ASSOCIATION BETWEEN SERUM URIC ACID LEVEL AND BODY MASS INDEX IN SEX- AND AGE-SPECIFIC GROUPS IN SOUTHWESTERN CHINA

Association of weight-adjusted waist index with hyperuricemia and gout among middle-aged and older adults in America: a cross-sectional analysis of NHANES 2007-2014

OBJECTIVES

The weight-adjusted waist circumference index (WWI), a novel obesity indicator, gives better accuracy in assessing both muscle and fat mass. Our goal was to evaluate the relationship between WWI and the occurrence of hyperuricemia/gout among middle-aged and older adults in America.

METHODS

We analyzed the National Health and Nutrition Examination Survey (NHANES) data from the 2007-2014 cycles. Logistic regression analyses, subgroup analyses, and restricted cubic splines (RCS) were performed to evaluate the association between WWI and hyperuricemia/gout prevalence.

RESULTS

A total of 5332 adults aged 50 years and above were included in this study. The prevalence of hyperuricemia and gout was 23.20% and 6.70% respectively. The fourth quartile of WWI was associated with a 56% higher risk for hyperuricemia, compared with the first quartile (OR = 1.56, 95% CI 1.07-2.27, P trend < 0.001). A similar association was found between continuous WWI increase and OR of hyperuricemia in the fully adjusted model (OR = 1.35, 95% CI = 1.13-1.61, P < 0.05). However, WWI was not significantly associated with the prevalence of gout. The RCS model suggested a significant linear relationship between WWI and the risk of hyperuricemia/gout. Stratification analysis showed that the positive associations of WWI with the risk of hyperuricemia were more pronounced in participants who were women, aged 50-59, smokers, no physical activity, non-diabetes, hypertension, and hyperlipidemia.

CONCLUSIONS

Our findings suggest a positive correlation between WWI and hyperuricemia among middle-aged and older adults in America. Employing WWI as a tool for hyperuricemia prevention may be meaningful. Key Points • Weight-adjusted waist circumference index is a new obesity evaluation index. • Weight-adjusted waist circumference index is associated with hyperuricemia not gout. • The association is more pronounced in participants who were women, aged 50-59, smokers, no physical activity, non-diabetes, hypertension, and hyperlipidemia.

Non-Interventional Weight Changes Are Associated with Alterations in Serum Uric Acid Levels

Background/Objectives: Serum uric acid is an established cardiovascular risk factor. Higher serum uric acid levels are associated with overweight and obesity. We assessed whether non-interventional weight changes affect serum uric acid levels. Methods: We performed a retrospective analysis of 19,193 participants referred to annual medical screening. Body mass index (BMI) and serum uric acid were measured annually. Subjects were divided into five groups according to changes in BMI between visits: large reduction (reduction of more than 5% in BMI), moderate reduction (reduction of more than 2.5% and 5% or less in BMI), unchanged (up to 2.5% change in BMI), moderate increase (increase of more than 2.5% and 5% or less in BMI), and large increase (increase of more than 5% in BMI). The primary outcome was serum uric acid level changes between visits. Results: A decrease in serum uric acid levels was evident as BMI decreased and an increase in serum uric acid levels was associated with an increase in BMI. The proportion of patients whose serum uric acid levels were increased by at least 10% between visits increased with the relative increase in BMI, while the proportion of patients whose serum uric acid levels were reduced by at least 10% decreased with the relative decrease in BMI. Conclusions: Non-interventional weight changes, even modest, are associated with significant alterations in serum uric acid levels. Our findings may aid in better risk stratification and the primary prevention of cardiovascular morbidity and mortality.

Relationship between serum uric acid in early pregnancy and gestational diabetes mellitus: a prospective cohort study

PURPOSE

The association between serum uric acid (UA) and gestational diabetes mellitus (GDM) was still unclear. Serum UA levels in pregnancy differed from that in non-pregnancy. This study aimed to investigate the changes of serum UA in early pregnancy, and to explore the association of serum UA with the risk of GDM.

METHODS

A prospective double-center study including 873 singleton pregnant women was conducted in Beijing, China since 2019 (clinical trial number: NCT03246295). Seventy-eight healthy non-pregnant women were selected to compare the changes of biomarkers in pregnancy. Spearman correlation and logistic regression analysis were performed to measure the relationship between serum UA in early pregnancy and GDM.

RESULTS

The incidence of GDM in our cohort was 20.27%(177/873). Compared with non-pregnant women, serum UA and creatinine decreased significantly during early pregnancy. Serum UA concentration in early pregnancy was significantly higher in GDM women than that in normal glucose tolerance (NGT) women [217.0(192.9, 272.0) μmol/l vs. 201.9(176.0, 232.0) μmol/l, p < 0.001]. After adjusted for confounding factors, elevated serum UA remained as an independent risk factor for GDM. The risk of GDM increased when serum UA was above 240 μmol/l (adjusted OR 1.964, 95% CI 1.296-2.977, p < 0.001), and stronger relationships between serum UA and GDM were observed in pregnant women aged over 35 years old and preBMI ≥ 24 kg/m2.

