The continuing success of potent antiretroviral therapy (ART) has resulted in dramatic reductions in HIV-associated morbidity and mortality. HIV-infected individuals are now living longer. This longer life span has exposed them to the effects of aging, and other host and environmental factors known to increase the risk of obesity, diabetes, and cardiovascular disease (CVD) in the general population.1 The HIV virus itself can cause lipid abnormalities including high triglycerides and low HDL cholesterol,2 and the side-effects of antiretroviral medications have also been associated with metabolic and body shape changes.3
Metabolic syndrome (MetS) is an aggregation of central obesity and metabolic abnormalities that confers an increased risk of CVD and type 2 diabetes.4 Since its introduction, the definition of MetS has been under scrutiny especially since it excludes known CVD risk factors such as smoking. The existence of MetS as a diagnostic entity is also controversial, and there is limited data in HIV-infected populations.
The age-adjusted prevalence of MetS in the adult US population is 34.3%5 in HIV-infected populations, the estimated prevalence ranges from 7%–45%.6 Data on the incidence of MetS in HIV-infected individuals receiving potent ART is limited by the cross-sectional nature of most of the studies. A US-based HIV-infected cohort that included both treatment-experienced and treatment-naive individuals reported an incidence of 1.2 per 100 person-months,7 and an international study of HIV-infected adults initiating ART reported an incidence of 12 per 100 person-years.8 Most of the existing data on factors associated with MetS are from cross-sectional studies6; few studies have examined factors associated with MetS among ARV-naive individuals after starting potent ART.
We examined the prevalence of MetS and factors associated with MetS in a large US-based cohort of HIV-infected ART-naive individuals at the time they started their ART regimens. Further, we determined the incidence of newly developed MetS in this ART-naive population after they had started their ART regimens through randomized clinical trials. We also examined the association of demographics, clinical factors, and ART use on the prevalence and incidence of MetS.
The AIDS Clinical Trials Group (ACTG) Longitudinal Linked Randomized Trials (ALLRT) is a prospective cohort of HIV-infected participants (age ≥13 years) randomized to receive ART regimens, immune-based therapies, or treatment strategies in selected ACTG clinical trials.9 ACTG sites that enrolled participants to ALLRT received approval by their designated institutional review boards to conduct this study, and all ALLRT participants provided written informed consent.
The present analysis included 2554 ART-naive individuals who enrolled in ALLRT from 3 parent trials (A5095, A5142, and A5202; enrollment period 2001–2007).10–12 The ART regimens used in these trials included either (1) 3 nucleoside reverse transcriptase inhibitors (NRTIs), (2) 2/3 NRTIs with a nonnucleoside reverse transcriptase inhibitor (NNRTI) or a boosted protease inhibitor (PI), or (3) an NNRTI with a boosted PI.
The “baseline” visit was the parent trial entry visit (before the start of ART). When individuals were enrolled in the parent trial, visits were scheduled according to the parent trial protocol. When the parent study ended, data collection continued according to the ALLRT protocol. Data were recorded by the study site staff using standard ACTG forms.
Definition of MetS
Since its introduction in 1998, various diagnostic criteria for MetS have been proposed13; The more widely used definitions for MetS are from International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute. In HIV-infected patients, there was 85% agreement in patient classification based on these 2 definitions.14 More recently, the International Diabetes Federation and American Heart Association/National Heart, Lung, and Blood Institute agreed upon a common definition, which was used in our study.13
Based on the Adult Treatment Panel III (ATPIII) criteria,13 MetS was defined as the presence of 3 or more of the following components: (1) waist circumference >88 cm in women or >102 cm in men; (2) blood pressure ≥130 mm Hg systolic or ≥85 mm Hg diastolic or use of antihypertensive medications; (3) triglycerides ≥150 mg/dL or use of lipid-lowering medications (niacin, fenofibrate, and gemfibrozil); (4) fasting blood glucose ≥100 mg/dL, physician-diagnosed diabetes or use of diabetic medications; (5) high-density lipoprotein cholesterol (HDL) <50 mg/dL in women or <40 mg/dL in men.
