ObjectiveTo examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors.MethodsThe conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.14.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.21.7 years of follow-up, 221 individuals developed functional limitation.ResultsHigher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6).ConclusionsHigher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses.

Health care resource consumption is a growing concern. The aim of this study was to examine the associations between diet quality and body mass index with health care resource use (HRU) in a cohort of advanced age. Participants in the Geisinger Rural Aging Study (n=5,993) were mailed demographic and dietary questionnaires in 2009. Of those eligible, 2,995 (50%; 1,267 male, 1,728 female; mean age 81.4±4.4 years) provided completed surveys. Multivariate negative binomial models were used to estimate relative risk and 95% CI of HRU outcomes with diet quality as assessed by the Dietary Screening Tool score and body mass index determined from self-reported height and weight. Poor diet quality was associated with a 20% increased risk for emergency room (ER) visits. Fruit and vegetable consumption was grouped into quintiles of intake, with the highest quintile serving as the reference group in analyses. The three lowest fruit and vegetable quintiles were associated with increased risk for ER visits (23% to 31%); the lowest quintile increased risk for inpatient visits (27%). Obesity increased risk of outpatient visits; however, individuals with class I obesity were less likely than normal-weight individuals to have ER visits (relative risk=0.84; 95% CI 0.70 to 0.99). Diets of greater quality, particularly with greater fruit and vegetable intake, are associated with favorable effects on HRU outcomes among older adults. Overweight and obesity are associated with increased outpatient HRU and, among obese individuals, with decreased ER visits. These findings suggest that BMI and diet quality beyond age 74 years continue to affect HRU measures.

In an aging population, potentially modifiable factors impacting mortality such as diet quality, body mass index (BMI), and health-related quality of life (HRQOL) are of interest. Surviving members of the Geisinger Rural Aging Study (GRAS) (n = 5,993; aged ?74 years) were contacted in the fall of 2009. Participants in the present study were the 2,995 (1,267 male, 1,728 female; mean age 81.4 ± 4.4 years) who completed dietary and demographic questionnaires and were enrolled in the Geisinger Health Plan over follow-up (mean = 3.1 years). Cox proportional hazards multivariate regression models were used to examine the associations between all-cause mortality and BMI, diet quality, and HRQOL. Compared to GRAS participants with BMIs in the normal range, a BMI < 18.5 was associated with increased mortality (HR 1.85 95%CI 1.09, 3.14, P = 0.02), while a BMI of 25-29.9 was associated with decreased risk of mortality (HR 0.71 95%CI 0.55, 0.91, P =0.007). Poor diet quality increased risk for mortality (HR 1.53 95%CI 1.06, 2.22, P = 0.02). Finally, favorable health-related quality of life was inversely associated with mortality (HR 0.09 95%CI 0.06, 0.13, P < 0.0001). Higher diet quality and HALex scores, and overweight status, were associated with reduced all-cause mortality in a cohort of advanced age. While underweight (BMI < 18.5) increased risk of all-cause mortality, no association was found between obesity and all-cause mortality in this aged cohort.

Abstract Objective To assess the association of diet-related practices and BMI with diet quality in rural adults aged ≥74 years. Design Cross-sectional. Dietary quality was assessed by the twenty-five-item Dietary Screening Tool (DST). Diet-related practices were self-reported. Multivariate linear regression models were used to analyse associations of DST scores with BMI and diet-related practices after controlling for gender, age, education, smoking and self- v. proxy reporting. Setting Geisinger Rural Aging Study (GRAS) in Pennsylvania, USA. Subjects A total of 4009 (1722 males, 2287 females; mean age 81·5 years) participants aged ≥74 years. Results Individuals with BMI < 18·5 kg/m2 had a significantly lower DST score (mean 55·8, 95 % CI 52·9, 58·7) than those individuals with BMI = 18·5–24·9 kg/m2 (mean 60·7, 95 % CI 60·1, 61·5; P = 0·001). Older adults with higher, more favourable DST scores were significantly more likely to be food sufficient, report eating breakfast, have no chewing difficulties and report no decline in intake in the previous 6 months. Conclusions The DST may identify potential targets for improving diet quality in older adults including promotion of healthy BMI, breakfast consumption, improving dentition and identifying strategies to decrease concern about food sufficiency.

