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A mechanistic model for atherosclerosis and its application to the cohort of Mayak workers

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Illustrations of the proposed model.For an explanation of the symbols see the main text. A: Pictorial representation of the first steps. B: Schematic depiction of the full model.
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pone.0175386.g001: Illustrations of the proposed model.For an explanation of the symbols see the main text. A: Pictorial representation of the first steps. B: Schematic depiction of the full model.

Mentions: Illustrations of the model can be found in Fig 1. The states and transitions are defined as:


A mechanistic model for atherosclerosis and its application to the cohort of Mayak workers
Illustrations of the proposed model.For an explanation of the symbols see the main text. A: Pictorial representation of the first steps. B: Schematic depiction of the full model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383300&req=5

pone.0175386.g001: Illustrations of the proposed model.For an explanation of the symbols see the main text. A: Pictorial representation of the first steps. B: Schematic depiction of the full model.
Mentions: Illustrations of the model can be found in Fig 1. The states and transitions are defined as:

View Article: PubMed Central - PubMed

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.

No MeSH data available.