Abstract
Background
In 2023, the American Heart Association introduced the cardiovascular-kidney-metabolic (CKM) syndrome to comprehensively assess multimorbidity involving obesity, diabetes, chronic kidney disease, and cardiovascular disease. This study aimed to explore the potential association between serum uric acid (SUA) level and advanced CKM syndrome.
Methods
This cross-sectional study analyzed data from U.S. adults collected in the National Health and Nutrition Examination Survey between 2011 and 2020. Participants were categorized into five CKM syndrome stages based on clinical severity. CKM syndrome stages 3 or 4 were considered advanced. Multivariable weighted Poisson regression with robust error variance were used.
Results
The final analysis included 15,839 patients diagnosed with CKM syndrome. Out of the total, 12,975 cases were classified as non-advanced CKM syndrome, and 2,864 cases were classified as advanced CKM syndrome. In the fully adjusted model, patients in the highest SUA quintile had a prevalence ratio of 1.32 (95% confidence interval: 1.14–1.53) for advanced stages compared to those in the lowest quintile, with a 7% increased risk per 1 mg/dL SUA elevation. Moreover, the restricted cubic spline curve indicated a significant increase in the likelihood of advanced stages when SUA level was ≥ 5.5 mg/dL.
Conclusions
In patients with CKM syndrome, elevated SUA levels correlated with a higher prevalence of advanced stages, demonstrating a dose-response relationship. Our data underscore the importance of SUA screening among CKM syndrome patients for identifying individuals at higher risk.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13019-026-04437-7.
Keywords: Cardiovascular-kidney-metabolic syndrome, Serum uric acid, National health and nutrition examination survey
Introduction
Multimorbidity, defined as the coexistence of two or more chronic conditions [1], presents a challenge to global medical practice and research. Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic diseases are the most common multimorbidities, persistently impacting human well-being and healthcare systems [2]. In 2023, the American Heart Association (AHA) proposed a framework for cardiovascular-kidney-metabolic (CKM) syndrome to enable a comprehensive assessment of these conditions [3]. CKM syndrome is categorized into five stages based on risk factors and established disease, from stage 0 (absence of risk factors) to stage 4 (clinical CVD). Besides, stages 3 and 4 are considered advanced because of subclinical or clinical CVD in CKM syndrome. Nearly 90% of US adults were identified with CKM syndrome (stage 1 or higher), with 15% diagnosed at advanced stages [4]. This high prevalence makes the understanding factors associated with advanced stages extremely urgent.
Uric acid is the final enzymatic product of purine metabolism. Recent research has recognized elevated serum uric acid (SUA) levels as an important risk factor for chronic diseases. There is a positive correlation between SUA and the incidence of obesity [5], diabetes [6], CKD [7] and CVD [8]. However, these previous studies primarily concentrated on diseases affecting individual systems. To our knowledge, the relationship between SUA and CKM syndrome multimorbidity has never been investigated. The elucidation of these relationships has important clinical and public health and implications. Specifically, a deeper understanding could facilitate efforts to identify individuals at higher risk for advanced CKM syndrome, thereby potentially improving patient outcomes and decreasing the burden on individuals and healthcare systems.
To address the research gaps, we conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Survey (NHANES) to thoroughly evaluate the relationship between SUA and advanced CKM syndrome.
Methods
Study design and participants
The NHANES, conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention, is a continuous, multistage probability sample survey assessing the nutritional and health status of the noninstitutionalized civilian U.S. population. Participants were interviewed at home about their demographic, socioeconomic, dietary, and health-related questions, along with laboratory tests at a mobile examination center. The NCHS Ethics Review Board approved all NHANES protocols, and informed consent was obtained from each participant.
As NHANES collected responses on Asian race as a separate category from the 2011–2012 cycle, data from NHANES 2011 to 2020 were used for the present study. Participants were excluded if they met any of the following criteria: (1) under 20 years of age (n = 22,867); (2) a pregnancy at baseline (n = 336); (3) lacking demographics data (marital status, household income, and educational attainment) (n = 3,768) or questionnaire data (smoking status, drinking status, physical activity, and diet quality) (n = 2,211); (4) missing data on estimated glomerular filtration rate (eGFR) or medication use (sodium-glucose cotransporter-2 inhibitors [SGLT2i], glucagon-like peptide-1 receptor agonists [GLP‑1 RA], allopurinol, febuxostat, and diuretics) (n = 1,808); (5) missing data on SUA levels (n = 2,903) or CKM syndrome indicators (n = 2,639). The study ultimately included 18,184 adult participants. The flowchart for participant enrollment is presented in Fig. 1.
Fig. 1.
Screening flow of participants. CKM, Cardiovascular-Kidney-Metabolic; NHANES, National Health and Nutrition Examination Survey
SUA measurement
SUA data were collected from various laboratory instruments over multiple NHANES cycles. SUA measurements in NHANES 2011–2016 were conducted using a Beckman UniCel DxC800 Synchron analyzer. And in NHANES 2017–2020, SUA measurements were obtained using a Roche Cobas 6000 analyzer. Previous studies have reported the combination of SUA from multiple cycles of the NHANES for analysis [9, 10].
