Quantity-effect correlation between water intake and serum uric acid in US adults: a cross-sectional study based on NHANES data

Introduction

Hyperuricemia (HUA) is a prevalent disease, affecting approximately 43 million individuals in the United States (U.S.) and 170 million individuals in China (1,2). Monosodium urate crystals are deposited on cartilage when the serum uric acid (SUA) concentration exceeds the saturating concentration (7 mg/dL) (3). This deposition represents an early stage in the development of gout and can result in direct mechanical damage to the joints (4). Intracellular urate possesses pro-oxidative properties (5,6). An increase in SUA levels can cause a variety of pathophysiological reactions, including DNA damage, oxidative stress, vascular smooth muscle cell proliferation, endothelial dysfunction, inhibition of nitric oxide production, and apoptosis (7-10). When cells interact with crystals, inflammatory factors, matrix metalloproteinases 9, and hydrolases are released, causing damage to multiple organs such as the heart, articular cartilage, bones, brain, and kidneys (11-14). HUA is an independent risk factor for chronic kidney disease, hypertension, diabetes mellitus, myocardial ischemia, and atherosclerosis (15-17). For every 1 mg/dL increase in SUA levels, the risk of cardiovascular death in patients with chronic kidney disease increases by 12% (18); patients with HUA have a 7% increased risk of kidney failure and a 15–23% increased risk of hypertension (6,19).

According to the 2020 American College of Rheumatology recommendations, drugs currently used to treat HUA, such as allopurinol, febuxostat, and probenecid, have numerous limitations (20). The common side effects of these drugs include gastrointestinal intolerance, rash, hematologic abnormalities, hepatotoxicity, arthralgia, potential cardiovascular risk, and urolithiasis (21-23). In addition, patients with asymptomatic HUA demonstrate low medication adherence, resulting in suboptimal drug treatment outcomes (24-26). For asymptomatic HUA, guidelines in the U.S., the United Kingdom, China, and other countries recommend non-pharmacologic therapies to reduce SUA levels, including reasonable exercise, a low-purine diet, and adequate water intake (27,28). A previous study suggests that established foods and specific dietary patterns account for less than 1% of the variance in SUA levels (29). In humans, approximately one-third of SUA is excreted through the digestive tract, while the remaining two-thirds is excreted through the kidneys (30). Thus, increasing water intake, which may promote SUA excretion and dilute SUA concentrations, appears to be an effective way to reduce SUA levels. However, the amount of water intake recommended in guidelines varies widely between countries and is mostly based on clinical experience (20,31-36). Further, only a few studies have reported a negative correlation between SUA levels and water intake (37), and large-scale quantity-effect correlation studies are lacking. Thus, water consumption recommendations for patients with HUA lack a solid scientific basis.

Using data from the 2009–2018 National Health and Nutritional Examination Surveys (NHANES), this cross-sectional study aimed to investigate the quantity-effect correlation between water intake and SUA levels. We present this article in accordance with the TRIPOD reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-699/rc).

Methods

Study population

We analyzed data from the NHANES, a prospective, ongoing cross-sectional study that investigates the health and nutritional status of Americans. NHANES uses a complex, multistage probability sampling design to represent the U.S. non-institutionalized civilian population, with oversampling of racial/ethnic minorities. Written informed consent was obtained from all participants for the NHANES and examination after obtaining approval from the National Ethical Review Board for Health Statistics Research. This cross-sectional study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

In this study, we used data from NHANES participants from 2009 through 2018 (the most recent years available). Inclusion criteria: (I) age ≥18 years; (II) complete data on water intake (total plain water/total moisture), SUA, and body weight; (III) no cancer diagnosis; (IV) non-pregnant status. Exclusion criteria: (I) missing key variables; (II) extreme water intake (<500 mL/day or >10,000 mL/day). After excluding participants with missing data on total plain water consumption (n=6,612), SUA data (n=13,735), body weight data (n=240), participants who drank less than 500 mL or more than 10,000 mL of water per day (n=10,181), participants younger than 18 years of age (n=2,119), participants with cancer (n=1,441), and pregnant women (n=191), a total of 15,174 participants were included in our analyses (Figure 1).

Figure 1 Flowchart showing the selection of the studied population. NHANES, National Health and Nutrition Examination Surveys.

Primary exposure

The primary exposure variable in our study was water intake (g), which included total plain water consumption and total moisture intake. Total plain water consumption included plain tap water, water from a drinking fountain, water from a water cooler, bottled water, and spring water. Total moisture intake included water ingested from food, beverages, and total plain water. The total plain water consumption (g) and total moisture intake (g) data were obtained from the Total Nutrient Intake dataset, which provides information on food (and beverage) consumption by participants.

