- Email: [email protected]
Psychological Determinants of Food Intake in Adults with Mood Disorders
SUNDAY, OCTOBER 7
Research & Practice Innovations: Nutrition Research, Biosciences, and Bioinformatics/Nutritional Informatics Association between Dietary Patterns in Older Adults and Obesity-Related Health Outcomes Author(s): P. Y. Hsiao,1 D. C. Mitchell,1 G. C. Wood,2 D. L. Coffman,3 D. Wheeler,1 T. J. Hartman,1 C. Still,2 G. L. Jensen1; 1Nutritional Sciences, The Pennsylvania State Univ., University Park, PA, 2Geisinger Obesity Inst., Danville, PA, 3The Methodology Center, The Pennsylvania State Univ., University Park, PA Learning Outcome: To be able to describe the 3 dietary patterns identified in a sample of older adults living in rural Pennsylvania and their association with obesity-related health outcomes. The prevalence of obesity-related health outcomes is increasing among older adults. Because it is thought that nutrition plays an important role in successful aging, there has been considerable interest in the association between dietary patterns of older adults and obesity-related health outcomes. Cluster analysis, utilizing data from four 24-hour dietary recalls, was used to derive dietary patterns in a subgroup of 260 participants from the Geisinger Rural Aging Study (mean age: 78.6 ⫾ 3.9 years). Prevalence (5-year follow-up) of cardiovascular disease, diabetes mellitus, hypertension, and metabolic syndrome was extracted from the outpatient electronic medical records using a validated data abstraction process. Logistic regression, adjusting for relevant covariates, was used to examine the associations between dietary patterns and health outcomes. ‘Sweets and Dairy’ (49%), ‘Health-Conscious’ (27%) and ‘Western’ (24%) dietary patterns were identified at baseline. Prevalence of hypertension in the ’Sweets and Dairy’, ‘Health-conscious’, and ‘Western’ pattern was 81.3%, 65.7%, and 79.0%, respectively. Compared to the ‘Health-Conscious’ pattern, those in the ‘Sweets and Dairy’ pattern had increased odds of hypertension; adjusted odds ratio (95% CI) was 2.17 (1.11-4.27). No significant associations were found for cardiovascular disease, diabetes mellitus, or metabolic syndrome with dietary patterns. Although there was a statistically significant relationship between dietary pattern and prevalence of hypertension, the practical clinical utility remains unclear. The null findings for a 5-year follow-up of any elderly sample for cardiovascular disease, diabetes mellitus and metabolic syndrome do not address the potential benefits of adopting a healthy dietary pattern at a younger age. Funding Disclosure: USDA 1950-51530-010-02G.
Statistical Program to Automate the Creation of Healthy Eating Index Scores Using Nutrition Data System for Research Output Author(s): D. C. Landy, J. M. Kurtz, T. L. Miller, D. A. Ludwig; Division of Pediatric Clinical Research, Univ. of Miami Miller Sch. of Med., Miami, FL Learning Outcome: Participants will be able to calculate Healthy Eating Index (HEI) scores and describe challenges associated with using output from NDSR. Participants will also be able to use an automated program to calculate HEI scores form unedited NDSR output. The application of dietary quality measures, such as the Healthy Eating Index (HEI), are often limited by the type and format of output from dietary analysis software, such as the Nutrition Data System for Research (NDSR, University of Minnesota), and by the complexities inherent in their calculation. With respect to calculating HEI scores using NDSR output, the necessary dietary variables are located in separate files, measured at different levels, and require further processing (example, separating fat amounts from individual foods into solid fats and oils). The HEI scoring algorithm is also somewhat tedious and complicated, requiring the calculation of ratios followed by segmented linear interpolation, and/or algebraic solutions. Further, when individuals have records for multiple days, HEI scores can be calculated using different methods (mean of day-specific scores or score of the mean day). We have created a SAS (SAS Institute, NC) program that automates this process, allowing users to easily calculate HEI scores with unedited NDSR output by simply specifying the output location and desired calculation method. For studies containing multiple days per individual, this program provides a comparison of the different calculation methods and estimates the reliability of the total HEI score as well as a prediction of how this reliability would change if the number of days per individual was varied. Although programs exist that automate some of these calculations, they require extensive front-end preprocessing of NDSR output, making them impractical to use with NDSR-conducted research, and provide no additional statistical insights to guide future dietary assessments. Funding Disclosure: American Heart Association.
