Association of Human Leukocyte Antigen Alleles and Hypersensitivity of Efavirenz/Nevirapine in HIV-Infected Chinese Patients.
Xiao-Yan ZhouChong-Xi LiJian-Bo ZhangJun-Ting TanXi -YangRowida A AlbarmaqiYu-Ye LiYi-Qun KuangPublished in: AIDS research and human retroviruses (2022)
To examine the association between human leukocyte antigen (HLA) and nevirapine (NVP)- and efavirenz (EFV)-induced cutaneous adverse reactions in human immunodeficiency virus (HIV) patients, we conducted a case-control study at our center consisting of 96 patients. Patients were further assigned based on the occurrence of cutaneous adverse events and the drugs involved. All patients were subjected to next generation sequencing (NGS)-based screening with focus on HLA phenotype, including the presence of HLA-B, HLA-C, and HLA-DRB1. Our data indicated that the HLA-C*01:02:01 allele presence was observed in 47.4% (18/38) of patients in the EFV-hypersensitivity group compared with 18.9% (7/30) in the control group [odds ratio (OR) = 5.837; 95% confidence interval (CI) = 1.727-19.722, p = .005]. In contrast, the occurrence of HLA-DRB1*08:03 was found to be significantly lower in the EFV-hypersensitivity group (4/38, 10.5%) compared with the corresponding control group (12/37, 32.4%) (OR = 0.148; 95% CI = 0.035-0.625, p = .009). In addition, the HLA-DRB1*04:05:01 antigen was expressed more frequently in the NVP-hypersensitivity group (23.8%, 5/21) compared with the control group (10.8%, 4/37) (OR = 7; 95% CI = 1.265-38.793, p = .026). Our data not only revealed a significant association between HLA-C*01:02:01 and EFV-induced cutaneous adverse reactions but may also shed light on defining the treatment for Chinese HIV patients.
Keyphrases
- end stage renal disease
- hiv infected
- human immunodeficiency virus
- ejection fraction
- chronic kidney disease
- newly diagnosed
- antiretroviral therapy
- peritoneal dialysis
- prognostic factors
- magnetic resonance
- hepatitis c virus
- magnetic resonance imaging
- computed tomography
- gene expression
- dna methylation
- deep learning
- drug induced
- machine learning
- hiv infected patients
- south africa
- artificial intelligence