The Genuine Innovations Tubeless Tire Repair Kit came out some time before 2009. So technically they don’t belong in a 2009 best of. But it wasn’t until a recent ride that these little gems came to my attention. And they are good enough to justify making a little exception here.

The concept is the same as plugs for car tires. The kit includes several “ropes” that are coated in some orange stuff, and an insertion tool. All you do is load a rope on the tool, find the hole, push in the plug, pool out the tool, and that’s about it.

The idea may not be a new one. In fact, anyone who has ever found a thorn in their tire in the middle of a ride and resisted the temptation to pull it out, has already used a “plug.”

Instead, the genius is how well it works to compliment the puncture protection of latex sealants. By fixing the tire from the outside, plugs are much faster and less messy than throwing in a tube or trying to patch a tire. Your back up is no longer a thorn vulnerable tube. And because you are not breaking the seal at the bead, you don’t need to worry about tires that are difficult to re-seat.

Sure, major sidewall cuts are still going to be a tire booting suck the fun out of riding experience.

For the remaining 99% of the time, flats are now relegated to a very minor inconvenience. Not bad for less than 10 bucks.

Anne Gripper has declared that the war on doping is being won,according to Cycling news. While the declaration may be a bit optimistic the article includes some interesting insights into the thinking of the BioPass Panel.

The first is that the “experts” basically aknowledge that hemoglobin and hematocrit values are relatively easy to manipulate and explain away. So while an abnormally high level is evidence of doping normal levels aren’t great evidence of clean competition (something to keep in mind when data is used as marketing.) Instead, it looks like the Reticulocyte count is their current go to value. This is a same impression that I came away with after looking at the Armstrong data from all sides.

Point number two is that they really aren’t using the passport yet to actually sanction athletes. Yes, a couple of cases have been brought forward as sort of a pilot study of the legal system. But at the moment it appears that suspicious trends are being used as a guide for traditional doping. The likely reason is that the 3 standard deviation cutoff effectively castrates the BioPassport. Such wide cutoffs are likely to pick up only reckless or unsophisticated doping practices. An unrealistic number of data points may be necessary for the current Bayesian network to narrow the predicted range sufficiently for a 3 standard deviation cutoff to be of much legal use. Suspicious trends however, say values around 2 standard deviations or improbable trends, are much more likely to be seen and can be used to more effectively target riders for traditional testing.

The final notable item is the statement that the war on doping is being won and that (normal) Retic values are the proof.

While I agree that it is likely harder to manipulate the Retic, you need a bit of a qualifier.

An abnormaly low retic can be bumped up with small doses of EPO, and possibly propped up a bit with altitude exposure as well. Masking a high Retic on the other hand would require large RBC transfusion.  So a lack of unusually high Retic counts makes high doses of EPO unlikely. But the absence of low Retics does not necessarily rule out blood transfusions or withdrawal of large dose EPO supplementation.  

With the the above in mind, it is encouraging that the BioPass data, according to Gripper’s statements, may show an end of the era of high dose EPO use.

An alternate cynical conclusion may be that Retic is already being manipulated by means that have not been reported yet.

Either way, the likely future of anti-doping will be to develop tactics and tests to pick up EPO micro-dosing, blood transfusions, and blood volume expansion.

The last series of articles took quite a bit out of me. One, it’s not easy putting that much time into anything when any given work week might be a hundred hour week, and any given shift might be a 30 hour shift. Two it’s not easy calling bullshit on your heroes. But sometimes the bullshit is a bit too much to take. At Least if your plan is to stick around for any length of time.

And so I’ve been thinking about “Going Long.”

It’s a thought that’s been rattling around in my head for some time now.

Some of the relevance is personal. I’ve now completed 8 years of higher education, 2 years of research, 2.5 years of residency. With just half a year between me and decent paycheck I’ll be taking on another 2 or more years of fellowship training because practicing anything but academic Sports-medicine would be a mistake.

Some of it is just plain wonderment about human nature or the rather the nature of some rare humans.

One such human recently made aware that there is a Tequila tree that lives on one of our local mountains. As it turns out the Tequilla tree was recently refilled for an upcomming special event. No that event is not the imminent arrival of LA for the kickoff team Radioshack training camp. Nor was the event the podium sweep of a world famous charity ride by a suspected doped Mexican team. No the special event is an unsanctioned combination hike and bike race.