CONCLUSION

The normal range of serum UA and creatinine in pregnant women were lower than those in non-pregnant women. It is essential to monitor serum UA concentrations since early pregnancy to alert and prevent GDM, especially in older and heavier pregnant women.

CLINICAL TRIAL NUMBER

NCT03246295.

Uric acid mediated the relationship between obesity and hypertension in children and adolescents: A population‑based cohort study

BACKGROUND AND AIM

Obesity and hyperuricemia (HUA) often coexist and have been widely accepted as risk factors for hypertension, but the role of uric acid (UA) in the relationship between obesity and hypertension remains unknown in children and adolescents.

METHODS AND RESULTS

A total of 7525 subjects aged 6-16 years were from the School-based Cardiovascular and Bone Health Promotion Program (SCVBH) at baseline (2017) and followed up in 2019. Multivariable logistic regression with interaction terms, cross-lagged panel analysis, and causal mediation model were applied to delineate the joint impact of obesity and HUA on hypertension, including the interaction effect, the temporal association, and the mediating effect of UA in the relationship between obesity and hypertension. There were 10.8 % of the participants with normotension at baseline developed hypertension after two years of follow-up. Cross-lagged panel analysis showed that the two-time point association was significant only from baseline BMI to follow-up UA (β1 = 0.302, P < 0.001), but not from baseline UA to follow-up BMI (β2 = 0.002, P = 0.745). Multivariable logistic regression showed that both obesity and HUA increased the risk of hypertension, but no interaction effect between HUA and obesity. The causal mediation analysis found that UA partially mediated the association between BMI and SBP (mediate proportion: 20.3 %, 95 % CI: 17.4-22.9 %) or DBP (mediate proportion: 11.9 %, 95 % CI: 3.9-18.2 %). The results were consistent in the analysis of systolic hypertension rather than diastolic hypertension.

CONCLUSIONS

It is mediating effect that UA played in the progress from obesity to hypertension, particularly systolic hypertension in children and adolescents.

Incidence density of hyperuricemia and association between metabolism-related predisposing risk factors and serum urate in Chinese adults: a cohort study

Background

Evidence regarding the association between metabolism-related indicators and serum urate (SU) is limited. We aimed to obtain the incidence density of hyperuricemia and to explore the association between metabolism-related predisposing risk factors and SU.

Methods

A total of 48,979 Chinese adults from the Beijing Physical Examination Center were included in the study. The partial least squares path model was used to explore the relationship between SU and metabolism-related risk factors. The generalized additive model was used for smooth curve fitting, showing the sex-specific associations of SU at follow-up with baseline fasting blood glucose (FBG) concentrations and age.

Results

The incidence density of hyperuricemia was 78/1000 person-years. Baseline SU, age, sex, obesity, FBG, and lipid metabolism were significantly associated with SU at follow-up (all P values <0.05). Non-linear relationships were found between the baseline FBG concentrations and SU at follow-up, while U-shaped associations were observed between baseline age and SU at follow-up.

Conclusions

The SU concentration is associated with several metabolism-related risk factors such as obesity and FBG. Recognition of these associations will aid in a deeper understanding of the multifaceted nature of SU regulation.

Insulin use and gout risk among patients with type 2 diabetes mellitus: a real-world cohort study in Shanghai, China

OBJECTIVES

The effect of insulin use on gout risk remains unknown. This study aimed to investigate the association between insulin use and gout risk among patients with type 2 diabetes mellitus (T2DM).

METHODS

Based on the Shanghai Link Healthcare Database, patients with newly diagnosed T2DM, with or without insulin exposure, were identified from January 1, 2014 to December 31, 2020, and followed until December 31, 2021. Apart from the original cohort, we also established a 1:2 propensity score-matched cohort. A time-dependent Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for gout incidence associated with insulin exposure.

RESULTS

A total of 414,258 patients with T2DM, including 142,505 insulin users and 271,753 insulin non-users, were enrolled in this study. After a median follow-up of 4.08 years (interquartile range, 2.46-5.90 years), the incidence of gout was significantly higher in insulin users than in insulin non-users (319.35 versus 302.20 cases per 100,000 person-years; HR 1.09, 95% CI 1.03-1.16). The results were robust in propensity score-matched cohort, sensitivity analyses, and stratified analysis of aspirin. In other stratified analyses, the association between insulin use and increased gout risk was found only in patients who were female, or aged 40-69 years, or without hypertension, dyslipidemia, ischemic heart disease, chronic lung disease, kidney disease, or not using diuretic.

CONCLUSIONS

Insulin use is associated with a significantly increased risk of gout among patients with T2DM. Key Points • The first real-world study to investigate the effect of insulin use on gout risk. • Insulin use is associated with a significantly increased risk of gout among patients with type 2 diabetes mellitus.

