Prediction of 24‐Hour Urinary Sodium Excretion Using Machine‐Learning Algorithms

Abstract

Background Accurate quantification of sodium intake based on self‐reported dietary assessments has been a persistent challenge. We aimed to apply machine‐learning (ML) algorithms to predict 24‐hour urinary sodium excretion from self‐reported questionnaire information. Methods and Results We analyzed 3454 participants from the NHS (Nurses' Health Study), NHS‐II (Nurses' Health Study II), and HPFS (Health Professionals Follow‐Up Study), with repeated measures of 24‐hour urinary sodium excretion over 1 year. We used an ensemble approach to predict averaged 24‐hour urinary sodium excretion using 36 characteristics. The TOHP‐I (Trial of Hypertension Prevention I) was used for the external validation. The final ML algorithms were applied to 167 920 nonhypertensive adults with 30‐year follow‐up to estimate confounder‐adjusted hazard ratio (HR) of incident hypertension for predicted sodium. Averaged 24‐hour urinary sodium excretion was better predicted and calibrated with ML compared with the food frequency questionnaire (Spearman correlation coefficient, 0.51 [95% CI, 0.49–0.54] with ML; 0.19 [95% CI, 0.16–0.23] with the food frequency questionnaire; 0.46 [95% CI, 0.42–0.50] in the TOHP‐I). However, the prediction heavily depended on body size, and the prediction of energy‐adjusted 24‐hour sodium excretion was modestly better using ML. ML‐predicted sodium was modestly more strongly associated than food frequency questionnaire‐based sodium in the NHS‐II (HR comparing Q5 versus Q1, 1.48 [95% CI, 1.40–1.56] with ML; 1.04 [95% CI, 0.99–1.08] with the food frequency questionnaire), but no material differences were observed in the NHS or HPFS. Conclusions The present ML algorithm improved prediction of participants' absolute 24‐hour urinary sodium excretion. The present algorithms may be a generalizable approach for predicting absolute sodium intake but do not substantially reduce the bias stemming from measurement error in disease associations.

Keywords

• dietary sodium
• food frequency questionnaire
• hypertension
• machine learning
• measurement error