Triglycerides, VLDL, and industrial carbohydrate-rich foods

Below are the coefficients of association calculated by HealthCorrelator for Excel (HCE) for user John Doe. The coefficients of association are calculated as linear correlations in HCE (). The focus here is on the associations between fasting triglycerides and various other variables. Take a look at the coefficient of association at the top, with VLDL cholesterol, indicated with a red arrow. It is a very high 0.999.

Whoa! What is this – 0.999! Is John Doe a unique case? No, this strong association between fasting triglycerides and VLDL cholesterol is a very common pattern among HCE users. The reason is simple. VLDL cholesterol is not normally measured directly, but typically calculated based on fasting triglycerides, by dividing the fasting triglycerides measurement by 5. And there is an underlying reason for that - fasting triglycerides and VLDL cholesterol are actually very highly correlated, based on direct measurements of these two variables.

But if VLDL cholesterol is calculated based on fasting triglycerides (VLDL cholesterol  = fasting triglycerides / 5), how come the correlation is 0.999, and not a perfect 1? The reason is the rounding error in the measurements. Whenever you see a correlation this high (i.e., 0.999), it is reasonable to suspect that the source is an underlying linear relationship disturbed by rounding error.

Fasting triglycerides are probably the most useful measures on standard lipid panels. For example, fasting triglycerides below 70 mg/dl suggest a pattern of LDL particles that is predominantly of large and buoyant particles. This pattern is associated with a low incidence of cardiovascular disease (). Also, chronically high fasting triglycerides are a well known marker of the metabolic syndrome, and a harbinger of type 2 diabetes.

Where do large and buoyant LDL particles come from? They frequently start as "big" (relatively speaking) blobs of fat, which are actually VLDL particles. The photo is from the excellent book by Elliott & Elliott (); it shows, on the same scale: (a) VLDL particles, (b) chylomicrons, (c) LDL particles, and (d) HDL particles. The dark bar at the bottom of each shot is 1000 A in length, or 100 nm (A = angstrom; nm = nanometer; 1 nm = 10 A).

If you consume an excessive amount of carbohydrates, my theory is that your liver will produce an abnormally large number of small VLDL particles (also shown on the photo above), a proportion of which will end up as small and dense LDL particles. The liver will do that relatively quickly, probably as a short-term compensatory mechanism to avoid glucose toxicity. It will essentially turn excess glucose, from excess carbohydrates, into fat. The VLDL particles carrying that fat in the form of triglycerides will be small because the liver will be in a hurry to clear the excess glucose in circulation, and will have no time to produce large particles, which take longer to produce individually.

This will end up leading to excess triglycerides hanging around in circulation, long after they should have been used as sources of energy. High fasting triglycerides will be a reflection of that. The graphs below, also generated by HCE for John Doe, show how fasting triglycerides and VLDL cholesterol vary in relation to refined carbohydrate consumption. Again, the graphs are not identical in shape because of rounding error; the shapes are almost identical.

Small and dense LDL particles, in the presence of other factors such as systemic inflammation, will contribute to the formation of atherosclerotic plaques. Again, the main source of these particles would be an excessive amount of carbohydrates. What is an excessive amount of carbohydrates? Generally speaking, it is an amount beyond your liver’s capacity to convert the resulting digestion byproducts, fructose and glucose, into liver glycogen. This may come from spaced consumption throughout the day, or acute consumption in an unnatural form (a can of regular coke), or both.

Liver glycogen is sugar stored in the liver. This is the main source of sugar for your brain. If your blood sugar levels become too low, your brain will get angry. Eventually it will go from angry to dead, and you will finally find out what awaits you in the afterlife.

Should you be a healthy athlete who severely depletes liver glycogen stores on a regular basis, you will probably have an above average liver glycogen storage and production capacity. That will be a result of long-term compensatory adaptation to glycogen depleting exercise (). As such, you may be able to consume large amounts of carbohydrates, and you will still not have high fasting triglycerides. You will not carry a lot of body fat either, because the carbohydrates will not be converted to fat and sent into circulation in VLDL particles. They will be used to make liver glycogen.

In fact, if you are a healthy athlete who severely depletes liver glycogen stores on a regular basis, excess calories will be just about the only thing that will contribute to body fat gain. Your threshold for “excess” carbohydrates will be so high that you will feel like the whole low carbohydrate community is not only misguided but also part of a conspiracy against people like you. If you are also an aggressive blog writer, you may feel compelled to tell the world something like this: “Here, I can eat 300 g of carbohydrates per day and maintain single-digit body fat levels! Take that you low carbohydrate idiots!”

Let us say you do not consume an excessive amount of carbohydrates; again, what is excessive or not varies, probably dramatically, from individual to individual. In this case your liver will produce a relatively small number of fat VLDL particles, which will end up as large and buoyant LDL particles. The fat in these large VLDL particles will likely not come primarily from conversion of glucose and/or fructose into fat (i.e., de novo lipogenesis), but from dietary sources of fat.

How do you avoid consuming excess carbohydrates? A good way of achieving that is to avoid man-made carbohydrate-rich foods. Another is adopting a low carbohydrate diet. Yet another is to become a healthy athlete who severely depletes liver glycogen stores on a regular basis; then you can eat a lot of bread, pasta, doughnuts and so on, and keep your fingers crossed for the future.

Either way, fasting triglycerides will be strongly correlated with VLDL cholesterol, because VLDL particles contain both triglycerides (“encapsulated” fat, not to be confused with “free” fatty acids) and cholesterol. If a large number of VLDL particles are produced by one’s liver, the person’s fasting triglycerides reading will be high. If a small number of VLDL particles are produced, even if they are fat particles, the fasting triglycerides reading will be relatively low. Neither VLDL cholesterol nor fasting triglycerides will be zero though.

Now, you may be wondering, how come a small number of fat VLDL particles will eventually lead to low fasting triglycerides? After all, they are fat particles, even though they occur in fewer numbers. My hypothesis is that having a large number of small-dense VLDL particles in circulation is an abnormal, unnatural state, and that our body is not well designed to deal with that state. Use of lipoprotein-bound fat as a source of energy in this state becomes somewhat less efficient, leading to high triglycerides in circulation; and also to hunger, as our mitochondria like fat.

