Patients with human immunodeficiency virus (HIV) infection have sustained alterations in metabolism (lipids and insulin/glucose homeostasis) and body composition (fat distribution) that are proatherogenic (the Figure). HIV infection itself and/or its therapies may contribute to these alterations (the Table); although most effects are reversible, there are some possibly irreversible consequences of treatment. With the relative restoration to health seen in the era of highly active antiretroviral therapy (HAART), many traditional risk factors and promoters of dyslipidemia and diabetes also are present; they interact with HIV-specific inducers to worsen dyslipidemia and to increase the prevalence of insulin resistance and diabetes. Figure. Overview of the effects of HIV and its therapies on CVD risk. The contribution of traditional risk factors must be kept in mind, and they may occur with increased prevalence in people with HIV infection (eg, smoking). HIV, likely through the inflammatory response, and antiretroviral therapies independently affect many of the mediators of CVD risk. The effects on lipids are a prominent but complex example; HIV infection lowers LDL levels, but antiretroviral therapy raises LDL back up to normal levels. The bidirectional arrows indicate associations, but there is not yet adequate proof of causality. The dotted arrow between body composition and CVD indicates that body fat is known to affect the mediators such as dyslipidemia and insulin resistance but may also have a direct effect. FFA indicates free fatty acids; ARV, antiretroviral. View this table: Table. Effects of HIV Treatment These disturbances in lipid and glucose metabolism and renal disease may contribute, at least in part, to the excess cardiovascular disease (CVD) morbidity and mortality observed in HIV-infected individuals (the Figure). However, the relative contribution to excess CVD risk of traditional CVD risk factors, especially smoking, compared with these infection- and treatment-specific complications requires clarification. More prospective data with multivariable modeling are needed. …
Read full abstract