1 DoReMi - Low Dose Research towards Multidisciplinary Integration Deliverable D5.17: Report for an integrated (biology-dosimetry-epidemiology) research project on occupational Uranium exposure (Task 5.8 CURE Final report) Due date: Month 60 Actual submission date: Month 63 (2 March 2015) Status: Final Nature Report Dissemination level RR = Restricted to a group specified by the consortium (including the Commission Services) Lead beneficiary IRSN Authors Olivier LAURENT (IRSN), Maria GOMOLKA (BfS), Richard HAYLOCK (PHE), Eric BLANCHARDON (IRSN), Augusto GIUSSANI (BfS), Will ATKINSON (Nuvia), Sarah BAATOUT (SCK•CEN), Derek BINGHAM (AWE), Elisabeth CARDIS (CREAL), Janet HALL (Institut Curie), Ladislav TOMASEK (SURO), Dominique LAURIER (IRSN) Contributors Sophie ANCELET, Christophe BADIE, Gary BETHEL, Jean-Marc BERTHO, Richard BULL, Cécile CHALLETON de VATHAIRE, Rupert COCKERILL, Estelle DAVESNE, Damien DRUBAY, Teni EBRAHIMIAN, Hilde ENGELS, Nora FENSKE, Michael GILLIES, James GRELLIER, Stephane GRISON, Yann GUEGUEN, Sabine HORNHARDT, Chrystelle IBANEZ, Sylwia KABACIK, Lukas KOTIK, Michaela KREUZER, Anne-Laure LE BACQ, James MARSH, Dietmar NOSSKE, Jackie O'HAGAN, Eileen PERNOT, Matthew PUNCHER, Roel QUINTENS, Estelle RAGE, Tony RIDDELL, Laurence ROY, Eric SAMSON, Maamar SOUIDI, Michelle TURNER, Nina WEILAND, Sergey ZHIVIN Approval WP5 Leader Simon Bouffler: 27 February 2015
Transcript
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DoReMi - Low Dose Research towards Multidisciplinary Integration
Deliverable
D5.17: Report for an integrated (biology-dosimetry-epidemiology) research project on occupational Uranium exposure
(Task 5.8 CURE Final report)
Due date: Month 60
Actual submission date: Month 63 (2 March 2015)
Status: Final
Nature Report Dissemination level
RR = Restricted to a group specified by the consortium (including the Commission Services)
Lead beneficiary IRSN
Authors
Olivier LAURENT (IRSN), Maria GOMOLKA (BfS), Richard HAYLOCK (PHE), Eric BLANCHARDON (IRSN), Augusto GIUSSANI (BfS), Will ATKINSON (Nuvia), Sarah BAATOUT (SCK•CEN), Derek BINGHAM (AWE), Elisabeth CARDIS (CREAL), Janet HALL (Institut Curie), Ladislav TOMASEK (SURO), Dominique LAURIER (IRSN)
Contributors
Sophie ANCELET, Christophe BADIE, Gary BETHEL, Jean-Marc BERTHO, Richard BULL, Cécile CHALLETON de VATHAIRE, Rupert COCKERILL, Estelle DAVESNE, Damien DRUBAY, Teni EBRAHIMIAN, Hilde ENGELS, Nora FENSKE, Michael GILLIES, James GRELLIER, Stephane GRISON, Yann GUEGUEN, Sabine HORNHARDT, Chrystelle IBANEZ, Sylwia KABACIK, Lukas KOTIK, Michaela KREUZER, Anne-Laure LE BACQ, James MARSH, Dietmar NOSSKE, Jackie O'HAGAN, Eileen PERNOT, Matthew PUNCHER, Roel QUINTENS, Estelle RAGE, Tony RIDDELL, Laurence ROY, Eric SAMSON, Maamar SOUIDI, Michelle TURNER, Nina WEILAND, Sergey ZHIVIN
Approval WP5 Leader Simon Bouffler: 27 February 2015
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SUMMARY The health effects of chronic exposure to uranium, as it occurs in populations of workers involved in
the nuclear fuel cycle and in the general population, are not well known. Experimental studies on the
effect of exposure to uranium have reported impairments of the cerebral function, genotoxic,
nephrotoxic and other biological effects, but the implications of these findings to human health are
not clear. Beside, a few epidemiological studies reported increases in lung, lymphatic/hematopoietic
cancers and cardiovascular diseases associated with occupational exposure to uranium, but most
available studies are descriptive or suffer from other limitations. New studies borrowing strengths
from modern biology approaches on the one hand, and from large epidemiological datasets on the
other hand, will have better potential to improve the characterization of the biological and health
effects of chronic exposure to low doses of uranium.
The CURE (Concerted Uranium Research in Europe) project was a 18-month concerted action funded
by the European Network of Excellence DoReMi (http://www.doremi-noe.net/). The aim of CURE
was to elaborate a collaborative research project on the biological and health effects of uranium
contamination, integrating epidemiology, biology/toxicology and dosimetry. It notably aimed to
evaluate the feasibility of a molecular epidemiology approach in cohorts of workers exposed to
uranium.
Existing cohorts of workers exposed to uranium (miners and nuclear industry employees involved in
uranium processing) in Belgium, Czech Republic, France, Germany, and the United Kingdom, plus
existing competences in epidemiology, biology and dosimetry among CURE partners, provided a
unique opportunity to develop a coherent multidisciplinary research project within a collaborative
framework. Contacts have been established with other teams involved in similar research projects
within and outside the European Union (e.g.: Russia, Kazakhstan, USA).
Based on the results of this concerted action, a large scale integrated collaborative project will be
proposed to improve the characterization of the biological and health effects associated with
uranium internal contamination in Europe. In the future, it might be envisaged to extend
collaborations with other countries outside the European Union, to apply the proposed approach to
other internal emitters and other exposure situations of internal contamination, and to open the
reflections to other disciplines interested in the effects of internal contaminations by radionuclides.
4.3.2. Biokinetic and dosimetric models ...................................................................................................... 42
4.3.3. Parameters of exposure .................................................................................................................... 43
4.3.4. Monitoring data ................................................................................................................................ 46
4.5.2. Characterizing the best estimates and probability distributions of model parameters and other relevant uncertainty sources .......................................................................................................................................... 55
4.5.3. Testing the feasibility to account for the uncertainty inherent in estimates of dose when estimating radiation-induced disease risk .......................................................................................................................... 56
4.5.4. Creating a synthetic cohort as a tool for testing the proposed methodologies ..................................... 57
1.1. Health effects of internal contaminations by radionuclides The assessment of long term health effects resulting from exposure to ionizing radiation is primarily
based on risk models developed from the Life Span study (LSS) of atomic bomb survivors who were
exposed to external photon and neutron radiation in Hiroshima and Nagasaki in 1945 (ICRP, 2007;
NRC, 2006; UNSCEAR, 2008a). By contrast, the major portion of the effective dose delivered to the
general population results from radiation emitted by radionuclides located inside the body after
internal contamination (UNSCEAR, 2008a). Many large populations of radiation workers are also
exposed internally to alpha radiation emitted by radionuclides such as uranium or plutonium.
Currently, estimates of risks associated with internal exposure to alpha radiation are derived from
the risk models based on LSS data, by way of applying a radiation weighting factor (wR) of 20 to the
absorbed alpha dose to account for the experimentally observed difference in risk (per unit absorbed
dose) for alpha particles as compared to photons (ICRP, 2007). However, there are concerns over the
reliability and accuracy of the conversion of risk between these very different types of exposure.
In 2009 the EURATOM-convened High Level and Expert Group (HLEG) considered the limited
knowledge about the effects of internal contamination by radionuclides to be a key scientific issue
for the purpose of radiation protection policy (HLEG, 2009). Subsequently, the effects of internal
contamination have been considered as a cross-cutting issue by European platforms in the field of
sources of exposure to natural uranium isotopes among the general population (Ansoborlo et al.,
2015; ATSDR, 2013).
Uranium has important industrial applications since uranium ore is the base material for the
production of nuclear fuel. Uranium is present in various chemical forms and isotopic compositions
at all stages of the nuclear fuel cycle. This cycle includes industrial processes such as mining, milling,
concentration, conversion and enrichment of uranium, fuel fabrication and reprocessing (see annex
1). Most civil reactors use enriched uranium, characterized by a higher proportion of 235U (2 to 5%)
than natural uranium (UNSCEAR, 2008b). Even higher degrees of enrichment can be encountered, for
instance during the production of nuclear weapons (ATSDR, 2013). The industrial process of
enrichment separates natural uranium into enriched uranium and depleted uranium, a by-product
characterized by a lower percentage of 235U (about 0.3%) than natural uranium. Depleted uranium
has been used in various ways, notably as counterweights or ballast in aircraft, shielding and military
applications (tank armor, munitions). Enriched uranium is more radioactive than natural uranium
owing to its higher percentages of 235U and 234U. Conversely, depleted uranium is less radioactive
than natural uranium since it contains a lower proportion of these two isotopes. Other, man-made
(artificial), isotopes of uranium can also be encountered at specific stages of the fuel cycle, such as 232U while handling reprocessed uranium (Guseva Canu et al., 2011).
In summary, interest in the incorporation of uranium into the body is justified by the widespread
exposure of the general population to natural uranium (Ansoborlo et al., 2015; ATSDR, 2013; Canu et
al., 2011) and by the more localized exposure to depleted uranium in settings where conflicts have
occurred (Krunic et al., 2005). In addition, there are many occupational settings in which workers are
exposed to uranium in various forms: natural, depleted, enriched, reprocessed (Laurier et al., 2012).
Since uranium is both a radionuclide and a heavy metal, it has the potential to exert both radiological
and chemical toxicities.
1.3. Evidence of uranium effects from experimental studies Uranium has been found to be genotoxic in both in vitro and in vivo experiments (ATSDR, 2013), even
in its depleted form which generates little alpha radiation (Bal et al., 2011; Stearns et al., 2005). A
recent study showed that depleted and enriched uranium have different genotoxic profiles: depleted
uranium has a low clastogenic but a high aneugenic potential, whereas enriched uranium has a high
clastogenic potential, suggesting additional radiotoxicity on top of chemical toxicity (Darolles et al.,
2010). These genotoxic properties clearly justify further investigations of the association between
uranium exposure and cancer.
In rats, inhalation of depleted uranium has been linked with the formation of DNA strand breaks in
broncho-alveolar lavage cells, possibly as a consequence of uranium-induced inflammation and
oxidative stress (Monleau et al., 2006). Also in rats, chronic inhalation of uranium ore dust was linked
with primary malignant and non-malignant lung tumor formation and although the malignant tumor
risk was not directly proportional to dose, it was directly proportional to dose rate (Mitchel et al.,
1999). In dogs, uranium inhalation was related to neoplasia and fibrosis in the lungs (Leach et al.,
1973). Although only limited experimental data are available on the effects of uranium inhalation on
the lung, their findings suggest the need for further experimental and epidemiological studies. This is
especially important since inhalation is a major route of exposure to uranium in the workplace.
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Once absorbed, uranium is deposited throughout the body and the highest levels are found in the
bones and kidneys (ATSDR, 2013). Many animal studies have reported damage to the kidney caused
by acute exposure to uranium (Gueguen and Rouas, 2012), which is considered to be the main target
organ for uranium toxic effects under such exposure conditions (ATSDR, 2013). Experimental studies
on the effect of chronic exposure to uranium in kidney have reported mixed findings. Some reported
nephrotoxic effects (Gilman et al., 1998; Zhu et al., 2009), whereas others unexpectedly did not find
such effects (Poisson et al., 2014).
The brain does not accumulate large quantities of uranium. However, some studies in rats have
reported associations between uranium exposure and impairments in cerebral function, suggesting
that the brain is an organ at risk for this nuclide (Houpert et al., 2005; Lestaevel et al., 2005).
The potential effects of uranium on the cardiovascular system have rarely been evaluated in
experimental studies. However, this question is of importance because of the relationships that have
been demonstrated between impaired renal function (which again, may be caused by uranium
(ATSDR, 2013)) and cardiovascular diseases (Currie and Delles, 2013). In addition, the inhalation of
particulate matter of less than 10 µm aerodynamic diameter is an established risk factor for
cardiopulmonary diseases (Araujo and Nel, 2009). Uranium bearing particles of such diameters are
encountered in uranium mines (Marsh et al., 2012) and at all stages of the nuclear fuel cycle
(Ansoborlo et al., 2002).
Further biological effects of uranium exposure have been identified by experimental research,
including oxidative stress, damage to the skin, impaired bone formation or reproductive function
(ATSDR, 2013).
A recent study in rats chronically exposed to low doses of uranium via drinking water showed that a
metabolomics approach using urine samples allowed the correct classification of exposed and non-
exposed animals (Grison et al., 2013). This suggests that metabolomics might be a useful tool to
identify new biomarkers (“fingerprints”) for uranium exposure in humans, as well as to identify
metabolic pathways activated by uranium exposure. This might in turn lead to the identification of
further biological functions and systems perturbed by uranium exposure, which would justify
complementary investigations of potentially related biological and health effects in epidemiological
cohorts.
In summary, it is important to emphasize that experimental studies on natural or depleted uranium
(both being only weakly radioactive, i.e. showing low specific activity) have reported significant
biological effects including genotoxicity, renal toxicity, lung damage and neuro-physiological
perturbations. This suggests that uranium, as a heavy metal, may cause harm by chemical toxicity,
aside from any radiation effects. In addition, experimental research confirmed that radiotoxicity can
occur in addition to chemical toxicity, especially when exposure is to enriched or reprocessed
uranium (Grignard et al., 2008; Houpert et al., 2005).
There are however, considerable uncertainties in the extrapolation of the biological effects at low
doses observed in animals, to possible disease risks in humans (ATSDR, 2013). These uncertainties
are related 1) to possibly different biological responses induced by uranium in animals and in humans
and 2) to the relations of such biological responses with disease risk quantification (e.g., the
predictive value of early biological effects on subsequent disease risk).
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It is therefore important to evaluate if exposure to uranium is associated with biological and health
effects in humans. The influence of different mixtures of uranium isotopes also appears worth
examining in this frame, in order to disentangle chemical from radiological effects (Zhivin et al.,
2014).
1.4. Evidence of uranium effects from epidemiological studies Cohorts of uranium miners and other workers have been identified as populations of priority interest
for the study of uranium health effects (Laurier et al., 2012). Therefore the present section will focus
on evidence from epidemiological studies conducted in occupational settings. Studies conducted in
non-occupational settings (e.g., in populations living around uranium processing sites) have mostly
employed ecological designs, and are therefore minimally informative. Molecular epidemiology
studies incorporating biomarkers will be reviewed in section 1.5.
Underground uranium miners are exposed to uranium ore dust and are therefore of interest to study
the health effects of uranium exposure. However, previous analyses in these miners predominantly
investigated the health effects related to the exposure to radon (a decay product of uranium or
thorium) (NRC, 1999), whereas the associations between uranium exposure and health outcomes
have been more rarely studied (e.g.: (Kreuzer et al., 2006; Rage et al., 2014)).
The doses from alpha radiation to the lung from uranium and other long-lived radionuclides (LLR)
present in uranium ore are indeed negligible when compared to those from radon exposure (Marsh
et al., 2012). This is particularly true for miners who worked before the implementation of radiation
protection standards (especially ventilation) in mines (Kreuzer et al., 2013; Rage et al., 2014;
Tomasek, 2012). In addition, potentially high correlations between lung doses from radon and from
long-lived radionuclides raise concerns about the possibility of disentangling the effect of uranium
exposure on lung cancer from that of radon exposure.
