Health disparities in gout.

PURPOSE OF REVIEW: Gout, the most common type of inflammatory arthritis in the world, is characterized by painful episodes of arthritis linked by asymptomatic intercritical periods of hyperuricemia. Once characterized as a disease of wealthy white men, contemporary evidence demonstrates gout disproportionately afflicts racial/ethnic minorities, Indigenous populations and other underrepresented groups leading to significant health disparities. RECENT FINDINGS: Herein, we review the current literature reporting a higher incidence and prevalence of gout in racial/ethnic minorities and Indigenous populations, in addition to a growing gout burden reported in females. We also examine how these population are more likely to receive suboptimal treatment for flares and chronic phases of gout. Additionally, we examine biologic and social health determinants that may be contributing to these findings. SUMMARY: Racial/ethnic minorities, Indigenous populations, and females have experienced a disproportionate rise in the prevalence and incidence of gout in recent years, are more likely to seek acute medical care and are less likely to receive optimal long-term care for gout with urate lowering therapy. Mechanisms underpinning these findings appear to be multifactorial and include differences in social determinants of care and in some cases may be due to population differences in select biologic factors such as differences in age, sex, genetics.

Changes in alcohol intake and serum urate changes: longitudinal analyses of annual medical examination database.

INTRODUCTION: Despite the established cross-sectional association between alcohol intake and serum urate (SU), its longitudinal association remains unknown. This study aimed to determine whether changes in alcohol intake have a clinically relevant association with SU change. METHOD: We conducted retrospective analyses using systematically collected annual medical examination data from October 2012 to October 2022 in a Japanese preventive medicine centre. The exposure was changes in alcohol intake between two consecutive visits. The association of SU changes with alcohol intake changes was estimated by mixed-effect linear regression with adjustment for relevant covariates. RESULTS: We analysed 63 486 participants (median age, 47.0 years; 55% women; 58.6% regular alcohol drinkers with a median of 1.4 drinks/day) with 370 572 visits. The median SU level was 5.3 mg/dL, and 506 (0.8%) participants had diagnoses of gout or hyperuricemia without medication use during the study period. Decreasing one daily alcohol intake had a clinically small association with SU changes (-0.019 (95% CI: -0.021 to -0.017) mg/dL). Beer had the largest association with SU (-0.036 (95% CI: -0.039 to -0.032) mg/dL for one beer decrease). Complete discontinuation of any alcohol from a mean of 0.8 drinks/day was associated with -0.056 mg/dL (95% CI: -0.068 to -0.043) decrease in SU; the association became larger in hyperuricemic participants (-0.110 mg/dL (95% CI: -0.154 to -0.066) for alcohol discontinuation from a mean of 1.0 drinks/day). CONCLUSIONS: This study revealed changes in alcohol intake had small associations with SU change at the general Japanese population level. Complete discontinuation of alcohol in hyperuricemic participants had only modest improvement in SU.

Gout risk in adults with pre-diabetes initiating metformin.

OBJECTIVE: Despite the strong association between gout and pre-diabetes, the role of metformin in gout among individuals with pre-diabetes remains uncertain. We compared the incidence rates of gout in adults with pre-diabetes starting metformin with those not using antidiabetic treatments. METHODS: We conducted a new-user, propensity score-matched cohort study using electronic health records from an academic health system (2007-2022). Pre-diabetes was defined based on haemoglobin A1c levels. Metformin users were identified and followed from the first metformin prescription date. Non-users of antidiabetic medications were matched to metformin users based on propensity score and the start of follow-up. The primary outcome was incident gout. Cox proportional hazards models estimated the HR for metformin. Linear regression analyses assessed the association between metformin use and changes in serum urate (SU) or C-reactive protein (CRP). RESULTS: We identified 25 064 individuals with pre-diabetes and propensity score-matched 1154 metformin initiators to 13 877 non-users. Baseline characteristics were well balanced (all standardised mean differences <0.1). The median follow-up was 3.9 years. The incidence rate of gout per 1000 person-years was lower in metformin users 7.1 (95% CI 5.1 to 10) compared with non-users 9.5 (95% CI 8.8 to 10.2). Metformin initiation was associated with a reduced relative risk of gout (HR 0.68, 95% CI 0.48 to 0.96). No relationship was found between metformin and changes in SU or CRP. CONCLUSIONS: Metformin use was associated with a reduced risk of gout among adults with pre-diabetes, suggesting that metformin may be important in lowering gout risk in individuals with pre-diabetes.

