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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.

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PURPOSE: This study aims to assess the disparities in choroidal thickness and optic disc parameters between individuals diagnosed with chronic gout and an age- and gender-matched control cohort. METHODS: This cross-sectional study involved 30 gout patients receiving treatment at the Rheumatology clinic, alongside 30 healthy control individuals matched for age and gender. A comprehensive ophthalmological assessment, encompassing visual acuity measurement, intraocular pressure evaluation, slit-lamp biomicroscopy, and dilated fundus examination, was conducted for all participants. Peripapillary retinal nerve fiber layer (RNFL), ganglion cell complex (GCC), and subfoveal choroidal thickness (SFCT) were quantified utilizing Spectral Domain Optical Coherence Tomography. RESULTS: The mean age within the study group was 54.53 ± 9.43 years, while the control group's mean age was 53.20 ± 10.36 years. In both the gout and control cohorts, there were 28 men and 2 women. No significant differences were observed in age and gender between the groups. Gout patients manifested thinner RNFL and GCC across all quadrants; however, statistically significant thinning was only evident in the nasal and inferior quadrants for RNFL. Despite a thinner SFCT observed in gout patients compared to controls, this discrepancy did not attain statistical significance. CONCLUSION: Chronic phase gout patients may display alterations in optic disc and macular parameters, alongside potential variations in choroidal thickness. Nevertheless, more controlled studies encompassing a larger participant pool are imperative to substantiate our findings.

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A 64-year-old male with a history of gout was seen with a swelling of het left acromioclavicular joint. Microscopic examination revealed monosodium urate crystals, confirming the diagnosis of tophaceous gout.

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Gout is inflammatory arthritis caused by monosodium urate crystal deposition in articular and non-articular structures. Acute gout flares are often monoarticular/polyarticular involving lower extremity joints characteristically involving 1st metatarsophalangeal joint. However, gout flares can also be polyarticular, involving upper extremity joints, especially in patients with multiple comorbidities and contraindications to urate-lowering therapies (ULT). Risk factors exacerbating gout flares include obesity, high alcohol and purine-rich food consumption, and the use of diuretics. Diagnosis requires synovial fluid analysis with direct visualization of monosodium urate crystals. Acute flares are managed with steroids, non-steroidal anti-inflammatory drugs, or colchicine. Long-term management includes lifestyle modifications including a heavy emphasis on weight loss, avoidance of alcohol, purine-rich foods, and diuretics. ULT is indicated in patients with 2 or more gout flares/year, tophi, or radiographic evidence of gouty arthropathy. Although allopurinol is the first-line ULT agent, it does carry a risk of inducing severe cutaneous adverse reactions, especially in patients with chronic kidney disease and patients harboring the HLA-B*5801 allele. Other ULT agents include febuxostat and probenecid. ULT is usually titrated to achieve goal serum uric acid (SUA) levels below 6 mg/dL. However, in patients with tophi, a lower SUA target of less than 5 mg/dL should be implemented for prompt urate crystal dissolution.

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BACKGROUND: The Oxidative Balance Score (OBS) is a systematic tool to assess the effects of diet and lifestyle in relation to oxidative stress. The association between OBS and gout has not been reported previously. We conducted a cross-sectional study to investigate the complex association between OBS and gout in US adults. METHODS: In all, 10 492 participants were included in this study. The exposure variable was OBS, which was scored by 16 dietary and four lifestyle factors. Multivariate logistic regression, subgroup analysis, and restricted cubic spline (RCS) regression were used to analyze the association between OBS and gout. RESULTS: Compared with the lowest OBS quartile group (Q1), the multivariate corrected odds ratio (OR) (95% confidence interval [C]) for the highest quartile of OBS (Q4) was 0.72 (0.52-1.00) (p = .13 for trend); furthermore, the RCS showed a negative linear relationship between OBS and gout (p-nonlinear = .606). CONCLUSION: In conclusion, the risk of gout is higher with high OBS. The prevalence of gout decreased with higher OBS. Diabetes may alter this negative correlation.

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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.