This hypothesis, and the theory outlined above, fit well with the numbers I have been seeing for quite some time from HCE users. Note that it is a bit different from the more popular theory, particularly among low carbohydrate writers, that fat is force-stored in adipocytes (fat cells) by insulin and not released for use as energy, also leading to hunger. What I am saying here, which is compatible with this more popular theory, is that lipoproteins, like adipocytes, also end up holding more fat than they should if you consume excess carbohydrates, and for longer.

Want to improve your health? Consider replacing things like bread and cereal with butter and eggs in your diet (). And also go see you doctor (); if he disagrees with this recommendation, ask him to read this post and explain why he disagrees.

Sports Science 2011: Talent vs training and Oscar P

Sports science in the media in 2011: Training, talent, doping and Oscar Pistorius

So yesterday was Day 1 of the fantastic UKSEM conference in London.  I gave a presentation on Sports Science in 2011, and that presentation is embedded in the post below.  I am a terrible judge of my own presentations, so I'll just say that mine went OK and hope that it did.  I always know instantly all the things I haven't explained clearly, when I was clumsy, when I repeated myself and when the point I was trying to make didn't quite come off!  But hopefully you can read quietly what I spoke about and it is better than the "live performance"!

I covered some of the more topical stories of the year, but given that I only had 30 minutes, I had to pick three, and they were:

1. The Kenyan dominance of the marathon, which provided a nice lead in to the training vs talent debate
2. Doping in cycling, in the context of how doping control changes doping behaviour
3. Oscar Pistorius, and the scientific cover-up and hatchet job he and his band of "scientists" got away with

The presentation again lacks my voice-over - I may at some stage do a "voice-over" when I have more time, but for now, it should suffice as a read through.  Below, I elaborate on part of the talk (the talent vs training part.  I may, in the future, do the same for the Pistorius section).

Email subscribers click here to be taken to site to view presentation. All others, click on the grey button, wait for loading, then hover over "More" and click "Fullscreen"

Sports Science in 2011 UKSEM on Prezi

The 10,000 hour concept

The biggest talking point, at least in the discussion I had with delegates afterwards, was the Training vs Talent debate (the first part of the talk).  Here, the only reason I included this was because I saw that Matthew Syed who wrote the book "Bounce" was on the programme after me, and his talk was called "The Science of Success".  So I decided that it would be good to have a little bit of science on the topic, because he doesn't provide it in support of his "training-sufficiency" position.

Effectively, Syed's thesis is this:  Genes and talent are over-rated, and great performers, whether they are sportsmen, doctors, musicians or businessmen, achieve expert performance not because of genetic factors or "talent", but because they accumulate enormous volumes of deliberate practice.  He has a few examples of this, and makes a compelling case, at least on the surface.

But when you really interrogate what he is saying, then you realise that the reality is that he is saying that in order to succeed at something at the highest level, to become an expert performer, you need to practice.  OK then... nobody should be surprised at this, and nor would they be.  The problem is that his (and Gladwell's) position seems to exist outside of a world where genetic factors also have an influence, and it's this exclusivity in his thinking that forces a closer look.

Unnecessarily polarizing the complexity of performance by ignoring genes and talent

So the issue is not that they advocate hard work and a lot of training, it is that they downplay the importance of talent or innate ability.  I emphasized this in my own talk, but it bears repeating - if Syed is correct, and the secret to success is training and accumulating many years and hours of practice, then Talent ID is a waste of time and money.  We should rather spend that money on getting 100 more children to train, because they should all (or most) become champions, provided they get through the required hours.

Note that this also completely overlooks the fact that children tend to do what they are good at, and that simply running a child through a "10,000 hour factory" is an imagined concept only.  I guess the real question is why are some children good at something almost within the first moments that they start it, thereby encouraging them to do it more?  It seems to me that this could be an innate difference too...

In a competitive sport, training is obviously a crucial determinant of success

But the theory that practice is important is so obvious it doesn't need emphasis.  As soon as you have competition, then within a narrow range of individuals (the top 10 tennis players, or the Olympic finalists, for example), training will become a crucial determinant of who wins and loses.

In "a small pond", where there is no competition, it's possible to succeed with talent alone.  Just think back to school level athletics, when there's no competition, a young athlete can show up on the day and dominate to win.  But the higher the level, the better the competition, the more important training becomes.  And those individuals who get attempt to by on talent alone are washed away in this more competitive landscape.  Syed made this point, and of course he's correct.  But the key is that the athlete who succeeds all the way to the Olympic podium is the one who dominated without training (that is, he's talented or genetically gifted), but then also trained incredibly hard to stay a champion as the competition intensified.  In otherwords, he has BOTH talent and training. 

In fact, I challenged him on this after his talk, and basically made the point that if he had walked into that venue today, with 200 people in the audience, and asked them to please raise their hands if they thought that sporting success was ENTIRELY genetic, he would have been the only person with his hand in the air.  He may have been laughed out the room had he tried to propose that the current belief is that success is all genetic.  Everyone knows that it is not.

Yet he seems to have arrived at this belief that someone out there believes that expert performance is achieved solely on the basis of genes and natural talent. Now, maybe I missed this in my studies, but I have not once heard this theory.  The established theory in sports science is that many, many years of training are required to hone and refine skills and physiology in order to become a world or Olympic champion.  The reality is that sports science does NOT believe that it's ALL in the genes, and nor do they believe that it's all about training.  So the first problem with the 10,000 hour concept is that it attacks a straw man that need not exist.

To polarize the debate the way that he (and others, most notably Malcolm Gladwell) have done is unnecessary, and it has quite important financial and policy implications for where money should be spent by sports federations and coaches to help improve performance.  Their books and emphasis are not without merit, certainly - they have emphasized how important it is that we recognize that not all young aspirant athletes develop equally, and that we may need to consider how coaching is provided to more children to prevent some from falling through the cracks.  But sports science already knew this.  What these books have done is spawn a theory that now says that practice is sufficient for expert performance, which it clearly is not.

The work of Elferink-Gemser, who presented today after Syed, confirmed this, because she has been studying the progress of young sportspeople for 10 years, and has found large differences between children in terms of how they respond to training sessions and coaching.  But more important, she finds that it is possible to predict which children will become professional within the first few years of them entering the sports academy.  In other words, by the time children are 15 or 16, there are already differences between those who will become "great" and those who are merely "good".  It has little to do with accumulating the "magical 10,000 hours".   The mere fact that these young athletes have such different responses to training tells you that you can't generalize potential performance to a group, and that the outcome of training will also differ between individuals.

