Associations Between Serum Uric Acid Concentrations and Cardiometabolic Risk and Renal Injury in Obese and Overweight Children

The role of the estimated glomerular filtration rate and body roundness index in the risk assessment of uric acid-lowering therapy-resistant gout in U.S. adults: evidence from the National Health and Nutrition Examination Survey (2007-2018)

OBJECTIVE

To explore the risk factors for uric acid-lowering therapy-resistant gout (UALT-RG) and its relationships with the estimated glomerular filtration rate (eGFR), body roundness index (BRI), and visceral adiposity index (VAI) via 2007-2018 National Health and Nutrition Examination Survey (NHANES) data.

METHODS

We calculated the BRI using waist circumference and standing height; the VAI using triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and body mass index (BMI); and the eGFR from serum creatinine levels. We also collected gout data. We explored the relationships of the eGFR, BRI, and VAI with UALT-RG risk via univariable and multivariable weighted logistic regression, trend analysis, and restricted cubic splines.

RESULTS

Among the 1,811 patients with gout, ∼9.08% had UALT-RG; these patients were more likely to have obesity, comorbid diabetes (36% [27-47%] vs. 25% [22-28%]) or impaired kidney function (eGFR < 60 mL/min/1.73 m2, 34.5% [27-43%] vs. 22.5% [20-26%]); be former smokers; and take colchicine (10% [5.6-19%] vs. 4.3% [2.8-6.7%]). Logistic regression and trend analysis suggested that an elevated BRI and decreased eGFR were independent risk factors and potential screening indicators for UALT-RG. Restricted cubic spline analysis revealed a negative linear trend between the eGFR and UALT-RG risk (p-overall < 0.0001) and a significant positive correlation between the BRI and UALT-RG risk (p-overall < 0.0001).

CONCLUSION

An increased BRI and decreased eGFR may be independent risk factors and assessment indicators for UALT-RG in U.S. adults. It is necessary to monitor serum urate levels more closely and conduct early multidisciplinary comanagement when gout is comorbid with visceral obesity and chronic kidney disease stages 3-5.

Association of Serum Uric Acid Levels with Cardiometabolic Factors in Adolescents with Obesity: A Cross-Sectional Study

INTRODUCTION

High serum uric acid (SUA) levels are known to be correlated with cardiometabolic factors in adults, but this relationship is less clear in the pediatric population, particularly given the undefined cutoff points for high SUA levels.

OBJETIVE

This study aimed to explore the associations between SUA levels and cardiometabolic factors in obese adolescents.

MATERIALS AND METHODS

We conducted a cross-sectional study of 391 adolescents aged 10 to 18 years with obesity (BMI > 95th percentile), assessing outcomes such as hypertriglyceridemia, reduced HDL cholesterol, hypertension, hyperglycemia, and insulin resistance. The statistical methods used to compare SUA levels with cardiometabolic factors included the Mann-Whitney U test and the chi-square test.

RESULTS

The results revealed that the median SUA level was 5.9 mg/dL, with significant differences between the sexes (5.5 mg/dL for girls and 6.1 mg/dL for boys). The highest SUA tertile (≥6.41 mg/dL) was associated with a significantly greater frequency of hyperglycemia and hypertriglyceridemia than the lowest tertile (p < 0.005).

CONCLUSIONS

The study concluded that higher SUA levels are significantly associated with specific cardiometabolic risks in adolescents with obesity, highlighting the importance of monitoring SUA levels in this population.

Metabolic and Anthropometric Effects of a Randomized Freely Chosen Exercise Prescription Program vs a Video-Based Training Program in Patients With Childhood Obesity: A Randomized Clinical Trial

Objectives When dealing with children and adolescents living with obesity, it is vital to be aware that exercise provides benefits in the prevention and control of non-communicable diseases and general well-being. The growing prevalence of childhood obesity makes it necessary to develop strategies aimed at controlling the barriers that limit the performance of exercise, which is why we designed a plan of exercise prescription through videos that could be accessible, free, and designed for exercise at home, as a complement to a multidisciplinary intervention program for lifestyle change. This study aimed to compare the effects of a video-based exercise prescription program (EPV) versus free-choice exercise (FCE) on anthropometric and metabolic indicators. Methods We conducted an open-label, randomized, controlled clinical trial. Patients aged eight to 16 years with obesity from the Child Unit of the General Hospital of Mexico were included. Forty-two participants finished the follow-up; 20 were boys, and 22 were girls. All patients were included in a multi-component program of healthy lifestyle changes and randomized to receive EPV (n=22) or FCE (n=20) for six months. Results The primary outcomes in both groups were a decrease in body mass index (BMI) (p < 0.001), a reduction in body fat mass (p < 0.001), and an increase in lean body mass (p = 0.003). Other outcomes observed were: in EPV, there was a decrease in low density lipid (LDL) (p=0.04); alanine aminotransferase (ALT) (p=0.002), aspartate aminotransferase (AST) (p=0.001) and uric acid (p=0.003) and an increase in high density lipid (HDL) (p=0. 002), while in FCE there was a decrease in Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (p=0.006), insulin (p=0.006), LDL (p=0.02), ALT (p=0.002), AST (p=0.004) and gamma-glutamyl transferase (GGT) (p=0.025). Conclusion Both EPV and FCE exercise prescription programs, as part of a multidisciplinary intervention for childhood obesity, had favorable effects on body composition and metabolic parameters.

