ISIS Report 16/10/12
Excess Cancers and Deaths with GM Feed: the Stats Stand Up
That cancers are found even with a small number of rats tested is
strong evidence that the GM feed and herbicide are carcinogenic Prof Peter Saunders
Please circulate widely and repost, but you must give the URL of the original and preserve all the links back to articles on our website. If you find this report useful, please support ISIS by subscribing to our magazine Science in Society, and encourage your friends to do so. Or have a look at the ISIS bookstore for other publications
2012, the research team led by Gilles-Eric Séralini at the University of Caen
published the findings of their feeding trial on rats to test for toxicity of Monsanto’s
genetically modified (GM) maize NK603 and/or Roundup herbicide in the online
edition of Food and Chemical Toxicology .
and his colleagues had previously found
evidence for toxicity of GM feed in data from Monsanto’s own experiments, which
they had obtained through a Freedom of Information demand . Monsanto
challenged their conclusions and, to no one’s great surprise the European Food
Standards Agency (EFSA) supported Monsanto . So the team decided to run their
own experiment, using an unusually large number of animals and over a period of
about two years, roughly the life expectancy of the rats, rather than the usual
90 days required in toxicity trials including Monsanto’s.
Séralini and his colleagues
found was that NK603 and Roundup are not only both
toxic as expected, but also carcinogenic, which was unexpected. The proportion
of treated rats that died during the experiments was much greater than the
controls; moreover, in almost all groups a higher proportion developed tumours,
and the tumours appeared earlier.
soon as the paper appeared, the GM lobby swung into action. In particular, the
Science Media Centre (SMC), a London-based organisation partly funded by
industry, quickly obtained quotes from a number of pro-GM scientists and
distributed them to the media . According to a report in Times Higher
Education , the SMC succeeded in influencing the coverage of the story
in the UK press and largely kept it off the television news.
has rebutted the pro-GM critics point by point on the CRIIGEN website . The
statistician Paul Deheuvels, a professor at the Université Pierre et Marie Curie
in Paris and a member of the French Académie des sciences, has now drawn attention to another serious error in the
criticisms : the complaint that Séralini used only 10 rats per group when the
OECD guidelines  recommend 50 for investigations on carcinogenesis. Because
the experiments did not follow the accepted protocol, their results, they
argue, can be safely ignored.
the first place, this was not a wilful disregard of the guidelines. The
experiment was designed to test for toxicity, and for that the recommended group
size is 10.
But Deheuvels pointed out that the fact Séralini
and his colleagues had used smaller groups than recommended makes the results
if anything more convincing, not less. That is because using a smaller
number of rats actually made it less likely to observe any effect. The fact that an
effect was observed despite the small number of animals made the result all the
To see why, we have to look carefully at how common statistical
tests are carried
out. We begin with a null hypothesis, which as the name suggests is essentially
the hypothesis that nothing unusual has happened. Here it is the hypothesis
that rats fed on GMOs and/or herbicide are no more likely to develop cancer
than the controls. Clearly, we would like to reject the null hypothesis if it is
false and accept it if it is true. But statistics is about taking decisions in
the face of uncertainty – if there were no uncertainty
there would be no need to use statistics – and so however careful we are, we may
come to the wrong conclusion.
are two ways in which we can go wrong. On the one hand, we can make a “Type 1
error” in rejecting the null hypothesis when it is correct. Here that would
mean reporting that GMO and/or herbicide are carcinogenic when they are not. Or,
we can make a “Type 2 error” in accepting the null hypothesis when it is false.
Here that would mean reporting that GMO and/or herbicide are not carcinogenic
when in fact they are.
we would like to design experiments to make either of those probabilities as
small as possible, but there is a problem. The two types of error are linked.
We can reduce the probability of making a Type 1 error by requiring stronger
evidence before we reject the null hypothesis. But if we do that we necessarily
require less evidence to accept it, but that increases the probability of
making a Type 2 error. We have to find a balance, and usually what we do is
insist that the probability of a Type 1 error must be very small,
conventionally 0.05. That’s the origin of the “significant at 5 percent” level.
probability of 0.05 is very small, so what we are saying is that we will only
accept that the effect is real if we can be convinced “beyond reasonable doubt”;
and most of the time that makes sense. If you’re thinking of installing a new
manufacturing process or a new way of running your farm, you want to be very
confident that it really is better before you make a major investment.
