Commentary: Disputing Ferrari’s altitude training claims, part 1
Editor’s note: The following is the first in a two-part series in which sports medicine physician Michael Puchowicz disputes claims made by Dr. Michele Ferrari in January that Lance Armstrong would have seen comparable benefits from altitude training without the use of EPO and other performance enhancing drugs.
The mind that once calculated Marco Pantani’s attacking Mean Ascent Velocity (VAM) without aid, and rightly predicted to “let him hang,” concluded recently that Lance Armstrong’s regimen of EPO and blood doping was no more effective than altitude training.
Michele Ferrari is no stranger to controversial commentary. He famously mused that “EPO is not dangerous, it’s the abuse that is. It’s also dangerous to drink 10 liters of orange juice.” At one time such defiance fueled “The Legend,” his reputation as the greatest performance enhancement doctor in cycling. But on the eve of a truth and reconciliation nearly 30 years in the making, the effect is altogether different. With Ferrari pushing ever further out on to untenable ground, one has to wonder, has he sacrificed his credibility? And if so, why?
The basis of Ferrari’s argument, which appeared on his website in late January, is twofold. First, he proposed that altitude training could produce an increase in hemoglobin (Hgb) mass on the order of five-to-10 percent. Second, he concluded that this change would be an increase equivalent to the one produced by Armstrong’s doping. In support of his argument, Ferrari cited four studies on the effects of altitude (Chapman 1998, Heinicke 2005, Wehrlin 2006, Garvican 2012) and his own opinion on the effects of doping. However, a closer look at the science shows that the data does not support his position.
To illustrate the effect of altitude on Hgb mass, we pooled data pooled from the four studies that Ferrari cited and combined into Figure 1. The Y axis shows the percent increase in Hgb mass from baseline following altitude training. The X axis represents days following the altitude exposure. Day 0 is Hgb mass recorded while still at altitude, and day 1 is the first day back at sea level and so on.
What is immediately apparent from the figure is that only one out of seven data points actually falls into the claimed five-to-10-percent range. Worse yet, the 9.2-percent value, which was recorded while the subjects were still at altitude, came from the smallest study and lacked a control group (Heinicke 2005). For the purposes of a cyclist riding a grand tour, it is hard to see any way that the data suggests the five-to-10-percent increase Ferrari claimed. From these studies, a rider might realistically expect a 2.8-4.5-percent increase in Hbg mass during the first week of a grand tour from a well-timed altitude camp. By mid-tour, the rider would experience a modest bump of 0.8-2.5 percent above baseline, and by the third week, whether any benefit would still be present is unclear from the data.
So, how does a scientist see something so completely different in the same data set?
Did he cherry-pick the best altitude data, minimize the dope effect, and try to paint a sympathetic picture to protect the legacy of a friend?
We developed Figure 2 by cherry-picking the best reported values from baseline, and ignoring time point or inappropriate study design for the purpose of looking at the effect of altitude on Hgb mass.
Is there credibility hiding in Ferrari’s analysis?
I emailed him with my concerns regarding his interpretation and he graciously replied.
First, Ferrari responded with data from additional studies. One showed a Hgb mass increase “by an average of six percent in a group of 20 athletes” (Friedmann 2005); the other, an impressive “average of 8.6 percent” (Saunders 2010). He did not mention that Saunders actually had two control groups. The effect of altitude over the first control group was, yes, 8.6 percent. The effect over the second control group was a more modest 5.5 percent, or a total of 7.2 percent when the data is pooled together. Ferrari also failed to mention that Friedmann’s study focused on a sample of elite junior athletes, a point to be kept in mind for later.
Since adding studies was now fair game, I completed my own literature review. Within the short time constraints, I was able to add seven additional studies that had adequate altitude exposure (Clark 2009, Robertson 2010a, Gore 1998, Robertson 2010b, Robertson 2010c, Gough 2012, Pottgiesser 2012, Wachsmuth 2012).
After adding the data from the two studies Ferrari cited (as well as the seven additional studies I found), only two data points make it into the five-10-percent range at a time point relevant to performance in a grand tour. The majority of points again fall consistently below five percent and the data again does not support his claim.