The three 'failings' of the 10,000 hour, "practice is sufficient" model

I think there are three key points about this 10,000 hour concept:

Firstly, if you can find ONE case of an exception, then you have disproved the "rule".  That is, if you can find a guy who trains 10,000 hours but doesn't succeed, then you have shown that it's not sufficient.  Or, if you can find a guy who trains only 5,000 hours, but who does succeed, then you have shown that it is not necessary.

And the truth is that both of these cases exist, everywhere.  Baker has shown it in triathlon, it has been found in chess (so it's not only "physiological" sports where innate ability seems to matter), and it has been found in football, wrestling, field hockey, skeleton.  Every single sport has examples of athletes who have shot to the top within a few years of starting the sport, and it is littered with athletes who fail despite doing 20,000 hours.  Today I spoke with a woman whose husband taught music for a school for gifted musicians in New York, and they discover children who within months of starting are playing at near-professional expert levels.  Now, unless those children have managed to get 10,000 hours of training in in one hour (by discovering how to slow down time), they have achieved expertise well before the theoretical minimum.

There's no question that talent, or innate ability, or genetics, play a role.

The second point is that there is no good evidence at all to suggest that 10,000 hours is required for expert performance.  The study that is always cited is a violin study, which found that expert violinists had accumulated an AVERAGE of 10,000 hours by the time they went to music school, whereas those who were merely good had done 8,000 hours.  Two problems.  First, you can't infer cause from this kind of retrospective study.  Who is to say that the talented, genetically gifted violinists didn't train more BECAUSE they had more talent from the age of 8?  Perhaps their innate ability was the catalyst to get them more practice (mom sends them for lessons, and they enjoy it).  And secondly, the study showed absolutely no indication of ranges or variance.  So we don't know whether there are some people who became experts with less training, and nor do we know whether some failed despite doing their 10,000 hours, because the author did not show that data.  I hope I don't have to emphasize that if either of these people exist, then the theory is wrong.

Which brings me to the third point about this theory - it is entirely unfalsifiable.  To the "evangelists" who proclaim that anyone can become an expert if they just practice enough, it's too easy and too convenient to simply dismiss the exceptions because they clearly didn't practice in the right way.  So if someone has done 25,000 hours and has not succeeded, then they simply say "He obviously didn't practice the right way".  Or if someone becomes an expert in only 3,000 hours (which happens, all the time), they say "He must have compressed his 10,000 hours into a third of the time".

So it's a completely unfalsifiable theory.  It cannot be proven, and it cannot be disproven.  Therefore, it does not belong in science.

What the science does say - "responders" and "non-responders"

What does belong in science are studies that look at how different individuals have been shown to adapt to training.  And sure enough, those studies exist, though Gladwell and Syed would never admit to them.  The Heritage study, for example, took hundreds of unrelated people and gave them standardized training programmes, and then measured the responses.

The result?  A complete spectrum, ranging from those who show absolutely no response to training, all the way to those who improve by more than 40% as a result of training.  And as expected by the scientific theory, the difference between these people can very reliably be linked to genetic factors.  Specifically, there are Single Nucleotide Polymorphisms (SNPs) which account for half this training resopnse.  Individuals who have 9 or fewer of the identified 21 SNPs are the "low-responders", whereas people who have 19 or more of these SNPs are "high responders".

The answer therefore is that it's not about having different genes, it could also be about having different variants of the same gene, the result being that you and I show completely different responses to training.  And you have to ask yourself, if you are a coach, would you rather have an individual who is a "high responder" or a "low responder"?  And more importantly, if you have $100,000 to invest in a sport, where do you spend it to find a champion?  On talent ID, to find those "high responders", or do you believe that anyone can succeed if you just spend the money to help them all do 10,000 hours of training?  In terms of policy, it's clear that the science, at least for this physiological variable, points you in the direction of finding the right people to spend the money on.  And that means understanding the value of genetic factors to performance.

And just to dispel the idea that skill-based activities benefit more from training, when you look at studies in chess, you find that there is a massive difference in the time taken to reach Master level - some do it in 3,000 hours, some have been at it for 25,000 hours and counting.  In darts, 15 years of practice (almost 15,000 hours) only accounts for 28% of the variability in performance.  In otherwords, 72% of the difference in performance between two players cannot be explained by the hours spent training.  In darts...

In sport, countless studies show that elite athletes get to the top within 6,000 hours of starting their sport, and the success of Talent ID programmes proves that talent transfer (something that is impossible if the 10,000 hour theory is correct) exists. 

Conclusion - training is the realization of genetic potential

The bottom line is that a theory of deliberate practice gives us one important message - if you want to succeed, practice.  Coaches around the world breathe a sigh of relief, you're not redundant.  But this is so obvious, I guess the reminder is always good though.

But the application of this theory, and the dismissal of genes that it somehow seems associated with, is a huge oversimplication and wrong, at least for sports.  Syed today argued about school performance, and about how teachers should downplay the idea that some children are more "talented" with numbers or better at mathematics than others.  And that's fine, because whatever helps people improve is great.  But if we're in the business of finding Olympic champions, then this theory has no place in its polarized form.

Not only this, but it could be extremely damaging.  If you take it literally, and you buy into a 10,000 hour concept, then you'll be obliged to start training a child at the age of about 10, because you need them to become world-class in their early-20s.  All good and well, except the evidence shows quite clearly that the earlier you start intensive training, the LESS likely you are to succeed.  And so there are all kinds of implications for how we manage children's sport participation.

The ultimate conclusion, in my opinion (and as always, I welcome your views), is that training is nothing more than the realization of genetic potential.  Without both, you will not become an Olympic champion (in a competitive sport, that is).  Training will improve everyone, and so everyone should be encouraged to train.  But genetic factors determine where we start, how we respond to training (trainability), how much training we can tolerate before burnout or injury (because let's face it, chess players rarely get injuries that force 6-week layoffs, like stress fractures), and finally, where the "performance ceiling" exists.

Training will get you to your ceiling, you'll realize your genetic potential.  But will it win you a medal?  Only if you chose your parents right!

Ross

P.S.  For a more detailed discussion of these issues, please do read the previous two articles I wrote on the subject:

1. A look at the 10,000 hour concept.  What does it say, and why it fails to pass the test of validity
2. The evidence for how genes influence elite sporting performance

Barefoot running: An overview

Barefoot running presentation: Overview of the science 

So last night, at the Sports Science Institute of South Africa where I'm based, I gave a presentation on barefoot running, aimed at the public.  A big topic, obviously, always guaranteed to pull a good crowd and generate lively debate.  Which it did.