Evaluation of metabolic syndrome components, serum uric acid levels and epicardial adipose tissue thickness in pubertal children by severity of obesity

BACKGROUND

We aimed to evaluate how the parameters used in the diagnosis of metabolic syndrome (MetS) and parameters such as epicardial adipose tissue (EAT) thickness, insulin resistance (IR), and serum uric acid (SUA) are affected according to the severity of obesity.

METHODS

A total of 120 obese patients aged 10-18 years were classified as class 1-2-3 according to their body mass index (BMI) score. SUA was measured and oral glucose tolerance tests were performed on all patients. MetS components were determined according to the International Diabetes Federation 2007 criteria. IR was calculated using homeostatic model assessment for insulin resistance (HOMA-IR) and whole body insulin sensitivity index (WBISI).

RESULTS

HOMA-IR was higher in the class 3 group than in the class 1 (p<0.001) and class 2 groups (p<0.01). WBISI was lower in the class 3 group than in the class 1 (p=0.015) and class 2 groups (p<0.01). EAT thickness was higher in the class 3 group than in the class 1 (p<0.01) and class 2 groups (p<0.01). No significant difference was found between class 1 and 2 groups for HOMA-IR, WBISI, and EAT thickness variables. The frequency of the MetS components was similar between the class of obesity groups (p=0.702). SUA and EAT thickness were significantly higher in the group with 2 and/or more MetS components than in the group with no MetS component. EAT thickness was positively and moderately correlated with SUA levels (Rho=0.319, p<0.001).

CONCLUSIONS

A more significant increase in cardiovascular disease risk factors, especially after class 2 obesity suggests that obese people should be followed closely and necessary interventions made for the prevention and progression of obesity. SUA and EAT thickness, an important risk factor affecting the obesity-related comorbidities, are positively correlated with each other and can be used in the follow-up of obese children.

Impact of Imperata Cylindrica polysaccharide on liver lipid metabolism disorders caused by hyperuricemia

Elevated uric acid levels are associated with lipid metabolism disorders. The effects of Imperata cylindrica polysaccharide (ICPC-a) were explored using a hyperuricemia mouse model and a uric acid-induced HepG2 hepatocyte model. ICPC-a significantly improved total cholesterol, triglycerides, low-density lipoprotein levels, and hepatic lipid deposition in hyperuricemia mice. The liver/body weight ratio decreased, and markers of liver damage, inflammation, and dyslipidemia improved. Metabolomics analysis suggested that ICPC-a modulates lipid metabolism by influencing the glycerophospholipid pathway and the enzyme LPCAT3. Stable HepG2 cell lines with knocked-down LPCAT3 were constructed, and western blot and RT-PCR were used to assess the impact of its knockdown on lipid metabolism under uric acid stimulation. In cells with reduced LPCAT3 expression, ICPC-a was still able to alleviate uric acid-induced lipid accumulation, though the effect was less pronounced compared to cells with normal LPCAT3 levels. However, the effectiveness was diminished compared to cells where LPCAT3 was not knocked down. These findings indicated that LPCAT3 was an important target through which ICPC-a regulated lipid metabolism disorders induced by hyperuricemia. These discoveries emphasized that ICPC-a, as a prebiotic, could modulate hepatic lipid accumulation and inflammation, contributing to the maintenance of hepatic lipid homeostasis.

Hypoxanthine ameliorates diet-induced insulin resistance by improving hepatic lipid metabolism and gluconeogenesis via AMPK/mTOR/PPARα pathway

AIM

Insulin resistance (IR) is a pivotal metabolic disorder associated with type 2 diabetes and metabolic syndrome. This study investigated the potential of hypoxanthine (Hx), a purine metabolite and uric acid precursor, in ameliorating IR and regulating hepatic glucose and lipid metabolism.

METHODS

We utilized both in vitro IR-HepG2 cells and in vivo diet-induced IR mice to investigate the impact of Hx. The HepG2 cells were treated with Hx to evaluate its effects on glucose production and lipid deposition. Activity-based protein profiling (ABPP) was applied to identify Hx-target proteins and the underlying pathways. In vivo studies involved administration of Hx to IR mice, followed by assessments of IR-associated indices, with explores on the potential regulating mechanisms on hepatic glucose and lipid metabolism.

KEY FINDINGS

Hx intervention significantly reduced glucose production and lipid deposition in a dose-dependent manner without affecting cell viability in IR-HepG2 cells. ABPP identified key Hx-target proteins engaged in fatty acid and pyruvate metabolism. In vivo, Hx treatment reduced IR severities, as evidenced by decreased HOMA-IR, fasting blood glucose, and serum lipid profiles. Histological assessments confirmed reduced liver lipid deposition. Mechanistic insights revealed that Hx suppresses hepatic gluconeogenesis and fatty acid synthesis, and promotes fatty acid oxidation via the AMPK/mTOR/PPARα pathway.

SIGNIFICANCE

This study delineates a novel role of Hx in regulating hepatic metabolism, offering a potential therapeutic strategy for IR and associated metabolic disorders. The findings provide a foundation for further investigation into the role of purine metabolites in metabolic regulation and their clinical implications.

