OBJECTIVE—To assess the prevalence in HIV-infected patients of the metabolic syndrome as defined by the National Cholesterol Education Program, i.e., three or more of the following components: abdominal obesity, hypertriglyceridemia, low HDL cholesterol, high blood pressure, and high fasting glucose.

RESEARCH DESIGN AND METHODS—In this cross-sectional study, 710 HIV-infected patients managed at the outpatient clinic of a tertiary hospital during 2003 completed the study protocol consisting of a medical examination and laboratory analysis after a 12-h overnight fast.

RESULTS—Metabolic syndrome prevalence was 17% and increased from 5.1% among HIV-infected patients under age 30 years to 27.0% for those aged 50–59 years. Age (per 10-year increment) (odds ratio [OR] 1.41 [95% CI 1.12–1.77]), BMI (1.27 [1.19–1.36]), past and present protease inhibitor exposure (2.96 [1.03–3.55] and 4.18 [1.4–12.5], respectively) were independently associated with the metabolic syndrome on logistic regression analysis. Furthermore, only stavudine (d4T) (1.74 [1.01–2.98]) and lopinavir/ritonavir (2.46 [1.28–4.71]) were associated with the metabolic syndrome after adjustment for age and BMI.

CONCLUSIONS—The prevalence of metabolic syndrome among these HIV-infected patients is similar to that previously reported in uninfected individuals. Of specific concern is the association of protease inhibitor exposure with the metabolic syndrome and, more specifically, with exposure to stavudine and lopinavir/ritonavir when individual antiretroviral drugs were analyzed.

Combination antiretroviral therapy (ART) has positively modified the natural history of HIV infection, leading to a significant reduction in morbidity and mortality. However, long-term toxicity is becoming recognized, and a variety of metabolic abnormalities including dyslipidemia, fat redistribution, high blood pressure, and insulin resistance have frequently been associated with this therapy, particularly when it contains protease inhibitors (1). This fact has raised the concern that the HIV-infected population in the long term may be at increased risk for cardiovascular disease, as has recently been described in two large prospective studies (2,3).

The metabolic syndrome is affecting the general population in epidemic proportions and is frequently associated with increased risk of cardiovascular morbidity and mortality (46). This aspect has been emphasized by the National Cholesterol Education Program Adult Treatment Panel (ATP) III (7), and a recently reviewed working definition of this syndrome has been drawn up (8). Insulin resistance plays a key role in the pathogenesis of the metabolic syndrome (4) and is frequently detected among HIV patients on ART (9). Furthermore, several traits of the metabolic syndrome in the general population overlap with common features of metabolic side effects associated with combination ART in the HIV-infected population. Although cardiovascular risk factors have been extensively evaluated in HIV-infected patients (3,10), the identification and management of metabolic disorders in those receiving combination ART has become a critical issue. The present study focuses on the prevalence and characteristics of the metabolic syndrome in HIV-infected patients and possible related factors, including individual antiretroviral drug exposure.

A cross-sectional study was carried out on HIV-infected patients managed at the outpatient Infectious Disease Unit of the Hospital del Mar, Barcelona, over a period of 1 year, from January through December 2003. The protocol study approved by the local ethics committee consisted of physical examination and laboratory analysis after a 12-h overnight fast. All participants were 20 years of age or older and were evaluated by trained physicians after giving their informed consent. Exclusion criteria included withdrawal of combination ART and evidence of clinical signs of active AIDS in the 3 months before entry because of their possible impact on anthropometric and laboratory parameters.

Age, sex, HIV disease status according to the 1993 Centers for Disease Control and Prevention (CDC) classification of HIV disease (11), HIV exposure (mutually exclusive in the following order: intravenous drug use, male homosexual activity, heterosexual activity), and type and duration of ART were recorded. Lipodystrophy was defined and categorized by the blinded physician-assessed (H.K.) presence of peripheral lipoatrophy (face, arms, legs, buttocks, and prominent veins), central lipohypertrophy (abdomen, breasts, dorsocervical region), and mixed lipodystrophy. Weight, height, and waist circumference were measured by standard methods. After the patient had rested for 10 minutes seated in a quiet room, blood pressure was measured in the left arm with the elbow flexed at heart level by the same physician (C.J.) using a 1042 Riester sphygmomanometer (Jungingen, Germany) with diastolic pressures at Korotkoff phase V (disappearance of sounds). Three readings were obtained, and the average of the second and third systolic and diastolic blood pressure readings was used in the analyses.

