Cardiovascular disease mortality of A-bomb survivors and the healthy survivor selection effect

Greater Odds for Angina in Uranium Miners Than Nonuranium Miners in New Mexico

OBJECTIVE

The aim of this study was to test the hypothesis that uranium miners in New Mexico (NM) have a greater prevalence of cardiovascular disease than miners who extracted the nonuranium ore.

METHODS

NM-based current and former uranium miners were compared with nonuranium miners by using cross-sectional standardized questionnaire data from the Mining Dust in the United States (MiDUS) study from 1989 to 2016.

RESULTS

Of the 7215 eligible miners, most were men (96.3%). Uranium miners (n = 3151, 43.7%) were older and diabetic, but less likely to currently smoke or use snuff (P ≤ 0.001 for all). After adjustment for covariates, uranium miners were more likely to report angina (odds ratio 1.51, 95% confidence interval 1.23 to 1.85) than nonuranium miners.

CONCLUSION

Our data suggest that along with screening for pulmonary diseases, uranium industry workers should be screened for cardiovascular diseases.

Dose-responses for mortality from cerebrovascular and heart diseases in atomic bomb survivors: 1950-2003

The scientific community faces important discussions on the validity of the linear no-threshold (LNT) model for radiation-associated cardiovascular diseases at low and moderate doses. In the present study, mortalities from cerebrovascular diseases (CeVD) and heart diseases from the latest data on atomic bomb survivors were analyzed. The analysis was performed with several radio-biologically motivated linear and nonlinear dose-response models. For each detrimental health outcome one set of models was identified that all fitted the data about equally well. This set was used for multi-model inference (MMI), a statistical method of superposing different models to allow risk estimates to be based on several plausible dose-response models rather than just relying on a single model of choice. MMI provides a more accurate determination of the dose response and a more comprehensive characterization of uncertainties. It was found that for CeVD, the dose-response curve from MMI is located below the linear no-threshold model at low and medium doses (0-1.4 Gy). At higher doses MMI predicts a higher risk compared to the LNT model. A sublinear dose-response was also found for heart diseases (0-3 Gy). The analyses provide no conclusive answer to the question whether there is a radiation risk below 0.75 Gy for CeVD and 2.6 Gy for heart diseases. MMI suggests that the dose-response curves for CeVD and heart diseases in the Lifespan Study are sublinear at low and moderate doses. This has relevance for radiotherapy treatment planning and for international radiation protection practices in general.

Cardiovascular diseases related to ionizing radiation: The risk of low-dose exposure (Review)

Traditionally, non-cancer diseases are not considered as health risks following exposure to low doses of ionizing radiation. Indeed, non-cancer diseases are classified as deterministic tissue reactions, which are characterized by a threshold dose. It is judged that below an absorbed dose of 100 mGy, no clinically relevant tissue damage occurs, forming the basis for the current radiation protection system concerning non-cancer effects. Recent epidemiological findings point, however, to an excess risk of non-cancer diseases following exposure to lower doses of ionizing radiation than was previously thought. The evidence is the most sound for cardiovascular disease (CVD) and cataract. Due to limited statistical power, the dose-risk relationship is undetermined below 0.5 Gy; however, if this relationship proves to be without a threshold, it may have considerable impact on current low-dose health risk estimates. In this review, we describe the CVD risk related to low doses of ionizing radiation, the clinical manifestation and the pathology of radiation-induced CVD, as well as the importance of the endothelium models in CVD research as a way forward to complement the epidemiological data with the underlying biological and molecular mechanisms.