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2016 marked the 30th anniversary of the Chernobyl Nuclear Power Plant accident. We and others wrote reviews for the 25th anniversary. Since then, additional papers have appeared and it seems timely to highlight lessons learned. To present, not a systematic review, but a commentary drawing attention to notable findings. We include not only recent reports and updates on previous results, but key findings from prior Chernobyl studies. The dose-dependent increase in Papillary Thyroid Cancer (PTC) following childhood I-131 exposure in Ukraine and Belarus has now been shown to persist for decades. Studies of post-Chernobyl PTCs have produced novel information on chromosomal rearrangements and gene fusions, critical to understanding molecular mechanisms. Studies of cleanup workers/liquidators suggest dose-related increases of thyroid cancer and hematological malignancies in adults. They also report increases in cardiovascular and cerebrovascular disease. If confirmed, these would have significant public health and radiation protection implications. The lens opacities following low to moderate doses found earlier are also a concern, particularly among interventional radiologists who may receive substantial lens doses. Finally, there is some, inconsistent, evidence for genetic effects among offspring of exposed persons. Further efforts, including improved dosimetry, collection of information on other risk factors, and continued followup/monitoring of established cohorts, could contribute importantly to further understand effects of low doses and dose-rates of radiation, particularly in young people, and ensure that appropriate public health and radiation protection systems are in place. This will require multinational collaborations and long-term funding.

The European EPI-CT study aims to quantify cancer risks from CT examinations of children and young adults. Here, we assess the risk of brain cancer. We pooled data from nine European countries for this cohort study. Eligible participants had at least one CT examination before age 22 years documented between 1977 and 2014, had no previous diagnosis of cancer or benign brain tumour, and were alive and cancer-free at least 5 years after the first CT. Participants were identified through the Radiology Information System in 276 hospitals. Participants were linked with national or regional registries of cancer and vital status, and eligible cases were patients with brain cancers according to WHO International Classification of Diseases for Oncology. Gliomas were analysed separately to all brain cancers. Organ doses were reconstructed using historical machine settings and a large sample of CT images. Excess relative risks (ERRs) of brain cancer per 100 mGy of cumulative brain dose were calculated with linear dose-response modelling. The outcome was the first reported diagnosis of brain cancer after an exclusion period of 5 years after the first electronically recorded CT examination. We identified 948 174 individuals, of whom 658 752 (69%) were eligible for our study. 368 721 (56%) of 658 752 participants were male and 290 031 (44%) were female. During a median follow-up of 5·6 years (IQR 2·4–10·1), 165 brain cancers occurred, including 121 (73%) gliomas. Mean cumulative brain dose, lagged by 5 years, was 47·4 mGy (SD 60·9) among all individuals and 76·0 mGy (100·1) among people with brain cancer. A significant linear dose-response relationship was observed for all brain cancers (ERR per 100 mGy 1·27 [95% CI 0·51–2·69]) and for gliomas separately (ERR per 100 mGy 1·11 [0·36–2·59]). Results were robust when the start of follow-up was delayed beyond 5 years and when participants with possibly previously unreported cancers were excluded. The observed significant dose-response relationship between CT-related radiation exposure and brain cancer in this large, multicentre study with individual dose evaluation emphasises careful justification of paediatric CTs and use of doses as low as reasonably possible. EU FP7; Belgian Cancer Registry; La Ligue contre le Cancer, L'Institut National du Cancer, France; Ministry of Health, Labour and Welfare of Japan; German Federal Ministry of Education and Research; Worldwide Cancer Research; Dutch Cancer Society; Research Council of Norway; Consejo de Seguridad Nuclear, Generalitat de Catalunya, Spain; US National Cancer Institute; UK National Institute for Health Research; Public Health England.

Exposure to ionizing radiation is ubiquitous, and it is well established that moderate and high doses cause ill-health and can be lethal. The health effects of low doses or low dose-rates of ionizing radiation are not so clear. This paper describes a project which sets out to summarize, as a restatement, the natural science evidence base concerning the human health effects of exposure to low-level ionizing radiation. A novel feature, compared to other reviews, is that a series of statements are listed and categorized according to the nature and strength of the evidence that underpins them. The purpose of this restatement is to provide a concise entrée into this vibrant field, pointing the interested reader deeper into the literature when more detail is needed. It is not our purpose to reach conclusions on whether the legal limits on radiation exposures are too high, too low or just right. Our aim is to provide an introduction so that non-specialist individuals in this area (be they policy-makers, disputers of policy, health professionals or students) have a straightforward place to start. The summary restatement of the evidence and an extensively annotated bibliography are provided as appendices in the electronic supplementary material.