Total cholesterol and triglycerides were determined using enzymatic methods in a Cobas Mira automatic analyzer (Baxter Diagnostics, Düdingen, Switzerland). HDL cholesterol was measured using separation by precipitation with phosphotungstic acid and magnesium chloride. Glucose was measured by the oxidase method. CD4 lymphocyte cell count and HIV RNA viral load (Nuclisens Easy Q HIV-1; Biomérieux, Boxtel, the Netherlands) were performed at the time of the study; the nadir of CD4 cell count and baseline viral load levels were recorded.

Definition of the metabolic syndrome

As detailed in the ATP III report (9), participants with three or more of the following criteria were defined as having the metabolic syndrome: waist circumference >102 cm in men and >88 cm in women; triglycerides ≥150 mg/dl (1.69 mmol/l); HDL cholesterol <40 mg/dl (1.04 mmol/l) in men and <50 mg/dl (1.29 mmol/l) in women; blood pressure ≥130/85 mmHg; and fasting glucose ≥110 mg/dl (6.1 mmol/l). Individuals met criteria for high blood pressure or high fasting glucose concentration if they were currently on antihypertensive or oral hypoglycemic therapies, respectively.

Statistical methods

Student’s t test was performed to assess differences between two means. When data were not normally distributed, the Mann-Whitney U test was used. Either χ2 test or Fisher’s exact test was used to test the degree of association of categorical variables. The 95% CIs for proportions are calculated according to the efficient score method (corrected for continuity): P ± 1.96 × sqrt[P(1 − P)/n] P ± 1.96 × square root[P(1 − P)/n]. Computed factors in the univariate analysis were age, sex, BMI, HIV transmission group (dichotomized as intravenous drug users versus sexual transmission), HIV clinical stage (dichotomized as asymptomatic, A stage of the CDC versus symptomatic, B and C stage of the CDC), current and nadir CD4 cell count, plasma HIV RNA categorized as detectable (>500 copies/ml) or undetectable, lipodystrophy, duration of ART and type of ART classified as antiretroviral naive, never protease inhibitor exposure, past protease inhibitor exposure, and current protease inhibitor exposure.

Variables demonstrating a univariate relationship (P < 0.05) with the outcome variable were included in the logistic regression analysis to assess the effect of independent variables on metabolic syndrome diagnosis. A P value <0.05 was considered statistically significant. Goodness-of-fit was verified with the Hosmer and Lemeshow statistic method. The variables included in the logistic regression model were age (per 10-year increment), BMI, HIV transmission group (intravenous drug users formed the reference group), CD4 nadir cell count, and type of ART classified as antiretroviral naive (the reference group), treated but never exposed to protease inhibitors, treated with past exposure to protease inhibitors, and treated with current exposure to protease inhibitors. The association between individual antiretroviral drug exposure and the metabolic syndrome was analyzed by the χ2 test. Drugs demonstrating a univariate relationship (P < 0.05) with the metabolic syndrome were included in the logistic regression analysis to assess their independent effect after adjustment for age and BMI. All statistical analyses of database results were performed with the Statistical Package for the Social Sciences (SPSS for Windows, v.11.5; Chicago, IL).

Among the 1,016 HIV-infected patients managed at the outpatient clinic of our hospital during 2003, 209 were excluded for age, ART withdrawal, or overt clinical disease that required hospital admission. Of the 807 eligible patients, only 710 (88%) completed the study protocol. Of these, 626 (88.2%) were on combination ART and 84 (11.8%) naive. Clinical characteristics of HIV infection and metabolic syndrome traits are listed in Tables 1 and 2, respectively. This shows that one or more features of metabolic syndrome were seen in 492 (69.3%) patients, two or more in 254 (35.8%), three or more in 121 (17%), four or more in 32 (4.5%), and five features were seen in 1 patient (0.1%). Thus, 121 patients (86 men, 35 women) met metabolic syndrome criteria, yielding a prevalence of 17% (95% CI 14–20%). This was significantly increased by age and rose from 5.1% in HIV-infected patients under age 30 to 27.0% in those aged 50–59. A total of 116 patients were on combination ART, and 5 were naive HIV-infected patients. Lipodystrophy was more common among participants with the metabolic syndrome compared with those without (50.4 vs. 33.8%; P = 0.0001). Hypertriglyceridemia (95%) was the most frequent trait of the metabolic syndrome, followed by low HDL cholesterol (71.1%), high blood pressure (67.8%), abdominal obesity (47.1%), and high blood glucose levels (46.3%).

