Comparative and longitudinal physiology

21 Jul 2015 Posted by
Let’s get right to it, because this is a longish post, on the concept of the measuring physiology and performance data for cycling.  Many people have called for Chris Froome (as the race leader and, to paraphrase Greg Lemond, the most exceptional cyclist we have ever seen) to publish his physiological data, the implicit assumption being that a single VO2max value will validate the performances produced in the mountains, showing that they are or are not feasible.

Unfortunately, it will not be quite this simple.  As an aside, however, lest people wonder why others are not being asked for their physiological data, recall that when Alberto Contador won the stage to Verbier in 2009, that performance inspired the same questions.

And he wasn’t even British…Anyone would (should) be questioned – the history of the sport has spawned skepticism, and performance merely draws attention to it.


Key concepts – mismatches and longitudinal comparisons linked to biology

So why then is it not as simple as validating a performance with a VO2max?  Many reasons, but primary among them are:

  • You’re in danger of setting up a circular argument
  • We aren’t in glaringly obvious mutant/extra-terrestrial/superhuman doping territory, with respects to performance, so the numbers won’t jump out at you as obviously doped

There are some situations in which a mismatch between performance and physiology might be obvious, and would indicate cause for suspicion (without ever being conclusive proof, I want to emphasize).  But the real value of this process thus depends on longitudinal tracking and the ability to compare riders not only to one another, but to themselves over time,  as well as to biology (as in, the passport).

This would enable you to then derive meaning by tracking how the performance changes with the physiology and underlying biology/hematology.  Let’s look at a few examples that may illustrate the pitfalls and work towards a better grasp of what the physiology means.


The illustrative case of Thibaut Pinot

To begin with, let’s use the case of Thibaut Pinot, who has released his data (go figure, transparency does exist), to illustrate a concept.  This is attractive because when you consider the physiological limitations to performance, there are effectively three ‘inputs’ or strings that must be pulled or understood, and Pinot has provided us with one (and a half) of them, so it simplifies the illustration.

The three variables are:

  1. VO2max – the one getting all the attention, but really only a third (if that) of the picture.  This is, for those unsure, the maximum amount of oxygen that the body can use, measured in ml/kg/min.  In the lab, it would be measured with an incremental test to exhaustion, where the cyclist rides at ever-increasing power output until fatigue, and VO2max is the highest recorded value
  2. Efficiency – the source of some controversy, depending how you define it (gross or delta efficiency) it’s easiest understood as the work produced as a function of the work required (metabolic efficiency).  In other words, if a rider is using 2000 kcal/min (you measure this using respiratory gases, based on how much energy a liter of oxygen provides depending on the energy source), but is producing 400 kCal, the rider has an efficiency of 20% (I realize I’m simplifying, but let’s keep this conceptual.  You can read more detail in the links provided)
  3. Sustainable exercise intensity – call it what you wish – any one of LT, FTP, CP – this refers to the percentage of maximum the cyclist can sustain for a given period.  Obviously the period is key – for 20 min, a cyclist might be able to hold 6W/kg, for 45 min, it drops to 5.7W/kg, whereas a 10 min is possible at 6.4 W/kg

It is the combination of 1, 2 and 3 that produce the ultimate W/kg performance.  That should be your first indication that simply publishing a VO2max value alone is not going to provide you with any definitive answers (and this works both ways – it’s neither exonerating or condemning)

Hope you’re still with me.  Let’s consider Pinot, for whom we have the following ‘facts’.

  • VO2max = 85 ml/kg/min (this is high, for a cyclist, but then we’re talking elite)
  • Sustainable power output for 20 min = 6.4 W/kg
  • Sustainable power output for 30 min = 6.1 W/kg
  • Sustainable power output for 45 min = 5.9 W/kg
  • Sustainable power output for 60 min = 5.7 W/kg

So one of the ‘moving parts’, VO2max is known, and so it reduces the assumptions we must make in order to illustrate some other concepts.  Let’s work on the assumption that Pinot has an efficiency of 23%.  This would be reasonable for a cyclist, and even generous for one with such a high VO2max.  Why that proviso?  Because by definition, people who are highly efficient will have a relatively lower VO2max, because they use oxygen efficiently, so even at their maximum work rates, they’re not consuming oxygen like a less efficient cyclist would (the fineprint here is that performance may be limited elsewhere in the ‘chain’ making the relationship at the extreme end of performance non-existent).

To use an analogy, if a car is very fuel efficient, it’s unlikely to burn fuel (energy, for which oxygen is a proxy) like a Bugati Veyron.  This is why, as has been shown in research, there is an inverse relationship between VO2max and efficiency in both professional cyclists, and well-trained cyclists, though other research has not found this relationship across a group of more widely varied physiology (Just a note on the first study mentioned there, the efficiency values were disputed as being too high, and the delta efficiency of a group of cyclists with VO2max > 70 was reported as 21.2% ± 0.5%)

Point is, assuming 23% is pretty favorable for the cyclist, in terms of its implications, as the following may illustrate:

Given Pinot’s maximal capacity of 85 ml/kg/min, and an efficiency of 23%, then using first principles and always making the assumptions for fuel use that produce the lowest physiological implication for the cyclist, you get the following:

  • For 20 min, 6.4 W/kg has an oxygen cost of 79 ml/kg/min, equating to 93% of maximum
  • For 30 min, 6.1 W/kg has an O2 cost of 75.3 ml/kg/min, equal to 88.5% of max
  • For 45 min, 5.9 W/kg has an O2 cost of 72.8 ml/kg/min, equal to 85.5% of max

Note in all these examples, the VO2 is a sub-maximal value, the cost of cycling at that power, with that efficiency.  It can then be related to the max value, as I’ve done, but shouldn’t be confused with the max.

