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.

Nut intake and hyperuricemia risk in young adults

OBJECTIVE

The relationship between dietary nut intake and hyperuricemia risk remains unclear. The aim of this study was to investigate the relationship between different nut intake and hyperuricemia risk with a cross-sectional study.

DESIGN

A semi-quantitative FFQ was adopted to collect dietary information. Biochemical and anthropometric parameters were measured by standard methods. Multivariate-adjusted logistic regression models were implemented to analyse the relationship between individual nut intake and hyperuricemia risk.

SETTING

Qingdao University in Shandong Province, China.

PARTICIPANTS

During 2018-2019, a total of 14 056 undergraduates (6862 males and 7194 females) aged 15-25 years participated in the study.

RESULTS

After adjusting for multiple confounding factors, compared with the lowest quartile, the highest quartile intakes of pine nut (95 % CI (0·51, 0·98)) was significantly associated with 29 % reduction in hyperuricemia risk, the highest quartile intake of walnut (OR = 0·78; 95 % CI (0·58, 1·05)) was marginally negatively associated with hyperuricemia risk.

CONCLUSIONS

The present study showed that the relationships between intakes of different nuts and hyperuricemia risk were different. Increased dietary intakes of walnut and pine nut are negatively associated with the hyperuricemia.