Radiation and Smoking Effects on Lung Cancer Incidence among Atomic Bomb Survivors

Radiation Effects Research Foundation, Hiroshima and Nagasaki, Japan.
Radiation Research (Impact Factor: 2.91). 07/2010; 174(1):72-82. DOI: 10.1667/RR2083.1
Source: PubMed


While radiation increases the risk of lung cancer among members of the Life Span Study (LSS) cohort of atomic bomb survivors, there are still important questions about the nature of its interaction with smoking, the predominant cause of lung cancer. Among 105,404 LSS subjects, 1,803 primary lung cancer incident cases were identified for the period 1958-1999. Individual smoking history information and the latest radiation dose estimates were used to investigate the joint effects of radiation and smoking on lung cancer rates using Poisson grouped survival regression methods. Relative to never-smokers, lung cancer risks increased with the amount and duration of smoking and decreased with time since quitting smoking at any level of radiation exposure. Models assuming generalized interactions of smoking and radiation fit markedly better than simple additive or multiplicative interaction models. The joint effect appeared to be super-multiplicative for light/moderate smokers, with a rapid increase in excess risk with smoking intensity up to about 10 cigarettes per day, but additive or sub-additive for heavy smokers smoking a pack or more per day, with little indication of any radiation-associated excess risk. The gender-averaged excess relative risk per Gy of lung cancer (at age 70 after radiation exposure at 30) was estimated as 0.59 (95% confidence interval: 0.31-1.00) for nonsmokers with a female : male ratio of 3.1. About one-third of the lung cancer cases in this cohort were estimated to be attributable to smoking while about 7% were associated with radiation. The joint effect of smoking and radiation on lung cancer in the LSS is dependent on smoking intensity and is best described by the generalized interaction model rather than a simple additive or multiplicative model.