According to the ALLRT protocol, blood pressure readings, fasting lipid panel, and glucose were assessed every 16 weeks, and waist circumference was measured every 48 weeks. For 800 individuals, the first available waist circumference measurement was within 16 weeks after baseline due to the timing of their ALLRT entry visit; for these individuals, the waist circumference at week 16 was considered as baseline.
At baseline, there were 307 (12%) of 2554 individuals with missing data on one or more of the MetS components and who could not be classified as having or not having MetS; these 307 individuals were excluded from the study population. Among the remaining 2247 individuals, 450 individuals with 3 or more components at baseline were defined as prevalent cases of MetS (even if they were missing data on other components). Those without MetS at baseline were categorized as one of the following: (1) those with 2 components and no missing data; (2) those with 1 component and missing data on ≤1 component, and; (3) those with no components and missing data on ≤2 components. Similarly, incident cases were assessed during follow-up among the 1797 individuals without MetS at baseline.
Prevalence of MetS at baseline (at ART initiation) was calculated as the number of cases at baseline divided by the total analyzed population. Binary regression (log-binomial) was used to evaluate associations between covariates and MetS at ART initiation. Baseline covariates examined included age (years), sex, race/ethnicity, years of education, history of cigarette smoking, body mass index (BMI, kg/m2), history of intravenous drug use, family history of CVD, HIV-1 RNA viral load (copies/mL), and CD4+ T-cell count (cells/mm3).
Incidence of MetS was evaluated among those who did not have MetS at baseline. Person-years at risk were calculated from baseline until first MetS diagnosis, death, end of follow-up, or June 30, 2009 (whichever came first). The incidence rate (IR) of MetS was calculated as the number of incident cases divided by the total person-years at risk. Exact 95% confidence intervals (CIs) were calculated under a Poisson distribution. Cox proportional hazards models and extended Cox models for time-dependent covariates were used to calculate unadjusted (HR) and adjusted hazard ratios and 95% CI. Baseline covariates examined included all those stated above. Time-varying covariates (updated every 48 weeks) included CD4+ T-cell count, HIV-1 RNA viral load, and ART use. Specifically, we examined time-updated class of ART use (for example PI-containing regimen versus regimen without PI) and time-updated use of specific antiretroviral medications.
Each subject’s follow-up time from baseline was divided into 48-week intervals. We examined the association between ART use (any current ART use during the 48 week period versus none), CD4+ T-cell count, and HIV-1 RNA viral load for a 48-week interval and risk of MetS in the next interval. The “last value carried forward” method was used for missing CD4+ T cell and HIV-1 RNA viral load values.
For both prevalence and incidence analyses, univariate models were used to examine the unadjusted associations between covariates and MetS. All covariates with a P value ≤0.1 were added to the multivariable model. For the final multivariable model, age, sex, and race/ethnicity were included regardless of P value. For all other variables, only those that were statistically significant (P < 0.05) were retained in the final multivariable model.
We determined the frequency of each combination of metabolic abnormalities for all prevalent and incident cases of MetS. For the incident cases, we examined the status of all MetS components at baseline to see if the metabolic abnormality was already present at baseline or developed during follow-up.
All analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC).
Of the 3761 participants who enrolled in the 3-parent studies, 2554 had enrolled in ALLRT. As stated in the Methods section, 307 of the 2554 individuals were excluded because there was not enough information to classify their MetS status at baseline. There were no significant differences in most demographic and clinical characteristics between the final study population (n = 2247) and the excluded individuals (n = 307); the mean (SD) fasting triglyceride level was higher in the excluded group than the final study population [221 (226) vs. 138 (117), P < 0.0001].