Non-lung cancer health outcomes are less likely to be affected by any confounding effect of radon
exposure, because doses from radon to the non-respiratory organs where uranium may accumulate
are very low (Marsh et al., 2012). By comparison, doses from LLR to some non-respiratory organs or
tissues (e.g.: kidney, red bone marrow, liver) may be non-negligible (Laurier et al., 2009). In addition,
the chemical toxicity of the uranium dust or other toxic effects resulting from the inhalation of
uranium bearing particles (which might be related to their chemical composition or to other, non-
radiological physical properties such as size (Schlesinger et al., 2006)) might increase the risk of
diseases other than lung cancer (e.g. including, but not necessarily limited to, non-cancer diseases of
the lung and kidney, and circulatory diseases). The associations between uranium exposure and
outcomes other than lung cancer have been evaluated in a few studies of uranium miners (Kreuzer et
al., 2006; Rage et al., 2014), but clearly deserve further investigation.
Epidemiological studies focusing on the health effects of uranium exposure at later stages of the fuel
cycle, where radon exposures are extremely low, have also been performed (ATSDR, 2013; Canu et
al., 2008; Zhivin et al., 2014). These studies included individuals engaged in uranium mills (Boice et
al., 2008; Kreuzer et al., 2014; Pinkerton et al., 2004)) and other uranium processing nuclear
industries such as those located at Port Hope, Canada (Zablotska et al., 2013), Fernald, Ohio, USA
(Ritz, 1999a; Silver et al., 2013), Mallinckrodt, Missouri, USA (Dupree-Ellis et al., 2000), Oak Ridge
National Laboratory, Tennessee, USA (Richardson and Wing, 2006; Yiin et al., 2009), Paducah,
Kentucky, USA (Chan et al., 2010; Figgs, 2013), Rocketdyne, California, USA (Boice et al., 2011),
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AREVA NC Pierrelatte, France (Guseva Canu et al., 2012; Guseva Canu et al., 2011), Springfields and
Capenhurst, UK (McGeoghegan and Binks, 2000a; McGeoghegan and Binks, 2000b).
However, most of these previous studies were limited by various factors (Canu et al., 2008; Zhivin et
al., 2014):
The statistical power of individual studies conducted in specific settings was limited by
relatively low numbers of workers included (usually a few hundred or thousands) and
sometimes short follow-up times at the time of publication. Because of the often moderate
to low level of exposures (and related low expected excess incidences of relevant diseases),
the numbers of workers and length of follow-up might not have been sufficient to provide
the power needed to detect statistically significant effects.
Reconstructing retrospective exposures for large populations of workers is challenging and as
a consequence many studies used indirect approaches for quantifying exposure. Individual
dose assessment based on urine analysis and other results of individual monitoring for
uranium (faeces, lung or whole body counts) would provide more accurate estimates and
thus increase the ability of studies to detect excess risk (Boice et al., 2006). In addition, most
analytical studies could not take into account the physical and chemical properties of
uranium (isotopic composition, chemical speciation, aerodynamic diameter of particles, and
resulting solubility). These properties may modify the mass and activity of the uranium
reaching different organs, and therefore might determine whether toxic effects of
radiological or chemical nature occur (Guseva Canu et al., 2011).
Finally, only a few studies have collected information on occupational exposures other than
uranium (e.g. (Chan et al., 2010; Guseva Canu et al., 2011; Silver et al., 2013) or on lifestyle
risk factors such as smoking (e.g.(Dupree et al., 1995)). If such factors are correlated with
uranium exposure, they might have biased risk estimates in studies which did not consider
them in the analysis.
As a result, individual epidemiological studies conducted so far do not provide reliable evidence on
the potential health risks associated with uranium exposure (Zhivin et al., 2014). Nevertheless, some
studies are suggestive of biologically plausible positive associations between uranium exposure and
lung cancer (Guseva Canu et al., 2011; Ritz, 1999b), lymphatic and hematopoietic tumours (Guseva
Canu et al., 2011; Yiin et al., 2009), circulatory diseases (Guseva Canu et al., 2012), and intestinal
cancer (Silver et al., 2013). Larger epidemiological studies with adequate individual dosimetry and
control for confounders are clearly required.
1.5. Evidence of uranium effects from molecular epidemiology So far, research on the biological and health effects of uranium has mostly been disconnected
between experimental and epidemiological studies. However, a few pioneering molecular
epidemiology studies have also been conducted.
Some of these studies investigated biological effects of exposure to depleted uranium in human
populations. These populations include Gulf war veterans exposed through friendly-fire incidents via
wound contamination and inhalation (McDiarmid et al., 2011a; McDiarmid et al., 2011b; McDiarmid
et al., 2013). In these veterans, only relatively weak genotoxic, pulmonary or renal adverse effects
have been associated with depleted uranium exposure so far. Other studies were carried out in
workers involved in the clean-up of conflict zones contaminated with depleted uranium munitions in
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the Balkans (Milacic, 2008; Milacic and Simic, 2009). In these workers, DNA alterations were found at
higher rates immediately after decontamination than before (Milacic, 2008). Damages to
chromosomes and cells were higher in workers exposed to depleted uranium than in controls groups
of workers (Milacic and Simic, 2009).
Other studies were conducted in populations drinking water naturally contaminated with uranium
(Kurttio et al., 2002; Kurttio et al., 2006; Mao et al., 1995; Selden et al., 2009; Zamora et al., 2009). In
these populations, several biomarkers of kidney function indicated some alterations associated with
uranium concentrations in drinking water (ATSDR, 2013). No indication of serious renal injury was
reported since values of biomarkers generally remained in the normal range (Ansoborlo et al., 2015).
However, a cautious interpretation is warranted since the cross sectional design of these studies did
not allow for a detailed characterization of the chronic effects of protracted, cumulated exposure to
uranium (Canu et al., 2011). Similar studies also reported a positive association between uranium
concentrations in water and bone resorption (as indicated by levels of serum type I collagen carboxy-
terminal telopeptide) (Kurttio et al., 2005) or with blood pressure (Kurttio et al., 2006).
Several molecular epidemiology studies were conducted in uranium miners, but generally with a
focus on radon rather than uranium effects (Brandom et al., 1972; Gomolka et al., 2012; Leng et al.,
2013; Li et al., 2014; Zolzer et al., 2012a; Zolzer et al., 2012b).
A few studies were conducted in other uranium workers (Clarkson and Kench, 1952; Dounce et al.,
1949; Eisenbud and Quigley, 1956; Luessenhop et al., 1958; Martin et al., 1991; Prat et al., 2011;
Thun et al., 1985). Most of them focused on renal biomarkers. The earliest studies failed to detect
nephrotoxic effects of uranium exposure, possibly since the biomarkers investigated were not
sensitive enough (ATSDR, 2013). However a study in uranium millers identified reduced renal
proximal tubular reabsorption in this population, as compared to other workers (Thun et al., 1985).
Another study in uranium workers reported a decrease of urinary osteopontin associated with
uranium exposure, suggesting kidney damage (Prat et al., 2011). Another study focused on
cytogenetic markers in blood lymphocytes and identified increases in asymmetrical chromosome
aberrations and sister chromatid exchanges in workers occupationally exposed to uranium (Martin et
al., 1991). An interesting biobank is being set up in uranium and plutonium workers in Russia
(Takhauov et al., 2015) but no result on the association between uranium exposure and biomarkers
is available yet.
So far, published molecular epidemiology studies conducted in uranium workers have focused only
on a limited number of biomarkers (mainly related to kidney function or cytogenetics) and generally
lacked proper organ dosimetry. Most of these studies have been cross sectional and therefore have
not allowed for a detailed assessment of the long-term effects of protracted exposure to uranium.
1.6. Room for improvement
Overall, in spite of experimental results demonstrating chemical and radiological toxicity of uranium
on living organisms, the biological and health effects of chronic uranium exposure in humans,
particularly those due to occupational exposure to uranium, are not well known. However, through
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conducting improved epidemiological and molecular epidemiology studies it should be possible to
improve the understanding and quantification of these effects.
1.6.1. Epidemiology
Despite the limitations of previous epidemiological studies regarding the quantification of the health
effects of uranium exposure, cohorts of uranium miners and uranium processing workers were
identified as part of DoRemi Task 5.5 as among the most potentially informative source of data for
the characterization of effects of internal contamination (Laurier et al., 2012). The strengths of these
studies include regular collection of dosimetric data and long-term follow-up of worker populations.
In order to allow for such cohorts to provide better insight into the health effects of uranium
exposure, several limitations of previous studies need to be addressed:
First, existing cohorts should be pooled after verification of their compatibility, in order to
increase the statistical power available for analyses and therefore improve the detection and
quantification of potential uranium-related health effects. Similar pooling and joint analyses
of cohorts have successfully been conducted in other populations of radiation workers and
miners (Cardis et al., 2005; Leuraud et al., 2011; NRC, 1999; Tomasek, 2014).
Second, the doses of cohort subjects should be estimated according to state-of-the-art
methods. Methodologies to enable uncertainty in the dose estimates to be incorporated in
dose response analyses should be developed.
Finally, available information on risk factors other than uranium exposure should be carefully
collected in order to adequately control for potential confounding or to take potential
interactions into account in subsequent epidemiological analyses.
It has been acknowledged that setting up multi-country joint epidemiological studies with reliable
dose estimates and using harmonized methods is a highly desirable, but complex, approach. Such
an approach requires preparatory work between researchers (epidemiologists, dosimetrists and
statisticians) in charge of the cohorts to describe and compare available datasets and methods,
and to define harmonized procedures for successfully pooling their data.
1.6.2. Molecular epidemiology
DoReMi task 5.5. has identified that research on the effects of internal contamination would benefit
from the integration of epidemiology and biology within the framework of molecular epidemiology.
Such integration would have a strong potential to improve the understanding and the quantification
of the biological and health effects of radiation exposure, notably resulting from internal
contamination by uranium and other radionuclides (Laurier et al., 2012; Pernot et al., 2012).
The rationale for integrating measurements of biomarkers in a large cohort study on the effects of
internal contamination by uranium is based upon three specific points: 1. to refine the assessment of
exposure to uranium by using biomarkers of exposure (e.g.: (Grison et al., 2013)) 2. to similarly
evaluate possible confounders by the use of confounder specific biomarkers, if available and 3. to
elucidate the different biological response mechanisms induced by uranium exposure in a human
population by using biomarkers of early and late biological effects (Pernot et al., 2012).
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If any association is observed between uranium exposure and the risk to develop a disease, point 3)
may provide insight into the physio-pathological processes involved in disease onset. This will help
assess the biological plausibility of the associations observed in epidemiological studies and inform
related judgment about causality. This might also help to identify pre-clinical biomarkers useful for
the surveillance of people exposed to uranium.
Again, it should be possible to overcome the main limitations of previous molecular epidemiology
studies, and to build on recent, as well as future, progresses of techniques for biological analyses:
Although most previous molecular epidemiology studies have been cross sectional, regular
medical check-ups conducted by the occupational medicine service during the workers’
careers have been identified as a unique opportunity for the prospective and repeated
collection of biological samples among workers (Laurier et al., 2012). A biobank centralizing
such samples over many years from a same cohort of individuals would be a valuable
resource for a detailed assessment of the long-term effects of protracted exposure to
uranium, as it is the case for other exposures (Palmer, 2007; Zins et al., 2010). Such a
prospective scheme is also of great interest to identify, or to validate, biomarkers of recent
exposures (e.g.: (Grison et al., 2013)) and their persistence or modifications over time.
In addition, the immediate collection of biological material from workers exposed to uranium
or other sources of radiation years to decades ago is of great interest in order to directly
investigate any persistent biomarkers of past exposures (Gomolka et al., 2012). If specific
diseases occurred in some of these workers, pathological archives (e.g. formalin fixed
paraffin embedded tissue from target organs (Wiethege et al., 1999)) may also be of great
interest to decipher uranium specific fingerprints in diseased tissues, for instance in tumor
tissues from cancer cases.
The need to use the best possible dosimetric estimates was mentioned above for
epidemiology, and this applies to molecular epidemiology as well. An efficient solution to
address this issue is to nest molecular epidemiology studies within established cohorts for
which dosimetric data have already been carefully collected over many years to decades
(Gomolka et al., 2012), or will be. This consideration also applies to many other factors for
which data are already available within cohorts (e.g., potential confounders).
Novel and already established biomarkers, notably originating from animal experiments, may
prove useful to better understand the biological effects of uranium exposure on its different
target organs, tissues and systems in humans. Some of these biomarkers have never been
tested in humans exposed to uranium, but appear worth investigating for instance in
uranium workers, as they might prove to be of interest to occupational health surveillance
before the onset of any disease (e.g., biomarkers of brain damage, inflammation or kidney
dysfunction).
Non-targeted (OMIC) approaches are also promising for the purpose of identifying not only
new biomarkers of exposure, but also biological (e.g., metabolic) pathways potentially
affected by uranium exposure (Grison et al., 2013), and help direct research towards related
pathways, functions/processes, organs and tissues.
Overall, modern biology approaches are developing fast and should lead to the identification
of new biomarkers of high interest in the forthcoming years or decades. Modern biobanking
methods allow for the long term conservation of biological material in suitable conditions for
later analyses of emerging biomarkers (Palmer, 2007; Zins et al., 2010). Diseases potentially
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related to current uranium exposure might only be diagnosed many years, even decades,
following exposure. Being able to investigate biological samples collected from the time of
exposure to possible disease onset using the latest available techniques for biological
analyses would be an invaluable asset for future research into potential mechanisms of
disease onset.
As for the preparation of pooled epidemiological studies, cooperation and coordination between
researchers (biologists, epidemiologists, dosimetrists and statisticians) is essential, in order to
undertake any successful and informative pilot molecular epidemiology study in a population of
uranium miners or workers. These discussions should result in a good understanding of the
expectations from the study field by each partner and related justifications, as well as the mutual
understanding of each partner’s (and discipline’s) role within the frame of molecular epidemiology
studies. To set up a molecular epidemiology study, the identification of biomarkers useful to study
the effects of uranium exposure in humans is necessary as well as the identification of populations
suitable for the collection of appropriate biospecimens. Valid biomarker typing can only been done in
biological samples of good quality. Therefore the definition of proper study instruments (standard
operating procedures, questionnaire, information and consent sheets), and the setting-up of an
optimal sampling scheme are essential requirements. These have to be a priori verified and tested in
a smaller feasibility study. The agreement and support of all stakeholders (workers, worker
representatives, employers, national ethics committees,) must be obtained before undertaking the
study. The experiences and feedback of other European initiatives in the field must be used
especially from the European Biobanking and Biomolecular Resources Research Infrastructure
(BBMRI) project (bbmri-eric.eu).
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2. OBJECTIVES OF THE CURE PROJECT
The objective of the concerted action CURE (Concerted Uranium Research in Europe) was to
elaborate a multidisciplinary and collaborative European research project, integrating epidemiology,
biology/toxicology and dosimetry to improve understanding and quantification of long-term
biological and health effects (including both risks of cancer and non-cancer diseases) associated with
uranium contamination.
This general objective included two specific aims:
• To prepare a common protocol for pooled epidemiological analyses of uranium miners and
workers in Europe, that would overcome the limitations of previous studies in the field, in
order to directly estimate the potential health risks associated with uranium exposure.
• To verify the feasibility of a molecular epidemiology approach (building biobanks and
measuring biomarkers) for integration into the risk assessment process and if feasible, to
elaborate a common protocol for the development of a molecular epidemiology study.
A parallel objective of the project was to identify and establish contact with similar research
programs, within and outside Europe.
3. PROJECT MANAGEMENT AND TASKS COMPLETED
3.1. General characteristics
CURE was a concerted action supported by the DoReMi EU FP7 network of excellence
(http://www.doremi-noe.net/) and coordinated by Institut de Radioprotection et de Sûreté Nucléaire
(IRSN, France). It was integrated as task 5.8 of DoReMi. The project duration was 18 months, from
July 1, 2013 to December 31, 2014.
