Plasma lipidomic profiling of thiopurine-induced leukopenia after NUDT15 genotype-guided dosing in Chinese IBD patients

Pan Li; Kang Chao; Zhanhua Hu; Lulu Qin; Ting Yang; Jing Mao; Xia Zhu; Pinjin Hu; Xueding Wang; Xiang Gao; Min Huang

doi:10.3389/fnut.2023.1138506

Plasma lipidomic profiling of thiopurine-induced leukopenia after NUDT15 genotype-guided dosing in Chinese IBD patients

Front Nutr. 2023 Jun 27:10:1138506. doi: 10.3389/fnut.2023.1138506. eCollection 2023.

Authors

Pan Li^#¹, Kang Chao^#^{2

3}, Zhanhua Hu¹, Lulu Qin⁴, Ting Yang¹, Jing Mao¹, Xia Zhu^{1

2

3}, Pinjin Hu^{2

3}, Xueding Wang¹, Xiang Gao^{2

3}, Min Huang¹

Affiliations

¹ Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China.
² Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Supported by National Key Clinical Discipline, Guangzhou, China.
⁴ School of Pharmaceutical Sciences, Guangdong Pharmaceutical University, Guangzhou, China.

^# Contributed equally.

Abstract

Introduction: Thiopurines, azathiopurine (AZA) and mercaptopurine (6-MP) have been regularly used in the treatment of inflammatory bowel disease (IBD). Despite optimized dosage adjustment based on the NUDT15 genotypes, some patients still discontinue or change treatment regimens due to thiopurine-induced leukopenia.

Methods: We proposed a prospective observational study of lipidomics to reveal the lipids perturbations associated with thiopurine-induced leukopenia. One hundred and twenty-seven IBD participants treated with thiopurine were enrolled, twenty-seven of which have developed thiopurine-induced leucopenia. Plasma lipid profiles were measured using Ultra-High-Performance Liquid Chromatography-Tandem Q-Exactive. Lipidomic alterations were validated with an independent validation cohort (leukopenia n = 26, non-leukopenia n = 74).

Results: Using univariate and multivariate analysis, there were 16 lipid species from four lipid classes, triglyceride (n = 11), sphingomyelin (n = 1), phosphatidylcholine (n = 1) and lactosylceramide (n = 3) identified. Based on machine learning feature reduction and variable screening strategies, the random forest algorithm established by six lipids showed an excellent performance to distinguish the leukopenia group from the normal group, with a model accuracy of 95.28% (discovery cohort), 79.00% (validation cohort) and an area under the receiver operating characteristic (ROC) curve (ROC-AUC) of 0.9989 (discovery cohort), 0.8098 (validation cohort).

Discussion: Our novel findings suggested that lipidomic provided unique insights into formulating individualized medication strategies for thiopurines in IBD patients.

Keywords: biomarkers; inflammatory bowel disease; lipidomic; machine learning; thiopurines.

Grants and funding

The project was sponsored by the National Nature Science Fund of China Grant Nos. 82020108031, 81700495, 81730103, 81573507, 81973398, and 81870382; the Guangdong Provincial Key of Laboratory Construction Foundation, Nos. 2017B030314030 and 2020B1212060034; the National Key Research and Development Program, No. 2016YFC0905003; the 111 Project, No. B16047.