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TESTS FOR
EPO ABUSE
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Will G
Hopkins PhD, Physiology and Physical Education, University of Otago, Dunedin
9001, New Zealand. Email: will.hopkins=AT=otago.ac.nz.
Sportscience 4(2), sportsci.org/jour/0002/inbrief.html#EPO, 2000 (1051
words)
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EPO
(erythropoietin) is a hormone produced by the kidneys. It travels via the
circulation to the bone marrow, where it keeps the supply of red cells
ticking over. More EPO means more red cells, which boost endurance
performance by transporting more oxygen to the muscles. A sojourn at real or
simulated altitude is a safe and legal way for an athlete to get more EPO.
Injections of EPO are even more effective: athletes can expect enhancements
in endurance performance a massive 5% or more (Sawka et al., 1996; Birkeland
et al., 2000). But injections of EPO can make the blood so thick with red
cells that it clots throughout the circulation and kills the athlete. Amongst
cyclists alone EPO abuse is thought to have caused 20 sudden deaths in recent
years. Not surprisingly, EPO is on the IOC's list of banned substances.
Unfortunately there has been no dependable and fair test
for EPO abuse. The International Cycling Union (UCI) now tests for the
thicker blood by measuring the proportion of red cells (the hematocrit or
packed-cell volume) in a blood sample. By itself, this test is not a good
indicator of EPO abuse, because a few athletes have a naturally high
hematocrit, while others can get a high proportion from altitude training. A
cyclist exceeding the upper limit is therefore not banned for EPO abuse, but
is simply not permitted to compete because of the health risk. In any case,
cyclists can cheat the test. When told they are to be tested, apparently they
have 10 minutes to report to the medical team. Why 10 minutes? A cynical
informant claims that's long enough for an athlete to run 500 ml of saline
into a vein. By diluting the blood, the saline immediately brings the
hematocrit down by a few percent. The normal hematocrit for "clean"
elite cyclists is around 44% (Saris et al., 1998; Schumacher et al., 2000).
So it's possible for a cyclist to take enough EPO to increase the hematocrit
to around 52%, then infuse saline just before the test to bring the
hematocrit back below the limit of 50% (or 51%, to allow for error of
measurement). As a bonus, the saline infusion itself almost certainly
enhances performance in long hot events like the Tour de France.
You can't really blame the athletes for cheating, or the
UCI for allowing the cheating to continue. Selfish behavior by athletes and
their sporting bodies is driven by genes that evolution can't eliminate from
the gene pool of social animals. Those of us who inherit the genes are driven
to cheating when we figure the rewards outweigh the risks. Sure, but one of
the rules of public competitive sport is that we must catch and punish the
cheats. What to do in the case of EPO abuse?
A better test would help. Right now there are two on
offer: a urine test and a blood test. The urine test, which has just been
published in Nature (Lasne and de Ceaurriz, 2000), is based on the technique
of immunoblotting to directly detect the artificial "recombinant"
EPO that the drug companies produce for treatment of patients. The authors of
the test first showed that it worked on urine from patients taking recombinant
EPO, then they turned their attention to frozen samples of urine from 102
cyclists in the 1998 Tour de France. A routine test for EPO revealed 28
positives. When the authors immunoblotted the 14 samples with the highest
concentration, all were positive for recombinant EPO. If we assume at least
half of the remaining 14 were positive, at least 20% of competitors abused
EPO during or immediately before the Tour.
Scientists at the Australian Institute of Sport have been
working on the blood test for the last couple of years, and they're hoping
the International Olympic Committee will adopt it for the games in Sydney.
The test is based on detecting the effects of EPO on red cells, rather than
EPO itself. The AIS team have found that a sensitive and specific indicator
of EPO injections is an increase in the number of immature red cells
(reticulocytes) in the blood. By analyzing the properties of these immature
cells with state-of-the-art equipment, they can distinguish between athletes
who have injected EPO and those who have had a natural increase in red cells
from real or simulated exposure to altitude.
