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Radiation Biology and Its Role in the Canadian Radiation Protection Framework

Leblanc, Julie E.; Burtt, Julie J.1

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doi: 10.1097/HP.0000000000001060
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The linear no-threshold (linear-non-threshold; LNT) model is a dose-response model that has long served as the foundation of the international radiation protection framework. A dose-response model plots cancer risk against radiation dose for high doses and extrapolates risk down to low doses where little data is available. The purpose of the LNT dose-response model is to inform the choice of appropriate dose limits and subsequent as low as reasonably achievable (ALARA) requirements, social and economic factors taken into account. The LNT model assumes that the risk of developing cancer increases proportionately with increasing radiation dose. The International Commission on Radiological Protection (ICRP) defines radiation-induced cancer risk as a nominal detrimental risk coefficient. The current cancer risk coefficient values (excluding hereditary effects) are 4.1% per Sv for adults and 5.5% per Sv for the whole population (ICRP 2007), which is largely based on epidemiological studies of the atomic bomb survivors. The Life Span Studies (LSS) of the atomic bomb survivors have examined the incidence and mortality of disease, principally cancer, against acute radiation doses. Recent LSS updates have found the dose response to be either linear or linear-quadratic, meaning that as dose increases so does cancer risk (Grant et al. 2017; Preston et al. 2007; Ozasa et al. 2012).

Generally speaking, at low doses (below 100 mSv) the natural incidence of cancer masks any cases that may be caused solely by radiation. Traditional epidemiological studies have not had sufficient statistical power to detect an excess cancer risk in the low-dose range. However, more recent pooled studies (Cardis et al. 2007; Zablotska et al. 2014; Bouville et al. 2015; Leuraud et al. 2015; Richardson et al. 2015) have provided strong evidence that a positive linear association exists between protracted low-dose radiation exposure and cancer risk. Prior to the publication of these pooled studies, the LNT model was often applied by extrapolating the risk of cancer at high doses (>1,000 mSv) down to low doses in a linear manner, independent of dose rate (ICRP 2007). The effect of dose, dose rate, and exposure time on risk is still largely debated (Ruhm et al. 2015). While the LNT model is applied in a regulatory context, it should not be used for individual or population-based cancer risk assessment as it does not consider individual parameters and uncertainties (ICRP 2007). More refined models, which consider the absorbed dose, the type of cancer, as well as the sex and age of an individual, are required for retrospective dose and risk assessments (ICRP 2007; UNSCEAR 2015, 2017).

The negative perception of the LNT model does not stem only from studies demonstrating nonlinear dose responses considered in this review, but it also stems from how the LNT model has been incorrectly applied. Using the LNT model strictly and correctly for radiation protection purposes would go a long way in improving how the model is perceived. Moving toward biologically based modeling could greatly improve how dose-response models are understood and used. Reducing the uncertainties surrounding radiation-induced cancer risk in the low-dose range can be achieved through radiation biology studies on cells and animals. The purpose of this critical review is to analyze whether the LNT model is still applicable for use by modern nuclear regulators for radiation protection purposes, or if there is sufficient scientific evidence (both epidemiological and radiobiological) supporting an alternate model on which to base regulatory dose limits.


Canadian regulatory context

In Canada, members of the public may participate in Canadian Nuclear Safety Commission (CNSC) proceedings (hearings or meetings). Past intervenors (members of the public and other stakeholders) in these proceedings have raised their concerns that the dose limits specified in the radiation protection regulations (SOR/2000-203) (Government of Canada 2017) are underestimating or overestimating radiation-induced cancer risk. These mixed concerns led the commission to request (CNSC 2016) CNSC staff to prepare a report of the current state of knowledge on the health effects of radiation. The information presented to the commission in November 2017 (CNSC 2017) served as the foundation for this critical review.