Ultraprocessed food consumption, genetic predisposition, and the risk of gout: the UK Biobank study.

OBJECTIVE: This study aimed to examine the interactions between ultraprocessed food (UPF) consumption and genetic predisposition with the risk of gout. METHODS: This prospective cohort study analysed 181 559 individuals from the UK Biobank study who were free of gout at baseline. UPF was defined according to the NOVA classification. Assessment of genetic predisposition for gout was developed from a genetic risk score of 33 single nucleotide polymorphisms. Cox proportional hazards were used to estimate the associations between UPF consumption, genetic predisposition and the risk of gout. RESULTS: Among the 181 559 individuals in the study, 1558 patients developed gout over 1 648 167 person-years of follow-up. In the multivariable adjustment model, compared with the lowest quartile of UPF consumption, the hazard ratio (HR) and 95% CI of the highest UPF consumption was 1.16 (1.01, 1.33) for gout risk, and there was a non-linear correlation between UPF consumption and the development of gout. In substitution analyses, replacing 20% of the weight of UPF in the daily intake with an equal amount of unprocessed or minimally processed food resulted in a 13% lower risk of gout (HR: 0.87; 95% CI: 0.79, 0.95). In the joint-effect analysis, the HR (95% CI) for gout was 1.90 (1.39, 2.60) in participants with high genetic predisposition and high UPF consumption, compared with those with low genetic predisposition and low UPF consumption. CONCLUSION: In summary, UPF consumption was found to be associated with a higher risk of gout, particularly in those participants with genetic predisposition to gout. Our study indicated that reducing UPF consumption is crucial for gout prevention.

A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. METHODS: A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. RESULTS: Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%. CONCLUSIONS: The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.

Empagliflozin and Risk of Incident Gout: Analysis from the EMPagliflozin Comparative Effectiveness and SafEty (EMPRISE) Cohort Study.

BACKGROUND: Hyperuricemia is frequently observed in patients with type 2 diabetes (T2D) and is associated with increased risk of gout and cardiovascular disease (CVD). Empagliflozin lowers serum urate levels by enhancing its urinary excretion. OBJECTIVE: To compare initiators of empagliflozin vs dipeptidyl peptidase-4 inhibitor (DPP4i) and initiators of empagliflozin vs glucagon-like peptide-1 receptor agonist (GLP-1RA) with respect to the risk of incident gout events. DESIGN AND PARTICIPANTS: Using three claims-based datasets from 08/2014 to 09/2019, we generated two cohorts (cohort 1: empagliflozin vs DPP4i; cohort 2: empagliflozin vs GLP-1RA) of adult patients with T2D and without prior history of gout or gout-specific medication dispensing separately in each dataset. To assess the risk of incident gout, we estimated hazard ratios (HR) and rate differences (RD) per 1000 person-years (PY) with their 95% confidence intervals (CI) before and after 1:1 propensity score (PS) matching adjusting for 141 baseline covariates. KEY RESULTS: We identified 102,262 pairs of 1:1 propensity score-matched adults in cohort 1 and 131,216 pairs in cohort 2. Over a mean follow-up period of 8 months on treatment, the risk of gout was lower in patients initiating empagliflozin compared to DPP4i (HR = 0.69: 95% CI (0.60-0.79); RD = - 2.27: 95% CI (- 3.08, 1.46)) or GLP-1RA (HR = 0.83: 95% CI (0.73-0.94); RD = - 0.99: 95% CI (- 1.66, - 0.32)). Results were consistent across subgroups (sex, age, body mass index, chronic kidney disease, heart failure, cardiovascular disease, and concurrent diuretic use) and sensitivity analyses. CONCLUSIONS: Among adults with T2D, the initiation of empagliflozin vs a DPP4i or GLP-1RA was associated with lower risk of incident gout, complementing results from a post hoc analysis of the EMPA-REG OUTCOME trial and previously published observational research focusing on the sodium-glucose co-transporter-2 inhibitor class in more narrowly defined study populations.

