Abstract
Objectives
Children 10–20 years old in the US are currently obese, showing suboptimal hydration as 60% fail to meet the US Dietary Reference Intakes for water. Studies have shown a significant inverse association between hydration status and body composition in children, although most failed to use the Dual-X-Ray Absorptiometry Scan (DEXA), the gold standard for body composition. Limited studies used an objective marker to measure hydration, such as urine specific gravity (USG) from a 24-h urine collection. Therefore, this study aimed to examine the association between hydration status (measured from USG in a 24-h urine sample and assessed from three 24-h dietary recalls) and body fat % and lean mass (assessed from a DEXA scan) in children (10–13 years, n=34) and adolescents (18–20 years, n=34).
Methods
Body composition was measured using DEXA, total water intake (mL/d) was assessed from three 24-h dietary recalls and analyzed using the Nutrition Data System for Research (NDSR). Hydration status was objectively measured using USG via 24-h urine collection.
Results
Overall body fat % was 31.7 ± 7.31, total water intake was 1746 ± 762.0 mL/d, and USG score was 1.020 ± 0.011 uG. Linear regressions showed significance between total water intake and lean mass (B=12.2, p<0.05). Logistic regressions showed no significant association between body composition and USG and total water intake.
Conclusions
Findings showed total water intake was significantly associated with lean mass. Future research should be conducted to explore other objective markers of hydration and with a larger sample.
Keywords: adolescents, body composition, children, hydration, nutrition
Introduction
Childhood obesity continues to be a major public health concern in the US, with 19.1% of boys and 17.8% of girls ages 2–19 years in the US being obese [1]. As rates increase, children are more susceptible to conditions, such as diabetes mellitus, hypertension, coronary artery disease, cancer, and sleep apnea [2], [3], [4]. Emerging evidence also shows an increased prevalence of children and adolescents with suboptimal hydration across the globe [5]. Evidence from the National Health and Examination Survey (NHANES) of 7,453 children ages 4–18 years and 3,248 adolescents ages 19–30 years in the US was examined. Average water intake was 577 mL/d in those ages 9–13 years, 866 mL/d in those ages 14–18 years, and 1,305 mL/d in those ages 19–30 years [6]. These levels are below the US Dietary Reference Intakes (DRI) of 2,100 mL/d for girls and 2,400 mL/d for boys ages 9–13 years [6], 2,300 mL/d for girls and 3,300 mL/d for boys ages 14–18 years, and 2,700 mL/d for women and 3,700 mL/d for men ages 19–30 years [6, 7]. Furthermore, the study using data from NHANES showed that 60% did not meet DRIs for total water intake [6].
Hydration may also have an impact on body composition as it may increase lipolysis, resulting in an increased metabolism and lower body weight [8]. Studies have shown an association between hydration and weight status [9], [10], [11], [12], [13], [14], mainly in adults using various markers to assess hydration status, such as urine osmolality, urine specific gravity, total body water (TBW), free water reserve (FWR), urine color, and thirst perception. A study using data from NHANES 2009–2012 of adolescents and adults ages 18–64 showed that those inadequately hydrated, which was measured by urine osmolality, had significantly higher BMI (p<0.001) and had higher odds of being obese (OR=1.59; 95% CI, 1.35–1.88, p<0.001) compared to those adequately hydrated [15]. Similar results were found in 1,500 Portuguese adults, with the greatest odds of obesity (OR=1.97, 95% CI, 1.06–3.66) in men in the highest osmolality tertile (≥602.1 mOsm/kg) compared to the lowest tertile [16]. For children and adolescents, less evidence is available associating an objective marker of hydration with weight or obesity. A study among 86 obese and 89 normal weight Italian children ages 7–11 years found that hypohydration, assessed using free water reserve (FWR), was significantly higher in obese children (34%) compared to children with healthy weights (20%) [17]. Another study of 371 Belgian children ages 7–13 years reported that BMI was a significant predictor of total fluid intake, assessed using urine osmolality, (β=0.110, p=0.038), although no significant association was found between fluid intake and hydration status measured using urine osmolality [18, 19]. Another study among 358 Spanish adolescents and adults ages 18–39 years found an inverse association between water intake, assessed from questionnaires, and BMI (r=−0.18, p<0.01) and body fat percentage (%) (r=−0.14, p<0.05) [19]. Although some studies associated hydration status assessed from questionnaires with weight status, to our knowledge, only three studies assessed hydration using an objective marker [19], [20], [21]. In addition, the studies did not evaluate body mass using Dual-energy X-ray absorptiometry (DEXA) which is considered the gold standard for measuring body composition [22]. Most of the studies evaluating the association between hydration and body composition were done primarily in European children and adolescents.