Search strategy

PubMed was the primary electronic database used to search for relevant published literature; no limit was placed on dates. The keyword search terms included radiation, biological mechanism, low dose, bystander, adaptive response, hyperradiosensitivity/increased radioresistance, hormesis, genomic instability, linear-non-threshold LNT model, supralinear, linear-quadratic, induced repair, and threshold. Select combinations of these search terms were also used. Reports by select international organizations (e.g., United Nations Scientific Committee on the Effects of Atomic Radiation [UNSCEAR], International Atomic Energy Agency [IAEA], and ICRP) were also considered. The authors also searched the bibliographies of the relevant literature and reports for additional publications that were not captured in the initial search. The sole language of publication considered was English.

Selection criteria

The large volume of relevant studies was further refined considering the following criteria: robust experimental design, detailed data analysis (sufficient controls), consideration of an alternate explanation of findings, biological plausibility of findings, and sufficient evidence to support conclusions. Studies retrieved from the search that met the predetermined selection criteria were assessed by the two authors independently. The authors resolved any disagreements by discussion or in consultation with an advisor.


In this paper, both the physical unit of absorbed dose (i.e., mGy) and the radiation protection quantity of effective dose (i.e., mSv) are discussed where appropriate. These quantities can be used interchangeably when referring to low linear energy transfer (LET) radiation. The effective dose is not a physical or measurable quantity and should be used only for setting dose limits for radiation protection purposes. The absorbed dose, on the other hand, is the appropriate quantity for individual health risk assessment (Fisher and Fahey 2017). UNSCEAR has characterized doses of low-LET radiation into the following categories: very low, low, moderate, and high. They are defined as <~10 mGy, ~10–100 mGy, ~100 mGy–1,000 mGy and >~1,000 mGy, respectively (UNSCEAR 2015). For the purpose of this critical review, these ranges are arbitrarily defined for both low- and high-LET radiation.

Background: dose-response models

Although all molecules can be damaged by radiation, DNA molecules are considered to be the most critical when assessing cancer risk. Furthermore, DNA double-strand breaks (DSBs) are considered to be the most serious type of DNA damage, as they are the most difficult to repair, posing a threat to the stability of the genome. Misrepair of DSBs is a hallmark of cancer development (Hanahan and Weinberg 2000). For this reason, DSBs are an excellent end point to measure experimental outcome. Rates of mutation, cell proliferation, and cell survival are also common end points used to measure the impact of radiation on cells. All of these end points are considered when establishing dose-response models, which typically plot cancer risk against radiation dose. Several different models have been proposed and the dose response can vary over a wide range of doses (Fig. 1). A single dose-response model does not accommodate all epidemiological data since there are differences in the outcomes considered, the characteristics of the radiation exposure, the tissues irradiated, and the individuals exposed. Given the lack of epidemiological data in the low-dose range, radiation biology studies attempt to explain the molecular pathways driving or preventing cancer development. This is because cancer development is strongly associated with the capacity of DNA damage to be repaired.

Fig. 1:
Radiation dose responses. A simplified graphic highlighting different dose-response models observed in either radiation biology or epidemiological studies. Epidemiological studies often evaluate the risk of stochastic effects (i.e., developing cancer), whereas radiation biology studies measure a particular biological effect (i.e., chromosome aberrations, cell survival, etc.). Of note, this graphic does not include error bars as none currently exist, and, theoretically, all of these dose-response models may overlap at a given dose point.

Linear and linear-quadratic dose-response models

As stated above, the LNT dose-response model assumes that there is a direct and proportional relationship between radiation dose and cancer risk. The LNT model is based on the assumption that radiation must interact, or hit, a critical target (i.e., DNA), and that the radiation damage incurred in that one cell is sufficient to generate a cancer. This assumption is referred to as the target theory; it explains how radiation-induced cell damage is based on probability. Asaithamby and Chen (2009) found that the number of DSBs formed at doses between 5 mGy and 1,000 mGy is consistent with a linear dose-response relationship. The target theory is also dependent on the defense systems (e.g., repair mechanisms and immune system) being less than 100% effective (UNSCEAR 2015). Although plausible, this scenario is unlikely to represent the true biological response of cells to radiation since it is now understood that several mutations are required to generate a cancer (Davoli et al. 2013; Martincorena et al. 2017).

