A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure.

In this article we review the history of key epidemiological studies of populations exposed to ionizing radiation. We highlight historical and recent findings regarding radiation-associated risks for incidence and mortality of cancer and non-cancer outcomes with emphasis on study design and methods of exposure assessment and dose estimation along with brief consideration of sources of bias for a few of the more important studies. We examine the findings from the epidemiological studies of the Japanese atomic bomb survivors, persons exposed to radiation for diagnostic or therapeutic purposes, those exposed to environmental sources including Chornobyl and other reactor accidents, and occupationally exposed cohorts. We also summarize results of pooled studies. These summaries are necessarily brief, but we provide references to more detailed information. We discuss possible future directions of study, to include assessment of susceptible populations, and possible new populations, data sources, study designs and methods of analysis.

Effects of confounding and effect-modifying lifestyle, environmental and medical factors on risk of radiation-associated cardiovascular disease.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. It has been known for some considerable time that radiation is associated with excess risk of CVD. A recent systematic review of radiation and CVD highlighted substantial inter-study heterogeneity in effect, possibly a result of confounding or modifications of radiation effect by non-radiation factors, in particular by the major lifestyle/environmental/medical risk factors and latent period. METHODS: We assessed effects of confounding by lifestyle/environmental/medical risk factors on radiation-associated CVD and investigated evidence for modifying effects of these variables on CVD radiation dose-response, using data assembled for a recent systematic review. RESULTS: There are 43 epidemiologic studies which are informative on effects of adjustment for confounding or risk modifying factors on radiation-associated CVD. Of these 22 were studies of groups exposed to substantial doses of medical radiation for therapy or diagnosis. The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate. Only four studies suggest substantial effects of adjustment for lifestyle/environmental/medical risk factors on radiation risk of CVD; however, there were also substantial uncertainties in the estimates in all of these studies. There are fewer suggestions of effects that modify the radiation dose response; only two studies, both at lower levels of dose, report the most serious level of modifying effect. CONCLUSIONS: There are still large uncertainties about confounding factors or lifestyle/environmental/medical variables that may influence radiation-associated CVD, although indications are that there are not many studies in which there are substantial confounding effects of these risk factors.

Revisiting an Inverse Dose-Fractionation Effect of Ionizing Radiation Exposure for Ischemic Heart Disease: Insights from Recent Studies.

Over the last two decades, there has been emerging evidence suggesting that ionizing radiation exposures could be associated with elevated risks of cardiovascular disease (CVD), particularly ischemic heart disease (IHD). Excess CVD risks have been observed in a number of exposed groups, with generally similar risk estimates both at low and high radiation doses and dose rates. In 2014, we reported for the first time significantly higher risks of IHD mortality when radiation doses were delivered over a protracted period of time (an inverse dose-fractionation effect) in the Canadian Fluoroscopy Cohort Study. Here we review the current evidence on the dose-fractionation effect of radiation exposure, discuss potential implication for radiation protection policies and suggest further directions for research in this area.

Prevalence of Thyroid Nodules in Residents of Ukraine Exposed as Children or Adolescents to Iodine-131 from the Chornobyl Accident.

