Rheumatoid arthritis (RA) afflicts approximately 1.5 million American adults and is a major cause of disability. As disease severity worsens, individuals with RA may experience functional decline that can impact dietary intake. The objective of this study is to assess the diet quality of individuals with RA using the Healthy Eating Index (HEI)-2010 and examine associations between diet quality and disease activity and functional status. This cross-sectional study assessed diet quality and disease activity and functional status in adults with RA. Participants completed seven-day weighed food records, which were scored using the HEI-2010. Participants had a fasting blood draw and completed the Multidimensional Health Assessment Questionnaire to determine disease activity and functional status. The mean age of individuals with RA (N = 84) was 53 ± 14 years, and 86.9% were female. The mean HEI-2010 total score was 58.7 ± 15.9, with 7.1% of participants scoring “good”, 58.3% “fair”, and 34.5% “poor”. Most participants did not adhere to recommended intakes of total fruit, total vegetables, whole grains, fatty acids, refined grains, sodium, and empty calories. An unadjusted multiple linear regression model found duration of morning stiffness and C-reactive protein concentration to be significant variables to inversely predict HEI-2010 total score. The diet quality of many individuals with RA needs improvement and may be related to functional disability associated with RA. Healthcare providers should encourage individuals with RA to meet dietary guidelines and maintain a healthy diet. Moreover, healthcare providers should be aware of the potential impacts of functional disability on diet quality in individuals with RA.

Hispanic women have a higher prevalence of weight associated complications in pregnancy. This ethnic disparity is likely related to behavior patterns, social circumstances, environmental exposures, and access to healthcare, rather than biologic differences. The objective was to determine associations between sociodemographic characteristics, health behaviors, and psychosocial stressors and gestational weight gain (GWG) in low-income Hispanic women. During pregnancy, information on sociodemographic characteristics, health behaviors, and psychosocial stressors were collected. Linear regression estimated mean differences in GWG by selected predictors. Multinomial logistic regression estimated odds of inadequate and excessive GWG by selected predictors. Five-hundred and eight women were included, 38% had inadequate and 28% had excessive GWG; 57% with a normal pre-pregnancy BMI had inadequate GWG. Compared to women with normal BMI, women with overweight or obesity were more likely to have excessive GWG (aRRR = 1.88, 95% CI: 1.04, 3.40 and aRRR = 1.98, 95% CI: 1.08, 3.62, respectively). Mean total GWG was higher among women who were nulliparous (ß = 1.34 kg, 95% CI: 0.38, 2.29) and those who engaged in ≥3 h of screen time daily (ß = 0.98 kg, 95% CI: 0.02, 1.94), and lower among women who were physically active during pregnancy (ß = −1.00 kg, 95% CI: −1.99, −0.03). Eating breakfast daily was associated with lower risk of inadequate GWG (aRRR = 0.47, 95% CI: 0.26, 0.83). Depressive symptoms and poor adherence to dietary recommendations were prevalent, but none of the psychosocial or dietary variables were associated with GWG. In this cohort of primarily immigrant, low-income, Hispanic women, there were high rates of poor adherence to diet and physical activity recommendations, and a majority of women did not meet GWG guidelines. Modifiable health behaviors were associated with GWG, and their promotion should be included in prenatal care.

Background/Objectives: The aim of this secondary analysis is to determine the baseline characteristics that are associated with a higher likelihood of weight-loss success in a personalized nutrition intervention. Methods: Data were analyzed in adults with abnormal glucose metabolism and obesity from a 6-month behavioral counseling randomized clinical trial. Participants were randomized to two calorie-restricted diets: a low-fat diet (Standardized) or a personalized nutrition diet leveraging a machine learning algorithm (Personalized). The gradient boosting machine method was used to determine the baseline variables (i.e., age, weight-loss self-efficacy) that predicted successful weight loss (≥5%) at 6 months in each study arm separately, using repeated five-fold cross-validation with 100 repetitions. Results: A total of 155 participants (Personalized: n = 84 vs. Standardized: n = 71) contributed data (mean [standard deviation]: age, 59 [10] y; 66.5% female; 56.1% White; body mass index (BMI), 33.4 [4.6] kg/m²). In both arms, higher baseline self-efficacy for weight loss was a predictor of weight-loss success. Participants with a higher BMI (p < 0.0001) in the Standardized arm and those who were older (p < 0.0001) in the Personalized arm were more likely to achieve successful weight loss. Conclusions: Future weight-loss interventions may consider providing tailored behavioral support for individuals based on weight-loss self-efficacy, BMI, and age.