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AIM: To quantify and characterise patients with coexistent septic arthritis (SA) and crystal arthritis (CA) (SACA) in an emergency department (ED) setting. METHODS: A single-centre, retrospective, 10-year observational study was conducted at a major referral centre. Patients with a positive joint aspirate for CA or SA carried out in ED, were included. The Newman criteria were utilised to define SA. RESULTS: Of the 567 patients included in the final analysis, 427 had CA and 140 had a final diagnosis of SA. Twenty-three point six percent of patients diagnosed with SA had concomitant CA, while 7.2% of patients diagnosed with CA had concomitant SA. The greatest predisposing factors for SACA were previous history of gout, rheumatoid arthritis, being immunocompromised or having joint metalware. Synovial fluid (SF) white cell count (WCC) showed excellent predictive capability for joint infection with the area under the receiver operating characteristic curves (AUROCs) of 0.81 and 0.87 for SA and SACA respectively. The receiver operating characteristic curves (ROCs) reported a SF WCC cutoff of 32,000/mm3 allowed for 100% sensitivity and approximately 50% specificity. CONCLUSIONS: SACA remains a small but important sub-group of patients at risk of misdiagnosis of CA alone. SF WCC of 32,000/mm3 may be a better cutoff than the traditionally accepted 50,000/mm3, possibly warranting inpatient admission for investigation and management of presumed SA.

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BACKGROUND/AIMS: The Gout Impact Scale (GIS), a part of the Gout Assessment Questionnaire 2.0, is used to measure gout-specific health-related quality of life (HRQOL). Although several studies have been conducted on the factors affecting the HRQOL of patients with gout, few have focused on lifestyle factors. This study aimed to investigate the correlation between lifestyle habits and HRQOL using the GIS in patients with gout. METHODS: We used data from the Urate-Lowering TheRApy in Gout (ULTRA) registry, a prospective cohort of Korean patients with gout treated at multiple centers nationwide. The patients were aged ≥18 years and met the 2015 American College of Rheumatology/European League Against Rheumatism gout classification criteria. They were asked to complete a GIS and questions regarding their lifestyle habits at enrollment. RESULTS: The study included 232 patients. 'Gout concern overall' scores in the GIS were significantly lower in patients who exercised more frequently and consumed soft drinks and meat less, and 'well-being during attack' scores were significantly lower in patients who consumed vegetables and exercised more frequently. The frequency of vegetable consumption had a negative linear relationship with the 'well-being during attack' and 'gout concern during attack' scores (p = 0.01, p = 0.001, respectively). The frequency of exercise had a negative linear relationship with the 'gout concern overall' and 'gout concern during attack' scores (p = 0.04 and p = 0.002, respectively). CONCLUSION: Patients with gout who frequently consumed vegetables and exercised regularly experienced less impact of gout, exhibiting a better GIS that represented HRQOL.

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Imaging methods capable of detecting inflammation, such as MR imaging and ultrasound, are of paramount importance in rheumatic disease management, not only for diagnostic purposes but also for monitoring disease activity and treatment response. However, more advanced stages of arthritis, characterized by findings of cumulative structural damage, have traditionally been accomplished by radiographs and computed tomography. The purpose of this review is to provide an overview of imaging of some of the most prevalent inflammatory rheumatic diseases affecting the lower limb (osteoarthritis, rheumatoid arthritis, and gout) and up-to-date recommendations regarding imaging diagnostic workup.

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We report two patients with pancreatic tophaceous gout diagnosed by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) of presumed cystic mass lesions. The first case involved a patient who had a recent episode of acute pancreatitis 6 months prior, with subsequent imaging concerning for a pseudocyst or mass lesion. The second case involved a patient with epigastric pain associated with a pancreatic head cystic mass and an erroneous original diagnosis of a mucinous pancreatic neoplasm on EUS-FNA. Diff-Quik stained direct smears on fresh material obtained from EUS-FNA of the lesions showed chalky debris with needle shaped negatively birefringent crystals consistent with gout. For the first case, the chalky material was not present on the H&E stained paraffin embedded formalin fixed cellblock slides. The importance of inclusion of cytologic specimen preparations to examine monosodium urate crystals is emphasized.