Background: Although childhood exposure to radioactive iodine-131 (I-131) is an established risk factor for thyroid cancer, evidence for an association with thyroid nodules is less clear. The objective of this study is to evaluate the association between childhood I-131 exposure and prevalence of ultrasound-detected thyroid nodules overall and by nodule histology/cytology (neoplastic/suspicious/non-neoplastic), size (<10 mm/≥10 mm), and number (single/multiple). Methods: This is a cross-sectional study of radiation dose (mean = 0.53 gray, range: 0.0003-31 gray) and screen-detected thyroid nodules conducted in 1998-2000 (median population age 21.5 years) in a cohort of 13,243 residents of Ukraine who were under 18 years at the time of the Chornobyl accident on April 26, 1986. Excess odds ratios per gray (excess odds ratio [EOR]/Gy) and confidence intervals (CIs) were estimated using logistic regression. Results: Among 13,078 eligible individuals, we identified 358 (2.7%) with at least one thyroid nodule. Significantly increased dose-response associations were found for all nodules and nodule groups with doses <5 Gy except individuals with non-neoplastic nodules. Among individuals with doses <5 Gy, the EOR/Gy for neoplastic nodules (5.35; CI: 2.19-15.5) was significantly higher than for non-neoplastic nodules (0.24; CI: 0.07-0.74), but the EOR/Gy did not vary by nodule size or number. Conclusions: Childhood exposure to I-131 is associated with an increased risk of thyroid nodules detected 12-14 years following exposure, and the risk for neoplastic nodules is higher than for non-neoplastic nodules. Analyses of incident thyroid nodules may help clarify dose-response patterns by nodule characteristics and provide insights into thyroid nodule etiology.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods.

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

Radiation Dose Does Not Affect the Predictive Value of Thyroid Biopsy for Diagnosing Papillary Thyroid Cancer in a Belarusian Cohort Exposed to Chernobyl Fallout.

INTRODUCTION: The Chernobyl nuclear accident exposed residents of contaminated territories to substantial quantities of radioiodines and was followed by an increase in thyroid cancer, primarily papillary thyroid cancer (PTC), among exposed children and adolescents. Although thyroid biopsy is an essential component of screening programs following accidental exposure to radioiodines, it is unknown whether the predictive value of biopsy is affected by different levels of environmental exposure. METHODS: A cohort of 11,732 Belarusians aged ≤18 years at the time of the Chernobyl accident with individual thyroid radiation dose estimates was screened at least once 11-22 years later. Paired cytologic conclusions and histopathologic diagnoses were possible for 258 thyroid nodules from 238 cohort members. Cytologic conclusions were divided into five reporting categories, with all follicular lesion aspirates combined into a single indeterminate category. Standard performance indicators, risk of malignancy (ROM), and odds ratios for a correct cytologic conclusion were calculated, both overall and according to quintile of thyroid radiation dose. RESULTS: The arithmetic mean thyroid dose estimate for the study group was 1.73 Gy (range: 0.00-23.64 Gy). The final histopathologic diagnosis was cancer for 136 of 258 biopsies (52.7%; 135 papillary and 1 follicular). The overall ROM was 96.7% for cytologies definite for PTC, 83.7% for suspicious for PTC, 33.0% for indeterminate, 8.1% for benign, and 31.0% for non-diagnostic. The ROM showed little change according to level of radiation exposure. Overall, there was no association between thyroid radiation dose and the odds ratio for a correct cytologic conclusion (p = 0.24). When analyzed according to dose quintile, the odds ratio for a correct conclusion increased two-fold at 0.10-0.29 Gy compared to a dose of 0.00-0.09 Gy and decreased at doses of 0.3-24 Gy (p value for linear trend = 0.99). CONCLUSIONS: At radiation doses received by a cohort of young Belarusians exposed to radioiodines by the Chernobyl accident, the predictive value of thyroid biopsy for diagnosing PTC was not significantly affected by level of radiation exposure.

Radiotherapy for non-cancer diseases: benefits and long-term risks.