A linear-quadratic dose-response model expresses the risk of cancer as the sum of two components, one proportional to dose (i.e., the linear term) and the other one proportional to the square of the dose (i.e., the quadratic term) (ICRP 2007), therefore increasing the slope of the fitted straight line at higher doses (UNSCEAR 2012). In general, mutational dose responses are linear-quadratic for low-LET and tend towards linearity as LET increases (ICRP 2007). The linear-quadratic model would be plausible if DNA repair is more effective following exposure at low doses than at higher doses. In other words, repair enzymes are not saturated because of fewer DNA damage sites, making repair easier. Furthermore, complex DNA damage may need several hits by radiation for repair to be induced, and the probability of several hits decreases with decreasing dose (UNSCEAR 2015).

Nonlinear dose-response models

Hormesis is defined as a stimulatory and beneficial effect of small doses of substances or processes (e.g., radiation, chemical, heat stress, free radicals), which in larger doses are detrimental (Wolff 1989). Thus, a hormesis dose-response model assumes that low doses of radiation are indeed beneficial with regard to any health impact. Radiation-induced hormesis effects can manifest as several beneficial observable phenomena such as stimulation of cell proliferation, anti-aging, stimulation of the immune system, countering autoimmune diseases, and prevention of cancer (Cui et al. 2017; Nowosielska et al. 2006; Rubner et al. 2012; Nakatsukasa et al. 2010; Tanaka et al. 2005; Tsukimoto et al. 2008). The hormesis dose-response model would be plausible if DNA repair and other protective mechanisms are stimulated following radiation exposure, thereby repairing radiation-induced damage as well as reducing the level of naturally occurring DNA damage.

The threshold dose-response model assumes that no increased cancer risk is incurred below a certain dose threshold. The threshold model would be plausible if the immune system was stimulated by low doses of radiation, thereby eliminating some additional spontaneously occurring cancer cells. In other words, if the number of cancer cells destroyed by the radiation-stimulated immune system were at least equal to the number of cancer cells produced by radiation, then a threshold dose would be expected (UNSCEAR 2015). Moreover, support for the threshold model would be plausible if DNA repair mechanisms could adequately cope with the level of natural and radiation-induced DSBs up to a certain threshold.

A supralinear dose-response model assumes that the slope of the fitted curve is greater at lower doses than at higher doses, creating an upward curvature in the low-dose range (UNSCEAR 2015). The supralinear model would be plausible if a certain amount of DNA damage is required for the DNA repair machinery to be activated. For example, at very low doses the amount of radiation-induced DSBs is very small compared to the natural incidence of DSBs and may not trigger repair. Following an exposure of 5 mGy, approximately 0.25 DSBs per cell are formed compared to the approximately 10 DSBs that occur naturally. Of note, unrepaired DNA damage induced by low doses of radiation may or may not cause problems later in life (UNSCEAR 2015).

An example of the supralinear dose-response model that is seldom highlighted is the induced-repair model. The survival probability of a cell after exposure to radiation is traditionally calculated using a linear-quadratic model. However, certain cell lines exhibit decreased cell survival at low to moderate doses compared to the survival predicted by extrapolation from the response to higher doses (i.e., between 1 and 5 Gy). As a result, Joiner and colleagues proposed the induced-repair model (Joiner et al. 1993). The induced-repair dose-response model is a biphasic dose-response model whereby: (1) lower doses are less capable of triggering DNA repair or other radioprotective mechanisms because there is not sufficient DNA damage to induce these mechanisms, resulting in increased radiosensitivity; and (2) higher doses inflict more DNA damage, subsequently triggering repair mechanisms, resulting in an increased radioresistance which eventually reaches the dose response approximated by the linear-quadratic model.