Uric Acid Levels in Overweight and Obese Children, and Their Correlation With Metabolic Risk Factors

BACKGROUND

Obesity is an ongoing medical condition that continues to rise on a global scale. Numerous metabolic disorders, such as type 2 diabetes mellitus, hyperlipidemia, and hyperuricemia, are closely associated with obesity. This particular study aims to investigate the occurrence of hyperuricemia and its association with other metabolic factors among children and adolescents aged 6 to 14 years who are overweight or obese residing in Riyadh, Saudi Arabia.

METHODS

In this research, a cross-sectional, descriptive, and analytical study was carried out on 339 children and adolescents. These participants were selected randomly from a list of patients who had sought medical care for overweight and obesity at the pediatric endocrinology, general pediatrics, and family medicine clinics in King Abdulaziz Medical City (KAMC), located in Riyadh, Saudi Arabia, in the period from January 2020 to January 2022. To gather the necessary data, the electronic medical records of the participating individuals were carefully reviewed, encompassing various relevant variables, including demographic characteristics, anthropometric measurements, serum uric acid levels, lipid profiles, and fasting blood sugar.

RESULTS

Out of the total 339 participants, 48 (14.2%) were identified as overweight, while 291 (85.8%) were classified as obese. The study revealed that the overall prevalence of hyperuricemia among the participants was 54%. There was an increased risk of hyperuricemia associated with male gender and elevated levels of LDL and TG.

CONCLUSION

The significant prevalence of hyperuricemia among children and adolescents who are overweight or obese is evident. In order to improve control and management of this condition, it is crucial to prioritize the promotion of a healthy lifestyle among these individuals.

Relationship between serum vitamin C and serum uric acid in people with different BMIs: results from the NHANES 2017-2018 and Mendelian randomization study

Objective

To examine the association of overweight/obesity and serum vitamin C (serum VC) with serum uric acid (SUA) and to assess causality using Mendelian randomization (MR).

Methods

4,772 participants from the National Health and Nutrition Examination Survey (NHANES), 2017-2018 were included in this study. Multivariate linear regression, variance inflation factor and quantile regression were used to analyze the relationships between overweight/obesity and serum VC and SUA levels. Secondly, Mendelian randomization (MR) was utilized to mitigate bias and prevent reverse causality in the observational study. Genetic variants associated with obesity (N = 13,848), vitamin C levels (N = 64,979) and serum uric acid levels (N = 343,836) were sourced from the most extensive genome-wide association studies (GWAS). The primary analytical method employed was inverse variance weighted (IVW).

Results

Based on the observational study, BMI was positively associated with SUA (β = 0.06, 95% CI: 0.05 to 0.07, p < 0.001) and serum VC was negatively associated with SUA (β = -0.14, 95% CI: -0.23 to -0.04, p = 0.005). In individuals with overweight/obesity (BMI > =25), the negative effects of serum VC on SUA enhanced with increasing serum VC. High serum VC level (Q4 level, above 1.19 mg/dL) reduced SUA (β = -0.30, 95% CI: -0.47 to -0.14, p < 0.001) in individuals with overweight/obesity compared to low serum VC level (Q1 level, below 0.54 mg/dL). IVW-MR analysis revealed a significant association between SUA levels and genetically elevated levels of VC (β = -0.03, 95% CI: -0.06 to -0.00, p = 0.029) and obesity (β = 0.06, 95% CI: 0.04 to 0.07, p < 0.001).

Conclusion

Cross-sectional observational analysis revealed that BMI exhibited a positive correlation with SUA levels and that serum VC was negatively correlated with SUA levels; moreover, moderate serum VC can reduce SUA, especially in individuals with overweight/obesity. There was evidence indicating a causal effect of VC and obesity on SUA. It highlights the importance of VC in the management of SUA levels, particularly in overweight/obese individuals. The findings might be helpful for the management of high SUA levels.

A Gender-, Age-, and Weight Status-Specific Analysis of the High Prevalence of Hyperuricemia Among Chinese Children and Adolescents with Obesity

Objective

To explore the gender-, age-, and weight status-specific prevalence of hyperuricemia (HUA) and its associated risk factors among Chinese children and adolescents with obesity.

Methods

A total of 1329 children aged 2-17 years, who were diagnosed with obesity and hospitalized in our center from January 2016 to December 2022 were recruited. They were divided into mild obesity, moderate obesity, and severe obesity groups. HUA was defined as fasting serum uric acid level >420 μmol/L for boys and >360 μmol/L for girls. Multivariate logistic regression analyses were performed to identify risk factors for HUA.

