not, it was built predominantly around fraud, conspiracy and other charges related to the violation of Armstrong's team's contract with the U.S. Postal Service.  Those who are proclaiming "innocence" are thus choosing to stop short of the point, at least as far as doping goes.

The result of this is that the federal investigation may have been dropped because of a simple balance between "cost" (time and financial) and "reward".  There has been no explanation for why the investigation has been dropped, and nor is there likely to be, leaving most to speculate and wonder what the reasons are.  One of the outcomes is that the ball is now firmly in the court of the anti-doping authorities, such as USADA, who can continue to pursue the case of doping against Armstrong.

There were reports, since confirmed, that USADA had been in contact with the investigators to gain access to the evidence they had collected as part of the criminal case.  Travis Tygart, the CEO of USADA, issued this statement following the US Attorney General's annoucement:

"Unlike the U.S. Attorney, USADA's job is to protect clean sport rather than enforce specific criminal laws. Our investigation into doping in the sport of cycling is continuing and we look forward to obtaining the information developed during the federal investigation."

Time will tell whether that evidence is forthcoming, and how it is acted upon, but certainly, the announcement that the investigation was ending is not the same thing as drawing a line under the issue.

Sports Illustrated yesterday carried this article expanding on the USADA investigation, for those interested in reading more.

The Contador verdict

Then third, and most relevant to cycling today (since he is the only active cyclist of the three), was the decision, finally, of CAS on the Alberto Contador case.  The end result of over two years of deliberations and court proceedings, protestations, accusations and cow slandering?  Four thousand pages of argument and counter-argument, a 98-page verdict, and a two year ban for Contador, which is really only 6 months, because it has been backdated to when the case began.

There are far better summaries of this case than I can provide in a short time.  For perhaps the best, read Matt Rendell's excellent analysis here.  He tackles issues of strict liability, the Contador defence (veal solomillo and clenbuterol, at 32 Euros a kilogram, apparently), and the UCI/WADA argument, which was built around the likelihood that the clenbuterol came from the infusion of plasma as part of Contador's alleged blood doping during that Tour de France.

This defence is particularly intriguing to me, because it has evidence supporting it, and therefore can be "proved" (in so far as "proof" seems to exist in these cases).  That evidence was described by Prof Michael Ashenden, one of the leading biological passport scientists.  He reported to CAS that Contador's reticulocyte percentages during the race were abnormally high, which would be indicative of EPO use, because that switches on red blood cell formation (for more on the biological passport, reticulocytes and how all this works, read this article that I wrote last year).  Contador's hemoglobin levels were also abnormal, compared to his biological passport history, leading the UCI, WADA and Ashenden to suggest that they were consistent with blood doping.

Ultimately, the CAS tribunal ruled that a blood transfusion was "very unlikely to have occurred", though I'm not sure why they came to this strong decision.  It was perhaps related to the biological passport's own internal requirements for a "strike", which I explained previously.  It was Ashenden's testimony, the legal back-and-forth it caused, its ultimate dismissal and then the CAS judges refusal to allow Ashenden to have a final session of questioning that was leaked to the press after the hearing, so unhappy were the WADA/UCI lawyers.

The legal battle - how law undermines the openness of science

All of which brings me to my main opinion/insight on these matters.  The fact of the matter is, as anti-doping becomes more sophisticated, it becomes more and fraught with the burden of scientific "proof". The reality is that science is open, it asks questions and only answers some of them!  It is rarely black or white, and the problem with this is that legal teams, armed with scientific experts of their own, can always, without fail, cast doubt on scientific findings.

To me, the findings of abnormal reticulocytes and hemoglobin concentration points very strongly to a likelihood of transfusion.  It doesn't prove it - the biological passport cannot prove anything in that way, and it has been designed like this to protect cyclists against false positive tests.  However, it points there, and so when a tribunal, dealing with the same evidence I'm seeing, concludes that a transfusion is "very unlikely to have occurred", I'm left mystified at their thought-processes.  At worst, they can conclude that a "transfusion is possible, but cannot be upheld given the physiological complexity of blood parameters".  But to dismiss it as "very unlikely"...?

A declaration like that ("very unlikely to have occurred") is definitive, it is black and white.  Science is grey, and so the two, science and law, seem to be very uneasy bedfellows.

