Evidence synthesizing the effects of acute body water losses on various markers of glycemic regulation, appetite, metabolism, and stress is lacking. Thus, the purpose of this review was to summarize the response of various hormonal changes involved in these physiologic functions to dehydration. A comprehensive literature search for peer-reviewed research in the databases PubMed, Scopus, CINAHL, and SportDiscus was conducted. Studies were included if they contained samples of adults (>18 years) and experimentally induced dehydration as measured by acute body mass loss. Twenty-one articles were eligible for inclusion. Findings suggested cortisol is significantly elevated with hypohydration (standard mean difference [SMD] = 1.12, 95% CI [0.583, 1.67], p < 0.0001). Testosterone was significantly lower in studies where hypohydration was accompanied by caloric restriction (SMD= −1.04, 95% CI [−1.93, −0.14], p = 0.02), however, there were no changes in testosterone in studies examining hypohydration alone (SMD = −0.17, 95% CI [−0.51 0.16], p = 0.30). Insulin and ghrelin were unaffected by acute total body water losses. Acute hypohydration increases markers of catabolism but has a negligible effect on markers of glycemic regulation, appetite, anabolism and stress. Given the brevity of existing research, further research is needed to determine the impact of hydration on glucagon, leptin, peptide YY and the subsequent outcomes relevant to both health and performance.

This study determined the beverage hydration index (BHI) and postprandial cardiac autonomic activity after consuming an isotonic beverage (IB) compared to distilled water (DW). Twenty-two participants (50% female; mean ± SD; age, 27 ± 3 year; height, 169.1 ± 12.6 cm; weight, 73.3 ± 13.8 kg; BF%, 23 ± 10%) completed two experimental trials where they consumed 1 L DW or an IB; after which urine volume and cardiac autonomic activity was measured through 240 min. Cardiac autonomic activity was quantified using heart rate (HR), log transformed heart rate variability measures (root mean square of successive R–R intervals; RMSSD; low frequency, LF; and high frequency, HF) and systolic time intervals (pre-ejection period, PEP). BHI was significantly greater after IB consumption at min 0 (MD [95% CI]; 1.31 [0.35, 2.27]), 180 min (0.09 [0.022, 0.16]), and 240 min (0.1 [0.03, 0.17]) compared to DW (p = 0.031). Net fluid balance was significantly greater in IB than DW at 180 min (90 [−16.80, 196.81]) and 240 min (106 [−13.88, 225.88]) (p = 0.037). HR decreased over time in both beverage trials but was higher following IB ingestion at 0 min (3.9 [−2.42, 10.22]), 30 min (5.3 [−0.94, 11.54]), and 60 min (2.7 [−3.42, 8.82]) (p = 0.0002). lnHF was greater 30 min post DW ingestion compared to IB (0.45 [−0.23, 1.13]) (p = 0.039). IB promotes greater fluid retention capacity compared to DW within 4 hours of consumption. The variations in cardiac autonomic measures may warrant further investigation in clinical populations (i.e., patients with autonomic failure).

Purpose This study determined fluid intake and physical activity behaviors among college students during the COVID-19 pandemic. Methods College students (n = 1014; females, 75.6%) completed an online survey during the Spring 2020 academic semester following the initial global response to the COVID-19 pandemic. Academic standing, habitation situation, and University/College responses to COVID-19 were collected. Participants completed the Godin Leisure-Time Exercise Questionnaire and a 15-item Beverage Questionnaire (BEVQ-15) to determine physical activity level and fluid intake behaviors, respectively. Results Females (1920 ± 960 mL) consumed significantly less fluid than males (2400 ± 1270 mL, p < 0.001). Living off-campus (p < 0.01) and living with a spouse/partner (p < 0.01) was associated with increased consumption of alcoholic beverages. 88.7% of participants reported being at least moderately active; however, Black/African American and Asian participants were more likely to be less active than their Caucasian/White counterparts (p < 0.05). Participants reporting no change in habitation in response to COVID-19 had a higher fluid intake (p = 0.002); however, the plain water consumption remained consistent (p = 0.116). While there was no effect of habitation or suspension of classes on physical activity levels (p > 0.05), greater self-reported physical activity was associated with greater fluid intake (std. β = 0.091, p = 0.003). Conclusions Fluid intake among college students during the initial response to the COVID-19 pandemic approximated current daily fluid intake recommendations. Associations between COVID-19-related disruptions (i.e., suspension of classes and changes in habitation) and increased alcohol intake are concerning and may suggest the need for the development of targeted strategies and programming to attenuate the execution of negative health-related behaviors in college students.