PURPOSE: The discovery of X-rays was followed by a variety of attempts to treat infectious diseases and various other non-cancer diseases with ionizing radiation, in addition to cancer. There has been a recent resurgence of interest in the use of such radiotherapy for non-cancer diseases. Non-cancer diseases for which use of radiotherapy has currently been proposed include refractory ventricular tachycardia, neurodegenerative diseases (e.g. Alzheimer's disease and dementia), and Coronavirus Disease 2019 (COVID-19) pneumonia, all with ongoing clinical studies that deliver radiation doses of 0.5-25 Gy in a single fraction or in multiple daily fractions. In addition to such non-cancer effects, historical indications predominantly used in some countries (e.g. Germany) include osteoarthritis and degenerative diseases of the bones and joints. This narrative review gives an overview of the biological rationale and ongoing preclinical and clinical studies for radiotherapy proposed for various non-cancer diseases, discusses the plausibility of the proposed biological rationale, and considers the long-term radiation risks of cancer and non-cancer diseases. CONCLUSIONS: A growing body of evidence has suggested that radiation represents a double-edged sword, not only for cancer, but also for non-cancer diseases. At present, clinical evidence has shown some beneficial effects of radiotherapy for ventricular tachycardia, but there is little or no such evidence of radiotherapy for other newly proposed non-cancer diseases (e.g. Alzheimer's disease, COVID-19 pneumonia). Patients with ventricular tachycardia and COVID-19 pneumonia have thus far been treated with radiotherapy when they are an urgent life threat with no efficient alternative treatment, but some survivors may encounter a paradoxical situation where patients were rescued by radiotherapy but then get harmed by radiotherapy. Further studies are needed to justify the clinical use of radiotherapy for non-cancer diseases, and optimize dose to diseased tissue while minimizing dose to healthy tissue.

A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods.

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.

Third mortality follow-up of the Mallinckrodt uranium processing workers, 1942-2019.

INTRODUCTION: Mallinckrodt Chemical Works was a uranium processing facility during the Manhattan Project from 1942 to 1966. Thousands of workers were exposed to low-dose-rates of ionizing radiation from external and internal sources. This third follow-up of 2514 White male employees updates cancer and noncancer mortality potentially associated with radiation and silica dust. MATERIALS AND METHODS: Individual, annualized organ doses were estimated from film badge records (n monitored = 2514), occupational chest x-rays (n = 2514), uranium urinalysis (n = 1868), radium intake through radon breath measurements (n = 487), and radon ambient measurements (n = 1356). Silica dust exposure from pitchblende processing was estimated (n = 1317). Vital status and cause of death determination through 2019 relied upon the National Death Index and Social Security Administration Epidemiological Vital Status Service. The analysis included standardized mortality ratios (SMRs), Cox proportional hazards, and Poisson regression models. RESULTS: Vital status was confirmed for 99.4% of workers (84.0% deceased). For a dose weighting factor of 1 for intakes of uranium, radium, and radon decay products, the mean and median lung doses were 65.6 and 29.9 mGy, respectively. SMRs indicated a difference in health outcomes between salaried and hourly workers, and more brain cancer deaths than expected [SMR: 1.79; 95% confidence interval (CI): 1.14, 2.70]. No association was seen between radiation and lung cancer [hazard ratio (HR) at 100 mGy: 0.93; 95%CI: 0.78, 1.11]. The relationship between radiation and kidney cancer observed in the previous follow-up was maintained (HR at 100 mGy: 2.07; 95%CI: 1.12, 3.79). Cardiovascular disease (CVD) also increased significantly with heart dose (HR at 100 mGy: 1.11; 95%CI: 1.02, 1.21). Exposures to dust ≥23.6 mg/m3-year were associated with nonmalignant kidney disease (NMKD) (HR: 3.02; 95%CI: 1.12, 8.16) and kidney cancer combined with NMKD (HR: 2.46; 95%CI: 1.04, 5.81), though without evidence of a dose-response per 100 mg/m3-year. CONCLUSIONS: This third follow-up of Mallinckrodt uranium processors reinforced the results of the previous studies. There was an excess of brain cancers compared with the US population, although no radiation dose-response was detected. The association between radiation and kidney cancer remained, though potentially due to few cases at higher doses. The association between levels of silica dust ≥23.6 mg/m3-year and NMKD also remained. No association was observed between radiation and lung cancer. A positive dose-response was observed between radiation and CVD; however, this association may be confounded by smoking, which was unmeasured. Future work will pool these data with other uranium processing worker cohorts within the Million Person Study.