How good are these tests? My guess--and it is a guess,
because no-one at the AIS will comment--is that the urine will test positive
only if the last injection of EPO was within a few days of the test. Any
earlier and the EPO will have disappeared from the circulation and therefore
from the urine. The blood test might detect an injection within the last
couple of weeks, because that's about how long it takes the new red cells to
mature. If the IOC decides to use either of these tests at the Sydney
Olympics, athletes will simply stop injecting a week or so before arriving at
the Games village. The ergogenic effect of a course of EPO injections lasts
several months, because that's the lifetime of red cells in the circulation
of athletes training hard. So the cheats will win again, but hopefully for
the last time. The real value of these tests will be apparent when they are
used for random testing between the Olympics. Athletes at Sydney will seem to
be clean. Athletes at Athens really will be clean.
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Sept 8: The blood and urine tests will both be used at
the Sydney Olympics. Any athlete who tests positive in both tests will be
disqualified. Some action may be taken against athletes who pass the urine
test but fail the blood test.
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Birkeland KI,
Stray-Gundersen J, Hemmersbach P, Hallen J, Haug E, Bahr R (2000). Effect of
rhEPO administration on serum levels of sTfR and cycling performance.
Medicine and Science in Sports and Exercise 32, 1238-1243
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Lasne F, de Ceaurriz J
(2000). Recombinant erythropoietin in urine. Nature 405, 635
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Saris W, Senden JMG, Brouns F (1998).
What is a normal red-blood cell mass for professional cyclists? Lancet 352,
1758
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Sawka MN, Joyner MJ, Miles DS,
Robertson RJ, Spriet LL, Young AJ (1996). The use of blood doping as an
ergogenic aid. Medicine and Science in Sports and Exercise 28(6), R1-R8
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Schumaker YO, Grathwohl D, Barturen JM,
Wollenweber M, Heinrich L, Schmid A, Huber G, Keul J (2000). Haemoglobin,
haematocrit and red blood cell indices in elite cyclists. Are the control
values for blood testing valid? International Journal of Sports Medicine
21, 380-385
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See also previous articles on
this topic by Stephen Seiler and Dave Martin under Blood Tests on our Sports Medicine index page.
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HAS YOUR PATIENT/CLIENT/SUBJECT CHANGED?
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Will G
Hopkins PhD, Physiology and Physical Education, University of Otago,
Dunedin 9001, New Zealand. Email: will.hopkins=AT=otago.ac.nz.
Sportscience 4(2), sportsci.org/jour/0002/inbrief.html#subject, 2000 (716
words)
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Any
time you test or measure someone, the value you get has "noise"
(random error) in it. If the noise is small, the test will reliably track any
changes in the subject. But if the noise is too big, any change you see could
be due to the noise rather than to any real change. So here's a really
important question: how can you tell whether the change you see is real or
noise?
Some researchers have had an honest shot at this question,
using a reference value for changes called 95% limits of agreement. According
to these researchers, you can trust an observed change only if it's greater
than the limits of agreement (Atkinson and Nevill, 1998). Sounds really cool,
but there's a problem: the limits of agreement are so big that clinically
important changes often fall within them. So if you use limits of agreement,
you may have to ignore an important change in your subject. I made this point
and others in a recent review of reliability in the journal Sports Medicine
(Hopkins, 2000). I also wrote about assessing an individual
on my stats pages and provided a spreadsheet to use when
making decisions about change. Since then Atkinson and Nevill have written a
letter to the editor, and the editor has invited me to respond. Their letter
and my response may appear in the October issue of the journal.
In writing the response, I've found that the best
reference value for making decisions about change is the magnitude of the
noise itself. I've previously called this noise the typical error. It's also
known as the standard error of measurement, the technical error of
measurement, and the within-subject standard deviation. Limits of agreement
are about 3 times as big as the typical error, which explains why they're too
big to use in clinical decision-making. I've also found that you have to keep
one eye on the smallest "signal"--the smallest clinically important
change in your subject. This element has been missing from previous
publications on the topic of measurement error. I've put the noise and the
signal together in the following advice for clinicians and other
practitioners:
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Use of Typical Error When Monitoring an
Individual
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- Find the noise in
your measure--the value of the typical error from a short-term
reliability study of individuals similar to the one you are
monitoring.
- Decide on the
smallest signal--the smallest clinically or practically worthwhile
change in the measure for the individual.
- If the noise is
less than the smallest signal, you can trust observed changes of any
magnitude between a single test and retest.