Dose-Response Relationship Between Alcohol Consumption and Gout Risk: Do Subtypes of Alcoholic Beverages Make a Difference?

OBJECTIVE: Although previous studies have explored the association of drinking with gout risk, we sought to explore the dose-response relationship and the evidence between subtypes of alcoholic beverages and gout risk. METHODS: The weekly alcoholic beverage consumption of patients in the UK Biobank was collected and calculated. The Cox regression model was applied to assess the effects of drinking alcohol in general and its subtypes on gout risk by calculating the hazard ratio (HR) and 95% CIs. Additionally, the restricted cubic splines were used to estimate the dose-response relationship between alcohol consumption and gout risk. To evaluate the robustness, we performed subgroup analysis across various demographic characteristics. RESULTS: During a mean follow-up period of 11.7 years, a total of 5728 new incident gout cases were diagnosed among 331,865 participants. We found that light alcohol consumption was linked to a slight decrease in gout incidence among female individuals (HR 0.78, 95% CI 0.65-0.94, P = 0.01), whereas there was no significant association in male individuals. Moreover, the dose-response relationship showed that drinking light red wine and fortified wine could reduce the gout risk, whereas beer or cider, champagne or white wine, and spirits increased the gout risk at any dose. CONCLUSION: Our study suggested a J-shaped dose-response relationship between drinking and gout risk in female individuals, but not in male individuals. For specific alcoholic beverages, light consumption of red wine and fortified wine was associated with reduced gout risk. These findings offer new insights into the roles of alcoholic beverages in gout incidence risk, although further validation is warranted.

Gout: one year in review 2023.

Gout is a chronic joint disease caused by the deposition of monosodium urate crystals into and around the articular tissues. In the last two years, new insights regarding diagnosis, genetic involvement, pathogenesis, comorbidities, and clinical data, have allowed the identification of new strategies to improve the control of the disease and its flares. In keeping, the discover of new mechanisms concerning crystal-induced inflammation have suggested new ways for the management not only of gout, but also other systemic diseases, mainly including renal and cardiovascular disorders. In this context it is very representative the case of colchicine which, given the surprising results obtained both in laboratory and clinical experiments, has recently received by FDA the approval for the prevention of cardiovascular disorders.

The prevalence and independent risk factors of elevated common carotid artery intima-media thickness and carotid plaque in patients with gout.

OBJECTIVES: Gout patients are at high risk of carotid atherosclerosis, which could be convincingly reflected by common carotid artery intima-media thickness (CCAIMT) and carotid plaque. The current study aimed to investigate the prevalence and risk factors of thick CCAIMT and carotid plaque in gout patients. METHODS: Comprehensive demographic characteristics, chronic comorbidities, disease features, and biochemical indexes (42 parameters) were obtained from 237 gout patients. CCAIMT and carotid plaque were evaluated by bilateral carotid artery ultrasound in gout patients and 80 healthy controls. RESULTS: The CCAIMT and carotid plaque percentage were increased in gout patients compared to healthy controls (both p<0.001). In detail, the prevalence of thick CCAIMT (>0.9 mm) and carotid plaque was 22.4% and 34.6% in gout patients, respectively. Forward-stepwise multivariate logistic regression model revealed that age (p<0.001, odds ratio (OR)=1.143], disease duration (p=0.001, OR=1.176), alkaline phosphatase (ALP) (p=0.002, OR=1.037), and low-density lipoprotein cholesterol (LDLC) (p=0.039, OR=2.144) were independently associated with elevated thick CCAIMT risk, while serum uric acid (SUA) (p=0.002, OR=0.992) exhibited an opposite trend; their combination well-identified thick CCAIMT risk [area under the curve (AUC)=0.910] by receiver operator characteristic (ROC) curve. Meanwhile, age (p<0001, OR=1.116), tophus (p=0.009, OR=3.523), and triglycerides (TG) (p=0.014, OR=1.323) were independently associated with a higher risk of carotid plaque, while SUA (p=0.008, OR=0.995) showed an opposite trend; their combination also well-identified carotid plaque risk (AUC=0.886) by ROC curve. CONCLUSIONS: Thick CCAIMT and carotid plaque are prevalent in gout patients, whose occurrence relates to age, disease duration, ALP, LDLC, SUA, TG, and tophus.

