BACKGROUND: Nutritional status is an important determinant of HIV outcomes. OBJECTIVE: We assessed the association between dietary patterns identified by cluster analysis and change in body mass index (BMI; in kg/m(2)), CD4 count, and viral load (VL). DESIGN: HIV-positive adult male subjects (n = 348) with a BMI >or= 20.5 were evaluated by biochemical, body composition, and dietary data. Cluster analysis was performed on 41 designated food groups derived from 3-d food records. Dietary clusters were compared for sociodemographic, nutrient intake, and clinical outcomes. Multivariate linear regression assessed associations between dietary clusters and change in BMI, CD4 count, and VL. RESULTS: We observed 3 dietary patterns: juice and soda; fast food and fruit drinks; and fruit, vegetable, and low-fat dairy. Subjects in the fast food and fruit drinks pattern had the lowest fiber intake, highest VL, and lowest CD4 count and had a lower income than did subjects in the other 2 clusters. Subjects in the fruit, vegetable, and low-fat dairy diet pattern had higher intakes of protein, fiber, and micronutrients and the highest BMI and CD4 count. Subjects in the juice and soda pattern had higher energy intakes and lowest BMI. On average, the fast food and fruit drinks cluster and fruit, vegetable, and low-fat dairy cluster gained 0.33 (P = 0.06) and 0.42 (P = 0.02), respectively, more in BMI than the juice and soda cluster across the study interval in a multivariate model. CONCLUSIONS: In a cohort of HIV-positive men, we identified 3 distinct dietary patterns; each pattern was associated with specific nutrition, demographic, and HIV-related variables.
BACKGROUND: Nutritional status is an important determinant of HIV outcomes. OBJECTIVE: We assessed the association between dietary patterns identified by cluster analysis and change in body mass index (BMI; in kg/m(2)), CD4 count, and viral load (VL). DESIGN:HIV-positive adult male subjects (n = 348) with a BMI >or= 20.5 were evaluated by biochemical, body composition, and dietary data. Cluster analysis was performed on 41 designated food groups derived from 3-d food records. Dietary clusters were compared for sociodemographic, nutrient intake, and clinical outcomes. Multivariate linear regression assessed associations between dietary clusters and change in BMI, CD4 count, and VL. RESULTS: We observed 3 dietary patterns: juice and soda; fast food and fruit drinks; and fruit, vegetable, and low-fat dairy. Subjects in the fast food and fruit drinks pattern had the lowest fiber intake, highest VL, and lowest CD4 count and had a lower income than did subjects in the other 2 clusters. Subjects in the fruit, vegetable, and low-fat dairy diet pattern had higher intakes of protein, fiber, and micronutrients and the highest BMI and CD4 count. Subjects in the juice and soda pattern had higher energy intakes and lowest BMI. On average, the fast food and fruit drinks cluster and fruit, vegetable, and low-fat dairy cluster gained 0.33 (P = 0.06) and 0.42 (P = 0.02), respectively, more in BMI than the juice and soda cluster across the study interval in a multivariate model. CONCLUSIONS: In a cohort of HIV-positive men, we identified 3 distinct dietary patterns; each pattern was associated with specific nutrition, demographic, and HIV-related variables.
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