Linear No-threshold Relationship Research Articles

Effects of historic radiation dose on the frequency of sex-linked recessive lethals in Drosophila populations following the Chernobyl nuclear accident

Contrary to the effects of high doses of radiation, the effects of low doses of radiation are still being investigated. Low doses and their non-targeted effects in particular are of special interest for researchers. The accident that occurred at the Chernobyl Nuclear Power Plant (NPP) gives researchers the opportunity to view these effects outside of a laboratory environment. For this paper, the relationship between low historic radiation doses and the persistent genetic damage observed in populations of fruit flies (Drosophila melanogaster) around the Chernobyl NPP over 3 years will be investigated. Data from Zainullin et al. (1992) on the frequency of sex-linked recessive lethals (SLRLs) in D. melanogaster around the Chernobyl NPP. To calculate the absorbed historic external dose, a method based on the Gaussian plume model was used to find the external dose from both plume shine and ground shine. The dose attributed to the ground shine dose made a greater contribution to the overall absorbed external historic radiation dose than the plume shine dose. For earlier generations of Drosophila living in the radioactive contaminated sites, the SLRL frequencies appeared to correlate with the dose in a linear no-threshold relationship. The later descendent generations appeared to have developed a radio-adaptive-like response. This work contributes to the understanding of historic dose effects on wildlife health following the accidental release of high mount of radioactive materials into the environment.

Estimation of occupational compensation based on a linear-quadratic methodology for the nuclear industry

Production of nuclear electricity is under scrutiny because of health issues connected with operation of nuclear facilities. National and international regulatory institutions aim to have regulations that ensure that any radiation dose received by the workers are kept as minimal as possible to reduce any risk on human health. Under these circumstances when a controlled nuclear facility is operating in standard conditions the possibility to have direct injuries connected by non-stochastic effects of ionizing radiation will happen only if regulations are violated. In addition, the stochastic effect of radiation may cause cancer. Nuclear power plants calculate the cost of potential health damage caused by ionizing radiation based on the Linear No-Threshold Relationship (LNT) between the dose and cancer risk. However, recent radiological research questions the validity of the LNT relationship for low and very low doses. In this paper, a new methodology based on a linear-quadratic function is proposed for the cost estimation of health risks induced by ionizing radiation, this new methodology results in significantly higher monetary cost for higher doses. At the same time the new methodology also results in lower monetary cost for low exposure levels and even zeros payment for environmental doses because they cannot be avoided. By adopting this new methodology it could provide motivation for nuclear facilities to improve health & safety measures.

Linear-No-Threshold Default Assumptions for Noncancer and Nongenotoxic Cancer Risks: A Mathematical and Biological Critique.

To improve U.S. Environmental Protection Agency (EPA) dose-response (DR) assessments for noncarcinogens and for nonlinear mode of action (MOA) carcinogens, the 2009 NRC Science and Decisions Panel recommended that the adjustment-factor approach traditionally applied to these endpoints should be replaced by a new default assumption that both endpoints have linear-no-threshold (LNT) population-wide DR relationships. The panel claimed this new approach is warranted because population DR is LNT when any new dose adds to a background dose that explains background levels of risk, and/or when there is substantial interindividual heterogeneity in susceptibility in the exposed human population. Mathematically, however, the first claim is either false or effectively meaningless and the second claim is false. Any dose-and population-response relationship that is statistically consistent with an LNT relationship may instead be an additive mixture of just two quasi-threshold DR relationships, which jointly exhibit low-dose S-shaped, quasi-threshold nonlinearity just below the lower end of the observed "linear" dose range. In this case, LNT extrapolation would necessarily overestimate increased risk by increasingly large relative magnitudes at diminishing values of above-background dose. The fact that chemically-induced apoptotic cell death occurs by unambiguously nonlinear, quasi-threshold DR mechanisms is apparent from recent data concerning this quintessential toxicity endpoint. The 2009 NRC Science and Decisions Panel claims and recommendations that default LNT assumptions be applied to DR assessment for noncarcinogens and nonlinear MOA carcinogens are therefore not justified either mathematically or biologically.

Cancer Risks from CT Scans: Now We Have Data, What Next?

