Mediating Effect of Serum Uric Acid in the Association Between Nocturia and Mortality

Article information

Int Neurourol J. 2025;29(4):277-285
Publication date (electronic) : 2025 December 31
doi : https://doi.org/10.5213/inj.2550126.063
1Department of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
2Department of Urology, Yeongwol Medical Center, Yeongwol, Korea
Corresponding author: Sung Tae Cho Department of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1 Singil-ro, Yeongdeungpo-gu, Seoul 07441, Korea Email: cst326@paran.com
Received 2025 May 18; Accepted 2025 September 1.

Abstract

Purpose

Nocturia is increasingly recognized as a natural consequence of aging and is commonly observed in the context of systemic dysfunction, with growing evidence linking it to elevated mortality risk. This study aims to elucidate whether serum uric acid (sUA) acts as a mediator in this associative pathway.

Methods

This study used population-based data from 2005 to 2014 to investigate the association between nocturia and mortality, incorporating sUA as a potential mediator. Using data from 12,522 participants, sUA levels were categorized into quartiles (Q1–Q4) to assess dose–response relationships in the context of nocturia and mortality.

Results

Participants in higher sUA quartiles were older and exhibited a greater burden of metabolic risk factors, including higher body mass index, waist circumference, and blood pressure. After adjustment, Cox regression analysis revealed a significant association between nocturia and increased all-cause and cardiovascular mortality. Restricted cubic spline regression indicated a J-shaped association between elevated sUA and increased mortality. Additionally, mediation analysis demonstrated that sUA partly mediated the relationship between nocturia and mortality.

Conclusions

Nocturia independently predicts mortality, irrespective of metabolic status and sUA levels. These findings underscore the pivotal mediating role of sUA, highlighting the necessity of an integrated approach to nocturia management. Future research should prioritize interventional strategies that target metabolic dysfunction as a means to reduce nocturia-associated mortality risk.

INTRODUCTION

The International Continence Society (ICS) defines nocturia as a need to wake up to urinate in the main sleeping period [1]. Nocturia is more prevalent in women than in men. As the nocturia becomes common after middle age (≥40 years), the prevalence in men exceeds that in women, particularly in the older population [2-4]. In a meta-analysis, nocturia was correlated with mortality, regardless of age and sex [5]. Nocturia interrupts sleep, disrupts circadian rhythms, contributes to cardio-metabolic disorders, and impacts mortality [6]. This means the impact of nocturia on mortality can vary depending on metabolic abnormalities.

Metabolic and inflammatory factors are increasingly recognized as contributors to lower urinary tract symptoms (LUTS) pathophysiology [7]. Serum uric acid (sUA) has been linked to systemic diseases independent of crystal deposition, resulting in obesity, insulin insensitivity, cardiovascular diseases, and metabolic syndrome [8]. Furthermore, sUA has emerged as a potential factor modulating prostate and bladder function [9-13]. Given that nocturia is one of the most bothersome LUTS, there is growing interest in understanding whether elevated sUA contributes to nocturia. However, the impact of uric acid on nocturia is underinvestigated.

Given the limited evidence, we conducted a large-scale analysis to explore the relationship between nocturia and mortality. Using robust statistical methods, our primary objective was to assess this association in middle-aged and older adults. Furthermore, we examined the potential mediating effects of sUA to evaluate its contribution to the nocturia-mortality pathway.

MATERIALS AND METHODS

Study Design and Data Source

The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional, nationally representative survey conducted in the United States that collects comprehensive health-related information through structured interviews, physical examinations, and laboratory assessments. Mortality outcomes were ascertained by linkage to the National Death Index through 2019, with a median follow-up duration of 107.3 months.

Of the 50,965 individuals who participated across five cycles (2005–2014), those aged ≥40 years who had completed the nocturia questionnaire, sUA measurements, and metabolic risk factor assessments were considered for inclusion. After excluding 38,443 participants due to missing data on key variables or incomplete follow-up information, a total of 12,522 individuals were retained for preliminary statistical analyses.