Definition of CKM syndrome
In this analysis, CVD and other comorbidities of CKM syndrome were defined by the participants’ response to the standardized questionnaire, physiological measurements, and laboratory examination. CKD was defined using the Kidney Disease Improving Global Outcomes classification based on eGFR and urinary albumin‑to‑creatinine ratio (Supplemental Table S1). The 10‑year CVD risk was calculated using the Framingham risk score, with point assignments and risk conversion tables for men and women provided in Supplemental Tables S2‑S5. High predicted 10‑year CVD risk was defined as ≥ 20%. CKM syndrome stage 0 encompassed participants with a body mass index (BMI) less than 25 kg/m2 (or < 23 kg/m2 if Asian ethnicity), with normal waist circumference (< 88 and < 102 cm for women and men, or < 80 and < 90 cm for women and men if Asian ethnicity), and without metabolic risk factors (hypertension, hypertriglyceridemia, metabolic syndrome, diabetes, prediabetes) or CKD. CKM syndrome stage 1 identified participants with BMI ≥ 25 kg/m2, or ≥ 23 kg/m2 if Asian ethnicity, with waist circumference ≥ 88/102 cm in women/men, or 80/90 cm in women/men if Asian ethnicity, or prediabetes. CKM syndrome stage 2 included participants with metabolic risk factors (hypertriglyceridemia, hypertension, metabolic syndrome, diabetes), or moderate-to-high-risk CKD. CKM syndrome stage 3 included participants with high predicted 10-year CVD risk (≥ 20%, based on the Framingham risk score [11]) or very-high-risk CKD. CKM syndrome stage 4 included participants with established clinical CVD, including coronary heart disease, angina, heart attack, heart failure, and stroke. Detailed definitions for each CKM stage are presented in Supplemental Table S6. Referring to the previous literature [4], CKM stage 1 and above was classified as CKM syndrome, with stages 3 and 4 recognized as advanced CKM syndrome stages due to their association with a high risk of CVD. According to the AHA framework, CKM syndrome is defined as stages 1 through 4, while stage 0 represents the absence of any CKM syndrome risk factors, that is, healthy individuals without excess adiposity, metabolic risk factors, CKD, or CVD. Therefore, we excluded participants with CKM stage 0 (n = 2,345) as they do not have CKM syndrome. The final analytic sample comprised 2,864 patients with advanced CKM syndrome (stages 3‑4) and 12,975 patients with non‑advanced CKM syndrome (stages 1‑2) (Fig. 1).
Other variables
The study’s demographic variables comprised age, sex, ethnicity, educational level, family income, and marital status. Ethnicity was divided into Mexican American, non-Hispanic Asian, non-Hispanic Black, non-Hispanic White, and Other. Educational level was categorized into three groups: less than high school, high school or equivalent, and college or above. Family income to poverty ratios were categorized as < 1, 1 to 3, and > 3; a higher income to poverty ratio represents a better family economic status [12]. Marital status was dichotomized into coupled, and single or separated. Smoking status, alcohol status, physical activity and diet quality were incorporated as self-reported lifestyle covariates. Smoking status was categorized as: never smokers (fewer than 100 cigarettes in a lifetime), former smokers (100 or more cigarettes in a lifetime but not currently smoking), and current smokers (100 or more cigarettes in a lifetime and currently smoking). Participants were categorized by alcohol consumption into non-drinkers, current heavy drinkers (≥ 3 drinks per day for females, ≥ 4 drinks per day for males, or binge drinking [≥ 4 drinks on same occasion for females, ≥ 5 drinks on same occasion for males] on 5 or more days per month), and current low to moderate drinkers (not meet the criterion above). Physical activity was classified as inactive (0 min/week), insufficient (1–74 min of high-intensity,1–149 min of moderate-intensity, or 1–149 min of combined leisure-time exercise per week), active (≥ 75 min of high-intensity,≥150 min of moderate-intensity, or ≥ 150 min of combined leisure-time exercise per week). The Healthy Eating Index-2015 (HEI-2015), which scores from 0 to 100 based on a participant’s 24-hour dietary recall, was utilized to assess overall diet quality, where higher scores reflect better diet quality [13].
eGFR and medication use were also included as covariates. eGFR (mL/min/1.73m2) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation based on serum creatinine. Medication use was extracted from the NHANES prescription medication files (RXQ_RX). Binary variables were created for the use of SGLT2i, GLP‑1 RA, allopurinol, febuxostat, and diuretics (yes/no for each class). These covariates were adjusted for in multivariable models.
Statistical analysis
All statistical analyses were conducted according to NCHS guidelines. To obtain nationally representative estimates, we used sampling weights, strata, and clustering variables that NHANES provided. Continuous baseline variables were reported as weighted mean value with weighted standard errors (SE), and categorical baseline variables were presented as number with weighted percentage (%). The patients were stratified into quintiles from Q1 to Q5 based on SUA levels. Generalized linear models were used to assess differences in continuous variables across the Q1-Q5 groups, while the chi-squared test was employed for categorical variables. The relationship between SUA level and advanced CKM syndrome were assessed using survey-weighted Poisson regression with robust error variance, as the outcome prevalence (15%) exceeded the 10% threshold [4] where odds ratios may substantially overestimate the true effect. Accordingly, prevalence ratios (PR) and 95% confidence intervals (CI) were reported instead of odds ratios. Three models were employed: a crude model without covariate adjustments, model 1 which included sociodemographic factors including age, sex, ethnicity, educational level, family income, and marital status, and model 2 which further incorporated smoking status, alcohol status, physical activity, HEI-2015, eGFR, and the use of SGLT2i, GLP‑1 RA, allopurinol, febuxostat, and diuretics. Variance inflation factor analysis indicated no multicollinearity among the included variables. All analyses accounted for NHANES survey weights, strata, and clustering variables using the svyglm function from the survey package in R with family = quasipoisson.
Furthermore, we used restricted cubic spline (RCS) model to examine the potential dose-response relationships, whether linear or non-linear. We employed the Akaike information criterion (AIC) to choose the RCS model with specific number of knots and adopted the model with the lowest AIC criterion value, which is considered as the best-fitting RCS model.