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PURPOSE OF REVIEW: Gout flares are a paramount component of disease burden inflicted by gout onto the patient. Furthermore, they are included in the core domain set for long-term gout studies recognized by Outcome Measures in Rheumatology. Along with a validated classification criterion for gout, gout investigators have turned their efforts into defining and characterizing the gout flare. This brief review will summarize the efforts that have been done to define and characterize a gout flare in clinical studies. RECENT FINDINGS: Recent findings include a validated definition of a gout flare that has been utilized in novel clinical studies, use of technology to monitor for gout flares and their effects on patient life, and qualitative analyses into the disease burden that a patient undergoes. SUMMARY: Although guidelines for core outcome domains have been well established, there is question in methods of measuring and reporting gout flares in long-term trials. Furthermore, there is question as to the effectiveness of the agreed upon instruments' abilities to fully capture the disease burden experienced by patients with gout. A combination of outcome measurements including binary data (gout flare present or absent) along with a comprehensive measurement of disease burden over time would theoretically provide a more accurate description of the disease and serve as a basis for intervention development.

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OBJECTIVE: To compare the clinical efficacy of febuxostat combined with a low-purine diet versus allopurinol combined with a low-purine diet in the treatment of gout. METHODS: In this prospective controlled trial, 98 gout patients admitted to our hospital from February 2021 to December 2022 were enrolled as study subjects. Patients were randomly assigned to the study group (febuxostat combined with a low-purine diet) and the control group (allopurinol combined with a low-purine diet), with 49 patients in each group. The therapeutic effect was evaluated based on joint function and serum uric acid levels after treatment, and classified into three levels: markedly effective, effective, and ineffective. The levels of inflammatory factors, including tumor necrosis factor-a (TNF-a), cytokine interleukin-1beta (IL-1ß), and interleukin (IL)-18 (IL-18), were collected. The Numeric Rating Scale (NRS) was used to assess the degree of pain in patients. Clinical indicators before and 6 months after treatment were compared between the two groups. RESULTS: There was no statistically significant difference in age and gender between the two groups. After 6 months of treatment, the effective rate in the study group (48 cases, 97.96%) was higher than that in the control group (42 cases, 85.71%), with a statistically significant difference (p = .027). At the same time, the study group had significantly lower levels of serum uric acid (162.39 µmol/L ± 17.23 µmol/L vs. S198.32 µmol/L ± 18.34 µmol/L, p < .001), creatinine (87.39 mmol/L ± 9.76 mmol/L vs. 92.18 mmol/L ± 9.27 mmol/L, p = .014), total cholesterol (3.65 mmol/L ± 0.65 mmol/L vs. 4.76 mmol/L ± 0.73 mmol/L, p < .001), and triglycerides (1.76 mmol/L ± 0.32 mmol/L vs. 2.28 mmol/L ± 0.41 mmol/L, p < .001) compared to the control group, with statistically significant differences (p < .05). After treatment, the levels of inflammatory factors and degree of pain in the study group were significantly lower than those in the control group (all p < .05). During the treatment process, the incidence of adverse reactions in the study group (2 cases, 4.08%) was lower than that in the control group (9 cases, 18.37%), with a statistically significant difference (p = .025). CONCLUSION: Febuxostat combined with a low-purine diet can reduce inflammatory factors and alleviate the degree of pain in gout patients, significantly improving their clinical symptoms.

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Background: The relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification. Methods: The dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process. Results: An initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output. Conclusion: The interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.

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BACKGROUND: Gout is a chronic inflammatory diseases caused by monosodium urate crystal deposition. However, the role of interleukin (IL)-36 in gout has not dbeen elucidated. METHODS: We enrolled 75 subjects, including 20 healthy controls (HC), 30 patients with acute gout attack and 25 patients in remission. Baseline data were obtained through clinical interrogation and laboratory data were obtained through tests of blood samples. Serum levels of IL-36α were detected using enzyme-linked immunosorbent assay. Spearman correlation analysis was used to investigate the correlation of IL-36α with other parameters. The diagnostic value of IL-36α was demonstrated using a receiver operating characteristic curve. RESULTS: The serum IL-36α level of gout patients in acute attack and remission stage was significantly higher than that of HC. Serum IL-36α was positively correlated with alanine transaminase (ALT) and aspartate transaminase (AST). Serum amyloid A (SAA) levels positively correlated with C-reactive protein levels and erythrocyte sedimentation rates. Glutamyl transpeptidase levels positively correlated with AST and ALT levels. CONCLUSION: In conclusion, serum IL-36α levels were elevated in patients with gout and correlated with the clinical markers of inflammation. Our findings suggest that IL-36α may be a novel inflammatory indicator for gout.

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