It's a topic I've covered in great detail before on this site, with approaches ranging from a look at the evidence for shoes, to the findings of the latest barefoot running research.  I fly to London tonight for the UKSEM conference, where I'll be chairing a debate on running injuries (among other talks), and which will probably be one of the highlights of the meeting, since it includes Daniel Lieberman and Benno Nigg, both of whom have done research on this subject.  So there'll be more to come from that, no doubt.

But for today, I just wanted to share with you the presentation that I did last night.  It will lack the sound and my explanations, of course, but most of it should be fairly self-explanatory.  For those who want to read through a more detailed description, you can read the article I wrote after the ACSM meeting earlier this year - most of the concepts covered in the presentation below are also described in that article.

I would say that the three key points about this whole debate are:

Evidence linking the mechanics to the injury outcome still lacking

There is as yet no conclusive evidence that either proves or disproves the benefits of shoes or barefoot running, or links the mechanical characteristics of barefoot running to a reduced risk of injury.  That is, for all the work showing how impact forces and loading rates are reduced when barefoot, it remains to be proven that this leads to lower injury rates.  I began last night's talk by saying that this was the first time a "scientific" presentation would be given with so little conclusive scientific evidence!  There are plenty of theories, of course, and some are sound, but we await the real evidence for the injury and performance side of the debate, which will come from long-term, prospective studies.

Recognize that running barefoot may be a skill and that people acquire skills at different rates (or not at all)

The evidence so far suggests that barefoot running produces some potentially beneficial changes, mostly related to how running form and kinetics are altered without shoes.  However, it also points to a potentially large group of people who, when running barefoot, may have increased risk of injury, especially early on - these are the people who continue to heel-strike when barefoot, and who may "force" a forefoot landing, leading to huge strain on the calf muscle and Achilles tendons.

The key point is that barefoot running (and thus running in general) should be recognized as a SKILL, and it is clear that we do not all have the ability to acquire skills equally.  Those who do not may be substantially worse off, and require much longer to make the adjustments.  Whether they should even try is a good question.

The issue however is not necessarily whether barefoot running is "good for you", but rather whether barefoot running helps us understand anything about how we run that might help us reduce injury risk.  If barefoot running provides these answers for a given runner, then of course it would be enormously beneficial.  But it may be that simply learning about barefoot running helps runners in shoes just as much!

It's also vital to recognize that huge differences may exist between individuals:  some adapt very quickly to minimalist shoes or barefoot running - these people are the "responders" and they tend to go on to become "evangelists" who tell everyone to throw away their shoes!  At the other extreme, however, are non-responders, who, for reasons unknown, will battle to run without "traditional shoes".  In both cases, we have to be careful about generalizing the "extreme" observation to the general population.  That's the mistake shoe companies made when telling everyone they needed all manner of gadgets in their shoe, and it's a mistake that people now make when advocating barefoot running.

We do not fully understand why some people adapt faster than others.  The studies required in the future need to assess how biomechanical and neuromuscular changes are learned and relearned when running barefoot, and then to establish whether this impacts on injury risk.  Those will come, in time.

Worth a try, or inclusion into training. But respect the length of the investment: Change management

In terms of advocacy, I believe that barefoot running will help most runners.  It may be as part of a training programme where barefoot running helps with adaptation because it loads the joints differently, activates muscles in different patterns and therefore provides a good training impulse. For some, barefoot running (or minimalist shoes) will go on to become the "only way".  For others, it will remain a training technique, and that's fine too.  But I'd certainly look at incorporating it, just for the training adaptations it provides.

The key, as mentioned in #2 above, is to recognize that going from shoes to either minimalist shoes or barefoot is a skill and involves a significant change.  Therefore, it's essential to respect the time that it will take to fully adapt to the different loading stresses associated with running either barefoot or in minimalist shoes.  I've given an illustration of a programme in the presentation, where I've 'budgeted' 12 weeks to build up to 40 minutes of solid running (plus 2 to 4 weeks of preparation).  Some people may take even longer than this - the question that has to be asked then is whether it's worth it?  Is a 6-month intervention worth the benefit, when the benefit hasn't yet been clearly established?  I doubt it.

Nevertheless, if you're sold on the idea of giving it a try, recognize that you're making a long-term investment, and that if you simply continue your normal training barefoot, you're pretty much guaranteed to get injured!

More to come in the future, I am sure.  Looking forward to meeting Lieberman for a few runs along the Thames, and we will be discussing the future research that needs to be done!

Until then, enjoy the presentation below!  Again, click the grey arrow, hover over "More" and click "Fullscreen".  Email subscribers click here to visit the site to view presentation

Ross

Barefoot running on Prezi

Read the previous article on barefoot running with more detailed explanations of the topics covered in the presentation

My transformation: How I looked 10 years ago next to a thin man called Royce Gracie

The photos below were taken about 10 years ago. The first is at a restaurant near Torrance, California. (As you can see, the restaurant was about to close; we were the last customers.) I am standing next to Royce Grace, who had by then become a sensation (). He became a sensation by easily defeating nearly every champion fighter that was placed in front of him. In case you are wondering, Royce is 6’1” and I am 5’8”. The second photo also has Royce’s manager in it – that is his wife. Their children’s names both start with the letter “K”. I wonder how big they are right now.

I think that at the time these photos were taken I weighed around 200-210 lbs. Even though I am much shorter than Royce, I outweighed him by around 40 lbs. Now I weigh 150 lbs, at about 11 percent body fat, and look like the photo on the top-right area of this blog - essentially like a thin guy who does some manual labor for a living, I guess. A post is available discussing the "how" part of this transformation (). I only put a shirtless photo here after several readers told me that my previous photo looked out of place in this blog.

My day job is not even remotely related to fitness instruction. I am a college professor, and like to think of myself as a scholar. I don’t care much about my personal appearance; never did. At least in my mind, putting up shirtless photos on the web should not be done gratuitously. If you are a fitness instructor, or an athlete, that is fine. In my case, it is acceptable in the context of telling people that a few minutes of mid-day sun exposure, avoiding sunburn, yields 10,000 IU of skin-produced vitamin D, which is about 20 times more than one can get through most "fortified" industrial foods.

