Lowering and Raising Serum Urate Levels: Off-Label Effects of Commonly Used Medications

Mechanism and use strategy of uric acid-lowering drugs on coronary heart disease

Coronary heart disease (CHD) is a serious cardiovascular illness, for which an elevated uric acid (UA) level presents as a considerable risk factor. This can be treated with UA-lowering drugs such as allopurinol and benzbromarone, which can reduce UA levels by the inhibition of UA production or by promoting its excretion. Such drugs can also be beneficial to CHD in other ways, such as reducing the degree of coronary arteriosclerosis, improving myocardial blood supply and alleviating ventricular remodeling. Different UA-lowering drugs are used in different ways: allopurinol is preferred as a single agent in clinical application, but in absence of the desired response, a combination of drugs such as benzbromarone with ACE inhibitors may be used. Patients must be monitored regularly to adjust the medication regimen. Appropriate use of UA-lowering drugs has great significance for the prevention and treatment of CHD. However, the specific mechanisms of the drugs and individualized drug use need further research. This review article expounds the mechanisms of UA-lowering drugs on CHD and their clinical application strategy, thereby providing a reference for further optimization of treatment.

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

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.

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.

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.

Exploring the Association between Low-dose Aspirin Intake and Hyperuricemia in Individuals over 40: A Cross-Sectional Study using NHANES Data (2011-2018)

BACKGROUND Long-term aspirin treatment was recommended for secondary prevention of cardiovascular and cerebrovascular disease. However, some studies reveal low-dose aspirin (LDA) can raise serum uric acid (SUA) levels. Thus, the purpose of this study was to analyze whether LDA intake is associated with hyperuricemia. MATERIAL AND METHODS Data was collected from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. All participants over 40 years old and who selected "preventive aspirin use" were included in the study. Logistic regression analyses were used to evaluate the relationship between LDA intake and hyperuricemia. The stratified analysis was based on race and estimated glomerular filtration rate (eGFR). RESULTS A total of 3540 participants were included in the study. Of them, 805 (22.7%) took LDA, and 190 (31.6%) had hyperuricemia. There was no significant association between hyperuricemia and LDA intake (OR=1.22, 95% CI: 0.97-1.54) after adjusting for confounding factors. However, further subgroup analysis by age showed a significant association between LDA intake and hyperuricemia (OR=3.44, 95% CI: 1.88-6.27) among those 40 to 50 years of age. After adjusting for confounding factors, the relationship was still significant (OR=2.28, 95% CI: 1.10-4.73); we also found that race (Hispanic American, OR=1.84, 95% CI: 1.11-3.06) and eGFR under 60 mL/min/1.73 m² (OR=1.94, 95% CI: 1.04-3.62) may play important roles in the development of hyperuricemia. CONCLUSIONS LDA does not increase the hyperuricemia risk in people over 40 years. However, those aged between 40 and 50 years, Hispanic American, and with impaired renal function should have careful evaluation during LDA treatment.

Differences in the Association Between Alcoholic Beverage Type and Serum Urate Levels Using Standardized Ethanol Content

IMPORTANCE

Differences have been observed in the association of serum urate levels with consumption of different types of alcoholic beverages. However, previous studies have not standardized the unit of intake for ethanol content, and only limited types of alcoholic beverages have been evaluated.

OBJECTIVE

To examine differences in the association of serum urate levels with various types of alcoholic beverages when their intakes are standardized for ethanol content.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study was conducted using data from participants aged 20 years or older who completed a medical checkup at St Luke's International University in Japan between October 1, 2012, and October 31, 2021. Participant demographics, blood test results, and lifestyle questionnaire data were used as covariates. Analysis was performed in December 2021.

EXPOSURES

Consumption of alcoholic beverages, including beer, sake (rice wine), shochu (Japanese spirit), wine, and whiskey.

MAIN OUTCOMES AND MEASURES

Serum urate levels were measured during the medical checkup. The beverage unit was standardized to 1 standard drink, which contained 20 g of ethanol. Multivariable linear regression including interaction terms of alcohol consumption and dominant alcoholic beverage was performed.

RESULTS

This study included 78 153 participants. Their mean (SD) age was 47.6 (12.8) years; 36 463 (46.7%) were men and 41 690 were women (53.3%). A total of 45 755 participants (58.5%) were regular alcohol drinkers. Consistent associations of serum urate levels with alcohol consumption were observed in the beer-dominant group, with β coefficients (for 1 standard drink per day) of 0.14 mg/dL (95% CI, 0.11-0.17 mg/dL; P < .001) for men and 0.23 mg/dL (95% CI, 0.20-0.26 mg/dL; P < .001) for women. A moderate increase in serum urate levels was observed in the wine-dominant group compared with a modest and nonsignificant increase in the sake-dominant group, with β coefficients (for 1 standard drink per day) for the latter group of 0.05 mg/dL (95% CI, -0.01 to 0.10; P = .10) for men and 0.04 mg/dL (95% CI, -0.05 to 0.14 mg/dL; P = .38) for women. Restricted cubic splines showed different patterns in associations of serum urate levels with ethanol intake by dominant alcoholic beverages.

CONCLUSIONS AND RELEVANCE

The results of this study suggest that the extent of the association of serum urate levels with alcohol intake was different for alcoholic beverages even after ethanol content was standardized. Higher beer consumption among men and women was consistently associated with higher serum urate levels, whereas sake was not associated with changes in serum urate levels. Therefore, alcoholic beverage type, in addition to ethanol content, should be considered as a factor contributing to hyperuricemia.

Establishment and Validation of Predictive Model of Tophus in Gout Patients

(1) Background: A tophus is a clinical manifestation of advanced gout, and in some patients could lead to joint deformities, fractures, and even serious complications in unusual sites. Therefore, to explore the factors related to the occurrence of tophi and establish a prediction model is clinically significant. (2) Objective: to study the occurrence of tophi in patients with gout and to construct a predictive model to evaluate its predictive efficacy. (3) Methods: The clinical data of 702 gout patients were analyzed by using cross-sectional data of North Sichuan Medical College. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze predictors. Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was developed for personalized risk assessment. (4) Results: Compliance of urate-lowering therapy (ULT), Body Mass Index (BMI), course of disease, annual attack frequency, polyjoint involvement, history of drinking, family history of gout, estimated glomerular filtration rate (eGFR), and erythrocyte sedimentation rate (ESR) were the predictors of the occurrence of tophi. Logistic classification model was the optimal model, test set area under curve (AUC) (95% confidence interval, CI): 0.888 (0.839-0.937), accuracy: 0.763, sensitivity: 0.852, and specificity: 0.803. (5) Conclusions: We constructed a logistic regression model and explained it with the SHAP method, providing evidence for preventing tophus and guidance for individual treatment of different patients.