Skip to main content
Download PDF
Download ePub

Distribution of water turnover by sex and age as estimated by prediction equation in Japanese adolescents and adults: the 2016 National Health and Nutrition Survey, Japan

Nutrition Journal volume 22, Article number: 64 (2023) Cite this article

Abstract

Background

Although water is essential to the maintenance of health and life, standard values for human water requirements are yet to be determined. This study aimed to evaluate the distribution of water turnover (WT) according to sex and age, estimated using a prediction equation, in Japanese adolescents and adults.

Methods

This cross-sectional study used data from the 2016 National Health and Nutrition Survey, Japan. Data were obtained from electronically available aggregated reports in the survey’s official website. Participants aged between 15 and 80 years (10,546 men, 12,355 women) were selected using stratified random sampling. WT was calculated considering lifestyle and environmental factors, and using an equation (coefficient of determination = 0.471) previously developed by the international doubly labelled water (DLW) database group. As data on physical activity levels (PAL) were not collected in the survey, we used two evaluation methods: (1) energy intake assessed by dietary records and (2) total energy expenditure measured by the DLW method reported in previous Japanese studies, divided by basal metabolic rate predicted using the equation. We evaluated the relationship between WT and age using a restricted cubic spline model.

Results

The average WT for the 15–19, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥ 70 years was 3291, 3151, 3213, 3243, 3205, 3104, and 2790 ml/day, respectively in men, and 2641, 2594, 2741, 2739, 2753, 2707, and 2482 ml/day, respectively in women. In the spline model, WT showed an inverse association with age in men older than 50 years, whereas women showed a reverse U-shaped relationship between WT and age (p for non-linearity < 0.001), although results differed with body weight adjustment. Similar results were found for both PAL evaluation methods, and the range of WT per body weight was 45–56 ml/day for both sexes.

Conclusions

We determined the standard values of WT in Japanese population using a prediction equation and national large-scale survey data. These findings may be useful for setting water requirements for dietary guidelines in future.

Peer Review reports

Background

When the fluid balance of the body is disturbed by the loss of water, humans experience hunger or thirst, and adjust primarily by consuming water through food and beverages [1]. Homeostatic functions maintain fluid levels in the body [2]. Although humans are thought to have evolved to utilise less water per day compared to other primates [3], a few days spent without consuming water can prove fatal to them [4, 5]. Therefore, it is important to evaluate the amount of water required for maintaining life and good health.

The requirement of water consumption as per guidelines from the United States and Canada [6] and the World Health Organisation (WHO) [7] are 2.7 L/day for adult women and 3.7 L/day for adult men. However, the Dietary Reference Intake in Japan does not indicate set water requirements for Japanese people [8]. Regarded as a gold standard method to evaluate the required amount of daily water consumption, water turnover (WT) can be measured using the deuterium or the double-labelled water method [3, 4, 9,10,11]. WT has been shown to differ between countries and regions with different environments [1, 4]. The sources of human water inputs are metabolic water produced by the metabolism of nutrients, respiratory and transcutaneous water entering the body through breathing and the skin, and pre-formed water derived from ingested food and fluids [9]. Therefore, clarifying the requirement of WT and pre-formed water in individuals is essential for establishing recommendations and setting goal values for water consumption to prevent dehydration and maintain body fluids [12].

To determine the water requirements for guidelines, standard values should be established by evaluating the mean and distributions of WT measured by DLW methods according to sex and age from a randomly selected sample population of Japanese individuals [8]. However, it is difficult to obtain such data because the DLW method is an expensive experimental technique. Intake values estimated from dietary records (DRs) are often underestimated [13,14,15], making it difficult to accurately estimate water as well as energy intake (EI) [11]. Recently, the international DLW database group, in which we are a participant, developed a prediction equation for WT measured by DLW methods [4]. This prediction equation included significant factors such as anthropometric, lifestyle, and environmental factors, and can obtain predicted WT for human in different countries and regions around the world. However, to the best of our knowledge, standard values of WT have not been considered, and we believe that it may be possible to estimate standard values for the Japanese population using this equation and national large-scale survey data. Thus, this study aims to estimate the expected distribution of WT by age and sex in Japanese adults based on inputs for the previously developed prediction equation [4] and lifestyle and environmental factors. In addition, we compare the distribution of pre-formed water estimated by the prediction equation with DRs. This study aims to obtain useful knowledge for setting standard values of WT using a prediction equation and national large-scale survey data. Further, we hypothesised that pre-formed-water estimated from DR was underestimated compared with those estimated by prediction equation.

Methods

Study design and data

This study was conducted in the following steps: (1) data were collected from electronically available aggregated reports; (2) WT was estimated according to age and sex from the collected data using the previously developed prediction equation, and (3) pre-formed water calculated by subtracting metabolic, respiratory, transcutaneous derived water from predicted WT was compared with the value estimated from DRs.