To determine the associations between diet quality, body mass index (BMI), and health-related quality of life (HRQOL) as assessed by the health and activity limitation index (HALex) in older adults.

The population is aging worldwide. Delayed mortality is associated with an increased burden of chronic health conditions, many of which have a dietary component. A literature search was conducted to retrieve and review relevant articles considering quality of diets in association with mortality in older adults aged 60 years and older. In the studies we reviewed, diet quality defined using either a priori methods, which characterize dietary patterns based on existing dietary guidelines, or a posteriori methods, which define dietary patterns through statistical methods met review criteria. Sixteen articles met criteria for review. Generally, dietary patterns that demonstrated greater adherence to diets that emphasized whole fruits and vegetables, whole grains, low-fat dairy, lean meats, and legumes and nuts were inversely associated with mortality. However, a priori methods have not yet demonstrated associations between diet and mortality in older adults in the United States. Development of new methods based on regional variations in dietary intake may offer the best approach to assess associations with mortality.

Nutrition status is a predictor of quality of life. This study sought to determine the association between diet quality, BMI, and health‐related quality of life (HRQOL) in a population of adults ≥74 y. The Diet Screening Tool (DST) is a 25 item questionnaire scored from 0 (poor) to 100 developed to assess diet quality in older adults. The Health and Activity Limitations Index (HALex) is a HRQOL measure scored on a 0.0 (death) to 1.0 (optimal health) scale. A total of 5,993 GRAS participants were mailed HRQOL and DST questionnaires with 2,975 (1260 male, 1715 female; mean age 81.4 ± 4.4) providing complete data. Multivariate linear regression models were used to analyze associations between HALex score, BMI and DST score after controlling for gender, age, education, and smoking. The mean adjusted HALex score for participants in the lowest quintile of DST scores was 0.72 ± 0.2 compared to 0.77 ± 0.2 for those in the highest quintile of DST Scores (p‐trend <0.01). Adjusted mean HALex scores were significantly lower for obese (BMI ≥30; 0.72 ± 0.21, P=0.006) and underweight individuals (BMI <18.5; 0.64 ± 0.23, P=0.02) and significantly higher for overweight individuals (BMI 25–29.9; 0.76 ± 0.18, P=0.02) compared to those with BMI 18.5–24.9 (0.73 ± 0.22). Poor diet quality, as assessed by the DST, is associated with lower HRQOL in older adults. USDA #1950–51530‐010–00Grant Funding Source: USDA

OBJECTIVE:To determine the relative validity of a population specific food frequency questionnaire (FFQ) and evaluate the effectiveness of the instrument for assessing nutritional risk in older adults. DESIGN:A cross-over design with participants completing two different dietary assessment instruments in random order. SETTING:The Geisinger Rural Aging Study (GRAS), a longitudinal study of over 20,000 adults living in the central, northern and eastern counties of Pennsylvania. PARTICIPANTS:A subset of GRAS consisting of 245 older adults (60% women) ranging in age from 70 to 95 years. MEASUREMENTS:Energy and nutrient intakes were assessed from two instruments: a population specific food frequency questionnaire (FFQ) and four 24-hour dietary recalls conducted over a two week period. RESULTS:Pearson correlation coefficients between the FFQ and dietary recalls for most nutrients were 0.5 or higher which suggests that the FFQ provided relatively valid estimates of macro and micronutrient intakes examined. Bland-Altman plots were generated to examine the agreement between instruments. Data are shown for energy, folate and zinc with close agreement at lower intakes indicative of risk for folate and zinc. Sensitivity results also showed that the FFQ was able to correctly classify individuals adequately at risk for most nutrients examined. CONCLUSION:This population specific FFQ appears to be a valid instrument for use in in evaluating risk for many nutrients that are of particular concern in older adults residing throughout many predominately rural counties in Pennsylvania.

