To estimate the 3-year risk of myocardial infarction (MI) among participants in the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) study.
To estimate the 3-year risk of myocardial infarction (MI) among participants in the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) study.
Conventional cardiovascular risk equations were applied to baseline data from the DAD study to estimate the 3-year risk of MI. Best estimates were obtained by simply applying the risk equations, with upper and lower limits based on worst case and optimistic case scenarios. Three-year risks of AIDS or death were also estimated based on a prognostic scoring system for patients receiving antiretroviral (ARV) treatment, and on estimated AIDS rates in untreated people with HIV for those patients not on ARVs or if they were to cease ARVs.
Analyses were based on 17 600 patients (24.3% female) recruited into the DAD study with baseline data and no previous MI. The overall 3-year risk of MI was estimated to be 0.72% (lower limit 0.35, upper limit 1.12%), corresponding to a total predicted 127 (65–197) MIs over a 3-year follow-up period. The risk was much greater for men than women (0.92% vs. 0.07%), with only three (2–8) MIs predicted in women. The 3-year risk of MI was estimated to increase from 0.30% (0.20–0.38%) in ARV naive patients to 1.07% (0.43–1.77%) in patients receiving ARVs from all three drug classes. The estimated 3-year risk of AIDS or death was in the range 6.2% to 11.1% in patients receiving ARVs if they continued treatment, and 22.5% to 29.4% if they ceased ARVs.
These models suggest that although the increase in relative risk of MI as a result of ARV treatment may be as high as threefold in a worst case scenario, the absolute risk is modest with a best estimate of 3-year risk less than or equal to 1% in all groups of patients, and is outweighed by the benefits of ARV treatment in terms of reduced risk of AIDS and death in most patients. As estimates are based on models not validated for people receiving ARV drugs, all estimates should be interpreted cautiously.
Standard treatment for HIV-infected patients now involves a combination of antiretroviral (ARV) treatments, usually including two or more nucleoside reverse transcriptase inhibitors (NRTIs) plus a protease inhibitor (PI) or a nonnucleoside reverse transcriptase inhibitor (NNRTI), or both. Treatment with combination ARVs is associated with a number of toxicities , including lipodystrophy syndrome which is accompanied by raised cholesterol and triglyceride levels and insulin resistance . These metabolic complications following ARV treatment would, if the typical risk factors for cardiovascular disease apply to people with HIV receiving ARVs, lead to increased rates of cardiovascular events, including myocardial infarction (MI) and stroke.
To assess the possible increased risk of cardiovascular disease in people with HIV receiving combination ARVs, the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) Study has been established. The DAD study is a collaborative study of 11 observational HIV cohorts in Europe, the USA and Australia which has recruited over 20 000 patients. Cardiovascular risk factors at enrolment, along with HIV-specific disease data on ARV treatment and outcomes, are collected and the incidence of MI and other cardiovascular events assessed according to WHO-MONICA criteria . The DAD study commenced in 1999, and first estimates of incidence data will become available in early 2003 (see http://www.cphiv.dk/dad for further details).
The objectives of this paper are, first, to estimate the numbers of MIs that might be expected in the DAD cohort during a 3-year follow-up period, according to baseline class of ARV treatment received, and, second, to provide some assessment of the relative importance of any increased risk of MI through ARV treatment in comparison to the expected number of AIDS diagnoses or deaths over the same period, were the treated individuals to continue, or, respectively, discontinue ARV therapy.
The DAD study is an observational study formed by the collaboration of previously established HIV cohorts. Eleven cohorts participate and contribute data on more than 20 000 HIV infected patients followed at 188 clinics in 20 countries situated in Europe, USA and Australia. The DAD study methodology has been described in detail previously . Briefly patients are followed prospectively during visits to out-patient clinics scheduled as part of regular medical care. Eligible patients are all under active follow-up at the time of initiation of the DAD protocol, irrespective of ARV treatment status. Patients were enrolled into DAD consecutively as they were seen in the clinic from the time the DAD study was implemented in each of the participating cohorts. The first cohorts started to include patients in December 1999, and all patients were included prior to 1 April 2001.