Results

The highest proportion of hospitalized obese children was aged 10-13 years comprising 677 (50.9%) followed by those aged 6-9 years comprising 348 (26.2%) whereas the least proportion was aged 2-5 years comprising 76 (5.7%). The above differences in age distribution were still present in subgroup analyses according to weight status. Most hospitalized obese children were boys (64.7%), especially in the severe obesity group (75.0%). The overall estimated prevalence of HUA in obese children was 54.8%. It presented a gradual increase trend over the last 7 years, with more rapidly in boys than in girls. Subgroup analysis by weight status showed that the prevalence of HUA was higher in children with moderate obesity (64.3%) and severe obesity (64.2%) when compared with mild obesity (48.2%) (P all<0.01). Boys reached a relatively high HUA incidence level (≥60%) at age 12, which occurred about 2 years later than in girls (age 10). With 12 years as the cut-off point, a high prevalence of HUA (≥60%) was observed in both genders. Multivariable logistic regression analyses showed that boy (OR=2.844, 95% CI 2.024-3.998), age (OR=1.253, 95% CI 1.155-1.360), BMI-Z score (OR=2.132, 95% CI 1.438-3.162), fasting blood glucose (OR=0.907, 95% CI 0.860-0.956), phosphorus (OR=4.123, 95% CI 2.349-7.239), alkaline phosphatase (OR=1.002, 95% CI 1.001-1.004), creatinine (OR=1.067, 95% CI 1.037-1.098), urea nitrogen (OR=1.193, 95% CI 1.032-1.378), aspartate aminotransferase (OR=1.016, 95% CI 1.002-1.030), triglycerides (OR=1.339, 95% CI 1.075-1.667), and high-density lipoprotein cholesterol (OR=0.381, 95% CI 0.160-0.910) were independently associated with odds of HUA (P all<0.05).

Conclusion

The prevalence of HUA in Chinese obese children and adolescents is unexpectedly high. Childhood HUA was significantly associated with obesity. Gender and age differences were observed in the association between childhood obesity and HUA. Obese children aged ≥12 years should be focused on screening the risk of HUA.

Salivary uric acid across child development and associations with weight, height, and body mass index

Introduction

Obesity during childhood is a serious and growing chronic disease with consequences for lifelong health. In an effort to advance research into the preclinical indicators of pediatric obesity, we examined longitudinal assessments of uric acid concentrations in saliva among a cohort of healthy children from age 6-months to 12-years (n's per assessment range from 294 to 727).

Methods

Using data from a subsample of participants from the Family Life Project (an Environmental influences on Child Health Outcomes Program cohort), we: (1) characterized salivary uric acid (sUA) concentrations from infancy to early adolescence by sex and race; (2) assessed changes in sUA levels across development; and (3) evaluated associations between sUA concentrations and measures of child weight, height, and body mass index (BMI). Across four assessments conducted at 6-, 24-, 90-, and 154-months of age, 2,000 saliva samples were assayed for UA from 781 participants (217 participants had sUA data at all assessments).

Results

There were no significant differences in sUA concentrations by sex at any assessment, and differences in sUA concentrations between White and non-White children varied by age. At the 90- and 154-month assessments, sUA concentrations were positively correlated with measures of child weight, height, and BMI (90-month: weight- ρ(610) = 0.13, p < 0.01; height- ρ(607) = 0.10, p < 0.05; BMI- ρ(604) = 0.13, p < 0.01; 154-month: weight- ρ(723) = 0.18, p < 0.0001; height- ρ(721) = 0.10, p < 0.01; BMI- ρ(721) = 0.17, p < 0.0001). Group based trajectory modeling identified two groups of children in our sample with distinct patterns of sUA developmental change. The majority (72%) of participants showed no significant changes in sUA across time ("Stable" group), while 28% showed increases in sUA across childhood with steep increases from the 90- to 154-month assessments ("Increasing" group). Children in the Increasing group exhibited higher sUA concentrations at all assessments (6-month: t(215) = -5.71, p < 0.001; 24-month: t(215) = -2.89, p < 0.01; 90-month: t(215) = -3.89, p < 0.001; 154-month: t(215) = -19.28, p < 0.001) and higher weight at the 24- and 90-month assessments (24-month: t(214) = -2.37, p < 0.05; 90-month: t(214) = -2.73, p < 0.01).

Discussion

Our findings support the potential utility of sUA as a novel, minimally-invasive biomarker that may help advance understanding of the mechanisms underlying obesity as well as further surveillance and monitoring efforts for pediatric obesity on a large-scale.

In Vitro Mimicking of Obesity-Induced Biochemical Environment to Study Obesity Impacts on Cells and Tissues

Obesity represents a heavy burden for modern healthcare. The main challenge facing obesity research progress is the unknown underlying pathways, which limits our understanding of the pathogenesis and developing therapies. Obesity induces specific biochemical environments that impact the different cells and tissues. In this piece of writing, we suggest mimicking obesity-induced in vivo biochemical environments including pH, lipids, hormones, cytokines, and glucose within an in vitro environment. The concept is to reproduce such biochemical environments and use them to treat the tissue cultures, explant cultures, and cell cultures of different biological organs. This will allow us to clarify how the obesity-induced biochemistry impacts such biological entities. It would also be important to try different environments, in terms of the compositions and concentrations of the constitutive elements, in order to establish links between the effects (impaired regeneration, cellular inflammation, etc.) and the factors constituting the environment (hormones, cytokines, etc.) as well as to reveal dose-dependent effects. We believe that such approaches will allow us to elucidate obesity mechanisms, optimize animal models, and develop therapies as well as novel tissue engineering applications.

Cross-sectional and longitudinal associations between lipid accumulation product and hyperuricemia

BACKGROUND AND AIMS

Lipid accumulation product (LAP) is a novel, sex-specific, index-describing lipid over accumulation. Previous studies used baseline LAP for predicting hyperuricaemia; however, the relationship between them is unclear. We aimed to investigate the relationship between LAP and the risk of hyperuricaemia in the Central Chinese population.

