Implementation of Cholesterol-Lowering Therapy to Reduce Cardiovascular Risk in Persons Living with HIV.
Stephani C WangGurleen KaurJoshua Schulman-MarcusScott PurgaSulagna MookherjeeCyndi MillerMandeep S SidhuRobert S RosensonPublished in: Cardiovascular drugs and therapy (2020)
The widespread availability of highly effective antiretroviral therapies has reduced mortality from opportunistic infections in persons living with HIV (PLHIV), resulting in an increase in atherosclerotic cardiovascular disease (ASCVD) and other chronic illnesses (Samji et al. 2013). Although there has been a decline in morbidity and mortality from ASCVD in the past several decades, contemporary studies continue to report higher rates of cardiovascular events (Rosenson et al. 2020). HIV has been identified as a risk enhancer for ASCVD by multiple professional guideline writing committees (Grundy Scott et al. 2019, Mach et al. 2020); however, the utilization of cholesterol-lowering therapies in PLHIV remains low (Rosenson et al. 2018). Moreover, the use of statin therapy in PLHIV is complicated by drug-drug interactions that may either elevate or lower the blood statin concentrations resulting in increased toxicity or reduced efficacy respectively. Other comorbidities commonly associated with HIV present other challenges for the use of cholesterol-lowering therapies. This review will summarize the data on lipoprotein-associated ASCVD risk in PLHIV and discuss the challenges with effective treatment. Finally, we present a clinical algorithm to optimize cardiovascular risk reduction in this high-risk population.
Keyphrases
- cardiovascular events
- low density lipoprotein
- cardiovascular disease
- hiv infected
- hiv positive
- coronary artery disease
- antiretroviral therapy
- human immunodeficiency virus
- hiv aids
- hiv testing
- hepatitis c virus
- men who have sex with men
- type diabetes
- primary care
- hiv infected patients
- south africa
- machine learning
- deep learning
- cardiovascular risk factors
- healthcare
- stem cells
- transcription factor
- electronic health record
- big data
- emergency department