The association between BMI and serum uric acid is partially mediated by gut microbiota

Obesity is a risk factor for the development of hyperuricemia, both of which were related to gut microbiota. However, whether alterations in the gut microbiota lie in the pathways mediating obesity's effects on hyperuricemia is less clear. Body mass index (BMI) and serum uric acid (SUA) were separately important indicators of obesity and hyperuricemia. Our study aims to investigate whether BMI-related gut microbiota characteristics would mediate the association between BMI and SUA levels. A total of 6,280 participants from Guangdong Gut Microbiome Project were included in this study. Stool samples were collected for 16S rRNA gene sequencing. The results revealed that BMI was significantly and positively associated with SUA. Meanwhile, BMI was significantly associated with the abundance of 102 gut microbial genera, 16 of which were also significantly associated with SUA. The mediation analysis revealed that the association between BMI and SUA was partially mediated by the abundance of Proteobacteria (proportion mediated: 0.94%, P < 0.05). At the genus level, 25 bacterial genera, including Ralstonia, Oscillospira, Faecalibacterium, etc., could also partially mediate the association of BMI with SUA (the highest proportion is mediated by Ralstonia, proportion mediated: 2.76%, P < 0.05). This study provided evidence for the associations among BMI, gut microbiota, and SUA, and the mediation analysis suggested that the association of BMI with SUA was partially mediated by the gut microbiota. IMPORTANCE Using 16S rRNA sequencing analysis, local interpretable machine learning technique analysis and mediation analysis were used to explore the association between BMI with SUA, and the mediating effects of gut microbial dysbiosis in the association were investigated.

Prevalence and associated factors of hyperuricemia among Chinese patients with diabetes: a cross-sectional study

Background

As a part of metabolic syndrome, hyperuricemia has a higher incidence in patients with diabetes than in the general population owing to various underlying factors.

Objectives

The objective of the present study was to investigate the prevalence of hyperuricemia among patients with diabetes and identify associated factors.

Design

A cross-sectional study.

Methods

Herein, we included patients with diabetes managed at nine healthcare centers in Chenghua District, Chengdu, from February 2021 to November 2021. Clinical data, lifestyle habits, and laboratory data were collected to determine the prevalence and factors associated with hyperuricemia.

Results

In total, we included 1577 patients with diabetes (males, 50.35%; females, 49.65%). The median serum uric acid level was 337.9 μmol/L, and the prevalence of hyperuricemia in patients with diabetes was 21.24%. The prevalence of hyperuricemia in male patients was significantly higher than in females (29.35% in males versus 13.03% in females, p < 0.001). Male patients with obesity (p = 0.006) or triglyceride (TG) ⩾ 1.7 mmol/L (p < 0.001) had a high risk of developing hyperuricemia, and hyperuricemia was negatively associated with estimated glomerular filtration rate (eGFR) ⩾ 60 mL/min/1.73 m2 (p < 0.001), glycosylated hemoglobin (HbA1c) ⩾ 7% (p < 0.001), fenofibrate (p = 0.010), and sodium-glucose cotransporter 2 (SGLT-2) inhibitors (p = 0.035). Considering females, overweight (p = 0.004), alanine transaminase (ALT) > 40 U/L (p < 0.001), and TG ⩾ 1.7 mmol/L (p = 0.015) showed a significant positive correlation with hyperuricemia, while eGFR ⩾ 60 mL/min/1.73 m2 (p < 0.001) was negatively associated with the risk of hyperuricemia.

Conclusion

Hyperuricemia is highly prevalent in patients with diabetes, especially in males. In addition to traditionally associated factors, fenofibrate and SGLT-2 inhibitors were also associated with the risk of hyperuricemia.

Registration

The study protocol was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn/), and the registration number was ChiCTR 2100042742.

Relationship between serum uric acid and estimated glomerular filtration rate in adolescents aged 12-19 years with different body mass indices: a cross-sectional study

Background

Globally, chronic kidney disease (CKD) is a growing public health concern. Serum uric acid (SUA) is an easily detectable and readily available biochemical indicator that has long been recognized as an independent risk factor for CKD. In addition, studies have indicated a potential relationship between SUA and body mass index (BMI). However, studies on the effect of SUA levels on the estimated glomerular filtration rate (eGFR) in adolescents with different BMIs are very rare.

Methods

Weighted multiple regression analysis was used to estimate the independent relationship between SUA and log-transformed eGFR. Additionally, we used a weighted generalized additive model and smooth curve fitting to describe the nonlinear relationships in the subgroup analysis.

Results

First, SUA was negatively associated with log-transformed eGFR even after adjusting for all covariates (β=-0.0177, 95% CI: -0.0203-0.0151, P<0.0001). Second, the results of the stratified analysis found that after adjusting for all covariates, the decrease in log-transformed eGFR due to changes in per SUA levels (Per 1, mg/dL increase) was elevated in female adolescents (β=-0.0177, 95% CI: -0.0216, -0.0138, P<0.0001), adolescents aged 12-15 years (β=-0.0163, 95% CI: -0.0200, -0.0125, P<0.0001) and black (β=-0.0199, 95% CI: -0.0251, -0.0148, P<0.0001) adolescents. Furthermore, we found that adolescents with a higher BMI had higher SUA levels, and the effect of SUA on eGFR was significantly higher in underweight adolescents (β=-0.0386, 95% CI: (-0.0550, -0.0223), P<0.0001).

Conclusion

SUA was negatively associated with the eGFR in adolescents aged 12-19 years. Furthermore, we found for the first time that SUA affects the eGFR differently in adolescents with different BMIs. This effect was particularly significant in underweight adolescents.