Extrapolating health risk from biological phenomena

Over the past few decades, results from many studies have shown that the LNT model cannot explain all biological phenomena. However, a dose-response model may not need to reflect the exact cellular response to radiation to inform the choice of dose limits that are both protective of persons and not overly restrictive. This concept is at the core of ICRP’s radiation protection principles of justification, optimization, and dose limitation (ICRP 2007). Although grounded in science, a judgement is made to satisfy these principles when choosing appropriate dose limits. The current effective dose limits in Canada are 1 mSv y−1 for members of the public and 50 mSv in a 1 y dosimetry period as well as 100 mSv over a 5 y dosimetry period for nuclear energy workers (Government of Canada 2017). An example of when judgement has impacted the choice of a regulatory dose limit is with regard to pregnant nuclear energy workers. In Canada, several exposure values for pregnant nuclear energy workers were specified at the federal level. In an attempt to harmonize these values, a national workshop with many stakeholders was held. The result of the extensive consultation was a dose limit of 4 mSv for the balance of the pregnancy, once declared. This differs from the 1 mSv y−1 recommended by ICRP (ICRP 2007). As the risk from 4 mSv and 1 mSv was deemed to be essentially the same, a higher value was desired by the workforce to minimize discrimination against female workers while being able to continue to apply the ALARA principal.

The regulatory implications of underestimating or overestimating radiation-induced cancer risk or whether risk estimates are appropriate are discussed below with regard to epidemiological and radiation biology studies.

Risk is underestimated

There is no epidemiological evidence for a dose-response model where the predicted risk is underestimated by the LNT model in the low-dose range. There is, however, evidence for hyperradiosensitivity in individuals with specific and very rare genetic diseases (Li-Fraumeni syndrome, heritable retinoblastoma, and ataxia telangiectasia, etc.), where DNA repair is insufficient. Due to being hypersensitive to radiation and other environmental stressors, these individuals are genetically predisposed to developing cancer (Kappel et al. 2015; Nandikolla et al. 2017; Olsen et al. 2001; Kamihara et al. 2017). Of note, standard risk assessment or dose limits would not apply in these situations.

There is mounting experimental evidence from radiation biology studies supporting a nonlinear dose response where the risk is greater than predicted by the LNT model (i.e., supralinear). One biological mechanism potentially supporting a supralinear dose-response model is the radiation-induced bystander effect; this mechanism results in radiation-like effects (e.g., chromosomal abnormalities, gene mutations, etc.) (Nagasawa and Little 1992) observed in nonirradiated cells responding to communication signals from directly irradiated neighboring cells at doses as low as 5 mGy (Kadhim et al. 2013; Mothersill and Seymour 2002). There is now firm evidence that bystander effects occur not only in vitro, but in vivo as well (Trosko 1996; Hatzi et al. 2015). The bystander effect has also been shown to be modulated by epigenetic changes (Koturbash et al. 2006, 2007, 2008; Kaup et al. 2006; Ilnytskyy et al. 2009; Pereira et al. 2014; Ilnytskyy and Kovalchuk 2011). The bystander effect can be induced by both high- and low-LET radiation and has been found to be dependent on dose, radiation quality, and cell density (Prise et al. 2003).

Another biological mechanism potentially supporting a supralinear dose-response model is radiation-induced genomic instability. Genomic instability is characterized by the accumulation of new genetic alterations observed in the progeny of irradiated cells, even multiple generations after the initial hit by radiation (Morgan et al. 1996; Kadhim et al. 1992). There is overwhelming evidence that genomic instability is a hallmark of cancer (Negrini et al. 2010). Genomic instability has been observed over a wide dose range, as low as 10 mGy but typically above 100 mGy (Huang et al. 2004, 2007). Genomic instability with respect to chromosomal abnormalities has been observed in vitro in mouse cells exposed to high-LET radiation (Kadhim et al. 1992) and other rodent cells (Marder and Morgan 1993) in human cells after exposure to both high- and low-LET radiation (Kadhim et al. 1994; Holmberg et al. 1993, 1995; Grosovsky et al. 1996) as well as in vivo (Watson et al. 2001). The induction and rate of genomic instability appears to be dependent on cell type, radiation quality, and genetic background (Watson et al. 2001; Kadhim et al. 1998; Little et al. 1997; Limoli et al. 2000; Smith et al. 2003).