At enrolment and at least every 8 months thereafter standardized data collection forms are completed at the sites providing information from physical examination, patient interview and patient case notes, concerning family history of coronary heart disease, patients' prior history of cardiovascular disease (CVD) and diabetes, cigarette smoking, blood pressure, therapy for diabetes mellitus, lipid-lowering and antihypertensive therapy, the presence of clinical signs of lipodystrophy and serum lipid levels [including total- and high density lipoprotein (HDL)-cholesterol, triglycerides, and information on fasting conditions]. Furthermore, all cumulative data characterizing the patient's underlying HIV infection since inclusion in each individual cohort are collected, including information on demography, ARV therapy, CD4 counts, HIV viral loads and AIDS defining clinical events (according to Centers for Disease Control and Prevention criteria ). All collected information is transformed into a standardized format and merged into a central data-set.
To compare any increased risk of MI with the appropriate decreased risk of AIDS, the 3-year probabilities of MI and AIDS were estimated in six predefined categories based on current use of ARV therapy regimen at the time of enrolment into the DAD study. These were: (i) naïve; (ii) treatment-experienced, but not currently receiving ARV therapy; (iii) currently receiving only NRTI; (iv) currently receiving NNRTI and NRTI but not PI; (v) currently receiving PI and NRTI but not NNRTI; or (vi) currently receiving PI, NNRTI and NRTI.
Estimates of the number of MIs were based on the risk-equations developed by Anderson et al. based on the Framingham study . The prediction equations are based on a parametric statistical model controlling for multiple CVD risk factors including age, gender, smoking, blood pressure, diabetes, and total- and HDL-cholesterol. Analyses were based on these equations in preference to other more recent scoring systems [7–11] for several reasons. First, these equations can be used to estimate risks over any time period, whereas other scoring systems give probabilities over a fixed time period, usually 5 or 10 years. Second, the equations give exact probabilities, whereas most of the scoring systems give probabilities to the nearest whole percent. Greater accuracy than this was needed to predict expected numbers based on the 17 600 patients included in DAD baseline analyses. Third, separate equations are provided for MI, stroke and coronary heart disease (CHD) death. These are the three major endpoints collected in the DAD study, and so, in due course, these equations will allow a direct comparison between observed and predicted numbers of events.
In the analyses presented here, the appropriate Anderson equations were used to predict the number of MIs, the primary endpoint in the DAD study, over a 3-year period according to ARV treatment class received at baseline.
Primary analyses were based on individual patient data, with imputation of any missing covariates. Imputation of missing values was performed in the following manner. First, missing binary covariates (smoker, diabetic) were imputed for an individual using the sex and country-specific mean from the DAD data. Second, missing continuous covariates (systolic and diastolic blood pressure, serum triglycerides, total cholesterol and HDL cholesterol) were imputed using linear regression, where the predictors included country, gender, age, ARV, smoking- and diabetes-status. Patients in DAD with history of a prior cardiovascular event were excluded from analyses because the risk equations do not apply to these patients who are at very high risk of subsequent events. The published equations include a term for ECG-left ventricular hypertrophy. This covariate was not available in DAD, but as this is a low prevalence condition , all patients could be included as not having this without underestimating the expected numbers to any great extent.
Predicted numbers of events were adjusted for the different baseline cardiovascular event rates in different countries based on WHO myocardial infarction mortality rates . Using the USA as the reference country, a relative risk was calculated for each country as the ratio of the observed event rates in that country compared to the US. The estimated MI rates were then adjusted for country-specific background rates using a proportional hazards approach.
