Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2026 Feb 25;21(2):e0337714. doi: 10.1371/journal.pone.0337714

Sex, age, and racial/ethnic disparities in hyperuricemia prevalence and risk factors among U.S. adults: An analysis of NHANES 2007–2018 data

Yadan Zou 1,#, Lina Zhang 1,#, Jing Xu 1, Ji Li 1, Jing Zhang 1, Ting Long 1, Ruohan Yu 1, Yanfeng Zhang 1, Zhongxing Zhao 2, Sheng-Guang Li 1,*
Editor: Toshiki Maeda3
PMCID: PMC12935233  PMID: 41739756

Abstract

Background

Hyperuricemia is a metabolic disorder linked to gout, kidney disease, and cardiovascular complications. Understanding its prevalence and risk factors across demographic groups is crucial for effective prevention and management.

Objectives

To evaluate the prevalence of hyperuricemia among U.S. adults by sex, age, and racial/ethnic groups, and identify common and sex-specific risk factors.

Methods

Data from 34,144 U.S. adults aged ≥20 years, obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2018, were analyzed. Hyperuricemia was defined as serum uric acid levels >7.0 mg/dL in males and >6.0 mg/dL in females. Prevalence estimates were calculated, and multivariate logistic regression models identified risk factors, adjusting for confounders such as body mass index (BMI), alcohol consumption, hypertension, renal function, and other variables. A sensitivity analysis excluding participants with a history of gout was conducted to evaluate the robustness of identified associations.

Results

The overall prevalence of hyperuricemia was significantly higher in males (21.1%) compared to females (17.1%, P < 0.001). Females surpassed males in both prevalence and absolute numbers after age 50–59; by age ≥ 80, the number of female cases was more than twice that of males. Non-Hispanic Black adults had the highest prevalence (23.9% in males, 23.4% in females). Key risk factors for both sexes included obesity (OR = 3.91 in males; OR = 4.76 in females), hypertension (OR = 1.65 in males; OR = 2.09 in females), and impaired renal function (eGFR < 30 mL/min/1.73 m²: OR = 3.72 in males; OR = 15.37 in females). Alcohol consumption was positively associated with hyperuricemia in males (OR = 1.25), but not significantly so in females. Diabetes showed opposite associations: protective in males (OR = 0.72) but a risk factor in females (OR = 1.22). Medication use exhibited expected directional effects: diuretic use was associated with significantly increased risk of hyperuricemia (OR = 2.67 in males; OR = 2.55 in females), while urate-lowering therapy was associated with reduced risk (OR = 0.57 in males; OR = 0.55 in females).

Conclusions

Hyperuricemia remains highly prevalent in the U.S., with notable disparities by sex, age, and race/ethnicity. Older women bear a particularly high burden, partly due to obesity, renal dysfunction, and diuretic use. Incorporating medication use into analyses strengthens the evidence for sex-specific risk profiles. These findings highlight the importance of considering targeted screening and prevention strategies in specific high-risk groups, such as older women and patients receiving diuretics.

Introduction

Hyperuricemia, defined by elevated serum uric acid (SUA) levels, is a metabolic disorder closely linked to gout and multiple chronic comorbidities such as chronic kidney disease (CKD), hypertension, diabetes mellitus, and cardiovascular diseases (CVD) [1–4]. The global prevalence of hyperuricemia has been increasing, paralleling the rise in obesity, hypertension, and other lifestyle-related disorders [5]. Given its widespread occurrence and associated health risks, understanding demographic variations and identifying key risk factors for hyperuricemia is essential for developing effective prevention and management strategies.

Sex differences in hyperuricemia prevalence have been consistently reported, with men typically exhibiting higher SUA levels compared to women. This disparity is primarily attributed to hormonal influences, particularly the uricosuria effect of estrogen in premenopausal women, facilitating renal excretion of uric acid, thereby maintaining lower SUA levels in this population [6]. After menopause, the protective effect of estrogen diminishes, leading to increased hyperuricemia prevalence among women [7]. However, comprehensive evaluations comparing both the prevalence and the absolute number of hyperuricemia cases between sexes across various age groups are limited, especially within nationally representative populations.

Age is another critical factor influencing hyperuricemia prevalence, reflecting a complex interplay between hormonal changes, metabolic shifts, and progressive renal impairment associated with aging [8]. While increased age is generally linked to elevated SUA levels, it remains uncertain whether hyperuricemia prevalence and absolute patient numbers among older women eventually exceed those among older men. Clarifying these trends is important for tailoring clinical interventions and targeting preventive measures to demographic groups particularly vulnerable to hyperuricemia-related health issues.

Racial and ethnic disparities in hyperuricemia prevalence have also been observed, potentially driven by genetic predispositions, socioeconomic status, dietary practices, and disparities in healthcare access. Previous studies suggest that Non-Hispanic Black and Hispanic populations may experience a higher prevalence of hyperuricemia compared to other racial and ethnic groups. Nevertheless, comprehensive analyses accounting simultaneously for sex and age differences within these groups remain scarce.

In this study, we analysed data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018 cycles, representing the U.S. adult population, to provide updated hyperuricemia prevalence estimates. We specifically examined variations in hyperuricemia prevalence by sex, age, and racial/ethnic subgroups and explored both common and sex-specific risk factors. We also seek to identify common and sex-specific risk factors associated with hyperuricemia, with a particular focus on examining whether the prevalence and actual number of women with hyperuricemia surpass those of men with increasing age. By addressing these objectives, we hope to fill gaps in the current understanding of hyperuricemia’s epidemiology in the United States.

Our findings are intended to inform targeted screening practices, guide personalized preventive strategies, and support broader public health initiatives aimed at reducing the clinical and societal burden of hyperuricemia and its associated conditions.

Methods

Ethics statement

To ensure ethical compliance, the protocols undergo review by the NCHS Ethics Review Committee, with informed consent obtained from all participants. Data from NHANES is publicly available through their official website (http://www.cdc.gov/nchs/nhanes.htm), accessed on March 1, 2022. Authors had no access to information that could identify individual participants during or after data collection.

Study design and population

This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007–2018. NHANES employs a multistage, stratified probability sampling design to assess health and nutritional status among the civilian, non-institutionalized U.S. population.

Participants aged ≥ 20 years who had relevant demographics, and health-related questionnaires were included. Initially, the dataset comprised 34770 participants. Among these, missing SUA values (3,405) were addressed through Multiple Imputation by Chained Equations (MICE) to minimize potential biases related to missing data (Fig 1).

Fig 1. Flowchart of Participant Selection from NHANES 2007–2018.

Fig 1

Open in a new tab

Data collection and variable definitions

Hyperuricemia Identification.

Hyperuricemia was defined as a serum uric acid (SUA) concentration >7.0 mg/dL (>416 μmol/L) in males and >6.0 mg/dL (>357 μmol/L) in females. These thresholds were based on the 2018 European Alliance of Associations for Rheumatology (EULAR) evidence-based recommendations for the diagnosis of gout [9] and the 2020 American College of Rheumatology (ACR) guideline for the management of gout [10]. This definition reflects the approximate physiological saturation point of monosodium urate under normal temperature and pH conditions, while also accounting for sex-specific differences in uric acid metabolism. Serum uric acid was measured enzymatically using a uricase-based method. Specifically, for NHANES 2007–2008 cycles, uric acid was measured using the Beckman Synchron LX20 analyzer, while from 2009–2018, measurements were performed using the Beckman Coulter UniCel DxC800 Synchron auto-analyzer. All laboratory measurements followed standardized NHANES protocols, including regular calibration, and were subject to rigorous quality control procedures.

Demographic variables.

A range of potential covariates was evaluated based on the existing literature [11], encompassing age, sex, marital status, race/ethnicity, education level, family income, gout, hypercholesterolemia, hypertension, diabetes, coronary heart disease, alcohol consumption, BMI, and GFR. Age was analyzed categorically (20–39, 40–59, 60–79, ≥ 80 years). Race/ethnicity was classified as non-Hispanic White, non-Hispanic Black, Mexican American, and other race/ethnicity groups. Marital status was categorised as married or living alone. Education level was divided into less than high school, high school graduate or GED, some college or associate degree, and college graduate or above. Family income was classified into three groups based on the poverty income ratio (PIR): low (PIR ≤ 1.0), medium (PIR > 1 to 2.0), and high (PIR > 3).

Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation, based on serum creatinine measured in MEC laboratory assessments.

Clinical and anthropometric Variables.

Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²), based on standardized anthropometric measurements collected during the MEC physical examination. Participants were classified according to standard categories: normal (<25 kg/m²), overweight (25–29.9 kg/m²), and obese (≥30 kg/m²). Hypertension was identified by either self-report of physician diagnosis, current antihypertensive medication use, or measured blood pressure ≥140/90 mmHg during NHANES examinations. Diabetes mellitus was defined by self-reported physician diagnosis, current use of diabetes medications (oral hypoglycemic agents or insulin), fasting plasma glucose ≥126 mg/dL, or HbA1c ≥ 6.5%.A history of gout, and coronary heart disease was determined based on self-reported physician-diagnosed conditions. Hypercholesterolemia was defined as a total serum cholesterol concentration ≥240 mg/dL [12], consistent with the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria and widely used in NHANES-based epidemiological studies. Total cholesterol was measured from blood samples obtained in the Mobile Examination Center (MEC) and analyzed in accordance with standardized laboratory protocols.

Renal function was assessed by estimated glomerular filtration rate (eGFR), calculated using the CKD-EPI equation. Chronic kidney disease (CKD) stages were categorized based on eGFR: normal (≥90 mL/min/1.73m²), mild(60–89 mL/min/1.73m²), moderate (30–59 mL/min/1.73m²)and severe impairment (<30 mL/min/1.73m²).Alcohol drinking status was determined using the survey question “In any 1 year, have you had at least 12 drinks of any type of alcoholic beverage?” Participants who answered “yes” were classified as alcohol drinkers.

Statistical analysis

Descriptive Analysis: Summarized demographic characteristics and hyperuricemia prevalence across subgroups using chi-square tests for categorical variables and t-tests for continuous variables.

Logistic Regression Analysis: Conducted univariate and multivariate logistic regression to identify risk factors for hyperuricemia [13]. Covariates included age, sex, race/ethnicity, BMI, diabetes, hypertension, CKD, alcohol intake, smoking status, and income. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported.

Due to potential reverse causality, a sensitivity analysis excluding participants with a history of gout was also conducted.

all models incorporated the NHANES complex survey design. Specifically, we applied the 2-year MEC examination weights (WTMEC2YR), divided by six to create a 12-year pooled weight, and specified strata (SDMVSTRA) and primary sampling units (SDMVPSU). Analyses were conducted using R (version 4.2.2) with the survey package, ensuring nationally representative estimates. In addition, use of diuretics (e.g., furosemide) and urate-lowering therapy (ULT, e.g., allopurinol, febuxostat) was included as binary covariates, derived from NHANES medication data files.

Stratified Analysis: To explore demographic disparities, stratified analyses were performed by age groups (20–29, 30–39,40–49, 50–59,60–69,70–79, ≥ 80 years), sex, and race/ethnicity.

Handling of missing data.

Missing serum uric acid values were addressed using Multiple Imputation by Chained Equations (MICE) [14], specifying five imputations to create complete datasets. MICE was chosen for its robustness in handling missing data across diverse demographic variables and its ability to reduce bias in the analysis.

Results

Characteristics of the study population

The final sample comprised 34,144 U.S. adults from the NHANES 2007–2018 cycles. Among them, 6,500 individuals were identified with hyperuricemia, including 3,522 males (54.18%) and 2,978 females (45.82%, Fig 1). The sample encompassed diverse ages, sexes, and racial/ethnic backgrounds.

Prevalence of hyperuricemia by sex, age, and race/ethnicity

Sex differences.

Males had a significantly higher overall prevalence of hyperuricemia (21.1%) compared to females (17.1%) (p < 0.001), affecting approximately 23.79 million males and 20.43 million females in the U.S. (Table 1).

Table 1. Prevalence of hyperuricemia, estimated number of U.S. adults affected, and mean serum uric acid levels by sex, age group, and race/ethnicity (NHANES 2007–2018).
Prevalence of hyperuricemia, % (95% CI) No. of US adults with hyperuricemia, millions Serum urate level, mean (95% CI) mg/dL
Male Female Both Male Female Both Male Female Both
Total population 21.1 (20.5, 21.7) 17.1 (16.5, 17.6) 19.0 (18.6, 19.5) 23.79 20.43 44.12 6.02 (6.00, 6.04) 4.87 (4.85, 4.89) 5.43 (5.42, 5.44)
Age
20-29 19.7 (18.2, 21.2) 8.7 (7.7, 9.8) 14.3 (13.4, 15.2) 4.42 1.87 6.28 5.98 (5.93, 6.02) 4.52 (4.48, 4.56) 5.26 (5.23, 5.29)
30-39 20.7 (19.2, 22.2) 8.8 (7.7, 9.8) 14.6 (13.7, 15.6) 4.29 1.81 6.03 6.05 (6.00, 6.10) 4.50 (4.46, 4.54) 5.27 (5.23, 5.30)
40-49 20.3 (18.8, 21.8) 10.5 (9.4, 11.6) 15.1 (14.1, 16.0) 4.27 2.24 6.40 6.02 (5.97, 6.07) 4.57 (4.53, 4.62) 5.25 (5.22, 5.28)
50-59 19.7 (18.2, 21.2) 18.3 (16.8, 19.7) 19.0 (18.0, 20.0) 4.09 3.98 8.08 5.93 (5.88, 5.98) 4.96 (4.91, 5.01) 5.44 (5.40, 5.47)
60-69 21.7 (20.2, 23.2) 24.2 (22.7, 25.8) 23.0 (21.9, 24.1) 3.30 4.06 7.35 6.05 (6.00, 6.10) 5.17 (5.12, 5.22) 5.61 (5.57, 5.64)
70-79 23.5 (21.6, 25.5) 30.3 (28.2, 32.4) 27.0 (25.5, 28.4) 1.95 3.06 4.97 6.09 (6.02, 6.15) 5.43 (5.36, 5.49) 5.75 (5.71, 5.80)
 ≥ 80 25.0 (22.4, 27.6) 31.8 (29.2, 34.4) 28.7 (26.8, 30.5) 1.09 2.35 3.37 6.04 (5.95, 6.13) 5.45 (5.37, 5.54) 5.73 (5.67, 5.79)
Race/ethnicity, n (%)
Non-Hispanic white 21.6 (20.6, 22.6) 17.9 (17.0, 18.8) 19.8 (19.1, 20.4) — — — 6.04 (6.01, 6.07) 4.90 (4.87, 4.93) 5.47 (5.45, 5.49)
Non-Hispanic black 23.9 (22.5, 25.3) 23.4 (22.0, 24.7) 23.6 (22.6, 24.6) — — — 6.12 (6.07, 6.17) 5.11 (5.06, 5.16) 5.60 (5.57, 5.64)
Mexican American 16.7 (15.2, 18.2) 11.5 (10.2, 12.7) 14.0 (13.1, 15.0) — — — 5.83 (5.78, 5.88) 4.64 (4.59, 4.69) 5.22 (5.19, 5.26)
Others 20.3 (19.0, 21.6) 13.5 (12.4, 14.5) 16.7 (15.9, 17.5) — — — 6.01 (5.97, 6.05) 4.73 (4.69, 4.76) 5.33 (5.31, 5.36)
Open in a new tab

Age trends.

In males, the prevalence of hyperuricemia remained consistently high from age 20 onwards, with minimal variation across age groups. Specifically, the prevalence was 19.7% in the 20–29 age group and remained relatively stable in older groups.

In contrast, females exhibited a progressive increase in hyperuricemia prevalence with age after 40–49 years. The prevalence rose from 8.7% in the 20–29 age group to 30.3% in the 70–79 age group, surpassing males after the age of 50–59 (Table 1 and Fig 2A).

Fig 2. (2A and 2B). Age- and Sex-Specific Patterns in Gout and Hyperuricemia Prevalence and Estimated Case Counts (NHANES 2007–2018).

Fig 2

Open in a new tab

Analyzing the estimated absolute number of affected individuals revealed distinct patterns between sexes. For males, the number of hyperuricemia patients exceeded 4 million in each age group before 50–59 years old. After age 50–59, despite the prevalence remaining stable, the estimated number of male patients in each age group showed a rapid decline (Table 1 and Fig 2B).

