HLA-DRB1 and HLA-DQB1 genetic polymorphisms and susceptibility to coronary atherosclerosis in a Northeast Chinese Population: A case-control study
Abstract
Atherosclerosis is a chronic inflammatory disease with increasing prevalence in Northeast China, where HLA class II molecules play an important immunoregulatory role. However, the contribution of specific HLA-DRB1 and HLA-DQB1 alleles to AS susceptibility in this population remains incompletely characterized, necessitating novel biomarkers for early detection. In this case-control study of 209 participants, HLA-DRB1 and HLA-DQB1 allele and phenotypic haplotype distributions were compared using odds ratios with 95% confidence intervals and Bonferroni correction for multiple comparisons. A total of 28 HLA-DRB1 and 12 HLA-DQB1 alleles were analyzed. The lowest P value for HLA-DRB1 was observed for DRB1*07:01 ( P = 0.077, corrected P = 1.000; OR = 1.994, 95% CI: 0.955–4.164), and that for HLA-DQB1 was observed for DQB1*02:02 ( P = 0.150, corrected P = 1.000; OR = 1.739, 95% CI: 0.850–3.558). Neither reached statistical significance, though both trended upward in the AS-susceptible group (DRB1*07:01: 12.59% vs. 6.76%; DQB1*02:02: 12.22% vs. 7.43%). The DRB1*07:01-DQB1*02:02 phenotypic haplotype was more frequent in the AS-susceptible group than in the control group (22.22% vs. 10.81%), with an OR of 2.357 (95% CI: 1.019–5.452). Although the association did not survive Bonferroni correction (corrected P = 1.000), the effect size suggested the signal was unlikely to be a trivial statistical artifact. In conclusion, the DRB1*07:01-DQB1*02:02 phenotypic haplotype was identified as a candidate risk marker for AS in the Northeast Chinese population, warranting validation in larger cohorts.
Article Details
Authors (17)
Miaomiao Niu
Jing Gao
Xiaodong Han
Ying Dong
State Key Laboratory of Natural Medicines, Jiangsu Key Laboratory of Drug Design and Optimization, and Department of Chemistry
Hui Wang
Yue Zhang
Dongming Zhao
Zijian Wang
School of Materials Science and Engineering
Fang Qi
Feng Wang
Qiuye Meng
Jinjiao Geng
Shuqi Wu
Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang Key Laboratory of Intelligent Sensing Technology and Advanced Medical Instrument, Department of Biomedical Engineering
Ying Wang
Ying Zhang
Chaoyang Guo
Hua Chen
Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands