Recently suggested novel implementation of the statistical distance measure (DM) for evaluating "physiological dysregulation" (PD) in aging individuals (based on measuring deviations of multiple biomarkers from baseline/normal physiological states) allows reducing high-dimensional biomarker space into a single PD estimate. Here we constructed DM using biomarker profiles from FRAMCOHORT (Framingham Heart Study) and CHS (Cardiovascular Health Study) Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center, and estimated effect of PD on total survival, onset of unhealthy life (proxy for "robustness") and survival following the onset of unhealthy life (proxy for "resilience"). We investigated relationships between PD and declines in stress resistance and adaptive capacity not directly observed in data. PD was more strongly associated with the onset of unhealthy life than with survival after disease suggesting that declines in robustness and resilience with age may have overlapping as well as distinct mechanisms. We conclude that multiple deviations of physiological markers from their normal states (reflected in higher PD) may contribute to increased vulnerability to many diseases and precede their clinical manifestation. This supports potential use of PD in health care as a preclinical indicator of transition from healthy to unhealthy state.

Despite evident success in clarifying many important features of Alzheimer's disease (AD) the efficient methods of its prevention and treatment are not yet available. The reasons are likely to be the fact that AD is a multifactorial and heterogeneous health disorder with multiple alternative pathways of disease development and progression. The availability of genetic data on individuals participated in longitudinal studies of aging health and longevity, as well as on participants of cross-sectional case-control studies allow for investigating genetic and non-genetic connections with AD and to link the results of these analyses with research findings obtained in clinical, experimental, and molecular biological studies of this health disorder. The objective of this paper is to perform GWAS of AD in several study populations and investigate possible roles of detected genetic factors in developing AD hallmarks and in other health disorders. The data collected in the Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), Health and Retirement Study (HRS) and Late Onset Alzheimer's Disease Family Study (LOADFS) were used in these analyses. The logistic regression and Cox's regression were used as statistical models in GWAS. The results of analyses confirmed strong associations of genetic variants from well-known genes APOE, TOMM40, PVRL2 (NECTIN2), and APOC1 with AD. Possible roles of these genes in pathological mechanisms resulting in development of hallmarks of AD are described. Many genes whose connection with AD was detected in other studies showed nominally significant associations with this health disorder in our study. The evidence on genetic connections between AD and vulnerability to infection, as well as between AD and other health disorders, such as cancer and type 2 diabetes, were investigated. The progress in uncovering hidden heterogeneity in AD would be substantially facilitated if common mechanisms involved in development of AD, its hallmarks, and AD related chronic conditions were investigated in their mutual connection.

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.