A third biological mechanism potentially supporting a supralinear dose-response model is low-dose hyperradiosensitivity/increased radioresistance (HRS/IRR). HRS/IRR is characterized by an increase in sensitivity to radiation between 10 and 300 mGy, followed by an increase in radioresistance between approximately 300 mGy and 1,000 mGy (Martin et al. 2014; Marples and Collis 2008; Marples and Joiner 1993; Marples et al. 2003). HRS/IRR has been confirmed in in vivo experiments (Martin et al. 2014; Qvarnstrom et al. 2017); however, most research has been conducted in vitro (Marples and Joiner 1993). Joiner et al. (1993) have suggested that the HRS/IRR response could be the result of an induced-repair mechanism. It is prevalent in animal cells, including in human cells (Martin et al. 2014), but not all cells display HRS/IRR, underlining the potential significance of cell type and origin. HRS/IRR can be observed following exposure to both low- and high-LET radiation (Martin et al. 2014; Marples and Collis 2008).

Based on radiation biology studies involving animals and cells, there is moderate evidence that some biological phenomena follow a supralinear dose response. However, these studies are not strongly supported by epidemiological evidence at low doses. Together, the weight of evidence does not support a supralinear dose-response model for radiation protection; therefore, lowering the dose limits would pose an unnecessary regulatory burden.

Risk is overestimated

There is very little epidemiological evidence supporting a dose-response model where the predicted risk is overestimated by the LNT model. Select researchers (Dobrzynski et al. 2015; Mortazavi et al. 2005) have speculated that the lack of increased cancer rates observed in populations in high natural background-radiation areas (e.g., Kerala, India; Yangjiang China) demonstrates the existence of hormesis. Others have claimed that the atomic bomb survivor data in fact supports a hormesis dose-response model (Doss 2013). However, the weight of evidence does not support these theories (Nair et al. 2009; Jaikrishan et al. 1999; Boice et al. 2010; Wei and Sugahara 2000; Ozasa et al. 2012; Grant et al. 2017).

There is strong experimental evidence from radiation biology studies supporting a nonlinear dose-response model where the risk is smaller than predicted by the LNT model (i.e., a hormesis model). Although the mechanistic details and identification of the key signaling molecules are not well known, the positive effects of hormesis have been observed in animal studies. For example, several studies have shown that chronic irradiation protects against the development of cancer in rodents (Hashimoto et al. 1999; Cheda et al. 2004; Hooker et al. 2004; Mitchel et al. 2008) and delays cancer progression in cancer-prone mice (Lemon et al. 2017a and b). Furthermore, chronic irradiation has been shown to increase survival in mice (Caratero et al. 1998; Nomura et al. 2013; Lacoste-Collin et al. 2007) and in dogs, where the response appears to vary according to genetic background (Cuttler et al. 2017). It must be noted that positive effects have not been consistently observed. Some researchers have found that exposing mice to chronic irradiation may have no effect on lifespan (Bustad et al. 1965; Mitchel et al. 1999; Courtade et al. 2002; Edin et al. 2016) or may result in a shortened lifespan (Thomson and Grahn 1989; Tanaka et al. 2003; Shin et al. 2010). Studying several end points (i.e., lifespan, neoplasm incidence, antineoplasm immunity, body weight, chromosome aberrations, gene mutations, alterations in mRNA and protein levels, and transgenerational effects) may offer better insights into the effects of low-dose exposures (Braga-Tanaka et al. 2018).

