Investigating the prevalence of hyperuricemia and its association with metabolic syndrome (MetS) and cardiometabolic risk factors (CMRFs) in Korean children and adolescents.
This cross-sectional survey used data from the 7th Korea National Health and Nutrition Examination Survey (2016–2017); 1,256 males and females aged 10–18 years were included. Hyperuricemia was defined as serum uric acid levels were >6.6 mg/dL at 10–11 years of age (both sexes), >7.7 mg/dL for males at 12–18 years of age and >5.7 mg/dL for females at 12–18 years of age. MetS was defined by the International Diabetes Federation criteria. Logistic regression analysis was used to analyze hyperuricemia-associated risk factors.
The prevalence of hyperuricemia was 9.4% (male, 8.4%; female, 10.5%) (
MetS, abdominal obesity, and BMI z scores were CMRFs significantly associated with hyperuricemia in Korean children and adolescents. Therefore, attention should be paid to hyperuricemia in patients with obesity or MetS.
Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors (CMFRs) of abdominal obesity, hypertension, dyslipidemia, and impaired fasting glucose [
The prevalence of hyperuricemia is increasing worldwide [
Korea National Health and Nutrition Examination Survey (KNHANES) is a nationwide survey conducted by the Korea Centers for Disease Control and Prevention (KCDC) to provide national statistics on national health and nutritional status. KCDC provides the basis for policy formulation and evaluation. A serum uric acid (SUA) test was added to participants aged 10 years or older in the 7th Survey conducted since 2016. The purpose of the present study was to investigate the prevalence of hyperuricemia and its association with MetS and CMRFs in Korean children and adolescents based on the data of KNHANES.
This study was conducted using data from 2016 and 2017 of the 7th KNHANES. A total of 1,522 children aged 10–18 years were in the 7th KNHANES. Among them, 97 children for whom there was not enough survey data, 144 who did not undergo uric acid test, and 25 whose MetS could not be evaluated were excluded. Finally, a total of 1,256 children were included in the study (
The participants’ sociodemographic factors (age, sex, residential regions, and household income) and health behaviors (smoking status, drinking status, and physical activity [sitting time] during the day) were checked through questionnaires. The areas of the participants’ residence were categories into 2 groups (rural or urban). Household income level was classified as low, middle-low, middle-high, and high with reference to the average monthly household equivalent income by the square root of the number of household members. Smoking status was categorized into 3 groups, according to their smoking habits or exposure to cigarette smoke in the past 30 days: nonsmokers (those who had never smoked), passive smokers (indoor exposure to smoking at home or public institutions), and current smokers (those who smoked at least 1 cigarette a day). Drinking status was divided into 3 groups, based on the frequency of alcohol intake in the past year: nondrinkers (<1 time per month), light drinkers (2–4 times per month), and heavy drinkers (≥2 times per week). The level of physical activity was self-reported, using the Global Physical Activity Questionnaire, in the KNHANES. Sitting time was defined as time (hr) of sitting for 1 day.
Anthropometric measurements were made by trained test takers in light clothing and naked shoes. Height was measured to the first decimal place (0.1 cm) using a stadiometer (Seca 225, Seca, Hamburg, Germany). Weight was measured to one decimal place (0.1 kg) using an electronic balance (GL600020, Gtech, Seoul, Korea). Body mass index (BMI) (kg/m2) was calculated by dividing body weight by the square of the body height. BMI status was defined by sex- and age-specific percentiles: normal (BMI<85th percentile), overweight (85th percentile≤BMI<95th percentile), and obesity (BMI≥95th percentile). Height, weight, and BMI were converted to z scores by using the 2017 Korean National Growth Charts [
MetS was confirmed via the International Diabetes Federation criteria [
This data was analyzed by applying weighted, stratified, and clustered variables to the data extracted from the complex stratified sample. Weights were calculated using the integrated weights in 2016 and 2017 using ‘itvex,’ which is the weight at the health interview and health examination. Statistical methods for comparing the general features of hyperuricemia and non-hyperuricemia were categorical variables using the complex sample crosstabs method and continuous variables using the complex sample generalized linear model. Values were presented as weighted mean±standard error or number of cases (weighted percent). Complex sample logistic regression analysis was used to analyze the risk factors associated with hyperuricemia in previous studies [