METHODS AND RESULTS

This large-scale observational study comprised a cross-sectional population sample and a prospective cohort of 44,294 healthy subjects. This study examined the association between LAP and the risk of hyperuricaemia in the total sample and subgroups using multiple logistic regression analysis and multivariate cox proportional hazards model analysis. As a result, there was a dose-response relationship between LAP and the risk of hyperuricaemia. The prevalence of hyperuricaemia was 13.4% in the cross-sectional study. During 9 years of follow-up, hyperuricaemia occurred in 928 (19.8%) participants. The corresponding hazard ratios after multiple adjustments of hyperuricaemia in the second, third and fourth quartile were 1.34 (95% confidence interval [CI], 1.04-1.72), 2.01 (95% CI, 1.54-2.63), and 2.44 (95% CI, 1.80-3.30)-fold higher vs. the first quartile, respectively. Subgroup analyses showed that the association between LAP and the risk of hyperuricaemia was more pronounced in females, individuals≤49 years old and subjects with eGFR ≥60 ml/min/1.73 m².

CONCLUSION

LAP was positively related to the risk of hyperuricaemia in the Central Chinese population, particularly in women, individuals≤49 years old and adults with relatively normal renal function. These findings suggested the potential of LAP as an independent risk indicator in preventing hyperuricaemia.

Correlation of uric acid with body mass index based on NHANES 2013-2018 data: A cross-sectional study

Clinical investigation of obesity-related risk factors aids in the early detection, prevention, and management of obesity. We aimed to examine the association between obesity and serum uric acid (sUA). A cross-sectional study was conducted including 18473 subjects from the National Health and Nutrition Examination Survey (NHANES). The exposure and outcome variables were sUA and body mass index (BMI), respectively. The weighted multivariate linear regression models and smooth curve fittings were conducted to assess the association between sUA and BMI. There were significantly positive correlations between sUA and BMI in both males and females (β = 1.414, 95% CI: 1.323-1.505, P < .0001, β = 1.853, 95% CI: 1.740-1.966, P < .0001, respectively). Furthermore, individuals in the higher sUA quartiles had higher BMI than those in the lowest quartile in both males and females. Subgroup analyses were stratified by race/ethnicity, results indicated the positive association of sUA with BMI in males remained in all races including Mexican American (β = 1.203, 95% CI: 0.965-1.442, P < .0001), other Hispanic (β = 1.126, 95% CI: 0.858-1.395, P < .0001), non-Hispanic White (β = 1.493, 95% CI: 1.343-1.642, P < .0001), non-Hispanic Black (β = 1.331, 95% CI: 1.122-1.540, P < .0001), and other races (β = 1.329, 95% CI: 1.115-1.544, P < .0001). And the positive association of sUA with BMI in females also remained in all races including Mexican American (β = 1.806, 95% CI: 1.520-2.092, P < .0001), other Hispanic (β = 2.033, 95% CI: 1.687-2.379, P < .0001), non-Hispanic White (β = 1.847, 95% CI: 1.657-2.037, P < .0001), non-Hispanic Black (β = 2.141, 95% CI: 1.874-2.408, P < .0001), and other races (β = 1.348, 95% CI: 1.081-1.615, P < .0001). The current cross-sectional study with 18473 US participants found that an elevated sUA was positively correlated with a higher BMI in males, females, and all kinds of races.

Detection and Quantification of the Relationship between the Ratio of Triglycerides over High-Density Lipoprotein Cholesterol and the Level of Serum Uric Acid: One Cross-Sectional Study

Background

Hyperuricemia acts as an independently known risk factor for diabetes, cardiovascular disease, and gout. It was previously reported that the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) is not only an important marker of cardiovascular disease, stroke, atherosclerosis, and insulin resistance but is also associated with an elevated level of serum uric acid. However, it is still poorly understood what the association is between TG/HDL-C and serum uric acid levels. Hence, the aim of this research was to determine this association.

Methods

A total of 5,402 participants who underwent physical examinations in 2021 were analyzed during our cross-sectional research. In order to verify this correlation between TG/HDL-C and uric acid, we performed both a generalized additive model (GAM) and a smoothing curve fit. We also performed receiver operating characteristic (ROC) curves for evaluation of differences in clinical risk factor models in identifying hyperuricemia risk before and after the introduction of TG/HDL-C.

Results

Upon adjustment for confounders, we found that there was a nonlinear positive correlation between TG/HDL-C and the level of uric acid, and the inflection point was 1.41. When TG/HDL-C was less than 1.41, the effect size was 40.56 (19.08-62.04, P = 0.0002), whereas when TG/HDL-C was more than 1.41 the effect size was 17.18 (3.70-30.65, P=0.0125). As shown by the ROC curve, a significant increase in the area under the curve (AUC) was observed upon the introduction of TG/HDL-C into the established risk factor model which elevated from 0.7206(0.7053-0.7359, P < 0.05) to 0.8291 (0.8175-0.8407, P < 0.05).

Conclusion

Therefore, TG/HDL-C is positively and nonlinearly correlated to the level of uric acid, and the inflection point is 1.41. Furthermore, TG/HDL-C leads to an improvement in hyperuricemia risk stratification.

Correlation of obesity, dietary patterns, and blood pressure with uric acid: data from the NHANES 2017-2018

BACKGROUND

Prevalence rates of hyperuricemia and gout are increasing. Clinical investigations of hyperuricemia-related risk factors aid in the early detection, prevention, and management of hyperuricemia and gout. Ongoing research is examining the association of obesity, dietary patterns, and blood pressure (BP) with serum uric acid (sUA).

