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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Association between Visceral Adiposity Index and Hyperuricemia among Steelworkers: The Moderating Effects of Drinking Tea.

BACKGROUND/OBJECTIVES: Steelworkers are more likely to have a higher prevalence of hyperuricemia due to their exposure to special occupational factors and dietary habits. The interrelationships of visceral adiposity index (VAI), hyperuricemia, and drinking tea remain uncertain. This study aimed to assess the association between VAI and hyperuricemia among steelworkers, and if drinking tea modified this association. METHODS: A total of 9928 steelworkers from Hunan Hualing Xiangtan Iron and Steel Company participated in this cross-sectional study. All participants completed a questionnaire, received anthropometric measurements, and provided blood samples for biochemical testing. Three logistic regression models were used to analyze the association between VAI and hyperuricemia. RESULTS: In this study, the prevalence of hyperuricemia was approximately 23.74% (males: 24.41%; females: 20.63%), and a positive correlation between VAI and hyperuricemia risk was observed. In multivariate logistic regression analysis, the risk of hyperuricemia increased 1.76 times (95% CI: 1.64-1.89) and 2.13 times (95% CI: 1.76-2.57) with the increase of ln VAI in males and females, respectively. For males, compared to quartile 1, the risk of hyperuricemia in the second, third, and fourth quartile of VAI were 1.75 (95% CI: 1.11-2.71), 2.56 (95% CI: 1.67-3.93) and 4.89 (95% CI: 3.22-7.43). For females, compared to quartile 1, the risk of hyperuricemia in the second, third, and fourth quartile of VAI were 1.99 (95% CI: 1.40-2.82), 2.92 (95% CI: 1.96-4.34) and 4.51 (95% CI: 2.89-7.02). Additionally, our study found that, compared with not consuming tea, drinking tea could reduce uric acid levels by 0.014 in male steelworkers (t = -2.051, p = 0.040), 0.020 in workers consuming smoked food (t = -2.569, p = 0.010), and 0.022 in workers consuming pickled food (t = -2.764, p = 0.006). CONCLUSIONS: In conclusion, VAI is positively correlated with hyperuricemia in steelworkers. Drinking tea may lower uric acid levels in male steelworkers and steelworkers who prefer smoked and pickled foods.

Comparison of the predictive value of four insulin resistance surrogates and hyperuricemia in women with recurrent pregnancy loss: A cross-sectional study.

BACKGROUND: Insulin resistance (IR), hyperuricemia (HUA), and recurrent pregnancy loss (RPL) elevate the risk of cardiovascular disease and metabolic disorders, while also impacting reproductive health. The relationship between IR, HUA, and RPL has not been thoroughly investigated. This study investigates the relationship between four IR surrogates and the risk of HUA in RPL patients. METHODS: Data from a real-world study on RPL in China were analyzed using multivariable regression to determine the relationship between HUA and triglyceride and glucose (TyG) index, triglyceride glucose-body mass index (TyG-BMI), triglyceride to high-density lipoprotein cholesterol (TG/HDL-c) ratio, and metabolic score for insulin resistance (METS-IR). The predictive ability of these surrogates for detecting HUA in RPL patients was evaluated using the area under the curve and receiver operating characteristic analysis. Sensitivity analysis was performed using bootstrapping resampling. RESULTS: The study included 769 patients with a mean age of 30 ± 4 years old, 8.32% of whom had HUA. Four IR surrogates were closely related to HUA in patients of RPL after adjusting for age, menstrual cycle, creatinine, alanine transaminase, aspartate transaminase, total cholesterol, homocysteine, and low-density lipoprotein, with area under the curve values of TyG index (OR = 0.693, 95% confidence interval [CI]: 0.626, 0.759), TyG-BMI (OR = 0.731 95% CI: 0.657, 0.805), TG/HDL-C (OR = 0.703, 95% CI: 0.641, 0.764), and METS-IR (OR = 0.728, 95% CI: 0.655, 0.799). Bootstrap resampling yielded similar results. CONCLUSIONS: The TyG index, TyG-BMI, TG/HDL-c, and METS-IR significantly correlated with HUA in patients with RPL. The TyG-BMI had the highest predictive value of the four IR surrogates.

Comparison of uric acid elevation between aspirin-ticagrelor and aspirin-clopidogrel during dual antiplatelet therapy.

OBJECTIVE: This study investigated whether serum uric acid levels are more elevated in the aspirin-ticagrelor group than in the aspirin-clopidogrel group. Materials and Materials and methods: We conducted a retrospective cohort study with patients between 2013 and 2020. Baseline and maximum serum uric acid levels within a 6-month follow-up period were analyzed to determine the increase in both groups. RESULTS: A total of 41,877 patients were enrolled. A statistically significant elevation of serum uric acid levels was found in the aspirin-ticagrelor group compared to the aspirin-clopidogrel group (odds ratio (OR; 95% confidence interval (CI)) = 1.36 (1.15 - 1.60), p < 0.001). Kidney dysfunction and diuretic use were also identified as risk factors for uric acid elevation. CONCLUSION: Monitoring serum uric acid levels is recommended during aspirin-ticagrelor therapy, especially in patients with kidney dysfunction or those using diuretics.