We used data from the 2016 National Health and Nutrition Survey, Japan (NHNS-J) [16,17,18]. This survey commenced in 1946, with the purpose of obtaining basic data necessary to receive food aid after World War II from each country under the General Headquarters of the Allied Forces. It is a cross-sectional household examination survey conducted in November of each year with the exception of 2016, when it was conducted from 1 October to 30 November because of the exceptionally large sample. Participants were selected using stratified random sampling from the 2010 census enumeration area of all 47 prefectures of Japan, with 10 districts per prefecture (only Tokyo, which has a large population, has 15 districts). In these areas, household members aged one year or older were included, except for the 13 districts affected by natural disasters, such as earthquakes and typhoons, in 2016. The number of households that gave valid responses to the dietary section of this survey was 10,745 out of 24,187 households (response rate = 44.4%). The present analyses included individuals aged 15–80 years who completed the dietary survey (10,546 men and 12,355 women).

Dietary intake was assessed from single-day semi-weighed household DRs, excluding trip or festivity days [19]. Before the dietary survey, participants were instructed on how to complete the DR by well-trained workers (mainly registered dietitians). A household member, usually responsible for preparing meals, was entrusted with weighing all food and beverages consumed by the household members and assigning approximate proportions of each dietary item to individual household members. When data were missing or illogical, workers visited the household and verified the portion sizes of food and beverages on the DR forms. Energy and nutrient intake were calculated from the weight of food and beverages consumed, based on the 2010 Standard Tables of Food Composition in Japan [20]. After the participants had completed the dietary survey, a physical examination was conducted in a facility within walking distance of their residence. The height and body weight were evaluated to the nearest 0.1 cm and 0.1 kg, respectively, using a stadiometer and weighing scale, with the participants barefoot and wearing light clothing. If participants were unavailable to measure these variables or could not travel to the facility, values were obtained from self-reports or measured at their homes. All data were available electronically in the form of aggregated reports in the official website of the Ministry of Health, Labour, and Welfare [21].

Estimation of WT

The WT was calculated using a previously developed equation derived from select variables using a multiple regression model with the WT measured by DLW methods as a dependent variable [4]. Data were collected from 5604 people including Japanese data that we provided for the ages of eight days to 96 years from across 23 countries and stored in the international DLW database, based on which a model predictive equation for WT in adults aged 18 years and older was developed. The coefficient of determination (R2) for this model was 0.471. The model used the following equation [4]:

$$\begin{aligned}C & = {\beta _0} + {\beta _1}\;PA{L_1} + {\beta _2}\;body\; weigh{t_2}\\&\quad + {\beta _3}\;se{x_3} (1\;if\;men,\;0\;if\;women) \\ &\quad + {\beta _4}\;humidit{y_4}\\&\quad + {\beta _5}\;athlete\; statu{s_5} (1\;if\;yes,0\;if\;no) \\ & \quad + {\beta _6}\;HD{I_6}(2\;if\;low\;HDI,1\;if\;middle\;HDI,0\;if\;high\;HDI) \\&\quad + {\beta _7}\;altitud{e_7} + {\beta _8}\;ag{e^2}_8 + {\beta _9}\;ag{e_9}\\&\quad + {\beta _{10}}\;temperatur{e^2}_{10} + {\beta _{11}}\;temperatur{e_{11}} \end{aligned}$$
(1)

where C represents WT. The intercept (β0) of the equation was − 713.1 ml. The coefficients of the binary variables of sex (β3), athlete status (β5), and human development index (HDI; β6), were 374.9 ml, 1070 ml, and 104.6 ml, respectively. The coefficients of the continuous variables of physical activity level (PAL; β1), body weight (β2), humidity (β4), altitude (β7), age2 (β8), age (β9), temperature2 (β10), and temperature (β11), were 1076 ml, 14.34 ml (kg), 5.823 ml (%), 0.4726 ml (m), − 0.3529 ml (years), 24.78 ml (years), 1.865 ml (℃), and − 19.66 ml (℃), respectively. The product of the above coefficients and all characteristic variables of the participants, such as PAL (continuous), body weight (continuous), sex (1 for men, 0 for women), humidity (continuous), athlete status (1 for yes, 0 for no), HDI (2 for low HDI countries, 1 for middle HDI countries, 0 for high-HDI countries), altitude (continuous), age2 (continuous), age (continuous), temperature2 (continuous), and temperature (continuous) were calculated. WT was calculated as the sum of the above products and intercepts.

Other variables

Calibrated EI was calculated by multiplying the EI estimated from the DR by 1.09, because it was previously reported that the values estimated from DRs were underestimated by approximately 9% compared with total energy expenditure (TEE), as measured using the DLW method in Japanese older adults [15]. The predictive basal metabolic rate (pBMR) was estimated using the equation by Ganpule et al. for Japanese individuals [22], given that this equation provided the best results in a comparison of several different equations for calculating pBMR [23]. We assumed that EI and TEE had similar values [24], and PAL was substituted with calibrated EI/pBMR to compensate for the absence of PAL data in NHNS-J. As water intake from food was not reported in NHNS-J, and the mean ratio of water from foods in the DRs of previous studies was 69% [25], water intake from food was estimated at 69% of the food weight from the DR. Based on the sum of water intake from food and beverages, pre-formed water was estimated. Japan was considered a high-HDI country in accordance with a previous study [4]. We were unable to evaluate athletic status in this survey, therefore, we assumed that there were no athletes across all age groups. For altitude, a value of 189.2 m was used for all participants, which is the average value for the inhabited area in Japan [26]. The temperature and relative humidity during the survey period adopted the mean values as on November 2016 in all surveyed areas determined from the database of the Japan Meteorological Agency (temperature: 10.9℃ and relative humidity: 74.7%) [27].

