Biography
Biography: Giulio Maria Pasinetti
Abstract
The convergence of several unique features of Alzheimer’s disease (AD) [e.g., heterogeneity, complex polygenic etiology, and prolonged asymptomatic pre-clinical phase of neurodegeneration] indicates the need for very large cohorts of well-characterized populations from diverse genetic/cultural backgrounds as potential volunteers for both: 1) longitudinal epidemiological studies to discover and/or validate putative risk factors, and 2) clinical studies for prospective validation of potential preventive interventions. In addition, many epidemiological studies indicate that people with diabetes are at higher risk of eventually developing AD or other dementias. A massive longitudinal database involving at-risk populations is an essential infrastructure needed in order to address the future needs of a major prevention initiative. Along with ‘Big-Data’, the field of therapy development will require novel computational capabilities to not only sort out the complex interactions between type 2 diabetes(T2D) and cognitive deterioration in AD, but also to discover-validate technologies for the early and accurate detection of the disease.We used data from public genome-wide association studies (GWAS) to explore the associationsingle-nucleotide polymorphisms (SNPs) between T2D and AD. Next, we explored the function of the T2D-AD shared GWAS SNPs by integrating pathway data withgene ontology data, expressional quantitative trait loci (eQTL), and co-expression networks.We found a significant overlap (p=4.9E-19) between association SNPs from large scale GWAS of T2D and AD.927 SNPs were associated with both ADand T2D with p≤0.01, and we found that these SNPs influence 190 genes in brain tissue and 416 genes in T2D-relevant peripheral tissues (liver and adipose). Interestingly, we found that certain mitochondria pathways and innate immune response pathways are particularly enriched in the AD and T2D GWAS. Collectively, by leveraging GWAS, eQTLs, gene co-expression networks, etc., we found that T2D and AD share common genetic risk factors, which may partially explain the epidemiological observation of the disease incidence correlation. A massive longitudinal database on health aging and pre-dementia or at-risk populations, such as T2D subjects, is essential to address the future needs of a major prevention initiative in AD.