Are there organizations that actually sanction these things?

The hike is estimated to be a 6 hour affair to the bike staging area. The hiking leg is followed by a 6 ish hour MTB ride. For those “that are in to that kind of thing” there is a Tequilla tree along the way. Entry is word of mouth. The only fee is for a shuttle seeing as it’s a 12 hour point to point in the desert. And the only prize, is probably something like first dibs next time you happen to be by a Tequila tree.

But the true genius of the event according to the founder is that it starts at midnight. That way you should be hitting the bike just as the blue is starting to crack across the sky. And by that time you will need every bit of motivation to actually climb aboard the bike. Unless of course, you are in so much pain that the only motivating factor is pain itself.

Bicycling magazine once wrote a good bike review. It was for a Pegoretti Marcelo, a steel racebike. They speculated that the bike was to stiff for the touring crowd and steel connoseurs. And too heavy to actually race. But they concluded that the bike was perfect for someone. To that someone they said,”We don’t know who you are, but you are a beautiful person.”

My current roadbike is an old style madone, carbon fiber sharkfin seat tube and all. My current MTB is a Niner Sir 9. Like a Marcelo a Sir 9 is not a bike that I would ever buy. Unless, I was planning On racing my first 24hr solo mtb, and my race bile was a recalled Jet.

At this point I have now ridden the stink out of the Sir9. I can honestly sat that the Jet does everything better in a faster, more comfortable… technical sense. Yet the Sir somehow is the absolute right bike for my first Solo 24 hour race.

Maybe I’m just kidding myself so that I don’t feel suckered that my proper race bike won’t be showing up untill way after Old Pueblo.

And maybe the reason that every ride I’ve done on the Sir has been at least 5 hours is because I can all of a sudden stick to a training plan.

It would be nice if one solo 24 hour aboard a steel hardtail was enough to prove one’s nature. More than likely it will just confirm that I tend to be a bit stubborn when it comes to ill-conceived ideas.

Whatever the case, 24 hours should be long enough to reach a point where every last bit of thought will be needed just to push a pedal.

And isn’t that all that anyone could really be looking for?

Whatever the case, here’s to Going Long.

This article is the last in a series (Part 1, Part 2, Part 3) discussing BioPassport data posted on Livestrong.com. The focus will be on the persistent low normal Retic values from 6/16 through the end of the Tour.

Why is a persistent low normal Retic important?

Reticulocytes are a marker of Red Blood Cell production. There are normal swings in Retic. However, a persistently high or low Retic is an indication that the body is trying to compensate for a persistent deficit or surplus of Red Blood Cells (Hgb mass/oxygen carrying capacity).

An unusually low or persistently low Retic indicates artificially manipulated surplus of RBCs. It can be a marker of exogenous EPO (during the washout period) or of blood transfusion.

The main natural factors that affect Retic are training status and altitude exposure.

Training typically causes an initial increase in Retic followed by a slight decrease over the course of the season.

Altitude exposure results in a potentially greater rise in Retic followed by a greater fall before returning to baseline over a shorter period of time. (Note that altitude studies show a range of effects on Retic from no response to effects lasting 1 or 2 weeks after return from altitude, an interesting discussion for another day.)

Even when Hgb and OFF score are within normal limits, it is still worthwhile to look at Retic. Because it is not affected by volume status, Retic may sometimes be a marker of manipulation even when Hgb is well within the normal range. (scroll down until you see a box of data)

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This chart shows the average Retic for three periods, Pre - Grand Tour, 4/30 through the Giro, and 6/16 through the Tour.

Is the low normal Retic the cumulative effect of a long season?

The modest drop from Pre-GT to the Giro is consistent with a training effect.  The larger drop during from the Giro to the Tour is less easily explained by a simple training effect.

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Next the Giro and Tour trends are compared.

Is the drop the result of riding a Grand Tour?

This comparison is difficult to make because the Retic is at the opposite end of the spectrum at the start of each tour, Giro 1.3, Tour 0.5. Ultimately, this comparison does not explain the Tour values.

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Finally, this last chart shows a possible predicted  effect of altitude exposure.

Can a response to altitude account for the Retic?