Psychological Determinants of Food Intake in Adults with Mood Disorders Author(s): K. M. Davison,1 B. J. Kaplan2; 1School of Nursing, Univ. of British Columbia, Vancouver, Canada, 2Department of Community Health, Univ. of Calgary, Calgary, Canada Learning Outcome: After listening or reading the abstract presentation, the participant will identify and describe eating behavior and psychiatric medication factors that can influence food intake in adults with mood disorders. Little is known about why food intakes of individuals with mood disorders differ from those of population norms; though it is speculated that psychiatric factors contribute to these differences. Using data from a cross-sectional nutrition study of 97 adults with verified mood disorders, we examined the relationships among diet quality (i.e., Canadian Healthy Eating Index (HEI) scores), measures of dietary restraint and disinhibition derived from Stunkard and Messick’s Three-Factor Eating Questionnaire (TFEQ), overall psychological functioning as measured by the Global Assessment of Functioning Scale (GAF), depression and mania symptoms (Hamilton Depression Scale; Young Mania Rating Scale), psychiatric medication use (i.e., antidepressants, mood stabilizers, and antipsychotics), body mass index and sociodemographic variables (i.e., age, gender, relationship status, education, and income). Results of multiple regression analyses indicated that disinhibition (coefficient ⫽ 2.68, SE ⫽ 1.20, t ⫽ 2.23, p ⬍ 0.05, 95% CI 0.29 to 5.06) was positively associated with diet quality; those taking antidepressants (coefficient ⫽ -6.25, SE ⫽ 2.85, t ⫽ -2.19, p ⬍ 0.05, 95% CI -11.93 to -0.57) had lower diet quality scores, Particular attention to eating attitude and behaviors of persons with mood disorders, especially those taking antidepressant medications, might help improve the quality of their dietary intakes. Further research in larger samples with general sample comparison groups is needed. Funding Disclosure: Danone Research Institute.
A-14
JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
September 2012 Suppl 3—Abstracts Volume 112 Number 9
Research & Practice Innovations: Nutrition Research, Biosciences, and Bioinformatics/Nutritional Informatics Association between Dietary Patterns in Older Adults and Obesity-Related Health Outcomes Author(s): P. Y. Hsiao,1 D. C. Mitchell,1 G. C. Wood,2 D. L. Coffman,3 D. Wheeler,1 T. J. Hartman,1 C. Still,2 G. L. Jensen1; 1Nutritional Sciences, The Pennsylvania State Univ., University Park, PA, 2Geisinger Obesity Inst., Danville, PA, 3The Methodology Center, The Pennsylvania State Univ., University Park, PA Learning Outcome: To be able to describe the 3 dietary patterns identified in a sample of older adults living in rural Pennsylvania and their association with obesity-related health outcomes. The prevalence of obesity-related health outcomes is increasing among older adults. Because it is thought that nutrition plays an important role in successful aging, there has been considerable interest in the association between dietary patterns of older adults and obesity-related health outcomes. Cluster analysis, utilizing data from four 24-hour dietary recalls, was used to derive dietary patterns in a subgroup of 260 participants from the Geisinger Rural Aging Study (mean age: 78.6 ⫾ 3.9 years). Prevalence (5-year follow-up) of cardiovascular disease, diabetes mellitus, hypertension, and metabolic syndrome was extracted from the outpatient electronic medical records using a validated data abstraction process. Logistic regression, adjusting for relevant covariates, was used to examine the associations between dietary patterns and health outcomes. ‘Sweets and Dairy’ (49%), ‘Health-Conscious’ (27%) and ‘Western’ (24%) dietary patterns were identified at baseline. Prevalence of hypertension in the ’Sweets and Dairy’, ‘Health-conscious’, and ‘Western’ pattern was 81.3%, 65.7%, and 79.0%, respectively. Compared to the ‘Health-Conscious’ pattern, those in the ‘Sweets and Dairy’ pattern had increased odds of hypertension; adjusted odds ratio (95% CI) was 2.17 (1.11-4.27). No significant associations were found for cardiovascular disease, diabetes mellitus, or metabolic syndrome with dietary patterns. Although there was a statistically significant relationship between dietary pattern and prevalence of hypertension, the practical clinical utility remains unclear. The null findings for a 5-year follow-up of any elderly sample for cardiovascular disease, diabetes mellitus and metabolic syndrome do not address the potential benefits of adopting a healthy dietary pattern at a younger age. Funding Disclosure: USDA 1950-51530-010-02G.
