Tests of the linear-no threshold theory for lung cancer induced by exposure to radon

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM2.5, may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute's Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross [Formula: see text] activity and indoor radon) and PM2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure's association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

Radiation and cancer risk: a continuing challenge for epidemiologists

This paper provides a perspective on epidemiological research on radiation and cancer, a field that has evolved over its six decade history. The review covers the current framework for assessing radiation risk and persistent questions about the details of these risks: is there a threshold and more generally, what is the shape of the dose-response relationship? How do risks vary over time and with age? What factors modify the risk of radiation? The example of radon progeny and lung cancer is considered as a case study, illustrating the modeling of epidemiological data to derive quantitative models and the coherence of the epidemiological and biological evidence. Finally, the manuscript considers the need for ongoing research, even in the face of research over a 60-year span.

Residential radon exposure and lung cancer risk: commentary on Cohen's county-based study

The large United States county-based study () in which an inverse relationship has been suggested between residential low-dose radon levels and lung cancer mortality has been reviewed. While this study has been used to evaluate the validity of the linear nonthreshold theory, the grouped nature of its data limits the usefulness of this application. Our assessment of the study's approach, including a reanalysis of its data, also indicates that the likelihood of strong, undetected confounding effects by cigarette smoking, coupled with approximations of data values and uncertainties in accuracy of data sources regarding levels of radon exposure and intensity of smoking, compromises the study's analytic power. The most clear data for estimating lung cancer risk from low levels of radon exposure continue to rest with higher-dose studies of miner populations in which projections to zero dose are consistent with estimates arising from most case-control studies regarding residential exposure.

Patient dose in multislice CT: why is it increasing and does it matter?

A brief review is presented of the reasons why multislice spiral/helical CT is associated with a higher radiation dose burden to the patient even than incremental CT. These include both intrinsic technological and geometric factors as well as simply a growing use of CT in an increasing number of applications. The typical magnitude of this dose burden is indicated and the basis for the anxiety that underpins it, namely the linear no-threshold (LNT) hypothesis, is discussed, together with the countervailing hypothesis that there is indeed a threshold for radiation harm in man and that the radiation doses associated with CT may lie below this threshold and may even be beneficial (radiation hormesis). There are as yet no certainties in this important area but it is argued that it is not a given that the doses associated with CT are harmful.

Smoking as a confounder in ecologic correlations of cancer mortality rates with average county radon levels

Cohen has reported a negative correlation between lung cancer mortality and average radon levels by county. In this paper, the correlation of U.S. county mortality rates for various types of cancers during the period 1970-1994 with Cohen's radon measurements is examined. In general, quantitatively similar, strongly negative correlations are found for cancers strongly linked to cigarette smoking, weaker negative correlations are found for cancers moderately increased by smoking, whereas no such correlation is found for cancers not linked to smoking. The results indicate that the negative trend previously reported for lung cancer can be largely accounted for by a negative correlation between smoking and radon levels across counties. Hence, the observed ecological correlation provides no substantial evidence for a protective effect of low level radon exposure.

Testing a BEIR-VI suggestion for explaining the lung cancer vs. radon relationship for U.S. counties

The BEIR-VI Report suggests that the large discrepancy between the observed lung cancer rate vs. radon exposure relationship for U.S. counties, and the predictions of linear no-threshold theory, may be explained by a strong negative correlation between smoking intensity and radon exposure. It proposes a model for testing that suggestion. We apply that model to the detailed data for U.S. counties; analysis shows that even a perfect negative correlation explains little more than half of the discrepancy, and the largest not-implausible correlation can explain less than a quarter of the discrepancy. We then extend the BEIR-VI suggestion to include a strong negative correlation between both the prevalence of smoking and the intensity of smoking. The largest not-implausible correlations can explain no more than 30% of the discrepancy. It is concluded that the previous interpretation of these data, that linear no-threshold theory fails this test, is sustained.

Hormesis: an adaptive expectation with emphasis on ionizing radiation

Non-linear fitness gradients with maxima between extremes are expected for any environmental variable to which free-living populations are exposed. For exceedingly toxic agents, including ionizing radiation, such deviations from linearity are close to zero exposure and are conventionally called hormesis. Accordingly, hormesis is an extreme version of the non-linear fitness gradients for general environmental stresses such as temperature fluctuations, for which maximum fitness occurs at the moderate temperature fluctuations to which free-living populations are most commonly exposed. Some metabolic reserves should occur under moderate temperature stresses because of the need for pre-adaptation enabling survival during exposure to occasional periods of more extreme stress, especially at species borders where selection for stress resistance is likely to be most intense. Because heat shock proteins are induced by all stresses, adaptation to extreme temperatures should translate into adaptation to other stresses. Consequently, metabolic reserves from adaptation to extreme temperatures in the past should translate into protection from correlated abiotic stresses, especially in human populations where modern cultural processes are now ameliorating exposure to extreme stresses. Ambient and man-made radiations of non-catastrophic dimensions should therefore lead to stress-derived radiation hormesis. Other stresses can, in principle, be incorporated into this model. Accordingly, evolutionary and ecological considerations suggest two components of hormesis in relation to ionizing radiation: background radiation hormesis based upon the background exposure to which all organisms on earth are subjected; and stress-derived radiation hormesis. Exposure under stress-derived radiation hormesis is considerably larger than under background radiation hormesis, so significant deleterious effects from non-catastrophic radiation normally may be impossible to detect. Suggestions are provided for testing such postulated deviations from the commonly assumed linear no-threshold (LNT) hypothesis for the biological consequences of exposure to radiation.

Importance of Radon as a Threat to Public Health

Since the discovery of x rays, the public has shown increasing concern about exposure to radiation. In the mid-1980s, with the dissemination of information about the ubiquitous nature of radon, this concern about radiation exposure has taken on a new perspective. As the general public realizes that exposure to radiation is an unavoidable part of life, questions arise as to how much exposure is acceptable when weighed against the costs of reducing the exposure. Because limited resources are available to protect the public's health and the environment, these resources need to be used wisely. The cost-effectiveness of the various options to lessen the potential adverse health effects from radon must be considered.

Hormesis: are low doses of ionizing radiation harmful or beneficial?

A review is provided of the literature on radiation hormesis, hormesis being any physiological effect that occurs at low doses and which cannot be anticipated by extrapolating from toxic effects noted at high doses. Epidemiological studies suggesting beneficial effects are considered, and experimental evidence for the existence of hormesis is then appraised. In the latter context, there are possible low-dose effects at the molecular level, at the cellular level and on the organism as a whole. It is concluded that while it is difficult to analyse the effects of low-dose radiation with statistical significance, the concept does permit the reconsideration of the validity of currently accepted notions.