METHODS

A cross-sectional study was conducted based on the National Health and Nutrition Examination Survey. The exposures included body mass index (BMI), dietary patterns, and BP. The outcome variable was sUA level. The weighted multivariate linear regression models and smooth curve fittings were used to assess the association of BMI, dietary patterns, and BP with sUA.

RESULTS

There was a significantly positive correlation between BMI and sUA (β = 0.059, 95% CI: 0.054 to 0.064, P < 0.00001). Overweight and obese individuals had higher sUA levels than those with the normal BMI (β = 0.451, 95% CI: 0.357 to 0.546, P < 0.00001; β = 0.853, 95% CI: 0.760 to 0.946, P < 0.00001; respectively). Dietary energy intake was positively correlated with sUA (β = 0.000, 95% CI: 0.000 to 0.000, P = 0.01057). Dietary intake of carbohydrate and fiber were negatively correlated with sUA (β = - 0.001, 95% CI: - 0.002 to - 0.000, P < 0.00001; β = - 0.008, 95% CI: - 0.011 to - 0.004, P = 0.00001; respectively). Moreover, systolic BP was positively correlated with sUA (β = 0.006, 95% CI: 0.003 to 0.009, P = 0.00002). However, no statistical differences were found about the associations of dietary intake of total sugars, protein, total fat, cholesterol, and diastolic BP with sUA.

CONCLUSIONS

The current cross-sectional investigation of a nationally representative sample of US participants showed that BMI, dietary energy intake, and systolic BP were positively correlated with sUA levels; dietary carbohydrate and fiber intake were negatively correlated with sUA levels. The findings might be helpful for the management and treatment of hyperuricemia and gout.

High uric acid levels in overweight and obese children and their relationship with cardiometabolic risk factors: what is missing in this puzzle?

OBJECTIVES

The prevalence of hyperuricemia, a common disorder, has been increasing. Moreover, the association between obesity, serum uric acid levels, and cardiometabolic markers in children is unclear. Therefore, this study aimed to analyze the inter-relationships between these factors in a sample of children aged 6-12 years.

METHODS

We evaluated 764 children and stratified them according to their body mass index (BMI). Blood pressure and uric acid, creatinine, lipid, and glycemic profiles were evaluated, and the estimated glomerular filtration rate (eGFR) and the homeostatic model assessment for insulin resistance (HOMA-IR) index were calculated.

RESULTS

There was a significant linear trend of increasing systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), total cholesterol, low-density lipoprotein cholesterol (LDLc), uric acid, insulin levels, and HOMA-IR index values corresponding with overweight and obese groups; however, high-density lipoprotein cholesterol (HDLc) levels decreased with increasing obesity. The mean creatinine level and eGFR were similar across all BMI groups. Uric acid levels were significantly correlated with BMI (r=0.527), waist circumference (r=0.580), SBP (r=0.497), DBP (r=0.362), TG (r=0.534), total cholesterol (r=0.416), LDLc (r=0.286), HDLc (r=-0.248), insulin (r=0.613), and HOMA-IR index (r=0.607). Multiple regression analyses showed that BMI (B=0.071; SE=0.012; p<0.001), TG (B=0.004; SE=0.001; p<0.001), LDLc (B=0.003; SE=0.001; p=0.006), and insulin (B=0.066; SE=0.007; p<0.001) (R²=0.460) were significant predictors of increased uric acid levels and explained 46% of the variability in uric acid in these children.

CONCLUSIONS

Our findings suggest that overweight or obese children are more likely to have higher uric acid levels. Moreover, several cardiometabolic risk factors were strongly associated with high uric acid levels.

Investigating the relationship between serum uric acid to high-density lipoprotein ratio and metabolic syndrome

Aims

This study aimed to determine a parameter to more easily diagnose metabolic syndrome and predict its probability of occurrence in high‐risk individuals.

Methods

In this cross‐sectional study, data related to the study population in the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS) were examined. Subjects were divided into two groups with and without metabolic syndrome, and the relevant factors such as the ratios of uric acid to high‐density lipoprotein (HDL) (UHR) in these two groups were compared, and the best cut‐off point was determined.

Results

Data related to 817 people including 96 people with metabolic syndrome and 721 people without metabolic syndrome were analysed. The mean UHR was significantly higher in patients with metabolic syndrome (14.76 ± 6.33%) compared with those without metabolic syndrome (10.0 ± 3.10%) (p < .001). People with high UHR are 2.9 times more at risk of metabolic syndrome and the best cut‐off point was 9.50% with 86% sensitivity and 55% specificity.

Conclusions

According to our study, UHR is also helpful in diagnosing metabolic syndrome and can also be used to screen people at risk for metabolic syndrome.

Association of uric acid with cardiovascular risk in Brazilian children and adolescents

BACKGROUND AND AIM

Hyperuricemia in adults is associated with cardiovascular risk factors. However, there is less data regarding this association in children and adolescents. Our purpose was to determine association between serum uric acid (SUA) and cardiovascular risk.