Prevalence of MetS at ART Initiation
At baseline, 450 (20%) of the 2247 individuals had MetS. Table 1 shows the baseline characteristics of the study population by baseline MetS status. At baseline, 88% of those with MetS and 76% of those without MetS were greater than 30 years of age. Among those with MetS, 47% were white, 27% were black, and 24% were Hispanic; among those without MetS, 42% were white, 35% were black, and 20% were Hispanic. In the multivariable model shown in Table 2, older age and high BMI were significantly associated with an increased prevalence of MetS, and black race was associated with a decreased prevalence of MetS at baseline. Baseline CD4+ T-cell count, HIV-1 RNA viral load, and other factors listed in Table 1 were not associated with MetS at baseline and were not included in the final model presented in Table 2.
Incident MetS During Follow-Up After ART Initiation
There were 478 incident cases of MetS among the 1797 individuals (5617 person-years of follow-up) who were MetS-free at baseline. The estimated IR of MetS was 8.5 per 100 person-years (95% CI = 7.8 to 9.3). Table 3 presents the unadjusted IRs for covariates of interest.
The factors that were independently associated with the incidence of MetS are presented in a multivariable model in Table 4. Age at study entry, race/ethnicity, baseline BMI, time-updated CD4+ T-cell count, time-updated HIV-1 RNA viral load, and time-updated use of a PI-based regimen during follow-up were significantly associated with incident MetS. The adjusted HR increased with increasing age and also with increasing BMI. Relative to a CD4+ T-cell count of ≤50 cells/mm3, the adjusted HR decreased from 0.68 (95% CI = 0.48 to 0.96) for a CD4+ T-cell count of 51–200 to 0.62 (95% CI = 0.43 to 0.90) for a CD4+ T-cell count of >500. The adjusted HR was 1.55 (95% CI = 1.25 to 1.92) for an HIV-1 RNA viral load ≤400 copies per milliliter compared with >400 copies per milliliter. Finally, use of a PI-based regimen was associated with an increased risk of MetS. We examined the prevalence of individual MetS components by PI use as follows: 26% of the 48-week time intervals when individuals were on a PI-based regimen had elevated triglycerides and 18% of the 48-week time intervals when individuals were on a non–PI-based regimen had elevated triglycerides; none of the other MetS components had notable differences by PI-based regimen use. When we stratified by time period (enrollment before and after 2005), the HRs for PI use were similar, but no longer statistically significant. Education, family history of CVD, and history of cigarette smoking were not associated with MetS in the multivariable model.
We also examined time-updated use of specific PIs. Among 5617 person-years of follow-up, 62% were from a non–PI-based regimen, 20% were from a lopinavir/ritonavir-containing regimen, 16% were from an atazanavir/ritonavir-containing regimen, and the rest were from other PI-containing regimens. In a model that adjusted for age, race/ethnicity, baseline BMI, time-updated CD4 and time-updated HIV-1 RNA, and excluded time on atazanavir/ritonavir regimens, the HR for a time-updated lopinavir/ritonavir-containing regimen was 1.28 (95% CI = 1.00 to 1.64, P = 0.05) relative to a regimen without any PIs. The HR was 1.12 (95% CI = 1.00 to 1.25, P =0.04) for a time-updated atazanavir/ritonavir-containing regimen relative to a regimen without PI (model adjusted for same covariates as above and excluded time on lopinavir/ritonavir regimens). The use of an NRTI-based regimen (overall NRTI use, stavudine use, abacavir use, tenofovir use) or a NNRTI-based regimen was not significantly associated (P > 0.05) with incident MetS.
Distribution of MetS Components Among Prevalent and Incident Cases
The combination of high blood pressure, high triglycerides, and low HDL was found among 40% of the prevalent and 49% of the incident cases. Figure 1 presents the distribution of factors defining MetS among the 478 incident cases at baseline and at MetS incidence. The Figure 1 shows that among the incident cases with low HDL as one of the components of incident MetS, 59% had low HDL at baseline. In contrast, among the incident cases with high triglycerides as one of the components of incident MetS, 58% developed hypertriglyceridemia during follow-up. Fifty-seven percent of the cases with high blood pressure as one of the components of incident MetS had developed the high blood pressure during follow-up.
In this large cohort of HIV-infected ART-naive individuals followed for a median of 2.8 years after ART initiation (maximum follow-up was 8.3 years), the incidence of MetS was 8.5 per 100 person-years. The most common set of MetS components during follow-up included high triglycerides, low HDL, and high blood pressure. In addition to age, race/ethnicity, and high BMI, low CD4+ T-cell counts, lack of virologic suppression, and use of a PI-based antiretroviral regimen were independently associated with a higher risk of developing MetS. To our knowledge, this is one of the few studies to prospectively examine factors associated with incident MetS in ART-naive HIV-infected individuals after ART initiation.
At ART initiation, 20% of the individuals had MetS; at the end of follow-up, the prevalence of MetS in those who were MetS free at ART initiation was 27%. A small study of 60 ARV-naive subjects (83% male) in Spain found that the prevalence of MetS increased from 17% to 25% after 48 weeks of ART.15 In a larger international study of 881 ARV-naive subjects (79% male), the prevalence before ART initiation was 8.5%, and incidence during follow-up after ART initiation was 12 per 100 person-years.8 Other studies reporting on MetS in HIV-infected populations have been predominantly cross-sectional or have included subjects already taking antiretroviral medications.6,7,16,17 Using our large cohort of ART-naive individuals, we were able to assess the prevalence of MetS before ART initiation and the progression to MetS during follow-up after ART initiation. Although some studies suggest an association between MetS and the risk of CVD and diabetes in the HIV-infected population, the role of MetS as an independent predictor of risk for CVD is unclear.8,18 Whether the incidence of MetS in our cohort contributes to a subsequent increased risk of CVD and diabetes in this population of ART-naive individuals after ART initiation needs to be examined. Majority of subjects in our study qualified for MetS based on hypertension, hypertriglyceridemia, and low HDL compared with NHANES, in which the majority qualified due to hypertension, hyperglycemia, and abdominal obesity.19 A possible explanation for this difference is that hypertriglyceridemia and low HDL are associated with HIV disease and ART, whereas in the general population, these conditions are probably the result of obesity and other lifestyle factors (exercise and alcohol consumption). Data comparing HIV-infected and HIV-infected populations are needed to examine if MetS confers similar CVD risk in both populations.
Previous reports on the association between CD4+ T-cell count and the risk of MetS are conflicting. Although some have found no association,17,18 2 cross-sectional studies found that a higher CD4+ T-cell count is associated with a higher risk of MetS.16,20 In contrast, in a cross-sectional study of 293 subjects, a CD4+ T-cell count less than 100 cells per cubic millimeter was associated with a higher risk of MetS.21 Using prospective data from our study, we were able to examine time-updated CD4+ T-cell levels in ART-naive subjects after ART initiation using a multivariable model that included demographic and other HIV-associated factors. Relative to CD4+ T-cell counts of ≤50 cells per cubic millimeter, the risk of MetS was decreased for each increasing level of CD4. The association of CD4+ T-cell count with MetS may be explained by higher levels of inflammation and immune activation among those with more advanced HIV disease. Data from the ALLRT cohort on inflammation and risk of diabetes show that those who developed diabetes had lower CD4+ T-cell counts.22 Residual immune activation/inflammation, similar to that observed by Brown et al, may also explain the association with time-updated CD4+ T-cell count even though there was a lack of association with baseline CD4 count. It is also possible that a higher CD4+ T-cell count is reflective of better adherence to medications and a healthier lifestyle consisting of a good diet and physical activity; and this then further reduces the risk of MetS. However, we did not have data on diet and exercise to evaluate this hypothesis.
