In testing the study hypotheses, the difficulty was not related to obtaining significant correlations of bioelectrical impedance to hydration status measures but rather to specifying its different relationship to different hydration-related compartments water, dissolved proteins, electrolytes because of an expected high degree of intercorrelation between them in healthy individuals.

Thus, any impedance parameter that was found to be correlated with one of the hydration-related compartments e. water would be expected to correlate almost equally well with the other compartments affecting hydration, such as dissolved proteins and non-osseous minerals i.

A second-order AIC with a correction for small sample sizes was used to rank significant models of the relation of BIA data to proximate composition for selecting the most parsimonious ones i. Significant negative effects on total body water or moisture percentage assessed by proximate composition analysis were found for both in series and in parallel obtained segmental BIA measures: resistance and reactance with and without adjustment to widths of segments, the reactance-to-resistance ratio, and various total impedance measures calculated by different equations Supplementary Table S2.

According to AIC, addition of body weight as a covariate did not improve the predictive ability of the models, but prediction of total body moisture percentage was improved if the segmental BIA models also included body length Supplementary Table S3.

According to AIC, the best fitting models were a segmental BIA model of a serially obtained reactance with the effect adjusted for body length and a model of a more complex equation involving the product of serially obtained reactance and resistance adjusted for total impedance with the final effect adjusted for body length.

The best fitting models for water-related effects from Supplementary Tables S2 and S3 were presented in Table 2. Thus, the quantity of water assessed segmentally or locally by reactance or a more complex bioimpedance-related equation could better represent the percent of body moisture or body hydration status after accounting for its distribution along the whole length of a particular body.

Significant positive effects on body protein percentage assessed by proximate composition analysis were found for in parallel obtained segmental BIA measures: reactance and total impedance, as well as a serially obtained segmental BIA measure: resistance, all adjusted for widths of segments Supplementary Table S2.

According to AIC, the prediction of body protein by these simple models did not improve if they also included body length or weight Supplementary Table S3. Significant interaction effects of the adjusted in parallel and serially obtained reactance and resistance were also found for body protein, but according to AIC, the models were poorer fit compared to the simple models Supplementary Table S3.

The same was found when body length was added to the interaction models. According to AIC, the best fitting models were simple segmental BIA models of serially obtained resistance unadjusted only approached significance in GLMM and adjusted for widths of body segments was significant in GLMM.

Two other simple segmental BIA indicators, in parallel obtained reactance unadjusted only approached significance in GLMM and adjusted for widths of body segments was significant in GLMM were close in model fit Supplementary Table S2. The best fitting models for protein-related effects from Supplementary Table S2 were presented in Table 2.

Serially obtained segmental BIA reactance and resistance, both adjusted for widths of segments and included in the same model, indicated significant negative and positive simple effects, respectively, on protein content.

The same models with in parallel obtained segmental BIA reactance and resistance indicated significant effects in the opposite direction, specifically positive and negative simple effects, respectively, on protein content.

Thus, quantity of proteins assessed locally or segmentally by serially obtained resistance the best fitting or in parallel obtained reactance a less fitting can represent the body protein with and without adjustment for widths of locally assessed segments and without accounting for the whole length or weight of the particular body as an individual trait.

Significant negative effects on body ash percentage assessed by proximate composition analysis were found for serially obtained BIA measures: phase angle, reactance-to-resistance ratio, reactance adjusted for widths of segments, a product of reactance and resistance adjusted for widths of segments and with additional adjustment for total impedance, as well as for an in parallel obtained BIA measure: resistance adjusted for widths of segments Supplementary Table S2.

According to AIC, the prediction of body ash was improved if the simple BIA models also included body length or weight best fitting models as covariates Supplementary Table S3.

Significant effects on body ash percentage were also found for various in serial and in parallel obtained BIA indicators when the indicators were added to models together Supplementary Table S2.

Serially obtained BIA reactance and resistance both included in the same model indicated significant opposite negative and positive simple effects, respectively, on ash content, while the same models with in parallel obtained BIA reactance and resistance indicated significant positive and negative simple effects, respectively, on ash content.

