Associations of polycyclic aromatic hydrocarbons exposure with serum uric acid and hyperuricemia in US adults: The role of systemic inflammation.

The associations of polycyclic aromatic hydrocarbon (PAH) exposure with serum uric acid (SUA) or hyperuricemia have been rarely assessed. We aimed to investigate the relationships between urinary PAH metabolites and SUA or hyperuricemia among US adults and to explore the mediating role of systemic inflammation in the associations. A total of 10,307 US adults were conducted to assess the associations of seven urinary hydroxy­PAH with SUA and hyperuricemia and evaluate the role of C-reactive protein (CRP), a biomarker of systemic inflammation, in such associations. Results showed that each 1-unit increase in ln-transformed 2-hydroxynaphthalene (2-OHNa), 1-hydroxyphenanthrene (1-OHPh), 2&3-hydroxyphenanthrene (2&3-OHPh) and total hydroxyphenanthrene (ΣOHPh) was associated with a 1.68 (95% confidence interval (CI): 0.19 to 3.17), 2.46 (0.78 to 4.13), 3.34 (1.59 to 5.09), and 2.99 (1.23 to 4.75) µmol/L increase in SUA, and a 8% (odds ratio (OR): 1.08, 1.02 to 1.15), 9% (OR: 1.09, 1.02 to 1.18), 13% (OR: 1.13, 1.05 to 1.22), and 12% (OR: 1.12, 95% CI: 1.03, 1.21) increase in hyperuricemia, respectively. Co-exposure of seven PAHs was positively associated with SUA and hyperuricemia, with 2&3-OHPh showing the highest weight (components weights: 0.83 and 0.78, respectively). The CRP mediated 11.47% and 10.44% of the associations of ΣOHPh and 2&3-OHPh with SUA and mediated 8.60% and 8.62% in associations of ΣOHPh and 2&3-OHPh with hyperuricemia, respectively. In conclusion, internal levels of PAH metabolites were associated with elevated SUA levels and the increased risk of hyperuricemia among US adults, and CRP played a mediating role in the associations.

Exosomal miRNAs in patients with chronic heart failure and hyperuricemia and the underlying mechanisms.

Chronic heart failure (CHF) combined with hyperuricemia (HUA) is a comorbidity that is hard to diagnose by a single biomarker. Exosomal miRNAs are differentially expressed in cardiovascular diseases and are closely associated with regulating most biological functions. This study aimed to provide evidence for miRNA as a new molecular marker for precise diagnosis of the comorbidity of CHF with HUA and further analyze the potential targets of differentially expressed miRNA. This controlled study included 30 CHF patients combined with HUA (Group T) and 30 healthy volunteers (Group C). 6 peripheral blood samples from Group T and Group C were analyzed for exosomal miRNAs by high-throughput sequencing and then validated in the remaining 24 peripheral blood samples from Group T and Group C by applying real-time PCR (RT-PCR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using R software to predict the differential miRNAs' action targets. 42 differentially expressed miRNAs were detected (18 upregulated and 24 downregulated), in which miR-27a-5p was significantly upregulated (P<0.01), and miR-139-3p was significantly downregulated (P<0.01) in Group T. The combination of miR-27a-5p and miR-139-3p predicted the development of CHF combined with HUA with a maximum area under the curve (AUC) of 0.899 (95 % CI: 0.812-0.987, SEN=79.2 %, SPE=91.7 %, J value = 0.709). GO and KEGG enrichment analysis revealed that the differentially expressed miRNAs had a role in activating the AMPK-mTOR signaling pathway to activate the autophagic response. Collectively, our findings suggest that upregulated exosomal miR-27a-5p combined with downregulated exosomal miR-139-3p can be used as a novel molecular marker for precise diagnosis of CHF combined with HUA and enhanced autophagy by AMPK-mTOR signaling pathway may be one pathogenesis of the differentially expressed miRNAs.

Hyperuricaemia elevates risk of short-term readmission and mortality in patients with heart failure.