Consistent with previous reports,16,21,7,23 HIV-1 RNA >400 copies per milliliter was associated with a higher risk of incident MetS in our cohort. A possible explanation for this finding is that HIV can cause lipid abnormalities including elevated triglycerides due to a combination of hepatic very low-density lipoprotein overproduction and reduced triglyceride clearance and low HDL due to poor nutritional state and weight loss.2,24,25 Indeed, we also found that high triglycerides and low HDL were the most common MetS components; similar observations have been reported in other studies.6
Specific ART regimens show associations with individual components of MetS. Proposed mechanisms for PIs can induce insulin resistance by inhibiting the glucose transporter 4 and by reducing peroxisomal proliferator-activated receptor activity, and NRTIs can cause mitochondrial toxicity that leads to fat cell apoptosis, elevated free fatty acids, and finally insulin resistance and hypertriglyceridemia.26,27 Evidence also suggests a link between MetS and PIs, and specifically an increased risk for lopinavir/ritonavir.7,28 For NRTIs, studies suggest an increased risk of MetS for those on stavudine17,28 or didanosine.7 In our study, we found an association between the use of a PI-based regimen and the risk of MetS; however, there was no association between NRTI use, NNRTI use, and MetS risk. The high prevalence of elevated triglycerides when subjects were on a PI-based regimen relative to when they were not on a PI-based regimen suggests that the association between PI use and MetS is probably mediated by high triglycerides. When we examined the use of specific antiretroviral medications, none were significantly associated with MetS in the multivariate model that included demographics, CD4+ T-cell count, and HIV-1 RNA viral load. Since enrollment in the parent studies began in 2001, some of the older regimens examined may no longer be in use. A next step to understanding the role of ART in the development of MetS would be to evaluate if newer ART drugs and regimens are associated with MetS.
Current treatment options for MetS include switching antiretroviral agents.26 Evidence suggests that switching a PI to certain NRTIs or NNRTIs in virally suppressed patients does not affect antiviral efficacy and may partly reverse metabolic changes.29 Whether such switching of regimens decreases the incidence of MetS needs to be evaluated.
Similar to findings from other HIV-infected populations, the risk of MetS increased with increasing age.20,23,28 The prevalence of MetS also varies by race/ethnicity, with lower prevalence in non-Hispanic black men than non-Hispanic white men possibly due to lower triglycerides, higher HDL, and lower waist circumferences in non-Hispanic black men.19,30 In our study that was largely male (80% male), compared with those of white race, non-Hispanic blacks had a lower risk of MetS. Obesity was independently associated with a higher risk of developing MetS in our cohort; relative to those with a normal BMI at baseline, the risk of MetS was almost 4 times higher in those who were obese. Prior studies in the general population and in the HIV-infected population have also reported a similar association between obesity and MetS.31 At entry, 31% and 8% of our cohort were overweight and obese, respectively; similar prevalence were noted in the US military cohort32 that concluded that HIV-infected patients are increasingly overweight or obese at HIV diagnosis and during the course of HIV infection.
Our study has some limitations. We had to exclude 307 individuals at baseline because of missing data on MetS components; however, a comparison of demographics and clinical characteristics showed that the excluded group was similar to the study population. We did not have data on alcohol use, exercise, or nutrition/caloric intake, all of which have been shown to be associated with MetS in other studies.17,20 Also, we did not have data on use of supplements such as fish oil that can be used to lower triglycerides.33 And finally, the regimens used in our study population only included a limited number of PIs; the majority of PI-based regimens in our study consisted of either lopinavir/ritonavir or atazanavir/ritonavir.
A primary strength of our study is the unique cohort of HIV-infected individuals who were treatment naive at entry, randomized to treatment regimens in clinical trials, and rigorously monitored and followed long-term after completion of the original clinical trial. This prospectively followed cohort had scheduled visits during their parent trial and during follow-up in ALLRT which ensured that all components of MetS were collected using standardized methods at regular intervals. This standardized data collection also made it possible for us to examine a large array of demographic and clinical risk factors for MetS.