We conducted multiple sensitivity analyses to assess the robustness of our findings. First, participants with baseline self-reported history of cancer were excluded. Second, excluding covariates with missing values may bias results. Thus, iterative imputation on covariates was conducted using a machine learning algorithm implemented through the missRanger R package. Third, to address potential collider bias from excluding CKM syndrome stage 0, we repeated the analysis in the full adult population (including stage 0) comparing advanced CKM syndrome (stages 3‑4) with non‑advanced stages (stages 0‑2). Fourth, we evaluated the relationship between hyperuricemia and advanced CKM syndrome. Hyperuricemia was defined as SUA levels ≥ 7.0 mg/dL in male and ≥ 6.0 mg/dL in female respectively [14]. In addition, stratified analyses were performed according to age (< 60 or ≥ 60 years), sex, ethnicity (white or non-white), educational level, family income, marital status, smoking status (never or ever/current), alcohol status (drinkers or non-drinkers), and physical activity. Interaction effects were evaluated by introducing a multiplicative interaction term between SUA levels and the respective stratification variable. In consideration of the different distribution of SUA levels between male and female, we also conducted RCS analyses in different sexes. All statistical analyses were conducted using R software (version 4.1.2), and complex survey analyses were conducted using the survey R package. Two-sided P values were computed, with significance indicated by P < 0.05.
Results
Baseline characteristics
This study included 15,839 patients with CKM syndrome from the NHANES 2011–2020. The average age of the patients was 49.97 (0.29) years, with 50.03% being male and 49.97% female. Table 1 presents the baseline characteristics of CKM syndrome patients. Compared with those in the lower SUA levels group, patients in the higher SUA levels group tended to be older, male, non-Hispanic white, more likely to be former smokers, heavy drinkers, and physically inactive, to have lower HEI-2015 scores, lower eGFR, and higher use of diuretics, and were more often in advanced stages (all P < 0.05).
Table 1.
Baseline characteristics of CKM Syndrome patients in the NHANES 2011–2020
| Characteristic | SUA level, mg/dL (N = 15839) | |||||
|---|---|---|---|---|---|---|
| Q1 < 4.3 |
Q2 4.3–5.1 |
Q3 5.1–5.8 |
Q4 5.8–6.7 |
Q5 > 6.7 |
P value | |
| CKM Syndrome patients, n | 3242 | 3290 | 3114 | 3088 | 3105 | |
| Age (years), mean (SE) | 49.14(0.43) | 49.74(0.45) | 50.88(0.39) | 49.80(0.49) | 50.31(0.40) | 0.021 |
| Sex, n (%) | < 0.0001 | |||||
| Male | 639(16.76) | 1249(36.91) | 1637(52.90) | 2070(68.20) | 2315(77.13) | |
| Female | 2603(83.24) | 2041(63.09) | 1477(47.10) | 1018(31.80) | 790(22.87) | |
| Race or ethnicity, n (%) | < 0.0001 | |||||
| Mexican American | 528(9.99) | 497(9.27) | 412(7.95) | 397(8.19) | 283(6.20) | |
| Non-Hispanic Black | 690(10.37) | 717(10.35) | 689(9.82) | 703(10.04) | 821(11.11) | |
| Non-Hispanic White | 1229(65.95) | 1285(66.17) | 1296(69.22) | 1262(68.06) | 1286(70.15) | |
| Non-Hispanic Asian | 309(4.28) | 291(3.81) | 304(4.28) | 304(4.36) | 331(4.58) | |
| Other | 486(9.41) | 500(10.41) | 413(8.74) | 422(9.36) | 384(7.95) | |
| Education level, n (%) | 0.508 | |||||
| Less than high school | 690(13.47) | 672(13.45) | 624(13.24) | 603(13.70) | 596(12.39) | |
| High school or equivalent | 687(22.45) | 744(22.53) | 746(25.07) | 731(23.12) | 767(24.47) | |
| College or above | 1865(64.07) | 1874(64.02) | 1744(61.69) | 1754(63.19) | 1742(63.15) | |
| Family PIR, n (%) | 0.284 | |||||
| <1.0 | 749(15.81) | 673(13.43) | 642(13.89) | 597(13.35) | 623(13.19) | |
| 1.0–3.0 | 1362(36.21) | 1418(37.19) | 1274(35.16) | 1311(36.44) | 1336(37.05) | |
| >3.0 | 1131(47.98) | 1199(49.38) | 1198(50.95) | 1180(50.21) | 1146(49.76) | |
| Marital, n (%) | 0.281 | |||||
| Coupled | 1895(64.15) | 1909(62.80) | 1879(65.37) | 1887(65.78) | 1862(63.79) | |
| Single or separated | 1347(35.85) | 1381(37.20) | 1235(34.63) | 1201(34.22) | 1243(36.21) | |
| Smoking status, n (%) | < 0.0001 | |||||
| Never | 1986(57.32) | 1861(55.77) | 1746(57.21) | 1607(51.91) | 1555(50.46) | |
| Former | 626(21.75) | 803(25.85) | 782(25.70) | 892(29.78) | 1004(33.57) | |
| Now | 630(20.93) | 626(18.37) | 586(17.09) | 589(18.31) | 546(15.98) | |
| Alcohol status, n (%) | < 0.001 | |||||
| Non-drinker | 1149(28.40) | 1024(25.34) | 987(25.61) | 931(25.79) | 929(23.20) | |
| Low to moderate drinker | 1552(53.69) | 1677(54.82) | 1565(55.00) | 1543(54.19) | 1530(52.02) | |
| Heavy drinker | 541(17.92) | 589(19.84) | 562(19.39) | 614(20.01) | 646(24.78) | |
| Physical activity, n (%) | 0.044 | |||||
