Santa Clara
University Biology Professor Michelle Marvier and her colleagues have recently
published a meta-analysis of field studies that concluded that Bt crops are
generally more benign for non-target invertebrates than chemical insecticides.
A second meta-analysis of lab studies found no harmful effects of Bt proteins
on honeybees. Although these reports will probably fail to convince skeptics,
they raise an important question: Can meta-analysis be used to tease meaningful
results out of a series of studies that, taken individually, are inconclusive?
Given the cost and methodological complexity of ecological studies, it’s
an important question.
The answer is affirmative only if the studies analyzed are sufficiently well
designed and conducted to yield useful data. The need to discard or discount
flawed studies presents a constant challenge to meta-analysts. Even when carefully
selected criteria for inclusion or exclusion of a data set are stated a priori,
researchers are accused of bias when they exclude a study that seems to favor
one point of view over another.
If we are to avoid the GIGO—garbage-in garbage-out—effect in meta-analysis,
incomplete and/or flawed studies must be excluded. A perfect example is the
study reported in PNAS by Rosi-Marshall et al. (2007) which claimed to show
that pollen from Bt-maize was injurious to caddisflies in a laboratory aquatic
ecosystem, but was flawed in numerous ways. For example, pollen produced by
currently available varieties of Bt-maize contains very low concentrations
of Bt toxin. In addition, the authors extrapolated from a laboratory experiment
to a field system based on a single study, an extrapolation that is problematic,
especially given that they used pollen in doses higher than the maximum encountered
under field conditions. Possibly the most damning of all is that they reported
elsewhere that they had failed to find these effects in the field (http://www.benthos.org/database/allnabstracts.cfm/db/Columbia2007abstracts/id/370),
an important fact that should have been disclosed in the PNAS paper. The omission
of those contrary findings arguably amounts to investigator misconduct.
Despite all the sensational publicity they may attract, such flawed studies
add no value to meta-analysis.
An additional challenge for researchers is the well-known “File Drawer
Effect.” Papers that show no differences—and especially no new
risks or harms—are seldom of interest to scientists or journalists and
are unpublishable. Thus, there is little incentive for scientists to spend
their careers proving that safe crops are safe. This skews the literature
in favor of papers that show effects, usually adverse. This phenomenon, combined
with the willingness of some scientists to skew their studies toward negative
results for a variety of reasons—good funding, publications, notoriety,
and politics among them—make meaningful meta-analyses exceedingly difficult.
Taking nothing away from the careful analysis performed by Marvier and her
colleagues, two important points emerge. First, as Marvier notes, “If
meta-analyses and large databases of completed studies were to become a routine
part of risk assessment, then there would not be the distraction of single
experiments capturing media attention and inappropriately alarming or comforting
the public and policy-makers.” It would be better if regulators were
allowed to make science-based decisions free from pressure and politics from
their over-seers in Congress and far from the maddening din of the media.
Second, perhaps we are asking all the wrong questions of all the wrong crops.
By that I mean simply that GM crops are more precisely engineered, have far
fewer and less random changes than conventional crops, and are far more thoroughly
researched; indeed pre-market safety reviews are not required for non-GM crops
even though they have more numerous, random, and potentially disruptive mutations
in their DNA. We also have over 12 years experience from more than 13 million
farmers on greater than 2 billion acres in 25 countries (Brooks and Barfoot,
2008) in profitably growing and harvesting GM crops, with no evidence of adverse
environmental or human health impact and mounting evidence of substantial
environmental benefit.
The greatest contribution Marvier and colleagues have made is to provide yet
one more independent objective analysis that points to the conclusion that
GM crops are as safe as any other crop produced by any other breeding technology;
all crops should be evaluated for safety in a risk-based fashion, independent
of the technology used to develop them.
References
Meta-studies for biosafety research, Michelle Marvier from the University
of Santa Clara (USA). http://www.gmo-safety.eu/en/news/712.docu.html
E.J. Rosi-Marshall et al. 2007. “Toxins in transgenic crop by products
may affect headwater stream ecosystems” PNAS 104 :16204-16208.
Graham Brookes and Peter Barfoot. 2008. Global Impact of Biotech Crops: Socio-Economic
and Environmental Effects, 1996-2006. AgBioForum, 11(1): 21-38. http://www.pgeconomics.co.uk/pdf/agbioforumpaper2008final.pdf
Source:
Bruce Chassy (15.12.09). Can meta-analysis help biosafety research?
Food Technology (http://foodtecheperspective.wordpress.com/2009/12/15/can-meta-analysis-help-biosafety-research/)
Reproduced by permission of the author.
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