Ten years after the suggestion that CT scans might produce a small cancer risk, Pearce et al have shown that this is almost certainly the case, and they have confirmed the numerical magnitude of the risks; more complete epidemiologic studies are needed and several are indeed in progress, but in the interim, estimation of medical radiation risks based on atomic bomb survivor data appears to yield reasonable results.

Fukushima Health Study Launched

Fukushima Health Study Launched

The Linear No-Threshold Relationship Is Inconsistent with Radiation Biologic and Experimental Data

The carcinogenic risk induced by low doses of ionizing radiation is controversial. It cannot be assessed with epidemiologic methods alone because at low doses the data are imprecise and often conflicting. Since the 1970s, the radiation protection community has estimated the risk of low doses by means of extrapolation from the risk assessed at high doses, generally by using the linear no-threshold (LNT) model. The LNT relationship implies proportionality between dose and cancer risk. This approach is based on one set of data and two hypotheses: (a) The relationship between dose and DNA damage in vivo seems linear from 1 mGy to 100 Gy with use of H2AX foci as a marker for DNA double-strand breaks (DSBs)—however, this marker is not specific (1); (b) each DSB is hypothesized to have the same probability of inducing cell transformation, irrespective of the quantity of DSBs present simultaneously in the cell; and (c) each transformed cell is hypothesized to have the same probability of developing into an invasive cancer, irrespective of the dose delivered to the tissue. The advances during the past 2 decades in radiation biology, the understanding of carcinogenesis, and the discovery of defenses against carcinogenesis challenge the LNT model, which appears obsolete (2–6). Life developed in a bath of ionizing radiation and solar ultraviolet radiation and created aerobic organisms requiring (a) defenses against the metabolically induced reactive oxygen species, (b) DNA repair, and (c) elimination of damaged cells. Several sets of data show the efficacy of these defenses to be much higher at low than at high doses and for fractionated or protracted irradiation than for acute irradiation. The LNT model was introduced as a concept to facilitate radiation protection (7). But the use of this model led to the claim that even the smallest dose (one electron traversing a cell) may initiate carcinogenesis—for instance, from diagnostic x-ray sources (8,9). This claim is highly hypothetical and has resulted in medical, economic, and other societal harm. The French Academies report (10) concluded that the LNT model and its use for assessing the risks associated with low doses are not based on scientific evidence. In contrast, the Biological Effects of Ionizing Radiation (BEIR) VII report (11) and that of the International Commission on Radiological Protection (ICRP) (12) recommended the use of the LNT model. We wish to update this debate by using recent radiation biologic and epidemiologic data.

Risks Associated with Low Doses and Low Dose Rates of Ionizing Radiation: Why Linearity May Be (Almost) the Best We Can Do

Deterministic and stochastic effects associated with high-dose ionizing radiation (x-ray) exposure have been known for almost as long as ionizing radiation itself (1–3). At lower doses, radiation risks are primarily stochastic effects, in particular, somatic effects (cancer) rather than the deterministic effects characteristic of higher-dose exposure (4–6). In contrast to deterministic effects, for stochastic effects, scientific committees generally assume that at sufficiently low doses there is a positive linear component to the dose response—that is, that there is no threshold (4–6). This does not preclude there being higher-order (eg, quadratic) powers of dose in the dose response that may be of importance at higher doses. It is on this basis that models linear (or linear-quadratic) in dose are often used to extrapolate the experience of the Japanese atomic bomb survivors (who were typically exposed at a high dose rate to moderate doses [average, 0.1 Sv]) to estimate risks from low doses and low dose rates (4–6). Most population-based cancer risk estimates are based primarily on the Japanese atomic bomb survivor Life Span Study (LSS) cohort data (4–6). However, evidence of excess risks comes from a large number of other studies as well. In the parallel editorial (7), evidence is presented for possible real (or at least “practical”) thresholds or “hormetic” (beneficial) effects of low doses of ionizing radiation. As we summarize here, and in contrast to the arguments of Tubiana et al (7), we judge that there is little epidemiologic or biologic evidence for these for cancer. The arguments are of three forms: (a) assessment of the degree of curvature in the cancer dose response within the Japanese atomic bomb survivors and other exposed groups (in particular, departure from linear or linear-quadratic curvature), (b) consistency of risks between the Japanese and other moderate- and low-dose cohorts, and (c) assessment of biologic data on mechanisms. Most of the information on radiation-induced cancer risk comes from (a) the Japanese atomic bomb survivors, (b) medically exposed populations, (c) occupationally exposed groups, and (d) environmentally exposed groups (6). In the higher-dose radiation therapy studies, where doses received are very much higher than in the LSS, sometimes in the range at which cell sterilization occurs, excess cancer risks per unit dose tend to be less than in comparable subsets of the LSS (8,9). However, as we show, risks in moderate- and low-dose medically and occupationally exposed groups are generally consistent with those in the LSS.