| Inactive | 1709(45.72) | 1687(46.31) | 1593(46.78) | 1601(46.73) | 1706(49.30) | |
| Insufficient | 848(30.33) | 884(27.20) | 838(29.05) | 778(27.14) | 778(29.09) | |
| Active | 685(23.95) | 719(26.49) | 683(24.17) | 709(26.13) | 621(21.61) | |
| HEI-2015, mean (SE) | 50.99(0.46) | 50.97(0.41) | 50.59(0.37) | 49.69(0.31) | 49.63(0.38) | 0.032 |
| eGFR (ml/min/1.73 m2), mean (SE) | 98.78(0.59) | 94.72(0.48) | 91.25(0.56) | 89.77(0.63) | 84.30(0.52) | < 0.0001 |
| Use of SGLT2i, n (%) | 0.070 | |||||
| Yes | 33(0.77) | 19(0.33) | 7(0.23) | 14(0.26) | 9(0.29) | |
| No | 3209(99.23) | 3271(99.67) | 3107(99.77) | 3074(99.74) | 3096(99.71) | |
| Use of GLP‑1 RA, n (%) | 0.759 | |||||
| Yes | 22(0.65) | 25(0.78) | 13(0.41) | 21(0.56) | 25(0.64) | |
| No | 3220(99.35) | 3265(99.22) | 3101(99.59) | 3067(99.44) | 3080(99.36) | |
| Use of allopurinol, n (%) | 0.080 | |||||
| Yes | 48(1.33) | 45(1.47) | 43(1.20) | 39(0.85) | 71(1.92) | |
| No | 3194(98.67) | 3245(98.53) | 3071(98.80) | 3049(99.15) | 3034(98.08) | |
| Use of febuxostat, n (%) | 0.381 | |||||
| Yes | 6(0.12) | 1(0.01) | 4(0.18) | 1(0.17) | 2(0.04) | |
| No | 3236(99.88) | 3289(99.99) | 3110(99.82) | 3087(99.83) | 3103(99.96) | |
| Use of diuretics, n (%) | < 0.0001 | |||||
| Yes | 266(6.94) | 347(9.56) | 460(13.49) | 545(15.46) | 899(24.55) | |
| No | 2976(93.06) | 2943(90.44) | 2654(86.51) | 2543(84.54) | 2206(75.45) | |
| CKM syndrome stage, n (%) | < 0.0001 | |||||
| Stage 1 | 1218(39.98) | 1154(39.96) | 948(34.18) | 858(31.82) | 616(23.66) | |
| Stage 2 | 1597(47.49) | 1625(44.82) | 1632(48.83) | 1631(51.67) | 1696(52.82) | |
| Stage 3 | 94(3.54) | 117(5.09) | 157(6.18) | 169(6.13) | 247(9.33) | |
| Stage 4 | 333(8.99) | 394(10.14) | 377(10.81) | 430(10.38) | 546(14.19) | |
| Advanced CKM syndrome, n (%) | 98.78(0.59) | 94.72(0.48) | 91.25(0.56) | 89.77(0.63) | 84.30(0.52) | < 0.0001 |
| No | 2815(87.47) | 2779(84.78) | 2580(83.01) | 2489(83.49) | 2312(76.48) | |
| Yes | 427(12.53) | 511(15.22) | 534(16.99) | 599(16.51) | 793(23.52) | |
Values are weighted mean (SE) for continuous variables or numbers (weighted %) for categorical variables
CKM, Cardiovascular-Kidney-Metabolic; eGFR, estimated glomerular filtration rate; GLP‑1 RA, glucagon-like peptide-1 receptor agonists; PIR, poverty income ratio; SGLT2i, sodium-glucose cotransporter-2 inhibitors; SUA, serum uric acid
Association of SUA level with advanced CKM syndrome
The results from survey-weighted multivariable Poisson regression with robust error variance were presented in Table 2. After adjustment for age, sex, ethnicity, educational level, family income, marital status, smoking status, alcohol status, physical activity, HEI-2015, eGFR, and the use of SGLT2i, GLP‑1 RA, allopurinol, febuxostat, and diuretics, the multivariable-adjusted PR for individuals in the highest quintile were 1.32 (95% CI, 1.14–1.53) for advanced CKM syndrome, as compared with the lowest quintile. For a one-unit increment in SUA level, the multivariable-adjusted PR of advanced CKM syndrome was 1.07 (95% CI, 1.03–1.10). In addition, we fitted dose-response curve for the relationship of the SUA level with advanced CKM syndrome risk by RCS. The use of AIC statistics suggested that the RCS model with 3 knots showed the best performance (lowest AIC: 10129.56) (Fig. 2A and Supplemental Table 7). The RCS showed a gentle slope of OR when SUA level was < 5.5 mg/dL, but an increase in OR when SUA level was ≥ 5.5 mg/dL, indicating the higher risks among patients with SUA levels ≥ 5.5 mg/dL (Fig. 2B).
Table 2.
Association between SUA level and advanced CKM syndrome
| SUA level, mg/dL | |||||||
|---|---|---|---|---|---|---|---|
| Q1 < 4.3 |
Q2 4.3–5.1 |
Q3 5.1–5.8 |
Q4 5.8–6.7 |
Q5 > 6.7 |
Per 1 mg/dL increment |
P for trend | |
| Crude model | 1 (reference) | 1.21(1.03,1.43) | 1.36(1.17,1.57) | 1.32(1.11,1.57) | 1.88(1.60,2.20) | 1.16(1.12,1.19) | < 0.0001 |
| Model 1 | 1 (reference) | 1.07(0.91,1.25) | 1.07(0.95,1.21) | 1.04(0.88,1.22) | 1.34(1.16,1.55) | 1.07(1.04,1.11) | < 0.0001 |
| Model 2 | 1 (reference) | 1.09(0.94, 1.27) | 1.10(0.97,1.24) | 1.03(0.87,1.22) | 1.32(1.14,1.53) | 1.07(1.03,1.10) | 0.004 |
Values are weighted PR (95% CI)
Crude model: no adjusted
Model 1: adjusted for sociodemographic variables (age, sex, ethnicity, educational level, family income, and marital status)
Model 2: Model 1 + smoking status, alcohol status, physical activity, Healthy Eating Index-2015, estimated glomerular filtration rate, and the use of sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, allopurinol, febuxostat, and diuretics
CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; PR, Prevalence ratios; SUA, serum uric acid
Fig. 2.