Metabolic syndrome-related factors in the univariate analysis are shown in Table 3. Patients with the metabolic syndrome presented higher age and BMI, lower percentage of intravenous drug users, and lower CD4 nadir cell count compared with those without the metabolic syndrome. Moreover, lipodystrophy and ART were associated with the metabolic syndrome. Lipodystrophy could indirectly be considered a component trait of the metabolic syndrome because its definition involves fat redistribution and, frequently, insulin resistance; thus, it was not included in the logistic regression analysis. Besides age and BMI, past and current exposure to protease inhibitors emerged as significantly and independently associated with metabolic syndrome in the logistic regression model (Table 4) (OR 2.96 [95% CI 1.03–3.55] and 4.18 [1.4–12.5], respectively). The relationship between individual antiretroviral drug exposure and the metabolic syndrome in the univariate and logistic regression analysis adjusted for age and BMI is shown in Table 5. Only stavudine (d4T) (1.74 [1.01–2.98]) and lopinavir/ritonavir (2.46 [1.28–4.71]) were independently associated with the metabolic syndrome.

Using ATP III criteria (7), 17% of HIV-infected patients in this sample were estimated to have the metabolic syndrome. This estimate is somewhat lower than that reported for the American population (12) using the same clinical definition or for the Spanish population (13). This could be due at least in part to the low number of women (28%) and patients over age 60 years (5%) in the present study, population subgroups with known higher metabolic syndrome prevalence. However, when comparing the age-specific prevalence of the metabolic syndrome, the prevalence among participants aged 30 through 50 was nearly identical in the present study of HIV-infected patients to that of the uninfected persons (12,13).

Although the prevalence of metabolic syndrome has been assessed in several populations (1215), previous data on metabolic syndrome among the HIV-infected population are limited and show an impressively high prevalence (16,17). Potential explanations for these dissimilar results include differences in study design, methodological aspects, and differences in the patient populations studied. In this respect, the present study has a relatively high proportion of intravenous drug users with a low prevalence of the metabolic syndrome (13%). On the other hand, when the present HIV-infected group was reanalyzed by the Data Collection on Adverse Events of anti-HIV Drugs Study criteria for age and sex, obesity, hypertension, hypercholesterolemia, low HDL, hypertriglyceridemia, and diabetes (3), prevalences of these cardiovascular risk factors were quite similar. Nevertheless, we must emphasize that the metabolic syndrome is a heterogeneous disorder, with substantial variability in the prevalence of component traits within and across populations.

Some aspects of the present study need to be highlighted. First, to date, this is the largest sample size study conducted in HIV-infected patients focusing specifically on the metabolic syndrome. Second, from at least three clinical criteria of the metabolic syndrome recommended by different organizations (7,18,19), we used ATP III criteria (7) because all five components can easily be evaluated in the clinical setting. The main difference in metabolic syndrome components of the present study compared with other studies conducted with HIV-infected or uninfected subjects (12,13,16,17) was the low prevalence of abdominal obesity (12.5%). Third, we ensured that blood samples were obtained after a 12-h overnight fast to avoid hypertriglyceridemia and hyperglycemia overdiagnosis. Finally, concerning blood pressure measurement and according to the American Heart Association (20), blood pressure was measured with the patient’s elbow flexed at heart level. From a clinical viewpoint, this apparently insignificant fact has important implications. In this respect, Villegas et al. (21) reported that 73% of health care workers failed to use proper arm and cuff positions, and Hemingway et al. (22) recently found blood pressure readings to be higher when the arm was parallel to the torso and would decrease by 8.8 to 14.4 mmHg with the forearm raised to a perpendicular position.