Serum Urate Levels and Ultrasound Characteristics of Carotid Atherosclerosis across Obesity Phenotypes

BACKGROUND

Existing evidence suggests a close link among high levels of serum urate (SU), obesity and carotid atherosclerosis. The aim of the present study was to evaluate the interrelations between SU levels and carotid atherosclerosis in subjects with different obesity phenotypes.

METHODS

In this study, a total of 2076 subjects (mean age 48.1 ± 13.1 years; 1307 women) were recruited: 59 with general obesity, 616 with central obesity, 715 with mixed (general-central) obesity and 686 non-obese. Anthropometric measurements, vascular risk factors, blood biochemistry analysis (including SU levels), and carotid ultrasound were performed. Ultrasound assessment included evaluation of intima-media thickness (IMT) and plaque characteristics, including number, total area and type (vulnerable vs. stable) of plaques.

RESULTS

After adjustment for potential confounders, the highest levels of SU were observed in subjects with mixed obesity, followed by subjects with central obesity, general obesity and the non-obese (309.4 ± 82.2 vs. 301.2 ± 73.1 vs. 272.9 ± 61.8 vs. 234.2 ± 59.8 μmol/L, respectively; F = 149.2, post hoc p < 0.001). Similarly, subjects with mixed and central obesity presented higher values of IMT compared to subjects with general obesity and the non-obese (0.68 ± 0.16 vs. 0.67 ± 0.16 vs. 0.62 ± 0.14 vs. 0.57 ± 0.13 mm, respectively; F = 54.2, post hoc p < 0.001). No difference in number, total area and type of plaques among obesity groups were attested (all p > 0.05). Significantly higher IMT values were observed in subjects with increased SU levels compared to subjects with normal SU levels (0.70 ± 0.10 vs. 0.62 ± 0.14 mm, p = 0.02) only within the central obesity group. Increasing levels of SU were associated with a higher frequency of increased IMT only in subjects with central obesity (OR 1.033, 95% CI 1.025-1.041). Similarly, SU levels yielded a satisfactory performance in detecting subjects with increased IMT (AUC 0.65, 95% CI 0.50-0.73, subjects with carotid plaques (0.62, 95% CI 0.55-0.68) and subjects with vulnerable plaque types (0.68, 0.59-0.76) only within the central obesity group.

CONCLUSIONS

Among the studied obesity types, the association between SU levels and markers of carotid atherosclerosis was of particular significance in subjects with central obesity.

Application of the Hoffmann, Bhattacharya, nonparametric test, and Q-Q plot methods for establishing reference intervals from laboratory databases

BACKGROUND

Reference intervals (RIs) are vital for interpreting laboratory biomarkers and enabling clinical decision-making. Among various RI-estimation methods, we explored the application value of Hoffmann, Bhattacharya, nonparametric test, and Q-Q plot methods for estimating the RI of urea, creatinine, and uric acid (UA).

METHOD

This cross-sectional study collected patient data recorded between January 2020 and April 2022 at the Chongqing University Central Hospital Laboratory Information System. The RIs of urea, creatinine, and UA levels were established using the Hoffmann, Bhattacharya, nonparametric, and Q-Q plot methods, and RI differences with different computational methods were verified using the reference change value (RCV%) of biological variability.

RESULTS

We included 16,474 and 123,570 patients in the physical examination and clinical groups, respectively. In the clinical group, differences in the RI upper limit of analytes with the four methods (excluding the Q-Q plot method) were within the permissible RCV% range; only the nonparametric test produced an RI of urea with the lower limit within the permissible RCV% range. In the physical examination group, the relative RI differences among the four methods (excluding the lower limit of RI obtained using the Q-Q plot) were all within the acceptable RCV% range; the relative deviation of the RI of UA with the four methods was within the acceptable RCV% range (excluding the lower RI limit obtained using the Q-Q plot and nonparametric test).

CONCLUSION

The Hoffmann and Bhattacharya methods may provide reliable RIs for indirect estimations of urea, creatinine, and UA based on laboratory datasets.

Hyperuricemia Associated with Low Skeletal Muscle in the Middle-Aged and Elderly Population in China

BACKGROUND

Previous studies have presented inconsistent results on the relationship between serum uric acid and skeletal muscle mass (SMM). We aimed to explore whether a higher serum uric acid level was associated with low SMM in the Chinese population.

METHODS

We performed a cross-sectional analysis of 6595 subjects aged 45 years or older. They were tested for fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, uric acid, blood urea nitrogen, creatinine, and estimated glomerular filtration rate. SMM was accessed by dual-energy x-ray absorptiometry using two approaches: weight-adjusted appendicular skeletal muscle mass (ASM)% and ASM/BMI (body mass index (kg/m²)). Low SMM was defined as a cut-off point of ASM/BMI<0.789 for men and<0.512 for women.

RESULTS

Compared with their normal group, patients with hyperuricemia had lower ASM% (29.33±2.33 vs 30.03±2.34 for males and 24.71±1.99 vs 25.19±2.07 for females, P<0.01) and ASM/BMI (0.83±0.10 vs 0.85±0.10 for male and 0.60±0.07 vs 0.62±0.07 for female), with a higher prevalence of the associated low SMM in both sexes (35.2 vs 26.5% for male and 10.5 vs 5.9% for female, P<0.01). Pearson analysis showed that ASM% and ASM/BMI were negatively correlated with SUA (male: ASM/BMI, r=-0.097, ASM%, r=-0.146; female: ASM/BMI, r=-0.151, ASM%, r=-0.157; all P<0.001). Logistic regression analysis showed a positive association of hyperuricemia with adjusted risk of low SMM association.