Uric acid and metabolic syndrome: Importance of hyperuricemia cut-off.

BACKGROUND: The relationship between HyperUricemia (HU) and Metabolic Sindrome (MS) and if Uric Acid (UA) should be inserted into MS definitions is a matter of debate. Aim of our study was to evaluate the correlation between UA and HU with Insulin Resistance (IR) and MS in a population of hypertensive patients. HU was defined with two cut-offs (the classic one of ≥6 mg/dL for women and ≥ 7 for men; the newly proposed URRAH one with ≥5.6 mg/dL for both sexes). METHODS: We enrolled 473 Hypertensive patients followed by the Hypertension Unit of San Gerardo Hospital (Monza, Italy). IR was defined through TG/HDL ratio and NCEP-ATP-III criteria were used for MS diagnosis. RESULTS: MS was found in 33.6 % while HU affected 14.8 % of subjects according to the traditional cut-off and 35.9 % with the URRAH cut-off. 9.7 % (traditional cut-off) and 17.3 % (URRAH's threshold) of the subjects had both HU and MS. UA level was significantly higher in MS group (5.7 vs 4.9 mg/dL, p < 0.0001) as well as for HU (29.0 vs 7.6 % and 51.6 vs 28.0 %, for classic and URRAH cut-off respectively, p < 0.0001 for both comparison). Logistic multivariable regression models showed that UA is related to MS diagnosis (OR = 1.608 for each 1 mg/dL), as well as HU with both cut-off (OR = 5.532 and OR = 3.379, p < 0.0001 for all comparison, for the classic cut-off and the URRAH one respectively). CONCLUSIONS: The main finding of our study is that UA and HU significantly relate to IR and MS. The higher the values of UA and the higher the cut-off used, the higher the strength of the relationship.

Association between 25 hydroxyvitamin D and serum uric acid level in the Chinese general population: a cross-sectional study.

BACKGROUD: The relationship between serum uric acid (SUA) and 25-hydroxyvitamin D (25(OH)D) has been variably characterized in existing literature, with inconsistent results regarding its nature and implications in the Chinese population. This study aims to clarify this association, considering the potential impact of vitamin D levels on SUA. METHODS: This cross-sectional study involved 7,086 individuals from the Second Affiliated Hospital of Zhejiang University School of Medicine, screened throughout 2020. We collected data on 25(OH)D, SUA, and other metabolic markers. Logistic regression models adjusted for confounding factors were utilized to analyze the relationships. RESULTS: Our findings illustrate a statistically significant inverted U-shaped relationship between 25(OH)D and SUA. The identified threshold effect at 28.82 ng/ml is pivotal; with 25(OH)D levels below this point associated with an increased risk of hyperuricemia (odds ratio: 1.0146, p = 0.0148), and levels above it offering protective benefits (odds ratio: 0.9616, p = 0.0164). CONCLUSIONS: Our findings confirm a nonlinear, inverted U-shaped correlation between 25(OH)D and SUA, emphasizing the importance of maintaining vitamin D levels within a specific range to effectively manage hyperuricemia. These results support the implementation of personalized vitamin D supplementation strategies to optimize metabolic health outcomes, highlighting the complex interplay between vitamin D status and uric acid levels.

Serum uric acid and pulmonary arterial hypertension: A two-sample Mendelian randomization study.

BACKGROUND: Observational studies have suggested a correlation between hyperuricemia and pulmonary arterial hypertension (PAH), yet the causal relationship remains uncertain. We aimed to establish this link using Mendelian Randomization (MR) methods. OBJECTIVES: Based on publicly accessible data, our study employs MR to determine the causal relationship between uric acid (UA) and PAH. METHOD: MR analysis was conducted among individuals of European descent. Genetic instruments linked to UA (p-value < 5 × 10-8) were extracted from the Chronic Kidney Disease Genetic Consortium and genome-wide association study databases. PAH risk genetic associations were sourced separately. We employed four MR methods (MR-Egger, weighted median, inverse variance weighted, and weighted mode) with selected instrumental variables to assess the causal association between UA and PAH. MR-PRESSO was used to evaluate pleiotropy and outlier Single Nucleotide Polymorphisms (SNPs), while Cochran's Q test and funnel plot assessed SNP heterogeneity. Leave-one-out analysis examined SNP impacts on causal assessment. RESULT: Two-sample MR analysis revealed a positive, causal relationship between UA levels and PAH. CONCLUSION: Our MR analysis provides robust evidence of a causal link between serum UA and PAH, suggesting UA's potential as a biomarker and therapeutic target for PAH.

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.