To date, there are no studies that have explored the relationship between hydration status via an objective marker and body composition using DEXA scan in children and adolescents in the US. Therefore, this study aimed to examine the association between hydration status (measured from USG in a 24-h urine sample and assessed from three 24-h dietary recalls) and body fat % and lean mass (assessed from a DEXA scan) in children and adolescents.
Materials and methods
Study design
This was a cross-sectional analysis to examine the association between hydration status (measured from USG in a 24-h urine sample and assessed from three 24-h dietary recalls) and body-fat % and lean mass using DEXA in a convenient sample of children (ages 10–13 years) and adolescents (ages 18–20 years).
Study population
The data for children (ages 10–13 years) were taken from the baseline visit of the MetA-Bone Trial, a trial to determine the effects of soluble corn fiber (SCF) supplementation for one year on bone metabolism in healthy children [23]. Inclusion criteria for children of the MetA-Bone Trial were age 10–13 years, had low calcium intake (2 or fewer servings of dairy products/day), and had adequate vitamin D levels. Children were excluded if they had chronic illnesses requiring medication use or if using regular calcium (>200 mg/d) or vitamin D supplements (>400 IU/d). Children were recruited throughout Miami-Dade schools, clinics, after-school programs, emails, online, among other strategies in South Florida. Parents interested completed a short pre-screening questionnaire to verify the eligibility of their child. If eligible, parents were asked to sign the consent form, and children were asked to sign the assent form, in which included consent and assent to perform DEXA analysis. The use of this data was approved by the Institutional Review Board of Florida International University (IRB-21-0429).
The data for adolescents (ages 18–20 years) was collected directly from college students at Florida International University. College students were considered an adolescent if they met the Center of Disease Control and Preventions (CDC) age group classification of 2–20 years [24]. Any adolescents with the presence of chronic illness requiring medication use were excluded. Adolescents were recruited using flyers distributed in classes or by email through faculty and staff. Those interested were explained the study and were asked a brief pre-screening questionnaire with the inclusion criteria. If eligible, participants were asked to sign the consent form. This study was approved by the Institutional Review Board of Florida International University (IRB-22-0045).
Measurements
General questionnaire
Participants were administered a questionnaire to determine their age, sex, race, and ethnicity.
Anthropometric measurements
Weight and height were obtained by the participant wearing light clothing, utilizing a standardized scale and a wall-mounted stadiometer. Both weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively [25]. Height was measured with the participant’s back and heels touching the vertical board of the stadiometer. The moveable headboard was adjusted based on the participant’s height and brought to the most superior point with sufficient pressure to compress the hair [25]. BMI percentiles for sex and age were calculated using the CDC standardized growth charts of 2–20 years [24].
Physical activity
Participants were asked to complete a short version of the International Physical Activity Questionnaire (IPAQ) to assess physical activity based on the intensity of the activities during the last seven days (e.g., moderate vs. vigorous) [26]. The metabolic equivalent of task (MET) was calculated for each type of activity and were then added to provide the total MET score. The total METs were classified as high physical activity if total MET score was at least 3,000 MET-min/week, moderate physical activity if total MET score was at least 600 MET-min/week, and low physical activity if they failed to meet either criterion for high or moderate physical activity.