Since Lance Armstrong is known for responsiveness to altitude, an effort was made to find best fit data from a group of “responders.” The Toluca 2000 data (Ashenden et al.) was gathered from a group of track cyclists who where followed over the course of a 25 day altitude camp, and two weeks after returning from altitude. This data was chosen for this article chosen because the group shows good responsiveness to altitude. Retic initially rises modestly above baseline then falls to a low point 1 week after return from altitude.

The composite chart above was constructed by superimposing the Toluca data over Armstrong’s taking care to line up altitude exposure. The Toluca curve required a significant downward adjustemnt to all the points so that the low points match up (Toluca baseline was 1.3) The idea behind the composite chart here is to get a sense if the trends might be consistent with those previously seen in response to altitude exposure.

What becomes evident from the composite prediction is that a rise in Retic between the end of the Giro and 6/16 may have been missed. The low Retic at 6/16 may be consistent with the fall after an initial peak. Similarly, the low point at 1 week after altitude exposure may be consistent with the low point after altitude exposure. Overal, assuming that the Retic did in fact trend up before falling you end up with a somewhat akward but plausible fit.

The explanation becomes less convincing when the Retic stays flat after reaching its low. The Retic does show a recovery, but it comes a week later than expected and is followed by another drop. In the end, altitude could be used to account for the initial drop in Retic, but it is not a solid explanation for a persistent low normal Retic.

Taking into account training status, the effect of a Grand Tour, and altitude exposure the analysis fails to fully explain the persistently low normal retic from 6/16 through the Tour. While other factors may be at play they probably have less of an impact. There is also the possibility of a synergistic effect, but it would be difficult to find data that supports this argument while excluding the possibility of manipulation at the same time.

In this series an attempt was made to aproach both sides of the discussion with a healthy scepticism. The result of this aproach was not a black and white answer. Instead, what is left are some unusual values that have not been adequately explained by the natural factors considered. Manipulation is not a conclusion that should be drawn lightly or without substantial proof. However, it should not be completely taboo and out of bounds either to discuss. Given cycling’s history, doping is simply a possibility that needs to be considered whenever results are otherwise difficult to explain.

Yes the Tour Hgb may be explainable. In fact, the Hgb may have behaved in a completely consistent manner.

In Article 1 of this series, we made the case that Lance Armstrong’s blood values should have reacted in a similar way to both grand tours, but they did not appear to. In article 2, we used calculations to illustrate how a Hgb might have been expected to trend down in the second grand tour.

The majority of the discussion can be summarized by this graph:

In the Giro Hgb trends down significantly while it trends up slightly in the Tour.

Expert opinion seems consistent with the conclusion of inconsistent trends but offers opposing explanations for the difference.

One thought is that something natural like dehydration secondary to something like diarrhea could skew the Hgb upwards in second half of the Tour.

The opposing view is that blood doping could cause the rise and would also account for the low normal Retic.

In this article, we consider the possibility that we missed something on our first pass.

The explanation proposed here is that the the expected drop in Hgb did in fact take place. It may simply have started before the second grand tour and reached a physiologic limit before the end of the race.

The clue to this possibility is listening to the Retic. During the Tour de France the Retic was consistently at the lower limits of normal. Usually a low normal Retic indicates that the body has a high normal total number of RBCs.

The observation of a stable Retic was touched upon in the last article and provided the justification to assume that changes in Hgb were not from changes in total RBC but from changes in blood volume.

What was not considered was the data from a few weeks before the Tour when this low normal Retic steady state was first achieved.

As it turns out, the Retic hits 0.6 on 6/16 and basically stays there until the last Tour data point.

On 6/16 the Hgb happens to be 16, a high normal value.

Now consider this graph which compares the Hgb trend during the Giro with data starting on 6/16 and continuing through the Tour.

Is this the missing drop in Hgb?

The profile of each curve is nearly identical for the first 25 days. Despite the flattening of the Tour line the overall trend is still a significant drop in Hgb as expected.

From 6/16 through the end of the Tour Retic is stable in the low normal range allowing the assumption that any drop in Hgb should be volume expansion.

In terms of estimated volume expansion, by the start of the tour the Hgb has already dropped to 14.3 indicating a 12% increase in blood volume, i.e. not dehydrated.

