Comparison of Urinary Sodium and Blood Pressure Relationship From the Spot Versus 24‐Hour Urine Samples. Issue 21 (5th November 2019) - Naser, Abu Mohd; Rahman, Mahbubur; Unicomb, Leanne; Doza, Solaiman; Anand, Shuchi; Chang, Howard H.; Luby, Stephen P.; Clasen, Thomas F.; Narayan, K.M. Venkat
Abstract : Background: We compared the relationship between sodium (Na) intake and blood pressure when Na intake was estimated from first‐ and second‐morning spot urine samples using the INTERSALT (International Study on Salt and Blood Pressure) formula, versus directly measured 24‐hour samples. Methods and Results: We collected 24‐hour urine and first‐ and second‐morning voids of 383 participants in coastal Bangladesh for 2 visits. We measured participants' blood pressure using an Omron ® HEM–907 monitor. To assess the shape of the relationship between urinary Na and blood pressure, we created restricted cubic spline plots adjusted for age, sex, body mass index, smoking and alcohol consumption, physical activities, religion, sleep hours, and household wealth. To assess multicollinearity, we reported variance inflation factors, tolerances, and Leamer's and Klein's statistics following linear regression models. The mean daily urinary Na was 122 (SD 26) mmol/d for the first; 122 (SD 27) mmol/d for the second; and 134 (SD 70) mmol/d for the 24‐hour samples. The restricted cubic spline plots illustrated no association between first‐morning urinary Na and systolic blood pressure until the 90th percentile distribution followed by a downward relationship; a nonlinear inverse‐V‐shaped relationship between second‐morning urinary Na and systolic blood pressure; and a monotonic upward relationship between 24‐hour urinary Na and systolic blood pressure. We found no evidence of multicollinearity for the 24‐hour urinary Na model. Conclusions: The urinary Na and systolic blood pressure relationship varied for 3 urinary Na measurements. Twenty‐four‐hour urinary Na captured more variability of Na intake compared with spot urine samples, and its regression models were not affected by multicollinearity.