Specifically, participants’ water intake was assessed using a 24-hour dietary recall method (from midnight to midnight of the day before the interview) from the Total Nutrient Intake Document. The U.S. Department of Agriculture’s Dietary Research Food and Nutrition Database converted the coding of the interview data into a calculation of total nutrient intake (http://www.ars.usda.gov/ba/bhnrc/fsrg). Two non-consecutive 24-hour dietary recalls were conducted. The first recall was conducted by an in-person interview at a Mobile Test Center. Three to 10 days later, the second recall was conducted by telephone by a physician. If the participant completed both recalls, total plain water consumption and total moisture intake were taken as the average of the two 24-hour dietary recalls. Otherwise, one reliable dietary recall was used. To better measure the water intake of each participant, water intake was divided by body weight to obtain the water intake per kilogram of body weight (g/kg), which was analyzed as the independent variable.

All the laboratory data were obtained using standardized accredited analytical methods. Plasma osmolality is a measure of the number of dissolved particles in a solution, and was calculated using the values of the chemical analyzer: (1.86 × Na) + (glucose/18) + [blood urea nitrogen (BUN)/2.8] +9. The DxC system uses an indirect (or diluted) ion-selective electrode method to determine the concentration of sodium in biological fluids. The concentration of glucose in biological fluids was determined by the oxygen rate method using a Beckman oxygen electrode. The concentration of BUN in serum or plasma was detected by enzymatic conductivity assay.

Outcome

The results of this study were SUA (mg/dL) measured from 2009 to 2018 using the Beckman UniCel^® DxC 800 Synchron and Roche Cobas 6000 (c501 module). The SUA measurements from the two laboratories were compared, and no significant differences were found. For more information on the SUA measurement process, visit www.cdc.gov/nchs/nhane.

Covariates

All the potential confounders were recorded and converted into categorical variables. The sociodemographic characteristics included sex (male or female), age (18–44, 45–64, or ≥65 years), race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other race), income-to-poverty ratios (Monthly Poverty Level Index <1.30, 1.30–1.85, or >1.85), and educational attainment (less than high school, high school or equivalent, or college or above). The health behavior characteristics included smoking status: assessed via questionnaire: “never smoker” (no history of smoking), “former smoker” (quit smoking for ≥1 month), or “current smoker” (smokes ≥1 cigarette/day in the past month). Physical activity: classified based on self-reported weekly duration and intensity of activities (e.g., walking, housework, sports) using metabolic equivalents (METs): “sedentary” (<150 MET-min/week), “low-to-moderate” (150–299 MET-min/week), or “high” (≥300 MET-min/week). Alcohol drinking status: determined by past 30-day alcohol intake: “non-drinker” (no alcohol consumption), “low-to-moderate drinker” (≤1 drink/day for females, ≤2 drinks/day for males), or “heavy drinker” (exceeding low-to-moderate limits). The laboratory data included high-density lipoprotein (HDL) (below normal, normal, or above normal), low-density lipoprotein (LDL) (normal, above normal), triglycerides (normal or above normal), total cholesterol (normal or above normal), glycohemoglobin (normal or above normal), and blood pressure (non-hypertensive or hypertensive). A priori adjustments were considered for all these potential covariates in the multivariable analysis.

Statistical analysis

To account for the population size and oversampling of specific subgroups, sampling weights provided by the NHANES were used. The data analysis was based on guidelines from the official website of the Centers for Disease Control and Prevention (CDC; https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). The categorical variables are presented as the number (percentage, weighted). Statistical differences between groups were examined using the Student’s t-test or Mann-Whitney U test (continuous variables), and Chi-squared test (categorical variables). A weighted multivariate linear regression analysis was used to assess the association between total plain water consumption/body weight and SUA, and the association between the total moisture intake/body weight and SUA. Subgroup analyses were also conducted for sex (male and female), age (<60 and ≥60 years), and race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, and other races). Additionally, generalized additive models and restricted cubic splines (smooth curve fittings) were used to explore the nonlinear association between total plain water consumption/body weight and SUA, as well as the nonlinear relationship between total moisture intake/body weight and SUA. A recursive algorithm was used to estimate the inflection points if nonlinear associations were observed from the smooth curve fitting. A two-piecewise linear regression model was constructed to calculate the threshold effect. To assess whether the best-fit model was linear or nonlinear, the log-likelihood ratio test was applied to calculate the P nonlinear value. All statistical analyses were performed using the statistical software EmpowerStats version 2.0 (http://www.empowerstats.com, X&Y solution, Inc.) and R version 3.6.1 (http://www.R-project.org, the R Foundation). A P value <0.05 was considered statistically significant for all analyses, including multivariate linear regression, subgroup tests, and mediation analysis. As suggested by VanderWeele (38), a mediation analysis was performed using the Sobel-Goodman analysis by Stata software (version 17.0) to determine whether serum plasma osmolality mediates the relationship between water intake and SUA values.