BACKGROUND: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. Serum uric acid (SUA), a product of purine metabolism, has been implicated in HF progression. However, the association between hyperuricaemia and the short-term readmission and mortality in patients with HF remains controversial. METHODS: In this retrospective cohort study, we analysed data from a HF database specific to the Chinese population. The primary endpoint was short-term readmission or all-cause mortality within 90 days. Participants with HF were categorised into normouricaemia group (NUA) and hyperuricaemia group (HUA) based on a SUA threshold of 420 µmol/L. The association between SUA and primary endpoint was evaluated using Kaplan-Meier survival curves and Cox regression analysis. RESULTS: Baseline characteristics revealed significant differences between NUA and HUA groups, with the latter exhibiting a higher prevalence of males, chronic kidney disease (CKD) and elevated levels of various biomarkers. During a 90-day follow-up, 493 (26.6%) participants reached the primary endpoint, with a higher incidence observed in the HUA group at 31.2%, compared with 20.1% in the NUA group. When a threshold effect was identified at 420 µmol/L, a non-linear association was observed between SUA and the primary endpoint. After adjusting for gender, age, New York Heart Association class, CKD, systolic blood pressure (SBP) and potassium, the HUA group exhibited a higher risk for the primary endpoint compared with the NUA group (HR: 1.40, 95% CI: 1.14 to 1.72, p=0.001). Additionally, the risk increased across quartiles of SUA (P for trend=0.002). Furthermore, stratified analyses indicated a stronger association in patients without CKD (P interaction=0.033). CONCLUSION: Hyperuricaemia is independently associated with an increased risk of short-term readmission and mortality in patients with HF. Our findings suggest that monitoring and managing SUA could be crucial in improving patient with HF outcomes.

Interaction of serum uric acid with overweight on hypertension: findings from the China Health and Nutrition Survey.

BACKGROUND: Both serum uric acid (SUA) levels and body mass index (BMI) are recognized as important risk factors for hypertension. The current study aimed to investigate the interaction effects between SUA levels and overweight (defined as BMI ≥ 24 kg/m2 in Chinese) on the incidence of hypertension among Chinese adults. METHODS: 1124 hypertensive participants and 7283 non-hypertensive participants, extracted from the China Health and Nutrition Survey (CHNS), were analyzed. Participants were categorized based on their SUA levels and BMI, to investigate the interaction effects between SUA levels and overweight on hypertension. RESULTS: In comparison with the reference group (BMI < 24 kg/m2 and in the 1st quintile of SUA), multivariable adjusted analysis demonstrated that the odds ratio (OR) (95% confidence interval, 95% CI) of hypertension for participants with overweight alone was 2.18 (1.41-3.37); for elevated SUA levels alone, the ORs (95% CIs) were 1.57 (1.08-2.30), 1.84 (1.24-2.74), 2.21 (1.47-3.32), and 2.48 (1.55-3.96) across SUA quintiles; and for the combined effect of higher SUA levels and overweight, the ORs (95% CIs) were 3.25 (2.19-4.82), 3.73 (2.51-5.55), 5.17 (3.42-7.80), and 6.21 (4.01-9.60). The relative excess risk due to interaction (RERI) was 3.26 (1.43-5.09) at the 5th quintile of SUA, indicating the presence of additive interaction between overweight and SUA levels on hypertension. CONCLUSION: Interaction between SUA levels and overweight on hypertension exists specifically at the highest quintile (Q5, > 6.39 mg/dL) of SUA among Chinese adults. Therefore, strategies to lower SUA levels could be considered as a potential approach to mitigate hypertension risk in overweight individuals within this specific subgroup.

Elucidating the role of gut microbiota dysbiosis in hyperuricemia and gout: Insights and therapeutic strategies.

Hyperuricemia (HUA) is a condition associated with a high concentration of uric acid (UA) in the bloodstream and can cause gout and chronic kidney disease. The gut microbiota of patients with gout and HUA is significantly altered compared to that of healthy people. This article focused on the complex interconnection between alterations in the gut microbiota and the development of this disorder. Some studies have suggested that changes in the composition, diversity, and activity of microbes play a key role in establishing and progressing HUA and gout pathogenesis. Therefore, we discussed how the gut microbiota contributes to HUA through purine metabolism, UA excretion, and intestinal inflammatory responses. We examined specific changes in the composition of the gut microbiota associated with gout and HUA, highlighting key bacterial taxa and the metabolic pathways involved. Additionally, we discussed the effect of conventional gout treatments on the gut microbiota composition, along with emerging therapeutic approaches that target the gut microbiome, such as the use of probiotics and prebiotics. We also provided insights into a study regarding the gut microbiota as a possible novel therapeutic intervention for gout treatment and dysbiosis-related diagnosis.

Effects of Acute and One-Week Supplementation with Montmorency Tart Cherry Powder on Food-Induced Uremic Response and Markers of Health: A Proof-of-Concept Study.

