2011 Annual Report
1a.Objectives (from AD-416)
1. Use population-based approaches to explore the role of dietary patterns and diet quality in relation to weight gain and obesity among rural older-aged adults.
2. Use population-based approaches to explore the role of dietary patterns and diet quality in relation to obesity-related chronic diseases, such as cardiovascular disease, metabolic syndrome, and diabetes, among rural older-aged adults.
3. Develop and validate GRAS dietary instruments with a population of rural adults aged 80 years of age or greater.
1b.Approach (from AD-416)
The Geisinger Rural Aging Study (GRAS) is a longitudinal study of health outcomes in relation to nutritional status among 21,645 community-dwelling Pennsylvanians aged greater than or equal to 65 years. Initial screenings for GRAS participants took place between 1994-1999. The baseline nutrition risk screening data includes height, weight, reported weight gain/loss, depression, polypharmacy, eating habits, food security, oral health, and functional status. Follow up rescreening at 3-4 year intervals has encompassed this information as well as additional queries of medical history, weight history, family history, dietary practices, medical co-morbidities, medication use, general health, depression, mobility/functional status, and healthcare resource use. Our prior investigations have highlighted the growing prevalence of obesity and ill health among these individuals. Findings suggest that many of these obese older persons consume poor quality diets and have associated micronutrient deficiencies. Dietary patterns/quality and their relationships with obesity and chronic diseases of older persons are poorly characterized; especially among “old older” persons 80 years of age or greater. Current investigations have supported the development of a Population Specific Food Frequency Questionnaire (PSFFQ) and a 37 item Diet Quality Screening Questionnaire (DQSQ). The DQSQ is an innovative instrument for assessment of diet quality that will be made widely available to investigators and practitioners. Our proposed next steps to further characterize the GRAS cohort include longitudinal follow up of obesity-related and other health outcomes in relation to dietary patterns and diet quality among 459 subjects for whom we have conducted comprehensive assessments 5-10 years previously. We will confirm and/or refine the results of our previous dietary patterns analyses with these data then determine associations among dietary intake, dietary patterns, diet quality lifestyle factors and anthropometric (height, weight, body-mass index) and available laboratory measures. We are particularly interested in evaluating associations with weight gain and obesity in older-aged adults. These analyses will be facilitated by use of the Geisinger Health Plan EPIC electronic database. Subsequent analyses will focus on the relationship between the aforementioned dietary variables and obesity-related chronic diseases, such as cardiovascular disease, metabolic syndrome, and diabetes. We will also administer the new DQSQ to the next wave of GRAS screening participants via mailing with telephone follow-up as needed. Based upon our rescreening experience we estimate that over the next two years we will have access to approximately 2,000 respondents (57% female; 53% 80 years of age or older). We will then monitor their health-related outcomes prospectively in relation to diet quality. Anticipated products of this research include better understanding of obesity-related and other health outcomes in relation to dietary patterns/quality among rural older persons and further validation of the DQSQ in relation to health outcomes among “old older” persons.
Progress made during FY 2011 has substantially addressed the milestones required to support the approved project plan objectives. Longitudinal databases for objectives 1 and 2 have been created, cleaned, and extensively tested. Data management tasks for ICD-9 CM codes have been successfully used to identify obesity-related chronic diseases and disease burden. Analyses are proceeding as planned. Body weight data for the study population was systematically evaluated. Several methods of identifying dietary patterns are currently being evaluated. Preliminary analyses using one methodology identified three dietary patterns which we labeled:.
3)Healthy. The Western dietary pattern was characterized by highest consumption of starchy vegetables, refined grains, red and processed meat, sweets and fats, whereas the Healthy pattern was characterized by high intakes of fruit, vegetables, whole grains, eggs, nuts and legumes. Individuals in the Small eaters’ dietary pattern were defined by generally low intakes. We are currently exploring other methods for identifying patterns. Once the patterns are finalized we will move on to evaluating the associations between the dietary patterns and obesity and obesity-related chronic disease outcomes. For objective 3 the DQSQs have been administered to the remainder of the screening cohort (n=3305 participants 74 years and older returned complete questionnaires to date; we expect a final number of approximately 4,000). Approximately fifty-three percent of these respondents are 80 years and older. The DQSQ data have been cleaned and scoring is in progress and nearly complete. The database will be linked to the EPIC database variables for the analyses required for objective 3. The progress made in addressing the objectives of the Geisinger Rural Aging Study directly relates to Component 3A of the National Program Action Plan to better understand the causes and consequences of obesity and related disorders in older persons.
Jensen, G.L., Hsiao, P. 2010. Obesity in older adults: relationship to functional limitation. Current Opinion in Clinical Nutrition and Metabolic Care. 13:46-51.
Johnson, M., Dwyer, J.T., Jensen, G.L., Miller, J.W., Speakman, J.R., Starke-Reed, P., Volpi, E. 2011. Challenges and new opportunities for clinical nutrition interventions in the aged. Journal of Nutrition. 141:535-541.
Miller, P.E., Mitchell, D.C., Harala, P.L., Pettit, J.M., Smiciklas-Wright, H., Hartman, T.J. 2010. Development and evaluation of a method for calculating the Healthy Eating Index-2005 using the Nutrition Data System for Research. Public Health Nutrition. 25:1-8.
Hsiao, P., Jensen, G.L., Hartman, T.J., Mitchell, D.C., Nickols-Richardson, S.M., Coffman, D.L. 2011. Food intake patterns and body mass index in older adults: a review of the epidemiological evidence. Journal of Nutrition in Gerontology and Geriatrics. 30(3):204-224.