In conclusion, in our large cohort of HIV-infected ART-naive persons, we found that apart from the traditional factors such as age and BMI, HIV-associated factors such as low CD4+ T-cell count and lack of virologic suppression are associated with an increased risk of developing MetS. In addition, use of a PI-based regimen was associated with an increased risk of developing MetS. Our findings support the importance of achieving both virologic suppression and immune restoration to avoid MetS. If indeed MetS increases the risk of diabetes and CVD, reducing MetS in HIV-infected populations may be an important step in reducing the burden of CVD and diabetes in this population.
The authors especially thank the study volunteers who participate in ALLRT/A5001; all the ACTG clinical units who enroll and follow participants; and the ACTG. The authors would also like to thank the A5001 protocol team.
1. Volberding PA, Deeks SG. Antiretroviral therapy and management of HIV infection. Lancet. 2010;376:49–62.
2. Grunfeld C, Pang M, Doerrler W, et al.. Lipids, lipoproteins, triglyceride clearance, and cytokines in human immunodeficiency virus infection and the acquired immunodeficiency syndrome. J Clin Endocrinol Metab. 1992;74:1045–1052.
3. Wohl DA, Brown TT. Management of morphologic changes associated with antiretroviral use in HIV-infected patients. J Acquir Immune Defic Syndr. 2008;49(suppl 2):S93–S100.
4. Kassi E, Pervanidou P, Kaltsas G, et al.. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48. doi: 10.1186/1741-7015-9-48.
5. Ford ES, Li C, Zhao G. Prevalence and correlates of metabolic syndrome based on a harmonious definition among adults in the US. J Diabetes. 2010;2:180–193.
6. Worm SW, Lundgren JD. The metabolic syndrome in HIV. Best Pract Res Clin Endocrinol Metab. 2011;25:479–486. doi: 10.1016/j.beem.2010.10.018.
7. Jacobson DL, Tang AM, Spiegelman D, et al.. Incidence of metabolic syndrome in a cohort of HIV-infected adults and prevalence relative to the US population (National Health and Nutrition Examination Survey). J Acquir Immune Defic Syndr. 2006;43:458–466.
8. Wand, Calmy A, Carey DL, et al.. Metabolic syndrome, cardiovascular disease and type 2 diabetes mellitus after initiation of antiretroviral therapy in HIV infection. AIDS. 2007;21:2445–2453.
9. Smurzynski M, Collier AC, Koletar S, et al.. AIDS clinical trials group longitudinal linked randomized trials (ALLRT): rationale, design, and baseline characteristics. HIV Clin Trials. 2008;9:269–282.
10. Gulick RM, Ribaudo HJ, Shikuma CM, et al.. Triple-nucleoside regimens vs. efavirenz-containing regimens for the initial treatment of HIV-1 infection: AIDS Clinical Trials Group (ACTG) Study A5095. N Engl J Med. 2004;350:1850–1861.
11. Riddler SA, Haubrich R, DiRienzo AG, et al.. Class-sparing regimens for initial treatment of HIV-1 infection. N Engl J Med. 2008;358:2095–2106.
12. Sax PE, Tierney C, Collier AC, et al.. Abacavir-lamivudine versus tenofovir-emtricitabine for initial HIV-1 therapy. N Engl J Med. 2009;361:2230–2240.
13. Alberti KG, Eckel RH, Grundy SM, et al.. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–1645. E-pub: October 5, 2009.
14. Samaras K, Wand H, Law M, et al.. Prevalence of metabolic syndrome in HIV-infected patients receiving highly active antiretroviral therapy using International Diabetes Foundation and Adult Treatment Panel III criteria: associations with insulin resistance, disturbed body fat compartmentalization, elevated C-reactive protein, and [corrected] hypoadiponectinemia. Diabetes Care. 2007;30:113–119.
15. Palacios R, Santos J, González M, et al.. Incidence and prevalence of the metabolic syndrome in a cohort of naive HIV-infected patients: prospective analysis at 48 weeks of highly active antiretroviral therapy. Int J STD AIDS. 2007;18:184–187.
16. Mondy K, Overton ET, Grubb J, et al.. Metabolic syndrome in HIV-infected patients from an urban, midwestern US outpatient population. Clin Infect Dis. 2007;44:726–734. E-pub: January 22, 2007.