Increased frequency of hepatic steatosis and fibrosis in patients with gout detected by transient elastography.

OBJECTIVES: It has been suggested that gout is associated with non-alcoholic fatty liver disease (NAFLD). Our aim was to assess NAFLD in gout patients using the validated non-invasive imaging technique, transient elastography (FibroScan). METHODS: FibroScans in consecutive gout patients in a single centre from 11/1/2016 to 11/1/2021 and reviewed retrospectively. FibroScan results include the E- score (kPA), measuring liver stiffness, and controlled attenuation parameter (CAP) score (dB/m), assessing steatosis. In addition, a FIB-4 fibrosis score was calculated. RESULTS: 47 gout patients (7 females, 14.9%; 40 males, 85.1%) underwent FibroScans. The mean age was 59.8 years, the mean body mass index (BMI) was 30.95 kg/m2, and gout duration 0-49 years. Tophi were present in 11 (26.2%). Comorbidities included dyslipidaemia (86.7%), diabetes mellitus (31.1%), known liver disease (33.3%), current alcohol consumption (46.8%), ALT or AST elevations (54.4%), and hyperuricaemia (53.7%). FibroScan results revealed hepatic steatosis (CAP >238 dB/m) in 40 (85.1%) and were significantly associated with BMI (r=0.53, p=0.0001) but not age, serum urate (SU), glucose, triglycerides, ALT, AST. FibroScan also revealed fibrosis (E score >7) in 9 (19.1%); severe fibrosis (cirrhosis) in 8. Fibrosis was significantly associated with age (p=0.03) and known liver disease (p=0.003) but not BMI, SU, or comorbidities. The FIB-4 score was significantly associated with the fibrosis score (r2=0.24, p=0.0009) but not with CAP, ALT, or AST. CONCLUSIONS: Despite not being associated with common gout comorbidities, fatty liver and liver fibrosis were common in this gout cohort, suggesting FibroScan screening in gout patients to assess NAFLD, irrespective of serum transaminase levels.

The association between triglyceride glucose index and gout: a cross-sectional analysis based on NHANES 2007-2018.

BACKGROUND: The triglyceride glucose (TyG) index, defined as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2], provides insights into overall metabolic status. However, the association between the TyG index and gout has not been investigated. Therefore, this study explored the correlation between the TyG index and gout. METHODS: Using data from the National Health and Nutrition Examination Survey, which was conducted from 2007 to 2018, this study investigated the relationship between the TyG index and gout. Demographic data and potential risk factors were analyzed and compared using t tests for continuous data and chi-square tests for categorical data. Logistic regression and subgroup analysis were performed to examine the association between the TyG index and gout. RESULTS: A total of 14,924 participants were enrolled, among whom 726 (4.86%) were diagnosed with gout. Without controlling for any covariates, a significant positive correlation was observed between an elevated TyG index and increased risk of gout, with an odds ratio (OR) of 2.07 and a 95% confidence interval (CI) ranging from 1.76 to 2.43. After full adjustment, this association remained statistically significant, with an adjusted OR of 1.43 and a 95% CI from 1.14 to 1.80. Subgroup analyses revealed significant interactions, particularly for females (OR = 2.55; 95% CI: 2.00-3.26), individuals with no military service history (OR = 2.15; 95% CI: 1.66-2.43), and those without diabetes (OR = 2.00; 95% CI: 1.64-2.43). CONCLUSION: A positive correlation was observed between the TyG index and gout. Consequently, further large-scale prospective studies are warranted for a comprehensive analysis of the role of the TyG index in gout.

Relationships between obesity and prevalence of gout in patients with type 2 diabetes mellitus: a cross-sectional population-based study.