Recent reports on the effect of low doses of ionizing radiation and its dose–effect relationship

Recently, the risk associated with low doses of ionizing radiation has gained new interest. Here, we analyze and discuss the major differences between two reports recently published on this issue; the report of the French Academy of Sciences and of the French Academy of Medicine published in March 2005, and the BEIR VII-Phase 2 Report of the American National Academy of Sciences published as a preliminary version in July 2005. The conclusion of the French Report is that the linear no-threshold relationship (LNT) may greatly overestimate the carcinogenic effect of low doses (<100 mSv) and even more that of very low doses (<10 mSv), such as those delivered during X-ray examinations. Conversely, the conclusion of the BEIR VII report is that LNT should be used for assessing the detrimental effects of these low and very low doses. The causes of these diverging conclusions should be carefully examined. They seem to be mostly associated with the interpretation of recent biological data. The point of view of the French Report is that these recent data are incompatible with the postulate on which LNT is implicitly based, namely the constancy of the carcinogenic effect per unit dose, irrespective of dose and dose rate.

Low mortality of British radiologists

CorrespondenceLow mortality of British radiologistsCorrections for this articleErratumR Doll, A Berrington and S C DarbyR Doll1Cancer Research UK/MRC/BHF Clinical Trial Service Unit and Epidemiological Studies Unit, 2Cancer Research UK Epidemiology Unit, Richard Doll Building, Old Road Campus, Rocsevelt Drive Oxford Ox3 7LF, UKSearch for more papers by this author, A Berrington1Cancer Research UK/MRC/BHF Clinical Trial Service Unit and Epidemiological Studies Unit, 2Cancer Research UK Epidemiology Unit, Richard Doll Building, Old Road Campus, Rocsevelt Drive Oxford Ox3 7LF, UKSearch for more papers by this author and S C Darby1Cancer Research UK/MRC/BHF Clinical Trial Service Unit and Epidemiological Studies Unit, 2Cancer Research UK Epidemiology Unit, Richard Doll Building, Old Road Campus, Rocsevelt Drive Oxford Ox3 7LF, UKSearch for more papers by this authorPublished Online:28 Jan 2014https://doi.org/10.1259/bjr/15501346SectionsPDF/EPUBFull Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail About"Low mortality of British radiologists." The British Journal of Radiology, 78(935), pp. 1057–1058 References 1 Berrington A, Darby SC, Weiss HA, Doll R. 100 years of observation on British radiologists: mortality from cancer and other causes 1897–1997. Br J Radiol 2001;74:507–19. Link ISI, Google Scholar2 Cameron JR. Radiation increased the longevity of British radiologists. Br J Radiol 2002;75:637–9. Link ISI, Google Scholar3 Simmons JA. On the linear no-threshold model. J Radiol Prot 2004;24:425–7. Medline ISI, Google Scholar4 Doll R, Peto R. Mortality among doctors in different occupations. Br Med J 1977;1:1433–6. Crossref Medline, Google Scholar5 Carpenter LM, Swerdlow AJ, Fear NT. Mortality of doctors in different specialties: findings from a cohort of 20000 NHS hospital consultants. Occup Environ Med 1997;54:388–95. Crossref Medline ISI, Google Scholar6 Matanoski GM, Seltser R, Sartwell PE, Diamond EL, Elliott EA. The current mortality rates of radiologists and other physician specialists: deaths from all causes and from cancer. Am J Epidemiol 1975;101:188–98. Crossref Medline ISI, Google Scholar7 Doll R, Peto R, Wheatley K, Grary R, Sutterland F. Martality in relation to smoking: 40 years' observations on male British doctors. BMJ 1994;309:901–11. Google Scholar8 Carpenter LM. Some observations on the healthy worker effect. Br J Ind Med 1987;44:289–91. Medline, Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited by5 Reasons Why Scoliosis X-Rays Are Not Harmful10 September 2020 | Dose-Response, Vol. 18, No. 3Are Restrictive Medical Radiation Imaging Campaigns Misguided? It Seems So: A Case Example of the American Chiropractic Association’s Adoption of “Choosing Wisely”1 May 2020 | Dose-Response, Vol. 18, No. 2Health effects of exposure to ionizing radiationHealth Impacts of Low-Dose Ionizing Radiation: Current Scientific Debates and Regulatory Issues19 September 2018 | Dose-Response, Vol. 16, No. 3Occupational Radiation Exposure and Health Effects among Medical WorkersThe Korean Journal of Medicine, Vol. 93, No. 3Radiation Exposure22 May 2014Cancer incidence and mortality after low-dosage radiation exposure: Epidemiological aspects1 July 2011 | Biophysics, Vol. 56, No. 2Radiation Hormesis: Historical Perspective and Implications for Low-Dose Cancer Risk Assessment18 January 2010 | Dose-Response, Vol. 8, No. 2Biological Effects of Ionizing Radiation29 March 2010DOES SCIENTIFIC EVIDENCE SUPPORT A CHANGE FROM THE LNT MODEL FOR LOW-DOSE RADIATION RISK EXTRAPOLATION?Health Physics, Vol. 97, No. 5La prévention du cancer et la relation dose–effet : l’effet cancérogène des rayonnements ionisantsCancer/Radiothérapie, Vol. 13, No. 4The Linear No-Threshold Relationship Is Inconsistent with Radiation Biologic and Experimental DataRadiology, Vol. 251, No. 1Cancérogenèse. Accroissement des connaissances et évolution des concepts22 June 2008 | Oncologie, Vol. 10, No. 5BJR Review of the Year – 20055 March 2014 | The British Journal of Radiology, Vol. 79, No. 939Related articlesErratum28 Jan 2014The British Journal of Radiology Volume 78, Issue 935November 2005Pages: 973-1064 © The British Institute of Radiology History ReceivedJanuary 08,2004RevisedMay 06,2005AcceptedJune 01,2005Published onlineJanuary 28,2014 Metrics Download PDF