Results

Participant characteristics

A total of 15,174 participants selected from NHANES 2009–2018 were included in this study after excluding participants with missing data. The weighted sociodemographic and medical characteristics of the participants are set out in Table 1. There were significant differences among the SUA groups (quartiles Q1–Q4: Q1, 0.4–4.4 mg/dL; Q2, 4.5–5.3 mg/dL; Q3, 5.4–6.3 mg/dL; Q4, 6.4–18 mg/dL) in terms of age, sex, race/ethnicity, educational level, income-poverty ratio, physical activity, direct HDL-cholesterol, triglyceride LDL-cholesterol, total cholesterol, glycohemoglobin, blood pressure, alcohol drinking, and smoking status.

Quantity-effect correlation between water intake/body weight and SUA

The results of the multivariate linear regression analyses are presented in Table 2 and Figure 2A,2B. In the unadjusted model, total plain water consumption/body weight was negatively correlated with SUA [β=–0.010, 95% confidence interval (CI): –0.012, 0.009, P<0.001]. After adjustment for confounders, this negative association was still present in model 2 (β=–0.005, 95% CI: –0.007, –0.004, P<0.001) and model 3 (β=–0.003, 95% CI: –0.004, –0.001, P<0.001). Total moisture intake/body weight was also negatively correlated with SUA in model 1 (β=–0.009, 95% CI: –0.011, –0.008, P<0.001), model 2 (β=–0.010, 95% CI: –0.011, –0.009, P<0.001), and model 3 (β=–0.007, 95% CI: –0.008, –0.006, P<0.001). After converting total plain water consumption/body weight and total moisture intake/body weight from continuous variables to categorical variables (quartiles), the participants in the highest quartile had a lower SUA than those in the lowest quartile by 0.114 mg/dL (95% CI: –0.169, –0.059, P<0.001) and 0.376 mg/dL (95% CI: –0.434, –0.319, P<0.001), respectively.

Figure 2 Quantity-effect correlation between water intake/body weight and SUA. (A) Linear association between total plain water consumption/body weight and SUA estimated using multivariate linear regression. (B) Linear association between total moisture intake/body weight and SUA estimated using multivariate linear regression. Each black point represents a participant, and the red dots were connected in a line by multivariate linear regression. (C) Nonlinear association between total plain water consumption/body weight and SUA estimated by a generalized additive model fitted with a smoothed curve. (D) Nonlinear association between total moisture intake/body weight and SUA estimated by a generalized additive model fitted with a smoothed curve. The shaded area indicates the 95% CI, and the solid line represents the smooth curve fit between variables. The outcomes were adjusted for all the covariates presented in Table 1. CI, confidence interval; SUA, serum uric acid.

The results of the subgroup analyses by age, gender, and race are presented in Table 2. Total plain water consumption/body weight remained negatively associated with SUA in those aged 45–64 years (β=–0.006, 95% CI: –0.009, –0.004, P<0.001) and at least 65 years (β=–0.007, 95% CI: –0.013, –0.002, P=0.007). In the stratified analyses by sex, total plain water consumption/body weight was only negatively correlated with SUA in women (β=–0.006, 95% CI: –0.008, –0.004, P<0.001). When stratified by race, total plain water consumption/body weight was only negatively correlated with SUA in non-Hispanic white participants (β=–0.004, 95% CI: –0.006, –0.001, P=0.003). In addition, the negative correlation between total water intake/body weight and SUA persisted in all subgroup analyses, regardless of age, sex, and race. Women generally had lower levels of SUA and were more sensitive to the reduction in SUA caused by increased drinking water (Figure S1).