CONCLUSIONS

In a middle-aged and elderly Chinese population, hyperuricemia is independently and positively associated with low SMM and can vary by age and gender.

The correlation of uric acid levels with biochemical parameters and dietary factors in individuals with asymptomatic hyperuricemia and gouty arthritis

Diet may play an important role in the development of asymptomatic hyperuricemia (ASH) and gouty arthritis (GOUT). However, the association between dietary factors and hyperuricemia remains unclear. Serum uric acid levels are affected by dietary factors. This study aimed to evaluate the correlation of uric acid levels with biochemical parameters and dietary factors in individuals with ASH and GOUT. This study was conducted in 145 individuals with ASH and GOUT. General characteristics of individuals were collected via face-to-face interviews. Food frequency questionnaire was used to obtain energy, macro- and micronutrients intakes. Biochemical parameters were obtained from patient files. The incidence of gout was higher in men comparing to women. Individuals in the GOUT group consumed more alcohol and higher serum levels of vitamin B₁₂, C-reactive protein (CRP), triglyceride, and uric acid. Individuals in the GOUT group had higher intakes of energy, protein, carbohydrate, fat, fructose, vitamin C, and vitamin B₁₂. Triglyceride, uric acid, CRP, vitamin B₁₂, and homeostatic model assessment of insulin resistance were found to be affected by high uric acid levels. Dietary factors can pose a risk for health problems in addition to GOUT and ASH, such as cardiovascular disease, diabetes, and obesity.

Positive association between body fat percentage and hyperuricemia in patients with hypertension: The China H-type hypertension registry study

BACKGROUND AND AIMS

The relationship between the body fat percentage (BFP) and hyperuricemia is still unknown in different gender subjects. The purpose of this study was to determine the magnitude of the association between the BFP and the presence of hyperuricemia in the sex-specific group among hypertensive patients.

METHODS AND RESULTS

We conducted a cross-sectional study enrolling 14,234 hypertensive participates from the Chinese Hypertension Registry Study. Body fat percentage (BFP) was calculated by simple anthropometric parameters. Hyperuricemia was defined as serum uric acid (SUA) level 420 umol/L in men and 360 umol/L in women. The mean BFP was 24.5% in men and 37.1% in women. Multiple logistic analyses showed that the relationship between BFP with the risk of hyperuricemia in a dose-dependent manner among both men (odds ratio [OR] 1.07, 95% CI 1.06, 1.09) and women (OR 1.08, 95% CI 1.06, 1.09) in the fully adjusted model. Subgroup analyses showed the positive association between BFP and the risk of hyperuricemia was consistent in all stratification subgroups (all P for interaction >0.05).

CONCLUSION

For patients with hypertension, BFP was positively associated with an increased risk of hyperuricemia among both men and women.

Relationship between uric acid and lipid accumulation product index by gender in Korean adults: The 2016 Korean National Health and Nutrition Examination Survey

AIMS

The present study assesses the relationship between uric acid (UA) and lipid accumulation product index (LAP) by gender among Korean adults.

METHODS

Data from 5670 subjects (2463 men and 3207 women) in the Korean National Health and Nutrition Examination Survey (KNHANES VII-1) 2016 were analyzed. LAP was divided into four quartiles (Q1-Q4).

RESULTS

There were several key findings. Compared with Q1, in the overall population, the odds ratios (ORs) of hyperuricemia (UA ≥ 7.0 mg/dL in men or UA ≥ 6.0 mg/dL in women) were significantly higher in Q2 [1.847 (95% confidence interval [CI], 1.325-2.575)], Q3 [3.050 (95% CI, 2.216-4.198)], and Q4 of LAP [6.367 (95% CI, 4.658-8.704)]. In men, the ORs of hyperuricemia were significantly higher in Q2 [1.658 (95% CI, 1.162-2.367)], Q3 [2.341 (95% CI, 1.656-3.308)], and Q4 [4.633 (95% CI, 3.290-6.525)] than Q1. In women, the ORs of hyperuricemia were significantly higher in Q2 [2.254 (95% CI, 1.085-4.680)], Q3 [5.402 (95% CI, 2.735-10.668)], and Q4 [11.025 (95% CI, 5.620-21.628)] than Q1. In addition, UA levels were positively associated with LAP level in men (r = 0.218; p < 0.001), women (r = 0.261; p < 0.001), and the overall population (r = 0.260; p < 0.001).

CONCLUSIONS

Hyperuricemia was positively associated with LAP in Korean men and women.

Prevalence and Risk Factors Associated with Hyperuricemia in the Pearl River Delta, Guangdong Province, China

Background

In China, the prevalence of HUA in the Pearl River Delta (PRD) region of Guangdong Province has not been extensively investigated. Therefore, this study investigated the prevalence of HUA and its related factors among people aged 20–99 years in nine cities in the PRD.