Simply applying the risk equations to the DAD baseline data assumes that any ARV-induced metabolic alterations immediately place individuals at an increased risk of MI which is similar to the risk at equivalent metabolic parameters in the background population. To give an indication of the uncertainty in predicted numbers of MIs as a result of this assumption, upper and lower limits, corresponding to optimistic case and worst case scenarios, were derived around our central estimates. Upper limits on the number of MIs predicted, corresponding to a worst case scenario, were obtained by assuming that features of the metabolic syndrome associated with ARV treatment [the presence of any combination of one or more of: lipodystrophy, obesity (body mass index > 30 kg/m2) and/or elevated triglycerides (≥ 2.3 mmol/L)] were equally important for the risk of MI as diabetes mellitus is in the background population. Lower limits, corresponding to an optimistic case scenario, were derived by assuming that the induced metabolic alterations are not immediately clinically relevant, and do not increase an individuals risk of MI over the short to medium term, e.g. the first 5–10 years. These lower limit predictions were obtained by replacing observed ratios of total to HDL cholesterol with ideal values (ratio of TC:HDL = 4.66/1.17 ). The differences between the upper and lower limits can also be used to gauge the maximal extent to which ARV treatment induced increases in MI risk would be reduced by stopping ARVs, assuming that treatment-induced metabolic alterations are associated with an immediate increase in risk, and likewise assuming that discontinuation of ARV leads to a complete normalization of risk factors and nullification of the associated risk of MI. This difference almost certainly overestimates the reduction in the risk of MI by stopping ARVs, but gives an idea of the maximum this reduction could be.
Further sensitivity analyses were also performed to assess the robustness of estimates to assumptions. Three-year predicted MI probabilities were calculated for each class of baseline ARV treatment based on: only those DAD patients with complete data, rather than imputing missing values; using diastolic rather then systolic blood pressure; by assuming all participants aged less than 30 years were 30 years old (in our best estimates we included participants aged less than 30 years with their exact age even though the risk equations are formally only applicable to people aged 30 years and older); and by assuming that all participants were aged 45. (The latter analyses provide an indication of the extent to which the risk may be attributed to ARV rather than to differences in age distribution between the groups).
To allow some comparison between the possibly increased risk of MIs induced by ARV treatment with the known effectiveness of ARVs in reducing AIDS-diagnoses and deaths, estimates were made of the expected numbers of AIDS diagnoses or deaths if patients continued their baseline ARV treatment, and if patients stopped all ARV therapy. For patients receiving ARV treatment at baseline, estimates of the expected number of AIDS diagnoses or deaths which would occur over a 3-year period if ARV were continued were based on a prognostic scoring system . This scoring system has been developed and validated for short-term prediction, which in the present analyses was extrapolated to provide 3-year probabilities. Estimates of expected AIDS diagnoses or deaths in patients not receiving ARVs, or if ARV treatment were stopped, were based on event rates by CD4 count and HIV viral load strata published by Mellors et al. . In estimates of AIDS diagnoses or deaths if patients were to cease ARV treatment, patients were assumed to have a CD4 count on ceasing ARV equal to their last CD4 count on treatment, and a HIV viral load equal to the median from those patients who have discontinued treatment. Patients with either missing CD4 count or HIV viral load values had the median value of the particular baseline ARV treatment group imputed.
Estimates were based on 17 600 patients from nine cohorts in DAD with baseline data available who were not reported to have had a previous MI (n = 252). Baseline demographic and clinical data are summarized by current baseline ARV drug class in Table 1. As has been reported previously , patients receiving combination ARVs at enrolment in the DAD study had raised cholesterol and triglyceride levels compared with patients not receiving ARVs, particularly those patients receiving all three classes of ARV drug. Patients receiving combination ARVs were also slightly older, more likely to be male, and more likely to be diagnosed with diabetes mellitus or lipodystrophy.