The mean SUA levels was 5.3±0.1 mg/dL, and the mean SUA levels in males and females were 5.9±0.1 mg/dL and 4.6±0.1 mg/dL, respectively (
Of the total 1,256 patients, 115 had hyperuricemia and its prevalence was 9.4%. The prevalence of males and females were 8.4% (55 of 655) and 10.5% (60 of 601), respectively (
The mean age was 14.8±0.1 years (15.2±0.2 years in hyperuricemic and 14.4±0.1 years in nonhyperuricemic participants) (
In hyperuricemic groups, age, weight, weight z scores, height, WC, BMI, BMI z scores, SBP, and DBP were higher than those of nonhyperuricemic group, and HDL-C was lower than those of nonhyperuricemic group (
The 2 groups had significant differences in BMI status (
Univariate and multivariate logistic regression analysis was performed to identify CMRFs associated with hyperuricemia (
In this study, the mean SUA levels of Korean children and adolescents differed by 5.9±0.1 mg/dL and 4.6±0.1 mg/dL for males and females, respectively. These differences were also reported in prior studies in other countries, and studies in Brazil and Japan for children and adolescents also found that males have higher SUA levels than females [
The prevalence of hyperuricemia is known to be higher in men than in women, and a study in Korean adults have also shown more prevalence in men than in women [
The prevalence of obesity and MetS was higher in hyperuricemic participants. Moreover, the prevalence of hyperuricemia in obese participants was higher than in nonobese participants. BMI z scores were a significant associated with hyperuricemia in multivariate analysis. These results are consistent with reports of a significant association between obesity and hyperuricemia in a study of overweight or obese children and adolescents [
The most powerful CMRF associated with hyperuricemia among the components of the MetS used in this study was abdominal obesity assessed by WC measurements. Also, the prevalence of abdominal obesity was higher in children with hyperuricemia than in those without hyperuricemia, consistent with the results of children and adolescent studies in the United States, Italy, and Taiwan [
The prevalence of hypertriglyceridemia and low HDL-C were high in children and adolescents with hyperuricemia. These results are consistent with previous reports. The results of studies on children aged 3–6 years in China also show a high prevalence of hypertriglyceridemia when hyperuricemia was present [
In children and adolescents with hyperuricemia, the proportion of high SBP, which is a component of MetS and a CMRF, was higher. Hyperuricemia is also associated with hypertension in studies on children and adolescents in Brazil and the United States [
There are several limitations to this study. First, as it is a cross-sectional study, the causal relationship of factors related to hyperuricemia could not be clarified. Second, there was no data on sex hormone levels and sexual maturity; therefore, the exact trend of changes in SUA levels and incidence of hyperuricemia with changes in pubertal stages was not revealed. Third, no blood insulin test was performed, and the correlation between hyperuricemia and insulin resistance could not be quantified accurately. However, this study is relevant as it is the first report, to the best of my knowledge, on the prevalence of hyperuricemia in Korean children and adolescents based on the data of the KNHANES. Second, this study confirmed that hyperuricemia is associated with MetS and CMRFs.
In conclusion, MetS, abdominal obesity, and BMI z scores were CMRFs significantly associated with hyperuricemia in Korean children and adolescents. Therefore, attention should be paid to hyperuricemia in patients with obesity or MetS.
No potential conflict of interest relevant to this article was reported.
I thank the Korea Centers for Disease Control and Prevention for providing the data.
Flow chart showing study population. KNHANES, Korea National Health and Nutrition Examination Survey.
Serum uric acid levels in Korean children and adolescents by sex and age.
Serum uric acid levels and prevalence of hyperuricemia by sex
Variable | Total (n=1,256) | Hyperuricemia (n=115) | No hyperuricemia (n=1,141) | |
---|---|---|---|---|
Serum uric acid (mg/dL) | ||||
Male | 5.9±0.1 | 8.3±0.1 | 5.7±0.1 | |
Female | 4.6±0.1 | 6.3±0.1 | 4.4±0.0 | |
Overall | 5.3±0.1 | 7.3±0.1 | 5.1±0.1 | |
Prevalence | 0.281 |
|||
Male | 655 | 55 (8.4) | 600 | |
Female | 601 | 60 (10.5) | 541 | |
Overall | 1,256 | 115 (9.4) | 1,141 |
Values are presented as weighted mean±standard error or number of cases (weighted percent).
Boldface indicates a statistically significant difference with
Comparison of prevalence of male and female children in the hyperuricemia group.
