Dose-responses for mortality from cerebrovascular and heart diseases in atomic bomb survivors: 1950-2003.
Helmut SchöllnbergerMarkus EidemüllerHarry M CullingsCristoforo SimonettoFrauke NeffJan Christian KaiserPublished in: Radiation and environmental biophysics (2017)
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.
Keyphrases
- heart failure
- healthcare
- atrial fibrillation
- cardiovascular disease
- public health
- mental health
- electronic health record
- type diabetes
- early stage
- high resolution
- radiation therapy
- risk assessment
- risk factors
- coronary artery disease
- cardiovascular events
- big data
- climate change
- single cell
- deep learning
- cardiovascular risk factors
- mass spectrometry