To be published in American Journal of Roentgenology, Nov. 2002 issue
Bernard L. Cohen
Dept. of Physics
University of Pittsburgh
Pittsburgh, PA 15260
Telephone: (412)624-9245
FAX: (412)624-9163
e-mail: blc@pitt.edu
In recent years, great efforts and substantial monetary
expenditures have been devoted to reducing radiation exposure from X-rays and
other medical procedures. This is motivated by the often quoted statement that
“any radiation dose, no matter how small, can cause cancer”. The basis for that
statement is the linear-no threshold theory of radiation carcinogenesis.
According to that theory, if 1 Gy (100 rads) of exposure gives a cancer risk R,
the risk from 0.01 Gy (1 rad) of exposure is R/100, the risk from 0.00001 Gy (1
millirad) is R/100,000, and so on. Thus the cancer risk is not zero regardless
of how small the exposure.
However, there has recently developed a strong sentiment
in the community of radiation health scientists to regard risk estimates in the
low-dose region based on linear-no threshold theory as being grossly
exaggerated or completely negligible. For example, the 6000 member Health Physics Society, the principal
organization for radiation protection scientists, has issued a position paper
[1] stating “Below 10 rad ….risks of health effects are either too small to be
observed or are non-existent”. In fact there is even substantial evidence that
low level radiation may be protective against cancer; a view known as
“hormesis”
The purpose of this paper is to review the basis for
linear-no threshold theory and to present some of the newly emerging
information that has caused this recent shift in sentiment.
The original basis for linear-no threshold theory, as
that theory emerged in the mid-twentieth century, was theoretical and very
simple. A single particle of radiation hitting a single DNA molecule in a
single cell nucleus of the human body can initiate a cancer. The probability of
such a cancer initiation is therefore proportional to the number of such hits,
which is proportional to the number of particles of radiation, which is
proportional to the dose. Thus the risk is proportional to the dose – this is
linear-no threshold theory..
The problem with this simple argument is that factors
other than initiating events affect the cancer risk. Human bodies have
biological defense mechanisms which prevent the vast majority of initiating
events from developing into a fatal cancer [2]. We give some of the most important examples including how they
are affected by low level radiation.
Our bodies produce repair enzymes which repair DNA damage
with high efficiency, 99.99% for single strand breaks and 90% for double strand
breaks [2]. We will show that low level radiation stimulates production of
these repair enzymes.
Apoptosis, a process by which damaged cells “commit
suicide”, is stimulated by low level radiation [3].
The immune system is important for preventing mutations
from developing into a cancer; we will show that low level radiation stimulates
the immune system, but high radiation levels depress it.
Many cancers are initiated by corrosive chemicals; there
are processes for scavenging these out of cells, and low leval radiation
stimulates these scavenging processes [4].
Radiation can alter cell cycle timing, extending the time
before the next cell division (mitosis); damage repair is effective only until
the next mitosis, so changing this available time can be important (Elkind M,
personal communication).
Since all of these biological defense mechanisms require
consideration, the above-described
basis for linear-no threshold theory is far too simple.
There is also direct and obvious evidence for failure of
the simple argument. The number of initiating events is roughly proportional to
the mass of the animal – more DNA targets mean more hits. Thus the simple
theory predicts that the cancer risk should be approximately proportional to
the mass of the animal. But the cancer risk in a given radiation field is very
similar for a 30 g mouse and a 70,000 g human. Our very definition of dose,
based on the energy absorbed per unit mass of tissue, which is proportional to
the number of radiation hits per unit target mass, would be misleading if only
the total number of hits (which is proportional to the number of initiating
events) were relevant regardless of the target mass.
Many aspects of the problem are now understood on the
molecular level [2]. DNA damage events naturally induced by chemical action of
free radicals and thermal processes occur roughly a million times per day in each of the trillions of cells in our
bodies, but only about one per cell per day remains unrepaired and survives as
long-term mutations; it is these that are normally responsible for human
cancers. The DNA damage from radiation is, on average, more severe, but taking
this into account, a dose of 0.1 Sv (10 rem) , which is near the upper limit of
low level radiation, is estimated to cause only 0.004 long-term mutations per
cell, a trivial addition to the one per cell per day from natural processes.
From this it seems evident that cancer initiating events
are not the controlling factor in determining the dose-response relationship
for radiation in the low level radiation
region as was assumed in linear-no threshold theory. The principal
effect of radiation is in modifying the biological defense mechanisms, rather
than in providing initiating events.
