paper challenges a common basic assumption in risk assessment, that
there is a threshold level of contamination below which no
effects are seen or caused. Their results and analysis present compelling
proof that this assumption is wrong.
did they find?
fundamental finding is that any addition of an estrogenic
contaminant with will cause an effect. For the system in
which they worked, there is no threshold. This is because in their
experimental system, endogenous estrogen is already at a high enough
level to exceed the threshold for causing an effect. Endogenous
estrogen is already activating the system. A contaminant doesn't
have to exceed the threshold because endogenous estrogen already
et al. show this to be the case experimentally, with a very
large sample size, and then show through theoretical analysis why
it is so. The combination of experimentation and theoretical analysis
is powerful. They conclude that their results demonstrate "that
no exogenous estrogen is without risk."
does this mean?
The "threshold assumption" is central to the entire approach
used by risk assessment for establishing the health safety of chemicals.
It has been widely accepted but rarely tested. Its use in regulatory
science has been a pragmatic step, not something based on theory
or on fact. This paper stands out as a strong test of that assumption-ong
because of the sample size and the analysis--and the assumption
comes up short.
Sheehan et al.: "Over 70,000 man-made chemicals that
have an aggregate value of billions of dollars are found in food,
water, air, or soil. Given the central role of the threshold assumption
in evalution of health safety, the exposure of all organisms to
synthetic chemicals, the importance of these chemicals in modern
society, and their huge production volume and economic value, it
is surprising that the threshold assumption has been so widely accepted
and so rarely tested."
is surprising. And it is deeply disturbing that when the assumption
is tested, as Sheehan et al. have done, it is disproven.
is this important?
This assumption is a key part of the way that safety standards
are set. In experiments designed to explore toxicity, an important
goal is to establish a "no observed adverse effect level"
or NOAEL. This is the level of exposure that produces no statistically
significant increase in adverse effects. Typically experiments start
at high contamination levels and work downward along the exposure
curve until no statistical effect is seen. That's the NOAEL. A fudge
factor is then added because of differences among species. Typically
this means that the NOAEL is divided by some number, often 100.
The assumption is that an exposure level calculated in this fashion
is safe, and it is used to determine acceptable exposure levels.
Sheehan et al. show is that this approach is wrong
if the contaminants share a common mechanism with endogenous chemicals
that are already at a level above a concentration sufficient to
cause an impact. (Note: there is another assumption
in this approach--that the dose-response relationship is monotonic--that
science is showing is also commonly violated by endocrine disrupting
compounds). That condition (sharing a common mechanism...) is likely
to be quite common for endocrine disrupting chemicals.
the basic message of this paper is that one of the fundamental assumptions
used to guide risk assessment doesn't work with endocrine disruption.
It's simply wrong.
did they do?
et al. worked experimentally with sex control in the red-eared
slider, a turtle in which sex determination is normally controlled
by temperature (via a mechanism in which the hormonal processes
involved in sex determination are temperature dependent; more...).
They exposed a series of turtle eggs at 28.6°C to a range of
doses of 17ß-estradiol. The temperature they chose normally
would have resulted in mostly males but some females. They then
determined the sex of each egg at hatching. They analyzed the results
using a theoretical construct based on the Michaelis-Menten equation,
which has been developed in basic chemistry to model enzyme kinetic
studies. The data from the large experiment fit the M-M model extraordinarily
well. Their analyses showed that any addition of exogenous
estrogen caused a change in the sex ratio of pool of eggs.