A generalisation of the method of regression calibration and comparison with the Bayesian 2-dimensional Monte Carlo method.

For many cancer sites it is necessary to assess risks from low-dose exposures via extrapolation from groups exposed at moderate and high levels of dose. Measurement error can substantially alter the shape of this relationship and hence the derived population risk estimates. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, much attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. In this paper we test a Bayesian model averaging method, the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, that has been fairly recently proposed against a very newly proposed modification of the regression calibration method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. We also compared both methods against standard regression calibration and Monte Carlo maximum likelihood. The Bayesian 2DMC method performs poorly, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5%, particularly when the magnitudes of classical and Berkson error are both moderate to large (20%-50%). The method also produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. By comparison the extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the Bayesian 2DMC and all other methods. The bias of the predicted relative risk at a variety of doses is generally smallest for extended regression calibration, and largest for the Bayesian 2DMC method (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between the other two methods.

A generalisation of the method of regression calibration.

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

A generalisation of the method of regression calibration.

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient \(\alpha\) are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient \(\beta\) are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for \(\beta\) that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient \(\beta\) are substantially upwardly biased.

Radiation exposure and leukaemia risk among cohorts of persons exposed to low and moderate doses of external ionising radiation in childhood.

BACKGROUND: Many high-dose groups demonstrate increased leukaemia risks, with risk greatest following childhood exposure; risks at low/moderate doses are less clear. METHODS: We conducted a pooled analysis of the major radiation-associated leukaemias (acute myeloid leukaemia (AML) with/without the inclusion of myelodysplastic syndrome (MDS), chronic myeloid leukaemia (CML), acute lymphoblastic leukaemia (ALL)) in ten childhood-exposed groups, including Japanese atomic bomb survivors, four therapeutically irradiated and five diagnostically exposed cohorts, a mixture of incidence and mortality data. Relative/absolute risk Poisson regression models were fitted. RESULTS: Of 365 cases/deaths of leukaemias excluding chronic lymphocytic leukaemia, there were 272 AML/CML/ALL among 310,905 persons (7,641,362 person-years), with mean active bone marrow (ABM) dose of 0.11 Gy (range 0-5.95). We estimated significant (P < 0.005) linear excess relative risks/Gy (ERR/Gy) for: AML (n = 140) = 1.48 (95% CI 0.59-2.85), CML (n = 61) = 1.77 (95% CI 0.38-4.50), and ALL (n = 71) = 6.65 (95% CI 2.79-14.83). There is upward curvature in the dose response for ALL and AML over the full dose range, although at lower doses (<0.5 Gy) curvature for ALL is downwards. DISCUSSION: We found increased ERR/Gy for all major types of radiation-associated leukaemia after childhood exposure to ABM doses that were predominantly (for 99%) <1 Gy, and consistent with our prior analysis focusing on <100 mGy.

Exposure to Ionizing Radiation and Risk of Dementia: A Systematic Review and Meta-Analysis.