Let’s leave it there, but hopefully you get the idea of how the three variables relate to one another.  If you increase the cyclist’s efficiency, say to 24%, then the oxygen cost at a given workload is reduced, and so the relative intensity (as a % of max) drops.  The corollary to that is that the cyclist would be able to sustain a higher power output at a given % of max.

But I digress.  Let’s take this illustrative case and look at the idea of testing the feasibility of performance based on physiology


Can physiology identify and flag a non-feasible performance?

Let’s now assume that you bring Marco Pantani into your lab.  You already have his performance data – let’s say it’s 6.7 W/kg for 40 min (that was Ferrari’s magic number).  He comes to your lab, with his steel postman’s bike, wearing his wellington boots (because that’s how they rode 15 years ago, apparently, until the light was seen…).

Unknown to you, he also has had the EPO of three men circulating in his bloodstream for a while, and his hematocrit is 57%.  No big deal.

He then produces the following physiological data (hypothetically):

  • VO2max 88 ml/kg/min
  • Efficiency 23% (I’ll keep to this as what I think is reasonable for someone with a high VO2max)

Now, for Pantani to produce 6.7 W/kg will cost him 82.7 ml/kg, which means he is riding at 94% of his max for 40 minutes.  Is that feasible?  I would argue not.  Especially at the end of a 4-hour stage of the Tour.  Instead, the only reason he was able to produce that performance is because doping enabled him to sustain an artificially high relative intensity for that long.

That’s exactly what the research shows incidentally – doped performances to exhaustion at a given power output are significantly better than undoped performances, something that is true for a range of diferent drugs, including huge increases in time to exhaustion at 80% max associated with EPO use.  This means that time-trial performance improves significantly, since the athlete can achieve a higher self-selected workload before they enter the realms of ‘impossibility’.

Very importantly for the performance and doping implication, there is evidence that the improvement in time-trial performance is tightly linked to an increase in hemoglobin and VO2max, but also that time-to-exhaustion increases significantly more than VO2max with prolonged EPO administration.  Why is this important?  Because it highlights that when we look for mismatches, we can look at performance as a function of VO2max and sustainable exercise intensity (#1 and #3 of our variables), and that they don’t necessarily track one another, as the following attempts to explain.


Matching (and mismatching) physiology and performance

This would be first case where the physiology and the performance don’t match.  You can make them match, however.  If for instance his physiological data said:

  • VO2max 90 ml/kg/min
  • Efficiency 24%

then riding at 6.7 W/kg would cost him 79.2 ml/kg/min, and that would be 88% of his maximum.  Is that feasible?  Pinot is able to ride at 86% for 40 minutes, so it’s not a stretch.  The stretch is the physiological inputs, and the combination of that VO2max with that efficiency, and the fact that he’d need to be at that workload for 40 min at the end of a Tour stage.  I’m not buying, but I also can’t say ‘impossible’.

If Pantani were riding at 6.4 W/kg, rather than 6.7 W/kg, then suddenly it looks even more feasible, even with the first case scenario of 23% efficiency and max of 88 ml/kg/min.  I would still argue those numbers are unrealistic, and have never been observed, but the point here is that even the most outrageous power output numbers can be justified if you just stack the decks heavily in favour of the cyclist (hi Andy Coggan).

Consider now a case of a cyclist with a VO2max of 80 ml/kg/min, significantly lower.  If that cyclist is riding at 6.1 W/kg (a number we estimate, with good reason to believe is accurate, was produced in this year’s Tour in the Pyrenees), then given a range of efficiencies, the following implications exist:

  • If efficiency = 22%, then VO2 = 78.7 ml/kg/min, or 98.4% of max (not possible – mismatch)
  • If efficiency = 23%, then VO2 = 75.3 ml/kg/min, 94% of max (not possible – mismatch)
  • If efficiency = 24%, then VO2 = 72 ml/kg/min, 90.1% of max (borderline, though I’m not buying, for that length, without some assistance – it’s higher than Pinot for 30 min in the illustrative case)
  • If efficiency = 25%, then VO2 = 69.2 ml/kg/min, 86.5% of max (feasible, mismatch gone)

In other words, if a cyclist has a maximal capacity of 80 ml/kg/min, they would need an exceptionally high efficiency in order to produce 6.1 W/kg.  Is this possible?  Yes.  Plausible, I’d argue not.  If I were a judge, I wouldn’t reach a verdict on that alone, but I’m also not buying the combination as typical, without good reason to.