Calculation of other water consumption

The carbon dioxide production rate (rCO2), metabolic, respiratory, transcutaneous, and pre-formed water were calculated using Eqs. 2–6. We assumed that EI and TEE showed similar values, and rCO2 (mg/day) was calculated using the calibrated EI (kcal/day), food quotient (FQ), and Eq. (2) referenced in a previous study [28]:

$$rC{O_2} = calibrated\,EI/(1.106 + (3.94/FQ))$$
(2)

We assumed that the respiratory quotient was equal to the FQ, which was calculated by multiplying the coefficients (protein: 0.8, fat: 0.7, carbohydrates: 1.0) and dietary protein, fat, and carbohydrate intake. Metabolic water (Wmet; ml/day) was calculated using Eq. (3) [9]:

$$\eqalign{{W_{met}} = & Calibrated{\mkern 1mu}\;EI \times (1/100,000) \cr & \left[ {{{0.119}_{\% fat}} + {{0.103}_{\% pro}} + {{0.150}_{\% carb}} + {{0.168}_{\% alc}}} \right] \cr}$$
(3)

The intake of fat (%fat), protein (%pro), carbohydrates (%carb), and alcohol (%alc) per calibrated EI, as estimated from the single-day DR, were multiplied by their coefficients and totalled. Metabolic water was estimated by multiplying the total value by the calibrated EI (kcal/day). Respiratory water (Wres; ml/day) was calculated from the concentration of water in the atmosphere, estimated from the average air temperature and relative humidity during the period when the DR was made, using Eq. (4) [9]:

$${W_{res}} = \left[ {absolute\,humidity/1000} \right] \times 0.035rC{O_2}$$
(4)

The mean temperature, relative humidity, and absolute humidity during the study were 10.9 °C, 74.7%, and 7.45 g/m3 in November 2016, respectively [27]. For respiratory air volume, 3.5% of the inhaled air was assumed to be CO2 and was calculated from the rCO2 obtained using above equation. Transcutaneous water (Wtrans; ml/day) was calculated using Eq. (5) [9]:

$${W_{trans}} = \left[ {{{0.18}_{absolute\;humidity}}/21.7} \right] \times 0.5 \times BSA \times 1.44$$
(5)

The transdermal absorption rate per m2 of body surface area (BSA) in an atmosphere saturated with water vapor (21.7 mg/L) was 0.18 g/m2. The BSA (m2) was estimated using the Dubois equation [29]. As clothing reduces the rate of evaporation of moisture from the skin, the clothing coefficient was assumed to be 50%. Pre-formed water (Wpre; L/day) was calculated by subtracting the metabolic, respiratory, and transcutaneous water from the WT using Eq. (6) [9]:

$${W}_{pre}= WT-\left[{W}_{met}+{W}_{res}+{W}_{trans}\right]$$
(6)

Pre-formed water includes the fluids consumed from food and drinks.

Statistical analysis

All analyses were performed after stratifying by age (15–19, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥ 70 years) and sex (male or female) according to the summary tables reported by the NHNS-J. For descriptive statistics, continuous and categorical variables of participant characteristics were expressed as mean and standard deviations or 95% confidence intervals (CI), and as numbers and percentages, respectively.

We used a restricted cubic spline model with three knots based on age distribution (5th (18 years), 50th (48 years), and 95th (77 years) percentiles) to evaluate the curvilinearity of the relationship between water consumption and age [30, 31]. The statistical significance of non-linearity was assessed using the Wald test, comparing the likelihood ratio of the spline model with the linear model, with p values of < 0.05 indicating a statistically significant non-linear relationship between the WT and age [32]. For the sensitivity analysis, we performed the same procedure using the TEE evaluated by DLW methods reported in previous Japanese studies [15, 33,34,35] because EI from DRs may not be uniformly underestimated in age groups [13, 14].

We compared the distribution of pre-formed water estimated by the prediction equation and DRs according to sex- and age-stratified models. The analysis results are presented as mean differences and 95% CI. To assess the relationship between underestimation of pre-formed water estimated from the DR and age, the p-value of the linear trend was calculated using a regression model and the continuous variable of age.

A two-tailed significance level of 5% was adopted and STATA MP, version 15.0 (StataCorp LP, College Station, TX, USA) was used for all analyses.

Results

Table 1 shows the participant characteristics stratified by age and sex in the analysed population. The number of participants increased with age in both sexes. Body weight was the highest for those aged 40–49 years, followed by an age-dependent decline up to ≥ 70 years in both sexes. The calibrated EI/pBMR tended to be high in the age ranges of 15–19 and ≥ 60 years. Comparison with the TEE reported in Japanese previous studies, calibrated EI took lower values, from approximately 3.5 (50–59 years in women) to 14.1% (30–39 years in men) but EI (uncalibration) took lower values, from approximately 11.5 (50–59 years in women) to 21.2% (30–39 years in men; Supplementary Table 1).

