No abstract

Background/Objectives While daily hydration is best assessed in 24-h urine sample, spot sample is often used by health care professionals and researchers due to its practicality. However, urine output is subject to circadian variation, with urine being more concentrated in the morning. It has been demonstrated that afternoon spot urine samples are most likely to provide equivalent urine concentration to 24-h urine samples in adults. The aim of the present study was to examine whether urine osmolality (UOsm) assessed from a spot urine sample in specific time-windows was equivalent to 24-h UOsm in free-living healthy children. Subjects/Methods Among 541 healthy children (age: 3-13 years, female: 45%, 77% non-Hispanic white, BMI:17.7 ± 4.0 kg m −2), UOsm at specific time-windows [morning (0600-1159), early afternoon (1200-1559), late afternoon (1600-1959), evening (2000-2359), overnight (2400-0559), and first morning] was compared with UOsm from the corresponding pooled 24-h urine sample using an equivalence test. Results Late afternoon (1600-1959) spot urine sample UOsm value was equivalent to the 24-h UOsm value in children (P < 0.05; mean difference: 62 mmol kg −1 ; 95% CI: 45-78 mmol kg −1). The overall diagnostic ability of urine osmolality assessed at late afternoon (1600-1959) to diagnose elevated urine osmolality on the 24-h sample was good for both cutoffs of 800 mmol kg −1 [area under the curve (AUC): 87.4%; sensitivity: 72.6%; specificity: 90.5%; threshold: 814 mmol kg −1 ] and 500 mmol kg −1 (AUC: 83.5%; sensitivity: 75.0%; specificity: 80.0%; threshold: 633 mmol kg −1). Conclusion These data suggest that in free-living healthy children, 24-h urine concentration may be approximated from a late afternoon spot urine sample. This data will have practical implication for health care professionals and researchers.

The purpose of this investigation was to assess the validity and reliability of a seven-day water frequency questionnaire (TWI-FQ) to estimate daily total water intake (TWI) in comparison to a water turnover objective reference value via deuterium oxide (D2O). Data collection occurred over 3 weeks, with a wash-out period during week two. Healthy adults (n = 98; 52% female; 41 ± 14 y; BMI, 26.4 ± 5.5 kg·m−2) retrospectively self-reported consumption frequencies of 17 liquids and 35 foods with specified volumes/amounts for weeks one and three via TWI-FQ. Standard water content values were utilized to determine the volume of water consumed from each liquid and food for calculation of mean daily TWI for each week. Diet records were completed daily during week two to estimate metabolic water production. To assess validity of the TWI-FQ, participants consumed D2O at the start of each week and provided urine samples immediately before ingestion, the following day, and at the end of the week to calculate water turnover. Metabolic water was subtracted from water turnover to estimate TWI. TWI-FQ validity was assessed via Bland-Altman plot for multiple observations. Reliability was assessed via intraclass correlation and Pearson's correlation between weeks. TWI-FQ significantly underestimated D2O TWI by −350 ± 1,431 mL·d−1 (95% confidence interval (CI): −551, −149 mL·d−1). TWI-FQ TWI was significantly correlated (r = 0.707, P &lt;0.01) and not different (198 ± 1,180 mL·d−1, 95% CI: −38, 435 mL·d−1) between weeks. TWI-FQ intraclass correlation = 0.706 was significant [95% CI: 0.591, 0.793; F(97, 98) = 5.799], indicating moderate test-retest reliability. While this tool would not be suitable for individual TWI assessment, the magnitude of bias may be acceptable for assessment at the sample-level.

Reproductive hormones can have significant non-reproductive physiological effects, including altering the regulation of body fluid volume. The purpose of this investigation was to explore the impact of sex and menstrual cycle (MC) phase on volume regulatory responses to 24-hour fluid restriction (24-h FR). Participants (men: n=12, 20±2y; women: n=10, 20±1y) were assigned two randomized and counterbalanced fluid prescriptions (Euhy: euhydrated, urine specific gravity, USG<1.020; Dehy: 24-h FR, USG>1.020). Men completed both (MEuhy, MDehy) while women completed both in the late follicular (days 10-13; FDehy, FEuhy) and mid-luteal (days 18-22; LDehy, LEuhy) phases. We measured body mass, plasma and urine osmolality (Posm, Uosm), USG, urine color (Ucol) and serum copeptin. 24-h FR yielded mild dehydration with no influence of sex or MC phase (P>0.05). Men exhibited higher copeptin following Dehy (pre: 8.2±5.2, post: 15.8±12.6, P=0.04) but women did not (FDehy - pre: 4.3±1.6, post: 10.5±6.9, P=0.06; LDehy - pre: 5.6±3.5, post: 10.4±6.2, P=0.16). In FDehy women, Posm increased following FR (pre: 288±2, post: 292±1, P=0.03) but not in men (pre: 292±3, post: 293±2, P=0.46). No MC differences were observed between %BML, Posm, Uosm, USG, Ucol, and serum copeptin (P>0.05). These results suggest that copeptin responses to mild dehydration elicited via 24-h FR were present in men only, regardless of MC phase in young healthy adults.

We investigated the impact of nutrient intake on hydration biomarkers in cyclists before and after a 161 km ride, including one hour after a 650 mL water bolus consumed post-ride. To control for multicollinearity, we chose a clustering-based, machine learning statistical approach. Five hydration biomarkers (urine color, urine specific gravity, plasma osmolality, plasma copeptin, and body mass change) were configured as raw- and percent change. Linear regressions were used to test for associations between hydration markers and eight predictor terms derived from 19 nutrients merged into a reduced-dimensionality dataset through serial k-means clustering. Most predictor groups showed significant association with at least one hydration biomarker: (1) Glycemic Load + Carbohydrates + Sodium, (2) Protein + Fat + Zinc, (3) Magnesium + Calcium, (4) Pinitol, (5) Caffeine, (6) Fiber + Betaine, and (7) Water; potassium + three polyols, and mannitol + sorbitol showed no significant associations with any hydration biomarker. All five hydration biomarkers were associated with at least one nutrient predictor in at least one configuration. We conclude that in a real-life scenario, some nutrients may serve as mediators of body water, and urine-specific hydration biomarkers may be more responsive to nutrient intake than measures derived from plasma or body mass.