The carcinogenic effect of low doses: the validity of the linear no-threshold relationship

The linear no-threshold relationship (LNT) has been advocated by the International Commission on Radiological Protection (ICRP) for assessing the carcinogenic effect of low dose irradiation (below 200 mSv). Initially, the main argument of its proponents was that risks were unlikely to be underestimated with LNT. Subsequently, it has been argued that: the traversal of a cell nucleus by one track can cause a double-strand break and since the probability of disrepair is never zero there is no inferior limit to the use of LNT; the repair probability of the complex DNA lesion caused by radiation is much smaller than that of the lesion caused by cell metabolism and is not much influenced by dose and dose rate; and this relationship is compatible with epidemiological and experimental data. Gradually, what was initially an hypothesis has become a dogma. The arguments of the opponents to LNT are given. In conclusion, for doses between 200 and 20 mSv the linear extrapolation could provide the superior limit of the risk although the actual risk might be much smaller. Assessment by LNT of the carcinogenic effect for doses below 10 mSv is not scientifically justified and should be banned. Doses below 1 mSv/year should not be taken into account for risk assessment.

Ecologic Bias revisited, A rejoinder to Cohenʼs response to “Residential 222Rn exposure and Lung Cancer: Testing the Linear no-Threshold theory with Ecologic Data”

In most rigorous epidemiologic studies, such as case-control and cohort studies, the basic unit of analysis is the individual. Each individual is classified in terms of exposure and disease status. However, in ecologic epidemiologic studies, the unit of analysis is some aggregate group of individuals. Summary measures of exposure and disease frequency are obtained for each aggregate, and the analyses focus on determining whether or not the aggregates with high levels of exposure also display high disease rates. The ecologic study design has major limitations, including ecologic confounding and cross level bias. Cohen has attempted to circumvent these limitations by invoking the linear no-threshold theory of radiation carcinogenesis to derive aggregate exposures from individual-level associations. He asserts that, while an ecologic study cannot determine whether radon causes lung cancer, it can test the validity of a linear-no threshold relationship between them. Cohen compares his testing of the linear no-threshold relationship between radon exposure and lung cancer to the practice of estimating the number of deaths from the person-rem collective dose, dividing the person-rem by the number of individuals in the population to derive the individual average dose, and then determining individual average risk by dividing the number of deaths by the number of individuals in the population. We show that Cohen's erroneous assumptions concerning occupancy rates and smoking effects result in the use of the wrong model to test the linear no-threshold theory. Because of these assumptions, the ecologic confounding and cross level bias associated with Cohen's model invalidate his findings. Furthermore, when more recent Iowa county lung cancer incidence rates are regressed on Cohen's mean radon levels, the reported large negative associations between radon exposure and lung cancer are no longer obtained.