We begin by presenting, as a follow-up on the previous
discussion, a few examples of how low level radiation affects biological
defense mechanisms. Cancers are initiated by genetic
damage in a cell nucleus. One type of
genetic damage that has been widely studied is chromosome aberrations and it
was long ago recognized that a high dose of radiation increases the number of
these. However, Table 1 shows an in
vitro example from Shadley and Dai
[5] of how that process is affected if the high dose is preceded a few hours
before by a low dose. In this case we
see that the number of chromosome aberrations caused by the high dose is
substantially reduced.
As an example of an in vivo experiment, Cai and Liu [6] reported that
exposure of mouse cells to 65 cGy (65 rad) caused chromosome aberrations in 38%
of bone marrow cells and in 12.6% of spermatocytes, but if these exposures are
preceded 3 hours earlier by an exposure to 0.2 cGy, these percentages are
reduced to 19.5% and 8.4% respectively.
There are many other examples of such experiments, both in vitro and in vivo , and the results are
usually explained as stimulated production of repair enzymes by low level
radiation. These are examples of what
is called “adaptive response” [7] -- the body adapts to effects of radiation by
developing protective responses.
There has been recent evidence of
this behavior from human exposures, based on comparing residents of a high
background radiation area (1 cGY/year) and a normal background radiation area
(0.1 cGy/year) in Iran [8]. When lymphocytes from these groups were exposed to
1.5 Gy (150 rad), the mean frequency of chromosome aberrations per cell was
0.098 +/-0.012 for the former versus 0.176 +/- 0.017 for the latter, a 4
standard deviation difference, presumably caused by adaptive response induced by
radiation in the high background area.
Another
type of experiment that reveals effects of “adaptive response” involves
detection of genetic mutations. As an
example of an in vitro experiment
[9], it was found that an X-ray exposure of 300 cGy to human lymphocytes
induced a frequency of mutations at the hprt locus of 15.5 x 10-6, but if this large exposure
was preceded 16 hours earlier by an exposure of 1 cGy, this frequency was
reduced to 5.2 x 10-6. As an
in vivo example [10], it was
found that the percentage of dominant lethal mutations in offspring resulting
from exposures of female drosophila to 200 cGy of X-rays before mating was
substantially reduced by preceding this high dose with an exposure to 2 cGy;
for different strains of drosophila and different oocyte maturities these
percentages were reduced from 42% to 27%, from 11% to 4.5%, from 40% to 36%,
from 32% to 12.5%, from 42% to 30%, and from 51% to 22%.
A
technique has been developed for directly observing repair of DNA base damage
[11]. It was found that preceding an exposure to 2 Gy of gamma radiation with
0.25 Gy 4 hours before reduced the time
for 50% DNA lesion removal from 100 minutes to 50 minutes.
One
might consider the possibility that adaptive response is only effective against large doses of radiation,
but there are data on its effectiveness against spontaneous transformation to
malignancy on cells with a predisposition to such transformation. This was
shown [12] for exposures of C3H 10T1/2 mouse cells where one day after exposure
to low doses of radiation the rate of spontaneous
neoplastic transformation was reduced by 78%. In a similar experiment [13] with
HeLa x skin fibroblast cells, the reduction was by 55%. In both of these
studies, the results had high statistical significance.
This
may be understood on a more basic level from effects of radiation on free
radical oxidants which normally cause the cancer initiating DNA damage and on
the antioxidants that scavenge them out of cells. A study of rat cells [14]
indicated that 50 cGy of X-ray exposure increases the amount of an antioxidant
(SOD) by about 25% and decreases the amount of lipid peroxide (oxidized cell
membrane) by about 20%. At much higher doses, these effects are reversed.
Since
the immune system destroys cells with persistent DNA damage and thus resists
the development of cancer, the effects of low level radiation on it are
relevant here. Such effects on several
different measures of the immune response [15] are listed in Table 2. We see that by each of these measures, the
immune response is increased by low level radiation.
One
study of this effect over a wide range of radiation doses [16] reports
increases in immune response by 80% in vitro and by 40% in vivo
at about 20 cGy followed by a rapid decrease to well below the
unirradiated level at doses above 50 cGy.