After the Tour starts the Hgb continues to trend down as one would expect hitting a low of 13.7, i.e. not ruling out blood doping but  not really supporting it either. The drop from 16 to 13.7 is a substantial 17% expansion in volume. This value even exceeds the change during the first grand tour of 14%.

From that point on Hgb bounces around recovering to 14.5. Is this evidence of dehydration?

Or is this evidence that there is simply a physiological limit to volume expansion, and that this limit was reached before the end of the Tour?

Consider the graph, again taking into account the 6/16 data point;

Even with the flattening of the curve blood volume expansion is still 10%.

Do the Hgb trends support manipulation?

Dehydration?

Or did the drop in Hgb begin early because of an intense build up lead up to the Tour?

If the 6/16 data point is included, the Hgb values do look more consistent with the Giro data. Dehydration also appears less likely as the overall trend is consistent with volume expansion rather than contraction.

The one problem with this reinterpretation, is that now the persistent low normal Retic values extend all the way from 6/16 through the end of the Tour. We’ll attempt to deal with this observation in the next and final article of this series.
Part 4

So far release of “Bio Passport” data has gone something like here’s the data from the last year. Look it doesn’t cross the doping cutoffs therefore this rider is clean. There have been discussions of historical norms and Bayesian something ‘ r ‘nuther, but no follow through with regard to a specific data set. Hopefully some of you have noticed this discrepancy, and that what has been marketed as such, is not exactly Biopassport science.

Cycling news picks up the story and gets into it a bit. While the focus of debate seems to be headed for a he said she said about wheter or not Lance Armstrong had diarrhea, I would like to redirect you this particular quote,

“A logical follow-up question… that is, even if Armstrong has a unique physiology, how do his Tour results square with those from the Giro, which showed over a five point drop in hematocrit? “I can speculate a lot between the two races and the differences between the two,” Damsgaard answered. “[But] these kinds of blood profiles should be exposed to the scientific algorithms, to the Bayesian model and WADA and UCI rules of upper and lower limits.””

The key here is that the debate right now rests on a failed Super EyeBall Test or SEB test as I like to call it. SEB testing is fun, but unfortunately it isn’t quite BioPassport.

To discuss the difference lets first go back to the SEB testing we did in in part 1 of this article. We took a look at numbers from Lance Armstrong’s data set and attempted to take the analysis a step further than simply comparing them to populational cutoffs.

To make that next step we had to make some assumptions;

1. The human body will always attempt to maintain a tight physiologic equilibrium, therefore it will respond to equivalent stress in a consistent way.

2. The greatest physiologic stress that a cyclist will experience is an elite performance in a grand tour.

3. All other stresses are not on the order of magnitude of a grand tour, therefore their influence should be washed out when assessing the physiologic response to a grand tour.

These assumptions allowed us to hold all outside factors as constant to test the hypothesis that the two data sets where essentially the same. In the debut use the LC SEB test it was determined that the data sets where not the same. Since they where different we had to look for some other factor on the order of magnitude of a grand tour to explain why the human body reacted differently to an equivalent stress.

From the Cycling News article it looks like the search will come down to Diarhea vs Dope. Based on your perssonal whiff test you will likely end up in one camp or the other, either way nobody wins.

However, my congratulations still go out to Cyclingnews. They are the first of the mainstreamers to rise from populational cutoffs and join the elite ranks of Local Cyclist and NY Velocity at a level of debate that still isn’t exactly Biopassport territory either.

What? SEB test does not equal BioPassport?

Say that again. Entire Biopassport articles written about an over-simplified analysis that wasn’t even really a proper Biopassport analysis?

Why would you do that?

For Local Cyclist the reason was the same one as why you might choose to debate the blood values of a mega-celebrity; It grabs your attention and creates a teachable moment. Additionally, the effect of using an approachable problem with an engaging question is that it enables you to sneek in the tedious ground work needed to tackle more complex issues. It also lets this author get write two articles instead of one.

Now that you are primed, lets look at some more numbers. Note that the calculations are to illustrate what Jakob Moerkeberg is saying and to set up the point of this article. Keep in mind that the calculations, particularly involving Retic, may stretch the limmits of validity.