Table 1 Participant characteristics stratified by age and sex
Full size table

Table 2 shows the distribution of water consumption according to age and sex estimated using the prediction equation. The average WT for the 15–19, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥ 70 years was 3291, 3151, 3213, 3243, 3205, 3104, and 2790 ml/day, respectively in men, and 2641, 2594, 2741, 2739, 2753, 2707, and 2482 ml/day, respectively in women. Figures 1 and 2 show the dose-response relationship between water consumption and age using the restricted cubic spline model. WT and pre-formed water showed an inverse association with age in men ≥ 50 years (p for non-linearity < 0.001), whereas WT and pre-formed water per body weight showed a strong dose-dependent negative association with age ≤ 50 years; however, no significant differences were observed in older men (L-shaped relationship). Conversely, women showed a reverse U-shaped relationship between water consumption and age (p for non-linearity < 0.001), and a slightly inverse linear association of WT and pre-formed water per body weight with age (p for trend < 0.001). Although water consumption calculated using calibrated EI was underestimated from 2.1 to 9.2% compared to the values estimated using TEE in previous studies, relationships between age and water consumption were corroborated by the main and sensitivity analyses, except for WT per body weight in women (Supplementary Tables 2–3 and Supplementary Figs. 1–2). From the results of the main and sensitivity analyses, the ranges of WT and pre-formed water per body weight were 45–56 and 37–47 ml/day, respectively, in both sexes. In addition, water consumption estimated using uncalibrated EI was underestimated from 6.7 to 14.3% compared to values estimated using TEE in previous studies (Supplementary Tables 4–5).

Table 2 Distribution of water consumption according to age and sex as estimated by the prediction equation
Full size table
Fig. 1
figure 1

Relationship between water consumption and age among men illustrated by a restricted cubic spline model. (A) water turnover, (B) pre-formed water, (C) water turnover per kilogram body weight (BW), and (D) pre-formed water per kilogram BW. Solid lines represent mean water consumption, dashed lines represent 95% confidence intervals

Full size image
Fig. 2
figure 2

Relationship between water consumption and age by a restricted cubic spline model among women. (A) water turnover, (B) pre-formed water, (C) water turnover per kilogram body weight (BW), and (D) pre-formed water per kilogram BW. Solid lines represent mean water consumption, dashed lines represent 95% confidence intervals

Full size image

We compared the distribution of pre-formed water estimated by the prediction equation and DRs according to sex- and age-stratified models (Table 3). Pre-formed-water estimated by DR was underestimated by approximately 20 to 40% compared with those estimated by prediction equation in both sexes. These underestimation levels were negatively associated with age (p for trend < 0.001). Similar results were obtained from the sensitivity analysis (Supplementary Table 6).

Table 3 Comparison of distribution of pre-formed water estimated by the prediction equation and dietary record according to a sex and age-stratified model
Full size table

Discussion

This study indicated the distribution of WT across age and sex in Japanese adolescents and adults, wherein WT was estimated using a prediction equation. WT and pre-formed water showed an inverse association with age in men aged ≥ 50 years, whereas a reverse U-shaped relationship between water consumption and age was observed in women. These results differed with adjustment for body weight. Pre-formed water estimated from DRs was approximately 20 to 40% underestimated compared with those estimated by the prediction equation in both sexes, and this underestimated amount was negatively associated with age. This is the first study to standardise values of water consumption in a Japanese population using a prediction equation and national large-scale survey data. These findings may be useful in setting water requirements for future dietary guidelines for the Japanese.

As previously stated, our results predict a different relationship of WT and age in men and women. The international DLW database group indicated that WT is greatest between the ages of 20–30 years in men and 25–60 years in women, and is lower in > 40 years men and > 65 years women [4]. Moreover, a previous study of American adults aged 40–79 years living in temperate climates reported that WT was inversely associated with age in both men and women [9]. Our findings are consistent with those of previous studies. These results suggest that age-related changes in WT in men aged > 50 years may be explained by changes in body weight because WT per body weight showed no significant differences with age after 50 years. In the 1998 National Nutrition Survey of Japan, 43.7% of all young Japanese women aged 15–39 indicated a desire to be ‘lean’ or ‘underweight’ [36], and the data we used also showed a reverse U-shaped relationship between body weight and age. This suggests that the low body weight of young women might contribute to the reverse U-shaped relationship between WT and age in women because similar findings were not observed in the results of WT per body weight. Considering that the logarithmic plots of WT and body weight in other mammals and humans are nearly linear [1], the target value for WT per body weight may also be applicable to individuals of different body masses living in similar environments. In environments with high external temperatures, fluid loss due to sweating increases, which increases daily water requirements [4, 5]. A previous study that used DRs over four days in each season to estimate the total water intake from food and drink (pre-formed water) observed higher values in the hot summer season than those in the winter [37]. The average annual temperature was 15.1ºC in Japan in 2012 [38], and the season in which the NHNS-J data were collected was closer to the average annual value than other seasons, suggesting that the autumn estimation may have reflected average annual water consumption better. Therefore, this study has the potential to provide standard values of average annual water consumption that can be used as targets when creating dietary guidelines applicable to public health policy aimed at avoiding inadequate water consumption.