BACKGROUND: The purpose of this study was to investigate the relationships between generalized, abdominal, and visceral fat obesity and the prevalence of gout in patients with type 2 diabetes mellitus (T2DM). METHODS: Data were obtained from the electronic medical databases of the National Metabolic Management Center (MMC) of Yuhuan Second People's Hospital and Taizhou Central Hospital (Taizhou University Hospital) between September 2017 and June 2023. Four obesity indicators were analyzed: waist circumference (WC), waist-to-hip ratio (WHR), body mass index (BMI), and visceral fat area (VFA). The relationships between these parameters and gout prevalence were analyzed using multivariate logistic regression and restricted cubic spline (RCS) analyses. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of the four parameters for gout. RESULTS: This cross-sectional study enrolled 10,535 participants (600 cases and 9,935 controls). Obesity was more common in patients with gout, and the obesity indicators were markedly higher in this group. After adjustment for confounders, obesity, as defined by BMI, WC, WHR, and VFA, was found to be associated with greater gout prevalence, with odds ratios (OR) of 1.775, 1.691, 1.858, and 1.578, respectively (P < 0.001). The gout odds ratios increased markedly in relation to the obesity indicator quartiles (P-value for trend < 0.001), and the obesity indicators were positively correlated with gout prevalence, as shown using RCS. The area under the ROC curve values for BMI, WC, WHR, and VFA were 0.629, 0.651, 0.634, and 0.633, respectively. CONCLUSION: Obesity-whether general, abdominal, or visceral fat obesity-was positively linked with elevated gout risk. But uncovering the causality behind the relationship requires further prospective study. Obesity indicators (BMI, WC, WHR, and VFA) may have potential value for diagnosing gout in clinical practice.

Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.

BACKGROUND: We aimed to determine whether integrating concepts from the notes from the electronic health record (EHR) data using natural language processing (NLP) could improve the identification of gout flares. METHODS: Using Medicare claims linked with EHR, we selected gout patients who initiated the urate-lowering therapy (ULT). Patients' 12-month baseline period and on-treatment follow-up were segmented into 1-month units. We retrieved EHR notes for months with gout diagnosis codes and processed notes for NLP concepts. We selected a random sample of 500 patients and reviewed each of their notes for the presence of a physician-documented gout flare. Months containing at least 1 note mentioning gout flares were considered months with events. We used 60% of patients to train predictive models with LASSO. We evaluated the models by the area under the curve (AUC) in the validation data and examined positive/negative predictive values (P/NPV). RESULTS: We extracted and labeled 839 months of follow-up (280 with gout flares). The claims-only model selected 20 variables (AUC = 0.69). The NLP concept-only model selected 15 (AUC = 0.69). The combined model selected 32 claims variables and 13 NLP concepts (AUC = 0.73). The claims-only model had a PPV of 0.64 [0.50, 0.77] and an NPV of 0.71 [0.65, 0.76], whereas the combined model had a PPV of 0.76 [0.61, 0.88] and an NPV of 0.71 [0.65, 0.76]. CONCLUSION: Adding NLP concept variables to claims variables resulted in a small improvement in the identification of gout flares. Our data-driven claims-only model and our combined claims/NLP-concept model outperformed existing rule-based claims algorithms reliant on medication use, diagnosis, and procedure codes.

Nonlinear association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and hyperuricemia in cancer patients: evidence from NHANES 2007-2018.

BACKGROUND: Evidence shows that cancer patients are more likely to have hyperuricemia (HUA) compared to the general population, with lipid metabolism playing a significant role. However, it is still unclear whether there is a non-linear relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and HUA in these patients. This study aims to explore the association between NHHR and HUA in cancer patients. METHODS: This study included participants from the NHANES database from 2007 to 2018. We used multivariable logistic regression, restricted cubic splines (RCS) analysis, and subgroup analysis to examine the association between NHHR and HUA and gout in cancer patients, as well as to investigate differences in this association among specific subgroups. RESULTS: A total of 2826 participants were included, with a HUA prevalence of 24.30%. Weighted multivariable logistic regression showed that for each unit increase in NHHR, the odds of HUA in cancer patients increased by 16% (95% confidence interval [CI]: 1.06, 1.29, P = 0.002). When NHHR was divided into tertiles, those in the highest tertile (Q3) had a 1.84 times higher odds of developing HUA compared to those in the lowest tertile (Q1) (95% CI: 1.32, 2.58, P < 0.001). However, there was no significant association with gout. RCS analysis further revealed a significant non-linear positive association, particularly among males. Subgroup analysis and interaction tests indicated a stronger association in cancer patients who did not have a history of stroke. CONCLUSION: There is a non-linear association between NHHR and HUA in cancer patients.