Metabolic conditions, such as gout, can result from elevated uric acid (UA) levels. Consuming high-purine meals increases UA levels. Therefore, people with hyperuricemia typically must avoid ingesting such foods. Polyphenols have been shown to reduce uric acid levels and tart cherries (TCs) are a rich source of phenolic and anthocyanin compounds. This proof-of-concept study evaluated whether ingesting TCs with a purine-rich meal affects the uricemic response. Methods: A total of 25 adults (15 males and 10 females, 85.0 ± 17 kg, 40.6 ± 9 years, 29.1 ± 4.9 kg/m2) with elevated fasting UA levels (5.8 ± 1.3 mg/dL) donated a fasting blood sample. In a randomized, double-blind, crossover, placebo-controlled, counterbalanced manner, participants ingested capsules containing 960 mg of a placebo (PLA) or concentrated TC powder containing 20.7 mg of proanthocyanins with a serving of hot soup (10 g of carbohydrate, 2 g protein, and 1 g fat) containing 3 g of purines (1 g of adenosine 5'-monophosphate, 1 g of disodium 5'-guanylate, and 1 g of disodium 5'-inosinate). Blood samples were obtained at 0, 60, 120, 180, and 240 min after ingestion to assess changes in uric acid levels and pharmacokinetic profiles. Cell blood counts, a comprehensive metabolic panel, cytokines, inflammatory markers, and subjective side effects ratings were analyzed on baseline (0 min) and post-treatment (240 min) samples. Participants continued consuming two capsules/day of the assigned treatment for one week and then repeated the experiment. Participants observed a 14-day washout and then repeated the experiment while ingesting the alternate treatment. Data were analyzed using general linear model (GLM) statistics with repeated measures, pairwise comparisons, and percentage change from baseline with 95% confidence intervals (CIs). Results: No statistically significant interaction effects or differences between treatments were seen in uric acid levels or PK profiles. Analysis of percent changes from baseline revealed that TC ingestion reduced the blood glucose levels following the ingestion of the high-purine meal (-4.2% [-7.7, -0.7], p = 0017). Additionally, there was some evidence that TC ingestion attenuated the increase from baseline in IL-1ß and IL-10 and increased INF-γ. No significant differences were seen in the remaining health markers or subjective side effects ratings. Conclusions: Acute and one-week TC supplementation did not affect the uricemic response to ingesting a high-purine meal in individuals with mildly elevated UA levels. However, there was some evidence that TC supplementation may blunt the glycemic response to ingesting a meal and influence some inflammatory cytokines. Registered clinical trial NCT04837274.

The associations of insulin resistance, obesity, and lifestyle with the risk of developing hyperuricaemia in adolescents.

BACKGROUND: Hyperuricaemia is common among obese children and adolescents, and is closely related to insulin resistance. The aim of this study was to explore the relationships between youth insulin resistance and hyperuricaemia, as well as their relationships with lifestyle factors in youths, to provide early guidance on the risk factors for hyperuricaemia in adolescents. METHODS: This study included 233 adolescents aged 10 to 20 years. Insulin resistance was evaluated via the homeostasis model assessment-insulin resistance (HOMA-IR) method. Binary logistic regression analysis was used to assess the associations of HOMA-IR with hyperuricaemia status and serum uric acid (UA) levels. The participants were subsequently divided into two groups, the noninsulin resistant group (HOMA-IR ≤ 3.2) and the insulin resistant group (HOMA-IR > 3.2), to further explore the factors that may affect the serum UA level. Finally, the predictive ability of different indicators of hyperuricaemia was evaluated via the ROC curve. RESULTS: Binary logistic regression analysis revealed a significant increase in the risk of developing hyperuricaemia for individuals with elevated HOMA-IR (p < 0.001) and insulin resistance (p < 0.01). Spearman's correlation analysis revealed a significant positive linear correlation between HOMA-IR and serum UA levels (r = 0.4652, p < 0.001). Among insulin-resistant adolescents, UA levels were positively correlated with weight ratings, frequency of staying up late, and sugary beverages intake. Notably, individuals who engaged in 1-3 h of weekly exercise had the lowest UA levels. The area under the ROC curve for HOMA-IR was 0.847 (cut-off value = 2.165, p < 0.001), and the optimal prediction model included HOMA-IR, BMI z-score, and other lifestyle factors (AUC: 0.870, p < 0.001)). CONCLUSION: HOMA-IR was identified as an independent risk factor for the development of hyperuricaemia and could be used as a sensitive indicator for the prediction its development in adolescents. In insulin-resistant adolescents with hyperuricaemia, maintaining normal weight, engaging in physical exercise for 1-3 h per week, avoiding staying up late and limiting sugary beverages intake are recommended to reduce the prevalence of hyperuricaemia among adolescents.

Association between per- and polyfluoroalkyl substances and hyperuricemia: sex-specific analysis.