Royce is such a nice guy that, after much insistence, he paid for the dinner, and then we drove to his house and talked until about midnight. He had told me of a flight the next morning to Chicago, so I ended the interview and thanked him for the wonderful time we had spent together. I had to talk him out of driving ahead of me to I-405; he wanted to make sure I was not going to get lost at that time of the night. This was someone who was considered a demigod at the time in some circles. A humble, wonderful person.

Royce helped launch what is today the mega-successful Ultimate Fighting Championship franchise (), which was then still a no holders barred mixed martial arts tournament. At the time the photos were taken I was interviewing him for my book Compensatory Adaptation, which came out in print soon after (). The book has a full chapter on the famous Gracie Family, including his father Helio and his brother Rickson.

I talked before about the notion of compensatory adaptation and how it applies to our understanding of how we respond to diet and lifestyle changes (). In this context, I believe that the compensatory adaptation notion is far superior to that of hormesis (), which I think is interesting but overused and overrated.

The notion of compensatory adaptation has been picked up in the field of information systems, my main field of academic research. In this field, which deals with how people respond to technologies, it is part of a broader theory called media naturalness theory (). There are already several people who have received doctorates by testing this theory from novel angles. There are also several people today who call themselves experts in compensatory adaptation and media naturalness theory.

The above creates an odd situation, and something funny that happened with me a few times already. I do some new empirical research on compensatory adaptation, looking at it from a new angle, write an academic paper about it (often with one or more co-authors who helped me collect empirical data), and submit it to a selective refereed journal. Then an "expert" reviewer, who does not know who the authors of the paper are (this is called a "blind" review), recommends rejection of the paper because “the authors of this paper clearly do not understand the notion of compensatory adaptation”. Sometimes something like this is added: “the authors should read the literature on compensatory adaptation more carefully, particularly Kock (2004)” - an article that has a good number of citations to it ().

Oh well, the beauty of the academic refereeing process …

UCT Research in 2011: Wrap-up

Wrapping up the 2011 academic year: UCT/ESSM research cocktail party conversation topics

Such a busy time recently, hence the big gap between posts!  I am off to London tomorrow for the UKSEM Conference, where I will be presenting three talks.  The first is on Sports Science in the media in 2011, where I'll tackle the topical stories of the year (Oscar Pistorius, doping in cycling and the Kenyan marathon dominance and the genetics vs training debate).

The second is on the fallacy and oversimplification of the 10,000 hour concept, because I saw from the conference programme that Matthew Syed of "Bounce" fame would be presenting a talk called "The Science of Success", and I feel it's important to at least counter this with some science....

That is, to present the proper scientific view of the role of genes in performance, because unlike Syed has said, genes do not play little to no role in performance, and it is definitely not "all about the training" (for more on this, you can read the posts I wrote back in August this year).

And then the third talk will be on doping and the limits to performance.  I guess it's topical again now, with the "sub-2 hour marathon debate" once again opening up, albeit very prematurely.  If you followed the Tour de France coverage on site these last few years, you'll also be aware of the idea that there is a physiologically believable performance limit, and that's the topic of the third talk at UKSEM.

Then I'm also going to chair a round-table discussion on running injuries, which features Daniel Lieberman (of barefoot running fame) and Benno Nigg (biomechanics guru), among others.  As a matter of fact, I'm giving a presentation tonight at the Sports Science Institute of SA on barefoot running, which I'll share with you as soon as it is done.

So all in all, UKSEM should provide plenty of fodder for the site in weeks to come.  Assuming I can find the time to post!

ESSM 2011: The academic year ends

But for today, I just wanted to do a recap of the year in research at the University of Cape Town, where I am jointly employed.  The unit is the Exercise Science and Sports Medicine research unit (ESSM for short), and last week, we held our annual year-end function.  This is a function where all those eager and interested "guinea-pigs" who have volunteered to be studied as part of our research get to come for a finger-dinner and listen to a few presentations on our research.  It's just feedback and information, mostly to say thank you for their time (and blood, sweat, tears and occasional muscle sample), but also to get sports science out, to translate it in a way that makes it more accessible.

My mission from the evening has always been to give each person one item of "cocktail party conversation".  That is, next time they're at a social event, whatever it is, they need to be able to say "Hey, I heard about this really interesting stuff being studied at Sports Science, where they're looking at..."

So my presentation on the evening was to summarize what the ESSM Unit had been doing in 2011.  Consider that we have about 40 people involved in research at a time, and that's no easy task - it means effectively trying to summarize 40 years of research, assuming each person has had a productive year, into a 30 min presentation!

But below is that presentation.  I created a mock-up newspaper, with "articles" featuring some of the research areas, and then I did a short interview with the relevant scientist responsible.  Each "interview" was 2 to 3 minutes long, where they elaborated on their work, a few questions, and then moved on as I took the audience through the "newspaper".

There's no sound, unfortunately, so the detail is absent.  But this is really just a filler and to showcase some fo the work that the unit is responsible for.  It doesn't get nearly enough air-time, in most instances.

Enjoy, and speak to you again from London!

Ross

P.S. Presentation may take a while to load.  Just click the grey "play" arrow, hover your cursor over "More" and click "Full-screen".


Oh, and if you get this in an email, please CLICK HERE to be taken to the site where you can watch the presentation

ESSM feedback 2011 on Prezi

The China Study II: How gender takes us to the elusive and deadly factor X

The graph below shows the mortality in the 35-69 and 70-79 age ranges for men and women for the China Study II dataset. I discussed other results in my two previous posts () (), all taking us to this post. The full data for the China Study II study is publicly available (). The mortality numbers are actually averages of male and female deaths by 1,000 people in each of several counties, in each of the two age ranges.

Men do tend to die earlier than women, but the difference above is too large.

Generally speaking, when you look at a set time period that is long enough for a good number of deaths (not to be confused with “a number of good deaths”) to be observed, you tend to see around 5-10 percent more deaths among men than among women. This is when other variables are controlled for, or when men and women do not adopt dramatically different diets and lifestyles. One of many examples is a study in Finland (); you have to go beyond the abstract on this one.

As you can see from the graph above, in the China Study II dataset this difference in deaths is around 50 percent!

This huge difference could be caused by there being significantly more men than women per county included the dataset. But if you take a careful look at the description of the data collection methods employed (), this does not seem to be the case. In fact, the methodology descriptions suggest that the researchers tried to have approximately the same number of women and men studied in each county. The numbers reported also support this assumption.