Conversely, among females, the estimated number of hyperuricemia patients before the 40–49 age group was lower than that of males. After age 50–59, the number of female patients increased rapidly, eventually approaching and surpassing the number of males. Although the number of female patients began to decline after the 60–69 age group, the difference compared to males continued to widen. By age 80 and above, the number of female hyperuricemia patients was more than twice that of males (Fig 2B).

Racial/Ethnic disparities.

Non-Hispanic Black adults had the highest prevalence of hyperuricemia (23.9% in males and 23.4% in females), followed by non-Hispanic Whites (21.6% in males and 17.9% in females). Mexican Americans had the lowest prevalence (16.7% in males and 11.5% in females, Fig 3A).

Fig 3. (3A and 3B). Racial/Ethnic and Sex-Specific Prevalence of Hyperuricemia Among U.S. Adults (NHANES 2007–2018).

Fig 3

Open in a new tab

Regarding sex differences, the prevalence in non-Hispanic Black males and females was nearly identical, showing almost no disparity (Fig 3A and 3B). In contrast, among non-Hispanic Whites, Mexican Americans, and other racial/ethnic groups, males had a significantly higher prevalence than females (P < 0.001).

Risk factors for hyperuricemia.

An analysis of hyperuricemia-related factors revealed both common and distinct risk patterns between sexes. After multivariate adjustment, BMI, gout, alcohol consumption, hypertension, and decreased renal function were common risk factors for both males and females. However, the impact of each factor and the role of specific variables exhibited significant gender differences (Table 2 and Table 3).

Table 2. Weighted Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Hyperuricemia in Males (NHANES 2007–2018).
Male with Hyperuricemia/n(%) Univariate OR (95%CI) Multivariate OR (95%CI) Univariate p Multivariate p
Age
 20-29 546 (19.70%) 1.00 (Referent) 1.00 (Referent)
 30-39 582 (20.66%) 1.09 (0.92, 1.28) 0.79 (0.66, 0.94) 0.340 0.011
 40-49 540 (20.31%) 1.02 (0.84, 1.24) 0.60 (0.49, 0.74) 0.840 < 0.001
 50-59 537 (19.74%) 0.90 (0.75, 1.08) 0.45 (0.35, 0.56) 0.265 < 0.001
 60-69 626 (21.73%) 1.04 (0.88, 1.23) 0.40 (0.32, 0.50) 0.649 < 0.001
 70-79 422 (23.55%) 1.09 (0.89, 1.34) 0.30 (0.23, 0.40) 0.416 < 0.001
  ≥ 80 269 (25.00%) 1.20 (0.97, 1.49) 0.28 (0.21, 0.38) 0.092 < 0.001
Race/ethnicity, n (%)
 Non-Hispanic white 1495 (21.61%) 1.00 (Referent) 1.00 (Referent)
 Non-Hispanic black 854 (23.88%) 1.10 (0.99, 1.22) 1.11 (0.99, 1.24) 0.080 0.084
 Mexican American 417 (16.69%) 0.78 (0.65, 0.92) 0.77 (0.64, 0.92) 0.004 0.006
 Others 756 (20.30%) 0.98 (0.85, 1.13) 1.18 (1.01, 1.39) 0.818 0.046
Education
 Some high school 861 (19.84%) 1.00 (Referent) 1.00 (Referent)
 High school or GED 864 (21.60%) 1.17 (1.02, 1.35) 1.07 (0.93, 1.25) 0.025 0.354
 Some college 1018 (22.66%) 1.23 (1.07, 1.41) 1.06 (0.90, 1.23) 0.004 0.502
 College graduate 779 (20.05%) 0.96 (0.82, 1.13) 0.94 (0.77, 1.15) 0.648 0.561
Marital status, n (%)
 Married or living with a partner 2229 (20.78%) 1.00 (Referent) 1.00 (Referent)
 Living alone 1293 (21.59%) 1.08 (0.96, 1.21) 0.87 (0.77, 0.98) 0.221 0.026
Ratio of family income to Poverty
  ≤ 1.0 703 (20.85%) 1.00 (Referent) 1.00 (Referent)
 1.0 to 2.0 954 (21.04%) 1.09 (0.96, 1.23) 1.04 (0.91, 1.19) 0.176 0.595
  ＞ 2.0 1865 (21.17%) 1.11 (0.98, 1.25) 1.07 (0.93, 1.24) 0.096 0.352
Body mass index
  ≤ 24.9 kg/m2 539 (11.32%) 1.00 (Referent) 1.00 (Referent)
 25.0 kg/m2 to 29.9 kg/m2 1181 (19.17%) 2.20 (1.89, 2.57) 2.11 (1.80, 2.48) < 0.001 < 0.001
  ≥ 30.0 kg/m2 1802 (31.09%) 4.16 (3.59, 4.83) 3.91 (3.32, 4.60) < 0.001 < 0.001
Alcohol Use
 No 843 (19.83%) 1.00 (Referent) 1.00 (Referent)
 Yes 2679 (21.49%) 1.14 (1.02, 1.28) 1.25 (1.10, 1.42) 0.021 0.001
Diabetes
 No 2979 (20.71%) 1.00 (Referent) 1.00 (Referent)
 Yes 543 (23.24%) 1.08 (0.93, 1.26) 0.72 (0.60, 0.87) 0.323 0.001
Hypercholesterolemia
 No 1456 (23.99%) 1.00 (Referent) 1.00 (Referent)
 Yes 2066 (19.40%) 0.73 (0.66, 0.82) 0.73 (0.65, 0.82) < 0.001 < 0.001
Hypertension
 No 1857 (17.29%) 1.00 (Referent) 1.00 (Referent)
 Yes 1665 (27.87%) 1.82 (1.65, 2.02) 1.65 (1.46, 1.86) < 0.001 < 0.001
Coronary heart disease
 No 3271 (20.77%) 1.00 (Referent) 1.00 (Referent)
 Yes 251 (25.98%) 1.19 (0.92, 1.54) 0.87 (0.63, 1.20) 0.180 0.403
Glomerular filtration rate (GFR)
 GFR ≥ 90mL/min 1597 (16.70%) 1.00 (Referent) 1.00 (Referent)
 GFR 60 to 89mL/min 1316 (23.24%) 1.43 (1.28, 1.60) 1.92 (1.66, 2.23) < 0.001 < 0.001
 GFR 30 to 59mL/min 545 (40.89%) 3.55 (2.96, 4.25) 6.10 (4.75, 7.84) < 0.001 < 0.001
 GFR < 30mL/min 64 (39.75%) 2.92 (1.80, 4.74) 3.72 (2.11, 6.54) < 0.001 < 0.001
Furosemide
 No 3260 (20.16%) 1.00 (Referent) 1.00 (Referent)
 Yes 262 (48.16%) 3.42 (2.66, 4.40) 2.67 (1.97, 3.61) < 0.001 < 0.001
Antihyperuricemic
 No 3477 (21.03%) 1.00 (Referent) 1.00 (Referent)
 Yes 45 (24.73%) 1.00 (0.64, 1.56) 0.57 (0.35, 0.95) 0.997 0.034
Open in a new tab

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status, history of gout, diuretic use, and urate-lowering therapy use.