Definition of Nocturia, sUA, and Cardiovascular Risks

We defined nocturia based on the self-reported data from the kidney condition questionnaire. Nocturia was defined as ≥2 nightly voids during the past year, as this threshold, although higher than the ICS definition (≥1 void), is generally regarded as bothersome and clinically meaningful [14].

sUA levels were measured using a timed endpoint enzymatic method with uricase on either the Beckman Synchron LX20 or the Beckman Coulter UniCel DxC800 platform, depending on the survey cycle. Uric acid is oxidized to allantoin and hydrogen peroxide, with the subsequent peroxidase-catalyzed chromogenic reaction monitored at 520 nm; the change in absorbance is directly proportional to the sUA concentration. All measurements adhered to the NHANES laboratory quality control protocol, incorporating internal and external reference standards to ensure accuracy and comparability across survey years.

For hypertension (HTN), participants were classified as hypertensive if their average of at least two blood pressure measurements met either a systolic threshold of 140 mmHg or higher, a diastolic threshold of 90 mmHg or higher, or if they were receiving antihypertensive medication. Individuals were considered to have dyslipidemia (DL) when fasting total cholesterol levels reached or exceeded 240 mg/dL, or if lipid-lowering drugs were in use. Diabetes mellitus (DM) was identified based on any of the following: random plasma glucose levels above 200 mg/dL, fasting plasma glucose levels of 126 mg/dL or higher, glycated hemoglobin (HbA1c) values of at least 6.5%, or ongoing treatment with antidiabetic medication.

Covariates

Baseline characteristics, including age, sex, race/ethnicity, body mass index (BMI), smoking status, alcohol consumption, sleep duration and comorbidities (e.g., HTN, DM, and DL) were adjusted for in the analysis.

Statistical Analysis

sUA levels were divided into quartiles (Q1–Q4) based on their distribution. Comparisons of baseline characteristics across sUA quartiles were performed using 1-way analysis of variance for continuous variables and the chi-square test for categorical variables. Kaplan-Meier survival analysis and Cox proportional hazards models were used to examine the association between nocturia and all-cause and cardiovascular mortality, adjusting for potential confounders. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported. For continuous mediators such as sUA, restricted cubic spline (RCS) models were applied to capture potential nonlinear relationships. The mediation effects of sUA in the pathway between nocturia and mortality were assessed using the “Regmedint” package. Estimates of direct, indirect, and total effects (TEs) were obtained. All statistical analyses were performed using R ver. 4.3.1 (R Foundation for Statistical Computing, Austria). Two-sided P-values less than 0.05 were considered statistically significant.

Ethical Considerations

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The dataset is publicly available and fully de-identified, and its use is approved by the Ethics Review Board (Protocols #2005–06 and #2011–17). Therefore, additional Institutional Review Board approval was not required for this analysis.

RESULTS

Baseline Characteristics

Table 1 presents the baseline characteristics of participants stratified by sUA quartiles. Individuals in higher sUA quartiles were older (mean age: 61.6 years in Q4 vs. 57.2 years in Q1) and demonstrated a higher prevalence of nocturia (≥2 episodes/night: 41.3% in Q4 vs. 35.4% in Q1). All-cause and cardiovascular mortality rates also increased across quartiles (all-cause mortality: 24.3% in Q4 vs. 12.9% in Q1; cardiovascular mortality: 7.2% vs. 2.9%). Participants with higher sUA levels exhibited a greater prevalence of HTN and DM, as well as higher BMI, waist circumference, systolic blood pressure, triglyceride levels, and serum creatinine. Conversely, they had lower HDL cholesterol and estimated glomerular filtration rate compared with those in lower quartiles. In contrast, participants in lower sUA quartiles were more likely to be female, have lower BMI and waist circumference, and report a lower burden of nocturia episodes. No significant differences were observed across quartiles in sleep duration or fasting glucose levels.