METHODS AND RESULTS

A fasting blood sample was collected from 1750 participants aged 6-17 years enrolled in a social project and public schools in Espírito Santo, Brazil. Internal cut-offs were generated to define high SUA (≥90th percentile of SUA concentration for sex and age group). Body mass index percentile (pBMI), body fat percentage (BFP) and muscle mass were determined by bioimpedance. Data are given as mean ± standard deviation. High SUA was associated with overweight/obesity (OR 3.7 CI 95% 2.7-5.0), high waist circumference (WC) (OR 3.9 CI 95% 2.9-5.4), low HDL (OR 2.0 CI 95% 1.5-2.8), high blood pressure (BP) (OR 1.8 CI 95% 1.1-3.2), high BFP (OR 4.1 CI 95% 2.7-6.4), metabolic syndrome (MetS) (OR 3.6 CI 95% 1.8-7.1) and insulin resistance (OR 1.7 CI 95% 1.1-2.7). Individuals in the fourth quartile of SUA, compared to those in the first quartile, showed higher age, pBMI, WC, BFP and muscle mass. Using a reference value of 5.5 mg/dL, the prevalence of hyperuricemia in the sample was 10.3% (CI 95% 8.9-11.7%).

CONCLUSION

Higher SUA values are associated with higher cardiovascular risk in childhood and adolescence. The main cardiovascular risk factors associated with hyperuricemia were overweight/obesity, high WC, dyslipidemia, high BFP, high BP, insulin resistance and MetS.

Serum Uric Acid Levels and Metabolic Indices in an Obese Population: A Cross-Sectional Study

OBJECTIVE

To analyze the association between serum uric acid (SUA) and metabolic state in obese inpatients and preliminarily explore potential mechanisms of hyperuricemia in obesity.

METHODS

A total of 153 obese inpatients were selected and assigned based on SUA level to the normal uric acid (NC group) or high uric acid (HUA) group. Patients' sex, age, height, weight, blood pressure, BMI, and prevalence of metabolic syndrome were collected and recorded. SUA, FPG, FIns, HOMA-IR, HOMA-IS, HbA_1c, TGs, TC, LDL-C, and HDL-C levels were tested. Pearson correlation analysis was performed to analyze the correlation between SUA and related metabolic indicators. Logistic regression was performed to analyze independent risk factors of hyperuricemia in obesity.

RESULTS

In the HUA group, the patients were predominantly males, and BMI, DBP, TGs, FPG, FIns, HOMA-IR, HOMA-IS, and metabolic syndrome were higher than those in the NC group (P<0.05), while HDL-C was lower than that in the NC group (P<0.05). There were no significant differences between the groups in TC or LDL-C. Pearson correlation analysis showed that in obese patients, SUA was positively correlated with BMI, FIns, HOMA-IR, HOMA-IS, TGs, andmetabolic syndrome and negatively correlated with age and HDL-C. Logistic regression showed that BMI, hyperinsulinemia, and insulin resistance were independent risk factors of hyperuricemia.

CONCLUSION

Development of hyperuricemia in obese populations might be correlated with hyperinsulinemia or insulin resistance.

Serum uric acid (SUA) in morbidly obese patients and its relationship with metabolic syndrome

OBJECTIVE

This study aimed to compare serum uric acid levels in normal-weight peers and morbidly obese patients. Our second objective is to evaluate whether hyperuricemia is associated with metabolic syndrome components in morbidly obese patients.

METHODS

Fifty morbidly obese patients (40 males and 10 females) and 50 control groups (37 males and 13 females) were included in the study. Their baseline clinical characteristics and laboratory data were recorded and compared. Kolmogorov-Smirnov test was used to examine the normality of variables. Data were compared using univariate tests including independent samples t-test, Mann-Whitney U test, and chi-square test.

RESULTS

Compared to control group CRP, uric acid, urea, fasting plasma glucose, triglyceride, ALT, and AST were significantly higher in morbidly obese patients. HDL-cholesterol and GFR were significantly lower in morbidly obese patients. There were no significant differences in creatinine, LDL-cholesterol, and total cholesterol levels between two groups.

CONCLUSION

Laboratory parameters, especially serum uric acid levels, were found to be higher in morbidly obese patients compared to the control group.

Integrated omics analysis reveals the alteration of gut microbe-metabolites in obese adults

Obesity, a risk to health, is a global problem in modern society. The prevalence of obesity was approximately 13% among world's adult population. Recently, several reports suggested that the interference of gut microbiota composition and function is associated with metabolic disorders, including obesity. Gut microbiota produce a board range of metabolites involved in energy and glucose homeostasis, leading to the alteration in host metabolism. However, systematic evaluation of the relationship between gut microbiota, gut metabolite and host metabolite profiles in obese adults is still lacking. In this study, we used comparative metagenomics and metabolomics analysis to determine the gut microbiota and gut-host metabolite profiles in six normal and obese adults of Chinese origin, respectively. Following the functional and pathway analysis, we aimed to understand the possible impact of gut microbiota on the host metabolites via the change in gut metabolites. The result showed that the change in gut microbiota may result in the modulation of gut metabolites contributing to glycolysis, tricarboxylic acid cycle and homolactic fermentation. Furthermore, integrated metabolomic analysis demonstrated a possible positive correlation of dysregulated metabolites in the gut and host, including l-phenylalanine, l-tyrosine, uric acid, kynurenic acid, cholesterol sulfate and glucosamine, which were reported to contribute to metabolic disorders such as obesity and diabetes. The findings of this study provide the possible association between gut microbiota-metabolites and host metabolism in obese adults. The identified metabolite changes could serve as biomarkers for the evaluation of obesity and metabolic disorders.