A test of the linear-no threshold theory of radiation carcinogenesis

It has been pointed out that, while an ecological study cannot determine whether radon causes lung cancer, it can test the validity of a linear-no threshold relationship between them. The linear-no threshold theory predicts a substantial positive correlation between the average radon exposure in various counties and their lung cancer mortality rates. Data on living areas of houses in 411 counties from all parts of the United States exhibit, rather, a substantial negative correlation with the slopes of the lines of regression differing from zero by 10 and 7 standard deviations for males and females, respectively, and from the positive slope predicted by the theory by at least 16 and 12 standard deviations. When the data are segmented into 23 groups of states or into 7 regions of the country, the predominantly negative slopes and correlations persist, applying to 18 of the 23 state groups and 6 of the 7 regions. Five state-sponsored studies are analyzed, and four of these give a strong negative slope (the other gives a weak positive slope, in agreement with our data for that state). A strong negative slope is also obtained in our data on basements in 253 counties. A random selection-no charge study of 39 high and low lung cancer counties (+4 low population states) gives a much stronger negative correlation. When nine potential confounding factors are included in a multiple linear regression analysis, the discrepancy with theory is reduced only to 12 and 8.5 standard deviations for males and females, respectively. When the data are segmented into four groups by population, the multiple regression vs radon level gives a strong negative slope for each of the four groups. Other considerations are introduced to reduce the discrepancy, but it remains very substantial. Since cigarette sales data are available only on a statewide basis, mean radon data for states are analyzed. The linear regression for lung cancer rates vs radon levels is also negative and has a much steeper slope than that for the county data. When cigarette sales per capita is introduced into the regression, the negative slope for dependence on radon level is essentially unchanged.

Lognormal Model for Health Risk Assessment of Fluctuating Concentrations

Health risk assessments of exposures to harmful materials increasingly are required because of legal and economic pressures. An important part of the procedure is the mathematical model for the dose-effects relationship. If a linear no-threshold relationship is assumed, then the mean of fluctuating concentrations may be used for the calculation of health risk. But the widely used PEL and TLV® values assume a threshold relationship. For this and for nonlinear relationships the calculation with the use of the mean concentration is inaccurate, because higher concentrations produce disproportionately higher effects. An appropriate mathematical model based upon lognormal concentrations and probit effects is proposed. Rather than monitoring concentrations for unlikely high values, the method requires estimation of their geometric mean and geometric standard deviation. A health risk assessment then may be calculated simply and conveniently from the charts and tables provided. The method clarifies some issues and the specifics of utilizing and improving the required data. The model should be useful for assessing health risks from fluctuating concentrations of most toxic compounds.

Lognormal model for health risk assessment of fluctuating concentrations.

Health risk assessments of exposures to harmful materials increasingly are required because of legal and economic pressures. An important part of the procedure is the mathematical model for the dose-effects relationship. If a linear no-threshold relationship is assumed, then the mean of fluctuating concentrations may be used for the calculation of health risk. But the widely used PEL and TLV values assume a threshold relationship. For this and for nonlinear relationships the calculation with the use of the mean concentration is inaccurate, because higher concentrations produce disproportionately higher effects. An appropriate mathematical model based upon lognormal concentrations and probit effects is proposed. Rather than monitoring concentrations for unlikely high values, the method requires estimation of their geometric mean and geometric standard deviation. A health risk assessment than may be calculated simply and conveniently from the charts and tables provided. The method clarifies some issues and the specifics of utilizing and improving the required data. The model should be useful for assessing health risks from fluctuating concentrations of most toxic compounds.