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Hydration, fluid intake, and related urine biomarkers among male college students in Cangzhou, China: a cross-sectional study-applications for assessing fluid intake and adequate water intake

Zhang, Na, Du, Songming, Tang, Zhenchuang, Zheng, Mengqi, Yan, Ruixia, Zhu, Yitang, Ma, Guansheng·2017·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph14050513
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Abstract

The objectives of this study were to assess the associations between fluid intake and urine biomarkers and to determine daily total fluid intake for assessing hydration status for male college students. A total of 68 male college students aged 18–25 years recruited from Cangzhou, China completed a 7-day cross-sectional study. From day 1 to day 7; all subjects were asked to complete a self-administered 7-day 24-h fluid intake record. The foods eaten by subjects were weighed and 24-h urine was collected for three consecutive days on the last three consecutive days. On the sixth day, urine osmolality, specific gravity (USG), pH, and concentrations of potassium, sodium, and chloride was determined. Subjects were divided into optimal hydration, middle hydration, and hypohydration groups according to their 24-h urine osmolality. Strong relationships were found between daily total fluid intake and 24-h urine biomarkers, especially for 24-h urine volume (r = 0.76; p < 0.0001) and osmolality (r = 0.76; p < 0.0001). The percentage of the variances in daily total fluid intake (R2) explained by PLS (partial least squares) model with seven urinary biomarkers was 68.9%; two urine biomarkers—24-h urine volume and osmolality—were identified as possible key predictors. The daily total fluid intake for assessing optimal hydration was 2582 mL, while the daily total fluid intake for assessing hypohydration was 2502 mL. Differences in fluid intake and urine biomarkers were found among male college students with different hydration status. A strong relationship existed between urine biomarkers and fluid intake. A PLS model identified that key variables for assessing daily total fluid intake were 24-h urine volume and osmolality. It was feasibility to use total fluid intake to judge hydration status.

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Research works printed but not linked: 36