Table 3. Weighted Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Hyperuricemia in Females (NHANES 2007–2018).
Female with Hyperuricemia/n(%) Univariate OR (95%CI) Multivariate OR (95%CI) Univariate p Multivariate p
Age
 20-29 235 (8.72%) 1.00 (Referent) 1.00 (Referent)
 30-39 252 (8.76%) 0.94 (0.73, 1.21) 0.67 (0.52, 0.86) 0.624 0.003
 40-49 319 (10.48%) 1.11 (0.87, 1.40) 0.57 (0.44, 0.73) 0.413 < 0.001
 50-59 515 (18.26%) 1.96 (1.61, 2.40) 0.72 (0.57, 0.91) < 0.001 0.008
 60-69 710 (24.23%) 3.03 (2.43, 3.78) 0.77 (0.60, 1.00) < 0.001 0.054
 70-79 553 (30.32%) 4.06 (3.20, 5.15) 0.70 (0.54, 0.92) < 0.001 0.013
  ≥ 80 394 (31.83%) 4.83 (3.90, 5.98) 0.76 (0.57, 1.01) < 0.001 0.060
Race/ethnicity, n (%)
 Non-Hispanic white 1244 (17.94%) 1.00 (Referent) 1.00 (Referent)
 Non-Hispanic black 880 (23.37%) 1.39 (1.24, 1.56) 1.33 (1.15, 1.53) < 0.001 < 0.001
 Mexican American 297 (11.47%) 0.56 (0.48, 0.65) 0.74 (0.62, 0.89) < 0.001 0.002
 Others 557 (13.46%) 0.70 (0.61, 0.81) 1.06 (0.89, 1.27) < 0.001 0.488
Education
 Some high school 771 (18.39%) 1.00 (Referent) 1.00 (Referent)
 High school or GED 722 (19.04%) 1.00 (0.87, 1.14) 1.04 (0.89, 1.22) 0.990 0.619
 Some college 964 (17.59%) 0.93 (0.81, 1.07) 1.12 (0.96, 1.31) 0.337 0.166
 College graduate 521 (13.14%) 0.64 (0.56, 0.73) 1.09 (0.92, 1.29) < 0.001 0.329
Marital status, n (%)
 Married or living with a partner 1404 (15.12%) 1.00 (Referent) 1.00 (Referent)
 Living alone 1574 (19.34%) 1.28 (1.14, 1.43) 1.00 (0.88, 1.15) < 0.001 0.951
Ratio of family income to Poverty
  ≤ 1.0 723 (17.50%) 1.00 (Referent) 1.00 (Referent)
 1.0 to 2.0 901 (18.87%) 1.03 (0.90, 1.17) 0.88 (0.74, 1.05) 0.699 0.152
  ＞ 2.0 1354 (15.89%) 0.83 (0.73, 0.94) 0.87 (0.73, 1.05) 0.005 0.146
Body mass index
  ≤ 24.9 kg/m2 361 (6.93%) 1.00 (Referent) 1.00 (Referent)
 25.0 kg/m2 to 29.9 kg/m2 710 (14.28%) 2.36 (1.93, 2.90) 2.04 (1.63, 2.55) < 0.001 < 0.001
  ≥ 30.0 kg/m2 1907 (26.31%) 5.42 (4.58, 6.42) 4.76 (3.89, 5.81) < 0.001 < 0.001
Alcohol Use
 No 1499 (19.05%) 1.00 (Referent) 1.00 (Referent)
 Yes 1479 (15.47%) 0.77 (0.68, 0.86) 1.15 (0.99, 1.33) < 0.001 0.068
Diabetes
 No 2272 (14.94%) 1.00 (Referent) 1.00 (Referent)
 Yes 706 (31.87%) 3.09 (2.69, 3.55) 1.22 (1.04, 1.42) < 0.001 0.016
Hypercholesterolemia
 No 1345 (18.72%) 1.00 (Referent) 1.00 (Referent)
 Yes 1633 (15.94%) 0.75 (0.67, 0.84) 0.72 (0.63, 0.82) < 0.001 < 0.001
Hypertension
 No 1060 (9.70%) 1.00 (Referent) 1.00 (Referent)
 Yes 1918 (29.51%) 4.05 (3.64, 4.50) 2.09 (1.83, 2.39) < 0.001 < 0.001
Coronary heart disease
 No 2815 (16.60%) 1.00 (Referent) 1.00 (Referent)
 Yes 163 (34.53%) 2.52 (1.99, 3.19) 0.75 (0.57, 0.98) < 0.001 0.039
Glomerular filtration rate (GFR)
 GFR ≥ 90mL/min 998 (9.46%) 1.00 (Referent) 1.00 (Referent)
 GFR 60 to 89mL/min 1128 (21.68%) 2.44 (2.15, 2.77) 2.14 (1.83, 2.51) < 0.001 < 0.001
 GFR 30 to 59mL/min 728 (48.92%) 9.38 (8.02, 10.98) 6.92 (5.46, 8.76) < 0.001 < 0.001
 GFR < 30mL/min 124 (68.51%) 25.84 (17.28, 38.64) 15.37 (9.65, 24.48) < 0.001 < 0.001
Furosemide
 No 2640 (15.71%) 1.00 (Referent) 1.00 (Referent)
 Yes 338 (54.25%) 7.61 (6.22, 9.30) 2.55 (1.98, 3.27) < 0.001 < 0.001
Antihyperuricemic
 No 2959 (17.02%) 1.00 (Referent) 1.00 (Referent)
 Yes 19 (41.30%) 3.16 (1.56, 6.42) 0.55 (0.21, 1.43) < 0.001 0.227
Open in a new tab

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status, history of gout, diuretic use, and urate-lowering therapy use.

Common risk factors

  1. Body Mass Index (BMI): Elevated BMI significantly increased the risk of hyperuricemia in both sexes. In the BMI ≥ 30 kg/m² group, the risk increased by 3.91-fold in males (OR = 3.91, 95% CI: 3.32–4.60, P < 0.001) and 4.76-fold in females (OR = 4.76, 95% CI: 3.89–5.81, P < 0.001).

  2. Hypertension: Hypertension significantly increased the risk of hyperuricemia in both sexes, with a 1.65-fold increase in males (OR = 1.65, 95% CI: 1.46–1.86, P < 0.001) and a 2.09-fold increase in females (OR = 2.09, 95% CI: 1.83–2.39, P < 0.001).

  3. Decreased Renal Function (eGFR): Decline in renal function was the strongest risk factor. Patients with eGFR < 30 mL/min/1.73 m² had an increased risk of 3.42-fold in males (OR = 3.42, 95% CI: 2.11–6.54, P < 0.001) and 15.37-fold in females (OR = 15.37, 95% CI: 9.65–24.48, P < 0.001).

  4. Alcohol Consumption: Alcohol use was associated with increased hyperuricemia risk in males (OR = 1.25, 95% CI: 1.10–1.42, P = 0.001), but not in females (OR = 1.15, 95% CI: 0.99–1.33, P = 0.068).

  5. Diabetes Mellitus: Diabetes showed opposite associations by sex. In males, diabetes was inversely associated with hyperuricemia (OR = 0.72, 95% CI: 0.60–0.87,P = 0.001). In contrast, in females, diabetes was positively associated (OR = 1.22, 95% CI: 1.04–1.42, P = 0.016).

  6. Medication Use: Medication variables demonstrated expected effects. Diuretic use (e.g., furosemide) was associated with higher odds of hyperuricemia in both sexes (males: OR = 2.67, 95% CI: 1.97–3.61, P < 0.001; females: OR = 2.55, 95% CI: 1.98–3.27, P < 0.001). Urate-lowering therapy was strongly protective (males: OR = 0.57, 95% CI: 0.35–0.95, P = 0.034; females: OR = 0.55, 95% CI: 0.21–1.43, P = 0.227).

Sex differences

Impact of age.

Males: As see in Table 2 (and S1 Table), After multivariate adjustment, age showed an inverse association with hyperuricemia risk. In the ≥ 80 years group, the risk decreased significantly (OR = 0.28, 95% CI: 0.21–0.38, P < 0.001).

Females: Univariate analysis demonstrated that increasing age significantly elevated hyperuricemia risk. However, after multivariate adjustment in the analysis excluding participants with gout, the association was substantially attenuated and became non-significant across all older age groups (e.g., for age ≥ 80 years, OR = 0.95, 95% CI: 0.74–1.22, P = 0.693), suggesting that the apparent age effect is largely mediated by other metabolic factors.

Discussion

In this nationally representative cross-sectional analysis of NHANES data from 2007 to 2018, we identified significant disparities in hyperuricemia prevalence according to sex, age, and racial/ethnic groups. Our findings highlight previously underappreciated patterns, notably the marked increase in hyperuricemia prevalence among females after the age of 50–59 years, ultimately surpassing males in both prevalence and absolute numbers. This finding underscore critical demographic differences and provide new insights into the epidemiology of hyperuricemia.

Sex and age disparities

Consistent with previous literature, males exhibited higher overall hyperuricemia prevalence than females [6]. However, a novel finding from our analysis is the clear age-related pattern observed among females, characterized by a substantial increase in hyperuricemia prevalence post-menopause. The narrowing and eventual reversal of the sex gap after age 50–59 years aligns with the hormonal hypothesis, specifically the loss of estrogen’s uricosuric effect in postmenopausal women [7]. This observation suggests hormonal changes significantly influence hyperuricemia risk in aging females, potentially mediated by estrogen’s effects on renal uric acid excretion.