General characteristics of participants with versus without hyperuricemia
Characteristic | Total (n=1,256) | Hyperuricemia (n=115) | No hyperuricemia (n=1,141) | |
---|---|---|---|---|
Age (yr) | 14.8±0.1 | 15.2±0.2 | 14.4±0.1 | |
Male sex | 655 (53.2) | 55 (47.5) | 600 (53.8) | 0.281 |
Residential regions | 0.664 | |||
Urban | 1,096 (88.8) | 100 (87.4) | 996 (88.9) | |
Rural | 160 (11.2) | 15 (12.6) | 145 (11.1) | |
Household income | 0.914 | |||
Low | 130 (11.2) | 13 (13.0) | 117 (11.0) | |
Middle-low | 285 (23.8) | 24 (23.2) | 261 (23.8) | |
Middle-high | 398 (31.0) | 41 (31.2) | 357 (31.0) | |
High | 443 (34.1) | 37 (32.7) | 406 (34.2) | |
Smoking status | 0.910 | |||
None | 857 (66.1) | 76 (65.1) | 781 (66.2) | |
Passive | 348 (29.0) | 34 (30.6) | 314 (28.8) | |
Current | 51 (4.9) | 5 (4.3) | 46 (5.0) | |
Drinking status | 0.079 | |||
None | 1,161 (90.3) | 104 (86.4) | 1,057 (90.7) | |
Light drinker | 80 (8.1) | 11 (13.6) | 69 (7.5) | |
Heavy drinker | 15 (1.6) | 0 (0) | 15 (1.7) | |
Sitting time (hr/day) | 11.2±0.1 | 11.2±0.4 | 11.2±0.1 | 0.997 |
Weight (kg) | 55.9±0.5 | 64.8±1.6 | 54.9±0.5 | |
Weight |
0.23±0.04 | 1.01±0.16 | 0.15±0.04 | |
Height (cm) | 162.5±0.4 | 164.7±1.0 | 162.3±0.4 | |
Height |
0.27±0.04 | 0.27±0.13 | 0.26±0.04 | 0.968 |
WC (cm) | 70.6±0.4 | 77.7±1.3 | 69.8±.4 | |
BMI (kg/m2) | 20.9±0.1 | 23.8±0.5 | 20.6±0.1 | |
BMI |
0.08±0.05 | 1.01±0.17 | -0.02±0.04 | |
SBP (mmHg) | 108.7±0.4 | 111.9±1.0 | 108.3±0.4 | |
DBP (mmHg) | 66.8±0.3 | 68.9±0.7 | 66.6±0.3 | |
TC (mg/dL) | 164.8±0.9 | 167.9±3.0 | 164.5±1.0 | 0.275 |
TG (mg/dL) | 84.9±1.7 | 95.9±6.1 | 83.7±1.6 | 0.050 |
HDL-C (mg/dL) | 51.9±0.3 | 49.2±1.0 | 52.2±0.3 | |
LDL-C (mg/dL) | 95.9±0.8 | 99.5±2.5 | 95.5±0.8 | 0.138 |
FPG (mg/dL) | 91.5±0.3 | 90.0±0.8 | 91.7±0.3 |
Values are presented as weighted mean±standard error or number of cases (weighted percent).
WC, waist circumference; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FPG, fasting plasma glucose.
Boldface indicates a statistically significant difference with
Prevalence by body mass index status, metabolic syndrome, and metabolic components in participants with versus without hyperuricemia
Variable |
Total (n=1,256) | Hyperuricemia (n=115) | No hyperuricemia (n=1,141) | |
---|---|---|---|---|
BMI status | ||||
Normal | 988 (78.9) | 59 (55.0) | 929 (81.3) | |
Overweight | 128 (9.5) | 20 (17.3) | 108 (8.7) | |
Obesity | 140 (11.6) | 36 (27.7) | 104 (10.0) | |
Metabolic syndrome | 30 (2.5) | 7 (6.3) | 23 (2.1) | |
Metabolic components | ||||
Abdominal obesity | 143 (11.5) | 38 (30.4) | 105 (9.5) | |
High TG | 106 (8.1) | 16 (14.4) | 90 (7.4) | |
Low HDL-C | 190 (15.5) | 31 (28.5) | 159 (14.2) | |
High BP | 46 (3.8) | 9 (6.1) | 37 (3.5) | 0.178 |
High SBP | 34 (2.5) | 9 (6.1) | 25 (2.2) | |
High DBP | 16 (1.6) | 1 (0.7) | 15 (1.6) | 0.358 |
Impaired FPG | 154 (11.8) | 10 (9.7) | 144 (12.0) | 0.496 |
Values are presented as number of cases (weighted percent).
BMI, body mass index; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; WC, waist circumference.
Boldface indicates a statistically significant difference with
BMI status was defined by sex- and age-specific percentiles: normal (BMI< 85th percentile), overweight (85th percentile≤BMI<95th percentile), and obesity (BMI≥95th percentile). Abdominal obesity (WC≥90th percentile in those aged 10–15 years, ≥90 cm in males and ≥80 cm in females aged 16–18 years), high TG (≥150 mg/dL), low HDL-C (<40 mg/dL in males aged 10–18 years and females aged 10–15 years, and <50 mg/dL in females aged 16–18 years), high BP (SBP≥130 mmHg or DBP≥85 mmHg), and impaired FPG (≥100 mg/dL). WC≥90th percentile for age and sex was estimated using reference data from the 2007 Korean National Growth Charts.
