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A mechanistic model for atherosclerosis and its application to the cohort of Mayak workers
Cristoforo Simonetto1, Tamara V Azizova2, Zarko Barjaktarovic1
1Helmholtz Zentrum München, Department of Radiation Sciences, Neuherberg, Germany.
Insights
We developed a new stochastic model to understand atherosclerosis development and its link to stroke risk. This model provides more reliable risk estimates by integrating biological mechanisms into epidemiological analysis.
Area of Science:
- Epidemiology
- Biostatistics
- Cardiovascular Disease Research
Background:
- Atherosclerosis is a complex disease influenced by age and various risk factors.
- Understanding the pathogenic processes of atherosclerosis is crucial for accurate risk assessment.
- Existing epidemiological models may not fully capture the biological mechanisms of disease progression.
Purpose of the Study:
- To propose a novel stochastic model for age-dependent atherosclerosis development.
- To assess the model's ability to describe epidemiological data on atherosclerotic lesions and stroke incidence.
- To evaluate the impact of risk factors like smoking, hypertension, and radiation on atherosclerosis progression.
Main Methods:
- Development of a stochastic model incorporating monocyte uptake, proliferation, and foam cell transition.
- Simulation studies to validate the model against age-dependent lesion prevalence.
- Application of the model to incidence data from a cohort of male workers.
- Goodness-of-fit analysis to identify the influence of risk factors.
Main Results:
- The proposed model adequately describes age-dependent atherosclerotic lesion prevalence and stroke incidence.
- Hypertension was found to significantly impact late-stage atherosclerosis progression.
- The model demonstrated comparable or superior performance to standard epidemiological models.
Conclusions:
- Mechanistic models offer a more scientifically grounded approach to epidemiological studies than purely statistical methods.
- Integrating biological evidence enhances the reliability of risk estimates for cardiovascular diseases.
- This study pioneers the application of mechanistic stochastic models to cardiovascular disease epidemiology.
Abstract:
We propose a stochastic model for use in epidemiological analysis, describing the age-dependent development of atherosclerosis with adequate simplification. The model features the uptake of monocytes into the arterial wall, their proliferation and transition into foam cells. The number of foam cells is assumed to determine the health risk for clinically relevant events such as stroke. In a simulation study, the model was checked against the age-dependent prevalence of atherosclerotic lesions. Next, the model was applied to incidence of atherosclerotic stroke in the cohort of male workers from the Mayak nuclear facility in the Southern Urals. It describes the data as well as standard epidemiological models. Based on goodness-of-fit criteria the risk factors smoking, hypertension and radiation exposure were tested for their effect on disease development. Hypertension was identified to affect disease progression mainly in the late stage of atherosclerosis. Fitting mechanistic models to incidence data allows to integrate biological evidence on disease progression into epidemiological studies. The mechanistic approach adds to an understanding of pathogenic processes, whereas standard epidemiological methods mainly explore the statistical association between risk factors and disease outcome. Due to a more comprehensive scientific foundation, risk estimates from mechanistic models can be deemed more reliable. To the best of our knowledge, such models are applied to epidemiological data on cardiovascular diseases for the first time.