24-h dietary recalls
Participants were asked to complete three 24-h dietary recalls; the first one was completed in-person, and the two others were completed in the next few days to represent 2 weekdays and 1 weekend day. The participant’s food description was reviewed with the participant to ensure completeness and correctness. The 24-h dietary recalls were analyzed using the dietary computer-based analysis software application ‘Nutrition Data System for Research (NDSR)’ developed at the University of Minnesota Nutrition Coordinating Center (NCC). The average of the three 24-h recalls was first calculated and then the following categories were grouped for beverages: water (bottled or tap), fruit juice (citrus juice, fruit juice without citrus, and other juices), milk (unflavored and flavored whole milk, reduced-fat milk, low-fat milk, and milk substitutes), sugar-sweetened beverages and soda (regular and diet), tea and coffee (sweetened and unsweetened), and total liquid from all beverages (all of these beverages combined). For foods, the following foods were grouped: other dairy products (cheese, yogurt, ice cream, and dairy substitutes), fruits, vegetables, grains (flour and dry mixes, bread, tortillas, muffins, crackers, pasta, and cereals), protein (chicken, pork, beef, fish, sausage, and eggs), oils and fats, sauces and condiments (salad dressing, barbeque sauce, hollandaise sauce, gravy, syrup, and jams), candies and desserts (chocolate, non-chocolate candy, pastries, danish, doughnuts, and cakes), nuts and seeds (nut and seed butters included), and salty snacks (bars, chips, and popcorn). For total water intake, the NDSR software analyzes the moisture from food via water content per serving of each food or ingredient and provides a total water intake from water contained in all beverages and foods.
Seasonal changes
To account for potential ambient temperature differences in data collection, we controlled for the mean ambient temperature by the date to which the data was collected in south Florida from 2018 to 2022 using data derived from the National Oceanic and Atmospheric Administration (NOAA) [27].
Body fat % and lean mass
This was measured through a whole-body scan using the Hologic-Dual-Energy-X-Ray Absorptiometry (DEXA). Participants were asked to lie down for 10 min in the DEXA scan bed while wearing no shoes and light clothing. This was conducted by a trained and certified personnel. The analysis provided data for body fat % and lean mass in grams [28]. Binary variables were created for these measures and was coded 0 as “below the median” and 1 as “above the median.”
Objective marker of hydration status
Participants were asked to collect urine for 24-h to assess hydration status using Urine Specific Gravity (USG). For this, participants were provided with a urine hat to initially collect the urine and then transfer it to sterile urine containers. Participants were instructed to store the urine in the refrigerator throughout their collection until they were brought back to the lab. The first sample was collected at the study visit and the remainder after the study visit. The total volume and USG were measured in the laboratory at FIU. For total volume, the urine from the different containers was combined in a volumetric cylinder. From the total volume, a sample of 0.3 ml was transferred into the ATAGO PAL 10 S digital pocket refractometer that utilizes a refractive index method to measure USG. The urine sample was placed on the refractometer’s prism and analyzed to reveal the measurement value within 3 s. Removal of the sample was cleaned using a Kim wipe along with the removal of excess moisture on the prism before the next sample. Zero testing was performed after each participant’s testing by placing three drops of deionized water with a plastic dropper on the prism service. The refractometer has a urinary USG range of 1.000–1.060 with a resolution of 0.001 [29]. USG refractometry score results are based on the number, mass, and chemical structure of the dissolved particles in the urine; therefore, the higher the number, the higher the concentration. Testing was conducted three times for each participant to verify stable USG values. If results varied, an additional measurement was made to provide consistent results. The average of the three measurements was used as the final level. USG values were also categorized as euhydrated (USG<1.020 μG) or dehydrated (USG≥1.020 μG), based on the American College of Sports Medicine for fluid replacement to sustain appropriate hydration of individuals performing physical activity [30].
