Project Number: 8050-51530-015-01-A
Project Type: Cooperative Agreement
Start Date: Aug 1, 2019
End Date: Jul 31, 2024
Objective 1: Continue to study the oldest participants in the existing Geisinger Rural Aging Study cohort, in relation to longitudinal health outcomes and mortality. Perform sub-analyses on all of those who survive to 100 years of age or older, whether currently surviving or not, with a focus on nutrition risk, quality of life and health outcomes as predictors of living to become a centenarian. Objective 2: Conduct secondary analyses of the entire existing Geisinger Rural Aging Study dataset to relate nutrition risk and other lifestyle variables at baseline enrollment with additional health outcomes not previously explored in our investigations (e.g., dementia).
The Geisinger Rural Aging Study (GRAS) was initiated between 1994-99 as a longitudinal study of health outcomes in relation to nutritional status among 21,645 individuals at least 65-years of age. Participants have been rescreened at 3-4-year intervals with questionnaires that encompass multiple domains of nutrition risk. Our investigations have found high prevalence of poor quality diets, obesity, and ill health. Low diet quality, as revealed by our Diet Quality Screening Questionnaire (DQSQ), is associated with low body mass index (BMI) and increased mortality risk. The GRAS dataset currently spans more than two decades and we now have the remarkable opportunity to extend our investigation to the oldest participants (at least 85 years). Out of the original GRAS cohort we have identified 4,245 participants that remain active in the Geisinger Healthcare System. We already have 130 confirmed centenarians (living or expired), and we project that an additional 70-170 will become available for investigation over the coming 5-years. We will continue to study the oldest participants in relation to health outcomes and mortality. By performing sub-analyses on those eligible to have survived to 100 years, we will discern whether nutrition risk, quality of life and health outcomes are predictors of living to become a centenarian. We will also conduct secondary analyses of the GRAS dataset to relate nutrition risk and other lifestyle variables with additional health outcomes like dementia. By studying the oldest of the old we can identify diet and lifestyle factors that promote healthy aging.