Cardiovascular safety of using non-steroidal anti-inflammatory drugs for gout: a Danish nationwide case-crossover study.

Gout attacks are treated with uric-lowering and anti-inflammatory drugs. In patients with gout, non-steroidal anti-inflammatory drugs (NSAIDs) could be both cardiovascular beneficial, due to their anti-inflammatory actions, and cardiovascular hazardous, due to their prothrombotic, hypertensive, and proarrhythmic side effects. We, therefore, examined the risk of cardiovascular events associated with NSAID use in patients with gout. We conducted a nationwide, population-based case-crossover study of all Danes ≥ 18 years of age with first-time gout during 1997-2020, who experienced a cardiovascular event (myocardial infarction, ischemic stroke, congestive heart failure, atrial fibrillation/flutter, or cardiovascular death) (n = 59,150). The exposure was use of NSAIDs, overall and according to type (ibuprofen, naproxen, or diclofenac). We used the dates 300, 240, 180, and 120 before the outcome date as reference dates. We used the Mantel-Haenszel method to calculate odds ratios (ORs) with 95% confidence intervals (CIs) of the association between NSAID use and cardiovascular events. NSAID use was overall associated with 12% decreased odds of a cardiovascular event (OR = 0.88, 95% CI: 0.85-0.91). This decreased odds ratio was observed for the use of ibuprofen (OR = 0.92, 95% CI: 0.88-0.97) and naproxen (OR = 0.85, 95% CI: 0.74-0.97), but not for the use of diclofenac (OR = 0.97, 95% CI: 0.90-1.05). Overall, use of NSAIDs was associated with decreased odds of all the individual components of the composite outcome. NSAIDs were not associated with an increased cardiovascular event rate when used in gout patients. Ibuprofen and naproxen appeared to have better cardiovascular risk profiles than diclofenac.

Bisphenol A and its alternatives bisphenol S and F exposure with serum uric acid levels, hyperuricemia, and gout prevalence among US adults: a nationally representative cross-sectional study.

BACKGROUND: Recent studies suggested inconclusive associations between bisphenols exposure and hyperuricemia risk. Our objective was to assess the potential association of bisphenol A (BPA) and its substitutes bisphenol S and F (BPS and BPF) exposure with serum uric acid (SUA) levels, hyperuricemia, and gout prevalence among US adults within the NHANES 2013-2016 datasets. METHODS: Multivariable linear and logistic regression models were used to explore the associations of urinary bisphenols concentrations with SUA levels, hyperuricemia, and gout prevalence, in total population and different sex groups. The restricted cubic spline (RCS) model was used to explore the dose-response relationship. RESULTS: In total population, doubling of urinary BPS and ∑BPs concentrations showed associations with an increase of 2.64 µmol/L (95% CI: 0.54, 4.74) and 3.29 µmol/L (95% CI: 0.59, 5.99) in SUA levels, respectively. The RCS model indicated a significantly "J"-shaped dose-response relationship between BPS exposure and SUA levels. Compared to the reference group of urinary BPS, males in the highest quartile displayed a 13.06 µmol/L (95% CI: 0.75, 25.37) rise in SUA levels. For females, doubling of urinary BPS concentrations was associated with a 3.30 µmol/L (95% CI: 0.53, 6.07) increase in SUA levels, with a significant linear dose-response relationship. In total population, doubling of urinary BPA concentrations showed a 1.05-fold (95% CI: 0.97, 1.14) adjusted risk of having hyperuricemia, with an inverted "U" curve. Doubling of urinary ∑BPs concentrations was associated with a 1.05-fold (95% CI: 0.96, 1.14) adjusted risk of hyperuricemia in total population, with a significant monotonic dose-response relationship. In females, doubling of urinary BPS concentrations was associated with a 1.45-fold (95% CI: 1.01, 2.08) adjusted increased risk of having gout, with a "J" shaped relationship. CONCLUSIONS: BPA and BPS exposure to some extent were associated with elevated SUA levels and increased risk of hyperuricemia, with different dose-response relationships and sex differences.