Perfluoroalkyl and polyfluoroalkyl(PFAS) substances are the most common environmental pollutants, which has an inconsistent association with hyperuricemia across different populations. This study explored the relationship between hyperuricemia and different gender PFAS and PFAS mixtures, using data from two cycles of the NHANES from 2015-2018. Weighted logistic regression results showed that the correlation between individual PFAS and hyperuricemia was significant only in men. Compared to the reference quartile, the fourth quartile of n-PFOA increased the risk of hyperuricemia in men (OR: 2.79, 95% CI: 1.50, 5.18). The Qgcomp model results showed that each quartile increase in the serum concentration of PFAS mixtures was associated with an increased likelihood of hyperuricemia in the total population, with odds ratios (OR) for men and women being 1.74 (95% CI: 1.26, 2.40), and 2.04 (95% CI: 1.35, 3.16), respectively. we concluded that PFAS might increase the risk of hyperuricemia in adults.

Systemic inflammation markers independently associated with increased mortality in individuals with hyperuricemia: Results from the NHANES prospective cohort study.

BACKGROUND: Hyperuricemia is associated with increased systemic inflammation. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are novel systemic inflammation markers and prognostic markers. However, no studies have evaluated the association between the SII/SIRI and mortality risk in individuals with hyperuricemia. This study aimed to investigate the predictive value of the SII and SIRI for all-cause and cardiovascular mortality in a large cohort of hyperuricemia patients. METHODS: We conducted a prospective cohort study using data from the National Health and Nutrition Examination Survey (NHANES) 2001-2020. Hyperuricemia was defined as serum uric acid (SUA) levels of ≥7 mg/dL in men and ≥6 mg/dL in women. The SII and SIRI were calculated based on complete blood count parameters. Associations with all-cause and cardiovascular mortality were analyzed using Cox proportional hazards models. Nonlinearity and effect modification were assessed using restricted cubic splines (RCS) and interaction analysis. RESULTS: Among the 6181 participants with hyperuricemia aged 20 years and older, over a total 181 months of follow-up, there were 936 all-cause deaths, of which 195 were cardiovascular mortality. In the fully adjusted models, the hazard ratios (HRs) were 1.73 (95% CI 1.42-2.13) for the SII and 2.18 (95% CI 1.82-2.62) for the SIRI with all-cause mortality. The adjusted HRs were 2.08 (95% CI 1.37-3.14) for the SII and 2.32 (95% CI 1.56-3.45) for the SIRI with cardiovascular mortality. Spline models identified nonlinear U-shaped (SII) and J-shaped (SIRI) relationships of inflammation markers with mortality. CONCLUSIONS: Elevated SII and SIRI are independent predictors of mortality in hyperuricemia patients. These inflammatory biomarkers may improve risk stratification in this high-risk population. Further research should evaluate utility in guiding preventive interventions.

Serum uric acid: an independent risk factor for cardiovascular disease in Pakistani Punjabi patients.

BACKGROUND: It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort. METHODS: This is a retrospective observational study in which 502 human subjects having CVD, hypertension and/or diabetes were grouped based on SUA levels as normouricemia (n = 266) and hyperuricemia (n = 236). Role of SUA was assessed in increasing the risk of CVD independent of other key confounding factors (i.e. age, gender, dyslipidemia, hypertension, diabetes, dietary and life-style habits). All clinical and biochemical data were analyzed in SPSS (ver. 20). RESULTS: Subjects aged 55 ± 13 years were of both genders (males: 52%). SUA levels were significantly different among clinical subtypes of CVD [i.e. acute coronary syndrome (ACS), myocardial infarction (MI) and heart failure (HF)]. Spearman correlation showed a significantly positive association between CVD and SUA (rho = 0.149, p < 0.001). Multivariate logistic regression of SUA quartiles showed that hyperuricemia is associated with CVD [3rd quartile: OR: 1.78 (CI: 1.28-2.48), p = 0.001 and 4th quartile: OR: 2.37 (CI: 1.72-3.27), p < 0.001]. Moreover, this association remained significant even after adjusting for confounding factors. CONCLUSION: This study showed that SUA is positively associated with CVD, thus it can act as an independent risk factor for CVD.

Association of uric acid and fructose levels in polycystic ovary syndrome.

