Objective: Previous studies exploring the relationship of neighborhood characteristics with metabolic conditions have focused on middle-aged adults but none have comprehensively investigated associations in older adults, a potentially vulnerable population. The aim was to explore the relationship of neighborhood characteristics with metabolic conditions in older women.
Design: Cross-sectional analysis.
Setting/participants: We studied 384 women aged 70-79 years, representing the two-thirds least disabled women in the community, enrolled in the Women's Health and Aging Study II at baseline. Neighborhood scores were calculated from census-derived data on median household income, median house value, percent earning interest income, percent completing high school, percent completing college, and percent with managerial or executive occupation. Participants were categorized by quartile of neighborhood score with a higher quartile representing relative neighborhood advantage. Logistic regression models were created to assess the association of neighborhood quartiles to outcomes, adjusting for key covariates.
Measurements: Primary outcomes included metabolic conditions: obesity, diabetes, hypertension, and hyperlipidemia. Secondary outcomes included BMI, HbA1c, blood pressure and lipids.
Results: Higher neighborhood quartile score was associated with a lower prevalence of obesity (highest quartile=13.5% versus lowest quartile=36.5%; p<0.001 for trend). A lower prevalence of diabetes was also observed in highest (6.3%) versus lowest (14.4%) neighborhood quartiles, but was not significantly different (p= 0.24 for trend). Highest versus lowest neighborhood quartile was associated with lower HbA1c (-0.31%, p=0.02) in unadjusted models. Women in the highest versus lowest neighborhood quartile had lower BMI (-2.01 kg/m2, p=0.001) and higher HDL-cholesterol (+6.09 mg/dL, p=0.01) after accounting for age, race, inflammation, and smoking.
Conclusion: Worse neighborhood characteristics are associated with adiposity, hyperglycemia, and low HDL. Further longitudinal studies are needed and can inform future interventions to improve metabolic status in older adults.