Baseline characteristics of participants stratified by sUA quartiles (Q1–Q4)

The Relationship Between Nocturia and Mortality

Kaplan-Meier survival analysis demonstrated a significant difference in mortality risk across sUA quartiles. For all-cause mortality, participants in the highest quartile (Q4) showed the lowest survival probability compared to the lower quartiles (Q1–Q3) (Fig. 1A). Similarly, cardiovascular mortality exhibited a graded increase with higher sUA quartiles, with Q4 dem-onstrating the steepest decline in survival (Fig. 1B). Cox proportional hazards regression confirmed these findings. As shown in Table 2, using Q4 as the reference category, Q1–Q3 showed significantly different risks of all-cause and cardiovascular mortality across all adjustment models. In the fully adjusted model (model 3), the HRs (95% CI) for all-cause mortality were 1.327 (1.164–1.513) for Q1, 1.227 (1.091–1.380) for Q2, and 1.256 (1.120–1.408) for Q3. For cardiovascular mortality, the corresponding HRs were 1.499 (1.157–1.942), 1.257 (1.006– 1.571), and 1.411 (1.131–1.761), respectively.

Fig. 1.

Kaplan-Meier survival curves according to serum uric acid (sUA) quartiles (Q1–Q4): (A) all-cause mortality; (B) cardiovascular mortality. Survival differences across quartiles were assessed using the log-rank test, with P<0.001 for both outcomes.

Cox proportional hazards regression for the association between sUA quartiles and mortality outcomes: (A) all-cause mortality and (B) cardiovascular mortality

(A) All-cause mortality

(B) Cardiovascular mortality

RCS regression demonstrated a significant nonlinear J-shaped association between sUA levels and mortality. For all-cause mortality, risk increased markedly at both low and high sUA concentrations, with the lowest risk observed around the midrange (χ²=109.91, P<0.001) (Fig. 2A). Cardiovascular mortality exhibited a comparable nonlinear association with a nadir in the intermediate range of sUA (χ²=64.01, P<0.001) (Fig. 2B).

Fig. 2.

Restricted cubic spline plots showing the adjusted hazard ratio (HR) of serum uric acid (sUA): (A) all-cause mortality; (B) cardiovascular mortality. The solid blue line represents the estimated HR, and the shaded area indicates the 95% confidence interval. The overall nonlinearity was assessed using the chi-square test (P<0.001).

Mediation Analyses

Mediation analyses revealed that sUA partially mediated the association between nocturia and all-cause mortality (Fig. 3A). The TE of nocturia on all-cause mortality was significant (HR, 1.984; 95% CI, 1.803–2.182), as were the total natural indirect effect (TNIE) (HR, 1.031; 95% CI, 1.018–1.044) and the total natural direct effect (TNDE) (HR, 1.909; 95% CI, 1.736–2.101). Similarly, for cardiovascular mortality (Fig. 3B), the TE of nocturia was significant (HR, 1.915; 95% CI, 1.617–2.267), with both the TNIE (HR, 1.031; 95% CI, 1.016–1.046) and TNDE (HR, 1.817; 95% CI, 1.534–2.153) demonstrating statistical significance, indicating a partial mediating role of sUA in this pathway.

Fig. 3.

Mediation analysis of the association between nocturia and mortality: (A) all-cause mortality with sUA as a mediator; (B) cardiovascular mortality with sUA as a mediator. HR, hazard ratio; OR, odds ratio; CI, confidence interval; sUA, serum uric acid; PNIE, pure natural indirect effect; TNIE, total natural indirect effect; PNDE, pure natural direct effect; TNDE, total natural direct effect; TE, total effect.

DISCUSSION

In this nationally representative study, nocturia was independently associated with an increased risk of all-cause and cardio-vascular mortality, a finding consistent across subgroups defined by sex, age, and metabolic status. To our knowledge, this is the first study to demonstrate a significant positive association between nocturia and sUA, which appears to partially mediate the observed mortality risk. These findings reinforce previous evidence regarding the adverse health implications of nocturia and emphasize its clinical relevance [5,15,16]. In addition, the slightly elevated mortality risk at low sUA levels may reflect frailty [17], malnutrition [18], or sarcopenia [19], conditions that are strongly linked to cardiovascular mortality. Thus, while the risk increase at low sUA appeared modest, both low and high sUA concentrations may indicate vulnerable metabolic states linked to higher mortality risk.