Positive associations between external radiation dose and non-cancer mortality have been found in a number of published studies, primarily of populations exposed to high-dose, high-dose-rate ionizing radiation. The goal of this study was to determine whether external radiation dose was associated with non-cancer mortality in a large pooled cohort of nuclear workers exposed to low-dose radiation accumulated at low dose rates. The cohort comprised 308,297 workers from France, United Kingdom and United States. The average cumulative equivalent dose at a tissue depth of 10 mm [Hp(10)] was 25.2 mSv. In total, 22% of the cohort were deceased by the end of follow-up, with 46,029 deaths attributed to non-cancer outcomes, including 27,848 deaths attributed to circulatory diseases. Poisson regression was used to investigate the relationship between cumulative radiation dose and non-cancer mortality rates. A statistically significant association between radiation dose and all non-cancer causes of death was observed [excess relative risk per sievert (ERR/Sv) = 0.19; 90% CI: 0.07, 0.30]. This was largely driven by the association between radiation dose and mortality due to circulatory diseases (ERR/Sv = 0.22; 90% CI: 0.08, 0.37), with slightly smaller positive, but nonsignificant, point estimates for mortality due to nonmalignant respiratory disease (ERR/Sv = 0.13; 90% CI: –0.17, 0.47) and digestive disease (ERR/Sv = 0.11; 90% CI: –0.36, 0.69). The point estimate for the association between radiation dose and deaths due to external causes of death was nonsignificantly negative (ERR = –0.12; 90% CI: <–0.60, 0.45). Within circulatory disease subtypes, associations with dose were observed for mortality due to cerebrovascular disease (ERR/Sv = 0.50; 90% CI: 0.12, 0.94) and mortality due to ischemic heart disease (ERR/Sv = 0.18; 90% CI: 0.004, 0.36). The estimates of associations between radiation dose and non-cancer mortality are generally consistent with those observed in atomic bomb survivor studies. The findings of this study could be interpreted as providing further evidence that non-cancer disease risks may be increased by external radiation exposure, particularly for ischemic heart disease and cerebrovascular disease. However, heterogeneity in the estimated ERR/Sv was observed, which warrants further investigation. Further follow-up of these cohorts, with the inclusion of internal exposure information and other potential confounders associated with lifestyle factors, may prove informative, as will further work on elucidating the biological mechanisms that might cause these non-cancer effects at low doses.

ObjectivesThis study investigated occupational exposure to radiofrequency electromagnetic fields (RF EMF) using two job-exposure matrices (JEMs) and risk of glioma.DesignPopulation-based family case–control study.SettingCases were recruited from participating hospitals in the Australian states of New South Wales, Queensland, Tasmania, Western Australia and Victoria between January 2013 and November 2017.ParticipantsThe study population consisted of 467 cases of glioma and 367 family controls recruited for the Australian Genomics and Clinical Outcomes of Glioma case–control study between 2013 and 2017. Participants completed questionnaires on demographic and other information, including a detailed occupational history.ExposuresExposure to RF EMF was estimated using both the multicountry case–control study INTEROCC JEM and the Canadian JEM (CANJEM).Primary outcome measuresORs and 95% CIs were calculated from logistic regression models adjusted for relatedness between cases and controls, sex, age, ethnicity, education level, smoking status and alcohol consumption.ResultsThere was no statistically significant positive association overall for risk of glioma when applying either JEM. For the highest compared with the lowest quartile of lifetime exposure, results using the INTEROCC JEM showed an OR of 0.74 (95% CI 0.47 to 1.15) for electric fields and 0.92 (95% CI 0.58 to 1.45) for magnetic fields, while the CANJEM showed an OR of 0.85 (95% CI 0.54 to 1.32). We also did not observe associations when applying different assumptions regarding latency or time windows or with glioma grade.ConclusionOverall, this study found no evidence of an association between occupational RF EMF exposure and glioma. Future research should focus on refining occupational RF EMF exposure assessment.

In order to achieve an integrated radio-frequency electromagnetic fields (RF-EMF) dose assessment, detailed information about source-specific exposure duration and output power is needed. We developed an Integrated Exposure Model (IEM) to combine energy absorbed due to use of and exposure to RF-EMF sources and applied it to a sample of the general population to derive population RF-EMF estimates. The IEM used specific absorption rate transfer algorithms to provide RF-EMF daily dose estimates (mJ/kg/day) using source-specific attributes (e.g. output power, distance), personal characteristics and usage patterns. Information was obtained from an international survey performed in four European countries with 1755 participants. We obtained median whole-body and whole-brain doses of 183.7 and 204.4 mJ/kg/day. Main contributors to whole-brain dose were mobile phone near the head for calling (2G networks) and far-field sources, whereas the latter together with multiple other RF-EMF sources were main contributors for whole-body dose. For other anatomical sites, 2G phone calls, mobile data and far-field exposure were important contributors. The IEM provides insight into main contributors to total RF-EMF dose and, applied to an international survey, provides an estimate of population RF-dose. The IEM can be used in future epidemiological studies, risk assessments and exposure reduction strategies.