Background: Multifrequency bioelectrical impedance devices such as the InBody 770 (IB770) offer faster measurements and lower costs compared with other body composition assessments. This study validated measures from IB770 against the deuterium oxide dilution technique (D2O) and DXA and compared a four-compartment (4C) model using total body water (TBW) derived from IB770 compared with D2O. Methods: A total of 55 adult females (mean ± SD, age: 21.1 ± 2.6 years) completed IB770 and DXA scans and the D2O protocol. Lin’s concordance correlation coefficients (CCCs), Bland–Altman analyses, and other equivalence tests evaluated agreement between IB770 and the criterion for measures of fat mass (FM), fat-free mass (FFM), and TBW individually and as part of 4C models. Results: There was substantial agreement between IB770 and D2O for TBW (MD = Mean Difference) (MD = 0.34 L, CCC = 0.98) and between the IB770 and DXA for FM (MD = −0.22 kg, CCC = 0.99). IB770 overestimated FFM compared with DXA (MD = 3.15 kg, CCC = 0.91). Both 4C models had almost perfect agreement for FM (CCC = 0.99), FFM (CCC = 0.99), and body fat percentage (CCC = 0.99). Conclusions: IB770 is valid for assessing TBW and can be used within the context of a 4C model in females.

Previous studies have identified links between fluid intake, hydration related hormones and cortisol measured at one timepoint but have not considered how hydration may influence cortisol dynamics throughout the day. This study assessed associations between hydration status (copeptin, urinary osmolality, urine volume) and habitual fluid intake with cortisol dynamics. The day before (DB) a 6-h laboratory visit, 29 male participants (age, 23±4y; BMI, 25.5 ± 4.3 kg/m²; body fat, 17.3 ± 9.3 %) provided 24-h urine samples and a fasted blood sample for hydration status assessment, recorded their 24-h fluid intake for three days prior, and provided 10 saliva samples to assess cortisol dynamics from DB into the evening of the laboratory visit. Calculated indices of cortisol dynamics included: nocturnal cortisol rise (NCR - salivary cortisol rise from bed to awakening), peak salivary cortisol (peak S_CORT - highest cortisol of all samples), cortisol awakening response (ΔCAR - difference between high morning sample and awakening sample), area under the curve with respect to ground (AUCG) and increase (AUCI), and diurnal cortisol slope (DCS - rate of change in cortisol from awakening to bed). The relationships between fluid intake or hydration status and cortisol dynamics were analyzed by separating participants into fluid intake tertile groups and by regressing cortisol dynamics on the continuous variables of total fluid intake (TFI) or hydration biomarkers. There were no between-group differences for ΔCAR (p = 0.89), AUCG (p = 0.57), AUCI (p = 0.48), peak S_CORT (p = 0.14), NCR (p = 0.95), DCS (p = 0.22), or serum cortisol (p = 0.61). TFI was not associated with log (peak S_CORT) (p = 0.49), ΔCAR (p = 0.61), AUCG (p = 0.76), or AUCI (p = 0.56). Copeptin was not associated with log (peak S_CORT) (p = 0.99), ΔCAR (p = 0.22), AUCG (p = 0.69) or AUCI (p = 0.18). Urinary hydration markers were not associated with any measures of cortisol dynamics (p > 0.05). These null effects were consistent when controlling for physical activity, sleep, and body fat percentage. In the absence of dehydrating stimuli, measures of fluid intake or hydration status may not be associated with cortisol dynamics in young healthy males.

Previous work focusing on understanding nutrient intake and its association with total body water homeostasis neglects to consider the collinearity of types of nutrients consumed and subsequent associations with hydration biomarkers. Therefore, the purpose of this study was to analyze consumption patterns of 23 a priori selected nutrients involved in osmotic homeostasis, as well as their association with 24 h urinary hydration markers among fifty African–American first-year college students through a repeated measures observation in a daily living setting. Through application of hierarchical clustering, we were able to identity four clusters of nutrients based on 24 h dietary recalls: (1) alcohol + pinitol, (2) water + calcium + magnesium + erythritol + inositol + sorbitol + xylitol, (3) total calories + total fat + total protein + potassium + sodium + zinc + phosphorous + arginine, and (4) total carbohydrates + total fiber + soluble fiber + insoluble fiber + mannitol + betaine. Furthermore, we found that consumption of nutrients in Cluster #2 was significantly predictive of urine osmolality (p = 0.004); no other clusters showed statistically significant associations with 24 h urinary hydration biomarkers. We conclude that there may be some nutrients that are commonly consumed concomitantly (at the day level), across a variety of settings and populations, and that a limited subset of the clustering of these nutrients may associate with body water status.