STUDY QUESTION: Is there a relationship between serum uric acid and fructose levels in polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Elevated serum uric acid levels in women with PCOS positively correlate with serum fructose levels, and elevated serum fructose levels are an independent risk factor for hyperuricemia in women with PCOS. WHAT IS KNOWN ALREADY: Our previous study suggested a link between elevated serum fructose levels and PCOS. Fructose is unique as it generates uric acid during metabolism, and high uric acid levels are associated with metabolic disorders and an increased risk of anovulation. However, the relationship between serum uric acid and fructose levels in women with PCOS remains unclear. STUDY DESIGN, SIZE, DURATION: In a case-control study of 774 women (482 controls and 292 patients with PCOS) between May and October 2020 at the Shengjing Hospital of China Medical University, the relationship between uric acid and fructose levels in women with PCOS was examined. Participants were divided into subgroups based on various factors, including BMI, insulin resistance, dyslipidemia, metabolic syndrome, and hyperuricemia. PARTICIPANTS/MATERIALS, SETTING, METHODS: Serum uric acid concentrations were measured using enzymatic assays, and serum fructose levels were determined using a fluorescent enzyme immunoassay. Dietary fructose data were collected through a validated food-frequency questionnaire of 81 food items. We applied restricted cubic splines to a flexibly model and visualized the linear/nonlinear relationships between serum uric acid and fructose levels in PCOS. Multivariate logistic analysis was executed to assess the association between serum fructose levels and hyperuricemia in PCOS. Human granulosa cell and oocyte mRNA profile sequencing data were downloaded for mapping uric acid and fructose metabolism genes in PCOS. Further downstream analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interactions were then carried out on the differentially expressed genes (DEGs). The correlation between uric acid and fructose metabolism genes was calculated using the Pearson correlation coefficient. The GeneCards database was used to identify DEGs related to uric acid and fructose metabolism in PCOS, and then several DEGs were confirmed by quantitative real-time PCR. MAIN RESULTS AND THE ROLE OF CHANCE: Both serum fructose and uric acid levels were significantly increased in women with PCOS compared with the control women (P < 0.001), and there was no statistically significant difference in dietary fructose intake between PCOS and controls, regardless of metabolic status. There was a positive linear correlation between serum uric acid and fructose levels in women with PCOS (Poverall < 0.001, Pnon-linear = 0.30). In contrast, no correlation was found in control women (Poverall = 0.712, Pnon-linear = 0.43). Additionally, a non-linear association was observed in the obese subgroup of patients with PCOS (Poverall < 0.001, Pnon-linear = 0.02). Serum uric acid levels were linearly and positively associated with serum fructose levels in patients with PCOS with insulin resistance, dyslipidemia, and metabolic syndrome. Furthermore, even after adjusting for confounding factors, elevated serum fructose levels were an independent risk factor for hyperuricemia in patients with PCOS (P = 0.001; OR, 1.380; 95% CI, 1.207-1.577). There were 28 uric acid and 25 fructose metabolism genes which showed a significant correlation in PCOS. Seven upregulated genes (CAT, CRP, CCL2, TNF, MMP9, GCG, and APOB) related to uric acid and fructose metabolism in PCOS ovarian granulosa cells were ultimately successfully validated using quantitative real-time PCR. LIMITATIONS, REASONS FOR CAUTION: Due to limited conditions, more possible covariates (such as smoking and ethnicity) were not included, and the underlying molecular mechanism between fructose and uric acid levels in women with PCOS remains to be further investigated. WIDER IMPLICATIONS OF THE FINDINGS: The results of this study and our previous research indicate that the high uric acid status of PCOS may be mediated by fructose metabolism disorders, highlighting the importance of analyzing fructose metabolism, and especially its metabolic byproduct uric acid, during the clinical diagnosis of PCOS. These results suggest the adverse effects of high uric acid in PCOS, and the importance of taking early interventions regarding uric acid levels to reduce the occurrence and development of further clinical signs, such as metabolic disorders in women with PCOS. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by: the National Natural Science Foundation of China (No. 82371647, No. 82071607, and No. 32100691); LiaoNing Revitalization Talents Program (No. XLYC1907071); Fok Ying Tung Education Foundation (No. 151039); and Outstanding Scientific Fund of Shengjing Hospital (No. 202003). No competing interests were declared. TRIAL REGISTRATION NUMBER: N/A.

Development and validation of a renal involvement model for patients with hyperuricemia: A cross-sectional study.

OBJECTIVE: To construct a prediction model for renal involvement in patients with hyperuricemia (HUA) based on logistic regression analysis, to achieve early risk stratification. METHOD: In this cross-sectional study, we collected data from the National Health and Nutrition Examination Survey (NHANES), and constructed a predicted model for renal involvement in HUA patients. The discriminative ability of the model was assessed using the receiver operating characteristic (ROC) curve. Model accuracy was evaluated using the Hosmer-Lemeshow test and calibration curve, while clinical utility was assessed using decision curve analysis (DCA). Furthermore, internal and external validation cohorts were also applied to validate the model. RESULTS: A total of 1669 patients from NHANES between 2007 and 2010 were included in the final analysis for modeling and validation. Six predictive factors including age, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Cr, Uric Acid (UA), and sex were identified by binary logistic regression analysis for renal involvement in HUA patients and used to construct a nomogram with good consistency and accuracy. The AUC values for the predictive model, internal validation, and external validation were 0.881 (95% CI: 0.836-0.926), 0.908 (95% CI: 0.871-0.944), and 0.927 (95% CI: 0.897-0.957), respectively. The calibration curves demonstrated consistency between the nomogram and observed values. The DCA curves of the model and validation cohort indicated good clinical utility. CONCLUSION: This study developed a predictive model for renal involvement in hyperuricemia patients with strong predictive performance and validated by internal and external cohorts, aiding in the early detection of high-risk populations for renal involvement.