3.2. Partners
CURE gathered together the main organizations in charge of conducting and/or analyzing cohort
studies of miners and other workers occupationally exposed to uranium in Europe, as well as
organizations with recognized expertise in internal dosimetry and/or in radiobiology.
In total 9 partners from 6 Countries (France, UK, Germany, Belgium, Czech Republic, Spain)
participated in the CURE project: Institut de Radioprotection et de Sûreté Nucléaire (IRSN, France),
Bundesamt für Strahlenschutz (BfS, Germany), Public Health England (PHE, United Kingdom), Nuvia
limited (United Kingdom), Atomic Weapons Establishment (AWE, United Kingdom), StudieCentrum
voor Kernenergie • Centre d'étude de l'Energie Nucléaire (SCK•CEN, Belgium) , Státní ústav radiační
ochrany (SURO, Czech Republic), Centre de Recerca en Epidemiologia Ambiental (CREAL, Spain),
Institut Curie (IC, France).
All partners were involved in the reviews conducted in DoReMi WP4 on pertinent cohorts for
radiation protection, in WP5 task 5.5 on cancer risk and internal contamination (Laurier et al., 2012),
The Belgian cohort of 1,650 workers employed at Franco Belge de Fabrication du Combustible in
Belgium still needs to be set-up using available computerized information.
4.1.2. Cohort subsets for dose response analyses
A dose response analysis will be most effective and informative if it is restricted to high quality data
and if potential for confounding bias is avoided. To this end a subset of data will be selected from
each cohort. Criteria for data inclusion differ between miners/millers and workers cohorts.
4.1.2.1. Cohorts of miners and millers
In these cohorts individual monitoring has usually not been conducted. Exposure in uranium miners
has been predominantly regulated by air sampling, as will be detailed in section 4.3. For all miners,
individual information on exposure to uranium, radon and external gamma radiation is already
available, mostly from ambient measurements and based on detailed job-exposure matrices. Much
individual level information has already been computerized, including internal doses to several
organs (lung, kidney, liver, red bone marrow) which were calculated within the framework of the FP6
Alpha risk project (Marsh et al., 2012). Doses to the stomach are also available for German miners.
The population used for dose-response analyses will be the same as that used for the broad analyses.
Specific sensitivity analyses will be based on the group of 6,227 millers and open pit miners, because
of their high uranium exposure and very low radon exposures. However, for millers the organ doses
have still to be calculated.
4.1.2.2. Cohorts of nuclear workers
In worker populations involved in other stages of the nuclear cycle, the evaluation of occupational
internal exposure to radionuclides has usually been based on individual monitoring of intakes of
radionuclides, as will be detailed in section 4.3. Routine bioassay measurement of radionuclides in
urine, faeces, whole-body or lungs has been conducted on a regular basis, and additional follow-up
measurements have been performed in case of incidents.
For the nuclear worker cohorts, the availability and quality of such data, and the ability to collect or
compute good quality estimates of dose from uranium and other alpha emitting nuclides is therefore
the most important criterion for inclusion in analyses of dose-response relationships. It was
proposed, as part of CURE, to focus on subgroups for which both external and internal dose
estimates could be available by mid-2016.
It is estimated that such data can be obtained for 4,500 workers from France, 24,700 from the UK
(15,000 from BNFL, 3700 from AWE and 6000 from UKAEA – doses in AWE and UKAEA cohorts are
being computed as part of the DoReMi AirDose UK project – task 5.5.2) and 800 from Belgium. In
total, a cohort of 30,000 workers would be available for the purpose of studying dose-response
relationships.
In some portions of this subset (described below), detailed information on potential confounders such as smoking, body mass index, blood pressure or occupational exposures other than uranium are
24
available. This will allow for an assessment the impact such factors on dose-response relationships, as part of sensitivity analyses:
In the TRACY cohort, an effort is underway to computerize individual information on risk
factors available from occupational medical files such as smoking habits, blood pressure,
body mass index, blood sugar and cholesterol, as well as chest X-ray examinations (Garsi et
al., 2014). Repeated information is available for these factors, generally on a yearly or more
frequent basis. Such data will be fully computerized for about 4,500 workers (for which dose
estimates will also be available) by the end of 2016.
Job exposure matrices (JEMs) based on a semi-quantitative approach have already been
developed for several facilities included in TRACY (e.g.: AREVA NC Pierrelatte (Guseva Canu
et al., 2009) and EURODIF (Guseva Canu et al., 2013)). These JEMs cover more than 5,000
workers formerly employed at these plants. They document the exposure of workers to
fluorinated products, aromatic solvents). In addition, these JEMs document the type of
uranium to which workers have been exposed (i.e. details on the degree of enrichment and
chemical form determining solubility) and are therefore of interest to refine dosimetric
estimates and conduct sensitivity analyses on the use of such information for dose
calculation. By mid-2016, a new JEM will be created for the Malvesi plant. In total, about
6500 workers from the TRACY cohort will be covered by JEMs at this time.
A first attempt at performing a JEM in Plutonium workers at the Sellafield plant in the UK is
currently being carried out. The aim in this setting is to improve the information on the
potential exposures of unmonitored workers. If successful it will provide a basis for
evaluating the feasibility to perform similar work on uranium exposed workers to identity the
types of uranium compounds workers were potentially exposed to.
In addition, cancer incidence data are available in the UK cohorts from the beginning of 1971 and will
also be available for the Belgian cohort from the beginning of 1999. This will allow for further
sensitivity analyses of the impact of using mortality vs incidence data when studying the association
between radiation dose and cancer.
4.1.3. Cohort subsets proposed for pilot molecular epidemiology studies
Regarding molecular epidemiology, this section will present groups of individuals from the
aforementioned cohorts, in which it has been proposed jointly by WP1 and WP3 to verify the
feasibility of collecting and biobanking biological samples, which would allow for subsequent
analyses of pertinent biomarkers. The details of the strategy proposed for the collection of the
biological data (standard operating procedures for biological sample collection, transportation,
storage and biomarker testing) will be presented in the “biology protocol for a molecular
epidemiology study ” section 4.2. below.
Epidemiologist, biologists and dosimetrists first examined the possibilities to launch prospective pilot
studies in workers currently active, which would allow for a repeated collection of samples over time
in the same workers. This collection scheme would allow for an analysis of biomarkers (of exposure,
then of early biological effects) before the onset of any radiation-induced disease so that the kinetics
25
of both uranium exposure and biomarkers, as well as their associations, can be studied over time.
Starting from these criteria, several potential population subgroups from the aforementioned
European cohorts of uranium workers have been proposed jointly by WP1 and WP3:
uranium workers currently employed at an AREVA uranium conversion plant in Malvesi
(France), N~250.
former uranium workers now involved in decommissioning activities in Belgium and non-
exposed workers, N~40.
Uranium millers and miners currently employed at the Rozna mine (Czech Republic), N~100.
The feasibility of collecting biological materials in these subcohorts was investigated in detail (see
section 4.2.5.).
In addition, information was collected more briefly about possible opportunities to collect biological
samples in uranium workers in other settings (or to analyze biological samples already collected
there) in the future. This includes uranium workers employed in European as well as non-European
countries. The contacts established with invited experts have been very important for that purpose:
Preliminary contacts have been established with Polat Kazymbet and Meirat Batkin (Astana
Medical University, Kazakhstan) to examine the feasibility to access samples previously
collected in Kazakh workers, or to collect new samples. Some samples have already been
collected in uranium workers from the Stepnogorsk site, notably for the purpose of gene
expression analyses (Abilmazhinova et al., 2014; Kazymbet, 2014).
Contacts have been established with Andrey Karpov and Ravil M Takhauov (Seversk
Biophysical Institute, Russia) to examine the feasibility to analyze samples previously
collected in uranium workers in Seversk (Russia), or to collect new ones. A large biobank has
been set up there (Takhauov et al., 2015).
Contacts have been established with Friedo Zölzer (University of South Bohemia) who has
conducted a pilot study in uranium miners and millers at the Mydlovary site (Czech
Republic)(Zolzer et al., 2012b). These miners and millers have already been sampled twice,
with a 90 % participation rate for the follow up one year after initial sampling.
First contacts have been established by Nuvia Ltd to assess whether a feasibility assessment
could be possibly conducted in uranium workers the UK (Dounreay, and Urenco at
Capenhurst).
Lastly, the possibility to examine biological samples collected a long time after exposure was also
examined. It is of great interest to identify persistent biomarkers of exposure or examine biomarkers
of late biological effects such as a specific biological signature of radiation-induced disease (e.g.
uranium specific “fingerprints” in tumor tissue). A biobank including former uranium miners retired
from the Wismut company (Germany) has been set up at BfS (Gomolka et al., 2012). This biobank
may provide interesting material for such analyses.
26
4.2. Biological protocol for a molecular epidemiology study
The aim of the biological protocol is to provide a strategy as well as standardized operating
procedures (SOPs) and study instruments to perform relevant biological sampling, biomarker
measurements and analysis in a cohort of uranium exposed workers, in order to constitute an
international biobank.
The rationales for establishing such a biobank and integrating measurements of biomarkers in a
cohort study have been presented in section 1.6.2. Briefly, the long-term goal is to set up a large
international molecular epidemiology study that will provide new insight into the biological effects
associated with occupational exposure to uranium and help refine the estimates of related health
risk. Another potential side product of this research might be the identification or validation of new
biomarkers useful to the surveillance of workers occupationally exposed to uranium.
Setting up an international biobank including biological samples from workers is quite a novel and
complex task. It is anticipated that approval of stakeholders and ethics as well as data safety
committees will be possible only if a clear strategy is provided together with a strong management
system that ensures the data analysis. The biological protocol therefore defines the following aspects
of the proposed study:
A list of biomarkers of interest to study in relation with uranium exposure.
A related list of biospecimens that should be collected for the measurement of the selected biomarkers (as well as of other biomarkers which may be proposed in the future).
Standardized operating procedures (SOPs) for biospecimen collection, processing,
transportation, storage and for biomarker testing/measurement. Some SOPs for functional
tests were also proposed (e.g: in order to assess the cerebral or circulatory functions of
workers) (Gomolka et al., 2014).
A questionnaire to collect the information needed for an adequate interpretation of
biomarkers and appropriate statistical analysis
An information sheet for workers and related form of consent to participate to the study
In addition, since the successful measurement of most biomarkers require stringent
conditions of sampling, processing, storage and transportation of specific biospecimen
(which may widely differ according to the biomarkers which are intended to be measured in
them), it is necessary to propose, assess, and if necessary refine, a precise logistic strategy
allowing for the collection of several biospecimens in one or more pilot cohort(s). Another
mandatory step before moving to a large scale molecular epidemiology study would be to
conduct one or more pilot studies for field testing. Such pilot studies would notably be of
interest to assess the participation rate among workers, the appropriate completion of
questionnaires, the applicability of SOPs in field conditions on a real scale (e.g.: for several
workers per day), the quality of resulting samples for the envisaged biomarker analyses.
Whilst launching such pilot studies was clearly beyond the time frame of the CURE project it
27
was recognized that preliminary feasibility studies should be conducted in the groups of pilot
cohorts proposed jointly by WP1 and WP3, to assess the compatibility of the proposed
logistics protocol with field conditions.
All these aspects are detailed in the biological protocol below.
4.2.1. Proposed biomarkers
A list of biomarkers of interest to study the biological effects of uranium has been proposed. This list
has been established by taking into consideration a priori the two main types of approaches
currently used in biomarkers research:
Targeted approaches. Biomarkers that were considered as potentially informative based on
the knowledge of uranium action in humans or animals were identified. This includes
biomarkers that may reflect damage to organs/tissues/systems identified as established or
potential targets of uranium according to published biokinetic, experimental or
epidemiological studies. This list of biomarkers covers early as well as late biological effects
in the target organs/tissues/systems (lung, kidney, heart, bone, blood, vessels, red bone
marrow and central nervous system). Because no biomarker identified so far is specific to
uranium exposure or to its suspected health effects, a multi-marker approach has been
proposed for most targeted biological endpoints. Using a panel of relevant biomarkers will
allow for a better delineation of the suspected targeted effects. Multi-marker approaches
will also be useful to explore the interplay of different possible biological pathways (Pernot
et al., 2012).
Non-targeted approaches (such as OMIC techniques) are highly dimensional multi-marker
approaches by nature. They aim at the collective characterization and quantification of pools
of biological endpoints that translate into the structure, function, and dynamics of
organisms. Such approaches are potentially able to identify new biomarkers of exposure
(Grison et al., 2013) and to detect perturbations of biological pathways by uranium exposure,
which have not been suspected so far. This might in turn lead to the identification of new
target organs/tissues/systems of uranium exposure and related health effects that can be
identified by new related biomarkers. In addition, quantitative meta analyses of differentially
regulated molecular levels by toxic compounds - from DNA to RNA to proteins to metabolites
and their regulatory factors (e.g. miRNA, DNA methylation) – should allow for a better/in-
depth understanding of their systemic action and induced biological mechanisms. Such
methods should help to improve radiation risk assessment in the future.
The various biological endpoints covered by the proposed list of biomarkers are listed below. The
detailed list of proposed biomarkers is provided in the standardized operating procedures produced
by CURE WP3 (Gomolka et al., 2014):
Lung cancer and other lung damages, because of first entrance and deposition by uranium
dust inhalation in the lung, and of suggestive results from animal and epidemiological
studies.
28
Renal damages, because of uranium accumulation and biological data from animal and
epidemiological studies indicating effects of uranium on kidney
Bone damages, because of uranium accumulation and long term storage in this tissue
Cardio-vascular damages, because of both direct and indirect effect of uranium through
kidney impairment, of the established effect of particle inhalation on the cardio-vascular
system, and of suggestive results from an epidemiological study
Lympho-hematopoietic system, because of clearance of uranium by macrophages and
uranium deposition in bones and lymph nodes, and suggestive results from an
epidemiological study
Brain damage, since changes in cognitive functions have been observed in animal
experiments but no data are available in humans
DNA damages to evaluate the radiotoxic and chemotoxic effects of uranium on genetic
material, using cytogenetic biomarkers
Metabolism because of previous identification of urinary metabolomic signatures of uranium
exposure in rats
Other, non-specific OMIC biomarkers to develop new hypothesis like RNA profiles and whole
genome RNA sequencing in order to detect fusion proteins (leukemia), alternative splicing
and micro RNA, DNA sequencing to detect methylation.
This extensive list includes a large number of biomarkers (as detailed in the SOPs), which may need
to be adapted in the context of a proposed study depending on practical constraints (amount of
funding available, samples for which expectations of successful collection are good). It is open for the
addition of emerging biomarkers of interest, since the proper collection and biobanking of the
biospecimen will provide the opportunity for a multitude of analyses in the future.
The defined biomarkers do not have to be all investigated in a first step. Indeed the quality of the
collected and stored material must allow for their future investigation, as well as for the future
analyses of emerging biomarkers. The best strategy for the identification of certain biomarkers
would be to wait until sequencing strategies become much more elaborated and cheaper.
4.2.2. Biospecimen to be sampled
As part of the CURE project, it was recommended to collect the following biospecimen from study
participants in order to investigate the proposed biomarkers:
Blood
Urine
Sputum
Nasal swabs
Saliva
Again, ad hoc SOPs are provided for the purpose of sampling biospecimen in the best conditions for
later measurement of specific biomarkers (Gomolka et al., 2014). In addition, general considerations
are provided below regarding the collection of these different types of biospecimen:
29
4.2.2.1. Blood
Blood is an important biospecimen to collect because of the high number of biomarkers that can be
measured in it with high quality. It has been estimated by members of the CURE project that a
volume of blood of about 50 ml per donor is acceptable, from both ethical and physiological point of
views. Mentioning preceding studies sampling similar quantities may help convince occupational
physicians who are not familiar with molecular epidemiology studies. For instance, up to 80 ml per
donor has been collected from retired miners as part of a pilot study for the Wismut Biobank
(Gomolka et al., 2012). In the UK biobank for which 500.000 individuals have been sampled a total
volume of 45 ml of blood has been collected per person (Elliott and Peakman, 2008).
