Responsável: Luciana Costa, Mafalda Bourbon e Astrid Vicent
Financiamento e data: Fundação Astra-Zeneca (2007-2010)
Introdution: Alzheimer’s disease (AD) has the highest prevalence among neurodegenerative diseases, which increases exponentially in the population from age 60 years onwards. The distinction between normal aging and AD is one first step to combat this disease efficiently. Because of the clinical interest in predicting patient evolution and prognosis, the identification of biomarkers and the unravelling of genetic factors underlying AD is of crucial importance. Several lines of evidence point to a contribution of defects in cholesterol homeostasis to the pathogenesis of AD, including the increased levels of total and LDL cholesterol found in patients, the known regulation of Amyloid-β protein (Aβ) accumulation by cholesterol and the association of the risk APOE4 genotype. A close relation between lipid metabolism, oxidative injury and AD pathology has also been widely cited, with documented involvement of abnormalities of iron (Fe) and copper (Cu)metabolism. Abnormalities in these various cell mechanisms and pathways can be monitored in peripheral tissues by measuring key molecules involved in cholesterol homeostasis and Fe/Cu metabolism.
Objectives: One main objective of this project is to identify biomarkers in peripheral blood involved in these mechanisms that correlate with disease (AD), and thus can be used as predictors of disease outcome. For this purpose, various key metabolites, such as the various forms of cholesterol and associated markers, as well as markers of iron and copper metabolism will be assessed and correlated with disease outcome in a sample of 100 AD patients and 100 elderly controls with no cognitive decline. A second goal of this study is the identification of genetic risk factors underlying AD. This is a complex disorder with a strong genetic component. However, the genetic information available does not explain in full the etiopathogenesis of AD (e.g. roughly 70% of the genetic variance of AD remains unexplained), and understanding of the genetic basis of these diseases allowing the development of effective treatment has not yet emerged.
Materials and methods: In our study, genetic analysis will particularly focus on candidate genes involved in lipid homeostasis and Fe/Cu metabolism, which are hypothesized to contribute to the pathogenesis of AD, and will involve high density SNP genotyping of candidate genes and haplotype risk analysis. Emphasis will be placed on testing functional polymorphisms in the regulatory and coding regions of the tested genes. Common statistical methods will be employed to detect association of tested polymorphic marker alleles/haplotypes. Multiparametric analysis will be carried out to evaluate the association between genotype at various specific loci and the specified traits, taking in consideration multiple risk factors such as age, gender and lifestyle. The Multifactor- Dimensionality Reduction (MDR) and Restricted Partition Method (RPM) will be used to model gene interactions at different loci in these complex qualitative and quantitative phenotypes. A recently developed method, LATAG, will be applied to our data to identify specific genetic variants associated to AD. This approach unifies two main goals of gene mapping, detecting association and estimating the location of the causative variation.
Expected results: It is expected that this study may bring new insights on genetic risk factors and biochemical markers/endophenotypes associated to AD, as well as add new information to what is already known of the susceptibility factors related to this disease.