1 Follower
12 Reads
  • Source
    • "Briefly, chronic or acute Ionising Radiation (IR) is known to increase reactive oxygen species in the plasma of radiology workers (Puthran et al., 2009), which can lead to genome instability and the generation of chromosome alterations in human lymphocytes (Goh et al., 1976), commonly associated with cancer in human lungs (Furukawa et al., 2010), in the mouse (Mukherjee et al., 2011) or cardiovascular disease (Picano and Vano, 2011), bystander (Rzeszowska-Wolny et al., 2009) and cataracts (Unescear, 2006). The cancer risk associated with radiation exposure has been widely studied and documented. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Abstract: Radiology is an essential technology in medicine and is used for organ diagnosis, radio-tracing and radiotherapy. The risks for the radiobiology workers are not assess sufficiently because measuring instruments fail to detect very low doses. This manuscript presents an investigation on the potential risks for radiobiology workers, due to the occupational exposure to low doses of irradiation. In this respect we used plasma samples from 16 subjects which were suppose to receive very low dose of irradiation in a longer time period. We used chromosomal aberrations, means of oxidative stress measurement and combined proteomics and bioinformatics in order to elucidate risks of such exposure. We found significant chromosome aberrations in lymphocytes and the increase of oxidative stress biomarkers in plasma. In addition the proteomic analysis shows differentially regulated proteins which three of them were verified by ELISA tests. This proteomic analysis picks out some interesting proteins that may belong to biomarkers panel of radiation exposure.
    International Journal of Low Radiation 03/2014; DOI:10.1504/IJLR.2014.060911
  • Source
    • "Patients with certain hereditary cancer syndromes can experience increased cancer risks when undergoing radiotherapy (Kleinerman 2009). Accumulating data, however , point to other potential modifiers of radiation-related disease risks, including cigarette smoking, reproductive factors, specific chemotherapy drugs, and possibly other factors (Travis et al. 2003; Cardis et al. 2005; Karagas et al. 2007; Furukawa et al. 2010; Egawa et al. 2012). Improvements in radiation dose measurement in the past few decades have been notable, but increasing knowledge about the long latency of many radiation-related late health effects motivates the need for high-quality historical dose reconstruction along with identification and incorporation of quantitative measures of uncertainty in dose. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Everyone is exposed to natural and manmade ionizing radiation that can originate from sources in the environment and in medical and occupational settings. There is notable variation, however, among individuals and across populations in the types of sources of radiation and in the frequency, level, and duration of exposure. Adverse health effects associated with radiation exposure have been known for decades, and ionizing radiation exposure has been linked with a broad range of different types of cancer and benign neoplasms as well as birth defects, reproductive effects, and diseases of the circulatory, hematologic, and neurologic systems. Our present understanding of radiation-related health risks derives primarily from multidisciplinary health risk (epidemiologic) studies that provide the key information on radiation-associated health outcomes, quantify radiation-related disease risks, and enhance understanding of mechanisms of radiation-related disease pathogenesis. Such information is central to quantifying risks in relation to benefits; addressing public concerns, including societal and clinical needs in relation to radiation exposure; and providing the database needed for establishing recommendations for radiation protection. Because of the importance of determining risks compared to benefits for all situations where exposure to ionizing radiation might result, it is useful for planning new health risks studies to categorize exposed populations according to the sources and types of radiation. This paper describes a wide range of populations exposed to radiation and the motivation and key methodological criteria that drive the rationale and priority of studying such populations. Also, discussed are alternative methods for evaluating radiation-related health risks in these populations, with a major focus on epidemiologic approaches. This paper concludes with a short summary of major highlights from radiation epidemiologic research and important unanswered questions.Introduction of Exposed Populations (Video 1:29,
    Health physics 02/2014; 106(2):182-95. DOI:10.1097/HP.0000000000000006 · 1.27 Impact Factor
  • Source
    • "Model-based evidence obtained from the literature for lung cancer A recent lung cancer analysis (Furukawa et al. 2010) indicated a complicated interaction between lung cancer and smoking based on an analysis applying the ERR model, that is, they applied generalized joint effect models, which they called ''generalized additive and multiplicative ERR interaction models'' and found stronger evidence than an earlier analysis (Pierce et al. 2003) against the additive approach. This indication partially supports the proposed method which provides the conclusion that, for lung cancer , projections should be based on the ERR model (ERR weight = 0.97). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Radiation-related risks of cancer can be transported from one population to another population at risk, for the purpose of calculating lifetime risks from radiation exposure. Transfer via excess relative risks (ERR) or excess absolute risks (EAR) or a mixture of both (i.e., from the life span study (LSS) of Japanese atomic bomb survivors) has been done in the past based on qualitative weighting. Consequently, the values of the weights applied and the method of application of the weights (i.e., as additive or geometric weighted means) have varied both between reports produced at different times by the same regulatory body and also between reports produced at similar times by different regulatory bodies. Since the gender and age patterns are often markedly different between EAR and ERR models, it is useful to have an evidence-based method for determining the relative goodness of fit of such models to the data. This paper identifies a method, using Akaike model weights, which could aid expert judgment and be applied to help to achieve consistency of approach and quantitative evidence-based results in future health risk assessments. The results of applying this method to recent LSS cancer incidence models are that the relative EAR weighting by cancer solid cancer site, on a scale of 0-1, is zero for breast and colon, 0.02 for all solid, 0.03 for lung, 0.08 for liver, 0.15 for thyroid, 0.18 for bladder and 0.93 for stomach. The EAR weighting for female breast cancer increases from 0 to 0.3, if a generally observed change in the trend between female age-specific breast cancer incidence rates and attained age, associated with menopause, is accounted for in the EAR model. Application of this method to preferred models from a study of multi-model inference from many models fitted to the LSS leukemia mortality data, results in an EAR weighting of 0. From these results it can be seen that lifetime risk transfer is most highly weighted by EAR only for stomach cancer. However, the generalization and interpretation of radiation effect estimates based on the LSS cancer data, when projected to other populations, are particularly uncertain if considerable differences exist between site-specific baseline rates in the LSS and the other populations of interest. Definitive conclusions, regarding the appropriate method for transporting cancer risks, are limited by a lack of knowledge in several areas including unknown factors and uncertainties in biological mechanisms and genetic and environmental risk factors for carcinogenesis; uncertainties in radiation dosimetry; and insufficient statistical power and/or incomplete follow-up in data from radio-epidemiological studies.
    Biophysik 11/2012; 52(1). DOI:10.1007/s00411-012-0441-x · 1.53 Impact Factor
Show more


12 Reads
Available from