2 per se (reviewed [12]). Nonetheless, there can be considerable variation of body weight status within a given group of centenarians [13], though to our knowledge the extent to which this may be associated with potential diﬀerences in dietary intake patterns or selected health conditions has not been explored. Thus, the objectives of this study were to explore associations (1) between BMI and dietary habits and (2) between underweight or overweight/obesity and health status in a population-based multiethnic sample of centenarians (98 years and above) from northern Georgia in the USA. It was hypothesized that overweight/obesity, but not underweight, would be associated with poorer dietary habits and that both overweight/obesity and underweight, as well as dietary habits, would be associated with speciﬁc health conditions in this population. 2. Methods 2.1. Study Population. This study was a secondary analysis of data collected by the Georgia Centenarian Study, a population-based multidisciplinary study conducted in 44 counties in northern Georgia (USA) from 2002 to 2005. The original study included 244 centenarians (deﬁned as age 98 and older). The sampling procedures and data collection methods have been described elsewhere [6, 14]. Brieﬂy, recruitment of participants from skilled nursing facilities was based on estimates of the institutionalized population of the area according to the 2000 US Census tabulations. The community dwelling participants resided in private residences and personal care homes and were recruited from voter registration lists. Participants were recruited to match census ﬁgures for gender and race/ethnicity (white or black; all were non-Hispanic) and were interviewed by trained personnel in their place of residence. All questionnaires and procedures were approved by the University of Georgia Institutional Review Board on Human Subjects. 2.2. Demographic, Nutrition, and Health Information. Infor- mation regarding age, gender, race/ethnicity, living arrange- ments, health conditions (cardiovascular disease, diabetes, hypertension, etc.), and behaviors (including tobacco and nutritional supplement use) were obtained from each par- ticipant (or his/her caregiver) by self-report. Questions regarding food intake, appetite, and weight change were adapted from the Mini-Nutritional Assessment [15, 16] and the response categories for food intake represented current frequency of consumption of food groups, including dairy products (milk, yogurt, and cheese); meat, ﬁsh, or poultry; orange/yellow vegetables; green vegetables; citrus and noncitrus fruit and juice. The total food score, ranging from 0 to 5, was based on comparisons with the Dietary Guidelines for Americans [17] 1,600-calorie meal pattern for sedentary older adults, as previously described [16]. Body weight and height were measured by interviewers, obtained from charts or via self-report. In addition, knee height was measured on the right leg, unless contraindicated, to the nearest 0.1 centimeter and used to predict stature as per the formulas of Chumlea et al. [18]. Body mass index (BMI) was calculated as weight (kg)/height (meters)2. Journal of Aging Research BMI calculated from observed/recorded height and weight was highly correlated with BMI calculated from predicted stature [18] and observed/recorded weight (r = 0.877; P < .0001). BMI calculated from observed/recorded height and weight was used to form three BMI classiﬁcations based on the National Institutes of Health criteria [19] for overweight/obesity and deﬁned as underweight ≤20 kg/m2, normal weight >20 and <25 kg/m2, and overweight/obese ≥25 kg/m2. Triceps skinfold (TSF) was measured on the right arm, unless contraindicated, by caliper to the nearest 0.1 millimeter. Mid-arm circumference (MAC) was measured on the right arm, unless contraindicated, to the nearest 0.1 cen- timeter. Systolic and diastolic blood pressure measurements were obtained with a brachial cuﬀ. 2.3. Biochemical Indices. Nonfasting blood samples were collected as previously described [6, 14]. Hemoglobin was assessed by a clinical diagnostic laboratory (LabCorp, Inc., Burlington, NC) and anemia was deﬁned as hemoglobin <12 g/dL for females or <13 g/dL for males [20]. 2.4. Exclusions from Data Analysis. Participants missing data for primary variables of interest were excluded from the present analyses. From the original sample of centenar- ians, 11 individuals were excluded due to missing data for BMI (n = 7; includes one double-amputee), food intake patterns (n = 1), average grip strength (n = 1), and/or systolic/diastolic blood pressure (n = 3). Overall characteristics of the 233 centenarians included in the study are given in Table 1. Compared to the included centenarians (n = 233), the excluded centenarians tended to be older (102.6 ± 3.6 versus 100.5 ± 1.9 yrs; P = .052), but did not diﬀer in gender (90.9% [excluded] versus 84.6% female), race/ethnicity (72.7% versus 79.0% white), or place of residence (36.4% versus 43.4% skilled nursing facility). 2.5. Statistical Analyses. Means, standard deviations, medi- ans, range of values, and/or frequencies were calculated. Diﬀerences between participants with diﬀerent BMI clas- siﬁcations were assessed with the Wilcoxon rank sum test for continuous variables and Chi square analysis for categorical variables. Probabilities reported are unadjusted for multiple tests. The level of signiﬁcance was set at P < .05. Because the food groups provide calories, a series of logistic regression analyses were performed with BMI ≤20 or BMI ≥25 as the dependent variable and gender, race/ethnicity, living arrangements, and reported intake of speciﬁc food groups as the independent variables. In addi- tion, because BMI can play a causative role as a risk factor for chronic disease, a second series of logistic regression analyses was performed with diabetes, anemia, or other chronic health conditions as the dependent variable and race, gender, residence, and BMI ≤20 or BMI >25 as the independent variables (Model 1). A ﬁnal series of logistic regression analyses was performed with diabetes, anemia, or other chronic health conditions as the dependent variable and race, gender, residence, total fruit, and vegetable intake and BMI ≤20 or BMI >25 as the independent variables (Model 2).