Hyperuricemia and Its Association With Ischemic Stroke

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey

Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin. Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the "Preventive Aspirin Use" questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia. Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia. Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin.

Effects of uric acid on ischemic diseases, stratified by lipid levels: a drug-target, nonlinear Mendelian randomization study

Although uric acid-lowering agents such as xanthine oxidase inhibitors have potential cardioprotective effects, studies on their use in preventing cardiovascular diseases are lacking. We investigated the genetically proxied effects of reducing uric acid on ischemic cardiovascular diseases in a lipid-level-stratified population. We performed drug-target Mendelian randomization (MR) analyses using UK Biobank data to select genetic instruments within a uric acid-lowering gene, xanthine dehydrogenase (XDH), and construct genetic scores. For nonlinear MR analyses, individuals were stratified by lipid level. Outcomes included acute myocardial infarction (AMI), ischemic heart disease, cerebral infarction, transient cerebral ischemic attack, overall ischemic disease, and gout. We included 474,983 non-gout individuals with XDH-associated single-nucleotide polymorphisms. The XDH-variant-induced uric acid reduction was associated with reduced risk of gout (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.78-0.93; P < 0.001), cerebral infarction (OR, 0.86; 95% CI, 0.75-0.98; P = 0.023), AMI (OR, 0.79; 95% CI, 0.66-0.94; P = 0.010) in individuals with triglycerides ≥ 188.00 mg/dL, and cerebral infarction in individuals with low-density lipoprotein cholesterol (LDL-C) ≤ 112.30 mg/dL (OR, 0.76; 95% CI, 0.61-0.96; P = 0.020) or LDL-C of 136.90-157.40 mg/dL (OR, 0.67; 95% CI, 0.49-0.92; P = 0.012). XDH-variant-induced uric acid reduction lowers the risk of gout, AMI for individuals with high triglycerides, and cerebral infarction except for individuals with high LDL-C, highlighting the potential heterogeneity in the protective effects of xanthine oxidase inhibitors for treating AMI and cerebral infarction depending on the lipid profiles.

Effectiveness and safety of different doses of febuxostat compared with allopurinol in the treatment of hyperuricemia: a meta-analysis of randomized controlled trials

BACKGROUND

The prevalence of hyperuricemia has increased steadily with the continuous improvement of living standards. Some studies have reported the clinical effectiveness and safety of different doses of febuxostat in comparison with allopurinol in hyperuricemia treatment, but the sample sizes of the studies have been small, and the results have been inconsistent. We designed this meta-analysis to evaluate the effectiveness and safety of different doses of febuxostat compared with allopurinol in the treatment of hyperuricemia.

METHODS

The Cochrane Library, Embase, PubMed, Web of Science and ClinicalTrials.gov databases were searched to identify randomized controlled trials (RCTs) comparing the use of febuxostat and allopurinol for the treatment of hyperuricemia. The effectiveness and safety of different doses of febuxostat and allopurinol in treating hyperuricemia were assessed using meta-analysis.

RESULTS

A total of 11 randomized controlled trials were included in the meta-analysis. The results of the meta-analysis showed that the percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less was higher among patients taking febuxostat (80 mg/d) than among patients taking allopurinol (200-300 mg/d) [RR = 1.79, 95% CI (1.55, 2.08), P < 0.00001]. However, there was no statistically significant difference in the percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less between febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.10, 95% CI (0.93, 1.31), P = 0.25]. There was also no statistically significant difference in the incidence of gout between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 0.97, 95% CI (0.64, 1.49), P = 0.91] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.13, 95% CI (0.81, 1.58), P = 0.48].No significant difference in the incidence of major adverse reactions as observed between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.16; 95% CI (0.43, 3.16), P = 0.77] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.06; 95% CI (0.79, 1.42), P = 0.70]. The incidence of adverse cardiovascular events did not differ significantly between the febuxostat (40 mg/d) and allopurinol (200-300 mg/d) [RR = 1.30; 95% CI (0.57, 2.95), P = 0.53] or between the febuxostat (80 mg/d) and allopurinol (200-300 mg/d) [RR = 1.79; 95% CI (0.74, 4.32), P = 0.20].

CONCLUSIONS

Febuxostat (80 mg/d) was associated with a higher percentage of patients achieving serum uric acid levels of 6.0 mg/dL or less than allopurinol (200-300 mg/d), however, febuxostat (80 mg/d) did not exhibit better efficacy in reducing the incidence of gout. More attention should be devoted to the adverse reactions caused by an increase in febuxostat doses.