17. Sobieszczyk ME, Hoover DR, Anastos K, et al.. Prevalence and predictors of metabolic syndrome among HIV-infected and HIV-uninfected women in the Women's Interagency HIV Study. J Acquir Immune Defic Syndr. 2008;48:272–280.
18. Worm SW, Sabin CA, Reiss P, et al.. Presence of the metabolic syndrome is not a better predictor of cardiovascular disease than the sum of its components in HIV-infected individuals. Diabetes Care. 2009;32:474–480.
19. Ervin RB. Prevalence of Metabolic Syndrome Among Adults 20 Years of Age and over, by Sex, Age, Race and Ethnicity, and Body Mass Index: United States, 2003–2006. National Health Statistics Reports: No 13. Hyattsville, MD: National Center for Health Statistics; 2009.
20. Alvarez C, Salazar R, Galindez J, et al.. Metabolic syndrome in HIV-infected patients receiving antiretroviral therapy in Latin America. Braz J Infect Dis. 2010;14:256–263.
21. Bonfanti P, De Socio GL, Marconi P, et al.. Is metabolic syndrome associated to HIV infection per se? Results from the HERMES study. Curr HIV Res. 2010;8:165–171.
22. Brown TT, Tassiopoulous K, Bosch RJ, et al.. Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care. 2010;33:2244–2249.
23. Squillace N, Zona S, Stentarelli C, et al.. Detectable HIV viral load is associated with metabolic syndrome. J Acquir Immune Defic Syndr. 2009;52:459–464.
24. Grunfeld C, Kotler DP, Hamadeh R, et al.. Hypertriglyceridemia in the acquired immunodeficiency syndrome. Am J Med. 1989;86:27–31.
25. Grunfeld C, Tien P. Difficulties in understanding the metabolic complications of the acquired immune deficiency syndrome. Clin Infect Dis. 2003;37(suppl 2):S43–S46.
26. Feigenbaum K, Longstaff L. Management of metabolic syndrome in patients with human immunodeficiency virus. Diabetes Educ. 2010;36:457–464. E-pub: March 26, 2010.
27. Tebas P. Insulin resistance and diabetes mellitus associated with antiretroviral use in HIV-infected patients: pathogenesis, prevention, and treatment options. J Acquir Immune Defic Syndr. 2008;49(suppl 2):S86–S92.
28. Jerico C, Knobel H, Montero M, et al.. Metabolic syndrome among HIV-infected patients: prevalence, characteristics, and related factors. Diabetes Care. 2005;28:132–137.
29. van Wijk JP, Cabezas MC. Hypertriglyceridemia, metabolic syndrome, and cardiovascular disease in HIV-infected patients: effects of antiretroviral therapy and adipose tissue distribution. Int J Vasc Med. 2012;2012:201027. E-pub: August 22, 2011.
30. Razzouk L, Muntner P. Ethnic, gender and age-related differences in patients with the metabolic syndrome. Curr Hypertens Rep. 2009;11:127–132. Review.
31. Bergesen BM, Schumacher A, Sandvik L, et al.. Important differences in components of the metabolic syndrome between HIV-patients with and without highly active antiretroviral therapy and healthy controls. Scand J Infect Dis. 2006;38:682–689.
32. Crum-Ciaflone N, Roediger MP, Elerly M, et al.. Increasing rates of obesity among HIV-infected persons during the HIV epidemic. PLoS ONE. 2010;5:e10106.
33. Gerber JG, Kitch DW, Fichtenbaum CJ, et al.. Fish oil and fenofibrate for the treatment of hypertriglyceridemia in HIV-infected subjects on anti-retroviral therapy: results of ACTG A5186. J Acquir Immune Defic Syndr. 2008;47:459–466.
Keywords:© 2012 Lippincott Williams & Wilkins, Inc.
metabolic syndrome; HIV; incidence; prevalence; ART-naive; risk factors