Wenyan Sun,1,* Xuetong Zhao,2– 4,* Nicola Dalbeth,5,* Robert Terkeltaub,6 Lingling Cui,1 Zhen Liu,1 Lin Han,1 Can Wang,1 Hui Zhang,1,7 Yiming Bao,2– 4 Changgui Li,1,7 Jie Lu1,7 

1Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, People’s Republic of China; 3CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Science and China National Center for Bioinformation, Beijing, People’s Republic of China; 4University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China; 5Department of Medicine, University of Auckland, Auckland, New Zealand; 6VA San Diego VA Healthcare Center, University of California San Diego, San Diego, CA, USA; 7Institute of Metabolic Diseases, Qingdao University, Qingdao, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jie Lu, Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, 266003, People’s Republic of China, Email 13127006046@163.com Changgui Li, Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, 266003, People’s Republic of China, Email changguili@vip.163.com

Objective: This study aimed to understand predictors of inadequate response (IR) to low-dose febuxostat treatment based on clinical variables.
Methods: We pooled data from 340 patients of an observational cohort and two clinical trials who received febuxostat 20 mg/day for at least 3 months. IR was defined as failure to reach the target serum urate level (sUA< 6 mg/dL) at any time point during 3 months treatment. The potential predictors associated with short- or mid-term febuxostat IR after pooling the three cohorts were explored using mixed-effect logistic analysis. Machine learning models were performed to evaluate the predictors for IR using the pooled data as the discovery set and validated in an external test set.
Results: Of the 340 patients, 68.9% and 51.8% were non-responders to low-dose febuxostat during short- and mid-term follow-up, respectively. Serum urate and triglyceride (TG) levels were significantly associated with febuxostat IR, but were also selected as significant features by LASSO analysis combined with age, BMI, and C-reactive protein (CRP). These five features in combination, using the best-performing stochastic gradient descent classifier, achieved an area under the receiver operating characteristic curve of 0.873 (95% CI [0.763, 0.942]) and 0.706 (95% CI [0.636, 0.727]) in the internal and external test sets, respectively, to predict febuxostat IR.
Conclusion: Response to low-dose febuxostat is associated with early sUA improvement in individual patients, as well as patient age, BMI, and levels of TG and CRP.

Keywords: gout, febuxostat, urate-lowering therapy, machine learning model