  1. Benefer, 2013, Water intake and post-exercise cognitive performance: An observational study of long-distance walkers and runners, Eur. J. Nutr., 52, 617, 10.1007/s00394-012-0364-y · DOI
  2. Vibhakar, 2009, Voluntary dehydration and cognitive performance in trained college athletes, Percept. Motor Skills, 109, 251, 10.2466/pms.109.1.251-269 · DOI · Paper page
  3. Edmonds, 2013, Water consumption, not expectancies about water consumption, affects cognitive performance in adults, Appetite, 60, 148, 10.1016/j.appet.2012.10.016 · DOI
  4. Arnaoutis, 2012, Water ingestion improves performance compared with mouth rinse in dehydrated subjects, Med. Sci. Sports Exerc., 44, 175, 10.1249/MSS.0b013e3182285776 · DOI
  5. Castellani, 2010, Effect of hypohydration and altitude exposure on aerobic exercise performance and acute mountain sickness, J. Appl. Physiol., 109, 1792, 10.1152/japplphysiol.00517.2010 · DOI · Paper page
  6. Dai, 2012, Dietary factors and risk of kidney stone: A case-control study in southern China, J. Renal Nutr., 23, 21, 10.1053/j.jrn.2012.04.003 · DOI
  7. Sorensen, 2012, Impact of nutritional factors on incident kidney stone formation: A report from the WHI OS, J. Urol., 187, 1645, 10.1016/j.juro.2011.12.077 · DOI
  8. Alaya, 2013, Idiopathic urolithiasis in Tunisian children: A report of 134 cases, Saudi J. Kidney Dis. Transplant., 24, 1055, 10.4103/1319-2442.118099 · DOI
  9. Strippoli, 2011, Fluid and nutrient intake and risk of chronic kidney disease, Nephrology, 16, 326, 10.1111/j.1440-1797.2010.01415.x · DOI
  10. Momeni, 2014, Does water hardness have preventive effect on cardiovascular disease?, Int. J. Prev. Med., 5, 159
  11. 2014, Correlation between water hardness and cardiovascular diseases in Mostar city, Bosnia and Herzegovina, J. Water Health, 12, 817, 10.2166/wh.2014.129 · DOI
  12. Catling, 2008, A systematic review of analytical observational studies investigating the association between cardiovascular disease and drinking water hardness, J. Water Health, 6, 433, 10.2166/wh.2008.054 · DOI
  13. Cheuvront, 2011, Reference change values for monitoring dehydration, Clin. Chem. Lab. Med., 49, 1033, 10.1515/CCLM.2011.170 · DOI
  14. Munoz, 2012, Hydration biomarkers and dietary fluid consumption of women, J. Am. Acad. Nutr. Diet., 112, 1056, 10.1016/j.jand.2012.03.036 · DOI · Paper page
  15. Perrier, 2013, Relation between urinary hydration biomarkers and total fluid intake in healthy adults, Eur. J. Clin. Nutr., 67, 939, 10.1038/ejcn.2013.93 · DOI · Paper page
  16. Erica, 2013, Hydration biomarkers in free-living adults with different levels of habitual fluid consumption, Br. J. Nutr., 109, 1678, 10.1017/S0007114512003601 · DOI · Paper page
  17. Perrier, 2013, Circadian variation and responsiveness of hydration biomarkers to changes in daily water intake, Eur. J. Appl. Physiol., 113, 2143, 10.1007/s00421-013-2649-0 · DOI · Paper page
  18. Mckenzie, A.L., Perrier, E.T., Guelinckx, I., Kavouras, S.A., Aerni, G., Lee, E.C., Volek, J.S., Maresh, C.M., and Armstrong, L.E. (2016). Relationships between hydration biomarkers and total fluid intake in pregnant and lactating women. Eur. J. Nutr., 1–10. · DOI
  19. Padrao, 2016, Urinary hydration biomarkers and dietary intake in children, Nutr. Hosp., 33, 35, 10.20960/nh.314 · DOI
  20. Ma, 2012, Fluid intake of adults in four Chinese cities, Nutr. Rev., 70, S105, 10.1111/j.1753-4887.2012.00520.x · DOI · Paper page
  21. Grigg, 2002, The worlds of tea and coffee: Patterns of consumption, GeoJournal, 57, 283, 10.1023/B:GEJO.0000007249.91153.c3 · DOI
  22. Liu, 2008, Food consumption patterns and their effect on water requirement in China, Hydrol. Earth Syst. Sci., 5, 27
  23. Institute of Medicine (US) (2005). DRI, Dietary Reference Intakes for Water, Potassium, Sodium, Chloride, and Sulfate, National Academy Press.
  24. Sm, 2013, Daily intake of plain water and beverages of primary and middle school students in four cities of China, Zhonghua Yu Fang Yi Xue Za Zhi, 47, 202
  25. Fu, C., Jiang, Z., Guan, Z., He, J., and Xu, Z.-F. (2008). Regional Climate Studies of China, Springer. · DOI
  26. Zhang, 2005, Race and sex differences and contribution of height: A study on bone size in healthy Caucasians and Chinese, Am. J. Hum. Biol., 17, 568, 10.1002/ajhb.20427 · DOI
  27. Wohlfarth, 1990, Genetic differences between the Chinese and European races of common carp: 5. Differential adaptation to manure and artificial feeds, Theor. Appl. Genet., 79, 693, 10.1007/BF00226885 · DOI
  28. Perrier, 2015, Twenty-Four-Hour Urine Osmolality as a Physiological Index of Adequate Water Intake, Dis. Markers, 2015, 1, 10.1155/2015/231063 · DOI · Paper page
  29. Pross, N., Demazières, A., Girard, N., Barnouin, R., Metzger, D., Klein, A., Perrier, E., and Guelinckx, I. (2014). Effects of changes in water intake on mood of high and low drinkers. PLoS ONE, 9. · DOI
  30. Finn, 2015, Agreement Between Omron 306 and Biospace InBody 720 Bioelectrical Impedance Analyzers (BIA) in Children and Adolescents, Res. Q. Exerc. Sport, 86, S58, 10.1080/02701367.2015.1042998 · DOI
  31. Du, 1989, A formula to estimate the approximate surface area if height and weight be known. 1916, Nutrition, 5, 303
  32. Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention (2009). China Food Composition, Peking University Medicine Press.
  33. Burchfield, 2015, 24-h Void number as an indicator of hydration status, Eur. J. Clin. Nutr., 69, 638, 10.1038/ejcn.2014.278 · DOI · Paper page
  34. Stookey, 2012, What is the cell hydration status of healthy children in the USA? Preliminary data on urine osmolality and water intake, Public Health Nutr., 15, 2148, 10.1017/S1368980011003648 · DOI
  35. Mckenzie, 2017, Urine color as an indicator of urine concentration in pregnant and lactating women, Eur. J. Nutr., 56, 355, 10.1007/s00394-015-1085-9 · DOI
  36. Armstrong, 2016, An empirical method to determine inadequacy of dietary water, Nutrition, 32, 79, 10.1016/j.nut.2015.07.013 · DOI

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Metadata

Authors
Zhang, N., Du, S., Tang, Z., Zheng, M., Yan, R., Zhu, Y., & Ma, G.
Affiliations
  • Department of Nutrition and Food Hygiene, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing 100191, China
  • Chinese Nutrition Society, 6 Guang An Men Nei Street, Xicheng District, Beijing 100053, China
  • Institute of Food and Nutrition Development, Ministry of Agriculture, 12 Zhong Guan Cun Nan Street, Haidian District, Beijing 100181, China
  • National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, 29 Nan Wei Road, Xicheng District, Beijing 100050, China
  • Department of Health Management and Service, Cangzhou Medical College, Higher Education District, Cangzhou 061000, China
  • Clinical Laboratory, Cangzhou Central Hospital, Xinhua District, Cangzhou 061000, China
ORCID
Zhang, Na
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2017-05-11
Volume
14
Issue
5
Pages
513
ISSN
1660-4601
DOI
10.3390/ijerph14050513
Cited by
40
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Danone Institute China (DIC2015-10)

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CoChat. (2026). Citation check: Hydration, fluid intake, and related urine biomarkers among male college students in Cangzhou, China: a cross-sectional study-applications for assessing fluid intake and adequate water intake. https://app.cochat.ai/papers/10.3390/ijerph14050513
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Zhang, N., Du, S., Tang, Z., Zheng, M., Yan, R., Zhu, Y., & Ma, G. (2017). Hydration, fluid intake, and related urine biomarkers among male college students in Cangzhou, China: a cross-sectional study-applications for assessing fluid intake and adequate water intake. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph14050513

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