Materials and Methods

We selected 6491 health check participants from 9 cities in the PRD and collected participants’ anthropometric and biochemical test results for a cross-sectional study. We included 6491 participants and assessed their blood pressure (BP), body mass index (BMI), total cholesterol (TC), triglycerides (TG), glucose (Glu) and serum uric acid (UA) to analyze the regional prevalence of HUA and its related factors. HUA was indicated when fasting serum UA level was >420 μmol/L in men and >360 μmol/L in women.

Results

Overall prevalence of HUA in our cohort was 34.05%; prevalence was higher in men than in women (41.53% vs 26.14%, P < 0.001). Characteristics associated with HUA were hypertension (odds ratio (OR), 5.506; 95% confidence interval (CI), 4.402–6.889), higher body mass index (BMI; OR: 1.746; 95% CI: 1.560–1.954), age 31–40 years (OR: 0.829; 95% CI: 0.706–0.973), age 61–70 years (OR: 1.434; 95% CI: 1.194–1.722) and age ≥71 years (OR: 1.742; 95% CI: 1.397–2.173). In all subjects, serum UA was positively correlated with Glu, TG and TC. After we adjusted for age, BMI and BP, multivariate logistic regression analysis showed that HUA risk factors were high TC (OR: 1.770; 95% CI: 1.459–2.147) and TG (OR: 1.961; 95% CI: 1.632–2.357) in men; and high Glu (OR: 1.508; 95% CI: 1.084–2.099), TC (OR: 1.341; 95% CI: 1.084–1.660) and TG (OR: 1.680; 95% CI: 1.290–2.187) in women.

Conclusion

The prevalence of HUA was relatively high in the PRD of Guangdong Province. Relevant governmental bodies should focus on early diagnosis, early treatment and early intervention.

Reduced renal function may explain the higher prevalence of hyperuricemia in older people

This study aimed to investigate the contribution of renal dysfunction to enhanced hyperuricemia prevalence in older people. A cohort of 13,288 Chinese people aged between 40 and 95 years were recruited from January to May 2019. Serum uric acid concentration and estimated glomerular filtration rate [eGFR] were measured. The associations between age or eGFR and serum uric acid or hyperuricemia were analyzed using linear or binary logistic regression adjusting for risk factors. Uric acid concentration and prevalence of hyperuricemia were greater in older participants. Adjustment for reduced renal function (eGFR < 60 mL/min/1.73 m²) eliminated the associations between older age and higher uric acid concentration and between older age and higher prevalence of hyperuricemia diagnosis, whereas adjustment for other risk factors did not change those associations. Lower eGFR was associated with higher uric acid concentration both before (β = - 0.296, P < 0.001) and after adjustment for age (β = - 0.313, P < 0.001). Reduced renal function was associated with hyperuricemia diagnosis both before (odds ratio, OR, 3.64; 95% CI 3.10-4.28; P < 0.001) and after adjustment for age (adjusted OR, 3.82; 95% CI 3.22-4.54; P < 0.001). Mean serum uric acid and prevalence of hyperuricemia were higher in people with eGFR < 60 mL/min/1.73 m² than those with eGFR ≥ 60 mL/min/1.73 m². The prevalence of reduced renal function increased with older age (P < 0.001). This study suggests that reduced renal function can explain the increased uric acid levels and hyperuricemia diagnoses in older people.

Urine Uric Acid Excretion Levels are Positively Associated with Obesity and Abdominal Obesity in Type 2 Diabetes Patients without Chronic Kidney Disease

PURPOSE

We aimed to investigate whether urine uric acid excretion (UUAE) levels are associated with obesity and abdominal obesity in patients with type 2 diabetes (T2D).

METHODS

There were 2785 type 2 diabetic patients in this cross-sectional study. Obesity was defined as BMI ≥ 25 kg/m², and abdominal obesity was defined as waist circumference (WC) ≥90 cm for men and WC ≥ 80 cm for women based on World Health Organization (WHO) recommendations for Asians. Chronic kidney disease (CKD) was defined as the estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m² and/or urinary albumin excretion (UAE) ≥300 mg/24h. 24-h UUAE was determined enzymatically using a single 24-hour urine collection. All the subjects were stratified into quartiles based on UUAE levels. Both obesity and abdominal obesity were compared among the UUAE quartile groups, respectively. Furthermore, the associations of UUAE with obesity and abdominal obesity were analyzed in both CKD and non-CKD patients, respectively.

RESULTS

There was an obvious increased trend in both obesity prevalence (36.2%, 41.5%, 46.3%, and 63.4%, respectively, p < 0.001 for trend) and abdominal obesity prevalence (58.1%, 61.2%, 64.7%, and 75.8%, respectively, p < 0.001 for trend) in patients with T2D across the UUAE quartiles after controlling for age, sex and diabetes duration. Multiple logistic regression analyses revealed independent associations between UUAE quartiles and obesity (p < 0.001) and abdominal obesity (p < 0.001) in all patients. However, UUAE was significantly associated with obesity and abdominal obesity only in the T2D patients without CKD (p < 0.001 in model 1, model 2, model 3 and model 4, respectively).

CONCLUSION

Increased UUAE levels were significantly associated with the presence of obesity, especially abdominal obesity in T2D patients without CKD.