The adaptive response is considered to be one of the major biological mechanisms supporting a hormesis effect and has been the most studied mechanistically to date. The adaptive response has been observed in vitro in both normal (Yang et al. 2016) and malignant cells (Murley et al. 2011; Boothman et al. 1996; Miller et al. 2016), while in other cases, the adaptive response has not been observed (Shi et al. 2016; Zhao et al. 2017; Boothman et al. 1996; Raaphorst and Boyden 1999; Dimova et al. 2008; Tapio and Jacob 2007; Morgan 2006). Considerable variability exists in the extent of the response, and this may be dependent on the cell type and biological end point studied (Prise et al. 2006). The adaptive response has not only been observed in vitro, but also in vivo (Nenoi et al. 2015; Yoshida et al. 1993; Yang et al. 2016; Tapio and Jacob 2007). Of note, Edin and colleagues (Edin et al. 2016) found that while the priming dose (300 mGy) did not affect the lifespan of mice, it did, however, protect them and increase their survival against the challenge dose (9,000–9,500 mGy). Furthermore, the adaptive response can be observed as a result of both low- and high-LET radiation exposures (Tapio and Jacob 2007).

The adaptive response has also been observed following a challenge dose of radiation, as a result of a priming dose mediated by the bystander effect (Iyer and Lehnert 2002a and b; Ojima et al. 2011; Tang and Loke 2015). While others have postulated that the bystander effect cannot induce the adaptive response, they have nonetheless shown that priming can protect against a bystander-mediated challenge dose (Zhou et al. 2003; Ren et al. 2013; Sawant et al. 2001), indicating that both can occur within the same cells.

Support for a sublinear dose-response model (i.e., hormesis) exists and is by far the strongest source of criticism of the continued use of the LNT model (Averbeck 2009; Feinendegen and Cuttler 2018; Cardarelli and Ulsh 2018; Calabrese et al. 2016; Calabrese and O’Connor 2014). Recently, Beyea (2017) explained how some of the widely publicized criticism of the LNT model largely stems from poor reanalysis of the data and misreading of text. While proof of principle has been well established in cell and animal models, the epidemiological evidence supporting a hormesis dose-response model is very weak. Of note, any proposed sublinear model would face a similar level of uncertainty that currently exists for the LNT model in the low-dose range. Based on the definition of hormesis, at some threshold the beneficial and stimulatory effects of radiation become detrimental. To date, there is no scientifically sound evidence identifying at what dose the hormetic transition occurs and hence where the appropriate dose limit would be putatively set.

Risk is appropriate

There is overwhelming epidemiological evidence supporting a linear or linear-quadratic dose-response model, meaning that as dose increases so does cancer risk (Grant et al. 2017; Preston et al. 2007; Ozasa et al. 2012; Hsu et al. 2013).The findings of the LSS have been supported by recent pooled epidemiological studies of chronically exposed nuclear energy workers (Cardis et al. 2007; Zablotska et al. 2014; Bouville et al. 2015; Leuraud et al. 2015; Richardson et al. 2015).

For decades, radiation biology studies have focused on establishing the rate of induction of DNA DSBs to extrapolate cancer risk. Rothkamm and Lobrich (2003) found the induction of DSBs to be linear across a wide dose range (1 to 100,000 mGy). It is now generally accepted that an exposure between 1,000 and 2,000 mGy of low-LET radiation will lead to the induction of approximately 35 DSBs per cell (Hall and Giaccia 2012). One of the best characterized markers for DSBs is histone H2AX. Following radiation exposure (or DSB induction), its phosphorylation (γ-H2AX) is directly linked to the absorbed dose and factors such as radiation quality, LET, cell type, and dose rate (Staaf et al. 2012), with peak phosphorylation occurring at approximately 30 min. However, the rate of disappearance of the γ-H2AX foci does not necessarily reflect the rate of DNA repair (Lobrich et al. 2010). In recent years, a significant effort has been put forth to investigate the apparent nonlinearity of DNA damage repair. Research in this area has included topics such as the existence of DNA repair centers (Neumaier et al. 2012), the ability of DSBs to move and merge with each other over micrometer distances (Vadhavkar et al. 2014), and the occurrence of DNA repair in a limited number of large domains (Georgescu et al. 2015). Additional research is required to support whether the induction and repair of radiation-induced DNA DSBs can be accurately modeled and thus act as a surrogate for cancer risk.