Participant will recognize that underweight is associated with poor diet quality in older adults and act to improve dietary habits such as promoting breakfast, providing strategies to improve food security, and assist with managing loss of appetite.

Background Information is limited on persistence of early beverage patterns throughout childhood and adolescence and their influence on long-term dietary intake.Objective To describe changes in beverage intake during childhood and assess beverage and nutrient intake from ages 5 to 15 years among girls who were consuming or not consuming sweetened carbonated beverages (soda) at age 5 years.Design/subjects Participants were part of a longitudinal study of non-Hispanic white girls and their parents (n=170) assessed biennially from age 5 to 15 years starting fall 1996.Statistical analyses At each assessment, intakes of beverages (milk, fruit juice, fruit drinks, soda, and tea/coffee), energy, macronutrients, and micronutrients were assessed using three 24-hour recalls. Analyses of longitudinal changes and the interaction between beverage type and age were conducted using a mixed modeling approach. Girls were categorized as either soda consumers or nonconsumers at age 5 years. A mixed modeling approach was used to assess longitudinal differences and patterns of change in beverage and nutrient intake between soda consumption groups.Results Early differences in soda intake were predictive of later soda and milk intake and of differences in selected nutrients. Relative to girls who were not consuming soda beverages at age 5 years, soda consumers at age 5 years had higher subsequent soda intake, lower milk intake, higher intake of added sugars, lower protein, fiber, vitamin D, calcium, magnesium, phosphorous, and potassium from ages 5 to 15 years.Conclusions Soda consumption at age 5 years predicted patterns of nutrient intake that persisted during childhood and into adolescence. Diets of soda consumers were higher in added sugars and lower in protein, fiber, calcium, vitamin D, magnesium, phosphorous, and potassium. Findings provide a more complex picture regarding the emergence of early beverage patterns and their predictive effects on nutrient intake across childhood and adolescence. J Am Diet Assoc. 2010;110:543-550.

This study is based on data from a nationally representative sample of U.S. households in the Continuing Survey of Food Intake by Individuals (CSFII 1994–96) to examine the relationship between the dietary intakes of the female heads of household (FHH) and 2–18 years old children residing in the same household (n=3144). Association between FHH's diet on childrens' intake was investigated by fitting multiple regression models controlling for socio‐demographic variables. Analysis was conducted by in STATA 10.1. FHH intake levels were only associated with those of male and female children for few food groups or nutrients: Regression coefficients were statistically significant between FHH's and male children's vegetables (0.63 (p‐value<0.001)) and vitamin A (0.65 (p‐value<0.001)) intakes while significant correlation coefficients between FHH's and female children's intake for vegetable intake was only 0.54 (p‐value<0.001) and meat (0.47 (p‐value<0.001)). This study indicates that correlations between dietary intakes of FHH and the intakes of the children residing in the same household were lower than anticipated. Further research is needed to understand the importance of adult modeling of dietary intake behavior on childrens' diets.Support from NIH grant number: R503HD050239

AbstractObjectiveTo develop and evaluate a method for calculating the Healthy Eating Index-2005 (HEI-2005) with the widely used Nutrition Data System for Research (NDSR) based on the method developed for use with the US Department of Agriculture’s (USDA) Food and Nutrient Dietary Data System (FNDDS) and MyPyramid Equivalents Database (MPED).DesignCross-sectional.SettingNon-institutionalized, community-dwelling adults aged 70 years and above.SubjectsTwo hundred and seventy-one adults participating in the Geisinger Rural Aging Study (GRAS) and 620 age- and race-matched adults from the National Health and Nutrition Examination Survey 2001–2002 (NHANES) were included in the analysis. The HEI-2005 scores were generated using NDSR in GRAS and compared to scores generated using FNDDS and MPED in NHANES.ResultsSimilar total HEI-2005 scores (mean 62·0 (se 0·75) in GRAS v. 57·4 (se 0·55) in NHANES) were estimated, and the individual components most strongly correlated with total score in both samples were compared. Cronbach’s coefficient α values of HEI-2005 were 0·52 in GRAS and 0·43 in NHANES.ConclusionsSince NDSR is commonly used for educational purposes, in clinical settings and in nutrition research, it is important to develop methodology for assessing diet quality through the use of HEI-2005 with this dietary analysis software application and its accompanying food and nutrient database. Results from the present study show that HEI-2005 scores can be generated with NDSR using the method described in the present study and the detailed USDA Center for Nutrition Policy and Promotion technical report as guidance.