Blood must be collected after overnight fasting, using classical blood collection sets with BD
Vacutainer-type, safety-lock tubes, pre-coated with special preservatives, each destined for a specific
use and the measurement of different sets of biomarkers. A well-defined blood collection protocol,
sampling instructions and the number and type of the collected tubes have to be communicated
clearly to nurses in charge of blood collection. Nurses should be trained to follow the designed
protocol. It should be noted that some centrifugation steps have to be performed shortly after
sampling either on site or in a classical medical laboratory located near the sampling site.
Participating individuals have to be informed about the risks associated with blood sampling
(venipuncture) by the medical staff a priori.
Adequate conditions for the processing, transportation and storage of blood samples can be
summarized as follow. These ideal conditions for sampling might need to be adjusted according to
the site specific conditions:
Table 1. preferred conditions for blood sampling, processing and storage
Purpose Tube type Biological Investigation
On-site Processing
Transportation Storage
DNA isolation K2-EDTA tubes DNA damage none room temperature
-20°C
Lymphocytes isolations Lithium–Heparin tubes
translocations none room temperature
Rapid isolation of lymphocytes (max 2 days)
Plasma collection after 12h of fasting
Lithium–Heparin tubes
metabolomics, cardiovascular or brain markers
plasma collection and freezing
dry ice -80°C
RNA isolation *PAXgene tubes genomics none room temperature
-20°C (24h) then -80°C
Serum collection after 12h of fasting
SST tubes cardiovascular and brain biomarkers
serum collection and freezing
dry ice -80°C
Plasma collection after 12h of fasting
K2-EDTA tubes kidney, cardiovascular and brain biomarkers
plasma collection and freezing
dry ice -80 °C
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4.2.2.2. Urine
Urine sampling is non-invasive and provides a biological sample that is of high interest as a matrix for
measuring kidney function biomarkers and metabolites. Two different protocols for urine collection
are proposed, each for a specific purpose, and are briefly summarized below:
A 24-hour collection time, used for studying of kidney biomarkers: The first morning urine sample
should be discarded, and then all urine should be collected over a 24 hour period until the first urine
sample of the following morning (see SOPs in (Gomolka et al., 2014)).
Following this collection, a second 2-hour urine sample should be obtained for metabolomics studies
and the analysis of additional kidney biomarkers. In order to control variation in metabolite
composition of the collected samples due to influencing factors (e.g. composition of meals before
sampling), a detailed list of recommendations (diet and physical activity restrictions) will be provided
before 2-hour urine sampling (see SOPs in (Gomolka et al., 2014)).
4.2.2.3. Sputum
The mucoid material that is expelled from the respiratory passages or the “breathing tubes” is called
sputum. Sputum is a highly interesting biological sample widely used in lung cancer studies, mainly
for the detection of specific gene methylation patterns as pre-diagnostic markers. It is also of interest
to investigate lung damages other than lung cancer (e.g., via measurement of the CC16 protein,
which is potentially indicative of obstructive diseases). In addition, sputum samples potentially of
interest for the measurement of uranium, as a potential source of information on the solubility of
uranium particles encountered in specific settings.
Again two different protocols can be envisaged. It is possible to collect either induced or
spontaneous sputum:
The collection of induced sputum is a quite complex procedure, as summarized below:
Preliminary spirometer test in order to evaluate breathing capacity of the participant.
Inhalation of a short acting β2-agonist and a succession of saline solutions of various
concentrations
Collection of induced sputum
Processing within 2 hours for cell isolation and conservation.
This collection requests the presence of a nurse and a private room for the participant. Its feasibility
in occupational settings would clearly need to be assessed via a pilot study in field conditions.
Alternatively, a less constraining procedure is to ask workers to collect morning spontaneous sputum
(Belinsky et al., 2005) at home over three days. A specimen can be obtained by coughing up sputum
from deep in the lungs and placing it in a bottle containing fixative. This fixative preserves the cells
until they can be prepared for evaluation in the laboratory. No freezing is necessary and samples may
even be sent by regular mail. On the logistics point of view, the coupling of such a collection with 24h
urine collection, which is also partly conducted at home, may be considered.
31
4.2.2.4. Nasal swabs
During inhalation, the nasal epithelium is directly exposed to environmental pollutants, including
airborne uranium particles. In addition the nasal epithelium is directly connected to, and may reflect
what happens in, the lung. Nasal swabs are of minimal cost and might provide important information
about exposure to uranium or biomarkers of early or late biological effects in the lung. For instance,
they can be used for the purpose DNA methylation assays. The procedure is not painful, but slightly
invasive. It requires gesture training from the nurse. The acceptability of such sampling appears
worth testing as part of pilot studies in workers.
4.2.2.5. Saliva
The usefulness of saliva samples for biomarker studies in radiation research (Pernot et al., 2014) or
to study the effects of internal contamination by uranium has not been extensively evaluated so far.
However, saliva is of interest as it contains a wide range of molecules, including proteins, peptides,
DNAs, hormones, metabolites and RNAs - including non-protein coding RNAs (ncRNAs) like
microRNAs (miRNAs). The use of saliva for genetic analyses has grown dramatically in recent years in
molecular epidemiology studies and recent characterizations of the salivary proteome and
transcriptome have highlighted the diagnostic potential of saliva. Saliva contains clinically
discriminatory protein and RNA biomarkers of oral cancer for example. Although some confounding
factors have to be taken into account (tooth brushing, oral disease, microbiome in the mouth,
circadian rhythm …) and that blood generally has a bigger potential, the collection of saliva is non-
invasive and easy to perform. A good level of acceptance can therefore be expected from
participants. It can be collected in large volumes in appropriate buffers and stored at room
temperature for subsequent genomic DNA and RNA isolation. Saliva might also be a useful
biospecimen for uranium measurements and dosimetric purposes, with potential to reflect lung and
oropharynx cavity contaminations. Although the relative usefulness of saliva samples versus other
samples for the purpose of studying the effects of uranium exposure cannot be guaranteed to date,
it would be of interest to test their potential as part of a pilot study in uranium workers.
4.2.2.6. Further considerations on biological samples
Again, the collection of biological samples proposed above corresponds to an ideal sampling plan. In
some settings, the collection of certain biological samples may be refused for practical reasons (e.g.
important changes needed in the schedule of the medical check-ups to integrate their collection, lack
of availability of a dedicated room, etc) or limited in volume (typically, competition between the
volume of blood collected for routine medical surveillance and the volume needed for the study).
Other adaptations may be needed according to field conditions. For instance, practical constraints
regarding temperature conditions and equipment or time to sample processing might not exactly fit
the proposed SOPs in some settings. Unless these conditions can be changed, the robustness of the
SOPs to the field conditions should be quickly tested on a few samples (e.g.; replication of conditions
in a lab) to make sure that the modifications of conditions/procedures do not impair the quality of
biological samples for future analyses.
32
Several biomarkers can be measured in different kinds of biological samples (e.g., CC16 marker for
lung damage can be measured in sputum, serum and plasma). This may represent alternative
solutions if one type of biological sample cannot be collected. Alternatively, measuring the same
biomarker in two different types of biospecimen (especially, more or less invasive or costly) may
prove of interest for the purpose of comparison and validation studies. Applications of such
comparisons would apply to workers studies and possibly other situations (emergency, accidents)
where time and resources are more limited for the collection of certain specimen (typically, blood or
sputum) or not easily acceptable for other reasons (e.g., studies in children).
4.2.3. Questionnaire
A questionnaire to be completed by all participants in the study at the time of biological sampling
was produced by CURE partners and is provided in (Gomolka et al., 2014). In complement to the
retrospective information available in medical records, it will allow confounding factors to be
identified and taken into account in the statistical analysis of biological data.
The questionnaire should ideally be filled-in by the worker at the center (on-site, before the blood
sampling) and if possible verified at least in part by the physician (or by an associate) during the
medical exam.
Specific and detailed questions are included in the proposed questionnaire, related to:
mean of default parameter values for absorption Types from (ICRP, 2015a)).
Mixture fr sr (d-1) ss (d
-1) fA
F+M+S 0.403 5.33 2.55 x 10-3 8.07 x 10-3
F+M 0.60 6.50 5.0 x 10-3 0.0120
M+S 0.105 3.0 2.55 x 10-3 2.10 x 10-3
F+S 0.505 6.5 1.0 x 10-4 0.0101
Reconstruction of workers histories
The doses will be calculated for workers monitored for uranium exposure through urine analyses on
or after a defined date which will be chosen as the earliest date for which reliable bioassay data are
available.
Workers employed at more than one participating facility or employed more than once at the same
facility will be identified and their occupational and dosimetric history will be reconstructed. External
and internal radiation doses received before employment in a participating facility, and in non-
participating facilities, will be obtained where available.
Some of the information required for dose reconstruction relates to the characteristics of the
plant/workplace/activity and may be entered only once and applied to all workers involved in the
workplace/activity. This includes the characteristics (chemical form, solubility type, etc.) of the
isotopes present in various work environments and the measurement techniques implemented
within specific time periods. On the other hand, some data will be specific to a worker. Where
feasible the exposure and its parameters will be defined by combining administrative files (successive
jobs in the career) with a job-exposure matrix (JEM) and a registry of incidents. Workplace specific
aerosol parameters may also be determined from a representative group of workers with sufficient
bioassay measurement data. With regard to the type and time of intake, a constant chronic intake
regime is assigned to each period of routine exposure during employment and acute intakes are
defined according to records of incidents and special monitoring (e.g. in-vivo, faecal results).
4.3.4. Monitoring data
4.3.4.1. Uranium miners and millers
Uranium and other long-lived radionuclides
Exposure to long-lived radionuclides (LLR) has been measured in mines in terms of gross alpha activity (h Bq m-3). The radionuclides considered include:
47
238U, 234U, 230Th, 226Ra, 210Pb and 210Po of the 238U chain
235U, 231Pa and 227Ac of the 235U chain
232Th, 228Ra, 228Th and 224Ra of the 232Th chain.
The assumed activity ratio of 235U/238U is 5/95 and the assumed activity ratios of 232Th/238U in different mines are given in (Marsh et al., 2009). It is also assumed that 25% of the 222Rn gas and 220Rn escapes from the ore dust particles (details for radon and its progeny are provided below). Because of its short radioactive half-life (4 s), 219Rn is assumed not to escape from the ore matrix. Under these assumptions about 7.6 alpha disintegrations occur per becquerel of 238U. Thus, the incorporated activity of 238U (Bq) is given by the gross alpha activity exposure (h Bq m-3) multiplied by the breathing rate (m3 h-1) and divided by 7.6 (Marsh et al., 2008).
For millers processing non-ore material, it is assumed that only the uranium nuclides were present in
the material with activity ratios typical for natural uranium (i.e. activity ratio of 235U/238U is 5/95 and
that of 235U/238U is 1). Therefore, to interpret the gross alpha activity measurements, it is assumed
that 2 alphas are emitted per disintegration of 238U.
Radon and progeny
The historical unit of exposure to radon progeny applied to the uranium mining environment and
available in the Czech, French and German cohort databases is the working level month (WLM),
defined as the cumulative exposure from breathing an atmosphere at a concentration of 1 working
level (WL) for a working month of 170 hours. A concentration of 1 WL is any combination of the
short-lived radon progeny in one liter of air that will result in the emission of 1.3 105 MeV of
potential alpha energy. To calculate the absorbed doses per WLM, the activity concentration (in h Bq
m-3) of each of the short-lived radon progeny (218Po, 214Pb, 214Bi) corresponding to 1 WL is required.
These concentrations for 1 WL can be uniquely determined from the equilibrium factor, F which is a
measure of the degree of disequilibrium between the radon gas and its progeny (ICRP, 1993). A value
of 0.4 for F is typical for a mine and the activity ratios assumed are consistent with the assumptions
of the NRC (NRC, 1991).
External gamma
The dose from external gamma exposure is readily available in the databases and recorded in mGy.
The operational quantities measured by dosimeters are converted into organ absorbed dose by the
application of correction factors taking into account the attenuation of the external radiation
spectrum by body tissues, as indicated in 3.2.
4.3.4.2. Uranium workers
Bioassay measurement results and normalisation
The data to be used in the dose assessment are values of uranium activity excreted per day (Bq/d).
Bioassay measurement results recorded as Bq/day or µg/day or otherwise indicated as already
representing daily excretion will be kept as they are. Other bioassay results will be converted to a
mass or activity of uranium excreted per day through normalisation by volume (using reference daily
excretion values of 1.6 L for males and 1.2 L for females) or by creatinine content (using reference
daily excretion values of 1.7 g for males and 1.0 g for females) depending on the information
available on the sample. Mass values for uranium will be converted to activity using any available
information or reasonable assumptions about the isotopic composition of the exposure material.
48
Detection limit and reporting limit
The sensitivity of a monitoring technique is defined by its detection limit (DL). Uranium analytical
techniques are relatively sensitive and, for some, the limit of detection could potentially be less than
excretion from normal dietary intake of uranium in some areas. In order to discriminate between
non-occupational, i.e. dietary, excretion of uranium and that resulting from occupational exposures,
a ‘reporting limit’ (RL) is sometimes employed. Results less than the detection limit or the reporting
limit are usually recorded only as being less than this limit, without indication of the raw
measurement value. However, more recent measurements of excretion in non-exposed persons
living in the same location as those potentially occupationally exposed to uranium at various sites
has indicated that normal dietary excretion levels can be much lower than the relevant RL. Results
from these surveys, at various sites, of actual levels of uranium in urine arising from dietary intake,
can be used to supplement/correct occupational monitoring data.
Scattering factors
The overall uncertainty of measurement may be described by a log-normal probability distribution
which geometric standard deviation is called a scattering factor (SF) (Marsh et al., 2007). For in vitro
measurement, the value of SF depends on the sampling procedure. Several of the participating
institutions have calculated SF for their own measurements, including values related to early
measurement techniques. Where such data are absent, the values elaborated by Working Group 7 of
EURADOS and published in the IDEAS guidelines (Castellani et al., 2013) are to be used.
External gamma
The dose from external gamma exposure is readily available in the databases and recorded in mGy.
The operational quantities measured by dosimeters are converted into organ absorbed dose by the
application of a correction factor taking into account the attenuation of the external radiation
spectrum by the body tissues, as indicated in 3.2.
4.3.5. Assessment procedure
4.3.5.1. Miners and millers
For miners and millers, the intake is calculated as the product of an activity concentration of a
radionuclide in the air by the breathing rate (considered in the “reference miner type” as explained
above) and by the time spent breathing this contaminated air (time integrated concentration). The
dose, calculated using the aforementioned biokinetic and dosimetric models, is proportional to the
intake and depends on the physico-chemical parameters of the radionuclide. For uranium and long-
lived radionuclides, a constant chronic inhalation for the year of exposure is assumed. Because of the
long half-lives and the long retention within the body, the inhaled LLR in the ore dust continue to
give a dose to the organs following exposure. This needs to be accounted for when calculating the
annual doses to a miner over a long period of time. For radon and progeny, an acute inhalation
intake regime is considered because of the short half-lives.
4.3.5.2. Uranium workers
Maximum likelihood approach
49
The intake of radionuclides for an individual worker will be evaluated using the identified likely
pattern of exposure (i.e. “intake regimes” - a set of chronic exposure periods and/or acute exposure
incidents), the relevant biokinetic model and parameter values for each intake regime, and the
available bioassay data for the individual. A maximum likelihood approach will be used to assess
intakes. It derives intake value(s) that maximise the probability of observing the bioassay
measurements, both real and censored values, under the hypothesis of the measurement error
model specified.