The multivariable adjusted RCS model for association of serum uric acid level with advanced CKM syndrome from NHANES 2011–2020. A The RCS model with 3 knots showed the lowest AIC statistics; B Association of serum uric acid level with advanced CKM syndrome from NHANES 2011–2020, P for non-linearity = 0.0012. AIC, Akaike information criterion; CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; NHANES, National Health and Nutrition Examination Survey; PR, prevalence ratios; RCS, restricted cubic spline
Sensitivity analyses
Supplemental Tables 8-Table 10 demonstrates the results of sensitivity analyses. The relationship between SUA levels and advanced CKM syndrome persisted after excluding participants with baseline self-reported history of cancer (Supplemental Table 8), performing iterative imputation on covariates (Supplemental Table 9). In addition, when we included all adults (CKM syndrome stages 0‑4) and compared advanced CKM syndrome (stages 3‑4) with non‑advanced stages (stages 0‑2) to avoid potential collider bias, the association remained robust (Supplemental Table 10). Besides, the significant association of hyperuricemia with advanced CKM syndrome was also observed (Supplemental Table 11).
Stratified analyses
The association of SUA levels with advanced CKM syndrome was similar across a wide range of subgroups stratified by age, ethnicity, educational level, family income, marital status, smoking status, alcohol status, and physical activity (Table 3). Meanwhile, the stratified analyses revealed a significant interaction between sex and SUA level (P value for interaction = 0.024). For male patients, the RCS with 3 knots showed a decrease in OR when SUA level was < 6.0 mg/dL, but an increase in OR when SUA level was ≥ 6.0 mg/dL (Fig. 3A-B and Supplemental Table 12). In contrast, the RCS with 3 knots showed a gentle slope of OR when SUA level was < 4.9 mg/dL, but an increase in OR when SUA level was ≥ 4.9 mg/dL in female patients (Fig. 3C-D and Supplemental Table 13).
Table 3.
Variables-stratified analyses for the association between SUA level and advanced CKM syndrome
| SUA level, mg/dL | ||||||
|---|---|---|---|---|---|---|
| Q1 < 4.3 |
Q2 4.3–5.1 |
Q3 5.1–5.8 |
Q4 5.8–6.7 |
Q5 > 6.7 |
P interaction | |
| Age | 0.283 | |||||
| < 60 | 1 (reference) | 0.89(0.63,1.24) | 1.08(0.79,1.49) | 0.92(0.61,1.38) | 1.26(1.08,1.97) | |
| ≥ 60 | 1 (reference) | 1.34(1.03,1.73) | 1.19(0.90,1.55) | 1.11(0.83,1.48) | 1.80(1.34,2.42) | |
| Sex | 0.024 | |||||
| Male | 1 (reference) | 1.09(0.67,1.76) | 1.00(0.63,1.59) | 0.83(0.55,1.26) | 1.20(0.75,1.93) | |
| Female | 1 (reference) | 1.07(0.80,1.44) | 1.15(0.85,1.57) | 1.14(0.79,1.65) | 2.42(1.70,3.46) | |
| Race or ethnicity | 0.314 | |||||
| Non-Hispanic White | 1 (reference) | 1.24(0.92,1.67) | 1.23(0.95,1.58) | 1.01(0.72,1.43) | 1.73(1.28,2.33) | |
| Non-White | 1 (reference) | 0.85(0.65,1.13) | 0.82(0.58,1.15) | 1.02(0.77,1.36) | 1.31(1.05,1.78) | |
| Educational level | 0.696 | |||||
| Less than high school | 1 (reference) | 0.89(0.56,1.41) | 1.07(0.69,1.64) | 1.02(0.62,1.67) | 1.57(1.01,2.45) | |
| High school or equivalent | 1 (reference) | 1.33(0.83,2.11) | 1.35(0.89,2.06) | 1.09(0.68,1.74) | 1.42(0.92,2.19) | |
| College or above | 1 (reference) | 1.10(0.80,1.53) | 1.01(0.72,1.41) | 0.97(0.67,1.40) | 1.64(1.16,2.31) | |
| Marital status | 0.482 | |||||
| Married | 1 (reference) | 1.22(0.87,1.71) | 1.11(0.82,1.49) | 0.98(0.70,1.35) | 1.71(1.28,2.29) | |
| Single or separated | 1 (reference) | 0.98(0.71,1.35) | 1.13(0.83,1.52) | 1.07(0.74,1.55) | 1.39(1.06,1.95) | |
| PIR | 0.537 | |||||
| < 1 | 1 (reference) | 0.81(0.57,1.13) | 0.92(0.64,1.32) | 0.73(0.44,1.23) | 1.26(0.81,1.96) | |
| 1–3 | 1 (reference) | 1.17(0.82,1.68) | 1.19(0.90,1.58) | 1.17(0.86,1.60) | 1.67(1.21,2.30) | |
| > 3 | 1 (reference) | 1.28(0.84,1.95) | 1.11(0.74,1.67) | 1.02(0.66,1.59) | 1.62(1.05,2.50) | |
| Smoking status | 0.882 | |||||
| Never | 1 (reference) | 1.12(0.81,1.55) | 1.09(0.81,1.46) | 0.89(0.62,1.26) | 1.55(1.08,2.21) | |
| Ever or current | 1 (reference) | 1.09(0.78,1.53) | 1.10(0.81,1.48) | 1.05(0.74, 1.48) | 1.46(1.00,2.13) | |
| Alcohol status | 0.463 | |||||
| Non-drinker | 1 (reference) | 0.82(0.58,1.18) | 1.07(0.79,1.47) | 0.73(0.47,1.15) | 1.79(1.28,2.51) | |
| Drinker | 1 (reference) | 1.33(0.97,1.83) | 1.16(0.86,1.55) | 1.20(0.89,1.63) | 1.52(1.12,2.06) | |
| Physical activity | 0.516 | |||||
| Inactive | 1 (reference) | 1.12(0.79,1.57) | 1.03(0.76,1.39) | 0.99(0.73,1.35) | 1.67(1.27,2.20) | |
| Insufficient | 1 (reference) | 1.41(0.97,2.05) | 1.22(0.84,1.77) | 1.18(0.77,1.81) | 1.70(1.08,2.67) | |
| Active | 1 (reference) | 0.80(0.39,1.61) | 1.40(0.72,2.70) | 0.83(0.41,1.70) | 1.17(0.52,2.60) | |
Values are weighted PR (95% CI). Model: each stratification was adjusted for age, sex, ethnicity, educational level, family income, marital status, smoking status, alcohol status, physical activity, Healthy Eating Index-2015, estimated glomerular filtration rate, and the use of sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, allopurinol, febuxostat, and diuretics except the stratification factor itself
CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; PR, Prevalence ratios; SUA, serum uric acid
Fig. 3.
