Location: Jean Mayer Human Nutrition Research Center On Aging
Project Number: 8050-51530-015-000-D
Project Type: In-House Appropriated
Start Date: May 1, 2019
End Date: Apr 17, 2024
Objective:
Objective 1: Continue to study the oldest old that survive 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. [NP107, C5, PS5A]
Sub-objective 1.A: Continue to study the oldest old that survive in the existing Geisinger Rural Aging Study cohort in relation to longitudinal health outcomes and mortality.
Sub-objective 1.B: Perform sub-analyses on all of those eligible to 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). [NP107, C5, PS5A]
Sub-objective 2.A: Conduct secondary analyses of the entire existing Geisinger Rural Aging Study dataset to relate nutrition risk and other lifestyle variables at baseline enrollment to dementia outcomes.
Sub-objective 2.B: Conduct secondary analyses of the entire existing Geisinger Rural Aging Study dataset to relate nutrition risk and other lifestyle variables at baseline enrollment to additional health outcomes not previously explored.
Approach:
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 =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. We continue to validate our diet quality screening questionnaire (DQSQ) in relation to health outcomes and mortality. We have found that DQSQ scores are significantly correlated with Healthy Eating Index (HEI) scores, which were based on 24-hour dietary recall. Those that were considered not at nutritional risk as determined by the DQSQ score had significantly higher HEI scores compared to those who were in the at-risk or possibly at risk groups. These results suggest that the DQSQ is a valid measure of diet quality even in the oldest old. Recent analysis has also revealed that higher diet quality is associated with lower mortality. Participants with the lowest diet quality scores had significantly increased risk of mortality compared to those with the highest scores. These findings suggest that diet quality may play an integral role in healthy aging with potential impact on dietary guidance for older adults. Further research will afford a unique opportunity to better characterize the impact of diet quality in a cohort of the oldest old which include a growing number of centenarians. Out of the original Geisinger Rural Aging Study (GRAS) cohort of 21,645 community-dwelling Pennsylvanians aged =65-years who completed a baseline nutrition risk screening at over 100 Geisinger clinic sites between 1994-1999, we have identified 4,245 participants that remain active in the Geisinger Healthcare System. Our analysis indicates that 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 old in relation to health outcomes and mortality. By performing sub-analyses on those eligible to have survived =100 years, we will discern whether nutrition risk, quality of life and health outcomes are predictors of living to become a centenarian. In particular, our robust longitudinal dataset now spans more than two decades and will provide unparalleled opportunities for secondary analyses that explore nutrition risk in relation to additional health outcomes like dementia. By studying the oldest old we can identify potentially modifiable diet and lifestyle factors to promote healthy aging.
