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Blood Pressure in relation to 24-Hour Urinary Sodium and Potassium Excretion in a Uruguayan Population Sample
Paula Moliterno1, Ramón Álvarez-Vaz2, Matias Pécora3
1Departamento de Nutrición Clínica, Escuela de Nutrición, Universidad de la República, Montevideo, Uruguay.
Abstract:
Many public health policies in Latin America target an optimized sodium and potassium intake. The aims of this study were to assess the sodium and potassium intake using 24-hour urinary analysis and to study their association with blood pressure in a Uruguayan population cohort using cluster analysis. A total of 149 participants (aged 20-85 years) were included in the study, and office blood pressure, anthropometric measurements, biochemical parameters in the blood, and 24-hour urine samples were obtained. The overall mean sodium and potassium excretion was 152.9 ± 57.3 mmol/day (8.9 ± 3.4 g/day of salt) and 55.4 ± 19.6 mmol/day, respectively. The average office systolic/diastolic blood pressure was 124.6 ± 16.7/79.3 ± 9.9 mmHg. Three compact spherical clusters were defined in untreated participants based on predetermined attributes, including blood pressure, age, and sodium and potassium excretion. The major characteristics of the three clusters were (1) high systolic blood pressure and moderate sodium excretion, (2) moderate systolic blood pressure and very high sodium excretion, and (3) low systolic blood pressure and low sodium excretion. Participants in cluster three had systolic blood pressure values that were 23.9 mmHg (95% confidence interval: -29.5 to -1.84) lower than those in cluster one. Participants in cluster two had blood pressure levels similar to those in cluster one (P = 0.32) and worse metabolic profiles than those in cluster one and three (P < 0.05). None of the clusters showed high blood pressure levels and high sodium excretion. No linear association was found between blood pressure and urinary sodium excretion (r < 0.14; P > 0.47). An effect of sodium and potassium intake on blood pressure levels was not found at the population level using regression or cluster analysis.
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