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Article

CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study

by
Wondmagegn Tamiru Tadesse
1,
Eulambius Mathias Mlugu
2,3,
Workineh Shibeshi
1,
Wondwossen Amogne Degu
4,
Ephrem Engidawork
1 and
Eleni Aklillu
2,*
1
Department of Pharmacology and Clinical Pharmacology, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa P.O. Box 9086, Ethiopia
2
Division of Clinical Pharmacology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital-Huddinge, 14186 Stockholm, Sweden
3
Department of Pharmaceutics and Pharmacy Practice, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam P.O. Box 65014, Tanzania
4
Department of Internal Medicine, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa P.O. Box 9086, Ethiopia
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(7), 1087; https://doi.org/10.3390/jpm12071087
Submission received: 22 May 2022 / Revised: 25 June 2022 / Accepted: 28 June 2022 / Published: 30 June 2022
(This article belongs to the Section Pharmacogenetics)
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Abstract

Long-term antiretroviral treatment (cART) increases the risk of glucose metabolism disorders (GMDs). Genetic variation in drug-metabolizing enzymes and transporters may influence susceptibility to cART-associated GMDs. We conducted a case-control study to investigate the association of pharmacogenetic variations with cART-induced GMDs. A total of 240 HIV patients on long-term efavirenz-based cART (75 GMD cases and 165 controls without GMDs) were genotyped for CYP3A4*1B, CYP3A5 (*3,*6), CYP2B6*6, UGT2B7*2, ABCB1 (c.3435C>T, c.4036A>G), and SLCO1B1 (*1b, *5). GMD cases were defined as the presence of impaired fasting glucose, insulin resistance, or diabetes mellitus (DM). Case-control genotype/haplotype association and logistic regression analysis were performed by adjusting for age, sex, and BMI. The major CYP3A haplotype were CYP3A5*3 (53.8%), CYP3A4*1B (17.3%), combinations of CYP3A4*1B, and CYP3A5*6 (10.9%), and CYP3A wild type (7%). CYP3A5*6 allele (p = 0.005) and CYP3A5*6 genotype (p = 0.01) were significantly associated with GMD cases. Multivariate analysis indicated CYP3A haplotype as a significant predictor of GMD (p = 0.02) and IFG (p = 0.004). CYP2B6*6 significantly predicted DM (p = 0.03). CYP3A haplotype and CYP2B6*6 genotype are independent significant predictors of GMD and DM, respectively, among HIV patients on long-term EFV-based cART.
Keywords: antiretroviral therapy; efavirenz; glucose metabolic disorder; HIV; pharmacogenetic variation; CYP3A5; CYP2B6; genotype; Ethiopia antiretroviral therapy; efavirenz; glucose metabolic disorder; HIV; pharmacogenetic variation; CYP3A5; CYP2B6; genotype; Ethiopia

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MDPI and ACS Style

Tadesse, W.T.; Mlugu, E.M.; Shibeshi, W.; Degu, W.A.; Engidawork, E.; Aklillu, E. CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study. J. Pers. Med. 2022, 12, 1087. https://doi.org/10.3390/jpm12071087

AMA Style

Tadesse WT, Mlugu EM, Shibeshi W, Degu WA, Engidawork E, Aklillu E. CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study. Journal of Personalized Medicine. 2022; 12(7):1087. https://doi.org/10.3390/jpm12071087

Chicago/Turabian Style

Tadesse, Wondmagegn Tamiru, Eulambius Mathias Mlugu, Workineh Shibeshi, Wondwossen Amogne Degu, Ephrem Engidawork, and Eleni Aklillu. 2022. "CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study" Journal of Personalized Medicine 12, no. 7: 1087. https://doi.org/10.3390/jpm12071087

APA Style

Tadesse, W. T., Mlugu, E. M., Shibeshi, W., Degu, W. A., Engidawork, E., & Aklillu, E. (2022). CYP3A and CYP2B6 Genotype Predicts Glucose Metabolism Disorder among HIV Patients on Long-Term Efavirenz-Based ART: A Case-Control Study. Journal of Personalized Medicine, 12(7), 1087. https://doi.org/10.3390/jpm12071087

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