In males, the prevalence of hyperuricemia remained relatively stable across age groups. However, after adjusting for confounders, a negative association between age and hyperuricemia risk was observed in most age groups (for group ≥80 years: OR = 0.28, P < 0.001). This unexpected inverse relationship may reflect healthier lifestyle adaptations, survival bias, or unmeasured confounders. Conversely, in females, the prevalence of hyperuricemia increased steadily with age, with a marked rise after menopause. However, after multivariate adjustment, the association between age and hyperuricemia risk became non-significant (for group ≥80 years, OR = 0.76, P = 0.733). This attenuation suggests a complex interplay of factors such as increased adiposity, postmenopausal hormonal changes, and metabolic comorbidities like hypertension and renal dysfunction [11,12,15].

Racial and ethnic disparities

Our study also identified pronounced racial/ethnic differences, notably the highest prevalence of hyperuricemia among Non-Hispanic Black individuals, consistent with previous NHANES analyses [11,13,14]. These findings point to potential genetic predispositions, socioeconomic influences, lifestyle factors, and healthcare access disparities [16,17]. The nearly identical prevalence rates between non-Hispanic Black males and females highlight the need for further investigation into the unique metabolic and environmental factors affecting this population.

Risk factors for hyperuricemia

Consistent with prior research, obesity, hypertension, alcohol use, and impaired renal function emerged as key risk factors for hyperuricemia in both sexes [1–4]. Among these, impaired renal function displayed the strongest association, particularly among females, highlighting the importance of renal health in uric acid metabolism. The association between obesity and hyperuricemia was robust, emphasizing obesity as a critical modifiable risk factor for hyperuricemia prevention strategies.

We also performed a sensitivity analysis excluding individuals with a reported history of gout. The consistent results observed in this analysis provide reassurance regarding the robustness of identified associations independent of gout history.

Medication use and sex differences

The observed associations between medication use and hyperuricemia provide important insights into the sex-specific patterns of disease burden. The stronger impact of diuretic use in older women may reflect their higher prevalence of hypertension and heart failure, conditions for which thiazide and loop diuretics are commonly prescribed. This pattern likely contributes to the sharp increase in hyperuricemia prevalence among women after menopause. By contrast, men’s greater access to urate-lowering therapy is consistent with their higher lifetime burden of gout and may partly attenuate sex differences in hyperuricemia prevalence. These findings underscore the necessity of incorporating medication use into epidemiological analyses, as failing to adjust for such covariates may obscure the true magnitude of other risk factors.

Beyond diuretics and urate-lowering therapy, the inverse relationship between hypercholesterolemia and hyperuricemia is noteworthy. This association may be explained by the widespread use of statins and other lipid-lowering medications, which have been shown to reduce serum uric acid concentrations [18,19]. The effect appeared more pronounced in males, suggesting potential sex-related differences in prescribing patterns or pharmacological response. Similarly, diabetes mellitus displayed sex-specific associations: a negative relationship with hyperuricemia was observed in men, but not in women. This discrepancy may reflect biological differences in insulin resistance, renal handling of uric acid, or the stage of diabetes progression, with glycosuria potentially enhancing uric acid excretion in males [20–22].

Historical context of NHANES data

Placing our results in historical context, earlier NHANES data showed that hyperuricemia prevalence among U.S. adults has gradually increased, from approximately 18.2% in the late 1980s (NHANES III) to around 21% in recent NHANES cycles (2007–2018) [11,13,14]. This rising trend parallels the increasing prevalence of obesity, hypertension, and metabolic syndrome over the same period. Importantly, our findings extend these observations by clarifying demographic trends, particularly emphasizing the previously underrecognized rise in hyperuricemia burden among older women, whose prevalence and absolute case numbers ultimately surpass those of men.

Earlier NHANES data indicated a gradual increase in hyperuricemia prevalence among U.S. adults over recent decades [11,13,14]. Our results reinforce and extend these observations by clarifying demographic patterns, particularly emphasizing the under-recognized burden among older women.

Clinical implications

While hyperuricemia’s role as a cause of cardiovascular and renal outcomes remains controversial and subject to ongoing debate [3,4], its role as an important modifiable risk factor for gout is well-established. Rather than universal screening, targeted screening and interventions may be warranted in high-risk groups—such as older women, individuals with impaired renal function, or patients on long-term diuretic therapy. Older women represent a particularly vulnerable population due to the dual impact of hormonal changes and metabolic comorbidities. Increased awareness and targeted screening in this demographic are crucial. Addressing modifiable risk factors such as obesity, alcohol consumption, and renal health maintenance could significantly reduce the prevalence and associated complications of hyperuricemia.

Strengths and limitations

This study has several strengths. It is based on a large, nationally representative sample from NHANES, enhancing the findings’ generalizability. Comprehensive multivariate analysis allowed for robust adjustment of confounders, providing reliable estimates of risk factors. Additionally, the focus on age- and sex-specific patterns adds new insights to the existing literature on hyperuricemia epidemiology.

However, some limitations should be noted. As a cross-sectional study, causal relationships cannot be established. The reliance on a single measurement of serum uric acid may introduce variability, though this limitation is inherent to many large-scale epidemiological studies. Moreover, detailed dietary information, particularly purine intake, and specific medication use beyond diuretics and urate-lowering therapy were not fully accounted for, which may influence uric acid levels. Despite these limitations, the study’s rigorous design and large dataset provide valuable contributions to understanding hyperuricemia prevalence and risk factors.

Future directions

Further longitudinal studies are needed to establish causal relationships and explore temporal trends in hyperuricemia prevalence, particularly among older women. Investigating the mechanisms underlying observed sex and racial/ethnic disparities can inform more effective, personalized interventions. Additionally, research on lifestyle and dietary interventions targeting modifiable risk factors may offer valuable insights into hyperuricemia prevention strategies.

Conclusion

This study highlights significant disparities in hyperuricemia prevalence across sex, age, and racial/ethnic groups. The novel finding that older women surpass men in prevalence and absolute numbers underscores the need for increased clinical focus on this demographic. Addressing modifiable risk factors and implementing targeted screening and intervention strategies may be warranted in specific high-risk groups, such as older women, patients with impaired renal function, and those receiving diuretics. Further prospective research will be critical to confirm and extend these findings.

Supporting information

S1 Table. Univariate and multivariate logistic regression analysis of risk factors for male hyperuricemia prevalence excluding gout participants (NHANES 2007–2018).

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status.

(DOCX)

pone.0337714.s001.docx (24.7KB, docx)
S2 Table. Univariate and multivariate logistic regression analysis of risk factors for female hyperuricemia prevalence excluding gout participants (NHANES 2007–2018).

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status.

(DOCX)

pone.0337714.s002.docx (21.8KB, docx)

Acknowledgments

We gratefully thank Jie Liu of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for his contribution to the statistical support, study design consultations, and comments regarding the manuscript.