The multivariable adjusted RCS model for associations of serum uric acid level with advanced CKM syndrome among male and female from NHANES 2011–2020. A The RCS model with 3 knots showed the lowest AIC statistics; B Association of serum uric acid level with advanced CKM syndrome among male from NHANES 2011–2020. P for non-linearity = 0.0095; C The RCS model with 3 knots showed the lowest AIC statistics; D Association of serum uric acid level with advanced CKM syndrome among female from NHANES 2011–2020; P for non-linearity = 0.0001. AIC, Akaike information criterion; CI, Confidence interval; CKM, Cardiovascular-Kidney-Metabolic; NHANES, National Health and Nutrition Examination Survey; PR, prevalence ratios; RCS, restricted cubic spline
Discussion
By using a large, national, representative sample of US adults, we evaluated the association of.
SUA levels with advanced CKM syndrome. In multivariable-adjusted models of the NHANES 2011–2020 data, CKM syndrome patients in the highest quintile of SUA level had a 32% higher prevalence of advanced stages, compared with those in the lowest quintile, and the estimated risk increase was 7% for per 1 mg/dL of SUA elevation. Moreover, the RCS curve demonstrated that when SUA level was ≥ 5.5 mg/dL, the likelihood of being in advanced stages had increased significantly among CKM syndrome patients.
Chronic non-communicable diseases like CVD, CKD, and metabolic dysfunctions, are leading causes of disability and mortality globally. According to the 2024 heart disease and stroke statistics update of the AHA, CVD is the leading causes of death in the world, responsible for nearly 20 million deaths in 2021 [15]. The global burden of CKD study reported that global all-age CKD mortality also increased 41.5% from 1990 to 2017 [16]. Approximately 529 million people worldwide suffered from diabetes in 2021, and more than 1.31 billion people are projected to have diabetes by 2050 [17]. Furthermore, growing concerns have been raised about the underlying pathophysiological interconnections between these diseases [18]. The above-mentioned epidemiological data and intricate relationship highlight the need for a better understanding and management of these conditions. In 2023, the AHA has introduced a framework of CKM syndrome to enhance multidisciplinary approaches to provide prevention and manage of these disorders [3]. Based on risk factors and established disease, CKM syndrome is categorized into five stages: stage 0 (no CKM risk factors), stage 1 (excessive or dysfunctional adiposity), stage 2 (presence of metabolic risk factors and CKD), stage 3 (subclinical CVD in CKM syndrome or its risk equivalents), stage 4 (clinical CVD in CKM syndrome). In addition, advanced CKM syndrome stages were defined as stages 3 or 4 because these identify individuals with subclinical or clinical CVD. Based on data from the NHANES 2011–2020 survey, almost 90% of U.S. adults satisfied the diagnostic criteria for CKM syndrome (stage 1 or higher) and 15% met the criteria for advanced stages [4]. The widespread occurrence of CKM syndrome in the U.S. population underscores the urgent need for strategies to better understand and manage this condition. However, managing conventional risk factors for CKM syndrome is insufficient to fully address the condition [3], highlighting the necessity for further research into other potential risk factors.
Uric acid is a degradation product from purines. Among the general population, higher SUA concentration is associated with increased mortality risk and a variety of chronic noncommunicable diseases, hinting at the adverse effect of high SUA level. A cohort study of 417,734 adults reported there was a gradual increase in the risk of myocardial infarction and congestive heart failure by increasing SUA concentrations [8]. Further, a cohort study using a national population-based longitudinal study in China showed that each 1 mg/dL increment in the SUA levels was associated with a 49% increased risk of incident CKD [7]. Meanwhile, another study containing 122,123 participants found that higher SUA levels increased the odds of having type 2 diabetes [6]. Based on NHANES 2011–2020 data, our study extended the previous findings to patients with CKM syndrome, revealing that high SUA levels were associated with advanced stages among CKM syndrome patients. Since SUA levels can be easily obtained through routine checkups, our findings may have significant clinical relevance and public health implications for risk stratification and hypothesis generation for future prospective studies.
Our study found a significant interaction between sex and SUA level. For male patients, we discovered an L-shaped association between SUA levels and advanced CKM syndrome; for female patients, the incidence of advanced CKM syndrome increased with the increase of SUA levels. This interesting conclusion seems to be consistent with previous studies. A recent study showed that SUA was an independent prognostic predictor for long-term outcomes of heart failure in male, but not in female [19]. Another study found that among cardiac patients with preserved ejection fraction, SUA was significantly associated with diastolic dysfunction in female but not in male [20]. Kawabe et al. reported an increased level of SUA was associated with major cardiovascular adverse events more strongly in female than in male with acute coronary syndrome [21]. Besides, a cross-sectional study of 216 CKD patients suggested that an association between SUA levels and the development of cardiac hypertrophy is more likely in female than in male [22]. Furthermore, one study of 1,938 newly diagnosed type 2 diabetes patients reported that elevated SUA was associated with higher risk of insulin resistance, and this association was more pronounced among female than male [23]. One possible explanation for the sex disparities in the association between SUA levels and the aforementioned diseases is sex steroid differences that result in distinct states of oxidative stress, inflammation, and immune response between male and female [24]. Testosterone increases the expression of urate transporter 1 (URAT1) [25], which leads to increasing SUA levels. On the other hand, estrogen decomposes URAT1 and decreases SUA levels [26].
