Location: Jean Mayer Human Nutrition Research Center On Aging
2021 Annual Report
Objectives
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.
Progress Report
Mortality: Most recent data confirmed 16,145 deaths out of the 21,645 (74%) original Geisinger Rural Aging Study (GRAS) participants, which has increased from 71% from our last reporting. This corresponds to an additional 741 participants with a deceased status.
Centenarians: There are currently 157 GRAS participants confirmed as centenarians and another 17 who are pending confirmation for a likely total of 174 centenarians (out of an estimated 200-300 centenarians as the project goal). All of these data have been extracted from the Electronic Health Records. There are approximately 500 others in the GRAS database that would be aged >100, but no information is currently available on their survival status. A small percentage of these would likely be centenarians. These data could be confirmed with our pending application to access the National Death Index data.
Dementia: Of 13,889 with eligible follow-up in the electronic health record (median follow-up 10.6 years), 2647 with diagnosed dementia. The overall 10-year incidence of new dementia was 14.0%, and it increased with baseline age (see table). Analysis has begun exploring potential associations between diet quality and dementia using DQSQ data collected in wave 4 (n=4009 individuals).
N total 1-year 3-year 5-year 10-year 15-year
Age <70 2583 0.2% 0.9% 1.7% 5.5% 12.1%
Age 70-79 8245 1.4% 3.5% 5.5% 13.0% 24.5%
Age 80-89 2814 4.8% 10.0% 14.6% 27.8% 43.3%
Age 90+ 247 9.5% 17.3% 22.0% 28.6% -
The accuracy of the electronic algorithm for identifying dementia was tested using chart review. The chart review process was informed by consultation with a Geisinger neurologist that has insight into how to identify dementia patients within the Geisinger electronic health record system (e.g., look for dementia medication use, with a diagnosis of memory loss, review clinic notes for results of mini-mental exam, and search for keywords within the electronic health record system). The chart review included a sample of N=26 Geisinger Rural Aging Study participants, including a random selection of n=10 without any dementia diagnosis codes, a random selection of n=6 with only a single dementia diagnosis code, and a random selection of n=10 with 2+ dementia diagnosis codes. Each of the selected patients was reviewed independently by two reviewers. These results were compared and were found to have a good agreement (22/26=85%, k=0.67). The four disagreements were re-reviewed, and a consensus was agreed upon. When assuming a dementia prevalence of 30%, the final review decision resulted in an overall accuracy (i.e. for the electronic algorithm to identify dementia) of 86% and resulted in the following other diagnostic test measures: Sensitivity = 83%, Specificity = 88%, PPV = 74%, NPV = 92%. The investigators agreed that these results were sufficient for implementation across the entire cohort.
Accomplishments
1. Among older adults, a higher quality diet may lower risk of developing Parkinson’s disease. Previous research has shown certain dietary behaviors such as the intake of a high saturated fat, low intakes of some vitamins and antioxidants or a low fiber diet may be related to higher risk of developing Parkinson’s disease; however, few studies have examined overall diet quality in older adults in relation to Parkinson’s disease. Using a validated diet quality screening instrument, we assessed the diets of 3,653 Geisinger Rural Aging Study adults over the age of 80 years with an average 6.94 years of follow up using electronic health records to identify those with a Parkinson’s disease diagnosis. We also included 4 other studies with diet quality and Parkinson’s disease status in a separate meta-analysis. In both analyses, higher diet quality was shown to lower the risk of developing Parkinson’s disease. These data suggest a healthy diet could prevent or delay the onset of Parkinson’s disease. This research also serves as a model for other research to study the relationship between diet and other neurodegenerative diseases such as dementia in the Geisinger Rural Aging Study cohort and others.
Review Publications
Liu, Y., Jensen, G., Muzi, N., Mitchell, D., Wood, C., Still, C., Gao, X. 2021. Diet quality and risk of Parkinson’s Disease: A prospective study and meta-analysis. Journal of Parkison's Disease. 11:337-347. https://doi.org/10.3233/JPD-202290.
Davis, B., Liu, Y., Stampley, J., Wood, G., Mitchell, D., Jensen, G., Gao, X., Glynn, N., Still, C., Irving, B. 2021. The association between poor diet quality, physical fatigability and physical function in the oldest-old from the Geisinger Rural Aging Study. Geriatrics. 6:41. https://doi.org/10.3390/geriatrics6020041.