- If the noise is
greater than the smallest signal, you can trust expected changes
greater than the noise, but you will need to do multiple tests before
you can trust any changes less than the noise.
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Researchers, don't feel left out! I've
come up with similar advice for you. Once again, the typical error and
smallest clinically important change are the crucial elements:
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Use of Typical Error in Research Design
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- Find the noise in
your measure--the value of the typical error from a reliability study
with individuals and a time frame similar to those of your intended
study.
- Decide on the
smallest signal--the smallest clinically or practically worthwhile
change in the measure for your study group.
- If the noise is
less than the smallest signal, you can use the measure to make precise
estimates of any experimental effects with a single test and retest
and a sample of modest size (<10 in a crossover; ><36 in a fully controlled trial). >
- If the noise is
greater than the smallest signal, the measure will provide acceptable
precision for effects smaller than the noise only with more testing
(more subjects, or more pre and post tests).
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I give the rationale for this advice in
my response in Sports Medicine. I am also emphatic that limits of agreement
should be abandoned as a clinical tool and marginalized as a measure of
reliability.
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Atkinson G, Nevill AM (1998). Statistical
methods in assessing measurement error (reliability) in variables relevant
to sports medicine. Sports Medicine 26, 217-238
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Hopkins WG (2000). Measures of
reliability in sports medicine and science. Sports Medicine 30, 1-15
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LIMITS TO PERFORMANCE
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Will G Hopkins
PhD, Physiology and Physical Education, University of Otago, Dunedin 9001,
New Zealand. Email: will.hopkins=AT=otago.ac.nz.
Sportscience 4(2), sportsci.org/jour/0002/inbrief.html#limits, 2000 (423
words)
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A
writer (Kurt Kleiner) preparing an article for an Olympics issue of New
Scientist contacted me recently about limits to world records. I flicked his
inquiry and my comments to the Sportscience mailing list for further comment.
The full text of the inquiry and the responses are linked
here. Below is a summary for the contributors, with a link to their
responses. See also the
article by Kurt at the New Scientist website. In summary: some people say
the running records are leveling off; others aren't so sure.
Will Hopkins
thought that the differences between training and nutrition programs accounted
for only a few percent. He thought that more people competing would lead to
better records for purely statistical reasons. He also suggested eugenics
arising from breeding between top athletes will lead to genetically superior
athletes. He thought (wrongly?) performances were plateauing now. There
haven't been big gains associated with drug use, so he suggested that top
athletes tend not to use the drugs (too much to lose), or that top athletes
might get less benefit anyway.
Stephen Seiler had
data on progression of records. In the last 50 years, improvements per decade
have been approximately: sprinting, 1%; distance running, 1.5%; jumping,
2-3%; pole vaulting, 5%; swimming, 5%; skiing, 10%. According to Stephen, the
increases show no sign of leveling off, even for sprinting. [But female times
have pretty-much leveled off: see Stephen's article on the gender gap.] Introduction of
drug testing appears to have made no difference [for the males, but it may
have stopped females getting faster]. Reasons for the increase: technology,
especially for transportation sports; more extreme outliers with increased
participation; interbreeding of athletes; individualized training; drugs? For
endurance events there is headroom in theory for ~10% improvement, if one
individual had current best values of maximum oxygen consumption, anaerobic
threshold, and exercise efficiency.
Michael Green wondered
if we would need to measure performances to an extra decimal place, as
performances plateau.
Frank Katch called our
attention to a recent article in Scientific American, in which the authors
suggested that manipulation of gene expression in muscles could produce
superfast sprinters.
Dan Wagman expressed
regret that gene doping might stop us from ever knowing what our natural
physiological limits are. He wondered whether sport psychologists will need
to specialize in sport performance dehancement strategies, to help stop
gene-doped athletes giving the game away.
Will Hopkins
commented on the paper in Scientific American. He noted that blood volume was
more important than proportion of muscle-fiber type as a determinant of
endurance performance. He was critical of the parallel the authors drew
between detraining in previous sedentaries and tapering in elite sprinters. He
was doubtful that turning on the expression of superfast Type IIb myosin in
human muscles would lead to enhanced sprint performance, especially if top
sprinters already have an optimum mix of Type IIa and IIx myosin.
©2000
Published Sept 2000
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