Compared to the prediction equation, our findings indicated that DRs underestimated pre-formed water by approximately 20–40%. This is similar to the results of a previous study that underestimated water consumption estimated from self-report dietary assessment methods such as DRs and 24-hour dietary recalls compared with water consumption measured from biomarkers [11]. In addition, we showed that these underestimation levels were negatively correlated with age. Data from the 2012 NHNS-J indicated that the under-reporting of EI is inversely associated with age, and these findings are consistent with ours [14]. However, under-reporting of EI was associated with older age in the National Health and Nutrition Examination Survey in the United States [13]. Therefore, the relationship between the underestimation of pre-formed water estimated by dietary assessment and age needs to be examined in each country and region. These findings corroborate the fact that it is difficult to accurately evaluate self-reported dietary intake due to systematic errors associated with subject characteristics [13,14,15, 39]. Further research is needed on whether predictive equations can be used to accurately estimate WT and pre-formed water intake by age and sex, as an alternative to DRs. It has been previously reported that a biomarker calibration approach for self-reported EI, as opposed to uncalibrated EI, is strongly associated with mortality [30] and the prevalence of frailty [31]. The guidelines for nutritional epidemiological studies recommend the use of biomarkers in the assessment of dietary intake [40]. Thus, our results may underscore the importance of estimating dietary intake using predictive equations that incorporate biomarkers. Using a prediction equation to estimate pre-formed water can partially solve the problem of systematic errors that have hindered nutritional epidemiological studies for decades, thereby bridging the gap in results from different studies [41].

This study has the advantage of using data from participants selected by stratified random sampling across 47 prefectures in Japan. Additionally, the sample size used to evaluate the distribution of water consumption in this study may be sufficient to provide strong support for our findings. However, there are some methodological limitations. First, self-reported data for dietary assessment may have been affected by systematic errors related to individuals’ characteristics, such as age and BMI [13,14,15, 39], which hinders the accurate evaluation of dietary intake. In addition, they may not reflect an individual’s habitual dietary intake because the DR was maintained for only one day [25]. Consequently, we may have underestimated WT due to the underestimation of EI in our study, in which EI/BMR was assumed to be PAL. Although the ratio of underestimation of EI from DRs to TEE measured by the DLW method in a previous study was corrected, it is possible that these relationships are not uniform across age groups. However, our results were similar after sensitivity analysis using data from previous Japanese studies that measured TEE using DLW methods. Second, we were unable to access any more data on the participants in this survey because we obtained all data from electronically available aggregated reports on the official website. In this context, the data used in the form of aggregated reports included the age category of 15–19 years. The equation used to predict WT was developed for people over 18 years of age, so there may have been systematic bias in this data that included people under 18 years of age. Moreover, it was not possible to calculate standard deviations or 95% CIs for some variables. Thus, our findings need to be re-evaluated using original data or other secondary databases such as National Health and Nutrition Examination Survey because many indicators in the equation may deviate significantly from the actual situation by using unified conjectured data as personal data could not be obtained. Third, although we used the data of participants selected by stratified random sampling, only 44.4% underwent the 2016 NHNS-J. Thus, these participants may have been more health-aware than the general Japanese population, opening our study to the possibility of selection bias. Fourth, owing to its cross-sectional design, this study is limited to describing population-level changes. Thus, it cannot be extended to understand how a specific individual’s water consumption changes with age [42]. Considering these limitations, extrapolation to participants with attributes and environmental conditions other than those included in this study should be performed with caution. Therefore, further longitudinal studies with repeated measurements and randomly sampled participants are needed to evaluate longitudinal changes in water consumption over time according to age and sex.

Conclusions

We determined the standard values of water consumption in the Japanese population using a predictive equation and national large-scale survey data. Our presented methodological approach may help assess water requirements in other regions or countries by using national nutrition survey data and prediction equations and is useful for setting target values for future dietary reference intakes. This method needs to be corrected for the underestimation of EI estimated by self-reported dietary assessment for a more accurate estimate of the standard value of WT.

Data Availability

Annual reports and summary tables of the National Health and Nutrition Survey, Japan are published on the official website of the survey by the Ministry of Health, Labour and Welfare. Results are published each year in the form of electronically available aggregated data [http://www.mhlw.go.jp/bunya/kenkou/kenkou_eiyou_chousa.html]. Moreover, individual-level data from the National Health and Nutrition Survey, Japan are electronically available for scientific research if approved by the Ministry of Health, Labour and Welfare through official application procedures under Article 33 of the Statistics Act; however, currently, these data are only available in Japanese.

References

  1. Swanson ZS, Pontzer H. Water turnover among human populations: effects of environment and lifestyle. Am J Hum Biol. 2020;32(1):e23365.