Statistical analysis
For descriptive statistics, categorical variables were presented as frequencies and percentages, while continuous variables were presented as mean ± standard deviation. Two-sample t-tests were used to compare mean values between age groups and chi-square and Fisher’s exact tests were used to compare differences in proportions between groups. To examine the association between hydration status (assessed from USG in a 24-h urine sample) and body fat % and lean mass in the sample (assessed from DEXA), a series of linear and logistic regressions were used, adjusting for age, sex, physical activity, and mean ambient temperature. To assess the overall fit of the regression equation and/or model to the observed data, fit indices (e.g., R2) were examined. Normality was tested using the Shapiro-Wilk test. For non-normally distributed variables, log-transformations were used to test linear associations. Regression coefficients were evaluated to further examine the individual contribution of each predictor variable to the equation and/or model. Medians or respective distributions were used for logistic regressions to categorize body fat% and lean mass outcome measures as below or above the median. Linear regressions were also used to explore the association between total water intake assessed from 24-h food recalls and body fat % and lean mass (assessed from DEXA). All analyses were performed using SPSS software Version 26.0 and significance was evaluated at an alpha level of 0.05.
Results
A total of 64 participants were included in the study consisting of 34 children and 34 adolescents. Table 1 shows the characteristics of these groups; sex distribution was similar between age groups, and most were Hispanic or Latino (72.1%), which was similar between age groups. There was a significant difference in the race distribution by age group. The overall group was classified as normal weight based on their BMI percentile with no difference by age group. Total water intake, physical activity, and lean mass were significantly higher in adolescents (18–20 years) compared to children (10–13 years; p<0.05). Though the study consisted of children with low calcium intake, total dairy intake (e.g., milk, cheese, yogurt, and ice cream) was 3.7 ± 2.44 and 3.05 ± 2.90 servings per day in children and adolescents, respectively, with no difference between age groups (p=0.33).
Table 1:
Characteristics of the study population (n=68).
| Variable | Overall (n=68) | Children 10–13 (n=34) | Adolescents 18–20 (n=34) | p-Value |
|---|---|---|---|---|
| Mean ± SD or n (%) | ||||
|
| ||||
| Age, year | 15.3 ± 3.92 | 12.0 ± 1.41 | 19.1 ± 0.64 | 0.000a |
| Sex | ||||
| Female | 34 (50.0) | 8 (44.4) | 18 (52.9) | 0.560 |
| Male | 34 (50.0) | 10 (55.6) | 16 (47.1) | |
| Race | ||||
| White | 43 (63.2) | 2 (11.1) | 28 (82.4) | 0.000a |
| African American | 5 (7.4) | 3 (16.7) | 1 (2.9) | |
| Asian | 3 (4.4) | 2 (5.9) | ||
| Native Hawaiian or other Pacific Islander | 2 (2.9) | 2 (5.9) | ||
| Other | 14 (26.9) | 13 (72.2) | 1 (2.9) | |
| Hispanic or Latino | 49 (72.1) | 24 (70.6) | 25 (73.5) | 0.919 |
| BMI percentile | 64.7 ± 26.0 | 62.0 ± 27.0 | 67.0 ± 24.7 | 0.412 |
| Body fat; % | 31.7 ± 7.31 | 31.7 ± 7.70 | 31.8 ± 7.02 | 0.967 |
| Lean mass, kg | 36.3 ± 10.8 | 28.8 ± 7.84 | 43.8 ± 7.68 | 0.000a |
| Total water intake, mL/d | 1,746 ± 762.0 | 1,384 ± 441.0 | 2,109 ± 843.9 | 0.001 |
| USG, μG | 1.020 ± 0.011 | 1.022 ± 0.015 | 1.018 ± 0.005 | 0.138 |
| IPAQ score, MET | 3,867 ± 4,108 | 3,194 ± 3,967 | 4,540 ± 4,194 | 0.178 |
aSignificant difference between age groups at p<0.05 by independent sample t-test or Chi-square. IPAQ, International Physical Activity Questionnaire, USG, urine specific gravity, USG, urine specific gravity.