Temporal relationship between body mass index and uric acid and their joint impact on blood pressure in children and adults: the Bogalusa Heart Study

OBJECTIVE

This study aimed to examine the temporal relationship between body mass index (BMI) and uric acid (UA), and their joint effect on blood pressure (BP) in children and adults.

METHODS

The longitudinal cohorts for temporal relationship analyses consisted of 564 and 911 subjects examined twice 5-14 years apart from childhood to adulthood. The cross-sectional cohorts for mediation analyses consisted of 3102 children and 3402 nondiabetic adults. Cross-lagged panel analysis models were used to examine the temporal relationship between BMI and UA, and mediation analysis models the mediation effect of UA on the BMI-BP association.

RESULTS

After adjusting for age, race, sex and follow-up years in children, and additionally smoking and alcohol drinking in adults, the path coefficients (standardized regression coefficients) from baseline BMI to follow-up UA (0.145 in children and 0.068 in adults) were significant, but the path coefficients from baseline UA to follow-up BMI (0.011 in children and 0.016 in adults) were not. In mediation analyses, indirect effects through UA on the BMI-systolic BP association were estimated at 0.028 (mediation effect = 8.8%) in children and 0.033 (mediation effect = 13.5%) in adults (P < 0.001 for both). Direct effects of BMI on systolic BP (0.289 in children and 0.212 in adults) were significant. The mediation effect parameters did not differ significantly between Blacks and Whites.

CONCLUSIONS

Changes in BMI precede alterations in UA, and the BMI-BP association is in part mediated through BMI-related increase in UA both in children and in adults. These findings have implications for addressing mechanisms of obesity hypertension beginning in early life.

Uric Acid Mediated the Association Between BMI and Postmenopausal Breast Cancer Incidence: A Bidirectional Mendelian Randomization Analysis and Prospective Cohort Study

BACKGROUND

Observational epidemiological studies have reported the associations of high body mass index (BMI) with elevated serum uric acid (UA) level and increased risk of postmenopausal breast cancer. However, whether UA is causally induced by BMI and functioned in the BMI-breast cancer relationship remains unclear.

METHODS

To elucidate the causality direction between BMI and serum UA, the bidirectional Mendelian randomization (MR) analyses were performed by using summarized data from the largest Asian genome-wide association studies (GWAS) of BMI and UA carried out in over 150,000 Japanese populations. Then, a total of 19,518 postmenopausal women from the Dongfeng-Tongji (DFTJ) cohort (with a mean 8.2-year follow-up) were included and analyzed on the associations of BMI and serum UA with incidence risk of postmenopausal breast cancer by using multivariable Cox proportional hazard regression models. Mediation analysis was further conducted among DFTJ cohort to assess the intermediate role of serum UA in the BMI-breast cancer association.

RESULTS

In the bidirectional MR analyses, we observed that genetically determined BMI was causally associated with elevated serum UA [β(95% CI) = 0.225(0.111, 0.339), p < 0.001], but not vice versa. In the DFTJ cohort, each standard deviation (SD) increment in BMI (3.5 kg/m²) and UA (75.4 μmol/l) was associated with a separate 24% and 22% increased risk of postmenopausal breast cancer [HR(95% CI) = 1.24(1.07, 1.44) and 1.22(1.05, 1.42), respectively]. More importantly, serum UA could mediate 16.9% of the association between BMI and incident postmenopausal breast cancer.

CONCLUSIONS

The current findings revealed a causal effect of BMI on increasing serum UA and highlighted the mediating role of UA in the BMI-breast cancer relationship. Controlling the serum level of UA among overweight postmenopausal women may help to decrease their incident risk of breast cancer.

Uric Acid Is Elevated in Children With Obesity and Decreases After Weight Loss

Introduction: Childhood obesity is an increasing condition associated with continuous obesity into adulthood and development of comorbidities. Adult studies show an association between serum uric acid (SUA) levels and body mass index (BMI). The aim of this retro perspective exploratory study was to investigate SUA in obese children and adolescents and the effects of a subsequent weight reduction. Materials and Methods: One hundred and seventy-one children (age 4-18), with obesity (i.e. BMI-SDS of +2 or higher) were included in a multifactorial lifestyle intervention. The children participating were annually measured for anthropometrics, blood samples and DEXA-scans for up to 3 years. Eighty-nine children were included for follow-up analysis. Results: After a follow-up of 20.7 ± 9.4 months a reduction in BMI-SDS of -0.34 ± 0.53 (p < 0.01) was observed. SUA was found to be positively associated with changes in BMI-SDS. SUA levels decreased in the 65 children who lost weight during the trial, conversely, SUA increased in the 23 children who gained weight during the trial (p < 0.01 between groups). Conclusion: SUA was found to correlate with measures of obesity and for the first time, this intervention demonstrates a positive relationship between SUA and weight reduction in children with obesity.

Association between Lipid Profiles and Serum Urate: A Cross-Sectional Study in Southwestern China

OBJECTIVE

The relationship between lipid profiles and serum urate has not been fully investigated. This study aims to investigate the sex- and age-specific association between lipid profiles and serum urate.

