- Since 1948, the Framingham Heart Study (FHS), under the direction of the National Heart, Lung, and Blood Institute (NHLBI), formerly known as the National Heart Institute, has been committed to identifying the common factors or characteristics that contribute to cardiovascular disease (CVD) over a long period of time in three generations of participants. Since 1971, the Boston University Aram V. Chobanian & Edward Avedisian School of Medicine (BUMC) has served as an NHLBI contractor and academic partner for the study. In 2020, the BUMC received an infrastructure grant from the National Institute on Aging (NIA) to establish the FHS Brain Aging Program (FHS-BAP). Since its inception, the FHS-BAP Data Core has played a crucial role in strengthening the data infrastructure necessary for studying brain aging, cognitive decline, Alzheimer’s disease (AD), and all causes of dementia.
- Our strategy for managing and sharing FHS data for AD research covers a wide range of information, including legacy data like demographics and risk factors, lab biomarkers, NP assessments, and Brain MRI/PET data. We also incorporate new clinical data and digital markers. Upholding FAIR principles, we ensure data integrity and accessibility. Through collaboration with FHS-BAP internal cores, contract staff, and external teams, we facilitate the collection, auditing, and sharing of curated datasets for analysis.
FHS-BAP Data
FHS data ranges from simple demographic and self-reporting data points to more complex multi-omic and digital data. In addition to these structured data, FHS has also been gathering unstructured data, especially in recent years.
Structured data
Phenotypic Data
The structured phenotypic data are commonly used data such as demographic, self-reported responses to questionnaires, clinical outcomes, lab test results, etc.
Genetic Data
DNA has been collected from blood and immortalized cell lines from Original Cohort, Offspring Cohort and the Third Generation Cohort (over 9,300 participants).
Multi-omic
Many of the multi-omic datasets comes under the SABRe projects – to identify the biomarker signatures of metabolic risk factors.
Digital
Unlike their unstructured counterparts, these digital data points are derived based on preset algorithms.
Unstructured data
Voice Data
Voice recording of neuropsychological assessment began in 2005, including raw voice recording, censored voice recording, and transcriptions.
Image Data
Image Data including Raw MRI brain scans (format DICOM) since 2002, and limited sample of the PET/Tau Scans.
Digital Pen
Including real-time pen motion recording during the digital clock drawing test, and other neuropsychological tests.