However, the models had poorer fit compared to simple models, according to AIC. An improvement in fit closer to simple models was found when body length or body weight most fitting models was additionally included in these complex models. According to AIC, the best fitting models were a serially obtained reactance-to-resistance ratio with and without the effect adjusted for body weight.

These models for ash-related effects from Supplementary Tables S2 and S3 were presented in Table 2. Thus, a relative quantity of ash assessed by the reactance-to-resistance ratio locally or segmentally can represent the total body ash after accounting for weight as a proxy of its distribution in the whole body.

Including body weight in the model improved the model fitting and its effect size but made the model non-significant. Significant negative effects on body protein percentage assessed by proximate composition analysis were found for an in parallel obtained whole-body BIA resistance adjusted for the distance between electrodes, serially obtained whole-body BIA phase angle, reactance-to-resistance ratio, and reactance with and without adjustment for the distance between the electrodes, as well as more complex whole-body BIA indicators: products of serially obtained reactance and resistance with and without adjustment for the distance between the electrodes, with additional adjustment for the serially obtained total impedance Supplementary Table S5.

Including body weight in the model only slightly improved the model fitting but decreased its effect size. Significant negative effects on total body ash percentage assessed by proximate composition analysis were found for an in parallel obtained whole-body BIA resistance with and without adjustment for the distance between electrodes, serially obtained whole-body BIA phase angle, reactance-to-resistance ratio, reactance with and without adjustment for the distance between electrodes, resistance with adjustment for distance between electrodes, as well as more complex whole-body BIA models: two with a product of serially obtained reactance and resistance with adjustment for the total impedance with and without additional adjustment of each measure in the equations for the distance between electrodes and one involving the serially obtained total impedance with adjustment for distance between electrodes Supplementary Table S5.

Including body weight in the model only slightly improved the model fitting but decreased its effect size and made the model non-significant. Supplementary Tables S1 and S4 present predicting effects of all segmental and whole-body BIA measures on body weight, width, and length.

In parallel obtained resistance of whole-body BIA adjusted to distance i. Body weight and widths of segments were not significantly related to percent of body moisture water , protein, ash, and fat. Body length was only significantly related to percent of body ash Supplementary Tables S2 and S5.

For the present study, fish was selected as a biological model to validate BIA equations in predicting proximate body components associated with hydration status such as water, proteins including dissolved colloid fraction, and minerals including non-osseous fraction and associated with nutrition status such as fat, all obtained directly by physicochemical methods.

Only these direct measures adjusted for inter-individual differences in weight i. These BIA equations were approved in the study with respect to the principal predictive values of impedance and its resistance and reactance components, as well as their various ratios and products assessed in series or in parallel, using whole-body and segmental BIA schemas that should be common across different biological species, but without inclusion of specific regression constants that should differ between different biological species The length of body, distance between electrodes, and weight as additional parameters of individual differences, or individual traits, were assessed for their impact in improvement of the predictive value of the BIA measures.

This confirmed the reliability of the design of the present study including the BIA measurement procedure and the lethal physicochemical method for the proximate composition analysis. Moreover, the study showed that models containing BIA measures obtained from segmental impedance readings could also predict these proximate measures of body composition.

However, best fitting models predicting the body compartments were different for BIA measures from segmental versus whole-body BIA readings. The main difference was related to different electrical compensation schemes used to obtained BIA measures for inclusion in prediction models: serial for segmental and parallel for the whole-body BIA readings.

In contrast, in the segmental BIA schema, the equations combining measures of resistance and reactance e. Length of body as an additional parameter of individual differences, or the individual trait, was found to improve the predictive value of the total water percentage by the segmental or local BIA if it was added in the regression formula.

With respect to between-subject variation in the body hydration status, the association of body moisture decrease with an increase in both serially obtained resistance and reactance bioimpedance measures indicated that the dehydration was probably related to or was interpreted as a mechanism of water transfer from ECW water decrease to ICW water increase.

Since reactance Xc is related to the dielectric properties, it is assumed that ICW should linearly and positively be correlated with the reactance Xc , while resistance R should linearly and negatively be correlated with ECW In addition, this purported ECW to ICW distribution shift was confirmed by a significant relationship of lower body moisture with a higher value for the product of the resistance and reactance adjusted for total bioimpedance.