It has been directly shown that the immune system provides resistance to metastasis of tumors. When tumor cells are transplanted into the groins of mice, the rate of their metastasis into the lung is cut about in half by total body irradiation with 15-30 cGy 12 days after the transplantation [17]. Doses above 50 cGy on the other hand, reduce the immune response, leading to increased rates of metastasis. A rat study showed that total body irradiation – but not tumor irradiation – with low level radiation reduces the rate of metastasis and increases infiltration into the tumor of immune system killer lymphocytes [19]. The latter effect was known much earlier [16]. Total body irradiation with low level radiation has also been shown to reduce tumor size [16, 20]. Clearly, total body irradiation with low level radiation stimulates the immune system.
All
of the work reported in this section shows that low level radiation has effects
that are protective against cancer. One must still consider the fact that this
same radiation can also initiate cancers occurring many years in the future,
perhaps in accordance with linear-no threshold theory – these two effects must
both be considered. But to ignore the former and consider only the latter as
has been done in the past is surely not justified.
The
final decisions on dose-response are always most heavily weighted on
experiences with exposures to humans.
The data on this are summarized in the next section.
The
principal data that have been cited by those in influential positions to
support linear-no threshold theory are solid tumors (all cancers except
leukemia) among the Japanese A-bomb survivors, and an IARC (International
Association for Research on Cancer) study of occupational doses to radiation
workers. The former data [21] are shown
in Figure 1, where the error bars represent 95% confidence limits (2 standard
deviations). If error bars are ignored,
the points do indeed suggest a linear relationship with intercept near zero
dose.
But
the data themselves give no statistically significant indication of excess
cancers for doses below 25 cSv. In
fact, it has been shown [22] that considering the three lowest dose points
alone, the slope of the dose-response curve has a 20% probability of being
negative (risk decreasing with increasing dose).
The
other evidence that has been cited as supporting linear-no threshold theory is
the IARC study [23] of 95,673 monitored
radiation workers in U.S., U.K., and Canada.
For all cancers except leukemia, there were 3,830 deaths but no
excess over the number expected.
The risk is reported as -0.07/Sv with 90% confidence limits (-04,+0.3).
There is surely no support for linear-no threshold theory here.
However,
for the 146 leukemia deaths, they do report a positive risk versus dose
relationship and claim that this supports linear-no threshold theory. Their data are listed in Table 3. It is obvious from those data that there is
no indication of an excess risk below 40 cSv (even the excess for >40 cSv is
by only 1.4 standard deviations). The
conclusion by the authors that this supports linear-no threshold theory is
based on an analysis which arbitrarily discards the data in Table 3 for which
o/e (observed/expected) is less than unity! They thus discard three of the
seven data points.
While
the solid tumor data on A-bomb survivors and the leukemia data on monitored
radiation workers are said to support linear-no threshold theory (although the
leukemia data on the former group and the solid tumor data on the latter group
do not), there are several studies that seem to contradict that theory. The data on leukemia among A-bomb survivors
[21] are shown in Figure 2, with error bars indicating 95% confidence
limits. These data strongly suggest a
threshold above 20 cSv.
A
similar behavior is found for breast cancer among Canadian women exposed to
X-ray fluoroscopic examinations for tuberculosis [24], the data for which are
shown in Figure 3. Here again, there
seems to be a decrease in risk with increasing dose at least up to 20 cSv.
The data on lung cancer among these
Canadian women [25], and also a one point study of 10,000 individuals in
Massachusetts [26] are shown in Figure 4. Here again we see a decrease in the
low dose region, in this case extending at least up to 100 cSv. In Figure 4, these data are compared with
lung cancer data for the Japanese A-bomb survivors, and we see there a
difference between the two data sets that is clearly statistically significant;
the A-bomb survivor data gives a much higher risk at all doses. This can perhaps be explained by the
difference between very high dose rate in the A-bomb survivors and the lower
dose rate from protracted fluoroscopic exams extending over several years. In
any case, Figure 4 must give one pause before accepting the widely practiced
approach of using A-bomb survivor data to predict risks from low level
radiation..
In 1957, there was an explosion in
an incredibly mismanaged radioactive waste storage facility at the U.S.S.R. Mayak nuclear weapons
complex in the Eastern Urals of Siberia, causing large radiation exposures to
people in nearby villages. A follow-up on 7852 of these villagers [26] found
that the rate of subsequent cancer mortality was much lower among these than
among unexposed villagers. The ratio for exposed to unexposed was 0.27 for 4
cGy, 0.39 for 12 cGy, and 0.28 for 50 cGy; for the latter two groups, the
differences are outside of the 95% statistical confidence limits.