The observed relationship between SUA level and increased risk of being in advanced CKM syndrome stages is biologically plausible. Elevated SUA may promote cardiovascular damage through multiple mechanisms. First, monosodium urate crystals can deposit in vascular walls, where they directly trigger local inflammatory responses, recruit macrophages, and promote the formation of atherosclerotic plaques [27]. Second, uric acid inhibits endothelial nitric oxide synthase activity, reducing nitric oxide bioavailability and impairing endothelium dependent vasodilation, which leads to increased vascular tone and promotes hypertension and target organ damage [28]. Third, uric acid activates the NOD-like receptor family pyrin domain containing 3 inflammasome in vascular endothelial cells and macrophages, amplifying the production of interleukin‑1β and other proinflammatory cytokines, thereby sustaining systemic and vascular inflammation [29]. Fourth, uric acid induces proliferation and migration of vascular smooth muscle cells, contributing to vascular remodelling, arterial stiffness, and increased afterload [30]. Additional pathways involve interactions with high‑sensitivity C‑reactive protein [31], sleep disorders [32], mental health disorders [33], and other risk enhancing factors of CKM syndrome [3]. The exact underlying mechanism still needs to be further studied and validated.
There are some shortcomings in this study. First, its cross-sectional study design precludes causal inferences, and specifically, we cannot determine whether elevated SUA levels preceded the transition from CKM syndrome stage 2 to advanced stages (3 or 4) due to the lack of longitudinal follow‑up. And reverse causality (i.e., advanced CKM syndrome leading to elevated SUA through renal impairment or medication use) remains possible. Well-designed cohort studies are still needed. Second, SUA levels for NHANES respondents were only available for a single time point, which can not reflect the changes of SUA levels in the long-term follow-up. Future studies with repeated measurements of SUA levels are warranted to investigate the effect of changing SUA levels on CKM syndrome. Third, other important variables such as echocardiographic parameters and coronary computed tomographic angiography imaging were unavailable, which may lead to underestimation of stages 3 and 4. Finally, the conclusions were derived from U.S. general population, and further research should be conducted to generalize our findings to other racial groups.
Conclusion
In conclusion, the findings of our cross-sectional study suggested that a higher concentration of SUA level was associated with a greater risk of being in advanced stages among CKM syndrome patients. Regular monitoring of SUA in CKM syndrome patients may act as a useful tool to identify individuals at high risk, thereby potentially guiding future prospective studies and risk stratification.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the National Center for Health Statistics of the Centers for Disease Control and Prevention for sharing the NHANES data. We also thank Home for Researchers (https://www.home-for-researchers.com/) for their linguistic assistance.
Abbreviations
- AHA
American Heart Association
- AIC
Akaike information criterion
- BMI
Body mass index
- CI
Confidence interval
- CKD
Chronic kidney disease
- CKM
Cardiovascular-Kidney-Metabolic
- CVD
Cardiovascular disease
- CVH
Cardiovascular health
- eGFR
Estimated glomerular filtration rate
- GLP‑1 RA
Glucagon-like peptide-1 receptor agonists
- HEI-2015
Healthy Eating Index-2015
- NCHS
National Center for Health Statistics
- NHANES
National Health and Nutrition Examination Survey
- PR
Prevalence ratios
- RCS
Restricted cubic spline
- SGLT2i
Sodium-glucose cotransporter-2 inhibitors
- SUA
Serum uric acid
- SE
Standard errors
- URAT1
Urate transporter 1
Author contributions
**Dingyuan Tu: ** Methodology, Formal analysis, Writing – original draft. **Yutang Li: ** Methodology, Data curation. **Yujuan Wang: ** Methodology, Software. **Jianming Wang: ** Conceptualization, Writing – review & editing.
Funding
This study was supported by grant from Liaoning Province Union Program - Application Fundamental Research Project (2023JH2/101700138) and Shenyang Joint Logistics Support Force Young Elite Cultivation Program (2025SL0012).
Data availability
The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.html).
Declarations
Ethics approval and consent to participate
Clinical trial number: not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Dingyuan Tu, Yutang Li and Yujuan Wang have contributed equally to this work.
References
- 1.Khunti K, Sathanapally H, Mountain P. Multiple long term conditions, multimorbidity, and co-morbidities: we should reconsider the terminology we use. BMJ. 2023;383:2327. [DOI] [PubMed] [Google Scholar]
- 2.Collaborators GBDCD. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148(20):1606–35. [DOI] [PubMed] [Google Scholar]
- 4.Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011–2020. JAMA. 2024;331(21):1858–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Maloberti A, Vanoli J, Finotto A, Bombelli M, Facchetti R, Redon P, Mancia G, Grassi G. Uric acid relationships with lipid profile and adiposity indices: Impact of different hyperuricemic thresholds. J Clin Hypertens (Greenwich). 2023;25(1):78–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kobayashi G, Okada H, Hamaguchi M, Kurogi K, Murata H, Ito M, Fukui M. Association between uric acid levels and incidence of type 2 diabetes: Population-based Panasonic cohort study 11. Diabetes Res Clin Pract. 2023;195:110179. [DOI] [PubMed] [Google Scholar]