However, best fitting models related to higher reactance to resistance ratio and higher absolute reactance could indicate a predominant effect of absolute ICW increase i.

Indeed, ICW has a higher resistivity than ECW primarily due to the high concentration of dissolved protein i. Both in parallel and in series obtained resistance and reactance in both whole-body and segmental schemas were similarly related to body moisture proposing that they detected a hydration status of a similar origin.

In the segmental BIA obtained in series schema, the protein content decrease was better predicted by a decrease of a resistance measure i. This proposes a distinct origin of these compartments and their correspondence to hydration status detected by serially and parallelly obtained electrical compensation schemas.

For example, ICW has a higher specific resistance than ECW primarily due to the high concentration of dissolved protein, which dramatically impedes ion movement associated with water resistivities regulated by non-osseous minerals presented in fluids as electrolytes: mainly chloride for ECW and mainly potassium for ICW Serially detected protein and minerals predominantly in the segmental models could indicate the decrease in body fluids with an increase in concentrations of electrolytes and dissolved proteins as in hyper-osmolal dehydration that was expected in the current design.

Parallelly detected protein and minerals predominantly in the whole-body models could indicate the decrease in body fluids with a decrease in concentrations of electrolytes and dissolved proteins mimicking eu-osmolal dehydration that was not expected in the current design.

Thus, the parallel schema might assess percent of electrolytes and dissolved proteins that were concentrated intracellularly. This corresponds to the proposal that bioimpedance measured at 50 kHz current obtained by a serial schema primarily reflects the ECW space, but a parallel bioimpedance model is more sensitive to changes in ICW Thus, the use of two in series and in parallel electrical compensation schemas could allow for measuring the osmolality hyper-, hypo- or iso-osmolality of different origin associated with dissolved protein or electrolytes , but not the hydration status hyper-, hypo- or iso-hydration separately in the ECW and ICW compartments by bioimpedance measures using the single 50 kHz frequency of electrical current.

This also confirms previous findings suggesting that electrolyte balance influences BIA measurements independently of fluid changes 12 , Both segmental and whole-body BIA readings in response to the single 50 kHz frequency did not predict the fat or lipid compartment of the body composition.

Additionally, most whole-body BIA models included the total length of the body in their equations, and their ability to accurately predict the body components may simply be related to the adjustment of BIA measures for body length or length squared Moreover, compared with models developed using only width i.

The present study did not include an assessment of the relative contribution of each segment in ventral and dorsal surfaces i. The validity of BIA models also invariably relies on the amount of contrast in the proximate composition of the studied samples that was not specially manipulated in the present study.

To adequately assess the ability of the BIA models to predict body compartments associated with hydration and osmolality status, future study should include a wider range of physiological states within a particular population and information on each of these states with cross-sectional e.

Since BIA works very similarly for a wide range of vertebrates from humans to fish 20 , the latter was used in this study as a model for the comparison of different BIA measures and equations to predict between-subject variance in proximate body measures of hydration status i.

This approach bypasses shortcomings of most non-lethal or in-vivo reference methods applied in human subjects affecting the precision of the related body compartment models. Moreover, their prediction by different equations corrected or uncorrected for body weight and length was probably related to different distribution of the hydration components between ICW and ECW spaces affecting not only their hydration, but separately also their osmolality status.

Some of the current findings showed that the application of both in series and in parallel electrical compensation schemas for BIA measurement at 50 kHz frequency of electrical current could guarantee that ECW differences do not corrupt the ICW and vice versa in assumed osmolality status assessment electrolyte balance coupled with dissolved protein level affecting, respectively, osmotic and oncotic pressures , but probably were not important for hydration status water balance assessment as two separate and relatively independent targets of the homeostatic regulation.

However, findings of indirect bioimpedance-derived measures of hydration and osmolality homeostasis obtained in fish should be transferred to humans after optimizing applicability of respective equations to bioimpedance measures obtained at different segmental and local anatomical portions of the human body Moreover, validity of these BIA models should be confirmed while controlling for potential confounding factors before implementation of the best techniques for the mathematical treatment of BIA data in practice.