Remnant cholesterol elevates hyperuricemia risk in the middle aged and elderly Chinese: a longitudinal study.

Evidence regarding the relationship between remnant cholesterol (RC) and hyperuricemia is limited. The purpose of this study is to investigate the association between RC and hyperuricemia in the middle aged and elderly Chinese. Information was extracted from the China Health and Retirement Longitudinal Study (CHARLS) survey 2011 and survey 2015. Four logistic regression models were established. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were applied to balance the baseline. Next, sensitivity analyses and restricted cubic spline (RCS) analysis were conducted to further explore the association. Cross-lagged panel model (CLPM) and mediation analysis were used to deduce the causal relationship between RC and hyperuricemia. This study contained 6,447 participants. A positive association between high RC and hyperuricemia was found in the full adjusted model (OR:1.80, P < 0.001). Similar results were also seen after PSM (OR:1.86, P < 0.001), IPTW (OR:1.80, P < 0.001) and sensitive analysis in non-overweight subgroups (OR:1.77, P < 0.001). Though non-linear relationship was not observed, CLPM exhibited that high level of RC can directly cause increase of blood uric acid (standardized ß = 0.005, P < 0.001). Moreover, mediation analysis suggested that the positive association can be mediated by hypertension (ß = 0.024; p = 0.004), CRP (ß = 0.050; p < 0.001) and WBC (ß = 0.024; p = 0.010). High level of RC is an independent risk factor for hyperuricemia, which can be mediated by inflammation and hypertension.

Kidney function, uric acid, and risk of atrial fibrillation: experience from the AMORIS cohort.

BACKGROUND: Uric acid closely relates to both kidney disease and atrial fibrillation (AF), yet the extent to which it influences the kidney-AF association remains uncertain. We examined the relationship between kidney function and risk of AF, accounting for uric acid levels. METHODS: A total of 308,509 individuals in the Swedish Apolipoprotein-Related Mortality Risk (AMORIS) cohort were included and their serum creatinine and uric acid were measured during 1985-1996. Ten-year incident AF was identified via linkage with the national registers. Glomerular filtration rate (eGFR) (ml/min/1.73 m2) was calculated with the 2009 Chronic Kidney Disease Epidemiology Collaboration equation. Hyperuricemia was defined as > 420 µmol/L for men and > 360 µmol/L for women. RESULTS: Over a mean follow-up of 9.4 years, 10,007 (3.2%) incident AF cases occurred. After adjusting for age, sex, cardiovascular diseases, total cholesterol, triglycerides, and glucose, individuals with low eGFR (< 30 and 30-59 ml/min/1.73 m2 ) had a higher risk of AF compared to those with normal eGFR (60-89) (hazard ratio (HR) = 1.72, 95% confidence interval (CI):1.29-2.30; HR = 1.10, 95% CI: 1.03-1.18, respectively). After further adjusting for uric acid levels, the association disappeared (HR = 0.97, 95% CI: 0.72-1.30; HR = 0.93, 95% CI: 0.86-1.00, respectively). When stratifying by hyperuricemia yes/no, eGFR < 30 ml/min/1.73 m2 was associated with higher AF risk in a small group of individuals without hyperuricemia (HR = 2.58, 95% CI: 1.64-4.07). CONCLUSION: Uric acid largely accounted for the relationship between eGFR and AF in this study. However, in individuals without hyperuricemia, eGFR in the lowest range (< 30 ml/min/1.73 m2) was still associated with increased risk of AF.

[Association of dietary patterns with serum uric acid and hyperuricemia in Chinese adults].

