Accelerating Alzheimer?s Disease Innovation and Discovery by Lowering Barriers to Framingham Cognitive Aging and Dementia Data The Framingham Heart Study (FHS) started in 1948 with the Original Generation 1 (Gen 1) cohort. This cohort has undergone 32 regular health examinations over 64 years in which many co-morbid features and risk factors (e.g., demographic, health, lifestyle, social networks, genetic/genomic, serum and plasma biomarkers) linked to future risk of dementia/Alzheimer?s disease (AD). Additionally, surveillance for incident dementia/AD has been underway since 1976. However, the complicated state of the data has resulted in a rich data set that uses a dissemination protocol that does not meet today?s principles for FAIR data (Findable, Accessible, Interoperable, Reusable). The proposed supplement seeks to modernize the data sharing process for the FHS brain health data to meet necessary standards for reuse by AMP-AD and other brain researchers. To meet this data sharing objective, we will prepare a master brain health dataset that includes all the coding programs and enhanced data dictionary necessary to process the raw data and convert into a more readily analyzable format. We will use the AMP-AD Knowledge Portal to broadly distribute the coding programs and documentation needed to understand and prepare FHS data for analysis, thereby eliminating redundant effort by researchers trying to use FHS data, particularly for the first time. We will also pursue alternative cloud- based strategies in consultation with FHS and the NHLBI.
The Framingham Heart Study (FHS) started in 1948, has collected many co-morbid features and risk factors (e.g., demographic, health, lifestyle, social networks, genetic/genomic, serum and plasma biomarkers) linked to future risk of dementia/Alzheimer?s disease (AD). However, the complicated state of the data has resulted in a rich data set that uses a dissemination protocol that does not meet today?s open principles. To aid AD researchers and specifically enrich the resources available through the AMP- AD portal to AD researchers, we will make a master health dataset that includes all the coding programs and enhanced data dictionary necessary to process the raw data and convert into a more readily analyzable format.