Table 2 shows the linear regressions between body composition with total water intake (assessed from 24-h food recalls). After adjusting for age, sex, physical activity, and mean ambient temperature, there was a significant positive association between total water intake and lean mass for the overall group (B=12.2, p<0.05) and in children (B=27.0, p<0.05). However, for body fat %, there was no significant association with total water intake for overall or by age groups after adjusting for confounders. An additional analysis (not shown) adjusting for mean dietary salt and sugar consumption was made to account for potential affects in total water intake [31, 32], although results remained the same.
Table 2:
Linear regression between total water intake (assessed from 24-h food recalls) and body composition (assessed from DEXA scan) in children (10–13 year) and adolescents (18–20 year).
| Simple linear regression | Adjusted linear regression | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall n=68 | Children (10–13 years) | Adolescents (18–20 years) | Overalla | Children (10–13 years)b | Adolescents (18–20 years)b | |||||||||||||
| (n=34) | (n=34) | (n=34) | (n=34) | |||||||||||||||
| B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | |
| Body fat % | −11.5 | 5.03 | 0.025 | −15.5 | 9.35 | 0.107 | −16.0 | 7.33 | 0.037 | −10.43 | 5.60 | 0.067 | −12.4 | 9.64 | 0.209 | −10.3 | 6.91 | 0.146 |
| Lean mass | 37.04 | 6.19 | 0.000 | 24.0 | 8.98 | 0.013 | 17.1 | 8.03 | 0.042 | 12.2 | 5.65 | 0.035 | 27.0 | 10.7 | 0.017 | 4.41 | 5.43 | 0.423 |
aAdjusted for age, sex, physical activity, and mean ambient temperature. bAdjusted for sex, physical activity, and mean ambient temperature. cStatistically significant values (p<0.05) are in bold.
Table 3 displays the linear regressions between body composition with hydration status (assessed from USG in 24-h urine samples). After adjusting age, sex, physical activity, and mean ambient temperature, no significant association was found between body fat % or lean mass and hydration status, overall or by age groups.
Table 3:
Linear regression between hydration status (assessed from USG in 24-h urine samples) and body composition (assessed from DEXA scan) in children (10–13 year) and adolescents (18–20 year).
| Simple linear regression | Adjusted linear regressiona | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall n=68 | Children (10–13 years) | Adolescents (18–20 years) | Overalla | Children (10–13 years)b | Adolescents (18–20 years)b | |||||||||||||
| (n=34) | (n=34) | (n=34) | (n=34) | |||||||||||||||
| B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | B | SE | p-Valuec | |
| Body fat %d | −0.77 | 0.80 | 0.329 | −0.78 | 0.90 | 0.391 | −0.95 | 2.43 | 0.698 | −0.89 | 0.69 | 0.201 | −1.04 | 0.79 | 0.196 | 0.70 | 2.17 | 0.748 |
| Lean massd | −0.53 | 1.17 | 0.654 | 0.71 | 0.91 | 0.443 | 0.68 | 2.66 | 0.801 | 0.81 | 0.70 | 0.254 | 0.94 | 0.95 | 0.333 | −1.11 | 1.65 | 0.504 |
aAdjusted for age, sex, physical activity, and mean ambient temperature. bAdjusted for sex, physical activity, and mean ambient temperature. cStatistically significant values (p<0.05) are in bold. dVariable was rescaled by dividing by 100.
Table 4 displays the logistic regressions between hydration status or total water intake with body composition. Hydration status was categorized as “euhydrated” and “dehydrated”, while total water intake, body fat %, and lean mass were categorized as “below the median” or “above the median.” After adjusting for age, sex, physical activity, and mean ambient temperature, no significant association was found between body fat % and lean mass and hydration status or total water intake. Results remained attenuated after adjusting for dietary salt and sugar consumption (results not shown).