|Current ART at enrolment||Naive||No current ART||NRTI only||Combination |
|Combination with |
PI plus NNRTI
|Number of patients (%)||2297 (13.1%)||1068 (6.1%)||1864 (10.6%)||3433 (19.5%)||7647 (43.4%)||1291 (7.3%)||17600 (100%)|
|Age (years; median, IQR)||36 (32–41)||38 (34–43)||39 (34–45)||39 (35–47)||39 (35–46)||41 (36–48)||39 (34–45)|
|Gender (% female)||31.6||30.7||30.9||23.5||21.1||17.2||24.3|
|CD4 count (cells/µL; median, IQR)||462 (305–659)||352 (189–539)||460 (303–639)||454 (288–649)||435 (277–630)||320 (183–488)||430 (270–622)|
|HIV-1 RNA (log; median, range)||4.0 (< 2.7–6.9)||4.4 (< 2.7–6.8)||< 2.7 (< 2.7–6.3)||< 2.7 (< 2.7–6.1)||< 2.7 (< 2.7–6.3)||< 2.7 (< 2.7–6.2)||< 2.7(< 2.7–6.9)|
|Body mass index > 30 kg/m2 (%)||4.8||3.7||3.0||3.9||3.1||2.2||3.4|
|Current smoker (%)||55.3||58.6||52.7||47.3||51.7||46.3||51.6|
|Family history of CHD (%)||11.3||8.5||12.1||12.5||10.6||12.9||11.2|
|Diabetes mellitus (%)||1.1||0.9||2.3||3.2||2.2||4.0||2.3|
|Total cholesterol (mmol/L; median, IQR)||4.3 (3.7–5.2)||4.4 (3.7–5.2)||4.6 (3.9–5.4)||5.2 (4.4–6.1)||5.3 (4.4–6.3)||5.9 (4.8–7.1)||5.1 (4.2–6.0)|
|HDL cholesterol (mmol/L; median, IQR)||1.1 (0.9–1.4)||1.1 (0.8–1.3)||1.2 (0.9–1.5)||1.2 (1.0–1.5)||1.1 (0.9–1.4)||1.1 (0.9–1.4)||1.1 (0.9–1.4)|
|Triglycerides (mmol/L; median, IQR)||1.3 (0.9–1.9)||1.5 (1.1–2.3)||1.4 (0.9–2.2)||1.6 (1.0–2.7)||1.9 (1.2–3.1)||2.5 (1.6–4.2)||1.7 (1.1–2.8)|
Estimated 3-year probabilities of MI by ARV drug class, both overall and by sex, are summarized in Table 2. The estimated 3-year risk of MI was fairly modest at 0.72% (optimistic case to worst case scenario 0.35–1.12%), but increased from 0.30% (0.20–0.38%) in DAD participants who have never received ARVs to 1.07% (0.43–1.77%) in participants receiving all three drug classes at enrolment. The best estimate of 3-year risk of MI was also much higher for men in the DAD study than women, 0.92% (0.47–1.42%) vs. 0.07% (0.05–0.19%). The projected number of MIs over a 3-year follow-up period in the DAD study was 127 (65–197), with the overwhelming majority of MIs projected to occur in men [123 (63–189) among 13.328 men]. Only three (2–8) MIs were projected to occur among the 4272 women in the study. More than half of the MIs [65 (35–96)] were projected to occur among the 16% of the individuals aged 50 or more (data not shown).
|ARV regimen||Three-year risk of MI (%)*||Expected number of MIs during 3 years†|
|Optimistic case |
|Worst case |
|Optimistic case |
|Worst case |
|No current ARV||0.54||0.31||0.83||6||3||9|
|Combination with NNRTI||0.70||0.38||1.10||24||14||38|
|Combination with PI||0.86||0.38||1.38||66||31||105|
|Combination with NNRTI + PI||1.07||0.43||1.77||14||6||23|
|No current ARV||0.78||0.44||1.16||6||3||8|
|Combination with NNRTI||0.94||0.49||1.43||23||13||36|
|Combination with PI||1.09||0.48||1.72||64||30||102|
|Combination with NNRTI + PI||1.29||0.52||2.09||14||6||22|
|No current ARV||0.07||0.04||0.17||0||0||1|
|Combination with NNRTI||0.09||0.06||0.23||1||0||2|
|Combination with PI||0.09||0.04||0.23||1||1||4|
|Combination with NNRTI + PI||0.11||0.06||0.30||0||0||1|
Some idea of the increased risk of MI due to ARV treatment can be gauged by comparing our best estimates of 3-year MI risk with the optimistic case scenario estimates, in which any patients with raised lipid measurements are taken to have ideal values. Using this approach, the largest increase in MI risk is in men receiving all three classes of ARV drugs, but the difference in absolute 3-year risk is still under 1%. In a worst case scenario, the ARV-associated increase in risk could be estimated as the difference between upper and lower limits, which at maximum would imply an increase in absolute risk of MI of 1.5% over 3 years.