Generalized additive models with smooth curve fittings were applied to estimate the nonlinear association between total plain water consumption/body weight and SUA, as well as the nonlinear relationship between total moisture intake/body weight and SUA. Using a two-piecewise linear regression model, a nonlinear negative correlation was found between total plain water consumption/body weight and SUA in all participants, which displayed an L-shaped curve with the inflection point of 7.591 mL/kg (Figure 2C and Table 3). More precisely, for participants with total plain water consumption/body weight less than 7.591 g/kg, each 1 g/kg increase in total plain water consumption/body weight was correlated with a 0.161 mg/dL decrease in SUA (95% CI: –0.201, –0.122, P<0.001). Once total plain water consumption/body weight was greater than 7.591 g/kg, SUA barely changed as total plain water consumption/body weight increased (β=–0.001, 95% CI: –0.003, 0.000, P=0.11). Similarly, total moisture intake/body weight showed a nonlinear inverse relationship with SUA in an L-shaped curve with an inflection point of 33.57 mL/kg (Figure 2D and Table 3). When the total moisture intake/body weight was less than 33.57 g/kg, SUA decreased by 0.025 mg/dL for every 1 g/kg increase in total moisture intake/body weight (95% CI: –0.030, –0.021, P<0.001). Once total moisture intake/body weight was greater than 33.57 g/kg (β=–0.003, 95% CI: –0.005, –0.002, P<0.001), SUA remained almost constant as total moisture intake/body weight increased.

Mediating effect of plasma osmolality between water intake and SUA values

Water intake may affect SUA values through blood dilution or increased urinary excretion. Plasma osmolality reflects blood dilution status, and can be used to quantitatively assess the mediating effect of blood dilution between water intake and SUA values. In this study, a Sobel-Goodman analysis was performed to explore the mediating effect. As detailed in Table 4, plasma osmolality mediated the relationship between water intake/body weight and SUA, with mediating effects of 9.14% and 5.84% for total plain water consumption/body weight and total moisture intake/body weight, respectively (P<0.001).

Discussion

Regardless of the presence of gout, higher levels of SUA can be detrimental to several body systems, contributing to conditions such as kidney damage, cardiovascular disease, and diabetes (39-41). Approximately two-thirds of SUA is eliminated from the body through urine from the kidneys (42); thus, increasing SUA excretion by drinking more water is consistent with established understanding. Current guidelines recommend that patients with asymptomatic HUA reduce SUA levels by increasing water intake (43); however, the amount of water intake recommended by different national guidelines varies widely and is mostly based on clinical experience (20,31,33,35,36). To provide evidence-based recommendations on water intake for patients with HUA, we conducted the present cross-sectional study in a nationally representative population of U.S. adults. Our primary objective was to investigate the quantity-effect correlation between water intake and SUA, and to explore whether there is a nonlinear relationship between water intake and SUA, and the mediating role of plasma osmolality.

We found a negative correlation between total plain water consumption/body weight and SUA, as well as a negative correlation between total moisture intake/body weight and SUA. Similar to the present study, Pokharel et al.’s cross-sectional study of 150 patients with gout and 150 patients with HUA showed that SUA levels were inversely correlated with total water intake (37). To gain insights into the relationship between water intake and SUA, we performed subgroup analyses. In our multivariate linear regression subgroup analyses, significant negative correlations were mainly observed among females, those aged over 65 years, and non-Hispanic white participants.

Women generally have lower SUA levels and are more sensitive to SUA reduction caused by increased water intake (44-46). This is partly due to estrogen and may also be related to the generally better lifestyles and lower alcohol consumption of women (47,48). For different ethnic groups, differences in the relationship between water intake and SUA may reflect genetic variation in water metabolism and SUA metabolism. Notably, the effects of water intake, metabolism, and physiology appear to be influenced by a variety of exogenous and endogenous factors such as diet, age, sex (hormonal status), and genetic background (49,50). These factors may lead to different sensitivities of SUA to water intake changes and different inflection points in stratified subpopulations.

To explore whether there was a nonlinear relationship between water intake and SUA, generalized additive models with smooth curve fittings were applied. We found a nonlinear inverse relationship between total plain water consumption/body weight and SUA, as well as a nonlinear inverse relationship between total moisture intake/body weight and SUA. Total plain water consumption/body weight and total moisture intake/body weight showed L-shaped curves with inflection points of 7.591 and 33.57 g/kg, respectively, suggesting that when water intake/body weight is below the inflection point, an appropriate increase in water intake may be an effective measure to ameliorate HUA. To examine this nonlinear relationship, we employed a two-piecewise linear regression model. A two-piecewise linear regression was selected to model the trend observed in the smooth curve fitting, which demonstrated discrimination at an inflection point. Notably, the use of three or more piecewise regressions, might have reduced the sample size in the segments, resulting in poor statistical power. Additionally, we found that the two-piecewise linear regression model easily explained the physiological efficacy of water intake on SUA, satisfying the trend of first decreasing and then stabilizing. Nevertheless, future studies should compare the pros and cons of different piecewise linear regressions for the mechanisms underlying the water intake/body weight-SUA relationship.