Logistic regression analysis of risk factors associated with hyperuricemia
Variable |
Univariate |
Multivariate model 1 |
Multivariate model 3 |
||||||
---|---|---|---|---|---|---|---|---|---|
OR | 95% CI | OR | 95% CI | OR | 95% CI | ||||
Age | 1.13 | 1.05–1.23 | 0.002 | 0.95 | 0.82–1.09 | 0.448 | 0.95 | 0.81–1.10 | 0.478 |
Female sex | 1.29 | 0.81–2.04 | 0.281 | 1.31 | 0.83–2.08 | 0.244 | 1.31 | 0.82–2.11 | 0.261 |
Residential regions (rural) | 1.15 | 0.61–2.19 | 0.659 | 1.22 | 0.63–2.39 | 0.555 | 1.17 | 0.57–2.42 | 0.669 |
Household income | |||||||||
Low | ref | ref | ref | ||||||
Middle-low | 0.82 | 0.38–1.78 | 0.615 | 0.95 | 0.41–2.20 | 0.907 | 0.88 | 0.37–2.08 | 0.771 |
Middle-high | 0.85 | 0.42–1.75 | 0.663 | 0.88 | 0.40–1.94 | 0.741 | 0.95 | 0.41–2.19 | 0.902 |
High | 0.81 | 0.40–1.63 | 0.548 | 0.95 | 0.43–2.08 | 0.894 | 0.92 | 0.41–2.06 | 0.842 |
Smoking status | |||||||||
None | ref | ref | ref | ||||||
Passive | 1.08 | 0.67–1.75 | 0.747 | 0.84 | 0.48–1.44 | 0.516 | 0.83 | 0.47–1.46 | 0.520 |
Current | 0.88 | 0.32–2.48 | 0.814 | 0.49 | 0.15–1.64 | 0.247 | 0.53 | 0.16–1.82 | 0.315 |
Drinking status | |||||||||
None | ref | ref | ref | ||||||
Drinker | 1.55 | 0.74–3.24 | 0.247 | 1.55 | 0.67–3.61 | 0.308 | 1.66 | 0.68–4.06 | 0.266 |
Sitting time (hr/day) | 1.00 | 0.90–1.11 | 0.997 | 1.01 | 0.91–1.11 | 0.899 | 1.01 | 0.91–1.11 | 0.890 |
BMI |
1.65 | 1.42–1.90 | 1.59 | 1.34–1.89 | |||||
BMI status | |||||||||
Nonobesity | ref | ref | ref | ||||||
Obesity | 3.47 | 2.12–5.69 | 1.17 | 0.59–2.36 | 0.650 | ||||
Metabolic syndrome |
3.16 | 1.21–8.26 | 3.05 | 1.17–7.92 | |||||
Metabolic components | |||||||||
Abdominal obesity | 4.14 | 2.58–6.67 | 3.38 | 1.72–6.63 | |||||
High TG | 2.09 | 1.12–3.90 | 1.61 | 0.83–3.14 | 0.159 | 1.63 | 0.84–3.18 | 0.152 | |
Low HDL-C | 2.41 | 1.46–3.98 | 1.42 | 0.80–2.52 | 0.234 | 1.23 | 0.66–2.30 | 0.515 | |
High BP | 1.76 | 0.81–3.84 | 0.153 | 1.04 | 0.39–2.77 | 0.935 | 1.00 | 0.36–2.76 | 0.997 |
Impaired FPG | 0.78 | 0.38–1.61 | 0.507 | 0.61 | 0.25–1.51 | 0.287 | 0.60 | 0.25–1.44 | 0.249 |
OR, odds ratio; CI, confidence interval; BMI, body mass index; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; FPG, fasting plasma glucose; WC, waist circumference.
Boldface indicates a statistically significant difference with
Obesity (≥95th percentiles, sex- and age-specific), abdominal obesity (WC≥ 90th percentile in those aged 10–15 years, ≥90 cm in males and ≥80 cm in females aged 16–18 years), high TG (≥150 mg/dL), low HDL-C (<40 mg/dL in males aged 10–18 years and females aged 10–15 years, and <50 mg/dL in females aged 16–18 years), high BP (systolic BP≥130 mmHg; diastolic BP≥85 mmHg), and impaired FPG (≥100 mg/dL). WC≥90th percentile for age and sex was estimated using reference data from the 2007 Korean National Growth Charts.
Adjusted for age, sex, residential regions, household income, smoking status, drinking status, and sitting time. Only the results of metabolic syndrome are shown.
Adjusted for the same set of variables in model 1 plus obesity and all components of metabolic syndrome.
Adjusted for the same set of variables in model 1 plus BMI