Shifting from the mean (average) data, Ferrari pointed to “individual increases up to 25 percent, with four athletes over 10 percent” in Friedmann (2005), as well as “individual improvements of up to 14 percent” in (Saunders 2010), and “seven-to-eight percent in the initial set of studies” (Wehrlin 2006, Garvican 2012). He said, “the reasons for these different responses may be genetic, nutritional (adequate intake of iron and protein), or related to training loads.”
However, increases as large as eight-to-10 percent can be found even in the control group, if looking at individuals (Robertson 2010a, Saunders 2010).
Ferrari correctly responded that “the method of measurement of Hgb mass is definitely subject to errors (see article on 53×12.com).”
However, it is not correct to blame errors or factors besides altitude for the 10-percent increase in the control subject while crediting altitude alone for the 25-percent increase in the experimental subject.
In this example, Ferrari is doing the scientific equivalent of flipping a coin 100 times and only counting the result if it comes up heads. The whole point of using the mean (average) values from large numbers and control groups is to try to get rid of the influence of noise on individual results so the real effect can be seen.
We followed up with two very direct questions:
“First, do you not consider the average value of the cohort as stronger evidence than an individual case?”
“Do you not consider studies with control groups to be stronger evidence than studies without a control?”
Ferrari responded, saying, “obviously from a scientific and statistical point of view, the average behavior of a group of subjects is more significant than individual behavior. But as a physician evaluating the individual patient, I have to further study the case if I did measure an effect on the individual, before ruling out that this cannot be true because it’s not ‘statistically significant.’ In the case of the effects of altitude, it is certain that there are ‘responders’ and ‘non-responders,’ as evidenced first by the study of Levine, as well as being confirmed by anyone who has experience as a trainer on the field.”
His response raised two final questions in this thread:
“Do you have data showing Armstrong’s response to altitude?”
“Do you have data showing Armstrong’s response to EPO?”
We also let him know that, “if not, [we] have to point out the problem in using the individual data points to suggest Armstrong was a super responder to altitude just as it would be wrong to use individual results to suggest he was a super responder to EPO.”
Ferrari did not respond further to this thread.
In the absence of Armstrong’s personal data, it is impossible to know where he might fall on the spectrum of altitude response. Without Armstrong’s data, credible science would consider the means (averages) of all the data at each time point to minimize the effect of uncontrolled variables, as we’ve done in Figure 3.
Poor science would weight the data according to its strength.
(Solid circles in Figure 4 highlight studies with control groups and are a stronger level of evidence. The open circles represent the studies without control groups.)
And credible science would look at the data that is relevant to the question at hand.
Of the available data, only two out of 16 data points fall in the claimed five-to-10-percent range in Figure 5. The higher of the points is still only 6.3 percent and comes from the study on junior athletes with no control group. In their discussion, the study authors hypothesized that because the subjects “were elite junior swimmers [they] undoubtedly still had the potential to increase total hemoglobin mass,” in contrast to veteran athletes who may reach a maximum from years of training-induced changes. It is not very strong.
The most credible conclusion is that the strongest data suggests that altitude would result in an increase in Hgb mass between one and five-percent at time points relevant to a grand tour. This is still well short of the five-to-10-percent range that Ferrari claimed.
In a parallel thread, Ferrari asked us to consider that “some elite athletes are used to planning and repeating three-to-four altitude sojourns of training for about two weeks every month, for example, in order to prepare for the [Tour de France]: four periods in the months of March, April, May, and June, about two weeks each time. So a total of six-to-eight weeks, which is about twice as much as proposed in published trials/studies. It is known [Hgb] mass increases by about one percent per week of altitude, both real and simulated (Berglund 1992, Clark 2009). The average life of red blood cells is about 120 days, for which it is logical to expect a sum of increments during a period of about four months.”