The number of people living with dementia is rising globally as life expectancy increases. Dementia is a multifactorial disease. Due to the ubiquity of radiation exposure in medical and occupational settings, the potential association between radiation and dementia, and its subtypes (Alzheimer's and Parkinson's disease), is of particular importance. There has also been an increased interest in studying radiation induced dementia risks in connection with the long-term manned space travel proposed by The National Aeronautics and Space Administration (NASA). Our aim was to systematically review the literature on this topic, and use meta-analysis to generate a summary measure of association, assess publication bias and explore sources of heterogeneity across studies. We identified five types of exposed populations for this review: 1. survivors of atomic bombings in Japan; 2. patients treated with radiation therapy for cancer or other diseases; 3. occupationally exposed workers; 4. those exposed to environmental radiation; and 5. patients exposed to radiation from diagnostic radiation imaging procedures. We included studies that considered incident or mortality outcomes for dementia and its subtypes. Following PRISMA guidelines, we systematically searched the published literature indexed in PubMed between 2001 and 2022. We then abstracted the relevant articles, conducted a risk-of-bias assessment, and fit random effects models using the published risk estimates. After we applied our eligibility criteria, 18 studies were identified for review and retained for meta-analysis. For dementia (all subtypes), the summary relative risk was 1.11 (95% CI: 1.04, 1.18; P = 0.001) comparing individuals receiving 100 mSv of radiation to those with no exposure. The corresponding summary relative risk for Parkinson's disease incidence and mortality was 1.12 (95% CI 1.07, 1.17; P <0.001). Our results provide evidence that exposure to ionizing radiation increases the risk of dementia. However, our findings should be interpreted with caution due to the small number of included studies. Longitudinal studies with improved exposure characterization, incident outcomes, larger sample size, and the ability to adjust for effects of potential confounders are needed to better assess the possible causal link between ionizing radiation and dementia.

Radon and lung cancer in the pooled uranium miners analysis (PUMA): highly exposed early miners and all miners.

OBJECTIVES: Radon is a ubiquitous occupational and environmental lung carcinogen. We aim to quantify the association between radon progeny and lung cancer mortality in the largest and most up-to-date pooled study of uranium miners. METHODS: The pooled uranium miners analysis combines 7 cohorts of male uranium miners with 7754 lung cancer deaths and 4.3 million person-years of follow-up. Vital status and lung cancer deaths were ascertained between 1946 and 2014. The association between cumulative radon exposure in working level months (WLM) and lung cancer was modelled as the excess relative rate (ERR) per 100 WLM using Poisson regression; variation in the association by temporal and exposure factors was examined. We also examined analyses restricted to miners first hired before 1960 and with <100 WLM cumulative exposure. RESULTS: In a model that allows for variation by attained age, time since exposure and annual exposure rate, the ERR/100 WLM was 4.68 (95% CI 2.88 to 6.96) among miners who were less than 55 years of age and were exposed in the prior 5 to <15 years at annual exposure rates of <0.5 WL. This association decreased with older attained age, longer time since exposure and higher annual exposure rate. In analyses restricted to men first hired before 1960, we observed similar patterns of association but a slightly lower estimate of the ERR/100 WLM. CONCLUSIONS: This new large, pooled study confirms and supports a linear exposure-response relationship between cumulative radon exposure and lung cancer mortality which is jointly modified by temporal and exposure factors.

Ionising radiation and cardiovascular disease: systematic review and meta-analysis.

OBJECTIVE: To systematically review and perform a meta-analysis of radiation associated risks of cardiovascular disease in all groups exposed to radiation with individual radiation dose estimates. DESIGN: Systematic review and meta-analysis. MAIN OUTCOME MEASURES: Excess relative risk per unit dose (Gy), estimated by restricted maximum likelihood methods. DATA SOURCES: PubMed and Medline, Embase, Scopus, Web of Science Core collection databases. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Databases were searched on 6 October 2022, with no limits on date of publication or language. Animal studies and studies without an abstract were excluded. RESULTS: The meta-analysis yielded 93 relevant studies. Relative risk per Gy increased for all cardiovascular disease (excess relative risk per Gy of 0.11 (95% confidence interval 0.08 to 0.14)) and for the four major subtypes of cardiovascular disease (ischaemic heart disease, other heart disease, cerebrovascular disease, all other cardiovascular disease). However, interstudy heterogeneity was noted (P<0.05 for all endpoints except for other heart disease), possibly resulting from interstudy variation in unmeasured confounders or effect modifiers, which is markedly reduced if attention is restricted to higher quality studies or those at moderate doses (<0.5 Gy) or low dose rates (<5 mGy/h). For ischaemic heart disease and all cardiovascular disease, risks were larger per unit dose for lower dose (inverse dose effect) and for fractionated exposures (inverse dose fractionation effect). Population based excess absolute risks are estimated for a number of national populations (Canada, England and Wales, France, Germany, Japan, USA) and range from 2.33% per Gy (95% confidence interval 1.69% to 2.98%) for England and Wales to 3.66% per Gy (2.65% to 4.68%) for Germany, largely reflecting the underlying rates of cardiovascular disease mortality in these populations. Estimated risk of mortality from cardiovascular disease are generally dominated by cerebrovascular disease (around 0.94-1.26% per Gy), with the next largest contribution from ischaemic heart disease (around 0.30-1.20% per Gy). CONCLUSIONS: Results provide evidence supporting a causal association between radiation exposure and cardiovascular disease at high dose, and to a lesser extent at low dose, with some indications of differences in risk between acute and chronic exposures, which require further investigation. The observed heterogeneity complicates a causal interpretation of these findings, although this heterogeneity is much reduced if only higher quality studies or those at moderate doses or low dose rates are considered. Studies are needed to assess in more detail modifications of radiation effect by lifestyle and medical risk factors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020202036.