As occurs in uninfected subjects (12,13), metabolic syndrome in HIV-infected patients is associated with age and BMI. Among the HIV-infected population, the new findings concern the additional independent association of metabolic syndrome in those with past and current exposure to protease inhibitors. The link between protease inhibitor exposure, lipodystrophy, and metabolic syndrome is not surprising because fat redistribution, hyperlipidemia, insulin resistance, and hyperglycemia have been extensively reported in subjects treated with protease inhibitors (23). In the present study, we went a step further because the association between individual antiretroviral drug exposure and the metabolic syndrome was evaluated. In this respect, only stavudine (d4T) and lopinavir/ritonavir were independently associated with the metabolic syndrome. Although a cross-sectional study is not the most appropriate method to assess this association, this finding is consistent with other studies in which lipid abnormalities tend to be more marked with ritonavir and lopinavir/ritonavir (24,25), even in uninfected subjects (26). In the present study, if the logistic regression analysis had been performed including lopinavir/ritonavir as ritonavir exposure, the adjusted OR for ritonavir would have been 2.23 (95% CI 1.38–3.61; P = 0.001). The possible role of specific nucleoside analogues to alter lipid profile is not well established; some studies suggest that stavudine may be associated with hypertriglyceridemia and hypercholesterolemia in comparison with other nucleosides or nucleotide analogues (27,28).

Limitations of the present study are mainly related to the observational design and cross-sectional nature of the current analyses as well as the clinical population studied. In this respect, the results reported herein are only associations from which no conclusions regarding causality can be drawn. Furthermore, it is not expected that many measurements will always be conducted in a uniform manner. This includes measurement of waist circumference and blood pressure and laboratory analyses of lipid and glucose levels. Finally, information on other environmental factors such as physical activity or diet was not collected.

Although the substantial benefits of combination ART clearly outweigh the increase in cardiovascular risk associated with this therapy, it must be borne in mind that with progressive aging of the HIV-infected population and the expected long-term use of combination ART, the need will arise to prevent an increased incidence of metabolic syndrome in this population. Because metabolic syndrome represents a cluster of modifiable cardiovascular risk factors, the present results may have significant implications for health care physicians.

Table 1—

Demographic, anthropometric, and HIV infection characteristics of the 710 patients

CharacteristicsValues
Age (years) 41.9 ± 9.2 
Male/female 511/199 
BMI (kg/m223.4 ± 3.9 
Waist circumference (cm)  
    Men 86.7 ± 9.9 
    Women 80.9 ± 11.4 
Current smokers (%) 479 (67.5) 
Transmission groups (%)  
    Intravenous drug users 293 (41.3) 
    Homosexuals 240 (33.8) 
    Heterosexuals 158 (22.2) 
    Other and unknown 19 (2.7) 
Known duration of HIV infection [months (IQR)] 113 (74–157) 
CD4 nadir cell count [× 106 cells/l (IQR)] 201 (76–329) 
CD4 cell count [× 106 cells/l (IQR)] 479 (302–688) 
Viral load <500 copies/ml (%) 442 (62.3) 
HIV disease category (%)*  
    A 339 (47.7) 
    B 139 (19.6) 
    C 232 (32.7) 
Median time ART [months (IQR)] 78 (42–104) 
ART exposure (%)  
    Naïve 84 (11.8) 
    Never protease inhibitor 158 (22.3) 
    Past protease inhibitor 264 (37.2) 
    Current protease inhibitor 203 (28.6) 
Lipodystrophy (%)  
    No fat redistribution 450 (63.4) 
    Lipoatrophy 152 (21.4) 
    Lipohypertrophy 108 (15.2) 
    Mixed lipodystrophy  
CharacteristicsValues
Age (years) 41.9 ± 9.2 
Male/female 511/199 
BMI (kg/m223.4 ± 3.9 
Waist circumference (cm)  
    Men 86.7 ± 9.9 
    Women 80.9 ± 11.4 
Current smokers (%) 479 (67.5) 
Transmission groups (%)  
    Intravenous drug users 293 (41.3) 
    Homosexuals 240 (33.8) 
    Heterosexuals 158 (22.2) 
    Other and unknown 19 (2.7) 
Known duration of HIV infection [months (IQR)] 113 (74–157) 
CD4 nadir cell count [× 106 cells/l (IQR)] 201 (76–329) 
CD4 cell count [× 106 cells/l (IQR)] 479 (302–688) 
Viral load <500 copies/ml (%) 442 (62.3) 
HIV disease category (%)*  
    A 339 (47.7) 
    B 139 (19.6) 
    C 232 (32.7) 
Median time ART [months (IQR)] 78 (42–104) 
ART exposure (%)  
    Naïve 84 (11.8) 
    Never protease inhibitor 158 (22.3) 
    Past protease inhibitor 264 (37.2) 
    Current protease inhibitor 203 (28.6) 
Lipodystrophy (%)  
    No fat redistribution 450 (63.4) 
    Lipoatrophy 152 (21.4) 
    Lipohypertrophy 108 (15.2) 
    Mixed lipodystrophy  