Elevated triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio increased risk of hyperuricemia: a 4-year cohort study in China

PURPOSE

Previous studies revealed that high serum uric acid (SUA) levels correlated with increased triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio. However, evidence is lacking regarding whether TG/HDL-C is an independent risk factor or just a simple marker of hyperuricemia. We aimed to investigate the relationship between TG/HDL-C and the risk of hyperuricemia in Chinese population.

METHODS

This retrospective cohort study involved 15,198 subjects (43.14 ± 13.13 years, 54.9% men) who were free of hyperuricemia at baseline. The association between TG/HDL-C and the risk of hyperuricemia, in the total sample and stratified by subgroups, was examined by multiple logistic regression analyses.

RESULTS

During 4 years follow-up, hyperuricemia occurred in 2365 (15.6%) participants. The cumulative incidence of hyperuricemia was significantly elevated with increasing TG/HDL-C quartiles (5.9, 10.8, 18.4, and 27.5%, respectively). After multivariate adjustment, the odds ratio for hyperuricemia in the highest compared with the lowest quartile of TG/HDL-C was 1.80 (95% confidence interval [CI] 1.49, 2.18), and each SD increment of TG/HDL-C ratio caused a 10% additional risk for hyperuricemia. Moreover, subgroup analyses showed that the association between TG/HDL-C and the risk of hyperuricemia was more pronounced in women and normal-weight adults. The results were consistent when analyses were restricted to participants without metabolic syndrome.

CONCLUSIONS

TG/HDL-C ratio was positively related to the risk of hyperuricemia in Chinese population, particularly in women and normal-weight individuals. These findings suggested the potential of TG/HDL-C ratio to serve as an independent risk indicator in the prevention of hyperuricemia.

Influence of muscle mass on the serum uric acid levels in children and adolescents

BACKGROUND AND AIM

Uric acid (UA) is an end-product of purine catabolism and its increase in blood is a risk factor for several diseases. UA levels in men are usually higher than in women. This difference is partially due to sex hormones. We sought to investigate the onset of sexual difference in UA levels during pubertal development and the determinants of UA levels in children and adolescents.

METHODS AND RESULTS

The muscle mass and fat mass were measured by multi-frequency bioelectrical impedance in a cross-sectional study involving 823 children and adolescents (both sexes; 6-18 years). Serum UA was determined using a commercially available kit. UA levels started to become higher in boys (5.0 ± 1.0 mg/dL) than in girls (4.1 ± 0.9 mg/dL) around 13 years. Boys in the highest quartile of muscle mass presented higher UA levels (5.2 ± 0.7 mg/dL) when compared with the third (4.2 ± 0.7 mg/dL), second (3.7 ± 0.9 mg/dL) and first (3.4 ± 0.9 mg/dL). Similarly, girls in the highest quartile of muscle mass presented higher UA levels (4.2 ± 0.7 mg/dL) when compared with the second (3.8 ± 0.9 mg/dL) and first (3.3 ± 0.9 mg/dL). Muscle mass explained 43.0% and 7.7% of the variability of UA in boys and girls, respectively.

CONCLUSION

Sexual differences in serum UA levels begin at puberty and partially result from a direct influence of muscle mass.

Asymptomatic Hyperuricemia Associates with Cardiometabolic Risk Indicators in Overweight/Obese but Not in Lean Adolescents

PURPOSE

In overweight/obese adolescents, asymptomatic hyperuricemia is associated with increased prevalence of metabolic syndrome, its components, and a higher cardiometabolic risk. Whether similar associations exist in lean hyperuricemic adolescents is unknown.

SUBJECTS AND METHODS

In 2424 adolescents (51.9% females) aged 16-19 years, anthropometric variables, blood pressure, uric acid, glucose, insulin, lipid profile, inflammatory markers, and renal function were determined. Continuous cardiometabolic score was calculated. Normouricemic vs hyperuricemic subjects were compared among lean and overweight/obese individuals of both sexes.

RESULTS

Females (5.4%) and males (13.3%) presented with hyperuricemia; among them 63% of females and 53% of males were lean. In both sexes, hyperuricemic lean and hyperuricemic overweight/obese adolescents displayed similar uric acid concentrations (eg, males: 455±30 vs 461±32 µmol/L, respectively, p=0.933). Lean normouricemic adolescents manifested significantly lower uric acid levels than their overweight/obese peers (eg, males: 333±46 vs 357±41 µmol/L, respectively, p<0.001). Lean normouricemic and hyperuricemic subjects presented similar cardiometabolic score (eg, males: 2.60±0.67 vs 2.64±0.60, respectively, p=0.998); among overweight/obese adolescents those with hyperuricemia displayed higher scores compared with their normouricemic counterparts (eg, males: 3.36±1.04 vs 4.21±1.65, respectively, p<0.001). A decision-tree model revealed phenotypes associated with higher uricemia, however, distribution of individuals with hyperuricemia among phenotypes was random.

CONCLUSION

In lean adolescents, hyperuricemia is not associated with cardiometabolic profile indicating an increased risk. Existence of this rather prevalent phenotype remains undetected unless lean and overweight/obese subjects are analyzed separately. Longitudinal studies are needed to elucidate the potential clinical consequences of asymptomatic hyperuricemia in lean subjects in later life.