Diagnosis of Gout as a Correlative Risk for Acute Myocardial Infarction in the Absence of Traditional Cardiovascular Risk Factors.

OBJECTIVES: We aimed to study the impact of gout as a correlative risk factor in the incidence of acute myocardial infarction (AMI) among patients without known MI risk factors. Our study population was obtained from the National Inpatient Sample (NIS) 2011-2018 using the International Classification of Diseases, Ninth and Tenth Revisions. METHODS: This study included patients without cardiovascular disease (CVD), and various outcomes were compared among patients with and without gout. Cohorts were weighted using an algorithm provided by the NIS, which allows for national estimates. Our primary endpoint was the odds of developing an MI, and secondary endpoints were adverse hospital events and length of stay. In total, 117,261,842 patients without CVD risk factors were included in this study, 187,619 (0.16%) of whom had a diagnosis of gout. RESULTS: Patients without CVD risk factors who had gout were older and more likely to be male compared with patients without gout. Among patients without CVD risk factors, the odds of having an AMI were significantly higher in those with gout compared with those without, even after adjusting for chronic nonsteroidal anti-inflammatory drug and oral steroid use. Moreover, patients without CVD risk factors and with gout were more likely to develop acute renal failure, acute thromboembolic event, shock, acute gastrointestinal bleed, and arrhythmia compared with those without gout. Furthermore, patients without CVD risk factors who were admitted with gout had higher mortality compared with those without gout. CONCLUSIONS: In our study, we found that patients without risk factors for AMI who had gout were more likely to develop AMI compared with those without gout. Furthermore, the same patients were more likely to develop other adverse outcomes. Even with proper management, these individuals should be monitored closely for coronary events.

Serum Urate and Recurrent Gout.

Importance: Approximately 12 million adults in the US have a history of gout, but whether serum urate levels can help predict recurrence is unclear. Objective: To assess associations of a single serum urate measurement with subsequent risk of acute gout flares and subsequent risk of hospitalizations for gout among patients in the UK with a history of gout. Design, Setting, and Participants: This retrospective study included patients with a history of gout identified from the UK between 2006 and 2010 who were followed up through Primary Care Linked Data medical record linkage until 2017 and through the Hospital Episode Statistics database until 2020. Exposures: Serum urate levels at enrollment. Main Outcome and Measure: Rate of recurrent acute gout, ascertained by hospitalization, outpatient, and prescription/procedure records, and adjusted rate ratios using negative binomial regressions. Results: Among 3613 patients with gout (mean age, 60 years; 3104 [86%] men), 1773 gout flares occurred over a mean follow-up of 8.3 years. Of these, 1679 acute gout flares (95%) occurred in people with baseline serum urate greater than or equal to 6 mg/dL and 1731 (98%) occurred in people with baseline serum urate greater than or equal to 5 mg/dL. Rates of acute gout flares per 1000 person-years were 10.6 for participants with baseline urate levels less than 6 mg/dL, 40.1 for levels of 6.0 to 6.9 mg/dL, 82.0 for levels of 7.0 to 7.9 mg/dL, 101.3 for levels of 8.0 to 8.9 mg/dL, 125.3 for urate levels of 9.0 to 9.9 mg/dL, and 132.8 for levels greater than or equal to 10 mg/dL. Rate ratio of flares were 1.0, 3.37, 6.93, 8.67, 10.81, and 11.42, respectively, over 10 years (1.61 [1.54-1.68] per mg/dL). Rates of hospitalization per 1000 person-years during follow-up were 0.18 for those with baseline serum urate less than 6 mg/dL, 0.97 for serum urate of 6.0 to 6.9 mg/dL, 1.8 for serum urate of 7.0 to 7.9 mg/dL, 2.2 for serum urate of 8.0 to 8.9 mg/dL, 6.7 for serum urate of 9.0 to 9.9 mg/dL, and 9.7 for serum urate greater than or equal to 10 mg/dL. Rate ratios of hospitalization for gout, adjusting for age, sex, and race were 1.0, 4.70, 8.94, 10.37, 33.92, and 45.29, respectively (1.87 [1.57-2.23] per mg/dL). Conclusions and Relevance: In this retrospective study of patients with a history of gout, serum urate levels at baseline were associated with the risk of subsequent gout flares and rates of hospitalization for recurrent gout. These findings support using a baseline serum urate level to assess risk of recurrent gout over nearly 10 years of follow-up.