Data are means ± SD and n (%) unless otherwise indicated.

*

A: asymptomatic, acute HIV, or persistent generalized lymphadenopathy; B: symptomatic, not A or C categories; and C: AIDS indicator conditions (11). IQR, interquartile range.

Table 2—

Component conditions of the metabolic syndrome of the 710 HIV-infected patients

VariablesValues
Waist circumference  
    >102 cm in men 43 (8.4%) 
    >88 cm in women 46 (23.1%) 
Blood pressure ≥130/85 mmHg 187 (26.3%) 
HDL cholesterol  
    <40 mg/dl in men 173 (33.9%) 
    <50 mg/dl in women 80 (40.2%) 
Triglycerides ≥150 mg/dl 306 (43.1%) 
Glucose ≥110 mg/dl 82 (11.5%) 
VariablesValues
Waist circumference  
    >102 cm in men 43 (8.4%) 
    >88 cm in women 46 (23.1%) 
Blood pressure ≥130/85 mmHg 187 (26.3%) 
HDL cholesterol  
    <40 mg/dl in men 173 (33.9%) 
    <50 mg/dl in women 80 (40.2%) 
Triglycerides ≥150 mg/dl 306 (43.1%) 
Glucose ≥110 mg/dl 82 (11.5%) 

Data are n (%).

Table 3—

Association between age, sex, HIV disease characteristics, lipodystrophy, ART, and the metabolic syndrome

Patients with metabolic syndromePatients without metabolic syndromeP
n (%) 121 (17) 589 (83)  
Age (years) 45.6 ± 10.2 41.2 ± 8.8 0.0001 
Sex (%)    
    Male 86 (71.1) 425 (72.2) 0.80 
    Female 35 (28.9) 164 (27.8)  
BMI (kg/m225.8 ± 3.7 22.9 ± 3.7 0.0001 
Transmission group (%)    
    Intravenous drug users 38 (31.4) 255 (43.3)  
    Others 83 (68.6) 334 (56.7) 0.01 
HIV disease category (%)*    
    A 63 (52.1) 341 (57.9) 0.23 
    B + C 58 (47.9) 248 (42.1)  
Known duration of HIV infection [months (IQR)] 111 (77–145) 115 (73–159) 0.9 
CD4 nadir cell count [× 106 cells/l (IQR)] 159 (60–297) 211 (80–337) 0.012 
CD4 cell count [× 106 cells/l (IQR)] 463 (306–654) 485 (301–694) 0.58 
HIV RNA <500 copies/ml    
    No (%) 43 (35.5) 225 (38.1) 0.60 
    Yes (%) 78 (64.5) 364 (61.9)  
Lipodystrophy (%)    
    No 60 (49.6) 390 (66.2)  
    Lipoatrophy 23 (19.0) 129 (21.9)  
    Lipohypertrophy 38 (31.4) 70 (11.9) 0.0001 
    Mixed lipodystrophy    
Median time ART [months (IQR)] 75 (54–97) 72 (29–100) 0.18 
Antiretroviral therapy exposure    
    Naive (%) 5 (4.1) 79 (13.4)  
    Never protease inhibitor (%) 24 (19.8) 134 (22.8) 0.02 
    Past protease inhibitor (%) 51 (42.1) 215 (36.4)  
    Current protease inhibitor (%) 41 (33.9) 161 (27.4)  
Patients with metabolic syndromePatients without metabolic syndromeP
n (%) 121 (17) 589 (83)  
Age (years) 45.6 ± 10.2 41.2 ± 8.8 0.0001 
Sex (%)    
    Male 86 (71.1) 425 (72.2) 0.80 
    Female 35 (28.9) 164 (27.8)  
BMI (kg/m225.8 ± 3.7 22.9 ± 3.7 0.0001 
Transmission group (%)    
    Intravenous drug users 38 (31.4) 255 (43.3)  
    Others 83 (68.6) 334 (56.7) 0.01 
HIV disease category (%)*    
    A 63 (52.1) 341 (57.9) 0.23 
    B + C 58 (47.9) 248 (42.1)  
Known duration of HIV infection [months (IQR)] 111 (77–145) 115 (73–159) 0.9 
CD4 nadir cell count [× 106 cells/l (IQR)] 159 (60–297) 211 (80–337) 0.012 
CD4 cell count [× 106 cells/l (IQR)] 463 (306–654) 485 (301–694) 0.58 
HIV RNA <500 copies/ml    
    No (%) 43 (35.5) 225 (38.1) 0.60 
    Yes (%) 78 (64.5) 364 (61.9)  
Lipodystrophy (%)    
    No 60 (49.6) 390 (66.2)  
    Lipoatrophy 23 (19.0) 129 (21.9)  
    Lipohypertrophy 38 (31.4) 70 (11.9) 0.0001 
    Mixed lipodystrophy    
Median time ART [months (IQR)] 75 (54–97) 72 (29–100) 0.18 
Antiretroviral therapy exposure    
    Naive (%) 5 (4.1) 79 (13.4)  
    Never protease inhibitor (%) 24 (19.8) 134 (22.8) 0.02 
    Past protease inhibitor (%) 51 (42.1) 215 (36.4)  
    Current protease inhibitor (%) 41 (33.9) 161 (27.4)  