Science being picked off, one by one, by the law

I have long held this opinion.  It began as a healthy skepticism of lawyers, and it was the Oscar Pistorius-CAS decision that pretty much condemned me to have zero confidence in the manner in which the law evaluates scientific evidence.  That decision was, to be blunt, a complete joke, and the CAS was manipulated by Hugh Herr and the rest of the Pistorius team, because they were able to exploit scientific "uncertainty" to win a legal verdict.

Then yesterday I read this absolutely brilliant piece by Lionel Birnie, in which he explains how the law attacks science and undermines it exactly because it is open.  As I was reading it, I found myself thinking "This piece could just as well have been written for the Pistorius case".  Science's greatest strength is its weakest point in anti-doping cases (and in cases like those of Pistorius).  Here is a section of Birnie's piece, which I highly recommend:

It seems that a lot of people love to put their faith in the law and yet are sceptical about science. The law is man-made (and therefore flawless) whereas what we know about science keeps changing (and therefore cannot be trusted). This applies to sport just as it does to many areas of life.

Science is attacked for its greatest strength – the fact that it cannot prove or disprove everything. Science is exploratory. It is open-minded and willing to accept that there may be another possibility, however slim the idea may seem. Science is never so arrogant as to presume it knows everything. 

When dealing with anti-doping cases, the law is exploitative in the sense that it seeks out areas where science is on shaky ground. It looks for loopholes and unpicks them ruthlessly. You could argue that science sees the dots and tries to work out how they are connected, while the law picks them off one by one. 

We have seen in many anti-doping cases how the defence lawyers work through the argument line by line, clause by clause, trying to prove or disprove.  And that is why we end up with such division among sports fans who are struggling to work out who the good guys are and who are the baddies.

So many jewels in that piece alone - "Science is open-minded and willing to accept that there may be another possibility", and "science cannot prove or disprove EVERYTHING".  Case in point - dehydration and performance.  There are scientists who maintain that any dehydration will compromise your performance.  There are others who argue that we can lose 2 to 8% of our fluid with no negative effects, and they cannot reconcile those two opinions, as simple a question as this may appear.  Science is full of areas of contention, and doping is perhaps one of the most complex.  The case of Pistorius is equally complex - the evidence certainly pointed to an advantage, but clever scientists, backed by even smarter lawyers, are able to "pick them off one by one".

Direction dependent - the verdict depends on who gets the final scientific "disproving" say

Therefore, what we had with Pistorius was a case of science trying to "join the dots" and create a picture that he had an advantage, while others picked off those points to cast doubt on this finding.  The key is to realize that this could have happened in either direction.  That is, it could have been designed in a way that said that Pistorius was clear to compete unless the IAAF could show that an advantage existed.  Then, the IAAF would have had the initiative and would have been able to cast doubt on evidence suggesting there was no advantage.  As it was, the question was asked in the other direction - the starting point was that Pistorius had an advantage, and this could be disproved (legally) by picking off the evidence.

Therefore, the decision you arrive at depends entirely on the direction from which you approach it, at least in terms of how the science is evaluated.  In anti-doping, this start point is determined by the concept of "strict liability" - the athlete has to show that the positive test was not the result of doping.  For Pistorius, the burden was with the IAAF to prove that the advantage existed, and so Pistorius' team were able to deflect every scientific finding with enough doubt to get the verdict, despite the scientific evidence (which didn't meet CAS' legal standard, clearly, though there were other factors in play here too)

Enter public relations

Lionel Birnie's great insight didn't end there, however.  He also recognized that it is a third party, Public Relations, that ultimately wields the biggest stick in cases like these.  He writes:

The court of public opinion is where the phoney war is fought. Over the past 18 months, while science and the law have been carefully preparing their arguments for serious scrutiny, the public are teased along as if they’re watching a Punch and Judy show.

PR is flashy. It comes up with catchy phrases that capture the public imagination and it wins hearts and closes off minds.  It is hardly surprising that most people will be turned off by the idea of wading through pages of legal and scientific argument. It is difficult, it strays well outside our areas of understanding and it makes our brains hurt.

Once again, this is so accurate for the Contador case, it's accurate for Armstrong, it's accurate for Pistorius (thanks Nike and about a dozen other sponsors).  Last year, Prof Peter Weyand, one of the researchers who did join the dots to see the advantage Pistorius had, wrote to me after I published his explanation of his research, and expressed frustration at how the general public do not want to wade through the complexities of the scientific argument.