As I said before, this is a well executed research project, for which Dr. Campbell and his collaborators should be commended. I may not agree with all of their conclusions, but this does not detract even a bit from the quality of the data they have compiled and made available to us all.

So there must be another factor X causing this enormous difference in mortality (and thus longevity) among men and women in the China Study II dataset.

What could be this factor X?

This situation helps me illustrate a point that I have made here before, mostly in the comments under other posts. Sometimes a variable, and its effects on other variables, are mostly a reflection of another unmeasured variable. Gender is a variable that is often involved in this type of situation. Frequently men and women do things very differently in a given population due to cultural reasons (as opposed to biological reasons), and those things can have a major effect on their health.

So, the search for our factor X is essentially a search for a health-relevant variable that is reflected by gender but that is not strictly due to the biological aspects that make men and women different (these can explain only a 5-10 percent difference in mortality). That is, we are looking for a variable that shows a lot of variation between men and women, that is behavioral, and that has a clear impact on health. Moreover, as it should be clear from my last post, we are looking for a variable that is unrelated to wheat flour and animal protein consumption.

As it turns out, the best candidate for the factor X is smoking, particularly cigarette smoking.

The second best candidate for factor X is alcohol abuse. Alcohol abuse can be just as bad for one’s health as smoking is, if not worse, but it may not be as good a candidate for factor X because the difference in prevalence between men and women does not appear to be just as large in China (). But it is still large enough for us to consider it a close second as a candidate for factor X, or a component of a more complex factor X – a composite of smoking, alcohol abuse and a few other coexisting factors that may be reflected by gender.

I have had some discussions about this with a few colleagues and doctoral students who are Chinese (thanks William and Wei), and they mentioned stress to me, based on anecdotal evidence. Moreover, they pointed out that stressful lifestyles, smoking, and alcohol abuse tend to happen together - with a much higher prevalence among men than women.

What an anti-climax for this series of posts eh?

With all the talk on the Internetz about safe and unsafe starches, animal protein, wheat bellies, and whatnot! C’mon Ned, give me a break! What about insulin!? What about leucine deficiency … or iron overload!? What about choline!? What about something truly mysterious, related to an obscure or emerging biochemistry topic; a hormone du jour like leptin perhaps? Whatever, something cool!

Smoking and alcohol abuse!? These are way too obvious. This is NOT cool at all!

Well, reality is often less mysterious than we want to believe it is.

Let me focus on smoking from here on, since it is the top candidate for factor X, although much of the following applies to alcohol abuse and a combination of the two as well.

One gets different statistics on cigarette smoking in China depending on the time period studied, but one thing seems to be a common denominator in these statistics. Men tend to smoke in much, much higher numbers than women in China. And this is not a recent phenomenon.

For example, a study conducted in 1996 () states that “smoking continues to be prevalent among more men (63%) than women (3.8%)”, and notes that these results are very similar to those in 1984, around the time when the China Study II data was collected.

A 1995 study () reports similar percentages: “A total of 2279 males (67%) but only 72 females (2%) smoke”. Another study () notes that in 1976 “56% of the men and 12% of the women were ever-smokers”, which together with other results suggest that the gap increased significantly in the 1980s, with many more men than women smoking. And, most importantly, smoking industrial cigarettes.

So we are possibly talking about a gigantic difference here; the prevalence of industrial cigarette smoking among men may have been over 30 times the prevalence among women in the China Study II dataset.

Given the above, it is reasonable to conclude that the variable “SexM1F2” reflects very strongly the variable “Smoking”, related to industrial cigarette smoking, and in an inverse way. I did something that, grossly speaking, made the mysterious factor X explicit in the WarpPLS model discussed in my previous post. I replaced the variable “SexM1F2” in the model with the variable “Smoking” by using a reverse scale (i.e., 1 and 2, but reversing the codes used for “SexM1F2”). The results of the new WarpPLS analysis are shown on the graph below. This is of course far from ideal, but gives a better picture to readers of what is going on than sticking with the variable “SexM1F2”.

With this revised model, the associations of smoking with mortality in the 35-69 and 70-79 age ranges are a lot stronger than those of animal protein and wheat flour consumption. The R-squared coefficients for mortality in both ranges are higher than 20 percent, which is a sign that this model has decent explanatory power. Animal protein and wheat flour consumption are still significantly associated with mortality, even after we control for smoking; animal protein seems protective and wheat flour detrimental. And smoking’s association with the amount of animal protein and wheat flour consumed is practically zero.

Replacing “SexM1F2” with “Smoking” would be particularly far from ideal if we were analyzing this data at the individual level. It could lead to some outlier-induced errors; for example, due to the possible existence of a minority of female chain smokers. But this variable replacement is not as harmful when we look at county-level data, as we are doing here.

In fact, this is as good and parsimonious model of mortality based on the China Study II data as I’ve ever seen based on county level data.

Now, here is an interesting thing. Does the original China Study II analysis of univariate correlations show smoking as a major problem in terms of mortality? Not really.

The table below, from the China Study II report (), shows ALL of the statistically significant (P<0.05) univariate correlations with mortality in 70-79 age range. I highlighted the only measure that is directly related to smoking; that is “dSMOKAGEm”, listed as “questionnaire AGE MALE SMOKERS STARTED SMOKING (years)”.

The high positive correlation with “dSMOKAGEm” does not even make a lot of sense, as one would expect a negative correlation here – i.e., the earlier in life folks start smoking, the higher should be the mortality. But this reverse-signed correlation may be due to smokers who get an early start dying in disproportionally high numbers before they reach age 70, and thus being captured by another age range mortality variable. The fact that other smoking-related variables are not showing up on the table above is likely due to distortions caused by inter-correlations, as well as measurement problems like the one just mentioned.

As one looks at these univariate correlations, most of them make sense, although several can be and probably are distorted by correlations with other variables, even unmeasured variables. And some unmeasured variables may turn out to be critical. Remember what I said in my previous post – the variable “SexM1F2” was introduced by me; it was not in the original dataset. “Smoking” is this variable, but reversed, to account for the fact that men are heavy smokers and women are not.

Univariate correlations are calculated without adjustments or control. To correct this problem one can adjust a variable based on other variables; as in “adjusting for age”. This is not such a good technique, in my opinion; it tends to be time-consuming to implement, and prone to errors. One can alternatively control for the effects of other variables; a better technique, employed in multivariate statistical analyses. This latter technique is the one employed in WarpPLS analyses ().