Objective: To analyze the dietary patterns of Chinese adults and explore the relationship with serum uric acid (SUA) and hyperuricemia (HUA). Methods: A total of 9 358 adults were selected in the 2018 China Health and Nutrition Survey. Dietary intake data were collected by three consecutive 24-hour dietary recalls and weighing method. The social demographic information of the survey subjects was obtained through questionnaire surveys. The dietary patterns were extracted using factor analysis, and the relationship between dietary patterns and SUA was analyzed using multiple linear regression analysis. The correlation between HUA and dietary patterns was analyzed using logistic regression analysis models. Results: Four dietary patterns were identified: northern (high intakes of wheat, other cereals,and tubers); modern (high intakes of fruit, dairy, eggs, and nuts); southern (high intakes of rice and vegetables);animal food-wine (high intake of organ meats, seafood, and wine). The multiple linear regression analysis results showed that the northern pattern was negatively correlated with SUA (ß=-0.438, 95%CI: -0.500--0.376); the modern pattern was negatively correlated with SUA (ß=-0.134, 95%CI: -0.219--0.049); the southern model was significantly correlated with higher SUA (ß=0.146, 95%CI: 0.079-0.214); the animal food-wine pattern was positively correlated with SUA (ß=0.188, 95%CI: 0.123-0.252). Logistic regression analysis showed that compared with the northern model score Q1 group, the risk of developing HUA was reduced in Q3 and Q4 groups, with ORs values of 0.777 (95%CI: 0.650-0.929) and 0.509 (95%CI: 0.423-0.613), respectively; and compared with the modern model score Q1 group, the higher the scores in Q3 and Q4 groups, the HUA was lower, with ORs of 0.793 (95%CI: 0.660-0.953) and 0.768 (95%CI: 0.631-0.934), respectively. Compared with the animal food-wine pattern score Q1 group, the risk of developing HUA was increased in both Q3 and Q4 groups (Q3 group: OR=1.224, 95%CI: 1.012-1.480; Q4 group: OR=1.312, 95%CI: 1.086-1.584). Conclusions: Dietary patterns are associated with HUA. The northern and modern patterns are related to lower SUA levels and reduced risk of HUA, while the animal food-wine pattern increases the risk of HUA.

Association between Body Mass Index and Serum Uric Acid: mediation analysis involving liver enzymes indicators.

BACKGROUND: Numerous studies have indicated a growing prevalence of hyperuricemia. Elevated levels of serum uric acid (SUA) have been established as influential factors in conditions such as obesity, metabolic syndrome, diabetes mellitus, gout, and cardiovascular disease. Overweight and obesity are closely related to an increase in SUA. Our objective is to demonstrate the mediating role of liver enzyme in the correlation between body mass index (BMI) and SUA. METHODS: A total of 5925 adults aged 18 to 65 were included in this cross-sectional study. Logistic regression and mediation analysis were used to investigate the relationship between BMI and hyperuricemia as well as liver enzyme levels. Standard methods were used to determine the biochemical indexes, including SUA, liver enzymes, and blood lipids in the collected samples. RESULTS: The study revealed that the prevalence of hyperuricemia was 28.0%. Furthermore, the prevalence of overweight and obesity was as high as 48.5%, with 70.7% of this subgroup presenting with hyperuricemia. There was a positive correlation between BMI and hyperuricemia, and elevated levels of liver enzymes (ALT, AST, GGT) were associated with a higher risk of hyperuricemia. The study also observed a positive correlation between BMI and liver enzymes (ALT, AST, GGT). The study findings suggested that ALT, AST, and GGT played significant mediating roles in the relationship between BMI and SUA. Specifically, the unadjusted model revealed that ALT and GGT accounted for 22.12% and 18.13% of the mediation effects, respectively. CONCLUSIONS: The study found that BMI is associated with hyperuricemia, where liver enzyme abnormalities may have a mediating role. It is suggested that being overweight or obese may affect liver enzyme levels, leading to increased SUA levels. Controlling weight and liver enzyme levels may help prevent and treat hyperuricemia.

Identifying reliable obesity indices for hyperuricemia among middle-aged and elderly populations: a longitudinal study.

BACKGROUND: Given the established link between obesity and hyperuricemia (HUA), the research want to investigate the relationship between different obesity indices and HUA, and further analyze which obesity index can better predict HUA. METHODS: The data were obtained from a longitudinal study involving middle-aged and elderly populations in Dalian, China. The research encompassed individuals who exhibited typical uric acid levels initially and tracked their progress over a three-year period. 8 obesity indices were evaluated retrospectively. Subgroup analyses were conducted to identify susceptible populations. Restricted cubic splines (RCS) were utilized to model the dose-response relationships between obesity indices and HUA. Receiver operating characteristic (ROC) curves were applied to visualize and compare the predictive value of both traditional and new obesity indices for HUA. RESULTS: Among 4,112 individuals with normal baseline uric acid levels, 950 developed HUA. Significant associations with HUA were observed for body mass index (BMI), waist circumference (WC), body roundness index (BRI), cardiometabolic index (CMI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), and abdominal volume index (AVI). Subgroup analysis indicated that all obesity indices proved more effective in assessing the onset of HUA in women without Metabolic Syndrome (MetS). Further analysis using RCS revealed non-linear dose-response relationships between LAP, CMI, VAI, and HUA in males, with similar non-linear relationships observed for all indices in females. The results from the ROC curves indicate that LAP may serve as a better predictor of HUA in males, and CVAI may serve as a better predictor in females. CONCLUSION: HUA is closely associated with obesity indices. Among females, CVAI emerges as the preferred predictive index for HUA. In males, LAP emerges as the preferred predictive index for HUA.