At the physiological level, uric acid is an antioxidant that scavenges superoxide, hydroxyl radicals, and singlet oxygen. However, at higher levels, uric acid can promote oxidative and renal damage, especially under metabolically morbid conditions. Konta et al. [9] reported that sUA levels were correlated with all-cause and cardiovascular mortality. This association was J-shaped with sUA at baseline in both men and women. Emerging evidence suggests that reducing uric acid levels may have a beneficial effect on LUTS and possibly nocturia, although direct proof for nocturia is limited, especially in women. Bang et al. [10] reported that gout (hyperuricemia and crystal arthritis) was associated with benign prostate hyperplasia (BPH) (treated ≥2 times) in the Korean population. Kukko et al. [11] reported that allopurinol use was negatively correlated with the risk of BPH diagnosis, surgery and related-medication use (5α-reductase inhibitors). In animal studies, urate-lowering medication inhibited prostatic inflammation and protected against ischemic injury of the bladder [12,20]. However, in a clinical longitudinal study, Hwang et al. [13] reported that higher sUA was associated with lower LUTS incidence in middle-aged men. This apparent discrepancy may reflect differences in study populations, baseline metabolic profiles, and the dual role of uric acid as both an antioxidant and a pro-oxidant depending on concentration and metabolic context. However, these results cannot be generalized to older, metabolically unhealthy individuals and women.

There are multiple mechanisms through which sUA may mediate the nocturia-mortality relationship. Circadian dysregulation caused by nocturia impairs insulin sensitivity, increases chronic inflammation, and promotes oxidative stress, all of which increase the risk of cardiovascular and renal diseases, which promotes hyperuricemia and further causes mortality. Conversely, elevated sUA promotes cardiovascular events by regulating molecular signals like NLRP3 (NOD-like receptor family, pyrin domain containing 3) inflammasome, triggering chronic systemic inflammation, which exacerbates oxidative stress, and endothelial dysfunction [21]. Although the mediation analyses demonstrated statistically significant indirect effects via sUA and metabolic syndrome (MetS), the magnitudes of these effects were modest. Therefore, these results should be interpreted with caution, and the clinical significance of these pathways remains limited. Further longitudinal and mechanistic studies are warranted to clarify whether sUA and MetS play substantial roles in the nocturia-mortality relationship. Another common comorbidity of nocturia is obstructive sleep apnea syndrome (OSAS). OSAS increases intermittent hypoxia, and the sleep fragmentation due to disordered breathing causes arousal, increases sympathetic tone, and causes nocturia [22]. Also, the obstruction of airflow during sleep decreases intrathoracic pressure and consequently increases venous return, dilates the right atrium, secretes atrial natriuretic peptide, and stimulates the excretion of sodium and water by the kidneys, particularly in the distal tubules and collecting ducts [23]. In OSAS patients, degradation of adenosine triphosphate increases sUA levels, which promotes cardiovascular and metabolic dysfunction [24-26].

Although the mediation analysis indicated that sUA partly mediated the relationship between nocturia and mortality, the effect size was modest. Therefore, this finding should be interpreted with caution, and its clinical significance appears limited.

There are a few limitations in our study. First, although a selfreported, simplified quantitative questionnaire for nocturia may facilitate data collection, it should not be considered a substitute for validated assessment tools such as the Nocturia Quality of Life questionnaire, voiding diaries, or urodynamic evaluations. Second, this dataset only contains nocturia status and metabolic changes at the initial visit, but the changes during the follow-up period were not accounted for in this study. Third, we used statistical methods to control for confounders, but the confounding effect on mediation analysis cannot be fully excluded. These limitations require further longitudinal prospective studies to verify the relationship.

In conclusion, this study identified nocturia as an independent predictor of all-cause mortality, even after adjusting for major metabolic risk factors. The observed mediation effect of sUA suggests that uric acid may contribute to the pathway linking nocturia to increased mortality risk. These findings highlight the importance of comprehensive management strategies that address both nocturia and underlying metabolic abnormalities. Future studies are warranted to determine whether targeted interventions, including metabolic optimization and uratelowering therapies, can reduce nocturia-associated mortality.

