Biodemography of Alzheimer’s Disease: Puzzling Connections with Health and Longevity Related Traits Anatoliy I. Yashin, Fang Fang, Mikhail Kovtun, Deqing Wu, Igor Akushevich, Arseniy Yashkin, Konstantin Arbeev, Alexander Kulminski, Eric Stallard, Ilya Zhbannikov, Svetlana Ukraintseva Introduction Alzheimer’s disease (AD) is a progressive degeneration of the brain, inducing memory decline, learning impairment, language and behavioral disturbances, depressive symptoms, and personality changes, resulting progressively in a marked decline in all mental activities and eventually in death. AD is most prevalent neurological health disorder in developed part of the world today. Recent estimates rank AD as the third cause of death for people older than 75 years of age. Despite substantial efforts to understand its causes and biological mechanisms the etiology of AD remains largely unknown. There are no medications that can notably influence rate of AD progression when it started. The multiple pathways of disease development and progression might be responsible for this situation. This indicates an urgent need for studying hidden heterogeneity of this health disorder. The availability of genetic data collected for individuals participated in longitudinal studies of aging, health, and longevity opens an unique opportunity for addressing questions about genetic component of this heterogeneity using genome wide association studies of AD. Useful insights on variability of biological mechanisms of AD development and progression can also be obtained from findings obtained in experimental and molecular biological studies of this health disorder. The non-genetic part of heterogeneity in AD can be evaluated from data on aging, health and longevity related traits collected in longitudinal and cross-sectional studies, Medicare Service Use files and other datasets. An important feature of age-related health decline observed in longitudinal data is dependence among chronic conditions caused by common genetic and non-genetic factors and processes. Some dependences demonstrate “strange” properties. For example AD positively correlates with Type 2 Diabetes Mellitus (T2DM) and negatively correlates with cancer. In this paper we present the results of genome wide association studies (GWAS) of AD obtained in our analyses of data from three longitudinal (CHS, FHS, HRS) and one case-control (LOADFS) datasets. Then we discuss research findings and review evidence of connections of AD with cancer and T2DM. Data and Methods We use data from the Framingham Heart Study (FHS), Cardiovascular Health Study, Health and Retirement Study, and Late Onset Alzheimer Disease Family Study to evaluate genetic factors associated with AD using genome-wide association studies (GWAS). In our analyses we use logistic regression model. To take family links into account in the logistic regression model we used the Glimmix program. The year of birth was used as observed covariate in separate analyses of males and females. To control for possible population stratification we calculated 20 principal components and used them as observed covariates (Price et al., 2006). In the joint analyses of data on males and females we also use gender as observed covariate. To estimate the influence of