Setting intake regimes
The assumed intake regime will consist of one or more periods of constant chronic intake, depending
on the work history, plus any acute intakes to be identified from operational data. Not all reported
incidents lead to acute intakes. Any incident that resulted in follow-up bioassay monitoring (i.e.
special sampling or in-vivo measurement) being performed will be assigned an acute intake date if
that monitoring yielded positive results.
The chronic intakes will then be assigned to each phase of a worker’s career involving a potential risk
of internal exposure. Just one chronic intake is required if the worker did the same job in the same
facility throughout his career, but a sequence of chronic intakes is required for a more complex work-
history. A job-exposure matrix is one possible way to specify the periods of constant potential
exposure within a work history. Bioassay monitoring is considered as the most reliable indication of
potential exposure. The assumed period of chronic exposure would therefore go from one
monitoring interval before the first measurement data (or from the date of first employment if this is
later) until the day of the last measurement data (or the end time of professional activity). A break in
bioassay monitoring over more than 3 monitoring intervals would indicate the cessation of potential
exposure during that break and no intake would be evaluated during that period. More precise
information is to be used when it is available.
Specification of biokinetic and error models
The biokinetic models indicated above will be applied. By default, intake is assumed to take place by
inhalation and is represented by the human respiratory tract model (ICRP, 2015a). If there is
indication of a wound intake, the wound model (NCRP, 2006) is to be applied. The particles cleared
from the airways to the gut and the unlikely ingestion intakes will be considered within the human
alimentary tract model (ICRP, 2006).
The particle size, pulmonary absorption type and wound retention category are fixed as explained
above. Plant-specific or incident-specific model parameter values will be used when available.
Aforementioned generic default values will be used in other cases (Blanchardon et al., 2014a). In
some cases it may be possible to infer individual-specific model parameters, where sufficient data
(e.g. faecal, in-vivo, air-sampling results) are available.
The measurement error model is log-normal with a geometric standard deviation, or scattering
factor (SF), depending on the monitoring technique as explained above. For results at or below the
limit of detection of a technique, then errors due to counting statistics may predominate and a
normal distribution may be used to describe errors.
Periods of chronic exposure with only censored bioassay data
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When all bioassay data within a chronic exposure period are less than the detection limit (DL) or
reporting level (RL), two intake values will be calculated for that period: (1) a minimum value of zero
and (2) a maximum value calculated by setting the last bioassay data as positive and equal to the
value of the DL/RL, while other bioassay data remaining censored. As a consequence, the doses for
the following years will be calculated as a range from a minimum dose to a maximum dose,
corresponding to the minimum and maximum values for former intakes.
Workers with bioassay data but no positive (above threshold) results could represent an important
part of the study population and their exclusion from the epidemiological study might lead to
selection bias (although this is to be evaluated in each cohort, by comparing the characteristics of
these workers with those having at least one positive result) and in this case, affect resulting risk
estimates. For those workers, annual doses will thus be calculated as a range from 0 to a maximum
value.
4.3.5.3. Quality assurance
The dose assessments for each cohort will be made by the dosimetrist associated with that cohort.
In order to ensure consistency in approach between the dosimetrists and to cross-check the results
of the various dose assessment software packages, samples of the assessments will be exchanged
between the dosimetrists.
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4.4. Methods for statistical analyses
Three forms of analyses are envisaged:
The first (“broad-base”) analysis will generate Standardised Mortality Ratios (SMRs) for a
range of diseases (both cancer and non-cancer) for which sufficient cases are observed
and/or that are of a priori interest regarding the suspected effects of uranium.
The aim of the second analysis will be to estimate a dose-response relationship between
uranium dose and disease risk.
The aim of the third analysis will be a preliminary exploration of the associations between
uranium exposure and the biomarkers selected for the study
Based on current knowledge about the biokinetics of soluble and insoluble uranium types,
information on biological effects stemming from animal experiments and suggested effects from the
few available epidemiological studies (see sections 1.3. and 1.4. for details) the diseases of interest to
the first two types of “classical” epidemiological analyses will be as follow:
Lung cancer
Lymphatic and haematopoietic cancers
Kidney cancer
Bone cancer
Digestive tract cancers
Cardiovascular diseases
Neurological diseases
Non-malignant respiratory diseases
Non-malignant renal diseases
These outcomes will be studied using mortality data. However, incidence data will also be available
for cancer outcomes in some cohorts. In addition, broad groupings of diseases or causes of death will
be examined:
All causes
All solid cancer
All solid cancer excluding lung cancer (with regard to miner studies)
Cancers related to smoking
Cancers not related to smoking.
Biomarkers of interest for analyses have been presented in section 4.2.1.
4.4.1. Broad base analyses
Two main pooled datasets will be available: one for miners/millers, and one for nuclear workers
(others may be defined for sensitivity analyses).
Standardised Mortality Ratios (SMR) will be computed in order to compare the mortality rates (from
the causes of death listed above) estimated from these pooled cohorts to the ones estimated in the
general populations from which the cohort members are drawn. As these analyses do not require
52
individual dose measurements for each subject, they will allow including the largest number of
subjects from each cohort. This will maximise the statistical power of the analyses to identify which
diseases are in excess in the cohorts.
Among nuclear workers, separate SMR values will be computed for those who were only exposed
externally to radiation and for those who were additionally (or instead) exposed internally to
uranium, and the ratio of these SMR’s will be computed. Any statistically significant difference
(deviation) of this ratio from one will indicate that uranium exposed workers are at different risk to
those only exposed to external sources of radiation. For miners and millers who were all exposed to
both external gamma radiation and uranium, the comparison will be made with the general
population only.
A range of additional sensitivity analyses may also be performed, for example:
Analysis of variation of SMRs with exposure to other nuclides e.g. plutonium in addition to
uranium
Analysis of variation of SMRs with age at first exposure, time since exposure, duration of
employment, between cohorts and countries.
Analysis of the impact of confounding factors. Examination of confounders would be
dependent on the information available in the cohorts with sufficient coverage. Possibilities
include stage of fuel reprocessing (or levels of enrichment), socio-economic status and in
some cohort subsets, contact with hazardous chemical or smoking. Even if smoking data are
not available for the whole cohorts, the risks for diseases potentially related to smoking
(IARC, 2004) can be compared to the risks for non-smoking related diseases in all cohorts.
The main focus of the pooled analyses will be on mortality, since mortality data (dates and causes of
deaths) will be available for all cohorts. However, it will also be possible to conduct similar analyses
with cancer incidence data (by computing Standardised Incidence Ratios) in countries where cancer
registries are available (United Kingdom and Belgium).
4.4.2. Dose-response analyses
The aim of these analyses will be to study the relationships between absorbed dose from uranium
and mortality from specific diseases or – where available –cancer incidence, accounting for potential
confounders including exposure to external gamma radiation exposure and other internal nuclides
than uranium. It will also aim to estimate separately external and uranium dose-response
relationships and to compare them. In doing so, it will be possible to empirically estimate the
radiation quality effect for comparison with the standard value for internal alpha radiation exposure
as proposed by (ICRP, 2007).
These analyses will initially be performed separately for the nuclear workers and miners/millers
groups. A final stage will be to compare the results between the nuclear workers and the
miners/millers to determine whether a single consistent estimate of uranium risk (and a single
radiation quality estimate) can be defined using a single pooled cohort of all the compatible workers
and miners/millers cohorts.
53
Ideally, Cox regression will be fitted to datasets providing information at individual level. However, in
the event that permission is not given to share data between partners at the individual level in a
timely fashion, standard Poisson regression will be rather used to fit dose-response relationships
from aggregated data (i.e., at a stratum level pooling homogeneous person-years according to sex,
attained age, calendar period, and other factors).
Where feasible, it would be also useful to perform further statistical analyses to study the potential
modifying impact of both time-dependent factors (e.g., age at exposure, time since exposure…) and
potential confounders (e.g., smoking, occupational exposures other than uranium) on the dose-
response relationships of interest. These analyses will be performed on groups of frequently
occurring diseases, e.g. all solid cancer, leukaemia excluding or including chronic lymphocytic
leukaemia, smoking and non-smoking related cancers, and on such individual cancer and non-cancer
disease groups in which a significant number of occurrences are observed (these are likely to include
lung cancer and circulatory system diseases).
4.4.3. Exploratory analysis of biological information
Because the primary objective of pilot studies for molecular epidemiology will be to determine
whether the standardized operating procedures (SOPs) proposed as part of the CURE project can be
implemented in an effective way in a prospective cohort, in the first place biological data will only be
available for a cross-sectional sample of workers.
Exploratory statistical analyses will focus on biomarkers measured in two subgroups of this cross-
sectional sample. One subgroup will be selected to have no (or low) exposure to uranium while the
other will be selected to be similar in other respects but to have high exposure to uranium. Standard
multivariate statistical analyses will be performed to determine potential differences in biomarker
measurements between the two groups.
Specific methods such as Partial Least Squares Regression-Discriminant Analysis (PLS-DA) may be
used for to the analysis of multidimensional data (e.g.: analyses of metabolomics data taking
potential confounders into account)(Bonvallot et al., 2013).
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4.5. Characterization and propagation of uncertainties
4.5.1. Introduction
Accounting for uncertainties in dosimetry and in dose-response models used in epidemiologic studies
of uranium workers and miners/millers is a complex issue and a vast field of investigation. This is due
to the complexity of dose assessment procedures, but also to the various sources of uncertainty that
can be identified (even if, some are of arguably less importance than others, see the UWG final
report (Giussani et al., 2014)) and to the advanced statistical methods that are needed to deal with
such uncertainties.
Research has been conducted, notably by partners of the CURE consortium, on the uncertainties
related to the quantification of radiation doses due to internal exposure to radionuclides (Davesne et
al., 2010; Li et al., 2011; Puncher et al., 2013). The impact of specific sources of uncertainty such as
exposure measurement error on the disease risk estimates is a relatively new but developing field in
radiation epidemiology (UNSCEAR, 2012), and several partners of the CURE project have begun to
explore it, e.g.: (Allodji et al., 2012; Hoffmann et al., 2014).
Several sources of uncertainty as well as their respective nature and possible impacts on dose and
risk estimates have been identified at several steps of the proposed scientific project (e.g.:
interpretation of bioassay, reconstruction of exposure conditions, dose calculation process, health
outcome ascertainment, epidemiological and biomarker analyses), as detailed in the UWG final
report.
Based on the uncertainty matrix developed by the UWG, but also of case studies conducted by UWG
members and of their experience of as part of various projects (see the UWG final report), it has
been possible to prioritize research needs in the field of health risk assessment pertaining to
occupational exposures to uranium. The following propositions should be considered as a partial
road map for a middle-to-long-term strategic work plan. It is not intended that all these research
questions are addressed within the scope of a single research project.
The priority issues to address were identified as follow:
Uncertainties on the lung solubility of uranium compounds encountered in some
occupational settings. Of all the parameters employed in the biokinetic models necessary for
retrospective reconstruction of internal radiation doses, those governing absorption of
radionuclides from the lungs to blood have the greatest impact on estimates of lung dose. It
is acknowledged that the impact on doses to other biokinetic and dosimetric endpoints than
lung will be more limited.
Censored bioassay data (i.e. measurement below DL or RL).
The identification of suitable statistical methodologies to account for the uncertainty
inherent in estimates of dose when estimating radiation-induced disease risk.
In order to address the aforementioned issues, the following research perspectives have been
proposed:
Characterizing the best estimates and probability distributions of model parameters and
other relevant uncertainty sources
55
Testing the feasibility to account for the uncertainty inherent in estimates of dose when
estimating radiation-induced disease risk.
Creating a synthetic cohort as a tool for testing the proposed methodologies
These research perspectives will be developed below:
4.5.2. Characterizing the best estimates and probability distributions of model parameters
and other relevant uncertainty sources
Deriving best estimates of parameter values is required whether the primary dose deliverable is
point estimates of dose or doses in the form of Bayesian (or other) probability distributions obtained
from uncertainty analyses. For these reasons significant effort should be directed into deriving prior
distributions for key parameters, particularly those representing “reducible” uncertainties (identified
in the uncertainty matrix (see the UWG final report)). Again, of all the biokinetic parameters, it is
those governing absorption from lungs to blood that have the greatest impact on estimates of lung
dose.
Bayesian methods to estimate uncertainties on doses were developed at PHE as part of the EU
funded projects Alpha-Risk and SOLO and under the US-funded JCCRER projects for Russian Mayak
workers. One aspect of this approach consisted in deriving informative prior probability distributions
for exposure parameters from data that directly relate to the workers of interest and the material
they were exposed to. In the dose estimation for Mayak workers, Bayesian inference was applied to
obtain best estimates of the dissolution parameters for plutonium nitrate and oxide compounds
from worker bioassay and autopsy data. The output from these calculations consisted in probability
distributions of absorption parameter values that were then applied to the whole cohort to calculate
uncertainties on doses. In the same way, other sources of uncertainty, such as aerosol size and
dispersion parameters, and likely intake levels, could be obtained from a review of workplace
monitoring data.
Similar approaches could be used to derive estimates of the absorption parameters for uranium
materials. Distributions (representing variability) derived for particle transport clearance rates and
other physiological parameters for the Mayak workers could be used as a reasonable starting point
for deriving distributions for individual specific parameters for the cohorts considered in CURE. It can
indeed be assumed that these distributions are likely to be material independent and similar across
cohorts. However, scope exists to further refine these distributions using relevant uranium worker
autopsy data, if available.
Therefore, priority should be given to obtaining best estimates of the rate of absorption of the
specific uranium materials that workers were exposed to and likely default intake rates; hence,
appropriate (informative) bioassay data from workers within the cohort will be identified for
Bayesian analysis to determine absorption rates. Failing this, the goal should be deriving reasonable
default values for exposures where the type of material is unknown.
Further information might come from a pilot study on biomarkers in sputum, saliva or nasal swabs. A
possible outcome of such work could be the identification of biological markers useful for the
characterization of lung solubility parameters and consequently for reducing the uncertainty on the
assumptions used in exposure/dose assessment.
56
Other sources of uncertainty such as qualitative modelling hypothesis (e.g.: modelling of activity
measurement error, correction for contribution of dietary intakes of uranium to bioassay) should
also be characterized in order to be integrated in a full uncertainty study.
4.5.3. Testing the feasibility to account for the uncertainty inherent in estimates of dose
when estimating radiation-induced disease risk
The development of an innovative approach to derive uncertainty on risk estimates from
uncertainties arising from the different sources identified in the matrix (see the UWG final report) is
a major challenge. The question of integrating dosimetry and epidemiology can be considered as one
global issue or, alternatively, as a succession of two smaller issues: 1) estimation of dose and its
associated uncertainty from uncertain exposure data; 2) evaluation of the risk and its uncertainty
from dose estimates and their uncertainties.
Several methods to account for the uncertainty inherent in dose estimates when estimating disease
risk have been discussed and proposed in the UWG final report.
All the proposed approaches need to be further evaluated, and their feasibility and validity in an
epidemiological study are still to be verified. Therefore, their application to the CURE cohorts would
not be realistic in a short time frame. Nonetheless, feasibility studies could be conducted using
smaller sets of representative subjects and then extended to all workers or miners/millers having
similar individual characteristics or experiencing similar conditions of exposure and monitoring.
Alternatively, a purposely created synthetic cohort (see below) could be used. These studies would
give important indications on the possible drawbacks and difficulties to be encountered in the
implementation of the methods to the full CURE cohorts.
4.5.3.1. Handling probability distributions representing uncertainties on doses as input data
for propagation in epidemiological analyses
A previous experience as part of the Alpha-risk project has showed that propagating distributions
reflecting both shared and unshared uncertainties on dose estimates (derived by a theoretically
correct Bayesian retrospective dosimetric framework) throughout epidemiological analyses can lead
to a series of difficulties, notably numerical problems due to large inter-/intra-cohorts variations and
scaling problems when manipulating the data due to different levels of apparent informativeness
between cohorts (and also within one cohort). However, it appears that if the priors are optimised
with respect to the bioassay data and, if the bioassay data then provide no additional information
regarding model parameter values (beyond that already specified by the priors), then the method
can be simplified in such a way that convergence can be achieved while preserving the shared error
structure. Nevertheless, this methodology is still in its infancy and needs to be further evaluated.