Uric acid plasma levels are associated with C-reactive protein concentrations and the extent of coronary artery lesions in patients with acute coronary syndromes

Many studies have pointed out that inflammation plays a pivotal role in pathophysiology of acute coronary syndromes (ACS) because several inflammatory molecules impair the endothelial functions in the coronary circulation and promote atherothrombotic events. Recently, many clinical/experimental evidences indicate that elevated plasma levels of uric acid (UA) might be considered a risk factor for developing ACS. It has been reported that elevated UA doses impair physiologic functions of endothelial cells, shifting them toward a pro atherothrombotic phenotype. In the present manuscript, we investigated the relationship between UA plasma levels, inflammatory burden, and extension of coronary atherosclerotic disease in patients with ACS. Patients with a clinical presentation of ACS (ST-elevated and non-ST-elevated myocardial infarction) admitted to the Vanvitelli Catheterization Laboratory at Monaldi Hospital in 2019, before the COVID-19 pandemia, were retrospectively analyzed. Biochemical profile, type of ACS presentation, as well as extension of coronary atherosclerosis were assessed. A total of 132 ACS patients were included in the analysis, and grouped into 3 tertiles according to the UA values (UA < 4.72 mg/dl, UA between 4.72 and 6.15 mg/dl, and UA > 6.15 mg/dl). Patients with UA plasma levels ≥ 6.15 mg/dL showed higher levels of C-reactive protein (mean of 5.1 mg/dL) as compared to patients with lower UA plasma levels. Moreover, the former group of patients showed higher levels of cardiac troponin and CPK, and presented more often with multivessel disease and complex coronary stenosis (type C of Ellis classification). Even though monocentric and with limited sample size, the present study shows that plasma levels of UA and hs-CRP are elevated in ACS patients and are associated with a more severe coronary disease, suggesting a potential role of UA in the pathophysiology of acute coronary events.

Uric Acid as a Risk Factor for Cardiovascular Diseases

Recently, hyperuricemia and its association with cardiovascular disease have been receiving more and more attention. The article talks about the role of uric acid in the body, the reasons for its increase and the impact of elevated uric acid levels on the cardiovascular system. The mechanisms of influence of hyperuricemia on the development and progression of cardiovascular diseases are presented. Medicinal and non-drug ways to reduce the level of uric acid in the blood are considered. From non-drug methods, attention is focused on the hypopurine diet, adequate drinking regimen and normalization of body weight. Separately, the effect on the level of uric acid of various drugs used in clinical practice is considered. In the light of current clinical guidelines, the issue of the need to prescribe drug therapy for asymptomatic hyperuricemia is discussed.

A novel aGAPSS-based nomogram for the prediction of ischemic stroke in patients with antiphospholipid syndrome

Background

Ischemic stroke (IS) is the most common and life-threatening arterial manifestation of antiphospholipid syndrome (APS). It is related to high mortality and severe permanent disability in survivors. Thus, it is essential to identify patients with APS at high risk of IS and adopt individual-level preventive measures. This study was conducted to identify risk factors for IS in patients with APS and to develop a nomogram specifically for IS prediction in these patients by combining the adjusted Global Anti-Phospholipid Syndrome Score (aGAPSS) with additional clinical and laboratory data.

Methods

A total of 478 consecutive patients with APS were enrolled retrospectively. All patients were randomly assigned to the training and validation cohorts. Univariate and multivariate binary logistic analyses were conducted to identify predictors of IS in the training cohort. Then, a nomogram was developed based on these predictors. The predictive performance of the nomogram for the training and validation cohorts was evaluated by determining areas under the receiver operating characteristic curve (AUROC) and creating calibration plots. A decision curve analysis (DCA) was conducted to compare the potential net benefits of the nomogram with those of the aGAPSS.

Results

During a mean follow-up period of 2.7 years, 26.9% (129/478) of the patients were diagnosed with IS. Binary logistic regression analysis revealed that five risk factors were independent clinical predictors of IS: age (P < 0.001), diabetes (P = 0.030), hyperuricemia (P < 0.001), the platelet count (P = 0.001), and the aGAPSS (P = 0.001). These predictors were incorporated into the nomogram, named the aGAPSS-IS. The nomogram showed satisfactory performance in the training [AUROC = 0.853 (95% CI, 0.802–0.896] and validation [AUROC = 0.793 (95% CI, 0.737–0.843)] cohorts. Calibration curves showed good concordance between observed and nomogram-predicted probability in the training and validation cohorts. The DCA confirmed that the aGAPSS-IS provided more net benefits than the aGAPSS in both cohorts.

Conclusion

Age, diabetes, hyperuricemia, the platelet count, and the aGAPSS were risk factors for IS in patients with APS. The aGAPSS-IS may be a good tool for IS risk stratification for patients with APS based on routinely available data.