Based on epidemiological and radiation biology studies, the weight of evidence does not suggest a change to the current radiation protection framework which uses the LNT model to set regulatory dose limits and ultimately manage risk (ICRP 2007). The LNT model remains the best model for practical radiation protection purposes (Boice 2017). Select advantages and weaknesses of the LNT model are listed in Table 1. The international radiation protection community uses the LNT model to inform regulatory dose limits since the advantages outweigh the weaknesses. Worker dose limits are chosen as such to limit the total occupational dose to less than 1 Sv (or 1,000 mSv) over a working life (ICRP 1991). The dose limits do not function alone, and licensees in Canada are required to implement the ALARA principle, which effectively reduces doses. Thus, workers should not approach, nor exceed, 1 Sv over their career, which could increase their cancer risk by 4.1% (ICRP 2007). The current Canadian baseline rate of cancer incidence (due to factors other than radiation) for adults is approximately 50%. Therefore, a worker who receives a nearly 1,000 mSv dose over his or her working lifetime increases lifetime cancer risk from approximately 50% to approximately 54%.

Table 1:
The strengths and weaknesses of the LNT model.


The LNT model cannot explain all experimental data. However, at the time of its inception, the LNT model was never intended to accurately reflect the true biological response in cells (Paunesku et al. 2017). Regardless of the polar opinions about the LNT model, a move toward biologically based modeling highlights the need for a better mechanistic understanding of radiation effects and, in theory, could alleviate some of the uncertainties at low dose (Ruhm et al. 2017; Dainiak et al. 2018; Paunesku et al. 2017; Mothersill and Seymour 2018).

As discussed, there is scientific evidence supporting several biological mechanisms, all of which are acting at low doses of radiation and do not necessarily follow a linear dose response. These biological mechanisms may occur concurrently, competitively, antagonistically, additively, or perhaps even synergistically, and are not yet fully understood. The experimental evidence included in this critical review does not suggest a direct causal relationship between individual biological mechanisms and human health effects, nor does the reviewed information clearly identify a model that improves upon the LNT model; all models face similar limitations in the low-dose range. The lack of causal relationship may be due to the fact that the cell/tissue-specific effects observed in in vitro and in vivo experiments do not reflect the true response in the body as a whole. This lack of evidence may also be due in part to the limitations of using uniform animal populations (i.e., same sex, age, diet, lifestyle, and genetic background) and comparing them with diverse human populations. Of note, the dose and dose-rate ranges used experimentally are typically higher than environmental exposures. It is possible that the different biological mechanisms impact an organism at the cellular level only and may not impact the overall cancer risk.

To date, what is understood by the scientific community is that if an increased risk exists at low doses, it is small since there is no epidemiological evidence of a measurable effect. Also, any additional unknown risks are minimized further with the application of the ALARA principle. The authors conclude that the current Canadian radiation protection framework is robust and protects all Canadians. Future studies will require coordinated multidisciplinary teams combining several fields of study, including genomics, proteomics, cell biology, molecular epidemiology, and traditional epidemiology, to achieve a greater understanding of the true biological effects induced by low doses of radiation. Once published and reproduced, the results of these future studies may or may not impact the international radiation protection framework.

To further reduce uncertainties in the low-dose range, the CNSC is funding and contributing to research projects, participating in national committees overseeing research, working with several other agencies and groups to establish a national low-dose research program, and participating in international discussions (i.e., International Dose Effect Alliance and Committee on Radiological Protection and Public Health). These efforts are geared to improving radiation protection, ultimately supporting our mandate of regulating the nuclear industry in Canada to protect the health, safety, and security of people and the environment; to implementing Canada’s international commitments on the peaceful use of nuclear energy; and to disseminating objective scientific, technical, and regulatory information to the public.


The authors wish to acknowledge the assistance of Annick Laporte with developing graphics and Patsy Thompson for reviewing an early version of this work. The authors would also like to thank CNSC and Health Canada staff for their valuable insights.


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cancer risk; dose; linear no-threshold; radiation; radiobiology

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