4.5.3.2. Determination and use of simple summary statistics to represent uncertainty on
dose estimates
A possible alternative approach could be to account for uncertainty on dose estimates through simple summary statistics: confidence or credible interval and standard deviation for example. Even though they are less informative, these summary statistics could be more easily used than a probability distribution on dose to account for dose uncertainty when estimating disease risk from dose-response models.
57
4.5.3.3. Directly accounting for exposure uncertainty when estimating risk
A Bayesian approach to account for exposure measurement error when estimating the risk of cancer
is currently under development at IRSN. This methodology derives risk directly from exposure and its
uncertainty. The main idea is to combine conditionally independent sub-models into a unique
framework, called Bayesian network, and then to infer the whole model under the Bayesian
paradigm, using, for instance, a Monte-Carlo Markov Chain algorithm. The independent sub-models
are:
a disease model describing, for each worker, the relationship between the true (latent)
radiation exposure and the time until death caused by that specific disease
a measurement model describing, for each worker, the conditional probability distribution of
his true (resp. observed) exposures given his observed (resp. true) exposures in case of
an exposure model describing, in case of classical measurement error only, the probability
distribution of the true exposures.
This so-called Bayesian structural approach is based on a unique and coherent framework in which all
the parameters of the measurement, exposure and disease models are estimated simultaneously
allowing radiation exposure uncertainty to be propagated into the risk parameters estimation. This
approach is currently being applied to refine the risk estimates for lung cancer due to radon
exposure in a uranium miners’ cohort. It could be extended and specifically adapted to the case of
uranium exposures of miners, millers and other nuclear workers.
4.5.4. Creating a synthetic cohort as a tool for testing the proposed methodologies
A so-called "synthetic cohort" can be created by generating simulated dosimetric and health
outcome data for a number of artificial subjects. Such a synthetic cohort would be used for a number
of purposes. A primary use would be evaluation and validation of methods currently used to
investigate uncertainties in dosimetric data. Since the cohort could be constructed to have a
particular risk and dose-response relationship, this would also allow a sensitivity analysis to be
carried out to quantify impacts of dosimetric uncertainties on measures of risk estimated by
epidemiologists. One significant advantage of using a synthetic cohort is the potential for sharing all
data (dosimetric and epidemiological databases) between members of a future project without the
usual constraints of data protection and ethics. A number of issues might be explored:
The possibility of using individual dose distributions for each member of the cohort within
analyses
Impact on risk estimates of attempting to account for both shared and unshared
uncertainties
Potential for combined analyses of cohorts with dosimetry of different levels of
informativeness
How to account for the reliability of assumptions used in the development of a Job Exposure
Matrix
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5. CONCLUSIONS
The CURE project has reached its objective to propose an integrated research proposal, thanks to an
effective collaboration and strong interactions between epidemiologists, biologists, dosimetrists and
biostatisticians. The CURE consortium proved to be a highly productive think-tank for future projects.
The work conducted has several major strengths:
The multidisciplinary approach adopted as part of CURE, integrating dosimetry,
epidemiology, biology/toxicology and biostatistics proved to be scientifically pertinent. It
allowed producing a research protocol with strong potential to improve both the
understanding and the quantification of the biological and health effects of uranium
exposure.
CURE has built a strong multidisciplinary consortium of epidemiologists, dosimetrists,
biologists, toxicologist and biostatisticians, conducting top-level research in their respective
fields. These researchers brought together a broad array of complementary skills, developed
understanding of their respective, and mutual, contributions. This aspect was essential to the
collective construction of the research project. This has prepared the consortium to the
implementation of future multidisciplinary research projects on the biological and health
effects of internal contaminations.
Thanks to the extensive experience of CURE partners and of invited experts, the
opportunities for future projects, but also their potential pitfalls, have been identified. This
allowed for the definition of an ambitious but realistic protocol, which can be implemented
in the short term.
The preparation work conducted as part of CURE will make it possible to conduct the first
pooled epidemiological analysis of uranium workers and miners in Europe. This will include
large cohorts with long follow-up (providing optimal statistical power to detect increased
risks), in which internal and external dose estimates are either readily available or are about
to be. Such a pooled epidemiological analysis will have potential to deliver results directly
relevant to radiation protection.
A full protocol has been prepared that will allow to test the integration of biological
indicators into epidemiological studies of uranium exposed workers, by conducting pilot
molecular epidemiology studies. This is a crucial step on the road map to the integration of
modern radiobiological and epidemiological research. The pilot studies will be nested within
established cohorts, which will allow capitalizing on carefully collected information
(including, but not limited to, dosimetric data). This will ensure both a high data quality and
cost effectiveness.
Contacts have been established with research teams involved in similar research projects
outside Europe (USA, Kazakhstan, Russia), as well as with experts from various fields of
expertise within Europe, notably in molecular epidemiology and biobanking. These
59
contributions proved to be very effective and the establishment of contacts with external
experts will be pursued in future projects.
Early dissemination work has been conducted, throughout oral and poster communications
in conferences and workshops (see annex 2). Further dissemination will be ensured by
submitting scientific papers directly resulting from the CURE project to peer reviewed
journals.
As stated above, a number of potential pitfalls have been identified by the CURE consortium, and
these will be anticipated.
Obtaining agreements from ethics committees (ethics of biological sampling, storage and use
of samples, data access, data protection) can be time consuming. Procedure vary from
country to country, but in each of them delays have to be anticipated. For all CURE cohorts,
the necessary agreements from ethics committees have already been collected, or
applications have been submitted and are currently being reviewed. This anticipation will
allow for the implementation of the proposed research protocol to be started in the short
term (i.e., before year 2016).
Obtaining agreements from stakeholders for molecular epidemiology studies (employers,
doctors, workers, worker representatives committees) can be challenging in some settings.
Biological sampling is only possible if there is interest and support to the project by the
workers who will provide biological samples, and by the medical centres where samples have
to be collected. Agreements from employers are needed if sample collections are organized
during medical check-ups. The local acceptability may depend upon the social and economic
context. The personal views of occupational physicians about the study goals will also
determine their willingness to authorize certain kinds of biological sampling (e.g., sputum
collection). Communication with all stakeholders (workers, worker representatives, doctors,
employers) is therefore a key issue. An information sheet was developed as part of the CURE
project to help in this process. For the pilot study proposed in Rozna (Czech Republic), the
agreements from all stakeholders except workers (at this stage), have already been obtained.
The individual consent to participate from voluntary workers will need to be obtained as part
of pilot studies.
The feasibility to set up molecular epidemiology studies in some settings also depends on
other practical constraints. The proposed biological sampling scheme needs to be compatible
with the usual organization of medical check-ups, and with local infrastructures. Last, in
some settings only a limited quantity of blood can be collected for the purpose of a study,
depending on the volume already collected for the purpose of routine surveillance. All these
conditions were carefully checked as part of the preliminary feasibility studies conducted in
the study settings proposed for pilot studies, and the logistic strategies were defined
accordingly.
The use of biomarkers in epidemiological analyses is a developing field. It is acknowledged
that the proposed list of biomarkers might not cover all the potential biological effects of
uranium. However, the list of proposed biomarkers can evolve in the future. It is open for
60
emerging biomarkers of interest, since the collection and proper storage of biospecimen
(biobanking) will give the opportunity for a multitude of future analyses.
It is acknowledged that the levels of exposure to uranium of workers currently active in
Western Europe are not representative of those encountered in the same settings a few
decades ago. It might be relevant in the future to launch other pilot studies in other settings
located outside Europe, where workers have been exposed to uranium at higher levels
during recent years.
Sources of uncertainties have been identified at several steps of the proposed project.
However, the work conducted by the UWG as part of CURE allowed identifying the sources
of uncertainty which should be addressed in priority, as opposed to others that are
considered to be of lower importance. Further work will be conducted to better characterize,
and whenever possible, reduce uncertainties. Methodologies have been proposed to
account for the uncertainty in estimates of dose when estimating radiation-induced disease
risk, and these will be evaluated as part of future projects.
Funding is to be found to apply the research protocol designed during the CURE project.
Financial support from the European Commission will be a major condition for launching
future projects. Nevertheless, the Commission will not be considered as the only source of
potential funding. Screening of potential complementary sources of funding at national and
international level will be regularly evaluated by each partner.
In the short term (i.e., within the next two years), major steps of the protocol developed as part of
CURE can be implemented. Depending upon availability of funding, it would be possible:
To complete or wherever needed, to improve internal dosimetry within established cohorts
of uranium workers and miners in Europe.
To estimate health risks associated with internal doses resulting from uranium exposure in
the pooled cohorts, which would be directly informative for radiation protection.
To better characterize the sources of uncertainties identified as priorities during the CURE
project, to reduce these uncertainties wherever feasible, and to test methodologies to
account for the uncertainty in estimates of dose when estimating radiation-induced disease
risk.
To set up pilot molecular epidemiology studies. This would allow : 1) testing the collection of
biological samples and of related information, which will provide precious feedback for
future projects. 2) testing the associations of several biomarkers with internal and external
radiation dose. Targeted approaches would provide information on the mechanisms of
biological responses to radiation at low dose. Non targeted approaches such as
metabolomics would be tested for the first time in humans, in relation with chronic exposure
to internal emitters. This would allow assessing whether new pools of biomarkers could be
identified as biological signatures of exposure in humans. Such signatures were recently
61
observed in rats exposed to uranium (Grison et al., 2013) and cesium (Grison et al., 2012). 3)
initiating the creation a biobank that would be an invaluable resource for future integrated
studies on the effects of chronic exposure to internal emitters and more generally, a
strategic investment for radiation protection research.
To further develop contacts established with other research teams interested in the
biological and health effects of uranium exposure outside Europe (USA, Kazakhstan, Russia,
Canada). This would include assessing the feasibility of pooling established epidemiological
cohorts in these countries with CURE cohorts or to set up new ones, but also of setting up
pilot molecular epidemiology studies in these countries.
To create closer links with research teams and organizations outside the field of uranium
research. This would include research on the effects of other internal emitters, as well as on
other exposure situations than occupational exposure. Exchanges with the MELODI,
EURADOS, ALLIANCE and NERIS platforms would be organized to better identify, and
wherever feasible to build on, possible synergies. Exchanges with researchers outside of the
radiation field would be organized, notably on the topic of molecular epidemiology, systems
biology, and advanced statistical approaches for the propagation of uncertainties in
regression models.
In order to complete the steps listed above, the CURE consortium will submit a proposal for funding
to the European Commission. The impacts of the proposed work would extend far beyond the issue
of uranium exposure in workers and miners, since the above steps are crucial for:
The improvement of knowledge on the health effects of exposure to internal emitters.
Especially, producing information about the magnitude of risks related with internal alpha,
compared with external gamma, radiation will help assess the validity of the current value
for the radiation weighting factor for alpha emitters (wR)(ICRP, 2007).
The integration of modern radiobiological and epidemiological research, which is a major
goal for future radiation protection research. A proof of principle is needed for this approach.
The successful constitution of a modern biobank and/or the validation of biomarkers of
exposure or effects would create important precedents. They would open the way to the
creation of large-scale international biobanks. In the future, such biobanks would become
major infrastructures for integrated radiobiological and molecular epidemiology research on
the effects of chronic exposure to internal emitters and more generally, for radiation
protection research. The proposed project would allow defining the bases of a road map
toward the integration of modern radiobiological and epidemiological research.
This proposal will build on at least 3 DoReMi tasks: CURE (task 5.8.), IntEmitUM (task 5.5.1) and
AIRDoseUK (task 5.5.2.). It will directly address research priorities identified by the HLEG (dose-
response relationship at low dose/low dose rate exposures for cancer; non cancer health effects;
internal exposure; influence of radiation quality). It will also addressing research priorities mentioned
in the Strategic Research Agendas of MELODI (again, dose and dose rate dependence of cancer risk;
improvement of the understanding of the mechanisms contributing to radiation risk at low dose and
dose-rate exposure; non-cancer effects; integration of biological approaches for radiation risk
62
evaluation in epidemiological research; effects of and risks associated with internal exposures;
differing radiation qualities and inhomogeneous exposures) and EURADOS (improvement in the
modelling of biokinetics and dosimetry of internal emitters, improvement of uncertainty evaluation
of doses, dosimetry for historical cohorts).
In fine, future projects building on reflections conducted as part of the CURE project will be able to
improve the estimates of risk related to exposure to uranium, but also of other incorporated
radionuclides and more generally of low doses of ionizing radiation, throughout to the integration of
dosimetry, biology, epidemiology and biostatistics. In the future, it might be envisaged to open the
reflections to other disciplines interested in the effects of internal contaminations by radionuclides.
63
REFERENCES
Abilmazhinova A, et al. Conference Paper: Gene expression profiles in uranium industry workers of Stepnogorsk city occupationally exposed to ionizing radiation. European Human Genetics Conference. Milan, Italy May 2014. (2014).
Allodji RS, et al. The performance of functional methods for correcting non-Gaussian measurement error within Poisson regression: corrected excess risk of lung cancer mortality in relation to radon exposure among French uranium miners. Statistics in medicine (2012) 31:4428-4443.
Ansoborlo E, et al. Determination of the physical and chemical properties, biokinetics, and dose coefficients of uranium compounds handled during nuclear fuel fabrication in France. Health physics (2002) 82:279-289.
Ansoborlo E, Lebaron-Jacobs L, Prat O. Uranium in drinking-water: A unique case of guideline value increases and discrepancies between chemical and radiochemical guidelines. Environment international (2015) 77C:1-4.
Araujo JA, Nel AE. Particulate matter and atherosclerosis: role of particle size, composition and oxidative stress. Particle and fibre toxicology (2009) 6:24.
Atkinson WD, Law DV, Bromley KJ. A decline in mortality from prostate cancer in the UK Atomic Energy Authority workforce. Journal of radiological protection : official journal of the Society for Radiological Protection (2007) 27:437-445.
ATSDR. Toxicological profile for uranium (2013) Atlanta, Georgia (USA): Agency for Toxic Substances and Disease Registry.
Bal W, Protas AM, Kasprzak KS. Genotoxicity of metal ions: chemical insights. Metal ions in life sciences (2011) 8:319-373.
Bečková V, Malátová I. Dissolution behaviour of 238U, 234U and 230Th deposited on filters from personal dosemeters. Radiat. Prot. Dosim. (2008) 129:469-472.
Belinsky SA, et al. Gene promoter methylation in plasma and sputum increases with lung cancer risk. Clinical cancer research : an official journal of the American Association for Cancer Research (2005) 11:6505-6511.
Blanchardon E, et al. Dosimetric protocol (CURE internal deliverable D2.2). DoReMi - Low Dose Research towards Multidisciplinary Integration - Task 5.8 “CURE project”. (2014a).
Blanchardon E, et al. Report on the evaluation of monitoring data and physico-chemical characterisation of radionuclides (CURE internal deliverable D2.1). DoReMi - Low Dose Research towards Multidisciplinary Integration - Task 5.8 “CURE project”. (2014b).
Boice JD, Jr., Cohen SS, Mumma MT, Chadda B, Blot WJ. A cohort study of uranium millers and miners of Grants, New Mexico, 1979-2005. Journal of radiological protection : official journal of the Society for Radiological Protection (2008) 28:303-325.
Boice JD, Jr., et al. Updated mortality analysis of radiation workers at Rocketdyne (Atomics International), 1948-2008. Radiation research (2011) 176:244-258.
Boice JD, Jr., et al. A comprehensive dose reconstruction methodology for former rocketdyne/atomics international radiation workers. Health physics (2006) 90:409-430.