Data Availability

This study is based on publicly available data from the U.S. National Health and Nutrition Examination Survey (NHANES). The dataset is maintained by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC). The data can be freely accessed at the NHANES website (https://www.cdc.gov/nchs/nhanes/). No special access privileges were required to obtain these data; other researchers can access the datasets in the same manner as the authors by selecting the relevant survey cycles (2007–2018) and downloading the data files provided.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Sun S, Chen L, Chen D, Li Y, Ma L, Hou Y, et al. Knowledge, Attitudes, and Practices about Hyperuricemia and Gout in Community Health Workers and Patients with Diabetes. Healthcare (Basel). 2024;12(11):1072. doi: 10.3390/healthcare12111072 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Si K, Chi J, Xu L, Dong B, Huang Y, Zhang H, et al. Tophi and carotid atherosclerosis in gout patients: Role of insulin resistance. Nutr Metab Cardiovasc Dis. 2024;34(5):1134–41. doi: 10.1016/j.numecd.2023.11.019 [DOI] [PubMed] [Google Scholar]
  • 3.Roddy E, Jordan KM, Giles I. Diagnosis and management of gout: are the British Society for Rheumatology and National Institute for Health and Care Excellence guidelines both needed?. Rheumatol Adv Pract. 2024;8(1):rkae007. doi: 10.1093/rap/rkae007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Eliseev MS, Novikova AM. Comorbidity in gout and hyperuricemia: prevalence, causes, prospects of urate lowering therapy. Ter Arkh. 2019;91(5):120–8. doi: 10.26442/00403660.2019.05.000232 [DOI] [PubMed] [Google Scholar]
  • 5.Chen-Xu M, Yokose C, Rai SK, Pillinger MH, Choi HK. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007-2016. Arthritis Rheumatol. 2019;71(6):991–9. doi: 10.1002/art.40807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Chuang S-Y, Lee S-C, Hsieh Y-T, Pan W-H. Trends in hyperuricemia and gout prevalence: Nutrition and Health Survey in Taiwan from 1993-1996 to 2005-2008. Asia Pac J Clin Nutr. 2011;20(2):301–8. [PubMed] [Google Scholar]
  • 7.Miao Z, Li C, Chen Y, Zhao S, Wang Y, Wang Z, et al. Dietary and Lifestyle Changes Associated with High Prevalence of Hyperuricemia and Gout in the Shandong Coastal Cities of Eastern China. [PubMed]
  • 8.Jiang J, Zhang T, Liu Y, Chang Q, Zhao Y, Guo C, et al. Prevalence of Diabetes in Patients with Hyperuricemia and Gout: A Systematic Review and Meta-analysis. Curr Diab Rep. 2023;23(6):103–17. doi: 10.1007/s11892-023-01506-2 [DOI] [PubMed] [Google Scholar]
  • 9.Richette P, Doherty M, Pascual E, Barskova V, Becce F, Castaneda J, et al. 2018 updated European League Against Rheumatism evidence-based recommendations for the diagnosis of gout. Ann Rheum Dis. 2020;79(1):31–8. doi: 10.1136/annrheumdis-2019-215315 [DOI] [PubMed] [Google Scholar]
  • 10.FitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, et al. 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care Res (Hoboken). 2020;72(6):744–60. doi: 10.1002/acr.24180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Chang P-Y, Chang Y-W, Lin Y-F, Fan H-C. Sex-Specific Association of Uric Acid and Kidney Function Decline in Taiwan. J Pers Med. 2021;11(5):415. doi: 10.3390/jpm11050415 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chou Y-H, Li C-C, Wu W-J, Juan Y-S, Chien T-M. Postmenopausal status increases the risk of uric acid stones. Exp Gerontol. 2024;196:112570. doi: 10.1016/j.exger.2024.112570 [DOI] [PubMed] [Google Scholar]
  • 13.Golmohammadi S, Tavasoli M, Asadi N. Prevalence and Risk Factors of Hyperuricemia in Patients with Chronic Kidney Disease and Non-Alcoholic Fatty Liver. Clin Exp Gastroenterol. 2020;13:299–304. doi: 10.2147/CEG.S253619 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Legendre B, Cerasuolo D, Dejardin O, Boyer A. How to deal with missing data? Multiple imputation by chained equations: recommendations and explanations for clinical practice. Nephrol Ther. 2023;19(3):171–9. doi: 10.1684/ndt.2023.24 [DOI] [PubMed] [Google Scholar]
  • 15.Stubnova V, Os I, Høieggen A, Solbu MD, Grundtvig M, Westheim AS, et al. Gender differences in association between uric acid and all-cause mortality in patients with chronic heart failure. BMC Cardiovasc Disord. 2019;19(1):4. doi: 10.1186/s12872-018-0989-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Guo K, Han Y, Liu S, Sun H, Lin X, Yang S, et al. Prevalence of and trends in hyperuricemia by race and ethnicity among US adolescents, 1999-2018. Arthritis Res Ther. 2024;26(1):193. doi: 10.1186/s13075-024-03427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.McCormick N, Lu N, Yokose C, Joshi AD, Sheehy S, Rosenberg L, et al. Racial and Sex Disparities in Gout Prevalence Among US Adults. JAMA Netw Open. 2022;5(8):e2226804. doi: 10.1001/jamanetworkopen.2022.26804 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Lin G-L, Lin H-C, Lin H-L, Keller JJ, Wang L-H. Association between statin use and the risk of gout in patients with hyperlipidemia: A population-based cohort study. Front Pharmacol. 2023;14:1096999. doi: 10.3389/fphar.2023.1096999 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Shao Q, Chi J, Che K, Zhou Y, Huang Y, Wang Y, et al. Association between low-density lipoprotein cholesterol levels and serum uric acid to serum creatinine ratio in Chinese male gout patients. Sci Rep. 2024;14(1):10121. doi: 10.1038/s41598-024-59992-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chen J, Liu Y, Guo H, Wang B, Sun Z, Yu J. Urine Glucose Excretion Attenuates the Association Between Lipid Accumulation Product and Serum Uric Acid in Subjects with Prediabetes. Diabetes Metab Syndr Obes. 2020;13:2297–305. doi: 10.2147/DMSO.S255908 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kuwabara M, Fukuuchi T, Aoki Y, Mizuta E, Ouchi M, Kurajoh M, et al. Exploring the Multifaceted Nexus of Uric Acid and Health: A Review of Recent Studies on Diverse Diseases. Biomolecules. 2023;13(10):1519. doi: 10.3390/biom13101519 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Mantovani A, Rigolon R, Pichiri I, Pernigo M, Bergamini C, Zoppini G, et al. Hyperuricemia is associated with an increased prevalence of atrial fibrillation in hospitalized patients with type 2 diabetes. J Endocrinol Invest. 2016;39(2):159–67. doi: 10.1007/s40618-015-0354-z [DOI] [PubMed] [Google Scholar]

Decision Letter 0

Toshiki Maeda

7 May 2025

Sex, Age, and Racial/Ethnic Disparities in Hyperuricemia Prevalence and Risk Factors Among U.S. Adults: An Analysis of NHANES 2007–2018 Data

PLOS ONE

Dear Dr. Li,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The review comments can be found at the end of this email. Once you have re-submitted your manuscript, it will be re-reviewed before any final decision on possible publication is made. However, I cannot assure guarantee that the manuscript will be accepted after revision.

Please submit your revised manuscript by Jun 21 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Toshiki Maeda

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for uploading your study's underlying data set. Unfortunately, the repository you have noted in your Data Availability statement does not qualify as an acceptable data repository according to PLOS's standards.

At this time, please upload the minimal data set necessary to replicate your study's findings to a stable, public repository (such as figshare or Dryad) and provide us with the relevant URLs, DOIs, or accession numbers that may be used to access these data. For a list of recommended repositories and additional information on PLOS standards for data deposition, please see https://journals.plos.org/plosone/s/recommended-repositories.

Additional Editor Comments:

Comment

Zou et al. evaluated the prevalence of hyperuricemia among U.S. adults by sex, age, and racial/ethnic groups and identified common and sex-specific risk factors using NHANES. As a result, they found significant disparities in hyperuricemia prevalence by sex, age, and race/ethnicity among U.S. adults. They also pointed out that females surpass males in prevalence and case numbers after age 50–59, which warrants greater clinical focus on older women. Although the findings are attractive and significant, there are some methodological concerns.

Major comments

  1. Is hyperuricemia solely hazardous? Hyperuricemia may be related to cardiovascular events, but the causal relationship is still ambiguous. I could not understand why the author underlined that targeted screening and interventions are essential for hyperuricemia.

  2. Gout is caused by the precipitation of uric acid, which is derived from hyperuricemia. The relationship between gout and hyperuricemia is apparent, so there is no need to elaborate on it further.

  3. Did you consider medication use? The discrepancy in sex may be attributed to differences in medication use between the sexes, and you should discuss this further.

Minor comments

  1. Please only suggest the results and avoid adding interpretation in the Results section. For example, “…, indicating that obesity is one of the most important modifiable risk factors.” in BMI. Such interpretation should be in the Discussion section.

  2. Typo: “base on the clinical guidelines….” Please use the uppercase letters. Additionally, please add the reference to the definition of hyperuricemia (“Hyperuricemia Identification” in “Data Collection and Variable Definitions”).

  3. I highly recommend using English editing by native.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Hello

This is a good article and it explores an important topic.

It seems that more studies are needed in this field in the future to be able to state with certainty the impact of exposure to such toxic substances.

Good luck.

Reviewer #2: This paper analyses NHANES 2007–2018 data to examine sex, age, and racial/ethnic disparities in hyperuricemia among U.S. adults. The authors report that hyperuricemia prevalence rises sharply among females after midlife, eventually surpassing males, and identify key risk factors including obesity, hypertension, alcohol use, and renal dysfunction. The findings highlight the need for targeted prevention strategies across demographic groups.

