A Method for Estimating 24-Hour Urinary Sodium Excretion by Casual Urine Specimen in Chinese Hypertensive Patients
Author(s) -
Yan V. Sun,
Hongyi Wang,
Hao Liang,
Yifang Yuan,
Chang Shu,
Yanwei Zhang,
Yihua Zhu,
Mingxin Yu,
Songnian Hu,
Ningling Sun
Publication year - 2021
Publication title -
american journal of hypertension
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.009
H-Index - 136
eISSN - 1941-7225
pISSN - 0895-7061
DOI - 10.1093/ajh/hpab020
Subject(s) - medicine , urine , creatinine , excretion , urinary system , sodium , urology , organic chemistry , chemistry
Background High salt intake is a known risk factor of hypertension, which in turn increases the risk of stroke and cardiovascular diseases. The aim of this study was to develop and evaluate a method for predicting 24-hour urinary sodium excretion (UNa24h) using casual urine specimens in Chinese hypertensive patients. Methods A total of 966 patients with hypertension were included from 8 provinces across China. A UNa24h prediction model (Sun_C method) was developed for males and females using linear regression based on age, weight, sodium concentration in the spot urine (UNaspot), and creatinine concentration in the spot urine (UCrspot). The data were split into the training (70%) and testing (30%) sets to, respectively, develop and evaluate the Sun_C method. Results Compared with the Kawasaki, INTERSALT, and Tanaka methods, Sun_C method achieved a low and consistent mean bias (1.1 mmol/d) within the range from 106 to 212 mmol/d of UNa24h (equivalent to NaCl intake of 6–12 g/d). In addition, the Sun_C method showed no significant difference between the measured and estimated UNa24h in a paired t-test (P = 0.689). At individual level, Sun_C method had 79.8% of individuals at the cutoff under ±30% level. Conclusions Sun_C method may prove a reasonable method to estimate the daily dietary sodium intakes (particularly in the range of 6–12 g/d of NaCl) in Chinese hypertensive patients using spot urine measurements. As the amount of data increases in the future, the performance of our formulae will be further improved.
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