Bonvallot N, et al. Metabolomics tools for describing complex pesticide exposure in pregnant women in Brittany (France). PloS one (2013) 8:e64433.
Brandom WF, Saccomanno G, Archer VE, Archer PG, Coors ME. Chromosome aberrations in uranium miners occupationally exposed to 222 radon. Radiation research (1972) 52:204-215.
Canu IG, Ellis ED, Tirmarche M. Cancer risk in nuclear workers occupationally exposed to uranium-emphasis on internal exposure. Health physics (2008) 94:1-17.
Canu IG, Laurent O, Pires N, Laurier D, Dublineau I. Health effects of naturally radioactive water ingestion: the need for enhanced studies. Environmental health perspectives (2011) 119:1676-1680.
Cardis E, et al. Risk of cancer after low doses of ionising radiation: retrospective cohort study in 15 countries. BMJ (2005) 331:77.
64
Carpenter L, Higgins C, Douglas A, Fraser P, Beral V, Smith P. Combined analysis of mortality in three United Kingdom nuclear industry workforces, 1946-1988. Radiation research (1994) 138:224-238.
Castellani CM, et al. IDEAS Guidelines (Version 2) for the Estimation of Committed Doses from Incorporation Monitoring Data. EURADOS Report 2013-01, Braunschweig, March 2013. (2013).
Chan C, et al. Mortality patterns among Paducah Gaseous Diffusion Plant workers. Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine (2010) 52:725-732.
Clarkson TW, Kench JE. Urinary excretion of amino acids by men absorbing heavy metals. Biochem J (1952) 62:361-371.
Currie G, Delles C. Proteinuria and its relation to cardiovascular disease. International journal of nephrology and renovascular disease (2013) 7:13-24.
Darolles C, et al. Different genotoxic profiles between depleted and enriched uranium. Toxicology letters (2010) 192:337-348.
Davesne E, Casanova P, Chojnacki E, Paquet F, Blanchardon E. Integration of uncertainties into internal contamination monitoring. Health physics (2010) 99:517-522.
Dounce AL, Roberts E, Wills JH. Catalasuria as a sensitive test for uranium poisoning. in : Voegtlin C, Hodge HC, ed. Pharmacology and toxicology of uranium compounds. Volume I. McGraw Hill, New York, NY 1949, pp 889-950. (1949).
Duport P, Robertson R, Ho K, Horvath F. Flow-through dissolution of uranium-thorium ore dust, uranium concentrate, uranium dioxide, and thorium alloy in simulated lung fluid. Radiat. Prot. Dosim. (1991) 38:121-133.
Dupree-Ellis E, Watkins J, Ingle JN, Phillips J. External radiation exposure and mortality in a cohort of uranium processing workers. American journal of epidemiology (2000) 152:91-95.
Dupree EA, Watkins JP, Ingle JN, Wallace PW, West CM, Tankersley WG. Uranium dust exposure and lung cancer risk in four uranium processing operations. Epidemiology (1995) 6:370-375.
Eisenbud M, Quigley JA. Industrial hygiene of uranium processing. A.M.A. archives of industrial health (1956) 14:12-22.
Elliott P, Peakman TC. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. International journal of epidemiology (2008) 37:234-244.
Engels H, et al. Radiation exposure and cause specific mortality among nuclear workers in Belgium (1969-1994). Radiation protection dosimetry (2005) 117:373-381.
Figgs LW. Lung cancer mortality among uranium gaseous diffusion plant workers: a cohort study 1952-2004. The international journal of occupational and environmental medicine (2013) 4:128-140.
Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research (1975.) 12:189–198.
Garsi J-P, et al. Half-century archives of occupational medical data on French nuclear workers: a dusty warehouse or gold mine for epidemiological research? Arhiv za higijenu rada i toksikologiju (2014) 65.
Gillies M, Haylock R. The cancer mortality and incidence experience of workers at British Nuclear Fuels plc, 1946-2005. Journal of radiological protection : official journal of the Society for Radiological Protection (2014) 34:595-623.
Gilman AP, et al. Uranyl nitrate: 28-day and 91-day toxicity studies in the Sprague-Dawley rat. Toxicological sciences : an official journal of the Society of Toxicology (1998) 41:117-128.
Giussani A, et al. Final Report of the Uncertainties Working Group (UWG). (2014). Gomolka M, et al. The German Uranium Miners Biobank – current status and perspectives in
radiation research. 4th International MELODI Workshop. 12-14 September 2012, Helsinki, Finland (2012).
65
Gomolka M, et al. CURE Biology Protocol (CURE internal deliverable D3.2). DoReMi - Low Dose Research towards Multidisciplinary Integration - Task 5.8 “CURE project”. (2014).
Grignard E, Gueguen Y, Grison S, Lobaccaro JM, Gourmelon P, Souidi M. Contamination with depleted or enriched uranium differently affects steroidogenesis metabolism in rat. International journal of toxicology (2008) 27:323-328.
Grison S, et al. Metabolomics identifies a biological response to chronic low-dose natural uranium contamination in urine samples. Metabolomics : Official journal of the Metabolomic Society (2013) 9:1168-1180.
Grison S, et al. The metabolomic approach identifies a biological signature of low-dose chronic exposure to cesium 137. Journal of radiation research (2012) 53:33-43.
Gueguen Y, Rouas C. [New data on uranium nephrotoxicity]. Radioprotection (2012) 47. Guseva Canu I, Faust S, Knieczak E, Carles M, Samson E, Laurier D. Estimating historic exposures at
the European Gaseous Diffusion plants. International journal of hygiene and environmental health (2013) 216:499-507.
Guseva Canu I, et al. Does uranium induce circulatory diseases? First results from a French cohort of uranium workers. Occupational and environmental medicine (2012) 69:404-409.
Guseva Canu I, et al. Uranium carcinogenicity in humans might depend on the physical and chemical nature of uranium and its isotopic composition: results from pilot epidemiological study of French nuclear workers. Cancer causes & control : CCC (2011) 22:1563-1573.
Guseva Canu I, et al. Comparative assessing for radiological, chemical, and physical exposures at the French uranium conversion plant: Is uranium the only stressor? International journal of hygiene and environmental health (2009) 212:398-413.
Haylock R, Kreuzer M, Laurent O, Samson E. CURE Epidemiology Intermediate report (CURE internal deliverable D1.1). DoReMi - Low Dose Research towards Multidisciplinary Integration - Task 5.8 “CURE project”. (2014a).
Haylock R, Kreuzer M, Laurent O, Tomasek L, Engels H. CURE Epidemiology protocol (CURE internal deliverable D1.2). DoReMi - Low Dose Research towards Multidisciplinary Integration - Task 5.8 “CURE project”. (2014b).
Henny J, Zins M, Goldberg M, Dagher G, Clement B. Guide pour la constitution d’une biobanque associée aux études épidémiologiques en population générale. (2013) Paris: Lavoisier.
HLEG. High Level and Expert Group (HLEG). HLEG Report on European Low Dose Risk Research. EUR 23884. Luxembourg, UK: Office for Official Publications of the European Communities; 2009. (2009).
Hoffmann S, Guihenneuc C, Ancelet S. A Bayesian hierarchical approach to deal with exposure measurement error in the analysis of the cancer risk associated with exposure to ionizing radiation. An application to the French cohort of uranium miners. Journée Nationale du groupe Biopharmacie et Santé de la Société Française de Statistique. November 2014, Paris. (2014).
Houpert P, Lestaevel P, Bussy C, Paquet F, Gourmelon P. Enriched but not depleted uranium affects central nervous system in long-term exposed rat. Neurotoxicology (2005) 26:1015-1020.
IARC. IARC Monographs on the evaluation of carcinogenic risks to humans. Tobacco Smoke and involuntary smoking. (2004) 83.
ICRP. Protection against radon-222 at home and work. ICRP Publication 65. . Ann. ICRP (1993) 23. ICRP. Human respiratory tract model for radiological protection. ICRP Publication 66. Ann. ICRP
(1994) 24. ICRP. Age-dependent doses to members of the public from intakes of radionuclides: Part 3. Ingestion
dose coefficients. ICRP Publication 69. . Ann. ICRP (1995a) 25. ICRP. Dose coefficients for intakes of radionuclides by workers. Replacement of ICRP Publication 61.
ICRP Publication 68. Ann. ICRP (1995b) 24. ICRP. Human alimentary tract model for radiological protection. ICRP Publication 100. Ann. ICRP
(2006) 36.
66
ICRP. The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Ann. ICRP 37 (2-4). (2007).
ICRP. Nuclear decay data for dosimetric calculations. ICRP Publication 107. Ann. ICRP (2008) 38. ICRP. Adult reference computational phantoms. ICRP Publication 110. Ann. ICRP (2009) 39. ICRP. Occupational Intakes of Radionuclides, Part 1. Annals of the International Commission on
Radiological Protection, Accepted for publication. (2015a). ICRP. Occupational Intakes of Radionuclides, Part 2. Annals of the International Commission on
Radiological Protection, Accepted for publication. (2015b). ICRP. Occupational Intakes of Radionuclides, Part 3. Annals of the International Commission on
Radiological Protection, Accepted for publication. (2015c). ICRP. Occupational Intakes of Radionuclides, Part 4. Annals of the International Commission on
Radiological Protection, Accepted for publication. (2015d). ICRP. Values of specific absorbed fractions (provisional title). In preparation. (2015e). ICRU. Fundamental Quantities and Units for Ionizing Radiation (Report 60). (1993). Johnson P, Atkinson WD, Nicholls JL. Updated analysis of mortality in workers at UK atomic weapons
establishments. Proceedings of the 6th International SRP symposium. SRP, London. (1999). Kazymbet P. Radiobiological and Radioecological Research in Uranium Mining Regions of Kazakhstan.
Presented at the Fifth International Scientific and Practical Conference "Biomedical and radioecological problems in the uranium mining regions" June 19 (2014) Astana, Kazakhstan.
Kreuzer M, et al. Mortality from internal and external radiation exposure in a cohort of male German uranium millers, 1946-2008. International archives of occupational and environmental health (2014).
Kreuzer M, Kreisheimer M, Kandel M, Schnelzer M, Tschense A, Grosche B. Mortality from cardiovascular diseases in the German uranium miners cohort study, 1946-1998. Radiation and environmental biophysics (2006) 45:159-166.
Kreuzer M, Schnelzer M, Tschense A, Walsh L, Grosche B. Cohort profile: the German uranium miners cohort study (WISMUT cohort), 1946-2003. International journal of epidemiology (2010) 39:980-987.
Kreuzer M, et al. Silica dust, radon and death from non-malignant respiratory diseases in German uranium miners. Occupational and environmental medicine (2013) 70:869-875.
Krunic A, Haveric S, Ibrulj S. Micronuclei frequencies in peripheral blood lymphocytes of individuals exposed to depleted uranium. Arhiv za higijenu rada i toksikologiju (2005) 56:227-232.
Kurttio P, et al. Renal effects of uranium in drinking water. Environmental health perspectives (2002) 110:337-342.
Kurttio P, et al. Kidney toxicity of ingested uranium from drinking water. American journal of kidney diseases : the official journal of the National Kidney Foundation (2006) 47:972-982.
Kurttio P, Komulainen H, Leino A, Salonen L, Auvinen A, Saha H. Bone as a possible target of chemical toxicity of natural uranium in drinking water. Environmental health perspectives (2005) 113:68-72.
Laurier D, et al. DoReMi workshop on multidisciplinary approaches to evaluating cancer risks associated with low-dose internal contamination. Radioprotection (2012) 47:119-148.
Laurier D, et al. Quantification of cancer and non-cancer risks associated with multiple chronic radiation exposures: Epidemiological studies, organ dose calculation and risk assessment. Alpha-Risk project deliverable D1.8. Analysis of risk among miners using organ dose calculation. Funded by the European Commission EC FP6 (Ref. FI6R-CT-2005-516483). European Commission DG XII, Brussels. (2009).
Leach LJ, Yuile CL, Hodge HC, Sylvester GE, Wilson HB. A five-year inhalation study with natural uranium dioxide (UO2) dust. II. Postexposure retention and biologic effects in the monkey, dog and rat. Health physics (1973) 25:239-258.
Leggett R, Marsh J, Gregoratto D, Blanchardon E. A generic biokinetic model for noble gases with application to radon. Journal of radiological protection : official journal of the Society for Radiological Protection (2013) 33:413-432.
67
Leng S, et al. Genetic variation in SIRT1 affects susceptibility of lung squamous cell carcinomas in former uranium miners from the Colorado plateau. Carcinogenesis (2013) 34:1044-1050.
Lestaevel P, Houpert P, Bussy C, Dhieux B, Gourmelon P, Paquet F. The brain is a target organ after acute exposure to depleted uranium. Toxicology (2005) 212:219-226.
Leuraud K, et al. Radon, smoking and lung cancer risk: results of a joint analysis of three European case-control studies among uranium miners. Radiation research (2011) 176:375-387.
Li K, et al. Alteration of cytokine profiles in uranium miners exposed to long-term low dose ionizing radiation. TheScientificWorldJournal (2014) 2014:216408.
Li WB, Janzen T, Zankl M, Giussani A, Hoeschen C. Uncertainties of organ-absorbed doses to patients from 18f-choline. Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, 796129 (March 16, 2011). (2011).
Luessenhop AJ, Gallimore JC, Sweet WH, Struxness EG, Robinson J. The toxicity in man of hexavalent uranium following intravenous administration. The American journal of roentgenology, radium therapy, and nuclear medicine (1958) 79:83-100.
Mao Y, et al. Inorganic components of drinking water and microalbuminuria. Environmental research (1995) 71:135-140.
Marsh JW, et al. Dosimetric calculations for uranium miners exposed to radon gas, radon progeny and long-lived radionuclides. Alpha-Risk project deliverables D5.5 & D5.6 Lung dosimetry model for uranium dust inhalation; organ dosimetry for radon progeny and long-lived radionuclides. Funded by the European Commission EC FP6 (Ref. FI6R-CT-2005-516483). European Commission DG XII, Brussels. (2008).
Marsh JW, et al. Alpha Miner Software Program (version 4.0). Alpha-Risk project deliverable D5.9. Final analysis of dose calculations and risk assessment for dose-response modelling. Funded by the European Commission EC FP6 (Ref. FI6R-CT-2005-516483). European Commission DG XII, Brussels. . (2009).
Marsh JW, et al. Evaluation of scattering factor values for internal dose assessment following the IDEAS guidelines: preliminary results. Radiation protection dosimetry (2007) 127:339-342.
Marsh JW, et al. Dosimetric calculations for uranium miners for epidemiological studies. Radiation protection dosimetry (2012) 149:371-383.
Martin F, Earl R, Tawn EJ. A cytogenetic study of men occupationally exposed to uranium. British journal of industrial medicine (1991) 48:98-102.
McDiarmid MA, et al. Measures of genotoxicity in Gulf war I veterans exposed to depleted uranium. Environmental and molecular mutagenesis (2011a) 52:569-581.
McDiarmid MA, et al. Longitudinal health surveillance in a cohort of Gulf War veterans 18 years after first exposure to depleted uranium. Journal of toxicology and environmental health. Part A (2011b) 74:678-691.
McDiarmid MA, et al. The Gulf War depleted uranium cohort at 20 years: bioassay results and novel approaches to fragment surveillance. Health physics (2013) 104:347-361.
McGeoghegan D, Binks K. The mortality and cancer morbidity experience of workers at the Capenhurst uranium enrichment facility 1946-95. Journal of radiological protection : official journal of the Society for Radiological Protection (2000a) 20:381-401.
McGeoghegan D, Binks K. The mortality and cancer morbidity experience of workers at the Springfields uranium production facility, 1946-95. Journal of radiological protection : official journal of the Society for Radiological Protection (2000b) 20:111-137.