I have the following comments:

1. Consider the issue of reverse causality: Hyperuricemia is a precursor condition to gout, as the authors also acknowledge in the introduction. Including “history of gout” as a covariate in the multivariable regression model may introduce reverse causality bias. I recommend conducting a sensitivity analysis excluding individuals with a history of gout to better assess the risk factors for hyperuricemia.

2. Clarify laboratory methods: Please provide a detailed description of how serum uric acid was measured in NHANES, including the assay method, equipment, and quality control procedures if available. This will improve reproducibility and methodological transparency.

3. Definition of hyperuricemia: Specify clearly which guideline was used to define hyperuricemia (e.g., ACR, EULAR, or another standard). Please cite the source and briefly discuss the clinical rationale for the chosen cut-offs.

4. Provide more detail on covariates: Describe key variables more clearly. For example, what were the specific categories of education? Were these self-reported during interviews, or collected through another method (e.g., online forms or medical examination)?

5. Expand on statistical analysis methods:

5.1: Specify which statistical software and packages were used.

5.2: Confirm whether survey weights were applied during analysis, given the complex NHANES sampling design.

5.3: Provide more information about how covariates were selected for inclusion in the multivariable models (e.g., based on prior literature, significance in univariable models, directed acyclic graphs, etc.).

5.4: Indicate whether regression diagnostic tests (such as checking for multicollinearity, goodness-of-fit, or influential observations) were conducted.

6. Contextualize findings with previous NHANES data: It would strengthen the discussion to mention hyperuricemia prevalence from prior NHANES cycles (e.g., 1988–1994, 1999–2000) and highlight any trends over time. This would help situate the current findings within the broader epidemiological context.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

PLoS One. 2026 Feb 25;21(2):e0337714. doi: 10.1371/journal.pone.0337714.r002

Author response to Decision Letter 1

3 Nov 2025

Dear Editor,

We thank you and the reviewers for your careful evaluation of our manuscript, “Sex, Age, and Racial/Ethnic Disparities in Hyperuricemia Prevalence and Risk Factors Among U.S. Adults: An Analysis of NHANES 2007–2018 Data.” We appreciate all the comments and suggestions, which have helped us improve the clarity and quality of the paper. We have revised the manuscript accordingly. Below, we provide a point-by-point response to each comment raised by the editor and reviewers. For clarity, the original comments are quoted in italics (indented), and our responses follow. The corresponding revisions in the manuscript have been highlighted in red. A clean version of the revised manuscript has also been uploaded.

Editor’s Comments

Comment 1 (Editor): “Is hyperuricemia solely hazardous? Hyperuricemia may be related to cardiovascular events, but the causal relationship is still ambiguous. I could not understand why the author underlined that targeted screening and interventions are essential for hyperuricemia.”

Response:

Thank you very much for raising this insightful question, which gives us the opportunity to clarify further the rationale behind emphasizing “targeted screening and intervention for hyperuricemia (HUA)” in our manuscript. We fully acknowledge that the causal relationship between hyperuricemia and cardiovascular events remains uncertain, a viewpoint supported by several recent Mendelian randomization studies and prospective cohort investigations1,2.

It was not our intention to imply that hyperuricemia is invariably harmful across all populations, nor to advocate for universal intervention in the general public. Our emphasis on “targeted screening and intervention” stems from the fact that, even in the absence of definitive causal evidence, extensive epidemiological studies have consistently shown that hyperuricemia is associated with an increased risk of gout3, progression of chronic kidney disease4, metabolic abnormalities, and incident cardiovascular events5. Moreover, among individuals with established cardiometabolic or renal risk factors—such as hypertension, obesity, or chronic kidney disease—elevated uric acid often serves as an early and modifiable biomarker6,7.

Therefore, by “targeted screening and intervention,” we refer to strategies focused on specific high-risk groups—such as those with recurrent gout attacks, impaired renal function, or multiple metabolic risk factors. In these populations, timely monitoring and evidence-based intervention when clinically indicated may help reduce the incidence of complications. We have expanded on the scope and rationale of this approach in the Discussion section to avoid overgeneralizing our recommendations to the general population8.

We have revised the Conclusions(L503-L506) to state that targeted screening and interventions may be warranted in specific high-risk groups, rather than implying it is essential for everyone. These changes ensure a balanced discussion that does not overstate the hazards of hyperuricemia while still highlighting the public health relevance of our findings.

Comment 2 (Editor): “Gout is caused by the precipitation of uric acid, which is derived from hyperuricemia. The relationship between gout and hyperuricemia is apparent, so there is no need to elaborate on it further.”

Response:

We agree and have streamlined the manuscript to avoid over-elaborating on the well-known relationship between hyperuricemia and gout. In the Introduction, we initially provided background on gout as a consequence of hyperuricemia; we have now condensed this to a brief statement for context only. Any redundant or lengthy explanation of gout’s relationship to hyperuricemia has been removed. This revision keeps the focus on our study’s objectives and findings, without reiterating established knowledge.

Comment 3 (Editor): “Did you consider medication use? The discrepancy in sex may be attributed to differences in medication use between the sexes, and you should discuss this further.”

Response:

Thank you for raising this critical point. In the revised manuscript, we have gone beyond discussion and included an analysis of two classes of medications available in NHANES that directly affect serum uric acid levels: diuretics and urate-lowering therapy (ULT).

Our weighted regression models demonstrate that the use of diuretics (e.g., furosemide) was strongly associated with increased odds of hyperuricemia in both sexes. After full adjustment, the odds ratio for males was 2.67 (95% CI: 1.97–3.61) and for females was 2.55 (95% CI: 1.98–3.27). Conversely, the use of urate-lowering medications (e.g., allopurinol, febuxostat) was significantly associated with a reduced risk of hyperuricemia. The adjusted odds ratio was 0.57 (95% CI: 0.35–0.95) in males and 0.55 (95% CI: 0.21–1.43) in females, thou-gh the result for females did not reach statistical significance, likely due to limited sample size. These findings confirm the expected biological effects of these medications and highlight their role as essential covariates.

We also note sex-related differences: diuretic use was more common in older women (likely due to treatment of hypertension and heart failure), which may contribute to the higher prevalence of hyperuricemia observed in females after midlife. On the other hand, men had a higher prevalence of urate-lowering therapy use, reflecting their higher lifetime burden of gout, which may partly offset hyperuricemia prevalence in males.

In the revised Abstract (L78–82), Methods (L244–240), Results (L333–339), and Discussion (L412–423), we explicitly describe the findings on medication use and interpret them as contributing to the observed sex disparity in hyperuricemia prevalence. With the inclusion of medication variables in the multivariable models (see Tables 2 and 3), estimates for other risk factors became more robust. This addition strengthens the manuscript by moving beyond speculation and providing direct evidence of the impact of medication use on serum uric acid levels.

Table 2. Weighted Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Hyperuricemia in Males (NHANES 2007–2018)

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status, history of gout, diuretic use, and urate-lowering therapy use.

Table 3. Weighted Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Hyperuricemia in Females (NHANES 2007–2018)

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status, history of gout, diuretic use, and urate-lowering therapy use.

Comment 4 (Editor): “Please only suggest the results and avoid adding interpretation in the Results section. For example, ‘…, indicating that obesity is one of the most important modifiable risk factors.’ in BMI. Such interpretation should be in the Discussion section.”

Response: We have revised the Results section to remove any interpretative statements, ensuring it focuses strictly on the empirical findings. Specifically, we deleted the phrase “indicating that obesity is one of the most important modifiable risk factors” from the Results section where we report the association with BMI.

We relocated this interpretative insight to the Discussion(L410-L412) section, where we discuss the implications of the strong association between obesity and hyperuricemia. The revised manuscript is as followed:

“The association between obesity and hyperuricemia was robust, emphasizing obesity as a critical modifiable risk factor for hyperuricemia prevention strategies”.

Throughout the Results, we now present the data without commentary, and we reserve interpretation, context, and implications for the Discussion. This change aligns the manuscript with the journal’s guidelines and clearly separates results from interpretation.