Why don’t more smoking-related variables show up on the univariate correlations table above? The reason is that the table summarizes associations calculated based on data for both sexes. Since the women in the dataset smoked very little, including them in the analysis together with men lowers the strength of smoking-related associations, which would probably be much stronger if only men were included. It lowers the strength of the associations to the point that their P values become higher than 0.05, leading to their exclusion from tables like the one above. This is where the aggregation process that may lead to ecological fallacy shows its ugly head.

No one can blame Dr. Campbell for not issuing warnings about smoking, even as they came mixed with warnings about animal food consumption (). The former warnings, about smoking, make a lot of sense based on the results of the analyses in this and the last two posts.

The latter warnings, about animal food consumption, seem increasingly ill-advised. Animal food consumption may actually be protective in regards to the factor X, as it seems to be protective in terms of wheat flour consumption ().

The marathon era: A seismic shift and commercial influence

The (r)evolution of the marathon: An unprecedented era

The marathon is in the midst of a quite extra-ordinary and unprecedented era.  This was encapsulated on the weekend by an incredible performance from Geoffrey Mutai in winning the New York Marathon in 2:05:05, breaking the difficult New York course record by an astonishing 2:38.

Only a week earlier, Wilson Kipsang, until then an unheralded name in marathon running, gave Patrick Makau's six-week old world record a real fright by running 2:03:42 in the Frankfurt Marathon.  

Kenyan dominance

But then again, perhaps we should not be surprised at what Kenyan marathon runners are producing this year.  Mutai's victory in New York wraps up the 2011 Major Marathon season, and it means, quite incredibly, Kenyans have won every single major marathon this year.  No exceptions.  They took London, Boston, Paris, Chicago, Berlin, Rotterdam, Amsterdam, New York and the IAAF World Championships in Daegu, Korea.  Even more amazingly, the course records in every single one of the World Marathon Majors has been broken THIS YEAR (all by Kenyans, of course).  The London record fell to Emmanuel Mutai (2:04:40), Moses Mosop won Chicago in 2:05:37, and then Geoffrey Mutai bagged two, first in Boston in that amazing 2:03:02 (admittedly, wind-aided), and then New York this past weekend.

And of course, there was Makau, who took Gebrselassie's world record in Berlin with his 2:03:38.  The world is used to Kenyan dominance in distance events, but not like this.  Looking back, Kenyans have consistently made up more than half of the world's top 20, so it's not too surprising.  However, their "monopoly" has always been broken by the odd Ethiopian, a Moroccan.  They have, to date, been absent in 2011 - Kenyans occupy every one of the top 20 places in the world-rankings, and the highest ranked non-Kenyan this year is Marilson dos Santos of Brazil in 21st place.  The best placed Ethiopian is Bekana Daba, down in 26th (he is also the first non-Kenyan to win a marathon of any significance this year - Houston in 2:07:04).

But in the larger scheme of things, Kenyan dominance aside, the marathon is currently in the midst of a quite remarkable "paradigm shift".  It was less than a decade ago that the world record stood at 2:05:42 (Khannouchi).  Jump ahead, and the average time of the top 10 in the world has been FASTER than this since 2009.  And consider this: having been the fastest time in history until 2002, FIVE men bettered it in 2008, 7 in 2009, 8 in 2010 and 9 in 2011.  The world record from a decade ago would now only just scrape into the top 10.  It is an incredible surge in both quality and depth, the likes of which have not been seen in any event before.

Putting the marathon evolution into context - the stats

To put this into context (and a graphical form), I looked back over the last eleven years of marathon running.  And below is a graph that is pretty heavy on data, but it shows four key stats:

1. The bars represent the AVERAGE time of the top 10 athletes per year
2. The blue diamond shows the world record time coming into the year (as it stood on Jan 1 of that year)
3. The black circle shows the fastest time in each year
4. Below the graph, two numbers - the top one is the number of runners who broke 2:07 and the lower number (in maroon) is the number of Kenyan athletes in the Top 20 of the world rankings

What you are looking at here is a shift in performances, particularly over the last three years.  For example, look at the black circles, showing the fastest time in the world each year - since 2007, the fastest time in the world has been better than the world record as it stood in 2007.  In other words, the performance that would have broken the world record in 2007 is now beaten yearly.  The average time of the top 10 men since 2009 has been at least 20 seconds faster than Khalid Khannouchi's 2003 world record of 2:05:38! 

This exceptional increase in quality has been accompanied by a huge growth in depth - the cumulative number of sub-2:07 performers in 2009, 2010 and 2011 is greater than the preceding eight years.  A typical year used to see between 5 and 10 sub-2:07 performers.  This year, it's 25 already, with 20 and 19 in the preceding years.

And finally, the Kenyan dominance has become, if anything, more dominant.  Marathon running has always been the domain of the east Africans.  The only year in the above graph when east Africans did not dominate was 2001 - back then, five out of the top 10 were Europeans.  However, every year since, Kenya have produced more than half of the top 20, with the bulk of the remaining places filled by Ethiopians.

Interestingly, the Ethiopians are now absent from the top lists.  Three will race in New York this week (Gebremariam, Kebede and Lelisa), but their absence has cleared the way for near-complete monopolization at the top by Kenya.

The progress of the evolution

That dominance aside, and looking at the larger picture, it is interesting to track the seismic shift in marathon running.  Back in 2003, when Paul Tergat broke 2:05 for the first time, it was a taste of what was to come, but interestingly, it didn't produce an immediate change in the way the marathon was raced, as we are seeing now.  Instead, the times in 2004, 2005 and 2006 went back to pre-Tergat days - the number of sub-2:07s declined, average time got slower and nobody came close to threatening 2:05 again.

This first drop then, was the "Tergat-effect", but it would take a few more years for the next kick to happen.  It was inevitably going to come, however, because in the 1990s, the world records on the track were being broken in astonishing amounts by the athletes who would soon move up in distance.  When Tergat and Gebrselassie, the two champions of that track-generation, eventually moved up to the marathon, it was inevitably going to break barriers.

Tergat was the first to succeed, whereas Gebrselassie took longer.  Not that he was unsuccessful, and he topped the yearly lists with amazing consistency.  But the big breakthrough took until 2007, when he ran 2:04:26 to win in Berlin.  That, in hindsight (and one must be careful to find patterns where there are none) was the performance that broke open the flood-gates.