Data are means ± SD and n (%), unless otherwise indicated.

*

A: asymptomatic, acute HIV, or persistent generalized lymphadenopathy; B: symptomatic, not A or C categories; and C: AIDS indicator conditions (11). IQR, interquartile range.

Table 4—

OR (95% CI) for the metabolic syndrome from multivariate analysis for selected variables

VariablesAdjusted OR (95% CI)P
Age (per 10-year increment) 1.41 (1.12–1.77) 0.003 
BMI 1.27 (1.19–1.36) 0.0001 
Sexual transmission 1.29 (0.80–2.08) 0.3 
CD4 nadir cell count 0.99 (0.99–1.00) 0.07 
Naives — 
ART never protease inhibitor exposure 2.1 (0.69–6.29) 0.19 
Past protease inhibitor exposure 2.96 (1.03–3.55) 0.04 
Current protease inhibitor exposure 4.18 (1.4–12.5) 0.011 
VariablesAdjusted OR (95% CI)P
Age (per 10-year increment) 1.41 (1.12–1.77) 0.003 
BMI 1.27 (1.19–1.36) 0.0001 
Sexual transmission 1.29 (0.80–2.08) 0.3 
CD4 nadir cell count 0.99 (0.99–1.00) 0.07 
Naives — 
ART never protease inhibitor exposure 2.1 (0.69–6.29) 0.19 
Past protease inhibitor exposure 2.96 (1.03–3.55) 0.04 
Current protease inhibitor exposure 4.18 (1.4–12.5) 0.011 

The goodness of fit was verified with the Hosmer and Lemeshow statistic method (P = 0.80).

Table 5—

Univariate and multivariate analyses of individual antiretroviral drugs and the metabolic syndrome