Statistical Program to Automate the Creation of Healthy Eating Index Scores Using Nutrition Data System for Research Output Author(s): D. C. Landy, J. M. Kurtz, T. L. Miller, D. A. Ludwig; Division of Pediatric Clinical Research, Univ. of Miami Miller Sch. of Med., Miami, FL Learning Outcome: Participants will be able to calculate Healthy Eating Index (HEI) scores and describe challenges associated with using output from NDSR. Participants will also be able to use an automated program to calculate HEI scores form unedited NDSR output. The application of dietary quality measures, such as the Healthy Eating Index (HEI), are often limited by the type and format of output from dietary analysis software, such as the Nutrition Data System for Research (NDSR, University of Minnesota), and by the complexities inherent in their calculation. With respect to calculating HEI scores using NDSR output, the necessary dietary variables are located in separate files, measured at different levels, and require further processing (example, separating fat amounts from individual foods into solid fats and oils). The HEI scoring algorithm is also somewhat tedious and complicated, requiring the calculation of ratios followed by segmented linear interpolation, and/or algebraic solutions. Further, when individuals have records for multiple days, HEI scores can be calculated using different methods (mean of day-specific scores or score of the mean day). We have created a SAS (SAS Institute, NC) program that automates this process, allowing users to easily calculate HEI scores with unedited NDSR output by simply specifying the output location and desired calculation method. For studies containing multiple days per individual, this program provides a comparison of the different calculation methods and estimates the reliability of the total HEI score as well as a prediction of how this reliability would change if the number of days per individual was varied. Although programs exist that automate some of these calculations, they require extensive front-end preprocessing of NDSR output, making them impractical to use with NDSR-conducted research, and provide no additional statistical insights to guide future dietary assessments. Funding Disclosure: American Heart Association.
Psychological Determinants of Food Intake in Adults with Mood Disorders Author(s): K. M. Davison,1 B. J. Kaplan2; 1School of Nursing, Univ. of British Columbia, Vancouver, Canada, 2Department of Community Health, Univ. of Calgary, Calgary, Canada Learning Outcome: After listening or reading the abstract presentation, the participant will identify and describe eating behavior and psychiatric medication factors that can influence food intake in adults with mood disorders. Little is known about why food intakes of individuals with mood disorders differ from those of population norms; though it is speculated that psychiatric factors contribute to these differences. Using data from a cross-sectional nutrition study of 97 adults with verified mood disorders, we examined the relationships among diet quality (i.e., Canadian Healthy Eating Index (HEI) scores), measures of dietary restraint and disinhibition derived from Stunkard and Messick’s Three-Factor Eating Questionnaire (TFEQ), overall psychological functioning as measured by the Global Assessment of Functioning Scale (GAF), depression and mania symptoms (Hamilton Depression Scale; Young Mania Rating Scale), psychiatric medication use (i.e., antidepressants, mood stabilizers, and antipsychotics), body mass index and sociodemographic variables (i.e., age, gender, relationship status, education, and income). Results of multiple regression analyses indicated that disinhibition (coefficient ⫽ 2.68, SE ⫽ 1.20, t ⫽ 2.23, p ⬍ 0.05, 95% CI 0.29 to 5.06) was positively associated with diet quality; those taking antidepressants (coefficient ⫽ -6.25, SE ⫽ 2.85, t ⫽ -2.19, p ⬍ 0.05, 95% CI -11.93 to -0.57) had lower diet quality scores, Particular attention to eating attitude and behaviors of persons with mood disorders, especially those taking antidepressant medications, might help improve the quality of their dietary intakes. Further research in larger samples with general sample comparison groups is needed. Funding Disclosure: Danone Research Institute.
A-14
JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
September 2012 Suppl 3—Abstracts Volume 112 Number 9