is not so obviously sensible when safety is concerned. If there is scientific
evidence that a product is hazardous, then it is hardly surprising if the
manufacturer would not want to withdraw it unless the evidence is very strong
indeed. The rest of us, however, might take a different view. Are we really
willing to accept NK603 maize, or Roundup herbicide, unless and until they have
been shown beyond reasonable doubt to be carcinogenic?
standard statistical test does seem to be the wrong way around, but that’s
partly because so far we have only been considering the Type 1 error, the false
positive. But as Deheuvels reminds us, there is also the Type 2 error, the
false negative. If NK603 and/or the herbicide are actually carcinogenic, what
is the probability that we will fail to observe that?
way to reduce the probability of a Type 2 error is to use larger groups. Because
we would expect carcinogenicity to be slower to appear and harder to detect
than toxicity, the group size for experiments on carcinogenicity should
be larger than for toxicity, and this is precisely what the OECD Guidelines
If the experiment had not detected carcinogenicity, that
might have been because the groups were too small. As the experiment did detect
it, that the groups were small is not an issue. The scientists who were asked
to supply sound bites for the Science Media Centre were quick to object that
Séralini and his group had used the protocol for testing toxicity rather than
the one for carcinogenesis. Had they taken a moment to ask themselves why the
two protocols are different, they would have realised that in using the
toxicity protocol (and remember, that was because it was what the experiment
was designed to test) Séralini and his group made it less likely that
they would detect carcinogenesis. To criticise a result because the experiment
was conducted in a way that was more conservative than required is
- Séralini G-E, Mesnage R, Gress S, Defarge N, Malatesta M,
Hennequin D and de Vendômois JS (2012), Long term toxicity of a Roundup
herbicide and a Roundup-tolerant genetically modified maize. Food and
Chemical Toxicity. http://dx.doi.org/10.1016/j.fct.2012.08.005
G-E, Cellier D and de Vendômois JS (2007). New analysis of a rat feeding
study with a genetically modified maize reveals signs of hepatorenal
toxicity. Archives of Environmental Contamination and Toxicity 52,
review of statistical analyses conducted for the assessment of the MON863
90-day rate feeding study, 2007, http://www.efsa.europa.eu/en/efsajournal/doc/19r.pdf
Media Centre press release: Expert Reaction to GM maize causing tumours in
rats. 19 September 2012,
troops check ‘poor’ GM study”, Paul Jump, Times Higher Education, 4
Research Team FAQs, accessed 12 October 2012, http://www.criigen.org/SiteEn/index.php?option=com_content&task=view&id=368&Itemid=1
Heuvels P. Étude de Séralini sur les OGM :
pourquoi sa méthodologie est statistiquement bonne. Le nouvel
observateur Le Plus, 2012, accessed 12 October 2012, http://leplus.nouvelobs.com/contribution/646458-etude-de-seralini-sur-les-ogm-pourquoi-sa-methodologie-est-statistiquement-bonne.html?utm_source=outbrain&utm_medium=widget&utm_campaign=obclick&obref=obinsource
Guidelines for the Testing of Chemicals 451: Carcinogenicity
There are 4 comments on this article so far. Add your comment
|Tim Eisenbeis Comment left 22nd October 2012 10:10:16|
Simply a superb explanation for those of us non-statisticians. Thank you! You're to be commended for diligently working to get the truth out before the people. If anyone is paying attention, they will benefit from listening to reason and careful scientific observation, blocking out the viciously selfish hype.
|John Byng Comment left 16th November 2012 22:10:28|
Please correct the typo in the twelth paragraph beginning "It is not so obviously sensible...". The words at the end of the paragraph should be:-
"...shown beyond reasonable doubt not to be carcinogenic?" Unfortunately the "not" is missing.
Otherwise a brilliant article.
|Peter Saunders Comment left 19th November 2012 08:08:17|
John: Thanks for your comment, but I meant it as it stands. The issue is whether we have to prove beyond reasonable doubt that Roundup is carcinogenic before we are permitted to restrict or forbid its use. It would of course be better if it were up to Monsanto to prove that it is not carcinogenic, but that isn’t how these things are done, at least not yet. I’m sorry if the language was a bit convoluted, but it’s not always easy to express statistical arguments precisely in ordinary language.
|mike olaughlin Comment left 7th December 2012 18:06:59|
i have cancer and i loved my corn aqnd sodas, etc....please stop this GMO stufff before its to late...