From the first tour data set we start with a Hgb of 14.8 that drop to 13 over the course of about three weeks. We will assume a hemodynamic steady state and just take the average of the Retic for the three weeks. We can then use the average Retic to estimate Red Blood Cell Production.

Simple right, Retic = Red Blood Cell production.

The justification for the geeks who might be interested:

During a steady state Red Blood Cell (RBC) turn over is about 100 days and Retic turnover is 1 day allowing you to say that Retic approximates RBC turnover i.e. it provides an estimate of RBC production. So if the average Retic is 1% then average daily RBC production is 1% of RBC total. An easy way to think of this is that if no RBCs are destroyed and production is at 1% your total number of RBCs would increase by 1% per day. Or if RBCs are undergoing normal destruction of 1% and you stopped making new ones your total number would drop by 1% per day.

So using the Retic from the first data set, which averages out to 0.97%, we can estimate RBC production to be 0.97%. Since 0.97% daily production is well within the 5% daily maximum capacity we can assume that the body is capable of meeting this demand. Therefore any drop in Hgb or Hct is not because your body couldn’t keep up causing a decrease in the number of Red Blood Cells, but because the numbers look lower because of dilution caused by your blood volume increasing.

In the first grand tour data set, to go from a Hgb of 14.8 to 13 you would need to increase blood volume by 14%.

Having made this calculation we can apply this information to the second grand tour data set and make some predictions based on the 14% increase in blood volume, and a RBC production of 0.97% from the first grand tour.

Starting with a Hgb of 14.3, a blood volume increase of 14% would give you a predicted drop in Hgb to 12.5 (z -2.65) by the end of the second grand tour.  Also, if an RBC production of 0.97% is necessary to match the rate of RBC destruction, as in the first grand tour, then you might expect the average RBC production of 0.6% in the second tour to cause a deficit of 0.37% per day, (0.97% -0.6%). Remember RBCs are always dieing, so if you don’t replace them fast enough your Hgb drops. Multiply that by 3 weeks and you should be down 8.5%. So the predicted final Hgb for the second grand tour might be as low as 11.28 (z -4.2).

For those who want to think about it another way, the final Hgb value of 14.5 may look like a nearly unchanged Hgb. However, staying at 14.5 is actually like the Hgb going up to 16.5 (z +2.54), given a 14% expansion in blood volume. Or if you want to account for the difference in Retic as well, you might calculate that staying at a Hgb of 14.5 is equivalent to jumping up to 17.7 (z +4.12). These numbers would certainly fail most people’s SEB test.

Of course that’s just speculation based on shaky science and bad math. But it does illustrates what your Super EyeBall test has been saying; The second set data set should look more like the first than it does.

So wouldn’t it be nice if your SEB test was on to something. Wouldn’t it be nice if you could just use the first data set to predict what the averages should be for the next data set. And what if you didn’t have to make tons of assumptions about all outside factors being the same, and didn’t have to choose between Dope and Diarhea. Instead, imagine plugging all the factors in exactly the way they were. What if you could do all this and end up with appropriately adjusted averages and cutoffs automatically tailor-fit to the athlete instead instead of the ill fitting populational norms. And wouldn’t it be nice if you could have these appropriate averages with their tight normal ranges to calculate a probability of doped vs clean?

Apparently you can. Its called the Biopassport.

Lets bring it all home with the example of Chuck running a quarter mile. You are Chuck’s time keeper but say you happen to be blind. I know it sucks but you still need to time Chuck at the track. You can’t see him but he swears up and down that he’ll run around the whole track and tell you when he crosses the line. So you say ok. You tell him to start and you listen closely as he runs off. At the far ends of the track you can’t quite here him but otherwise it sounds like he went all the way around. You do it again and again standing at different parts of the track trying to make sure he goes all the way around. Now Chuck gets his best time. You like Chuck but can’t be sure he didn’t cheat. You can compare his time to what an average person might do. But since the range is so wide (average can mean anything from toddler to Olympian) you only know if Chuck cheated if he’s way faster than the world record, or maybe you happen to be standing at the right part of the track at the right time to catch Chuck cutting across the field. At this point you’ve done the equivalent of pre-Biopassport testing.