Notes

Grant/Fund Support

This research was supported by Hallym University Research Fund 2024 (HURF-2024-62).

Research Ethics

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The dataset is publicly available and fully deidentified, and its use is approved by the Ethics Review Board (Protocols #2005–06 and #2011–17). Therefore, additional Institutional Review Board approval was not required for this analysis.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTION STATEMENT

· Conceptualization: SJK, STC

· Data curation: SJK

· Formal analysis: SJK

· Funding acquisition: STC

· Methodology: SJK, STC

· Visualization: SJK, SGP, SP, OK, YGL, STC

· Writing - original draft: SJK, STC

· Writing - review & editing: SJK, STC

References

1. Hashim H, Drake MJ. Basic concepts in nocturia, based on International Continence Society standards in nocturnal lower urinary tract function. Neurourol Urodyn 2018;37(S6):S20–4.
2. Burgio KL, Johnson TM, Goode PS, Markland AD, Richter HE, Roth DL, et al. Prevalence and correlates of nocturia in community-dwelling older adults. J Am Geriatr Soc 2010;58:861–6.
3. Tikkinen KA, Tammela TL, Huhtala H, Auvinen A. Is nocturia equally common among men and women? A population based study in Finland. J Urol 2006;175:596–600.
4. Coyne KS, Sexton CC, Thompson CL, Milsom I, Irwin D, Kopp ZS, et al. The prevalence of lower urinary tract symptoms (LUTS) in the USA, the UK and Sweden: results from the Epidemiology of LUTS (EpiLUTS) study. BJU Int 2009;104:352–60.
5. Pesonen JS, Cartwright R, Vernooij RWM, Aoki Y, Agarwal A, Mangera A, et al. The impact of nocturia on mortality: a systematic review and meta-analysis. J Urol 2020;203:486–95.
6. Fitzgerald MP, Litman HJ, Link CL, McKinlay JB. The association of nocturia with cardiac disease, diabetes, body mass index, age and diuretic use: results from the BACH survey. J Urol 2007;177:1385–9.
7. He Q, Wang Z, Liu G, Daneshgari F, MacLennan GT, Gupta S. Metabolic syndrome, inflammation and lower urinary tract symptoms: possible translational links. Prostate Cancer Prostatic Dis 2016;19:7–13.
8. Dehlin M, Jacobsson L, Roddy E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat Rev Rheumatol 2020;16:380–90.
9. Konta T, Ichikawa K, Kawasaki R, Fujimoto S, Iseki K, Moriyama T, et al. Association between serum uric acid levels and mortality: a nationwide community-based cohort study. Sci Rep 2020;10:6066.
10. Bang WJ, Choi HG, Kang HS, Kwon MJ, Kim JH, Kim JH, et al. Increased risk of benign prostate hyperplasia (BPH) in patients with gout: a longitudinal follow-up study using a national health screening cohort. Diagnostics (Basel) 2023;14:55.
11. Kukko V, Kaipia A, Talala K, Taari K, Tammela TLJ, Auvinen A, et al. Allopurinol and risk of benign prostatic hyperplasia in a Finnish population-based cohort. Prostate Cancer Prostatic Dis 2018;21:373–8.
12. Abdel-Fattah MM, Abo-El Fetoh ME, Afify H, Ramadan LAA, Mohamed WR. Probenecid ameliorates testosterone-induced benign prostatic hyperplasia: implications of PGE-2 on ADAM-17/EGFR/ERK1/2 signaling cascade. J Biochem Mol Toxicol 2023;37:e23450.
13. Hwang J, Ryu S, Ahn JK. Higher levels of serum uric acid have a significant association with lower incidence of lower urinary tract symptoms in healthy Korean men. Metabolites 2022;12:649.
14. Zumrutbas AE, Bozkurt AI, Alkis O, Toktas C, Cetinel B, Aybek Z. The prevalence of nocturia and nocturnal polyuria: can new cutoff values be suggested according to age and sex? Int Neurourol J 2016;20:304–10.
15. Moon S, Kim YJ, Chung HS, Yu JM, Park II, Park SG, et al. The relationship between nocturia and mortality: data from the National Health and Nutrition Examination Survey. Int Neurourol J 2022;26:144–52.
16. Bliwise DL, Howard LE, Moreira DM, Andriole GL, Hopp ML, Freedland SJ. Nocturia and associated mortality: observational data from the REDUCE trial. Prostate Cancer Prostatic Dis 2019;22:77–83.
17. Ungar A, Rivasi G, Di Bari M, Virdis A, Casiglia E, Masi S, et al. The association of uric acid with mortality modifies at old age: data from the uric acid right for heart health (URRAH) study. J Hypertens 2022;40:704–11.
18. Tseng WC, Chen YT, Ou SM, Shih CJ, Tarng DC. U-shaped association between serum uric acid levels with cardiovascular and allcause mortality in the elderly: the role of malnourishment. J Am Heart Assoc 2018;7:e007523.
19. Liu X, Chen X, Hu F, Xia X, Hou L, Zhang G, et al. Higher uric acid serum levels are associated with sarcopenia in West China: a crosssectional study. BMC Geriatr 2022;22:121.
20. Shin JH, Chun KS, Na YG, Song KH, Kim SI, Lim JS, et al. Allopurinol protects against ischemia/reperfusion-induced injury in rat urinary bladders. Oxid Med Cell Longev 2015;2015:906787.
21. Raya-Cano E, Vaquero-Abellán M, Molina-Luque R, De Pedro-Jiménez D, Molina-Recio G, Romero-Saldaña M. Association between metabolic syndrome and uric acid: a systematic review and meta-analysis. Sci Rep 2022;12:18412.
22. Venkataraman S, Vungarala S, Covassin N, Somers VK. Sleep apnea, hypertension and the sympathetic nervous system in the adult population. J Clin Med 2020;9:591.
23. Zeidel ML. Renal actions of atrial natriuretic peptide: regulation of collecting duct sodium and water transport. Annu Rev Physiol 1990;52:747–59.
24. Crossland RF, Durgan DJ, Lloyd EE, Phillips SC, Reddy AK, Marrelli SP, et al. A new rodent model for obstructive sleep apnea: effects on ATP-mediated dilations in cerebral arteries. Am J Physiol Regul Integr Comp Physiol 2013;305:R334–42.
25. Sánchez-Lozada LG, Lanaspa MA, Cristóbal-García M, García-Arroyo F, Soto V, Cruz-Robles D, et al. Uric acid-induced endothelial dysfunction is associated with mitochondrial alterations and decreased intracellular ATP concentrations. Nephron Exp Nephrol 2012;121:e71–8.
26. Yang Z, Lv T, Lv X, Wan F, Zhou H, Wang X, et al. Association of serum uric acid with all-cause and cardiovascular mortality in obstructive sleep apnea. Sci Rep 2023;13:19606.