Background: No rapid methods exist for screening overall dietary intakes in older adults.Objective: The purpose of this study was to develop and evaluate a scoring system for a diet screening tool to identify nutritional risk in community-dwelling older adults.Design: This cross-sectional study in older adults (n = 204) who reside in rural areas examined nutrition status by using an in-person interview, biochemical measures, and four 24-h recalls that included the use of dietary supplements.Results: The dietary screening tool was able to characterize 3 levels of nutritional risk: at risk, possible risk, and not at risk. Individuals classified as at nutritional risk had significantly lower indicators of diet quality (Healthy Eating Index and Mean Adequacy Ratio) and intakes of protein, most micronutrients, dietary fiber, fruit, and vegetables. The at-risk group had higher intakes of fats and oils and refined grains. The at-risk group also had the lowest serum vitamin B-12, folate, beta-cryptoxanthin, lutein, and zeaxanthin concentrations. The not-at-nutritional-risk group had significantly higher lycopene and beta-carotene and lower homocysteine and methylmalonic acid concentrations.Conclusion: The dietary screening tool is a simple and practical tool that can help to detect nutritional risk in older adults. Am J Clin Nutr 2009;90:177-83.

BACKGROUND:Increased consumption of sweetened beverage has been linked to higher energy intake and adiposity in childhood. OBJECTIVE:The objective was to assess whether beverage intake at age 5 y predicted energy intake, adiposity, and weight status across childhood and adolescence. DESIGN:Participants were part of a longitudinal study of non-Hispanic white girls and their parents (n = 170) who were assessed biennially from age 5 to 15 y. At each assessment, beverage intake (milk, fruit juice, and sweetened beverages) and energy intake were assessed by using three 24-h recalls. Percentage body fat and waist circumference were measured. Height and weight were measured and used to calculate body mass index. Multiple regression analyses were used to predict the girls' adiposity. In addition, at age 5 y, girls were categorized as consuming <1, > or =1 and <2, or > or =2 servings of sweetened beverages. A mixed modeling approach was used to assess longitudinal differences and patterns of change in sweetened beverage and energy intake, adiposity, and weight status by frequency of sweetened beverage intake. RESULTS:Sweetened beverage intake at age 5 y, but not milk or fruit juice intake, was positively associated with adiposity from age 5 to 15 y. Greater consumption of sweetened beverages at age 5 y (> or =2 servings/d) was associated with a higher percentage body fat, waist circumference, and weight status from age 5 to 15 y. CONCLUSION:These findings provide new longitudinal evidence that early intake of sweetened beverages predicts adiposity and weight status across childhood and adolescence.

This cross-sectional study of 2- to 12-year-olds living in medically underserved areas examined the proportion of children meeting the food group intake recommendations for fruits, vegetables, total grains, dairy, and meat/meat alternatives by age group and body weight status. Based on 24-hour recalls collected between July 2004 and March of 2005, mean food group intake and deviation from the recommended intake amounts were determined (actual intake minus recommended intake). Measured weight and height were used to calculate body mass index z scores using the Centers for Disease Control and Prevention growth charts. Data analyses were done for two age groups (2- to 5-year-olds and 6- to 12-year-olds) (n=214), by weight status categories (underweight or healthy weight [< 85 th percentile], overweight [85 th to 94 th percentile], or obese [> or = 95th percentile]), and were repeated for the subset of children with biologically plausible reports. The majority of children lived in low-income households. More 2- to 5-year-olds met intake recommendations compared with 6- to 12-year-olds. Overall, the proportion of children meeting the food group intake recommendations was low with the exception of the meat group, which was met by 52% and 93% of the 2- to 5- and 6- to 12-year-old children, respectively. There was a positive association between the proportion of younger children meeting the fruits or total grains recommendation and increasing body weight. The data support the importance of community-level nutrition intervention programs to improve children's diet quality in low-income, medically underserved areas and suggest that such interventions may help reduce the risk of obesity.