Now say that you know Chuck is 13. Suddenly you can change the average to something more specific for 13 year olds and the range will narrow as well. Add in that Chuck is a great wide receiver and the average might go up a bit while the range narrows even closer. Then you plug in all the times from Chuck’s previous runs and readjust your average and range. Pretty soon anything that isn’t completely consistent is going to start to stick out. Congratulations you just constructed a Bayesian Network, a theoretical model based on probability which takes into account all available information. Finish the work off by taking the new time and calculating the probability that he cheated using the Bayesian Network (BN) that you just created.

In straightforward language, the idea is that you start with an average value from the population and your normal range is that of the population. Now start plugging in test results to adjust the average and narrow the normal range. Then you use Bayesian Networks to predict where the average should be adjusted to given the all of the available information. Now set cutoffs at a point that the probability of anything beyond the cutoff is practically proven cheating. Finally, see if your result fits.

What about Armstrong’s data sets.

I’m assuming that the z scores provided are calculated using an appropriate BN. If so, only the Hgb of 13 falls outside of 2 standard deviations. This value while outside of normal doesn’t cross the threshold of 3.09 standard deviations needed for conclusive proof of doping. Even after crossing this threshold the data would still require review by an expert panel including further analysis and calculations to rule out other explanations.

So how did we go wrong with our SEB test? Maybe we didn’t. At this point, the BNs are still in their early incarnations. To the best of my knowledge they don’t yet use the data the same way that we did in the eyeball test. For example the factors currently used in the BN are along the lines of age, gender, race, and altitude exposure. They are not yet taking the change in Hgb from first to last day of one grand tour to plug into the calculation of an appropriate Hgb for the last day of a second grand tour. At least that type of work is not yet published.  Also, while blood parameters are combined in the OFF score and Abnormal Blood Profile score they are used more in a pattern recognition manner. I am not aware of published literature showing them being used as explicit factors in a BN Hgb probability calculation.

I realize the conclusion is anti-climactic. Fortunately, more definitive answers will likely come. The beauty of the Biopassport is that the data isn’t going anywhere and BNs continue to learn as additional data is collected. BNs can also be taught new tricks by adding in factors that where previously not considered. It will be interesting to see just how far down the rabbit hole ultimately goes.

Even without the addition of new tricks, there is a lot more to discuss. Only 2 out of 7 hematologic markers are being considered here. The OFF score is sitting this one out and the Abnormal Blood Profile Score hasn’t even made an appearance. Nor did we mention the possibility of using multi-tiered sanctions for lowerer cutoffs, or bother to ask about calculating practically proven clean.

But for now, I hope that you can appreciate that the Biopassport is on the verge of unlocking an incredibly powerful new tool for doping prevention.

Finally, I would like to publicly thank Lance Armstrong. More than any other person he has the most to lose by releasing results. And it is the magnitude of what is at stake that has truly brought the BioPassport front page awareness it deserves.
Part 3 Part 4

P.S. If your eyes haven’t glazed over I encourage you to get to a library and start with this.

Any minute now the cycling media is going to unleash a deluge of in depth articles analyzing the recently released bio passport data. Actually, that’s a lie. No instead, you the average cyclist will have to do your own analysis. I know, I know every time bio-passport data gets mentioned it comes with a disclaimer that only super doping experts are capable of looking at it without going blind. Fortunately, that’s not exactly true either.

So lets dive in and look at some Grand Tour data from two cyclists, MJ and BT.

MJ
Hgb 14.8 13.6 13.0
Hgb z 0.39 -1.42 -2.32
Retic 1.3 0.7 0.9

BT
Hgb 14.3 13.7 14.4 14 14.5
Hgb z -0.36 -1.19 -0.2 -0.7 -0.05
Retic 0.5 0.5 0.7 0.5 0.7

So what are we looking at. Hgb stands for Hemoglobin mass concentration. Hemoglobin is the stuff in the red blood cells that carries oxygen. For simplicity, red blood cells = hemoglobin = oxygen = performance.

Rider MJ starts the tour with Hgb 14.8 and drifts down to 13.0. Rider BT starts at 14.3 starts to drift down pops up, drifts down, and pops up again to 14.5 just higher than where he started.

Hgb z stands for Hemoglobin z score. A z score is an indicator of where you are within your normal range, also called standard deviation. Typically, you are considered to be out of your normal range if you are higher than +2 (2 standard deviations above average) or less than -2 (2 standard deviations below your average).