- 7.Wu N, Xia J, Chen S, Yu C, Xu Y, Xu C, Yan T, Li N, Liu Y, Pan XF. Serum uric acid and risk of incident chronic kidney disease: a national cohort study and updated meta-analysis. Nutr Metab (Lond). 2021;18(1):94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Holme I, Aastveit AH, Hammar N, Jungner I, Walldius G. Uric acid and risk of myocardial infarction, stroke and congestive heart failure in 417,734 men and women in the Apolipoprotein MOrtality RISk study (AMORIS). J Intern Med. 2009;266(6):558–70. [DOI] [PubMed] [Google Scholar]
- 9.Li B, Chen L, Hu X, Tan T, Yang J, Bao W, Rong S. Association of Serum Uric Acid With All-Cause and Cardiovascular Mortality in Diabetes. Diabetes Care. 2023;46(2):425–33. [DOI] [PubMed] [Google Scholar]
- 10.He Y, Feng J, Zhang B, Wu Q, Zhou Y, He D, Zheng D, Yang J. Serum uric acid levels and risk of cardiovascular disease in type 2 diabetes: results from a cross-sectional study and Mendelian randomization analysis. Front Endocrinol (Lausanne). 2023;14:1251451. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.D’Agostino RB, Sr., Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53. [DOI] [PubMed] [Google Scholar]
- 12.Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, L.R. C. : National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2 2013; (161):1–24. [PubMed]
- 13.Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, Wilson MM, Reedy J. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Tan Y, Fu Y, Yao H, Wu X, Yang Z, Zeng H, Zeng Z, Liang H, Li Y, Jing C. Relationship between phthalates exposures and hyperuricemia in U.S. general population, a multi-cycle study of NHANES 2007–2016. Sci Total Environ. 2023;859(Pt 1):160208. [DOI] [PubMed] [Google Scholar]
- 15.Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347–913. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Collaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ndumele CE, Neeland IJ, Tuttle KR, Chow SL, Mathew RO, Khan SS, Coresh J, Baker-Smith CM, Carnethon MR, Despres JP, et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation. 2023;148(20):1636–64. [DOI] [PubMed] [Google Scholar]
- 19.Fu K, Cheng C, Su C, Teng J, Qiao L, Xiao J, Ji X, Lu H, Chen W. Gender differences in the relationship between serum uric acid and the long-term prognosis in heart failure: a nationwide study. Cardiovasc Diabetol. 2024;23(1):131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Nogi S, Fujita S, Okamoto Y, Kizawa S, Morita H, Ito T, Sakane K, Sohmiya K, Hoshiga M, Ishizaka N. Serum uric acid is associated with cardiac diastolic dysfunction among women with preserved ejection fraction. Am J Physiol Heart Circ Physiol. 2015;309(5):H986–994. [DOI] [PubMed] [Google Scholar]
- 21.Kawabe M, Sato A, Hoshi T, Sakai S, Hiraya D, Watabe H, Kakefuda Y, Ishibashi M, Abe D, Takeyasu N, et al. Gender differences in the association between serum uric acid and prognosis in patients with acute coronary syndrome. J Cardiol. 2016;67(2):170–6. [DOI] [PubMed] [Google Scholar]
- 22.Yoshitomi R, Fukui A, Nakayama M, Ura Y, Ikeda H, Oniki H, Tsuchihashi T, Tsuruya K, Kitazono T. Sex differences in the association between serum uric acid levels and cardiac hypertrophy in patients with chronic kidney disease. Hypertens Res. 2014;37(3):246–52. [DOI] [PubMed] [Google Scholar]
- 23.Hu X, Rong S, Wang Q, Sun T, Bao W, Chen L, Liu L. Association between plasma uric acid and insulin resistance in type 2 diabetes: A Mendelian randomization analysis. Diabetes Res Clin Pract. 2021;171:108542. [DOI] [PubMed] [Google Scholar]
- 24.Mitchell T, De Miguel C, Gohar EY. Sex differences in redox homeostasis in renal disease. Redox Biol. 2020;31:101489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hosoyamada M, Takiue Y, Shibasaki T, Saito H. The effect of testosterone upon the urate reabsorptive transport system in mouse kidney. Nucleosides Nucleotides Nucleic Acids. 2010;29(7):574–9. [DOI] [PubMed] [Google Scholar]
- 26.Matsubayashi M, Sakaguchi YM, Sahara Y, Nanaura H, Kikuchi S, Asghari A, Bui L, Kobashigawa S, Nakanishi M, Nagata R, et al. 27-Hydroxycholesterol regulates human SLC22A12 gene expression through estrogen receptor action. FASEB J. 2021;35(1):e21262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Dalbeth N, Alhilali M, Riordan P, Narang R, Chhana A, McGlashan S, Doyle A, Andres M. Vascular deposition of monosodium urate crystals in gout: analysis of cadaveric tissue by dual-energy computed tomography and compensated polarizing light microscopy. Arthritis Rheumatol. 2022;74(7):1295–6. [DOI] [PubMed] [Google Scholar]
- 28.Gherghina ME, Peride I, Tiglis M, Neagu TP, Niculae A, Checherita IA. Uric Acid and Oxidative Stress-Relationship with Cardiovascular, Metabolic, and Renal Impairment. Int J Mol Sci 2022, 23(6):3188. [DOI] [PMC free article] [PubMed]
- 29.Yin W, Zhou QL, OuYang SX, Chen Y, Gong YT, Liang YM. Uric acid regulates NLRP3/IL-1beta signaling pathway and further induces vascular endothelial cells injury in early CKD through ROS activation and K(+) efflux. BMC Nephrol. 2019;20(1):319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Savale L, Akagi S, Tu L, Cumont A, Thuillet R, Phan C, Le Vely B, Berrebeh N, Huertas A, Jais X et al. Serum and pulmonary uric acid in pulmonary arterial hypertension. Eur Respir J 2021, 58(2):2000332. [DOI] [PubMed]
- 31.Yang T, Ding X, Wang YL, Zeng C, Wei J, Li H, Xiong YL, Gao SG, Li YS, Lei GH. Association between high-sensitivity C-reactive protein and hyperuricemia. Rheumatol Int. 2016;36(4):561–6. [DOI] [PubMed] [Google Scholar]
- 32.Zhou C, Gu M, Yin L, Yin W, Liu J, Zhu Y, Yin K, Lei C, Xu Z, Yang X. Low serum uric acid levels may be a potential biomarker of poor sleep quality in patients with Parkinson’s disease. Sleep Med. 2023;105:9–13. [DOI] [PubMed] [Google Scholar]
- 33.Black CN, Bot M, Scheffer PG, Snieder H, Penninx B. Uric acid in major depressive and anxiety disorders. J Affect Disord. 2018;225:684–90. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The National Health and Nutrition Examination Survey dataset is publicly available at the National Center for Health Statistics of the Center for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes/index.html).