Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Fast track – JSH2025 Tokyo
  • Published:

Challenges in improving the equation for estimating 24-h urinary sodium excretion from casual urine in hypertensive patients taking antihypertensive drugs: addressing overestimation, especially at low sodium excretion levels

Abstract

The estimation formula by Tanaka et al. for predicting the 24-h urinary sodium (Na) excretion (24Na) from a single causal urine sample is widely used. However, it overestimates values in the low 24Na range. We aimed to develop a formula to improve the accuracy, particularly for samples with 24Na < 2 g/day. Stored data from 187 hypertensive patients (mean age, 66.1 years; 56.7% female) who underwent both 24-h home urine collection and a fasting morning causal urine test the following day were analyzed. We used a machine learning approach to extract conditional branches based on the threshold relationships among the variables. The proposed estimation formula was constructed by adding a correction term to the Na/Creatinine(Cr) ratio in Tanaka’s formula and the modified formula was applied to each conditional branch. The correction terms included body mass index (BMI), age, and concentration of causal urine Na and were applied in different forms according to each branch. Compared with the Tanaka method, our method improved the agreement rate by ~25% and reduced the disagreement rate by 25% in samples with 24Na < 2 g/day. The correlation coefficient was higher (Ours: 0.51, Tanaka: 0.29), the range of error with 24Na was narrower (Ours: 4.89, Tanaka: 5.69), and the percentage of absolute errors for <1 g improved by 9.8%. Although developed from a specific dataset, our formula is useful for low-24Na samples prone to misestimation by the conventional formula and may improve the accuracy of dietary salt intake assessments from causal urine.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group. N Engl J Med. 2001;344:3–10.

    Article  CAS  PubMed  Google Scholar 

  2. Whelton PK, Appel LJ, Espeland MA, Applegate WB, Ettinger WH Jr, Kostis JB, et al. Sodium reduction and weight loss in the treatment of hypertension in older persons: a randomized controlled trial of nonpharmacologic interventions in the elderly (TONE). TONE Collaborative Research Group. JAMA. 1998;279:839–46.

    Article  CAS  PubMed  Google Scholar 

  3. Gupta DK, Lewis CE, Varady KA, Su YR, Madhur MS, Lackland DT, et al. Effect of dietary sodium on blood pressure: a crossover trial. JAMA. 2023;330:2258–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ohta Y, Tsuchihashi T, Onaka U, Miyata E. Long-term compliance of salt restriction and blood pressure control status in hypertensive outpatients. Clin Exp Hypertens. 2010;32:234–38.

    Article  CAS  PubMed  Google Scholar 

  5. Campbell NRC, Whelton PK, Orias M, Cobb LL, Jones ESW, Garg R, et al. It is strongly recommended to not conduct, fund, or publish research studies that use spot urine samples with estimating equations to assess individual’s sodium(salt) intake in association with health outcomes: a policy statement of the World Hypertension League, International Society of Hypertension and Resolve to Save Lives. J Hypertens. 2023;41:683–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tanaka T, Okamura T, Miura K, Kadowaki T, Ueshima H, Nakagawa H, et al. A simple method to estimate populational 24-h urinary sodium and potassium excretion using a casual urine specimen. J Hum Hypertens. 2002;16:97–103.

    Article  CAS  PubMed  Google Scholar 

  7. Zhou L, Tian Y, Fu JJ, Jiang YY, Bai YM, Zhang ZH, et al. Validation of spot urine in predicting 24-h sodium excretion at the individual level. Am J Clin Nutr. 2017;105:1291–96.

    Article  CAS  PubMed  Google Scholar 

  8. Kelly C, Geaney F, Fitzgerald AP, Browne GM, Perry IJ. Validation of diet and urinary excretion derived estimates of sodium excretion against 24-h urine excretion in a worksite sample. Nutr Metab Cardiovasc Dis. 2015;25:771–79.