A $10 million study was conducted of
shipyard workers involved in servicing U.S. Navy nuclear-propelled ships,
comparing those who were and were not occupationally exposed to radiation [28];
the former group had exposures above 0.5cSv (0.5 rem) and average exposures of
5 cSv, while the latter group had exposures below 0.5 cSv. The cancer mortality
rate for the exposed was only 85% of that for the unexposed, a difference of
more than 4 standard deviations. Hiring procedures, medical surveillance, job type,
and other factors were the same for both groups, so the often used explanation
of “healthy worker effect” does not apply here.
Stimulation of the immune system by low level radiation is being used on an experimental basis for medical treatment of non-Hodgkins lymphoma with total body and half body irradiation. This radiation was administered to one group of patients (“irradiated” group), but not to an otherwise similar “control” group, before both groups were given similar other standard treatments such as chemotherapy with or without accompanying high radiation doses to tumors. In one such study [17], after 9 years, 50% of the control group, but only 16% of the irradiated group had died. In a 25 year old study [29] with different standard treatment, 4-year survival was 70% for the irradiated group versus 40% for the controls. In another study in that time period [30] with a more advanced chemotherapy, 4-year survival was 74% for the irradiated group versus 52% for the control group.
Probably the most significant human
data on low level radiation is still in the research stage, but preliminary
results [31] seem very interesting. In Taipai and other areas of Taiwan, 1700
apartment units were built using steel contaminated with Cobalt-60, exposing
10,000 occupants for 16 years to an average, according to preliminary
estimates, of 4.8 rem in the first year and 33 rem in total. From national
Taiwan statistics, 173 cancers and 4.5 leukemias would be expected from natural
sources, and according to linear-no threshold theory, there should have been 30
additional leukemias. However a total of only five cancers and one leukemia
have occurred among these people.
The above described data deal with
radiation by X-rays and gamma rays (and some neutrons for the A-bomb
survivors). There are also impressive relevant data from radiation with alpha
particles. One such study is of bone and head cancers among dial painters,
chemists, and others occupationally exposed to ingested radium [32]. There were no tumors among those with
exposures below 1,000 cGy, but for dose ranges centered about 1800, 3500, 7500,
and 20,000 cGy, 25% to 38% in each category developed tumors. Elaborate
analyses of these data shows that a linear-no threshold fit is statistically
unsupportable and a threshold behavior is strongly suggested..
Several studies have reported that
workers who inhaled plutonium, resulting in sizable radiation exposures to
their lungs, have lower lung cancer mortality rates than those not so-exposed
[33-35]. Contrary to media-generated impressions, there is no record of cancer
deaths resulting from human exposure to plutonium.
Very
strong evidence against linear-no threshold theory is provided by a very
extensive study of lung cancer rates versus average radon exposure in homes for
1729 U.S. counties (more than half of all U.S. counties with 90% of the U.S.
population) [36]. Plots of age-adjusted
rates are shown in Figures 5a and 5c where, rather than showing individual
points for each county, these are grouped into intervals of radon exposure
(shown on the base-line along with the number of counties in each group) and
plotted as the mean value of m for each group, its standard deviation indicated
by the error bars, and the first and third quartiles of the distribution. Figures 5b and 5d show these data corrected
for prevalence of cigarette smoking. Note that when there is a large number of
counties in an interval, the standard deviation of the mean is quite small. We
see, in Figure 5, a clear tendency for lung cancer rates, with or without
correction for smoking prevalence, to decrease with increasing radon
exposure, in sharp contrast to the increase expected from the
supposition that radon can cause lung cancer, shown by the line labeled
“Theory”, based on linear-no threshold theory.
These data have been analysed for over 500 possible confounding factors,
including socioeconomic, geographic, environmental and ethnic associations
[37], but the conclusion remains firm that linear-no threshold theory fails
very badly by grossly over-estimating the cancer risk from low level radiation.
In the 1960s and 1970s, many animal
studies were conducted on cancer risk vs dose, utilizing X-rays, gamma rays,
and beta rays with both external exposures and injection of radioactive
materials [38]. Nearly all of these studies indicate, with high statistical
significance, that linear-no threshold theory over-estimates the cancer risk
from low level radiation.
Ingenious experimental techniques
have been developed for observing the effects of a single alpha particle
hitting a single cell. It was found [39] that the probability for
transformation to malignancy from N particle hits on a cell is much greater
than N times the probability for transformation to malignancy from a single
hit. This is a direct violation of linear-no threshold theory, indicating that
estimated effects based on extrapolating the risk from high exposure,
represented by N hits, greatly exaggerates the risk from low level exposure as
represented by a single hit.