    Article  PubMed  Google Scholar 

  2. Popkin BM, D’Anci KE, Rosenberg IH. Water, hydration, and health. Nutr Rev. 2010;68(8):439–58.

    Article  PubMed  Google Scholar 

  3. Pontzer H, Brown MH, Wood BM, Raichlen DA, Mabulla AZP, Harris JA, Dunsworth H, Hare B, Walker K, Luke A, et al. Evolution of water conservation in humans. Curr Biol. 2021;31(8):1804–1810e1805.

    Article  CAS  PubMed  Google Scholar 

  4. Yamada Y, Zhang X, Henderson MET, Sagayama H, Pontzer H, Watanabe D, Yoshida T, Kimura M, Ainslie PN, Andersen LF, et al. Variation in human water turnover associated with environmental and lifestyle factors. Science. 2022;378(6622):909–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sawka MN, Cheuvront SN, Carter R. 3rd: human water needs. Nutr Rev. 2005;63(6 Pt 2):30–9.

    Article  Google Scholar 

  6. The National Academies Press: Dietary Reference Intake. 2005. Available: https://nap.nationalacademies.org/catalog/10490/dietary-reference-intakes-for-energy-carbohydrate-fiber-fat-fatty-acids-cholesterol-protein-and-amino-acids. Accessed 6th August 2023.

  7. Grandjean AC. Water Requirements, Impinging Factors, and Recommended Intakes. World Health Organization 2004:25–34. Available: https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/referencespapers.aspx?referenceid=1830742. Accessed 6th August 2023.

  8. Ministry of Health, Labour and Welfare: Dietary Reference Intakes for Japanese. (2020). Available: https://www.mhlwgojp/content/10900000/000862500pdf. Accessed 6th August 2023.

  9. Raman A, Schoeller DA, Subar AF, Troiano RP, Schatzkin A, Harris T, Bauer D, Bingham SA, Everhart JE, Newman AB, et al. Water turnover in 458 American adults 40–79 year of age. Am J Physiol Renal Physiol. 2004;286(2):F394–401.

    Article  CAS  PubMed  Google Scholar 

  10. Johnson EC, Peronnet F, Jansen LT, Capitan-Jimenez C, Adams JD, Guelinckx I, Jimenez L, Mauromoustakos A, Kavouras SA. Validation testing demonstrates efficacy of a 7-Day fluid record to Estimate Daily Water Intake in Adult Men and Women When Compared with total body water turnover measurement. J Nutr. 2017;147(10):2001–7.

    Article  CAS  PubMed  Google Scholar 

  11. Chang DC, Stinson EJ, Dodd KW, Bowles HR, Herrick KA, Schoeller DA, Barrett B, Votruba SB, Krakoff J, Kavouras SA. Validation of Total Water Intake from the automated self-administered 24-h Recall, 4-d Food records, and a food frequency questionnaire using doubly labeled Water. J Nutr 2023; S0022-3166(23)72555-8.

  12. Miller JD, Workman CL, Panchang SV, Sneegas G, Adams EA, Young SL, Thompson AL. Water Security and Nutrition: current knowledge and Research opportunities. Adv Nutr. 2021;12(6):2525–39.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Murakami K, Livingstone MB. Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003–2012. Br J Nutr. 2015;114(8):1294–303.

    Article  CAS  PubMed  Google Scholar 

  14. Murakami K, Livingstone MBE, Okubo H, Sasaki S. Prevalence and characteristics of misreporting of energy intake in Japanese adults: the 2012 National Health and Nutrition Survey. Asia Pac J Clin Nutr. 2018;27(2):441–50.

    PubMed  Google Scholar 

  15. Watanabe D, Nanri H, Sagayama H, Yoshida T, Itoi A, Yamaguchi M, Yokoyama K, Watanabe Y, Goto C, Ebine N, et al. Estimation of Energy Intake by a food frequency questionnaire: calibration and validation with the doubly labeled Water Method in Japanese Older people. Nutrients. 2019;11(7):1546.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Saito A, Imai S, Htun NC, Okada E, Yoshita K, Yoshiike N, Takimoto H. The trends in total energy, macronutrients and sodium intake among Japanese: findings from the 1995–2016 National Health and Nutrition Survey. Br J Nutr. 2018;120(4):424–34.

    Article  CAS  PubMed  Google Scholar 

  17. Ikeda N, Takimoto H, Imai S, Miyachi M, Nishi N. Data Resource Profile: the Japan National Health and Nutrition Survey (NHNS). Int J Epidemiol. 2015;44(6):1842–9.

    Article  PubMed  Google Scholar 

  18. Katanoda K, Matsumura Y. National Nutrition Survey in Japan–its methodological transition and current findings. J Nutr Sci Vitaminol (Tokyo). 2002;48(5):423–32.

    Article  CAS  PubMed  Google Scholar 

  19. Iwaoka F, Yoshiike N, Date C, Shimada T, Tanaka H. A validation study on a method to estimate nutrient intake by family members through a household-based food-weighing survey. J Nutr Sci Vitaminol (Tokyo). 2001;47(3):222–7.