Milacic S. Health investigations of depleted-uranium clean-up workers. La Medicina del lavoro (2008) 99:366-370.
Milacic S, Simic J. Identification of health risks in workers staying and working on the terrains contaminated with depleted uranium. Journal of radiation research (2009) 50:213-222.
Mitchel RE, Jackson JS, Heinmiller B. Inhaled uranium ore dust and lung cancer risk in rats. Health physics (1999) 76:145-155.
68
Monleau M, De Meo M, Paquet F, Chazel V, Dumenil G, Donnadieu-Claraz M. Genotoxic and inflammatory effects of depleted uranium particles inhaled by rats. Toxicological sciences : an official journal of the Society of Toxicology (2006) 89:287-295.
NCRP. Development of a biokinetic model for radionuclide-contaminated wounds and procedures for their assessment, dosimetry and treatment. National Council on Radiation Protection and Measurements Report No. 156, NCRP: Bethesda, USA. (2006).
NRC. National Research Council. Comparative dosimetry of radon in mines and homes. National Academy Press (Washington, DC). ISBN 0-309-04484-7. (1991).
NRC. National Research Council, Committee on Health Risks of Exposure to Radon. Health effects of exposure to radon (BEIR VI). Washington, DC: National Academy Press. (1999).
NRC. National Research Council. Health risks from exposures to low levels of ionizing radiation (BEIR VII), phase 2. Board on radiation effects research. National Academies Press, Washington. (2006).
Palmer LJ. UK Biobank: bank on it. Lancet (2007) 369:1980-1982. Pernot E, Cardis E, Badie C. Usefulness of saliva samples for biomarker studies in radiation research.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology (2014) 23:2673-2680.
Pernot E, et al. Ionizing radiation biomarkers for potential use in epidemiological studies. Mutation research (2012) 751:258-286.
Pinkerton LE, Bloom TF, Hein MJ, Ward EM. Mortality among a cohort of uranium mill workers: an update. Occupational and environmental medicine (2004) 61:57-64.
Poisson C, et al. Chronic uranium exposure dose-dependently induces glutathione in rats without any nephrotoxicity. Free radical research (2014) 48:1218-1231.
Prat O, et al. From cell to man: evaluation of osteopontin as a possible biomarker of uranium exposure. Environment international (2011) 37:657-662.
Puncher M, Birchall A, Bull RK. A Bayesian analysis of uncertainties on lung doses resulting from occupational exposures to uranium. Radiation protection dosimetry (2013) 156:131-140.
Rage E, Caer-Lorho S, Drubay D, Ancelet S, Laroche P, Laurier D. Mortality analyses in the updated French cohort of uranium miners (1946-2007). International archives of occupational and environmental health (2014).
Rage E, et al. Risk of lung cancer mortality in relation to lung doses among French uranium miners: follow-up 1956-1999. Radiation research (2012) 177:288-297.
Richardson DB, Wing S. Lung cancer mortality among workers at a nuclear materials fabrication plant. American journal of industrial medicine (2006) 49:102-111.
Riddell AE. An analysis of the Impact of Aerosol Lung Solubility Assumptions on Dose Estimates. Proc. of the Seventh Internal Symposium of the Society for Radiological Protection, Cardiff. (2005).
Ritz B. Cancer mortality among workers exposed to chemicals during uranium processing. Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine (1999a) 41:556-566.
Ritz B. Radiation exposure and cancer mortality in uranium processing workers. Epidemiology (1999b) 10:531-538.
Samson E, et al. Mortality from cancer and non cancer diseases in the French cohort of uranium processing workers (TRACY). (in preparation).
Samson E, Piot I, Zhivin S, Acker A, Laroche P, Laurier D. Mortality profile of the French cohort of uranium processing workers. Occupational and environmental medicine (2014) 71 Suppl 1:A23.
Schlesinger RB, Kunzli N, Hidy GM, Gotschi T, Jerrett M. The health relevance of ambient particulate matter characteristics: coherence of toxicological and epidemiological inferences. Inhalation toxicology (2006) 18:95-125.
69
Selden AI, et al. Nephrotoxicity of uranium in drinking water from private drilled wells. Environmental research (2009) 109:486-494.
Silver SR, et al. Mortality and ionising radiation exposures among workers employed at the Fernald Feed Materials Production Center (1951-1985). Occupational and environmental medicine (2013) 70:453-463.
Stearns DM, et al. Uranyl acetate induces hprt mutations and uranium-DNA adducts in Chinese hamster ovary EM9 cells. Mutagenesis (2005) 20:417-423.
Takhauov RM, et al. The Bank of Biological Samples Representing Individuals Exposed to Long-Term Ionizing Radiation at Various Doses. Biopreservation and biobanking (2015).
Thun MJ, Baker DB, Steenland K, Smith AB, Halperin W, Berl T. Renal toxicity in uranium mill workers. Scandinavian journal of work, environment & health (1985) 11:83-90.
Tomasek L. Lung cancer mortality among Czech uranium miners-60 years since exposure. Journal of radiological protection : official journal of the Society for Radiological Protection (2012) 32:301-314.
Tomasek L. Effect of age at exposure in 11 underground miners studies. Radiation protection dosimetry (2014) 60:124-127.
UNSCEAR. United Nations Scientific Committee on the Effects of Atomic Radiation. UNSCEAR 2006 Report to the General Assembly with Scientific Annexes, Effects of Ionizing Radiation. Vol. 1: Report and Annexes A and B New York, NY: United Nations. (2008a).
UNSCEAR. United Nations Scientific Committee on the Effects of Atomic Radiation. UNSCEAR 2006. Sources and effects of Ionizing Radiation. Annex B. Exposure of the public and workers from various sources of radiation. (2008b).
UNSCEAR. United Nations Scientific Committee on the Effects of Atomic Radiation. Sources, effects and risks of ionizing radiation. UNSCEAR 2012 Report to the General Assembly. Scientific annexes A and B. Annex B. Uncertainties in risk estimates for radiation-induced cancer. New York, NY: United Nations. (2012).
Wiethege T, et al. German uranium miner study--pathological and molecular genetic findings. German Uranium Miner Study, Research Group Pathology. Radiation research (1999) 152:S52-55.
Yiin JH, et al. A nested case-control study of multiple myeloma risk and uranium exposure among workers at the Oak Ridge Gaseous Diffusion Plant. Radiation research (2009) 171:637-645.
Zablotska LB, Lane RS, Frost SE. Mortality (1950-1999) and cancer incidence (1969-1999) of workers in the Port Hope cohort study exposed to a unique combination of radium, uranium and gamma-ray doses. BMJ open (2013) 3.
Zamora ML, Zielinski JM, Moodie GB, Falcomer RA, Hunt WC, Capello K. Uranium in drinking water: renal effects of long-term ingestion by an aboriginal community. Archives of environmental & occupational health (2009) 64:228-241.
Zhivin S, Laurier D, Guseva Canu I. Health effects of occupational exposure to uranium: do physicochemical properties matter? International journal of radiation biology (2014) 11:1-33.
Zhu G, Xiang X, Chen X, Wang L, Hu H, Weng S. Renal dysfunction induced by long-term exposure to depleted uranium in rats. Archives of toxicology (2009) 83:37-46.
Zins M, et al. The CONSTANCES cohort: an open epidemiological laboratory. BMC public health (2010) 10:479.
Zolzer F, et al. Persistence of genetic damage in lymphocytes from former uranium miners. Cytogenetic and genome research (2012a) 136:288-294.
Zolzer F, et al. Micronuclei in lymphocytes from currently active uranium miners. Radiation and environmental biophysics (2012b) 51:277-282.
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LIST OF PARTICIPANTS TO THE CURE PROJECT
Sophie Ancelet, IRSN Will Atkinson, NUVIA Ltd Sarah Baatout, SCK•CEN Christophe Badie, PHE Gary Bethel, NUVIA Ltd Jean-Marc Bertho, IRSN Derek Bingham, AWE Eric Blanchardon, IRSN Richard Bull, NUVIA Limited Elisabeth Cardis, CREAL Cécile Challeton De Vathaire, IRSN Rupert Cockerill, AWE Estelle Davesne, IRSN Damien Drubay*, IRSN Teni Ebrahimian, IRSN Hilde Engels, SCK•CEN Nora Fenske, BfS Michael Gillies, PHE Augusto Giussani, BfS Maria Gomolka, BfS James Grellier, CREAL Stephane Grison, IRSN Yann Gueguen, IRSN Janet Hall, IC Richard Haylock, PHE Sabine Hornhardt, BfS Chrystelle Ibanez, IRSN Lukas Kotik, SURO Michaela Kreuzer, BfS Olivier Laurent, IRSN Dominique Laurier, IRSN Anne-Laure Le Bacq, SCK•CEN James Marsh, PHE Dietmar Nosske, BfS Jackie O'hagan, PHE Eileen Pernot , CREAL Matthew Puncher, PHE Roel Quintens, SCK•CEN Estelle Rage, IRSN Tony Riddell, PHE Laurence Roy, IRSN Eric Samson, IRSN Maamar Souidi, IRSN Ladislav Tomasek, SURO Michelle Turner, CREAL Nina Weiland, BfS Sergey Zhivin*, IRSN
*PhD student
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LIST OF INVITED EXPERTS
Naomi Allen, University of Oxford (UK)
Jeri Anderson, National Institute for Occupational Safety and Health (USA)
Meirat Batkin, Astana Medical University (Kazakhstan)
Steven Belinsky, Lovelace Respiratory Research Institute (USA)
Georges Dagher, Inserm (France)
Andrey Karpov, Seversk Biophysical Research Center (Russia)
Polat Kazymbet, Astana Medical University (Kazakhstan)
Richard Wakeford, University of Manchester (UK)
Friedo Zölzer, University of South Bohemia (Czech Republic)
Petr Otahal, National Institute for Nuclear, Chemical and Biological Protection (Czech Republic)
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GLOSSARY ADU: ammonium diuranate AMAD: activity median aerodynamic diameter AWE: Atomic Weapons Establishment BBMRI: Biobanking and Biomolecular Resources Research Infrastructure BfS: Bundesamt für Strahlenschutz CRA: clinical research associate CREAL: Centre de Recerca en Epidemiologia Ambiental CURE: Concerted Uranium Research in Europe EDTA: Ethylenediaminetetraacetic acid HRTM: human respiratory tract model HATM: human alimentary tract model IC: Institut Curie IRSN : Institut de Radioprotection et de Sûreté Nucléaire JCCRER: Joint Coordinating Committee for Radiation Effects Research JEM: job exposure matrix LD: limit of detection LLR: Long Lived Radionuclides LSS: Life Span Study MOX: mixed oxide fuels PHE: Public Health England RL: reporting limit SF: scattering factor SMR: Standardised Mortality Ratios (SMRs) SCK•CEN: StudieCentrum voor Kernenergie • Centre d'étude de l'Energie Nucléaire SOP: standard operating procedure SURO: Státní ústav radiační ochrany TBP: tributyl phosphate UKAEA: United Kingdom Atomic Energy Authority WL: working level WLM: working level month WP: work package WR: radiation weighting factor
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ACKNOWLEDGEMENTS The CURE project was supported by the EU FP7 DoReMi Network of Excellence (grant agreement:
249689)
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ANNEXES
Annex 1. Simplified scheme of the nuclear fuel cycle
Annex 2. Early dissemination activities
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Annex 1. Simplified scheme of the nuclear fuel cycle.
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Annex 2. Early dissemination activities Oral presentations: Laurier D, Gomolka M, Haylock R, Atkinson W, Bingham D, Baatout S, Tomasek L, Cardis E, Hall J,
Blanchardon E. Concerted Action for an Integrated (biology-dosimetry-epidemiology) Research
project on Occupational Uranium Exposure. Fifth International MELODI Workshop, Bruxelles
(Belgium), Oct 2013.
Laurier D, Gomolka M, Haylock R, Atkinson W, Bingham D, Baatout S, Tomasek L, Cardis E, Hall J,
Blanchardon E. The CURE project: a Concerted Action for an Integrated (biology-dosimetry-
epidemiology) Research project on Occupational Uranium Exposure. Fourth congress of International
Radiation Protection Association (IRPA), Genova (Switzerland), June 24, 2014.
Laurent O, Gomolka M, Haylock R, Atkinson W, Bingham D, Baatout S, Tomasek L, Cardis E, Hall J,
Blanchardon E, Laurier D. Concerted Uranium Research in Europe: the CURE project. Conference
"Biomedical and radio-ecological problems in the uranium mining regions". Astana (Kazakhstan),
June 19, 2014.
Laurier D. Progress of the CURE project. 3rd DoReMi periodic meeting. Munich (Germany), July 9,
2014
Laurier D, Gomolka M, Haylock R, Atkinson W, Bingham D, Baatout S, Tomasek L, Cardis E, Hall J,
Blanchardon E. Concerted Action for an Integrated (biology dosimetry-epidemiology) Research
Project on Occupational Uranium Exposure. 3rd DoReMi periodic meeting. Munich (Germany), July 9,
2014
Poster presentations:
Laurent O, Gomolka M, Haylock R, Giussani A, Atkinson W, Bingham D, Baatout S, Tomasek L, Cardis
E, Hall J, Blanchardon E, Laurier D. Concerted Uranium Research In Europe (CURE): Recent Progresses
And Perspectives. MELODI annual workshop, Barcelona (Spain), October 7-9 2014
CURE deliverables:
Laurier D, Roy L, Haylock R, Rage E, Blanchardon E, Giussani A, Gomolka M, Bertho J-M. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”CURE. Kick-off meeting report (CURE internal deliverable 4.1), November 2013. Haylock R, Kreuzer M, Laurent O, Samson E. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. CURE Epidemiology Intermediate report (WP1) (CURE internal Deliverable D1.1), May 2014. Banchardon, E, Bingham D, Bull R, Challeton - de Vathaire C, Davesne E, Giussani A, Hochstrat B, Kreuzer M, Le Bacq A.-L., Noßke D, Marsh J, Rage E, Riddell T, Samson E, Tomášek L, Tschense A, Zhivin S. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Report on the evaluation of monitoring data and physico-chemical characterisation of radionuclides (WP2) (CURE internal Deliverable D2.1), May 2014.
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Gomolka M, Badie C, Baatout S, Bertho J-M, Ebrahimian T, El Saghire H, Gueguen Y, Hall J, Hornhardt S, Ibanez C, Kabacik S, Pernot E, Roy L. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Biology intermediary report (WP3) (CURE internal Deliverable D3.1), May 2014. Haylock R, Kreuzer M, Laurent O, Tomášek L, Engels H. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Epidemiology protocol (WP1) (CURE deliverable D1.2), December 2014. Blanchardon E, Bingham D, Bull R, Challeton - de Vathaire C, Cockerill R, Davesne E, Giussani A, Le Bacq A.-L., Noßke D, Marsh J, Riddell T, Tomášek L. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Dosimetric protocol (WP2) (CURE deliverable D2.2), December 2014. Gomolka M, Hornhardt S, Weiland N, Roy L, Bertho J-M, Gueguen Y, Ibanez C, Ebrahimian T, Grison S, Souidi M, Hall J, Badie C, Kabacik S, Pernot E, Turner M, Baatout S, Quintens R, Zölzer F, Belinsky S. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Biology Protocol (WP3) (CURE deliverable D3.2), December 2014. Giussani A, Ancelet S, Atkinson W, Bethel G, Bingham D, Blanchardon E, Bull, R, Cockerill R, Davesne E, Fenske N, Grellier J, Hall J, Haylock R, Hornhardt S, Kotik L, Laurent O, Laurier D, Marsh J, Nosske D, Puncher M, Rage E, Riddell T, Samaga D, Tomasek L. DoReMi - Low Dose Research towards Multidisciplinary Integration Task 5.8 “CURE project”. Final Report of the Uncertainties Working Group (UWG), December 2014.
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