2008 ushers in the "brave new world" with Wanjiru and Geb showing the way

The following year was 2008, and Gebrselassie went even better, breaking the sub-2:04 barrier, and all of a sudden, a host of other runners were breaking 2:06 - call this the "Gebrselassie-effect".  It's not shown on the graph, but in 2007, Gebrselassie was the only man under 2:06 (so the "Tergat-effect" had still not caught on).  In 2008, however, it's a different story - five men broke 2:06, the first time since 2003 that anyone other than Gebrselassie had done it.  Of course 2008 was notable for the breaking of another barrier - 2:04, when Haile Gebrselassie ran his 2:03:59 world record in Berlin.

That Berlin performance was perhaps not even the most significant marathon of 2008.  As long-time friend Jim Ferstle points out in his comment below, it was Sammy Wanjiru's performance in Beijing that may have been the real catalyst for the attitude of marathon runners today.  For it was his courage and fearlessness, on a hot, humid day in Beijing that transformed the marathon from an event that demanded caution to one where aggression would be rewarded.  Jim wrote about this recently, ahead of the Chicago Marathon, and he sums up the "Wanjiru-effect" very nicely.

Remember, no Kenyan had ever won gold in the marathon - it was a glaring omission from the world's dominant distance nation, and Wanjiru responded to that pressure in a manner rarely seen.  Tactics?  Fast and hard early, conditions be damned.  Many would have warned against a suicidal pace early on - Wanjiru seemed to interpret this as a means to kill off any pretenders to gold.  It was, and remains, one of the great dominant marathon performances ever seen.  And so the next time you watch a major city marathon and a Kenyan man is surging at 30km while on world record pace, think of Sammy Wanjiru...

And so perhaps it was the combination of Wanjiru showing the world a new attitude towards marathon running, and Gebrselassie showing it a new target, a 2:03:xx, that lit the way into the era we now find ourselves.  In 2009, we saw two men race head-to-head and run 2:04:27 in Rotterdam, and another six would break 2:06 that year.  So in total, that's EIGHT sub-2:06 performances, and now the floodgates were open and runners, most of the Kenyan, were pouring through it.

Commercial forces: Driving marathon at the expense of track

Kenyan dominance is however nothing new, so something else must be in play to explain why the last three of four years have seen such a shift in the event.  It's still too early to tell if this is just a golden patch (and if we're being fooled into seeing a pattern where there isn't one), or if something real is driving it, but there are some good debates about it.  There's always a good discussion of genetics, training, opportunity and culture when it comes to Kenyan distance running, and that's a debate I will save for another time.  For those who want a taste of what the talent vs training discussion involves, you can read Part 1 and Part 2 of my series on this from a few months ago.

But one of the key factors driving the current shift, I believe, is the growing commercial value of the marathon (for the event organizers, that is). In the past, there was a pretty well-established "pipeline" that led a great runner onto the track for a few years before he turned to the marathon later.  The exception was the athlete who never quite possessed the speed to compete over 10,000m, and who would turn to marathons early.  But this runner had a capacity of maybe 28-min for 10km, and so was always going to battle to run much faster than 2:08.  However, as a result of being in this pathway, the best track runners often stepped up to the marathon when they were 5% beyond their very best, in the twilight of their careers.  The result is that the "experiment" of taking an athlete with 26:30 potential (ala Terget and Geb) and exposing them to marathon training and racing had never really happened.

However, this seems to have changed, and runners with tremendous pedigree are racing marathons much sooner than was the case a decade ago (it would be very interesting to compare the average age of the winners and top 10 of the big six marathons now and in 2000).  The late, great Sammy Wanjiru was perhaps the first to make this move, racing marathons at 20.  He pulled along a host of others, again, mostly from Kenya, who are racing marathons having by-passed that track pipeline.

From a commercial point of view, it's really interesting to consider why the marathon attracts so much money compared to track.  It's mostly dictated by sponsorships and very importantly, who owns the rights to the event.  For potential sponsors, the ability to engage with many thousands of runners (New York had 47,000 for example, who succeeded in a lottery that attracted a staggering 140,000) is superior to taking out what is effectively a billboard at an athletics meeting made of up 100 athletes and 25,000 fans for one night only.

The total exposure and awareness is much higher for a marathon, because the event is self-standing, the focal point of all sponsor advertising, and usually involves much more television coverage and a week-long expo to allow what is called experiential marketing.  Being seen is not enough given the clutter in the 'marketplace' - what sponsors want is to be experienced, and the marathon affords more opportunity to leverage the sponsorship to potential customers.

Allied to this, there may be more focus on health and participation, and also a reduction in the sponsorship spend in general as a result of the economic climate, and the net result is that an event for the masses may appear far more valuable to a potential sponsor than an elite spectator event.  What money is available is funneled to those 'products', and therefore, a relatively smallish event like the Frankfurt Marathon can attract valuable sponsorships.  

It is not for nothing that the sponsorship value of the New York marathon rose 30% in the last year, despite the economic conditions that are negatively affecting sponsorships in many other sports (including athletics).  The marathon, at least at that large scale, remains 'recession-proof'.  The impact all this will have on track and field is another matter entirely.  In the words of one agent, "Track is Usain Bolt and clapping hands", and it has a fight on its hands to sustain interest.

These marathon sponsorships in turn find their way down to athletes in the form of appearance fees (essential, because guaranteed income always trumps potential income, it's a valuable premium) and prize money for successful athletes.

And athletes are not exactly in short supply - the demand to race from within Kenya is extra-ordinary, and for every Kipsang, Mutai or Makau, there could be ten more who COULD produce similar performances given the right support and circumstances.  The end result is that there are now dozens of lucrative opportunities, extreme competition to secure those opportunities, and performance is being driven ever faster as a result.  The culture of the sport in Kenya further tears down barriers, and so more and more athletes are recognizing a) how fast they can run, inspired by others, and b) what riches and rewards await them when they do.

All in all, it's a perfect melting pot, and a possible (and brief) explanation of what may be driving the graph above.  Of course, there's much more to it than this, and the genetic discussion is too good to miss, especially for a sports science point of view.  Perhaps for another time!

Until then, enjoy Kipsang's pursuit of the World Record from Frankfurt, and a really great marathon finish - I agree with the Letsrun.com guys, this is what the sport needs to get even more commercial value!

Ross