Alisson Diego Machado1, Fernanda Silva Nogueira Dos Anjos2, Maria Alice Muniz Domingos3, Maria Del Carmen Bisi Molina4, Dirce Maria Lobo Marchioni5, Isabela Judith Martins Benseñor6, Silvia Maria de Oliveira Titan7. 1. MSc. Dietitian, Department of Nephrology, Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo (SP), Brazil. 2. Dietitian, Department of Nephrology, Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo (SP), Brazil. 3. MD. Nephrologist, Department of Nephrology, Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo (SP), Brazil. 4. PhD. Dietitian and Associate Professor, Health Sciences Center, Universidade Federal do Espírito Santo (UFES), Vitória (ES), Brazil. 5. PhD. Dietitian and Associate Professor, Department of Nutrition, Faculdade de Saúde Pública (FSP), Universidade de São Paulo (USP), São Paulo (SP), Brazil. 6. MD. Associate Professor, General Medicine Unit, Hospital Universitário (HU), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo (SP), Brazil. 7. MD. Research Investigator, Department of Nephrology, Hospital das Clínicas (HC), Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo (SP), Brazil.
Abstract
BACKGROUND: Despite evidence that diet is very important in relation to chronic kidney disease (CKD) progression, studies in this field are scarce and have focused only on some specific nutrients. We evaluated the energy, macronutrient and micronutrient intakes and dietary patterns of non-dialysis CKD participants in the PROGREDIR study. DESIGN AND SETTING: Cross-sectional study; CKD cohort, São Paulo, Brazil. METHODS: Baseline data on 454 participants in the PROGREDIR study were analyzed. Dietary intake was evaluated through a food frequency questionnaire. Dietary patterns were derived through principal component analysis. Energy and protein intakes were compared with National Kidney Foundation recommendations. Linear regression analysis was performed between energy and nutrient intakes and estimated glomerular filtration rate (eGFR), and between sociodemographic and clinical variables and dietary patterns. RESULTS: Median energy and protein intakes were 25.0 kcal/kg and 1.1 g/kg, respectively. In linear regression, protein intake (β = -3.67; P = 0.07) was related to eGFR. Three dietary patterns (snack, mixed and traditional) were retained. The snack pattern was directly associated with male gender (β = 0.27; P = 0.006) and inversely with diabetes (β = -0.23; P = 0.02). The traditional pattern was directly associated with male gender (β = 0.27; P = 0.007) and schooling (β = 0.40; P < 0.001) and inversely with age (β = -0.01; P = 0.001) and hypertension (β = -0.34; P = 0.05). CONCLUSIONS: We identified low energy and high protein intake in this population. Protein intake was inversely related to eGFR. Dietary patterns were associated with age, gender, schooling level, hypertension and diabetes.
BACKGROUND: Despite evidence that diet is very important in relation to chronic kidney disease (CKD) progression, studies in this field are scarce and have focused only on some specific nutrients. We evaluated the energy, macronutrient and micronutrient intakes and dietary patterns of non-dialysis CKD participants in the PROGREDIR study. DESIGN AND SETTING: Cross-sectional study; CKD cohort, São Paulo, Brazil. METHODS: Baseline data on 454 participants in the PROGREDIR study were analyzed. Dietary intake was evaluated through a food frequency questionnaire. Dietary patterns were derived through principal component analysis. Energy and protein intakes were compared with National Kidney Foundation recommendations. Linear regression analysis was performed between energy and nutrient intakes and estimated glomerular filtration rate (eGFR), and between sociodemographic and clinical variables and dietary patterns. RESULTS: Median energy and protein intakes were 25.0 kcal/kg and 1.1 g/kg, respectively. In linear regression, protein intake (β = -3.67; P = 0.07) was related to eGFR. Three dietary patterns (snack, mixed and traditional) were retained. The snack pattern was directly associated with male gender (β = 0.27; P = 0.006) and inversely with diabetes (β = -0.23; P = 0.02). The traditional pattern was directly associated with male gender (β = 0.27; P = 0.007) and schooling (β = 0.40; P < 0.001) and inversely with age (β = -0.01; P = 0.001) and hypertension (β = -0.34; P = 0.05). CONCLUSIONS: We identified low energy and high protein intake in this population. Protein intake was inversely related to eGFR. Dietary patterns were associated with age, gender, schooling level, hypertension and diabetes.