Article information Continued

Fig. 1.

Kaplan-Meier survival curves according to serum uric acid (sUA) quartiles (Q1–Q4): (A) all-cause mortality; (B) cardiovascular mortality. Survival differences across quartiles were assessed using the log-rank test, with P<0.001 for both outcomes.

Fig. 2.

Restricted cubic spline plots showing the adjusted hazard ratio (HR) of serum uric acid (sUA): (A) all-cause mortality; (B) cardiovascular mortality. The solid blue line represents the estimated HR, and the shaded area indicates the 95% confidence interval. The overall nonlinearity was assessed using the chi-square test (P<0.001).

Fig. 3.

Mediation analysis of the association between nocturia and mortality: (A) all-cause mortality with sUA as a mediator; (B) cardiovascular mortality with sUA as a mediator. HR, hazard ratio; OR, odds ratio; CI, confidence interval; sUA, serum uric acid; PNIE, pure natural indirect effect; TNIE, total natural indirect effect; PNDE, pure natural direct effect; TNDE, total natural direct effect; TE, total effect.

Table 1.

Baseline characteristics of participants stratified by sUA quartiles (Q1–Q4)

Characteristic Q1 (n = 3,442) Q2 (n = 3,129) Q3 (n = 3,063) Q4 (n = 2,888) P-value
sUA 3.93 ± 0.55 5.11 ± 0.26 6.02 ± 0.29 7.56 ± 0.92 < 0.001
Age (yr) 57.16 ± 12.26 59.43 ± 12.09 59.66 ± 12.06 61.58 ± 12.22 < 0.001
 < 65 2,455 (71.3) 2,061 (65.9) 1,964 (64.1) 1,687 (58.4) < 0.001
 ≥ 65 987 (28.7) 1,068 (34.1) 1,099 (35.9) 1,201 (41.6)
Race/ethnicity < 0.001
 Mexican American 591 (17.2) 467 (14.9) 461 (15.1) 275 (9.5)
 Other Hispanic 353 (10.3) 314 (10.0) 248 (8.1) 192 (6.6)
 Non-Hispanic White 1,683 (48.9) 1,536 (49.1) 1,513 (49.4) 1,443 (50.0)
 Non-Hispanic Black 568 (16.5) 568 (18.2) 620 (20.2) 768 (26.6)
 Other race 247 (7.2) 244 (7.8) 221 (7.2) 210 (7.3)
Sex < 0.001
 Male 844 (24.5) 1,458 (46.6) 1,982 (64.7) 2,073 (71.8)
 Female 2,598 (75.5) 1,671 (53.4) 1,081 (35.3) 815 (28.2)
BMI (kg/m2) 27.27 ± 5.89 29.07 ± 6.16 29.83 ± 6.18 31.20 ± 6.59 < 0.001
Alcohol drinker < 0.001
 < 12 Alcohol drinks/yr 1,213 (35.2) 950 (30.4) 778 (25.4) 699 (24.2)
 ≥ 12 Alcohol drinks/yr 2,229 (64.8) 2,179 (69.6) 2,285 (74.6) 2,189 (75.8)
Smoking status < 0.001