Comment 5 (Editor): “Typo: ‘based on the clinical guidelines….’ Please use the uppercase letters. Additionally, please add the reference to the definition of hyperuricemia (‘Hyperuricemia Identification’ in ‘Data Collection and Variable Definitions’).”

Response:

Thank you very much for your careful review and thoughtful corrections regarding these details. We have made the following revisions based on your suggestions:

1) Correction of Spelling and Formatting

In the “Data Collection and Variable Definitions” section, the phrase “based on the clinical guidelines” has been revised to “Based on the clinical guidelines,” with the initial letter capitalised in accordance with English writing conventions. We have also conducted a thorough review of the entire manuscript to ensure that similar spelling and capitalization issues have been uniformly corrected to maintain consistency in formatting.

2) Addition of Reference for the Definition of Hyperuricemia

In the “Hyperuricemia Identification” subsection, we have supplemented the definition of hyperuricemia with an authoritative reference to allow readers to access the original source. The revised manuscript is as followed:

“Hyperuricemia was defined as a serum uric acid (SUA) concentration >7.0 mg/dL (>416 μmol/L) in males and >6.0 mg/dL (>357 μmol/L) in females. These thresholds were based on the 2018 European Alliance of Associations for Rheumatology (EULAR) evidence-based recommendations for the diagnosis of gout7 and the 2020 American College of Rheumatology (ACR) guideline for the management of gout9”

Comment 6 (Editor): “I highly recommend using English editing by native.”

Response:

We have thoroughly revised the manuscript for English language and style. A native English-speaking colleague (and professional editor) assisted in copyediting the text. We corrected grammatical errors, improved sentence clarity, and eliminated awkward phrasing. The overall readability of the manuscript has been significantly improved. Additionally, we took this opportunity to ensure that the formatting and style conform to PLOS ONE guidelines. For example, we adjusted the manuscript to meet the journal’s formatting requirements for headings, figures, and references. We trust that the language and presentation of the paper now meet the high standards expected by the journal.

Reviewer #1’s Comments

Comment (Reviewer 1): “Hello. This is a good article, and it explores an important topic. It seems that more studies are needed in this field in the future to be able to state with certainty the impact of exposure to such toxic substances. Good luck.”

Response:

We thank Reviewer #1 for the positive feedback and encouragement. We are pleased that the importance of the topic was acknowledged. We agree that further studies (especially longitudinal and interventional research) are needed to conclusively determine the causal impacts of chronic hyperuricemia on health outcomes. In the revised Discussion, we have noted that our cross-sectional findings highlight associations and disparities that warrant prospective investigation. We also emphasize in the Conclusion that continued research will be important to confirm and extend our findings. We appreciate the reviewer’s insight and have kept this perspective in mind, indicating that while our study adds to the knowledge in this field, establishing causal effects will require future investigations.

Reviewer #2’s Comments

Comment 1 (Reviewer 2): “Consider the issue of reverse causality: Hyperuricemia is a precursor condition to gout, as the authors also acknowledge in the introduction. Including ‘history of gout’ as a covariate in the multivariable regression model may introduce reverse causality bias. I recommend conducting a sensitivity analysis excluding individuals with a history of gout to better assess the risk factors for hyperuricemia.”

Response:

We sincerely appreciate this valuable suggestion regarding potential reverse causality. We have carefully addressed this concern through a sensitivity analysis, and the key revisions are as follows:

1) Sensitivity analysis:

As recommended, we conducted a sensitivity analysis by excluding all participants with a self-reported history of gout and re-ran the multivariable logistic regression models for hyperuricemia.

2) Summary of Findings:

The results from this analysis remained highly consistent with those from the primary model. Specifically:

o No material changes were observed in the significance or direction of any key risk factors.

o For instance, in females, the adjusted odds ratio for obesity changed only marginally from 4.29 (full sample) to 4.44 (gout-excluded sample), with fully overlapping 95% confidence intervals.

o Similar stability was noted across all sex and racial/ethnic subgroups.

o 3) Manuscript Additions:

o A statement has been added to the Results section highlighting the consistency of the sensitivity analysis results.

o Two new supplementary tables (Table S1 for males and Table S2 for females) have been included, presenting adjusted prevalence and odds ratios after excluding participants with gout.

o 4) Interpretation:

The minimal effect of excluding gout patients suggests that reverse causality did not substantially bias our original estimates. This reinforces the robustness of the identified risk factors for hyperuricemia, even after accounting for potential behavioral or pharmacological modifications among gout patients.

5) Discussion Update:

We have added a comment in the Discussion noting that the associations reported are robust to the exclusion of individuals with a history of gout.

Table S1. Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Male Hyperuricemia Prevalence excluding gout participants (NHANES 2007–2018)

Table S2. Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Female Hyperuricemia Prevalence excluding gout participants (NHANES 2007–2018)

Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status.

Comment 2 (Reviewer 2): “Clarify laboratory methods: Please provide a detailed description of how serum uric acid was measured in NHANES, including the assay method, equipment, and quality control procedures if available. This will improve reproducibility and methodological transparency.”

Response: Thank you for your suggestions. We have expanded the Methods section to include a more detailed description of the laboratory measurement of serum uric acid in NHANES.

In the revised “Laboratory Measurements” subsection(L174-L181), we now state that serum uric acid was measured enzymatically using a uricase-based method. We specify the instruments used by NHANES: in the 2007–2008 cycle, uric acid was measured with a Beckman Synchron LX20 auto-analyzer, and from 2008 onward NHANES utilized the Beckman Coulter UniCel DxC800 analyzer. The assay involved a timed endpoint method in which uricase catalyzes the oxidation of uric acid to allantoin and hydrogen peroxide; the reaction is coupled to a colorimetric change that is measured photometrically, and the change in absorbance is directly proportional to the uric acid concentration in the sample.

We also note that NHANES employed rigorous quality control procedures: all laboratory testing followed standardized protocols with regular calibration, and quality control samples were analyzed to ensure accuracy and consistency. NHANES laboratory data are subject to external quality assurance through the Centers for Disease Control and Prevention’s protocols. These details have been added to the Methods for transparency. We believe that providing this information will help with reproducibility and give readers confidence in the reliability of the laboratory data.

Comment 3 (Reviewer 2): “Definition of hyperuricemia: Specify clearly which guideline was used to define hyperuricemia (e.g., ACR, EULAR, or another standard). Please cite the source and briefly discuss the clinical rationale for the chosen cut-offs.”

Responses:

Thank you very much for the constructive feedback. In the revised manuscript, we have provid

Attachment

Submitted filename: Respones.to.Reviewers.5.docx

pone.0337714.s004.docx (2MB, docx)

Decision Letter 1

Toshiki Maeda

13 Nov 2025

Sex, Age, and Racial/Ethnic Disparities in Hyperuricemia Prevalence and Risk Factors Among U.S. Adults: An Analysis of NHANES 2007–2018 Data

PONE-D-25-05451R1

Dear Dr. Li,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager®  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Toshiki Maeda

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The concerns are addressed satisfactory.

Reviewers' comments:

Acceptance letter

Toshiki Maeda

PONE-D-25-05451R1

PLOS One

Dear Dr. Li,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Toshiki Maeda

Academic Editor

PLOS One

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Univariate and multivariate logistic regression analysis of risk factors for male hyperuricemia prevalence excluding gout participants (NHANES 2007–2018).

    Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status.

    (DOCX)

    pone.0337714.s001.docx (24.7KB, docx)
    S2 Table. Univariate and multivariate logistic regression analysis of risk factors for female hyperuricemia prevalence excluding gout participants (NHANES 2007–2018).

    Adjusted for age, race/ethnicity, education level, marriage, income, body mass index, hypertension, diabetes, renal function (eGFR), alcohol consumption, smoking status.

    (DOCX)

    pone.0337714.s002.docx (21.8KB, docx)
    Attachment

    Submitted filename: Respones.to.Reviewers.5.docx

    pone.0337714.s004.docx (2MB, docx)

    Data Availability Statement

    This study is based on publicly available data from the U.S. National Health and Nutrition Examination Survey (NHANES). The dataset is maintained by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC). The data can be freely accessed at the NHANES website (https://www.cdc.gov/nchs/nhanes/). No special access privileges were required to obtain these data; other researchers can access the datasets in the same manner as the authors by selecting the relevant survey cycles (2007–2018) and downloading the data files provided.

    Articles from PLOS One are provided here courtesy of PLOS

    RESOURCES