A Bayesian network model for predicting cardiovascular risk.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascular diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations. METHODS: We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions. RESULTS: The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision- support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners' use. CONCLUSIONS: Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.

Changes in the brain and plasma Aß peptide levels with age and its relationship with cognitive impairment in the APPswe/PS1dE9 mouse model of Alzheimer's disease.

Double transgenic mice expressing mutant amyloid precursor protein (APPswe) and mutant presenilin 1 (PS1dE9) are a model of Alzheimer-type amyloidosis and are widely used in experimental studies. In the present work, the relationships between brain and plasma amyloid-ß peptide (Aß) levels and cognitive impairments were examined in male APPswe/PS1dE9 double transgenic mice at different ages. When compared with non-transgenic littermates, APPswe/PS1dE9 mice exhibited significant learning deficits from the age of 6months (M6), which were aggravated at later stages of life (M8 and M12). Sporadic brain amyloid plaques were observed in mice as early as M3 and progressively increased in number and size up to M12. A similar increase was observed in brain insoluble Aß levels as assessed by enzyme-linked immunosorbent assay (ELISA). In particular, the levels of brain insoluble Aß peptides rose steeply from M4 to M6. Interestingly, this pronounced amyloid deposition was accompanied by a temporary fall in the concentration of brain soluble and membrane-bound Aß peptides at M6 that rose again at M8 and M12. The plasma levels of Aß40 and Aß42 decreased with advancing age up to M8, when they stabilized at M12. This decrease in plasma Aß levels coincided with the observed increase in insoluble brain Aß levels. These results could be useful for developing plasma Aß levels as possible biomarkers of the cerebral amyloidosis and provide advances in the knowledge of the Aß peptide biochemical changes that occur in the brain of Alzheimer's disease patients.