DrugnUnivariate
Multivariate
OR*95% CIPOR*95% CIP
Zidovudine 557 2.16 1.22–3.84 0.007 1.75 0.81–3.73 0.15 
Lamivudine 557 2.16 1.22–3.84 0.007 1.37 0.64–2.93 0.42 
Didanosine 385 1.9 1.26–2.87 0.002 1.37 0.81–2.32 0.24 
Stavudine 380 1.8 1.20–2.71 0.004 1.74 1.01–2.98 0.044 
Abacavir 123 1.47 0.91–2.38 0.11    
Tenofovir 67 1.07 0.55–2.06 0.84    
Nevirapine 249 1.44 0.96–2.14 0.07    
Efavirenz 188 1.4 0.92–2.15 0.12    
Indinavir 338 1.1 0.74–1.63 0.63    
Nelfinavir 189 1.75 1.15–2.65 0.008 1.35 0.84–2.17 0.22 
Ritonavir 101 1.24 0.73–2.12 0.43    
Saquinavir 96 1.44 0.85–2.44 0.18    
Amprenavir 17 2.74 0.99–7.56 0.06    
Lopinavir/Ritonavir 70 1.8 1.02–3.21 0.04 2.46 1.28–4.71 0.007 
DrugnUnivariate
Multivariate
OR*95% CIPOR*95% CIP
Zidovudine 557 2.16 1.22–3.84 0.007 1.75 0.81–3.73 0.15 
Lamivudine 557 2.16 1.22–3.84 0.007 1.37 0.64–2.93 0.42 
Didanosine 385 1.9 1.26–2.87 0.002 1.37 0.81–2.32 0.24 
Stavudine 380 1.8 1.20–2.71 0.004 1.74 1.01–2.98 0.044 
Abacavir 123 1.47 0.91–2.38 0.11    
Tenofovir 67 1.07 0.55–2.06 0.84    
Nevirapine 249 1.44 0.96–2.14 0.07    
Efavirenz 188 1.4 0.92–2.15 0.12    
Indinavir 338 1.1 0.74–1.63 0.63    
Nelfinavir 189 1.75 1.15–2.65 0.008 1.35 0.84–2.17 0.22 
Ritonavir 101 1.24 0.73–2.12 0.43    
Saquinavir 96 1.44 0.85–2.44 0.18    
Amprenavir 17 2.74 0.99–7.56 0.06    
Lopinavir/Ritonavir 70 1.8 1.02–3.21 0.04 2.46 1.28–4.71 0.007 

OR adjusted for age and BMI.

C.J. is the recipient of a grant from the Fundación Institut Municipal d’Investigació Mèdica.

We thank Miss Christine O’Hara for review of the English version of the manuscript.