He is 100% correct.  I share this frustration, and when I read drivel like the recent Outside magazine or New York Times pieces on Pistorius, it's tremendously frustrating because one half of the scientific team (Herr) are making idiotic claims that have no basis in evidence or reality, while the other half (Weyand) are being circumspect and scientifically cautious.  Public relations looks at this with glee, because it's so easy to back the extreme view, however false or inaccurate it may be.  That fuels the fire, and the public are watching, to borrow Birnie's phrase, "a Punch and Judy show".

The general public, and therefore the general media who cater to them, do not want to peel back layer after layer of scientific explanation to truly understand a case.  They want simple answers, black and white, and science is incapable of providing them.  PR, on the other hand, thrives on simple answers.  Backed by legal complexities, it's not difficult to see why so many people are confused, and therefore choose to hear one message over another without necessarily understanding it.

The fight against doping - the danger of crippling complexity

So, for doping, there is a real problem.  Anti-doping is becoming so complex that it may end up crippling itself in the court of law.  The more dots there are to join (the role of science), the more points there are to attack (the role of law).  The end result is that the cost of prosecution will sky-rocket, it will become increasingly difficult to enforce test results, and the public, ultimately the "paying" customer, will be turned off by the complexity.  Enter the PR firms.

There is an anti-doping future, then, which exists on the internet and is waged by PR firms and athletes, who build mountains for anti-doping authorities to climb.  All of this is a call to action, though it beats me what the solution might be.

The end result for Contador is that he'll be able to race in the Vuelta this year.  We still don't know if he did anything wrong - having dismissed the Contador argument of contaminated beef, having dismissed the UCI/WADA argument of a blood transfusion, the CAS tribunal ends up concluding that a "contaminated supplement" is more likely the source, and therefore grounds for a ban (read part 2 of Rendell's excellent analysis for more on this).

Contador's results have been annulled, and so Andy Schleck is your Tour de France champion from 2010 (a hollow victory).  It could have been worse, of course - I was actually surprised that CAS did reach the decision it did, I fully expected Contador to be cleared, and so perhaps there is some hope left.  Whether such a long, and expensive process, changes the anti-doping game in the future remains to be seen.  Your thoughts welcome, and I realize that there is so much to the verdict and the argument that I haven't covered, but I highly recommend Rendell's pieces, both Part 1 and Part 2 on the judgment.

 The "upside down" VO2max protocol

The other interesting story, one that has garnered some great discussion on our Twitter account, is the recent study that found that VO2max is increased when you do a reverse protocol that starts out at a high power output and decreases (as opposed to the normal progressive increase to fatigue).  The implication of this finding is that the VO2 "max" concept is incorrect, which is something many already knew, but it calls into question the idea that oxygen delivery or use is limiting during maximal exercise. After all, if VO2max can be increased and then maintained simply by doing something different, despite maximal effort, then how was it the limitation in the first place?  The implication of your answer to this question is rather important!

The study is therefore a hook for the idea that something else regulates performance, though it doesn't establish precisely what that is.  There is the suggestion that the brain is in control, and that's so obvious many people will dismiss it as "too easy".  But there are many reasons to suggest this, and I'll cover these in a blog post as soon as I can.  The bottom line, regarding this study at least, is that it's fairly obvious, and not as outrageous as it may seem.  But the reaction of people who see it tells the story of sports science and the VO2max theory, which has long been full of holes, but remains entrenched among many as the explanation for maximal performance.  This is akin to proclaiming that the world is flat.  Someone has to point out that it is round, and as obvious as this may be (the idea that the brain is command is equally obvious), this study adds to that realization.

Perhaps even more important are the implications of this.  People make the incorrect leap that it's about "mind over matter". The idea that the brain controls exercise is not the same as saying that our mental capacities determine performance.  This is obvious.  It's not "mind" over matter, but "brain" over matter - it's still physiology, so let's not get too carried away with the idea that we can "believe" ourselves into being elite athletes.  Certainly, psychology is crucial, and belief is essential, but the physiological limits still exist, and the regulation of performance is still physiological!  Can we do more with the right mental approach?  Of course, but that's a parallel area of performance management.

More to come...

Ross