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Background We compared the relationship of past and contemporary sodium (Na) intake with cardiometabolic biomarkers. Methods and Results A total of 1191 participants' data from a randomized controlled trial in coastal Bangladesh were analyzed. Participants provided 24‐hour urine Na (24UNa) data for 5 monthly visits. Their fasting blood glucose, total cholesterol, triglycerides, high‐density lipoprotein, blood pressure, and 24‐hour urine protein were measured at the fifth visit. Participants' mean 24UNa over the first 4 visits was the past Na, and 24UNa of the fifth visit was the contemporary Na intake. We estimated the prevalence ratios of elevated cardiometabolic biomarkers and metabolic syndrome across 24UNa tertiles by multilevel logistic regression using participant‐, household‐, and community‐level random intercepts. Models were adjusted for age, sex, body mass index, smoking, physical activity, alcohol consumption, sleep hours, religion, and household wealth. Compared with participants in tertile 1 of past urine Na, those in tertile 3 had 1.46 (95% CI, 1.08–1.99) times higher prevalence of prediabetes or diabetes mellitus, 5.49 (95% CI, 2.73–11.01) times higher prevalence of large waist circumference, and 1.60 (95% CI, 1.04–2.46) times higher prevalence of metabolic syndrome. Compared with participants in tertile 1 of contemporary urine Na, those in tertile 3 had 1.93 (95% CI, 1.24–3.00) times higher prevalence of prediabetes or diabetes mellitus, 3.14 (95% CI, 1.45–6.83) times higher prevalence of proteinuria, and 2.23 (95% CI, 1.34–3.71) times higher prevalence of large waist circumference. Conclusions Both past and contemporary Na intakes were associated with higher cardiometabolic disease risk.

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.