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and D. conceived the experiment, and together with S. carried it out; D. advanced the hypothesis, designed, and carried out the data processing and analysis and wrote the paper. In study we used the Water Balance Questionnaire WBQ , a self-administrated semi-quantified food frequency questionnaire especially designed and validated with urine hydration biomarkers Malisova et al.

The validity and reliability in Turkish version of the WBQ was made by Sen which includes a total of seven episodes: a individual's sociodemographic characteristics, b lifestyle characteristics, c physical activity status, d food consumption frequency, e drinking water and beverage consumption, the frequency f of fluid elimination from the body, and g the trends in fluid intake.

Water balance was calculated by subtracting the total amount of water loss from the body through sweat, defecation, and urine from the total amount of water intake into the body by consuming beverages, foods, and drinking water.

To determine the amount of water intake into the body to foods and beverages through food and fluid consumption, frequency form was calculated with computer-assisted nutrition program developed for the Turkey Nutrition Information System BeBiS 7.

Using the International Physical Activity Questionnaire IPAQ to determine water loss due to sweating in the calculation of body water loss, individuals' time for high, moderate, light walking level physical activity level, and resting was questioned Biernat et al.

Also, the amount of water discharged from the body according to the daily urination frequency and the defecation frequency of the individuals were used for the calculation of the amount of water loss. World Health Organization's WHO classification was used in the assessment of BMI WHO The normal range is The data obtained from the research were evaluated with the SPSS 22 version statistical package program.

To examine the relationship between continuous and two-group variables, t -test was used for normally distributed data and Mann—Whitney U test for non-normally distributed data.

Differences between groups in categorical variables were controlled by Student's t -test and chi-square test. The one-sample t -test was used to compare the total daily water intake calculated on the WBQ of the individuals and the DRI recommendations. One-way ANOVA test for normally distributed data in data grouped more than two; Kruskal—Wallis test was used for non-normally distributed data.

Correlations were evaluated using Pearson's correlation coefficient. Partial correlations between water intake, energy intake, and beverage consumption adjusted for age, gender, body weight, and physical activity were calculated. The sociodemographic and lifestyle characteristics of the individuals included in the study are shown in Table 1.

A total of individuals, including men The mean age of individuals was While Most of the individuals Those who did not use any medication or food supplements Only 5. Figure 1 shows the mean daily beverage consumption of individuals according to their physical activity status.

Hot beverages were consumed the most after water. In all groups, consumption of vegetables and fruit juices, energy and sports drinks, and alcoholic beverages were lower compared with other beverages.

Table 2 shows individuals' status of meeting the DRI water intake recommendations. Accordingly, it is seen that the mean total water intake TWI of men and women met the DRI recommendation.

The mean of all food and beverage intake was 6, Also, the contribution of beverages to total energy intake TEI was 7. The mean TWI was 3, Also, the contribution of beverages to TWI was While the contribution of all beverages to water intake was 2, WBQ, Water Balance Questionnaire; g, gram; kcal, kilocalories; p 1 , significance between total weight consumed value in men and women; p 2 , significance between contribution to energy intake in men and women; p 3 , significance between contribution to water intake in men and women.

The hydration status calculated with the WBQ according to gender is shown in Table 4. When the TWI and total water loss of the individuals were evaluated, a positive net water balance 2, The hydration status calculated with the WBQ according to BMI groups is shown in Table 5.

A positive net water balance was found in all BMI groups. Although not statistically significant, as BMI increased, net water balance value increased.

Correlations between water intake, energy intake, and beverage consumption are shown in Table 6. Partial correlations between water intake, energy intake, and beverage consumption adjusted for age, gender, body weight, and physical activity using the WBQ.

Although many factors such as age, gender, physical activity, drug use, and illness affect hydration status, adequate hydration is an essential component of health at every stage of life.

This study evaluated the hydration status of Turkish young adults in the COVID pandemic using the WBQ, which includes physical activity status, food and beverage consumption frequency, water consumption, and water loss by urine and feces. Hydration reflects a balance between TWI and loss.