Inaccurate reporting of energy intake makes it difficult to study the associations between diet and weight status. This study examined reported energy intake at age 9 years as a predictor of girls' body mass index (BMI) at age 11 years, before and after adjusting for parents' BMI and girls' pubertal status. This prospective, observational cohort study included 177 non-Hispanic white girls and their parents. When the subjects were 9 years of age, three 24-hour recalls were used to categorize girls as plausible or implausible over-reporters and under-reporters based on previously published methods. Height and weight was measured to calculate BMI. Linear and hierarchical regression analyses were used to predict girls' BMI. Results revealed that girls who under-reported had significantly higher BMIs than plausible and overreporters. Among the total sample and among implausible reporters, reported energy intake was not a significant predictor of BMI; however, among plausible reporters, reported energy intake explained 14% of the variance in BMI and remained a significant predictor after adjusting for parental BMI and girls' pubertal status. Systematic bias related to underreporting in dietary data can obscure relationships with weight status, even among young girls. A relatively simple analytical procedure can be used to identify the magnitude and nature of reporting bias in dietary data.

Poor nutrient intakes are an adaptable risk factor associated with numerous public health outcomes. As older adults represent a vulnerable population for low nutrient intakes they may be ideal candidates for DSU. Usual nutrient intakes were estimated with and without DSU using the mean nutrient intake from 4, 24‐hour recalls in the Geisinger Rural Aging Study (114 m, 158 f; mean age 78.5 ± 6). We examined the impact of DSU on the proportion of our sample who met the EAR and AI for selected nutrients, as well as on the Mean Adequacy Ratio (MAR), an index of nutrient intake (range of 0–1). The nutrients used to calculate the MAR included calcium, folate, pantothenic acid, magnesium, zinc, potassium and Vitamins A, C, D, E, K, B6, and B12. The majority of the sample (74%) reported DSU. The MAR for our sample without accounting for DSU was 0.68 but increased to 0.82 with DSU. Using the EAR, the prevalence of inadequate intakes for nutrients was higher if DSU wasn't accounted for, and a similar trend was observed for those nutrients with an AI. Notably, only 1% of the sample met the AI for Vitamin D through diet alone; however, when DSU was factored in 68% were meeting the AI. Given the widespread prevalence of DSU in older adults and the large contribution of DSU to total nutrient intakes, it is imperative to account for DSU when estimating nutrient adequacies within groups. Supported in part by NIH R21AG023179‐01A1, USDA #58‐1950‐4‐401.

Carbohydrate-restricted diets have been promoted for the management of central obesity, a feature of metabolic syndrome. This study evaluated the impact of a reduced-carbohydrate diet provided in a typical outpatient setting on outcomes associated with metabolic syndrome. Adults older than 21 years who met the criteria for metabolic syndrome were recruited (n = 21) and received 2 nutritional counseling sessions. Changes in body weight, blood pressure, and anthropometric, glucose, and lipid outcomes were assessed. Participants lost a mean (±SD) of 5.0 (±3.4) kg (P < .05). There was a significant reduction in waist circumference, body mass index, and systolic and diastolic blood pressure (all P < .01). No significant improvement in glucose or lipoprotein level was observed. Only 50% of participants met the criteria for metabolic syndrome at the end of the study. A reduced-carbohydrate diet can be effective in promoting weight loss and is accompanied by improvements in body composition and blood pressure over 3 months.