Dependence of
latent period on dose
There
is a substantial body of data, both on animals and on humans, indicating that
the latent period between radiation exposure and cancer death increases with
decreasing exposure; these are reviewed in References 38 (older data) and 40
(more recent data). This leads to the obvious conclusion that for low enough
exposures, the latent period exceeds the normal life span, so no actual cancers
develop. Thus there is an
effective threshold.
This
effect alone, even in the absence of all considerations discussed previously,
would invalidate linear-no threshold theory as applied to low level radiation..
The conclusion from the evidence reviewed in this paper
is that the linear-no threshold theory fails very badly in the low dose region,
grossly over-estimating the risk from low level radiation. This means, for
example, that the cancer risk from diagnostic X-rays is much lower than given
by usual estimates, and may well be zero.
The author acknowledges a great debt to
Myron Pollycove for help in pointing out references and providing explanations
involved in this paper.
Table 1: Effects of pre-exposure to 5 cGy on
Chromosome aberration in human lymphocyte cells induced by 400 cGy of X-rays 6
hours later [5]
dicentrics & rings deletions
donor 400
cGy (5 + 400) cGy 400 cGy (5+400)cGy
#1 136 92 52 51
#2 178 120 62 46
#3 79 50 39 15
#4 172 42 46 34
#5 134 106 58 41
Table 2: Effects of radiation on immune
response. Different columns are percent
of response to various tests in unexposed mice to response in mice exposed as
indicated [15]. PFC = plaque forming cell; MLC = mixed lymphocyte culture, used
as test of T-cell function; Con A =
concanavalin-A, lectin that stimulates
T-lymphocytes; NK = natural killer cells which recognize and kill tumor cells;
ADCC = anti-body dependent cell mediated cytotoxicity, which assists NK
activity.
Test 2.5 cGy 5
cGy 7.5cGy
PFC Reaction 110 143 174
MLC Reaction 109 133 122
Reaction to Con A 191 155 530
NK activity 112 109 119
ADCC Activity 109 128 132
Table 3: Leukemia deaths from International
Association for Research on Cancer (IARC) Study [23]. The final column is thr
ratio of observed to expected, O/E
Dose (cSv) Observed Expected O/E
0-1 72 75.7 0.95
1-2 23 21.2 1.08
2-5 20 21.8 0.92
5-10 12 11.3 1.06
10-20
9 7.8 1.15
20-40
4 5.5 0.73
>40
6 2.6 2.3
Figure Legends
Note for web page users: Figures are not shown here but
are available in item #3 on my web page. Fig. 1 here is Fig. 1 there. Fig. 2
here is Fig. 3 there. Fig. 3 here is Fig. 4 there. Fig. 4 here is Fig. 5 there.
Fig. 5 here is Fig. 9 there.
Fig. 1: Plot shows excess deaths from
solid tumors per 100 “expected” among Japanese A-bomb survivors (1950-1990)
versus their dose [21]. Error bars are 95% confidence limits.
Fig. 2: Plot shows excess deaths from
leukemia per 100 “expected” among Japanese A-bomb survivors (1950-1990) versus
their dose [21]. Error bars are 95% confidence limits.
Fig. 3: Plot shows standardized death rates per million person-years from breast cancer among Canadian women after irradiation in fluoroscopic examinations versus their radiation dose [24]. Error bars are 95% confidence limits.
Fig. 4: Plot shows relative risk of
mortality from lung cancer versus dose to lung, with 95% confidence limits. In
upper figure with expanded vertical scale, circles are from [25] and diamond is
from [26]. In lower figure [25], solid line connects data from Canadian
fluoroscopy patients, and dashed line connects data from A-bomb survivors.
Fig. 5a: Plot shows lung cancer mortality rates
(age-adjusted) for males versus average radon level in homes for U.S. counties
[36]. See explanations in text.
Fig. 5b Plot shows lung cancer mortality
rates for males from Fig. 5a, corrected for smoking prevalence.
Fig. 5c:
Plot shows lung cancer mortality rates (age-adjusted) for females versus
average radon level in homes for U.S. counties [36]. See explanations in text.
Fig. 5d:
Plot shows lung cancer mortality rates for females from Fig. 5c,
corrected for smoking prevalence.
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