TWI consists of water from a variety of sources, namely, drinking water, beverages, fluid, and solid foods. In another study by Malisova et al. When the TWI and total water loss of the individuals were evaluated, a positive net water balance was found.

Individuals' TWI was found to be higher than other studies Malisova et al. It is thought that this situation may be related to the pandemic as well as the diversity of the study groups, levels of education, eating habits, physical activity levels, and the health awareness of the population.

In addition to the frequency of beverage consumption in our study, the determination of the amount of water in foods using the food frequency questionnaires instead of the h food consumption record used in other studies may be another reason.

Available data on gender differences in hydrated status are insufficient and inconsistent. While there are studies showing that women are more vulnerable to insufficient fluid intake Haveman-Nies et al.

In this case, a higher amount of water loss by high and moderate physical activity in men and a higher amount of water loss in urine in women were significantly effective.

Considering the TWI of individuals, Also, according to Turkey Nutrition and Health Survey TBSA data, the mean daily water consumption of individuals was found to be 1, In this study, the mean daily water consumption was found lower than the general Turkish population.

This may be due to economic and health-related differences between the general population and young, healthy adults also may be related to individuals being less active due to the pandemic. The water content in food, as well as drinking water and other beverages intake, may have a role in body weight homeostasis by affecting overall diet energy density.

Recent data have shown that hydration status was associated with the energy density of the diet and dietary profile Pérez-Escamilla et al. At the same time, it is known that factors such as water intake and hydration status are effective in weight regulation and obesity development Ang et al.

In the National Health and Nutrition Examination Survey NHANES study, which represented adults aged 18—64 years from to , a significant association between higher BMI and inadequate hydration was reported Chang et al.

Similarly, another study using NHANES data showed that obese adults were more likely to have hypohydration, and the association between increased water intake and hypohydration was weaker in obese than adults with underweight or normal body weight Rosinger et al.

In a recent study, no significant relationship was found between individuals' hydration attitudes or water intake and BMI Veilleux et al. In this study, TWI was 4, The contribution of water from beverages to TWI was In many studies, the contribution of beverages and foods to TWI was found to be similar O'Connor et al.

Similarly, hot drinks such as tea and coffee were the most contributing drinks for Australian adults Sui et al. The hot beverages The reason for the high contribution of hot beverages to TWI may be that the study was conducted during the winter months.

Many beverages contribute to the TEI. The beverage that made the most contribution was milk 2. In our study, the contribution of beverages to TEI was found to be relatively low, and this may be due to the higher consumption of hot beverages with very low energy content, as shown in the study.

The main limitation of the present study is represented by a self-reported questionnaire, which may lead to the actual misreporting of data. Since retrospective food and beverage consumption frequency methods are based on individuals' memory and recall skills, it created difficulties in reflecting the estimation of actual consumption.

In addition, a comprehensive evaluation was made with the WBQ to estimate the hydration status of individuals without the need for laboratory methods. The findings cannot be generalized for all age groups, as the study data mainly consist of young adults.

However, this study is important for estimating the hydration status of young adults in Turkey during the COVID pandemic. These data will be necessary in order to formulate public health recommendations. In conclusion, our study gives an overview of the characteristics of the water intake of Turkish young adults.

In all BMI groups, men and, women, a positive net water balance was found. As the COVID pandemic continues, studies are needed on hydration status in the more balanced populations in terms of BMI and age groups.

In addition, more research should be done to examine hydration status in different populations to determine the optimal water intake level. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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MATERIALS AND METHODS. COMPETING INTEREST. Article Navigation. Research Article June 23 a Department of Nutrition and Dietetic, Bandirma Onyedi Eylul University, Balikesir , Turkey. E-mail: nurselsahin bandirma.

This Site. Google Scholar. Senay Catak ; Senay Catak. b Department of Nutrition and Dietetic, Aydin Adnan Menderes University, Aydin , Turkey. Gamze Akbulut Gamze Akbulut. c Department of Nutrition and Dietetic, Gazi University, Ankara , Turkey.

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