Table 4:
Logistic regression between hydration categories and total water intake categories and body composition categories in the sample.
| Body compositiona | Overall | |||
|---|---|---|---|---|
| OR (95% CI) | p-Valued | Adjusted OR (95% CI) | p-Valued | |
| Overall b | ||||
|
| ||||
| Body fat % and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 2.05 (0.78, 5.39) | 0.147 | 1.48 (0.47, 4.64) | 0.499 |
| Lean mass and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 1.03 (0.37, 2.90) | 0.951 | 0.90 (0.25, 3.26) | 0.868 |
| Body fat % and total water intakea | ||||
| Above the median | 1 | 0.790 | 1 | 0.960 |
| Below the median | 0.79 (0.30, 2.05) | 0.93 (0.31, 2.79) | ||
| Lean mass and total water intakea | ||||
| Above the median | 1 | 1 | ||
| Below the median | 2.01 (0.70, 5.76) | 0.193 | 2.44 (0.68, 2.44) | 0.172 |
|
| ||||
| Children c | ||||
|
| ||||
| Body fat % and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 2.04 (0.52, 8.00) | 0.306 | 1.26 (0.23, 6.89) | 0.790 |
| Lean mass and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 1.27 (0.33, 4.87) | 0.732 | 1.42 (0.34, 5.98) | 0.636 |
| Body fat % and total water intakea | ||||
| Above the median | 1 | 1 | ||
| Below the median | 2.04 (0.52, 8.00) | 0.306 | 3.71 (0.64, 21.6) | 0.144 |
| Lean mass and total water intakea | ||||
| Above the median | 1 | 1 | ||
| Below the median | 2.04 (0.52, 8.00) | 0.306 | 2.84 (0.62, 13.1) | 0.180 |
|
| ||||
| Adolescents c | ||||
|
| ||||
| Body fat % and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 2.06 (0.52, 8.18) | 0.303 | 1.65 (0.31, 8.86) | 0.557 |
| Lean mass and hydration status | ||||
| Euhydrated (USG<1.020 μG) | 1 | 1 | ||
| Dehydrated (USG≥1.020 μG) | 0.38 (0.04, 4.09) | 0.426 | 0.47 (0.03 6.64) | 0.580 |
| Body fat % and total water intakea | ||||
| Above the median | 1 | 1 | ||
| Below the median | 0.30 (0.07, 1.22) | 0.091 | 0.40 (0.08, 1.98) | 0.262 |
| Lean mass and total water intakea | ||||
| Above the median | 1 | 1 | ||
| Below the median | 3.42 (0.31, 36.8) | 0.309 | 2.38 (0.18, 31.06) | 0.507 |
aReference category is above the median. bAdjusted for age, sex, physical activity, and mean ambient temperature. cAdjusted for sex, physical activity, and mean ambient temperature. dStatistically significant values (p<0.05) are in bold.
Discussion
To our knowledge, this is the first study to examine the association between hydration status and body composition in a sample of US children and adolescents in south Florida. Total water intake was positively associated with lean mass (B=12.2, p<0.05) overall, after adjusting for sex, physical activity, and mean ambient temperature. In children, total water intake was positively associated with lean mass (B=27.0, p<0.05) after adjusting for covariates. However, USG or hydration status was not associated with body fat % or lean mass in the linear or in the logistic regression models.
There are only a few studies that have found an association between body composition and an objective marker of hydration status in children and adolescents. A study in Poland with 264 children ages 7–15 years showed an increase in the odds of dehydration due to excess body fat % measured by urine osmolality (OR 2.39, 95% CI 1.15, 4.94), although this study used a Tanita analyzer to assess body composition and a 24-h urine collection was not used [33]. Another study conducted in the United Kingdom with 936 children and adolescents ages 4–22 years showed a significant association between hydration (measured by total body water; TBW) and FFM (β=0.59, p<0.01) [34]. A study in Italy with 175 children ages 7–11 years found similar results with BMI and hydration status using the free water reserve (FWR) equation and two 24-h urine collections (p<0.05), although did not use DEXA for analysis of body composition [17].