Rider MJ starts the tour with a Hgb z of 0.39, just above average but well within the normal range. By the end of the tour he’s at -2.32, out of his normal range on the low side.
Rider BT starts the tour at -0.36 just below average but well within the normal range. Initially, he also trends down, to -1.19, below average but within the normal range. Then he recovers to -0.05 or basically back to average by the end of the Tour.

Retic stands for reticulocytes or immature (new) red blood cells. For simplicity Retic = how fast your body is making red blood cells.

Rider MJ starts out with a Retic of 1.3 at the upper end of normal. His Retic comes to the low side before recovering to 0.9. Overall MJ came in producing a lot of red blood cells and maintained decent production.

Rider BT starts out at 0.5 at the lower limit of normal trending up a little but still low. Overall, BT had a suppressed production of red blood cells.

To summarize, MJ comes in with an above average Hgb and a ramped up red blood cell production (ed. still within normal range), maintains decent red blood cell production but his Hgb still drops low, well outside his normal range. BT on the other hand comes in with with a low Hgb and suppressed red blood cell production (ed. but still within the normal range), never really ramps up red blood cell production yet finishes with a higher Hgb than when he started.

Before we try to make an interpretation, lets consider the relationship between Hgb and Retic, and the factors that effect blood values.

Hgb level is a balance between red blood cell destruction and production. It goes up when red blood cells are being produced faster than they are destroyed. It goes down when cells are destroyed faster than they are produced.

Accelerated destruction of red blood cells can be caused by, normal wear and tear, significant physical stress, and illness.

Slowing of destruction can be caused by; rest (less normal wear and tear).

Production of red blood cells (high Retic) can be caused by; low Hgb, altitude training, rest after physical stress, and EPO.

Suppression of red blood cell production (low Retic) can be caused by; high Hgb, physical stress, and illness.

Special considerations:

Dehydration: Hgb looks higher than it really is.

Volume expansion: Hgb looks lower than it really is.

Normal Patterns:
Grand Tour: Hgb decreases despite decent Retic count although retic should trend down as well.

Altitude: A modest rise in Retic is followed by modest rise in Hgb, that should not overcome significant physical stress. Returning to lower altitude should cause a tapering off of Retic and Hgb.

Recovery from physical stress: Initially Hgb and Retic are low followed by a moderate rise in Retic and Hgb. The effect should be suppressed by significant physical stress.

Chronic stress: Low Hgb, low Retic, and poor performance.

Dehydration: Hgb is higher than expected, variable Retic, and performance is decreased.

Enhanced patterns

Epo: Look for a large rise in Retic followed by rise in Hgb, persistently elevated Retic despite high Hgb, and the effect is strong enough to overcome significant physical stress.

Sudden stop of EPO: High Hgb combined with a very low Retic. Retic should fall dramatically and precede a significant fall in Hgb.

Blood transfusion: High Hgb very low retic, maintenance of Hgb despite significant physical stress and low Retic.

Blood withdrawal: Low Hgb despite high retic without some other explanation for red cell destruction.

Masking:

Transfusion plus volume expansion: Very low Retic despite what looks like a low to normal Hgb without other cause for suppression.

EPO plus volume expansion: High retic normal HgB, and an effect that can overcome significant physical stress.

Transfussion plus EPO micro dose: High Hgb, low normal Retic, and an effect that can overcome significant physical stress.

Transfusion plus volume expansion with EPO micro dose: Low to normal Hgb, low normal retic, and an effect that can overcome significant physical stress.

Abrupt stop of EPO plus altitude: Very high Hgb, with low normal retic.

Getting back to our scenario rider MJ started the tour with high normal Hgb and a revved up Retic count. If the body continues to keep production high when Hgb is high normal it must be responding to something. Possible explanations include response to altitude, recovery from significant physical stress, EPO or some combination. The values trended down as expected with a grand tour.

BT started the the tour with low normal Hgb and a very low Retic. This situation is the opposite where something must be preventing the body from revving up production bring up Hgb. Possible explanations include chronic physical stress and illness. Or Hgb may be high but look low on testing because of volume expansion. During the stress of the grand tour however, he maintained his Hgb despite poor production (low Retic). Explanations include particularly hardy red blood cells that survive longer than expected, a tour hard enough to suppress his Retic but not hard enough to break down red cells, dehydration(Hgb looks higher than it actually is), or blood transfusions plus minus EPO.