Plasma osmotic pressure is measured by the million moles of solutes per liter of solution, and reflects the concentration of solutes such as sodium, chloride, and glucose in the blood. Increased water intake leads to an increase in blood volume, which dilutes the concentration of solutes in the blood (51,52). In this study, we found a negative correlation between water intake and SUA, and hypothesized that increasing water intake, in addition to increasing SUA excretion in urine, may also dilute uric acid concentration by decreasing plasma osmotic pressure. Thus, we analyzed the relationship between water intake and plasma osmolality using total moisture intake and total plain water consumption as independent variables, and found consistent nonlinear negative correlations (Figure S2 in the supplementary material). We also analyzed the relationship between plasma osmolality and SUA and found an S-shaped nonlinear positive correlation between plasma osmolality and SUA (Table S1 and Figure S3), suggesting that plasma osmolality mediates the increase in water intake to reduce SUA levels. Specifically, the plasma osmolality-mediated effects of total plain water consumption/body weight and total water intake/body weight in reducing SUA were 9.14% and 5.84%, respectively. These findings suggest that increased water intake reduces SUA concentrations primarily by increasing renal excretion of SUA, whereas blood dilution-mediated effects are relatively weak.

Strengths and limitations

This study had the following strengths and limitations. The study population was representative and distributed throughout the U.S. with a large sample size. Two 24-hour dietary recalls were used to obtain the mean water intake data, thereby reducing random measurement errors. In addition, generalized additive models were used to solve nonlinear problems. To our knowledge, we found the first evidence of a nonlinear relationship between water intake and SUA in U.S. adults. While this study focuses on U.S. adults, the NHANES’s large, nationally representative sample (n=15,174) overcomes limitations of small-scale studies—detecting nonlinear relationships and inflection points (7.591 g/kg for total plain water) that are unobservable in smaller cohorts. These findings address guideline variability by providing evidence-based threshold values, which can be adapted to diverse populations. Additionally, large sample sizes minimize random error, support robust subgroup analyses, and enhance the reliability of non-pharmacological HUA recommendations—justifying the need for this large-scale study. However, this study also had some limitations. First, due to the cross-sectional nature of the study, causality could not be inferred. Second, the SUA levels in the study population were predominantly within the physiological range, and the nonlinear relationship may not apply to patients with HUA and gout. Third, SUA was significantly higher in males than in females, but the association between SUA and total plain water/body weight was not significant in males, which should be examined in future studies. Finally, due to incomplete follow-up information, we were unable to provide customized water intake recommendations for HUA patients with increased metabolism or decreased excretion based on the analysis of the NHANES data.

Future research should address several unresolved questions to refine recommendations for lowering SUA levels. First, prospective cohort studies or randomized controlled trials are necessary to establish the causal relationship between water intake and SUA reduction, as the current cross-sectional design cannot determine causality. Second, studies targeting patients with HUA or gout are required to determine whether the observed inflection points (7.591 g/kg for total plain water intake/body weight and 33.57 g/kg for total moisture intake/body weight) are applicable to these clinical groups. Third, exploring the mechanisms underlying sex differences, such as whether testosterone masks the effects of water intake in males, could inform personalized guidance. Fourth, investigating interactions between water intake and other factors (e.g., purine intake, urate-lowering medications) would enhance comprehensive SUA management. Finally, long-term data on water intake (beyond 24-hour recalls) should be collected to assess its sustained impact on SUA levels.

Conclusions

This cross-sectional study found a negative correlation between water intake/body weight and SUA, particularly in females. Moreover, the negative relationship exhibited nonlinear characteristics, following an L-shaped curve with inflection points at 7.591 and 33.57 g/kg for total plain water consumption/body weight and total moisture intake/body weight, respectively. These results suggest that an appropriate increase in water intake may be an effective measure to ameliorate HUA when water intake/body weight is below the inflection point. Plasma osmolality was found to play a mediating role in the reduction of SUA by water intake. The plasma osmolality-mediated effects of total plain water consumption/body weight and total water intake/body weight in reducing SUA were 9.14% and 5.84%, respectively.

Acknowledgments

The data used in this study were obtained from the NHANES. We would like to thank all the NHANES staff and participants for their contribution.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-699/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-699/prf

Funding: This work was supported by the National Natural Science Foundation of China (No. 82201774 to K.Y.Z.; Nos. 82202933, 82203633, 82173204, and 82220108004 to W.J.Q.), and the Young Talent Support Program of Xi’an (No. 959202313006 to K.Y.Z.). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-699/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all participants for the NHANES and examination after obtaining approval from the National Ethical Review Board for Health Statistics Research.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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