These two very recent publications [see attached] are the latest in a long series –– on the topic of the Linear No-Threshold (LNT) Model, which (since about 2o11) has been carefully dissected historically by Ed Calabrese (School of Public Health, Univ Mass). To see the previous email-thread on this topic, please go to GeneWhisperer.com and search the GEITP archives for ‘LNT Model’.
Reviewing the history briefly: Researchers in 1900-1920 began to explore whether it was possible to induce mutations in plants and animals (and to have these genetic changes passed on to their progeny (transgenerational phenotypic changes). Hermann Joseph Muller (1890–1967), an American geneticist, reported in 1927 that “x-rays were able to induce gene mutations in fruit flies” (Drosophila), barely beating three independent teams of botanists who likewise had reported inducing transgenerational phenotypic changes in plants with x-rays, as well as radium. Muller’s findings –– that x-irradiation caused mutational changes down to “zero threshold” –– quickly transformed the mutation research field. His conclusion was that there is no “safe dose” for irradiation; miniscule amounts are able to cause mutations. For his discovery, Muller received the Nobel Prize in 1946. The first paper [attached] clarifies the historical foundations of the LNT single-hit dose-response Model, its unique dependence upon the gene-mutation interpretations of Muller in 1927, and how this interpretation became widely accepted by the scientific community and regulatory agencies.
The first paper [attached] shows:
[a] Muller’s claim (that x-ray-induced transgenerational phenotypic changes were due to gene mutations) was an interpretation lacking conclusive evidence;
[b] The induced transgenerational phenotypic changes were due to chromosomal deletions and aberrations, rather than Muller’s proposed gene “point-mutations”;
[c] These developments undermine the historical and scientific foundations of the LNT single-hit Model, because it was built upon Muller’s gen- mutation interpretation;
[d] Muller and other leading U.S. radiation geneticists chose to collude in a series of articles to promote acceptance of the LNT Model, creating deliberate deceptions and misrepresentations of the scientific record;
[e] These deceptive practices would infiltrate and culminate in the (1956) actions of the U.S. National Academy of Sciences/Biological Effects of Atomic Radiation (NAS/BEAR I) Genetics Panel –– that recommended adoption of the LNT Model by regulatory and public health agencies worldwide;
[f] The mouse data used to provide the experimental basis for the subsequent reaffirmation of the LNT Model for cancer risk assessment was similarly problematic, i.e. the Biological Effects of Ionizing Radation (BEIR I) (1972) Committee used a flawed historical control group that significantly over-estimated risk in the low-dose zone, yielding a linear dose-response;
[g] Use of a corrected historical control value yields a threshold –– rather than the linear dose-response; and
[h] This new assessment indicates that the LNT Model has been flawed from the start, yet national and international regulations continue to be based upon it.
Therefore, cancer risk assessment has lived with a poorly appreciated, complex, and seriously flawed history that has undermined policies and practices of regulatory agencies in the U.S. and worldwide from the late 1950s to the present. The amount of time, effort and money spent –– based on a flawed original conclusion –– is staggering. And reminiscent of another more recent (1988-present) expensive example of purported “global warming.”
The second paper [attached] is a historical analysis indicating that it is highly likely that the Nobel Prize-winning research of Hermann Muller was never peer-reviewed. The published paper of Muller lacked a “Research Methods” section, cited no references, and failed to acknowledge and discuss the work of Gager and Blakeslee [Proc Natl Acad Sci USA 1927; 13: 75] that claimed to have induced gene mutations via ionizing radiation –– 6 months prior to Muller’s non-data paper [Muller, Science 1927; 66: 84]. Despite being well acclimated into the scientific world of peer-review, Muller chose to avoid the peer-review process on his most significant publication. It would appear that Muller’s actions were strongly influenced by his desire to claim preeminence for “the discovery of gene mutation.” The actions of Muller have important ethical lessons and implications today, when self-interest surpasses one’s obligations to society and the scientific culture that support the quest for new knowledge and discovery.
Environmental Pollution 2o18; 241: 289–302
Philosophy, Ethics, and Humanities in Medicine 2018; 13: 6 (https://doi.org/10.1186/s13010-018-0060-5)
COMMENT The two attached files provide an interesting (and balanced) “argument on both sides”, pro-LNT versus anti-LNT, concerning (at least) radiation-induced effects. (I have no vested interests in either side of this debate.)
The first file (from Roger McClellan) is a summary of the latest commentary from the National Council on Radiation Protection and Measurements (NCRP). This commentary comprises a critical review of 29 high-quality epidemiologic studies of populations exposed to radiation in the low-dose and low-dose-rate range. These epidemiologic studies “support the continued use of the LNT Model for radiation protection.” This is in accord with judgments by other national and international scientific committees, based on somewhat older data, indicating that no alternative dose-response relationship appears more pragmatic, or prudent, for radiation-protection purposes –– than the LNT Model.
The second file (from Ed Calabrese) is a recent publication by B.R.Scott, analyzing four studies of nuclear workers (exposed to low-dose radiation in their occupation). These studies were carried out essentially by the same group of epidemiologists. It was concluded that, whereas large radiation doses suppress our bodies’ multiple natural defenses (barriers) against cancer –– these barriers are “enhanced” by low-radiation doses, thereby decreasing cancer risk. Therefore, this analysis essentially “renders the LNT model to be inconsistent with the data.”
Commentary_No27_overview.pdf (178 KB)[Open as Web Page]; Scott-epidemiology crtique-1.pdf (193 KB)
Dear Dan: Thanks very much for including me on your last email. In case it may be of interest to you and your colleagues, I am providing the open-access paper (see the latest attached pdf file), which summarizes findings that are inconsistent with the LNT Model and that are essentially ignored by LNT advocates.
As you may be aware, epidemiologic studies are now essentially the last defense for the LNT Model as it applies to low-dose cancer risk assessment for ionizing radiation. Thanks very much –– for providing to your GEITP list of participants –– my recent paper (Scott 2018) that critiques several epidemiologic studies of nuclear workers. It is pointed out in that paper that methods used in epidemiologic studies of cancer risks for low-level radiation exposure appear not to have been rigorously tested for their reliability and accuracy, so far as generating radiation dose-response relationships.
A method to conduct this testing rigorously is briefly outlined in the paper. The testing would be based on simulated data generated by modelers, using stochastic multivariate models (e.g. radiation dose would be but one of several key variables). Other key variables would be factors such as age, time since radiation exposure, etc. In this case, the underlying model would be known (but only by those who generate the simulated data). The simulated data (several data sets) for populations of given sizes would then be analyzed by epidemiologists –– using their preferred approaches to generating radiation dose-response relationships.
Methods employed by the epidemiologists could then be evaluated for their reliability and accuracy, based on the radiation dose-response relationship generated. For example, should findings indicate an LNT response to radiation dose –– and LNT (related to radiation) is the wrong model for the data used –– then this would indicate that methods employed in the epidemiologic study would need to be improved, before findings based on epidemiologic methods employed could be considered reliable for low-radiation doses.