This is one reason why a cyclist might NOT want their physiology to be measured or disclosed – the only way they can produce the performance is to ride at an artificially high intensity, and the combination of testing would potentially reveal that.

Hopefully these cases give you an indication of where the numbers start to indicate (not prove) suspicion, and as I and others have tried to point out numerous times, we’re dealing in probabilities here, and that’s fine – biology is noisy and complex.


Let’s deal with the circular argument pitfall

I mentioned near the top of this post that one of the big issues about publishing a single line of data is that you run a risk of setting up a circular argument.  You end up chasing your tail.  Why?

Recall the three contributors to cycling performance:

  1. VO2max
  2. Cycling efficiency
  3. Sustainable workload

Doping with EPO, say, will alter all three of those.  It would increase the maximum capacity because it would allow more work to be done, hence more oxygen will be consumable.  It MAY increase efficiency, though I’ve seen evidence only that it enhances lipid oxidation but not direct measurements of efficiency in this caliber of athlete.  And it will definitely increase sustainable workload – a cyclist will ride for longer at a given power output, or could nudge power output up for a given period.

The problem, then, is that if you get the cyclist in the laboratory at the SAME time that they are doping AND producing the performances you are linking to the physiology, then all you will ever see is the effect of doping on BOTH the physiological tests and the performance.  That is, you will not know whether you are looking at remarkable physiology or doped physiology, because the performance is being related to the physiology and both are, concurrently, affected by doping.

Let’s assume, for instance, that you have Pantani (or anyone else – Lance, Jan, Froome) in your lab, and they are either fresh off, or about to produce 6.1W/kg for 45 minutes on Plateau de Beille.  That is the performance you measure.

The physiology you measure says:

  • VO2max 86ml/kg/min (I am using this value because it is Nairo Quintana’s)
  • Efficiency 23%

You’ve probably got the idea by now, but 6.1W/kg for this cyclist happens if they ride at 87.5% of maximum for 45 minutes.  High but not ridiculous (though at the end of a long stage, I remain skeptical).  If you want to allow for a range of efficiencies from 22% to 25%, they’re at 91% to 81%.

Problem is, in the absence of doping this cyclist might have a VO2max of 80 ml/kg/min, and might be capable of producing 5.7 W/kg, but you’re measuring the outcome of doping – physiology and performance, and have no chance of knowing about it.


Longitudinal testing & biological linkage is key

For this reason, the most important concept about this link between physiology and performance, and the idea that you can detect doping, is that you need to do it at repeated times during the season, just like the biological passport concept, because change and comparison is just as important as a single picture.  You need to watch the movie, not view a photograph.

Even repeated tests won’t nail down the mismatch one might be looking for, because variables like VO2max and sustainable exercise intensity as a % of max vary over a season quite normally, with training, but if you can somehow marry repeated testing to biological measurements, then you have a chance of seeing patterns that can be attributed to doping, more than to those natural changes.

One risk of the current call for a disclosure of physiology is that it’ll simply validate a doped or non-doped performance and you’d be none the wiser.  So if anyone out there is interested, don’t put too much stock in a once-off test.  It’s better than average cadence on a stage, mind you.  And it might flag a mismatch or two, as per the cases I described previously.


In conclusion

At the risk of exhausting myself, and you, on this Tour de France rest day, let me leave it there, for I feel I would be labouring the point if I continued.

This post is meant to illustrate concepts, and highlight that the calls for a VO2max data are nowhere near likely to provide a simple solution to a complex matter.

My calls to anyone interested are:

  • Performance and physiology must be measured more than once – it’s the longitudinal comparisons that would provide value.  They must also be linked to the biological variables that doping would alter, as a means to identify explanations for any changes that might be observed.  In other words, we need to see the movie, with a cast of performance, physiology and biology, not a single picture of each at totally different times.  This would help avoid that pitfall of a circular argument I describe.
  • The measurements must be done in multiple riders – the goal here is not simply to track one person.  As I wrote the other day, data in the absence of context is meaningless.  Other riders provide that context.  Thus, if I am a team wishing to earn back some trust, and I have nothing to hide, I provide longitudinal, repeated measurements of multiple riders over the course of multiple seasons.  Its not going to prove beyond all doubt that I’m clean or doped, for all the reasons I’ve tried to illustrate above, but it would sure be a show of good faith and an attempt to be transparent
  • I am under no illusions as to the complexity of this potential undertaking.  I realize and respect the issues of calibration, control, repeatability etc.  However, I have also learned in life (generally, this is true, not only for this issue) that too many people fail to take even that first, single step, because they are too preoccupied with what the fifth or sixth step might be.  Or because they see the problems beyond the horizon, and thus never leave the shore.  We can’t choose blindness just because we can’t have 20/20 vision.
  • Pick your metaphor, but I’ll take a vision for a better situation than we currently have, despite the challenges, than to concede defeat.  I feel progress in this performance discussion has already been made, thanks to the efforts of Ammattipyoraily, Antoine (yes, him) and Mike.  As more call for transparency, we will be in a better place than we are now, even if it is just the process of asking questions that gets us there.

On that note, thanks for getting this far, and enjoy the rest day.

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Happy resting!


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