1
Carr A, Samaras K, Burton S, Law M, Freund J, Chisholm DJ, Cooper DA: A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors.
AIDS
12
:
51
–58,
1998
2
Mary-Krause M, Cotte L, Simon A, Partisani M, Costagliola D, the Clinical Epidemiology Group from the French Hospital Database: Increased risk of myocardial infarction with duration of protease inhibitor therapy in HIV-infected men.
AIDS
17
:
2479
–2486,
2003
3
The Data Collection on Adverse Events of Anti-HIV Drugs (DAD) Study Group: Combination antiretroviral therapy and risk of myocardial infarction.
N Engl J Med
349
:
1993
–2003,
2003
4
Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, Nissen M, Taskinen MR, Groop L: Cardiovascular morbidity and mortality associated with the metabolic syndrome.
Diabetes Care
24
:
683
–689,
2001
5
Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT: The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men.
JAMA
288
:
2709
–2716,
2002
6
Ninomiya JK, L’Italien G, Criqui MH, Whyte JL, Gamst A, Chen RS: Association of the metabolic syndrome with history of myocardial infarction and stroke in the Third National Health and Nutrition Examination Survey.
Circulation
109
:
42
–46,
2004
7
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults: Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).
JAMA
285
:
2486
–2496,
2001
8
Grundy SM, Brewer BH Jr, Cleeman JI, Smith SC Jr, Lenfant C; American Heart Association; National Heart, Lung, and Blood Institute: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition.
Circulation
109
:
433
–438,
2004
9
Leow MK, Addy CL, Mantzoros CS: Human immunodeficiency virus/highly active antiretroviral therapy-associated metabolic syndrome: clinical presentation, pathophysiology, and therapeutic strategies.
J Clin Endocrinol Metab
88
:
1961
–1976,
2003
10
Saves M, Chene G, Ducimetiere P, Leport C, Le Moal G, Amouyel P, Arveiler D, Ruidavets JB, Reynes J, Bingham A, Raffi F, the French WHO MONICA Project and the APROCO (ANRS EP11) Study Group: Risk factors for coronary heart disease in patients treated for human immunodeficiency virus infection compared with the general population.
Clin Infect Dis
37
:
292
–298,
2003
11
Centers for Disease Control and Prevention: 1993 Revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults.
MMWR Recomm Rep
41
:
1
–19,
1992
12
Ford ES, Giles WH, Dietz WH: Prevalence of the metabolic syndrome among U.S. adults: findings from the Third National Health and Nutrition Examination Survey.
JAMA
287
:
356
–339,
2002
13
Ascaso JF, Romero P, Real JT, Lorente RI, Martinez-Valls J, Carmena R: Abdominal obesity, insulin resistance, and metabolic syndrome in a southern European population.
Eur J Intern Med
14
:
101
–106,
2003
14
Al-Lawati JA, Mohammed AJ, Al-Hinai HQ, Jousilahti P: Prevalence of the metabolic syndrome among Omani adults.
Diabetes Care
26
:
1781
–1785,
2003
15
Meigs JB, Wilson PW, Nathan DM, D’Agostino RB Sr, Williams K, Haffner SM: Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies.
Diabetes
52
:
2160
–2167,
2003
16
Gazzaruso C, Sacchi P, Garzaniti A, Fratino P, Bruno R, Filice G: Prevalence of metabolic syndrome among HIV patients.
Diabetes Care
25
:
1253
–1254,
2002
17
Hadigan C, Meigs JB, Wilson PWF, D’Agostino RB, Davis B, Basgoz N, Sax PE, Grinspoon S: Prediction of coronary heart disease risk in HIV-infected patients with fat redistribution.
Clin Infect Dis
36
:
909
–916,
2003
18
Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus: provisional report of a WHO consultation.
Diabet Med
15
:
539
–553,
1998
19
Einhorn D, Reaven GM, Cobin RH, Ford E, Ganda OP, Handelsman Y, Hellman R, Jellinger PS, Kendall D, Krauss RM, Neufeld ND, Petak SM, Rodbard HW, Seibel JA, Smith DA, Wilson PW: American College of Endocrinology position statement on the insulin resistance syndrome.
Endocr Pract
9
:
237
–252,
2003
20
Frohlich ED, Grim C, Labarthe DR, Maxwell MH, Perloff D, Weidman WH: Recommendations for human blood pressure determinations by sphygmomanometers: report of a special task force appointed by the Steering Committee, American Heart Association.
Hypertension
11
:
210A
–222A,
1988
21
Villegas I, Arias IC, Botero A, Escobar A: Evaluation of the technique used by health-care workers for taking blood pressure.
Hypertension
26
:
1204
–1206,
1995
22
Hemingway TJ, Guss DA, Abdelnur D: Arm position and blood pressure measurement.
Ann Intern Med
140
:
74
–75,
2004
23
Carr A: HIV lipodystrophy: risk factors, pathogenesis, diagnosis and management.
AIDS
17 (Suppl. 1)
:
141
–148,
2003
24
Periard D, Telenti A, Sudre P, Cheseaux JJ, Halfon P, Reymond MJ, Marcovina SM, Glauser MP, Nicod P, Darioli R, Mooser V: Atherogenic dyslipidemia in HIV-infected individuals treated with protease inhibitors: the Swiss HIV cohort study.
Circulation
100
:
700
–705,
1999
25
Walmsley S, Bernstein B, King M, Arribas J, Beall G, Ruane P, Johnson M, Johnson D, Lalonde R, Japour A, Brun S, Sun E, the M98–863 Study Team: Lopinavir-ritonavir versus nelfinavir for the initial treatment of HIV infection.
N Engl J Med
346
:
2039
–2046,
2002
26
Lee GA, Seneviratne T, Noor MA, Lo JC, Schwarz JM, Aweeka FT, Mulligan K, Schambelan M, Grunfeld C: The metabolic effects of lopinavir/ritonavir in HIV-negative men.
AIDS
18
:
641
–649,
2004
27
Eron JJ Jr, Murphy RL, Peterson D, Pottage J, Parenti DM, Jemsek J, Swindells S, Sepulveda G, Bellos N, Rashbaum BC, Esinhart J, Schoellkopf N, Grosso R, Stevens M: A comparison of stavudine, didanosine and indinavir with zidovudine, lamivudine and indinavir for the initial treatment of HIV-1 infected individuals: selection of thymidine analog regimen therapy (START II).
AIDS
14
:
1601
–1610,
2000
28
Lafeuillade A, Jolly P, Chadapaud S, Hittinger G, Lambry V, Philip G: Evolution of lipid abnormalities in patients switched from Stavudine- to tenofovir-containing regimens.
J Acquir Immune Defic Syndr
33
:
544
–546,
2003

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.