    Article  CAS  PubMed  Google Scholar 

  20. The Council for Science and Technology., Ministry of Education, Culture, Sports and Technology: Standard Tables of Food Composition in Japan. 2010.

  21. Ministry of Health, Labour and Welfare. : The National Health and Nutrition Survey, Japan. Available: http://www.mhlwgojp/bunya/kenkou/kenkou_eiyou_chousahtml. Accessed 6th August 2023.

  22. Ganpule AA, Tanaka S, Ishikawa-Takata K, Tabata I. Interindividual variability in sleeping metabolic rate in Japanese subjects. Eur J Clin Nutr. 2007;61(11):1256–61.

    Article  CAS  PubMed  Google Scholar 

  23. Miyake R, Tanaka S, Ohkawara K, Ishikawa-Takata K, Hikihara Y, Taguri E, Kayashita J, Tabata I. Validity of predictive equations for basal metabolic rate in Japanese adults. J Nutr Sci Vitaminol (Tokyo). 2011;57(3):224–32.

    Article  CAS  PubMed  Google Scholar 

  24. Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL, Swinburn BA. Quantification of the effect of energy imbalance on bodyweight. Lancet. 2011;378(9793):826–37.

    Article  PubMed  Google Scholar 

  25. Watanabe D, Nanri H, Yoshida T, Yamaguchi M, Sugita M, Nozawa Y, Okabe Y, Itoi A, Goto C, Yamada Y, et al. Validation of Energy and Nutrition Intake in Japanese Elderly individuals estimated based on a short food frequency questionnaire compared against a 7-day Dietary Record: the Kyoto-Kameoka Study. Nutrients. 2019;11(3):688.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zaiki M, Oguchi T, Kagawa Y, Takahashi A, Koike S, Yamauchi M. Relationships between distribution of Japanese residential areas and topography. CSIS Discussion Paper 2005, 68:1–13 (In Japanese).

  27. Japan Meteorological Agency. : Information on weather. Available: http://www.jmagojp/jma/en/menuhtml. Accessed 6th August 2023.

  28. Speakman JR, Yamada Y, Sagayama H, Berman ESF, Ainslie PN, Andersen LF, Anderson LJ, Arab L, Baddou I, Bedu-Addo K, et al. A standard calculation methodology for human doubly labeled water studies. Cell Rep Med. 2021;2(2):100203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Du Bois D, Du Bois EF. Clinical calorimetry Tenth Paper. A formula to Estimate the approximate surface area if Height and Weight be known. Arch Intern Med. 1916;17:863–71.

    Article  CAS  Google Scholar 

  30. Watanabe D, Yoshida T, Watanabe Y, Kimura M, Yamada Y, Kyoto-Kameoka Study Group. Doubly labelled water-calibrated energy intake associations with mortality risk among older adults. J Cachexia Sarcopenia Muscle. 2022;14(1):214–25.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Watanabe D, Yoshida T, Nanri H, Watanabe Y, Date H, Itoi A, Goto C, Ishikawa-Takata K, Sagayama H, Ebine N, et al. Association between the prevalence of Frailty and doubly labeled Water-Calibrated Energy Intake among Community-Dwelling older adults. J Gerontol A Biol Sci Med Sci. 2021;76(5):876–84.

    Article  PubMed  Google Scholar 

  32. Watanabe D, Yoshida T, Watanabe Y, Yamada Y, Miyachi M, Kimura M. Dose-response relationships between objectively measured Daily steps and Mortality among Frail and Nonfrail older adults. Med Sci Sports Exerc. 2023;55(6):1044–53.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ishikawa-Takata K, Kaneko K, Koizumi K, Ito C. Comparison of physical activity energy expenditure in Japanese adolescents assessed by EW4800P triaxial accelerometry and the doubly labelled water method. Br J Nutr. 2013;110(7):1347–55.

    Article  CAS  PubMed  Google Scholar 

  34. Ishikawa-Takata K, Tabata I, Sasaki S, Rafamantanantsoa HH, Okazaki H, Okubo H, Tanaka S, Yamamoto S, Shirota T, Uchida K, et al. Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire. Eur J Clin Nutr. 2008;62(7):885–91.

    Article  CAS  PubMed  Google Scholar 

  35. Ishikawa-Takata K, Naito Y, Tanaka S, Ebine N, Tabata I. Use of doubly labeled water to validate a physical activity questionnaire developed for the Japanese population. J Epidemiol. 2011;21(2):114–21.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hayashi F, Takimoto H, Yoshita K, Yoshiike N. Perceived body size and desire for thinness of young Japanese women: a population-based survey. Br J Nutr. 2006;96(6):1154–62.

    Article  CAS  PubMed  Google Scholar 

  37. Tani Y, Asakura K, Sasaki S, Hirota N, Notsu A, Todoriki H, Miura A, Fukui M, Date C. The influence of season and air temperature on water intake by food groups in a sample of free-living Japanese adults. Eur J Clin Nutr. 2015;69(8):907–13.

    Article  CAS  PubMed  Google Scholar 

  38. Ma C, Yang J, Nakayama SF, Honda Y. The association between temperature variability and cause-specific mortality: evidence from 47 Japanese prefectures during 1972–2015. Environ Int. 2019;127:125–33.