Another explanation of course is that each rider has a unique physiology that responds differently to preparation and the grand tour itself. This argument is supported by both riders staying well within the cutoff parameters for Hgb, and retic count. OFF scores where well within the normal range as well. The only notable deviation is that rider MJ had a Hgb z score less than -2 indicating a drop well below his usual range. The drop may be entirely consistent with the stress of a grand tour which is likely to be greater than the physical stress during training.

Are the riders clean? Did they dope? Certainly there is no sanctionable evidence. But consider the scenario that rider MJ and BT are not two different riders but the same rider at two different times. Does this scenario change your interpretation of the data?

The point isn’t to answer the question of whether Lance Armstrong is clean or not. What is more important is to begin to demystify the Bio Passport. Hopefully, the type of discussion above can give the average enthusiast the tools and confidence to start looking at the data themselves. Otherwise, we’re stuck with the stories that begin and end with so and so released data, and sanctions so after the fact that no one ever wins.
Part 2 and Part 3 are now up. Part 4

(Note that I am sure that a lot of information here is not entirely correct. I welcome commentary to fill the wholes and redirect the misguided spots.)

I’ve been thinking about writing this post ever since I flipped open the September issue of Bicycling magazine to the review of the new Pinarello Dogma. The only part of the article worth looking at is the price, $17,900 as tested. At that price, is there really any question that it will be laterally stiff yet vertically compliant? So without further introduction I give you a first for Local Cyclist, a top 10 list.

Top 10 you couldn’t make this sh– up.

10. Pinarello Dogma. Sure the bike had Lightweight wheels and electronic Dura Ace, but 18 grand?

9. $1500 cycling shoes. Anything that makes your feet comfortable while riding a bike is worth every penny right? Except that the competition didn’t triple their price after pics of custom “Nike”s started showing up on the internet.

8. Contador time trial phenom.

7. The Butterfly Bike.

6. Tie between Shimano Di2 and the SRAM XX cassette.

5. Joe Papp posts blood values

4. Vino 4 Ever.

3. Tie between Fixies and BikeSnob’s obsession with them.

2. Cycling is clean!

1. Just kidd ing no really nothing to see HERE

+1. I’m all for clean sports but there is something unsettling about the para-religious overtones in the anti-doping movement, especially when our self-proclaimed patron saints are Millar, Papp, and Vaughters.

For a bit more on the winners;
Solo Male
Mathew Lee, and here
Tandem
Jay and Tracey Petervary
Solo Female
Jill Homer

Truvativ Noir 1.1crankset, 32t chainring.

Jump Stop

SRAM PG 990 cassette 11-34t.

SRAM X.0 Medium cage derailleur.

Overall the set up works well. 1×9 is less expensive, lighter, and easier to maintain than a triple or double set up. And the gearing that you give up is very minimal. Why manufactures continue to push 3×9 and 2×10 is beyond me.

With this particular setup the gearing will be a bit tall for a lot of 29er folks (it is spot on for 26 inch bikes). Cat 1 and 2 racers would probably do fine with it. But long for long steep climbs a 30t ring or 11-36 cassette would be better.

Chain retention to the inside is an occasional issue. Without anything tensioning the chain from underneath it can come off the ring if the bike is bouncing up and down and the cranks rotate backwards. Once the chain is off, the Jump Stop is too flimsy to get it back on by pedalling. Realistically, its not a problem while riding as long as you don’t pedal backwards to reset your feet in rough sections.

The Noir crankset comes with a carbon fiber outer ring. It is NOT a bashguard and will get destroyed if you treat it like one. But it is light and does a great job of preventing the chain from coming off to the outside.

The best setup might be the new MRP guide. I haven’t had a chance to try it, but it looks very promising.

Also, remember to get the chain as short as you possibly can so its just long enough to let the suspension cycle completely when you’re in the big cog.

Lastly, the chain-line is actually just about spot on with the chainring in the middle position. All the cogs are accessible, and I haven’t noticed any premature chain wear.