 Nonsmoker 1,890 (54.9) 1,628 (52.0) 1,456 (47.5) 1,301 (45.0)
 Previous smoker 862 (25.0) 894 (28.6) 1,006 (32.8) 1,102 (38.2)
 Current smoker 690 (20.0) 607 (19.4) 601 (19.6) 485 (16.8)
Sleeping time (hr/day) 6.85 ± 1.42 6.83 ± 1.43 6.82 ± 1.41 6.84 ± 1.47 0.842
Waist circumference (cm) 94.36 ± 14.11 100.43 ± 14.42 103.37 ± 14.11 107.53 ± 14.89 < 0.001
SBP (mmHg) 126.27 ± 19.81 128.88 ± 19.30 129.58 ± 19.31 131.63 ± 20.36 < 0.001
DBP (mmHg) 70.64 ± 12.33 71.14 ± 12.81 71.93 ± 14.00 71.60 ± 14.92 0.001
Fasting glucose (mg/dL) 108.01 ± 51.19 108.25 ± 41.97 106.65 ± 36.92 109.10 ± 35.66 0.160
Serum cholesterol (mg/dL) 199.01 ± 39.87 199.72 ± 42.90 199.22 ± 41.89 197.89 ± 45.26 0.400
Triglyceride (mg/dL) 141.84 ± 147.62 157.69 ± 125.45 165.65 ± 121.90 179.17 ± 138.96 < 0.001
HDL cholesterol (mg/dL) 59.05 ± 17.39 53.96 ± 16.01 51.36 ± 15.32 48.64 ± 14.91 < 0.001
Serum creatinine (mg/dL) 0.80 ± 0.33 0.90 ± 0.42 0.98 ± 0.44 1.13 ± 0.45 < 0.001
eGFR (mL/min/1.73 m²) 91.55 ± 17.68 85.33 ± 18.68 82.44 ± 19.55 73.88 ± 22.66 < 0.001
Hypertension < 0.001
 No 2,416 (70.2) 1,948 (62.3) 1,736 (56.7) 1,262 (43.7)
 Yes 1,026 (29.8) 1,181 (37.7) 1,327 (43.3) 1,626 (56.3)
Dyslipidemia < 0.001
 No 2,798 (81.3) 2,419 (77.3) 2,393 (78.1) 2,240 (77.6)
 Yes 644 (18.7) 710 (22.7) 670 (21.9) 648 (22.4)
Diabetes mellitus < 0.001
 No 3,024 (87.9) 2,720 (86.9) 2,641 (86.2) 2,429 (84.1)
 Yes 418 (12.1) 409 (13.1) 422 (13.8) 459 (15.9)
Nocturia (≥ 2 episodes/night) 1,219 (35.4) 1,142 (36.5) 1,079 (35.2) 1,192 (41.3) < 0.001
Nocturia (episodes/night) < 0.001
 0 884 (25.7) 782 (25.0) 817 (26.7) 629 (21.8)
 1 1,339 (38.9) 1,205 (38.5) 1,167 (38.1) 1,067 (36.9)
 2 723 (21.0) 676 (21.6) 653 (21.3) 690 (23.9)
 3 336 (9.8) 305 (9.7) 278 (9.1) 332 (11.5)
 4 82 (2.4) 99 (3.2) 83 (2.7) 100 (3.5)
 ≥5 78 (2.3) 62 (2.0) 65 (2.1) 70 (2.4)
Follow-up period (mo) 109.38 ± 37.54 107.65 ± 37.88 108.00 ± 37.68 103.65 ± 39.59 < 0.001
All-cause mortality < 0.001
 Censored 2,998 (87.1) 2,616 (83.6) 2,534 (82.7) 2,187 (75.7)
 Deceased 444 (12.9) 513 (16.4) 529 (17.3) 701 (24.3)
Cardiovascular mortality < 0.001
 Censored 3,341 (97.1) 2,992 (95.6) 2,932 (95.7) 2,681 (92.8)
 Deceased 101 (2.9) 137 (4.4) 131 (4.3) 207 (7.2)