    Article  PubMed  Google Scholar 

  9. He FJ, Ma Y, Campbell NRC, MacGregor GA, Cogswell ME, Cook NR. Formulas to estimate dietary sodium intake from spot urine alter sodium-mortality relationship. Hypertension. 2019;74:572–80.

    Article  CAS  PubMed  Google Scholar 

  10. Imai E, Yasuda Y, Horio M, Shibata K, Kato S, Mizutani Y, et al. Validation of the equations for estimating daily sodium excretion from spot urine in patients with chronic kidney disease. Clin Exp Nephrol. 2011;15:861–67.

    Article  CAS  PubMed  Google Scholar 

  11. Segawa H, Kanno Y. Kidney disease and salt intake. Jpn J Nephrol. 2019;61:574–78.

    CAS  Google Scholar 

  12. Arakawa K, Tominaga M, Sakata A, Tsuchihashi T. Does casual urine Na/K ratio predict 24 h urine Na/K ratio in treated hypertensive patients? Comparison between casual urine voided in the morning vs. 24 h urine collected on the previous day. Hypertens Res. 2025;48:772–79.

    Article  CAS  PubMed  Google Scholar 

  13. Ohya Y, et al. The Japanese Society of Hypertension guidelines for the management of hypertension (JSH 2025). Hypertension Res. 2025;48:2500–11.

    Article  Google Scholar 

  14. Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull. 1945;1:80–3.

    Article  Google Scholar 

  15. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hastie T, Tibshirani R, Friedman J The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. New York, USA: Springer Nature; 2009.

  17. edregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-lean: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

    Google Scholar 

  18. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.

    Article  CAS  PubMed  Google Scholar 

  19. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied linear statistical models. 5th ed. Boston, USA: McGraw-Hill; 1996.

  21. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference; 2010. https://proceedings.scipy.org/articles/Majora-92bf1922-011.pdf.

  22. Zhou L, Stamler J, Chan Q, Van Horn L, Daviglus ML, Dyer AR, et al. INTERMAP Research Group. Salt intake and prevalence of overweight/obesity in Japan, China, the United Kingdom, and the United States: the INTERMAP Study. Am J Clin Nutr. 2019;110:34–40.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ling CX, Li C. Data mining for direct marketing: problems and solutions. In Proceedings of International Conference on Knowledge Discovery from Data (KDD 98). 1998, pp. 73–79. https://cdn.aaai.org/KDD/1998/KDD98-011.pdf. Accessed 13 August 2023.

  24. Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. 2nd ed. New York: Springer; 2005. pp. 875–86.

  25. Jędrusik P, Symonides B, Gaciong Z. Estimation of 24-hour urinary sodium, potassium, and creatinine excretion in patients with hypertension: can spot urine measurements replace 24-hour urine collection?. Pol Arch Intern Med. 2019;129:506–15.

    PubMed  Google Scholar 

  26. Brown IJ, Dyer AR, Chan Q, Cogswell ME, Ueshima H, Stamler J, et al. INTERSALT Cooperative Research Group. Estimating 24-hour urinary sodium excretion from casual urinary sodium concentrations in Western populations: the INTERSALT study. Am J Epidemiol. 2013;177:1180–92.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hamaya R, Wang M, Juraschek SP, Mukamal KJ, Manson JE, Tobias DK, et al. Prediction of 24-hour urinary sodium excretion using machine-learning algorithms. J Am Heart Assoc. 2024;13:e034310. https://doi.org/10.1161/JAHA.123.034310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery; 2016. pp. 785–94. https://doi.org/10.1145/2939672.2939785.

  29. Atool implementing our estimation formula. 2025. https://first-screening.com/news/post-335/. Accessed 1 December 2025.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Morio Matsumoto.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matsumoto, M., Arakawa, K., Asai, K. et al. Challenges in improving the equation for estimating 24-h urinary sodium excretion from casual urine in hypertensive patients taking antihypertensive drugs: addressing overestimation, especially at low sodium excretion levels. Hypertens Res 49, 539–549 (2026). https://doi.org/10.1038/s41440-025-02539-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41440-025-02539-8

Keywords

Search

Quick links