The Life Span Study (LSS) of Japanese atomic bomb survivors has served as the primary basis for estimates of radiation-related disease risks that inform radiation protection standards. The long-term follow-up of radiation-monitored nuclear workers provides estimates of radiation-cancer associations that complement findings from the LSS. Here, a comparison of radiation-cancer mortality risk estimates derived from the LSS and INWORKS, a large international nuclear worker study, is presented. Restrictions were made, so that the two study populations were similar with respect to ages and periods of exposure, leading to selection of 45,625 A-bomb survivors and 259,350 nuclear workers. For solid cancer, excess relative rates (ERR) per gray (Gy) were 0.28 (90% CI 0.18; 0.38) in the LSS, and 0.29 (90% CI 0.07; 0.53) in INWORKS. A joint analysis of the data allowed for a formal assessment of heterogeneity of the ERR per Gy across the two studies (P = 0.909), with minimal evidence of curvature or of a modifying effect of attained age, age at exposure, or sex in either study. There was evidence in both cohorts of modification of the excess absolute risk (EAR) of solid cancer by attained age, with a trend of increasing EAR per Gy with attained age. For leukemia, under a simple linear model, the ERR per Gy was 2.75 (90% CI 1.73; 4.21) in the LSS and 3.15 (90% CI 1.12; 5.72) in INWORKS, with evidence of curvature in the association across the range of dose observed in the LSS but not in INWORKS; the EAR per Gy was 3.54 (90% CI 2.30; 5.05) in the LSS and 2.03 (90% CI 0.36; 4.07) in INWORKS. These findings from different study populations may help understanding of radiation risks, with INWORKS contributing information derived from cohorts of workers with protracted low dose-rate exposures.

Risk of depression increased in the general population after the COVID-19 pandemic outbreak. By examining the interplay between genetics and individual environmental exposures during the COVID-19 lockdown, we have been able to gain an insight as to why some individuals are more vulnerable to depression, while others are more resilient. This study, conducted on a Spanish cohort of 9218 individuals (COVICAT), includes a comprehensive non-genetic risk analysis, the exposome, complemented by a genomics analysis in a subset of 2442 participants. Depression levels were evaluated using the Hospital Anxiety and Depression Scale. Together with Polygenic Risk Scores (PRS), we introduced a novel score; Poly-Environmental Risk Scores (PERS) for non-genetic risks to estimate the effect of each cumulative score and gene-environment interaction. We found significant positive associations for PERS Soc (Social and Household), PERS Life (Lifestyle and Behaviour), and PERS Env (Wider Environment and Health) scores across all levels of depression severity, and for PRS B (Broad depression) only for moderate depression (OR 1.2, 95% CI 1.03–1.40). On average OR increased 1.2-fold for PERS Env and 1.6-fold for PER Life and PER Soc from mild to severe depression level. The complete adjusted model explained 16.9% of the variance. We further observed an interaction between PERS Env and PRS B showing a potential mitigating effect. In summary, stressors within the social and behavioral domains emerged as the primary drivers of depression risk in this population, unveiling a mitigating interaction effect that should be interpreted with caution.

Background INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. Methods In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. Results Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. Conclusion While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.

The increasing risk of acute large-scale radiological/nuclear exposures of population underlines the necessity of developing new, rapid and high throughput biodosimetric tools for estimation of received dose and initial triage. We aimed to compare the induction and persistence of different radiation exposure biomarkers in human peripheral blood in vivo. Blood samples of patients with indicated radiotherapy (RT) undergoing partial body irradiation (PBI) were obtained soon before the first treatment and then after 24 h, 48 h, and 5 weeks; i.e. after 1, 2, and 25 fractionated RT procedures. We collected circulating peripheral blood from ten patients with tumor of endometrium (1.8 Gy per fraction) and eight patients with tumor of head and neck (2.0-2.121 Gy per fraction). Incidence of dicentrics and micronuclei was monitored as well as determination of apoptosis and the transcription level of selected radiation-responsive genes. Since mitochondrial DNA (mtDNA) has been reported to be a potential indicator of radiation damage in vitro, we also assessed mtDNA content and deletions by novel multiplex quantitative PCR. Cytogenetic data confirmed linear dose-dependent increase in dicentrics (p < 0.01) and micronuclei (p < 0.001) in peripheral blood mononuclear cells after PBI. Significant up-regulations of five previously identified transcriptional biomarkers of radiation exposure (PHPT1, CCNG1, CDKN1A, GADD45, and SESN1) were also found (p < 0.01). No statistical change in mtDNA deletion levels was detected; however, our data indicate that the total mtDNA content decreased with increasing number of RT fractions. Interestingly, the number of micronuclei appears to correlate with late radiation toxicity (r2 = 0.9025) in endometrial patients suggesting the possibility of predicting the severity of RT-related toxicity by monitoring this parameter. Overall, these data represent, to our best knowledge, the first study providing a multiparametric comparison of radiation biomarkers in human blood in vivo, which have potential for improving biological dosimetry.