Values are presented as mean±standard deviation or number (%).

sUA, serum uric acid; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate.

Table 2.

Cox proportional hazards regression for the association between sUA quartiles and mortality outcomes: (A) all-cause mortality and (B) cardiovascular mortality

(A) All-cause mortality

Quartile (vs. Q4) Model 1
Model 2
Model 3
HR 95% CI P-value HR 95% CI P-value HR 95% CI P-value
Q1 2.012 1.786–2.266 < 0.001 1.321 1.166–1.497 < 0.001 1.327 1.164-1.513 < 0.001
Q2 1.551 1.384–1.738 < 0.001 1.259 1.122–1.413 < 0.001 1.227 1.091-1.380 < 0.001
Q3 1.479 1.321–1.655 < 0.001 1.261 1.126–1.412 < 0.001 1.256 1.120-1.408 < 0.001
Q4 Ref Ref Ref

(B) Cardiovascular mortality

Quartile (vs. Q4) Model 1
Model 2
Model 3
HR 95% CI P-value HR 95% CI P-value HR 95% CI P-value
Q1 1.611 1.257–2.065 < 0.001 2.609 2.056–3.310 < 0.001 1.499 1.157–1.942 0.002
Q2 1.351 1.086–1.681 0.007 1.714 1.381–2.127 < 0.001 1.257 1.006–1.571 0.044
Q3 1.459 1.171–1.817 < 0.001 1.761 1.415–2.192 < 0.001 1.411 1.131–1.761 0.002
Q4 Ref Ref Ref

Model 1: unadjusted; model 2: adjusted by age, sex, and race/ethnicity; model 3: adjusted by age, sex, race/ethnicity, body mass index, waist circumference, smoking status, alcohol consumption, sleep duration and comorbidities including hypertension, diabetes mellitus, and dyslipidemia.

sUA, serum uric acid; HR, hazard ratio; CI, confidence interval.