METHODS

This was a cross-sectional study involving 122,351 participants aged 18-99 years from a check-up centre in Southwestern China. Generalized additive models and smooth curve fitting were conducted to explore the association between components of lipid profiles and serum urate. Furthermore, multivariate linear and logistic regression models were also performed.

RESULTS

In generalized additive models, the fitted smoothing curves showed that serum urate fluctuated in a small range with total cholesterol, LDL-C, or HDL-C raising. After adjusting for confounders, the differences in serum urate progressively increased with raising serum triglycerides quartiles. The likelihood (odds ratio, OR) for developing serum urate > 420 μmol/L significantly increased in the highest quartile of triglycerides than in the lowest quartile, in hypertriglyceridemia than in normal triglycerides, and with 1 mmol/L increment in triglycerides in all sex- and age-specific groups. Furthermore, the increased OR (95% confidence interval) was higher in females than in males compared with their respective controls.

CONCLUSIONS

Serum urate and the likelihood for developing serum urate >420 μmol/L increased with triglycerides raising. Females were in a higher likelihood for developing serum urate >420 μmol/L than males with raising triglycerides. With changes in total cholesterol, LDL-C, or HDL-C, serum urate fluctuated in a small range.

Food Consumption is Associated with Hyperuricemia in Boys

INTRODUCTION

Hyperuricemia is related to health issues among children and adolescents, once the uric acid concentration is associated with metabolic syndrome, hypertension, insulin resistance, obesity, and dyslipidemia. However, few studies are addressing uric acid levels and food uptake in this age group.

AIM

To verify the association between food consumption and uric acid in children and adolescents.

METHODS

This is a cross-sectional study developed with 2335 children and adolescents of both genders aged 6-17 years old. Blood collection was performed after 12 h of fasting. Uric acid values were classified according to tertiles, in which the highest tertile was considered as hyperuricemia. Food consumption was evaluated by weekly consumption frequency questionnaire. Pearson correlation and logistic binary regressions were used for statistical analysis. Models were adjusted for age, systolic blood pressure, body mass index (BMI), and skin color/ethnicity.

RESULTS

It was found an association between red meat consumption and hyperuricemia only in boys in the crude model (OR = 1.56; 95% CI 1.12; 2.18). Also, there was an association between pasta (OR = 1.52; 95% CI 1.11; 2.10) with hyperuricemia in boys, when adjusted age, systolic blood pressure, BMI, and skin color/ethnicity.

CONCLUSION

The knowledge of food patterns which are predisposing factors for the increase in serum uric acid levels is important for the implementation of strategies and public health policies for health promotion among children and adolescents.

Association of serum uric acid with visceral, subcutaneous and hepatic fat quantified by magnetic resonance imaging

Elevated serum uric acid (SUA) is associated with a variety of medical conditions, such as hypertension, diabetes and obesity. Analyses investigating uric acid and obesity were primarily conducted using anthropometric measures like BMI and waist circumference. However, different adipose tissue depots might be differentially affected in uric acid metabolism. We analyzed the relation of SUA with visceral, subcutaneous and hepatic fat as quantified by Magnetic Resonance Imaging in N = 371 individuals from a cross-sectional sample of a population-based cohort. Associations of SUA and fat depots were calculated by regressions adjusted for potential confounders. We found that SUA was correlated with all fat measures (e.g. Pearson’s r between SUA and hepatic fat: 0.50, 95%-CI: 0.42, 0.57). Associations with visceral and hepatic fat, but not with subcutaneous fat, remained evident after adjustment for anthropometric measures (e.g. visceral fat: β = 0.51 l, 95%-CI: 0.30 l, 0.72 l). In conclusion, these results show how different adipose tissue compartments are affected by SUA to varying degrees, thus emphasizing the different physiological roles of these adipose tissues in uric acid metabolism.

Retrospective Analysis of the Correlation between Uric Acid and Thyroid Hormone in People with Normal Thyroid Function

OBJECTIVE

This study adopts the method of retrospective analysis to collect general information and laboratory results of physical examination population, hoping to clarify the correlation between uric acid and thyroid hormone.

METHODS

The subjects of the study were healthy subjects who underwent physical examination at the Sir Run Run Shaw Hospital affiliated to the Medical College of Zhejiang University from January 2016 to December 2018. Demographic information and medical history of all subjects were recorded through an electronic health system. Serum uric acid (SUA) was grouped by quartiles. Statistical analyses were performed with R version 3.5.1.

RESULTS

A total of 48,526 subjects were included in the analysis. Gender ratio, age, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, FBG, HbA1c, TG, HDL-C, ALT, AST, FT3, FT4, and TSH were significantly different among the uric acid groups. The regression coefficients of SUA in the TSH, FT3, and FT4 regression models were B = 1.000 (95% CI 1.000-1.000, p = 0.009), B = 0.999 (95% CI 0.999-0.999, p < 0.001), and B = 1.001 (95% CI 1.001-1.001,  p < 0.001), respectively. There was a significant dose-dependent relationship between FT4, FT3, and SUA gradient.

CONCLUSIONS

Under normal thyroid function, there were significant differences in TSH, FT3, and FT4 between groups with different uric acid levels. Uric acid levels were linearly correlated with FT3 and FT4, but not with TSH.