    Article  PubMed  Google Scholar 

  39. Watanabe D, Yoshida T, Yoshimura E, Nanri H, Goto C, Ishikawa-Takata K, Ebine N, Fujita H, Kimura M, Yamada Y et al. Doubly labelled water-calibration approach attenuates the underestimation of energy intake calculated from self-reported dietary assessment data in Japanese older adults. Public Health Nutr 2021:1–11.

  40. Lachat C, Hawwash D, Ocke MC, Berg C, Forsum E, Hornell A, Larsson C, Sonestedt E, Wirfalt E, Akesson A, et al. Strengthening the reporting of Observational studies in Epidemiology-Nutritional Epidemiology (STROBE-nut): an extension of the STROBE Statement. PLoS Med. 2016;13(6):e1002036.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Prentice RL, Howard BV, Van Horn L, Neuhouser ML, Anderson GL, Tinker LF, Lampe JW, Raftery D, Pettinger M, Aragaki AK, et al. Nutritional epidemiology and the women’s Health Initiative: a review. Am J Clin Nutr. 2021;113(5):1083–92.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Watanabe D, Murakami H, Gando Y, Kawakami R, Tanisawa K, Ohno H, Konishi K, Sasaki A, Morishita A, Miyatake N, et al. Association between temporal changes in Diet Quality and concurrent changes in Dietary Intake, Body Mass Index, and physical activity among Japanese adults: a longitudinal study. Front Nutr. 2022;9:753127.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge the staff of the 2016 National Health and Nutrition Survey, Japan for their valuable contributions. We wish to express our gratitude to all the participants for their cooperation in this study. We would like to thank Editage (www.editage.jp) for English-language editing.

Funding

This study was conducted with support from JSPS KAKENHI through a research grant provided to Daiki Watanabe (grant number 23K16780).

Author information

Authors and Affiliations

  1. Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa-city, Saitama, 359-1192, Japan

    Daiki Watanabe & Motohiko Miyachi

  2. National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, 17-34 Senrioka-Shimmachi, Settsu-city, Osaka, 566-0002, Japan

    Daiki Watanabe & Motohiko Miyachi

  3. Graduate School of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa-city, Saitama, 359-1192, Japan

    Yumiko Inoue

Authors
  1. Daiki Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yumiko Inoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Motohiko Miyachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors’ responsibilities were as follows: study conception and design: D.W., Y.I., and M.M.; analyses and interpretation of data: D.W. and Y.I.; drafting of the manuscript: D.W.; revised the manuscript: D.W., Y.I., and M.M.; provided the data: D.W. and Y.I.; approved the manuscript: all the authors.

Corresponding author

Correspondence to Daiki Watanabe.

Ethics declarations

Ethics approval and consent to participate

The National Health and Nutrition Survey, Japan, was conducted according to the guidelines laid down in the Declaration of Helsinki. In the National Health and Nutrition Survey, Japan study procedures as well as the risks associated with participating were explained and written informed consent was obtained from all the participants. An ethical review was deemed unnecessary by the Research Ethics Committee of Waseda University because we used only data of annual reports and summary tables that were anonymised and contained no information that could be used to identify the individuals, in accordance with the Ethical Guidelines of Epidemiological Research established by the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour and Welfare.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

: Supplementary Table 1. Difference in energy intake assessed by dietary record and previous total energy expenditure data measured by the doubly labelled water method. Supplementary Table 2. Comparison of distribution of water turnover estimated by a prediction equation using calibrated energy intake assessed by dietary record with previous total energy expenditure data measured by the doubly labelled water method according to a sex- and age-stratified model. Supplementary Table 3. Comparison of distribution of pre-formed water estimated by prediction equation using calibrated energy intake assessed by dietary record with previous total energy expenditure data measured by the doubly labelled water method according to a sex- and age-stratified model. Supplementary Table 4. Comparison of distribution of water turnover estimated by prediction equation using uncalibrated energy intake assessed by dietary record with previous total energy expenditure data measured by the doubly labelled water method according to a sex- and age-stratified model. Supplementary Table 5. Comparison of distribution of pre-formed water estimated by prediction equation using uncalibrated energy intake assessed by dietary record with previous total energy expenditure data measured by the doubly labelled water method according to a sex- and age-stratified model. Supplementary Table 6. Results of sensitivity analysis for comparison of distribution of pre-formed water estimated by a prediction equation and dietary record according to a sex- and agestratified model. Supplementary Figure 1. Results of sensitivity analysis for the relationship between water consumption and age among men illustrated by a restricted cubic spline model. Supplementary Figure 2. Results of sensitivity analysis for the relationship between water consumption and age by a restricted cubic spline model among women.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Watanabe, D., Inoue, Y. & Miyachi, M. Distribution of water turnover by sex and age as estimated by prediction equation in Japanese adolescents and adults: the 2016 National Health and Nutrition Survey, Japan. Nutr J 22, 64 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s12937-023-00896-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12937-023-00896-z

Keywords

Nutrition Journal

ISSN: 1475-2891

Contact us