Postmenopausal status increases the risk of uric acid stones.

OBJECTIVE: This study investigated the impact of menopause on stone composition in women with urolithiasis. STUDY DESIGN: A cross-sectional study was conducted from March 2013 to March 2018. Women with urolithiasis patients were divided into two groups according to their menopause status. MAIN OUTCOME MEASURES: The clinical demographic characteristics, stone removal, stone composition, and urine chemistry were investigated. Univariate and multivariate survival analyses were performed to identify risk factors for the risk of uric acid stones. RESULTS: Our study enrolled 1221 female patients with stone diseases, 783 (64.1 %) of whom were postmenopausal (66 patients surgically menopause and 717 patients naturally menopause). Postmenopausal women had higher rates of diabetes and hyperuricemia, a higher serum uric acid level, a higher urinary specific gravity, and a lower estimated glomerular filtration rate. Stone analysis revealed calcium oxalate stones in 66.2 % of the patients, apatite stones in 19.4 %, calcium oxalate and calcium phosphate stones in 7.7 %, uric acid stones in 4.4 %, struvite stones in 2.0 %, and brushite stones in 0.2 %. Postmenopausal women had a higher rate of uric acid stones. Multivariate analysis confirmed that postmenopausal status and hyperuricemia were independent risk factors of uric acid stones. Postmenopausal women required more invasive procedures to remove the stones, and they had lower self-voiding rates. CONCLUSIONS: Postmenopausal women had higher rates of stone episodes, specifically related to uric acid stones. Given the prevalence and impact of chronic kidney diseases, factors that impede optimal renal function management in women must be identified to provide tailored treatment recommendations.

Associations between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and hyperuricemia: a cross-sectional study.

BACKGROUND AND OBJECTIVE: The value of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) assessment in the context of metabolic abnormalities is growing in importance. Nevertheless, the relationship between NHHR and hyperuricemia (HUA) is unknown. This study seeks to investigate the relationship between NHHR and HUA. METHODS: The data derived from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) included 7,876 adult participants. The multivariable logistic regression model, subgroup analysis and smooth fitting curve were utilized in order to investigate the association between NHHR and HUA. RESULTS: In the fully adjusted model 3, NHHR was significantly associated with HUA. Specifically, participants in the highest quartile of NHHR had 1.95 times higher odds of HUA prevalence compared to those in the lowest quartile [2.95 (2.39, 3.64), P < 0.0001]. Although the overall trend suggested a positive association, further analysis using smooth fitting curves and threshold effect analysis indicated that this association was nonlinear, with an inflection point at 5.8. The positive association persisted across different HUA definitions and after removing outliers. Subgroup analysis showed significant interactions between NHHR and HUA in different races and diabetes statuses. The odds of HUA prevalence were higher among non-diabetic participants [1.40 (1.32, 1.49), P < 0.0001] compared to diabetic participants [1.18 (1.06, 1.32), P = 0.0031]. Mexican Americans had the lowest odds of HUA prevalence [1.09 (0.92, 1.27), P = 0.2413] compared to other races. CONCLUSIONS: There is a significant positive association between NHHR and HUA, indicating that NHHR may serve as a potential risk assessment maker for HUA, although further prospective studies are needed for validation.

Effect of Dotinurad on Serum Uric Acid Concentration in Chronic Kidney Disease Patients Treated with Febuxostat.

BACKGROUND: Febuxostat is recommended for treatment of severe hyperuricemia in chronic kidney disease (CKD). We previously reported a significant positive correlation between fractional excretion of uric acid (FEUA) and estimated excretion of uric acid (eEUA) in patients receiving febuxostat and proposed that the addition of uricosuric agents could further decrease serum uric acid (sUA) levels by enhancing FEUA and eEUA in patients treated with febuxostat. METHODS: This retrospective study included 34 patients with CKD who were categorized into three groups (G3-G5) according to their estimated glomerular filtration rate (eGFR). The effects on sUA, FEUA, and eEUA of adding dotinurad (0.5 mg/day) to febuxostat (10 mg/day) were evaluated in these patients. Specifically, we examined changes in sUA, FEUA, and eEUA in each group after the addition of dotinurad. RESULTS: Dotinurad significantly increased FEUA in all groups and notably decreased sUA in groups G3 and G4 but not in group G5. There was no significant change in eEUA in any group. Dotinurad maintained the significant positive correlation between FEUA and eEUA in patients receiving febuxostat. CONCLUSIONS: This study is the first to show the effect of combining dotinurad with febuxostat in lowering sUA levels in G3 and G4 patients. Additional research is required in order to clarify the pharmacological mechanisms of dotinurad in patients with CKD.