Metabolomic Profile of Children Conceived With Medically Assisted Technologies.

CONTEXT: Assisted reproductive technologies (ART) and non-in vitro fertilization fertility treatments (NIFT) are treatments for infertility. These technologies may have long-term health effects in children such as increased hypertension, glucose intolerance, and hypertriglyceridemia. Few studies have compared children born following ART and NIFT to those conceived spontaneously by subfertile couples. OBJECTIVE: This work aimed to describe metabolic differences in children conceived by ART and NIFT compared to children conceived spontaneously by infertile couples. METHODS: Children conceived by parent(s) receiving infertility care at the University of California, San Francisco, between 2000 and 2017 were invited to participate in the Developmental Epidemiological Study of Children born through Reproductive Technology (DESCRT). Serum metabolomic analyses were conducted using samples from 143 enrolled children (age range 4-12 years, 43% female) conceived using NIFT or ART (with fresh or frozen embryos with and without intracytoplasmic sperm injection [ICSI]) and children conceived spontaneously by subfertile couples. Principal component analysis and multivariable regression were used to compare the distribution of metabolites between groups. RESULTS: There was no separation in metabolites based on treatment or sex. NIFT-conceived children showed no differences compared to spontaneously conceived controls. Only spontaneously conceived children had different metabolomics profiles from children conceived from fresh ART, frozen ART, and all ICSI. Pantoate and propionylglycine levels were elevated in fresh ART compared to the spontaneous group (P < .001). Propionylglycine levels were elevated in the ICSI (both fresh and frozen) vs the spontaneous group (P < .001). Finally, 5-oxoproline levels were decreased in frozen ART compared to the spontaneous group (P < .001). CONCLUSION: NIFT-conceived children did not show any metabolic differences compared with spontaneously conceived children. The metabolic differences between ART-conceived children and children conceived spontaneously were small but unlikely to be clinically significant but should be examined in future studies.

Radiation and CNS effects: summary of evidence from a recent symposium of the Radiation Research Society.

This article summarizes a Symposium on 'Radiation risks of the central nervous system' held virtually at the 67th Annual Meeting of the Radiation Research Society, 3-6 October 2021. Repeated low-dose radiation exposure over a certain period could lead to reduced neuronal proliferation, altered neurogenesis, neuroinflammation and various neurological complications, including psychological consequences, necessitating further research in these areas. Four speakers from radiation biology, genetics and epidemiology presented the latest data from their studies seeking insights into this important topic. This symposium highlighted new and important directions for further research on mental health disorders, neurodegenerative conditions and cognitive impairment. Future studies will examine risks of mental and behavioral disorders and neurodegenerative diseases following protracted radiation exposures to better understand risks of occupational exposures as well as provide insights into risks from exposures to galactic cosmic rays.