Association between gout and the risk of osteoporosis and fractures: a meta-analysis.

OBJECTIVE: The relationship between gout and osteoporosis is poorly clarified, and the association between gout and fractures incidence remains controversial. Hence, in the present study, we aimed to comprehensively evaluate the available literature to elucidate whether gout is associated with an increased risk of both osteoporosis and fractures. MATERIALS AND METHODS: We conducted an exhaustive search of pertinent literature published until 20 March 2023, in well-recognized databases, namely Medline, Embase and Cochrane Library, focusing on examining the association between gout and the risk of osteoporosis or fracture. Meta-analysis was performed to aggregate the relative risks (RR) using random- or fixed-effects models. Sensitivity analyses were conducted iteratively, whereby each study was removed sequentially to gauge its impact on the overall outcome. Publication bias was assessed using Egger's and Begg's tests. This study was registered with PROSPERO (registry number: CRD42022376822). RESULTS: Herein, we included 10 observational studies comprising a total of 1,606,095 participants. An independent population sample of four studies validated the significant association between gout and osteoporosis (RR = 1.25, 95% confidence interval [CI] 1.05-1.48), with the results demonstrating robustness. However, our analysis did not detect any association between gout and fracture risk when compared with the control group (RR = 1.09, 95%CI 0.99-1.19), along with high heterogeneity (p for heterogeneity = 0.000; I2 = 79.7%). Further subgroup analysis revealed that gout is positively associated with fracture risk in the Chinese population (RR = 1.17, 95%CI 1.14-1.21), with no evidence of heterogeneity (p for heterogeneity = 0.420; I2 = 0.00%). CONCLUSION: Our meticulous evaluation of the available literature indicates that gout has no discernible impact on fracture incidence, although it is positively associated with an enhanced risk of osteoporosis. Therefore, it is imperative to prioritize preventive measures to prevent osteoporotic complications in individuals diagnosed with gout.

The Prevalence and Factors Associated with Coronary Heart Disease in Patients with Gout.

Gout is associated with increased risk of cardiovascular disease (CVD) morbidity and mortality. Therefore, an association between coronary heart disease (CHD) and gout deserves careful examination. AIM: . The aim of this study was to determine the prevalence of CHD and factors associated with CHD in patients (pts) with gout. MATERIALS AND METHODS: . The study involved 286 male patients with gout, age 51.2 [42.8; 59.4] years (ys), disease duration 6.2 [3.8; 12.1] ys. All patients underwent standard clinical examination screening traditional risk factors (TRFs) of CVDs. We estimated the adjusted odds ratio (OR) and 95% confidence interval (95% CI). RESULTS: . CHD was found in 111 out of the 286 pts (38.8%), MI had a history in 29.7%. Compared to individuals with CHD, participants without CHD were older (56.7[52.1; 61.1] vs 46.2[40.6; 53.4] ys), had longer duration of gout (9.3[4.7; 15.1] vs 5.6[3.3; 9.7] ys) (for all p < 0.05). Abdominal obesity (OR, 3.6; 95% CI, 1.2-10.9), family history of CHD (OR, 2.2; 95% CI, 1.3-3.7), disease duration of gout more 10 ys (OR, 2.8; 95% CI, 1.6-4.7), age of gout onset < 35 ys (OR, 5.5; 95% CI, 2.6-11.7), intraosseous tophi (OR, 3.03; 95% CI, 1.8-5.01), nephrolithiasis (OR, 1.7; 95% CI, 1.04-3.04), renal failure (OR, 5.6; 95% CI, 2.7-11.4), serum total cholesterol (TC), (OR, 1.6; 95% CI, 1.0-2.8), serum creatinine (OR, 2.5; 95% CI, 1.2-5.1), increased the risk for CHD in patients with a gout. CONCLUSIONS: . The prevalence of CHD was 38.8% among individuals with gout (one-third of patients had a history of MI 29.7%). Our study showed that both TRFs of CVD and the severity of gout and a history of renal failure contribute to the development of CHD in patients with gout.