The one study that I’ve found that looked directly at cumulative effect similar to what Ferrari proposed followed elite swimmers over the course of multiple coach-prescribed two-week altitude camps alternated with two-week blocks of no altitude (Roberston 2010c). The swimmers showed average gains of 0.9 percent during the camps. However, because of Hgb mass decreases between the camps, there is no measurable difference between starting and finishing mean data in the published figure. The study certainly doesn’t support Ferrari’s proposed cumulative effect. However, since the altitude exposure used was lower than studies finding larger effects, this study does not necessarily end debate on this point.
Red blood cell destruction
Continuing the analysis, working against Ferrari’s hypothesis are two factors related to the fall in natural EPO when altitude exposure is stopped. The first is neocytolysis, which is the destruction of young red blood cells (Rice 2001). The second is suppression of the red blood cell (RBC) production.
Ferrari’s counter-argument held that “neocytolysis, assumed it takes place after a stay at altitude in athletes who continue to train intensely, will be even more present after a transfusion, because it’s stored blood…,” but his statement does not make theoretical sense. Neocytolysis is specifically the destruction of young, not older, RBCs. So, if anything, the older age of stored blood might protect transfused cells from the process of neocytolysis.
Similarly, pointing to the 120-day lifespan of red blood cells is a red herring. The length of time the Hgb mass will stay elevated is determined by the balance between RBC destruction and production. If production is less than normal or destruction is increased, Hgb mass will fall. The rate that it will fall is not determined by the lifespan of an RBC under normal conditions, but the balance between the former and the latter. So, in determining if an accumulation effect is even possible, it is important to look at two things: the Hgb mass at the later time points (not the peak values), and the rate of expected decrease in the Hgb mass.
In response, Ferrari stated, “after altitude training, I am more interested in the ability of performance (increased for more than 16 days since the end of the exposure) rather than the measurement (which is approximate) of [Hgb] mass.”
Again, Ferrari pivoted away from his error.
Lacking further input from Ferrari, consider the strongest Hgb mass data that is available, shown in Figure 6.
First, these studies used three-week altitude exposures, which are greater than the two-week blocks proposed by Ferrari. Therefore, the data points would need to be adjusted downward accordingly. Next, from the controlled studies with multiple time points, the data suggests that Hgb mass falls fairly rapidly after altitude exposure is stopped. If this same rate of fall occurred after a two-week exposure, it would be unlikely that there would be much remaining elevation in Hgb mass before starting the next two-week altitude exposure.
As a whole, the data suggests that altitude exposure, whether from a continuous three-week block or from accumulation from repeated two-week blocks, would not result in a five-to-10-percent increase in Hgb mass at a time point relevant to a grand tour. Without any of Armstrong’s personal data to suggest otherwise, Ferrari’s altitude claim is not credible.
Part 2 of this analysis will look at the effect of EPO and blood doping on Hgb mass with further discussion from Dr. Ferrari.
Michael Puchowicz is a former lab rat turned sports medicine physician. After the last two years of laying waste to grammar, farce, and pretense in the shadowy world of anonymous cycling blogs and Twitter rants, he’s finally caved and gone legit. Please don’t mistake his views for those of his employer, his friends, or anyone else linked by real or perceived affiliations through the medical and science communities. To do so, would surely end his uncompromising pursuit of all things true and glorious in cycling.
Author’s note: I do thank Dr. Ferrari for engaging in this discussion. I very strongly disagree with the actions for which he is banned from cycling, but I wish him no ill will as a person. There is some concern that engaging Ferrari gives him a chance to pivot away from his own culpability and distracts from more important efforts to clean up the sport. I agree with this concern. However, the questions being addressed have defined cycling for too long for the sport to be able to move on without closure.
In the Hgb mass figures, data from studies with a control group was plotted as the percentage above baseline compared to the control group. Data from studies without control groups was plotted as the percentage above baseline. Data was extracted from graphs when the numerical value was not available. All reasonable effort was made to not introduce additional error.
The Chapman 1998 study was designed to look at the differences in physiological parameters between “responders” and “non-responders.” In order to make this data set usable for the analysis of the effect of altitude, the two groups were pooled together and the average change from baseline for all subjects was used.
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