Similar to the present study, other studies have found significant associations between total water intake assessed from questionnaires with body composition in both children and young adults. For example, Garcia et al. found in 372 adolescents ages 12–18 years in Spain a significant inverse association between water intake assessed from the hydration status questionnaire (HSQ) and body fat % (β=−0.284, p<0.01) via bioimpedance analysis [20, 35]. Garcia et al. also found similar results in the study involving 358 adolescents and adults ages 18–39 years showing an inverse association between water intake assessed using the HSQ and fat body mass (FBM) (p<0.05), waist circumference (p<0.05), and weight (p<0.05) [19]. Another study conducted in Spain by Milla-Tobarra et al. with 366 children ages 9–11 years reported a positive association between water intake assessed from 24-h dietary recalls with BMI (p<0.001), body fat (p<0.01), and fat-free mass (p<0.01), assessed via bioimpedance analysis [21]. However, none of these studies assessed body composition using DEXA and they were conducted primarily in European children and adolescents.
There are several mechanisms that may explain how hydration status is associated with weight or obesity. One mechanism is in relation to the anti-diuretic hormone (ADH) that is released as a result to an increase of plasma osmolality during dehydration, in which ADH is responsible for stimulating thirst to help regulate body fluid. During increased plasma osmolality (insufficient water intake), the precursor of ADH, known as serum copeptin, once elevated results in an increase in fat accumulation. Another mechanism previously mentioned is the process of lipolysis that occurs in all triglyceride storing tissues, although adipose tissue is known to reflect a majority of lipolytic activity. During hyperosmolality (insufficient water intake), adipocytes become dehydrated, resulting in excess triglycerides due to an inability to metabolize free fatty acids in the mitochondria. Therefore, a relationship between hydration and weight status may be evident.
The strength of the present study is the utilization of a DEXA scan to analyze body composition, the use of an objective marker of hydration, and three 24-h dietary recalls to assess total water intake. Furthermore, the use of accurate measures such as lean mass provides a more detailed indicator of obesity in children [36]. Body fat % has been commonly used in practice, however, cannot be evaluated independently from lean mass due to its dependence on height and body proportion differences [37]. Body fat indices such as lean mass has been used as a preferred measure due to its inclusion of non-skeletal muscle components to evaluate changes in body fat % in recent studies, although no significant association or trend was shown in the present study between body fat % and lean mass and hydration status when running both linear and logistic regressions [37]. Among the limitations is the small convenience sample size of children and adolescents from south Florida. A larger sample may be needed to find significant associations between body composition and hydration status with USG. USG was chosen as the objective marker for the present study instead of urine osmolality, urine color, FWR, and TBW due to its ability to adapt to pediatric populations and use without technical expertise, suitable to studies with a larger sample size and limited resources [38]. However, other measures may impact the association with USG as protein, glucose, ketones, bilirubin, and urobilinogen may alter the results if present in urine [39, 40]. During proteinuria, each 10 g/L protein may increase USG by 0.003 when measured by refractometry. Similar is shown for every 10 g/L of glucose may increase USG by approximately 0.002 when compared to other objective markers of hydration [39], [40]. Several markers should be used together to explore these associations in this population to account for limitations in objective measures.
In conclusion, our findings showed that total water intake was significantly associated with body fat % and lean mass in the linear regression models, in which may provide insight to the importance of children and adolescent’s water intake to have a lower body fat % and greater lean mass. Future research should be conducted to explore other objective markers of hydration that may serve as a better predictor to body fat % along with a larger sample to explore the associations between hydration and body composition in this population.
Acknowledgments
We would like to thank Julia Leone from the Department of Dietetics and Nutrition, Stempel School of Public Health, at Florida International University for her contribution in the data collection.
Footnotes
Research funding: This study was funded in part by the National Institutes of Health (Eunice Kennedy Shriver National Institute of Child Health and Human Development, NICHD), grant number 1R01HD098589-01.
Author contributions: PC and CP: facilitated and conducted the study, wrote the manuscript. MATF, RG, and PC collected all questionnaires, biospecimens, and anthropometrics. CP: project oversight and was responsible for the final content of the manuscript; and all authors critically reviewed the manuscript and approved the final version.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: The research related to human use has complied with all the relevant national regulations, institutional polices, and in accordance with the tenets of the Helsinki Declaration and has been approved by the authors’ Institutional Review Board (IRB-22-0045).
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