Sensitivity analyses were performed to gauge how robust the estimates of 3-year MI risk were to the assumptions made (Table 3). Best estimates of 3-year risk of MI were recomputed for DAD participants with complete data (0.65%), by assuming that any subjects aged less than 30 years were 30 years old (0.72%), by assuming all subjects were aged 45 years (0.74%), and basing estimates on diastolic rather than systolic BP (0.64%). In all these sensitivity analyses, the estimated 3-year MI risk was similar to our overall best estimates (0.72%). Furthermore, the increasing estimated risk of MI with combination ARVs at enrolment to DAD was a consistent finding (Table 3). The analysis which assumed all subjects were aged 45 did, however, give a slower increasing gradient in 3-year MI risk according to class of ARV dug received, from 0.44% in naive patients to 1.02% in patients receiving all three ARV drug classes, indicating that a proportion of the estimated increased risk of MI due to ARV treatment is due to patients receiving treatment being older. However, the proportion of increased risk is probably underestimated because blood pressure and cholesterol, which independently carry an increase risk of MI, are both increased with older age.
|ARV regimen||Three-year risk of MI (%)*|
Sensitivity analysis estimates based on
|Subjects with |
|Subjects all aged |
at least 30 years
|Subjects all aged |
|Diastolic rather |
than systolic BP
|No current ARV||0.54||0.47||0.54||0.61||0.48|
|Combination with NNRTI||0.70||0.72||0.71||0.68||0.63|
|Combination with PI||0.86||0.74||0.86||0.90||0.77|
|Combination with NNRTI + PI||1.07||1.09||1.07||1.02||0.96|
Three-year risks of AIDS or death were estimated if patients were to continue their ARV treatment based on a recent scoring system, and if patients were to cease antiretrovirals based on AIDS event rates in untreated patients (Table 4). The estimated 3-year risk of AIDS or death in patients receiving ARVs if treatment was continued was estimated to range between 6.2% and 11.1%. In naive patients and patients not currently receiving ARVs, the 3-year risk of AIDS assuming that ARVs were not started was estimated to be 25.0% and 36.1%, respectively. If patients receiving ARVs at enrolment into DAD were to stop their ARVs, the 3-year risk of AIDS was estimated to range between 22.5 and 29.4. In comparison, the proportion of patients estimated to be at comparable risk, > 10%, of MI over 3 years was very low, the highest proportion being 1.1% of patients receiving drugs from all three ARV classes (Table 4).
|ARV regimen||Best estimate |
risk MI (%)
|Best estimate percentage |
subjects > 10%
risk MI in 3 years
|Estimated 3-year |
risk AIDS or death
if ARVs continued (%)†
|Estimated 3-year |
risk of AIDS if
ARVs stopped (%)‡
|No current ARV||0.54||0.1||NA||36.1|
|Combination with NNRTI||0.70||0.2||6.2||23.0|
|Combination with PI||0.86||0.6||7.1||23.7|
|Combination with NNRTI + PI||1.07||1.1||11.1||29.4|
To give some indication of the longer term cardiovascular risks in DAD patients, the best estimates of overall cumulative risk of MI by ARV drug class over a 10-year period from baseline are summarized in Fig. 1. As might be expected, the risk of MI gradually accelerates over time as a result of increasing age. Discrepancies in MI risk between untreated patients and patients receiving ARVs also continue to increase over the period, although these differences need to be viewed cautiously as they are unadjusted for age and sex differences. However, the cumulative 10-year risk of MI is estimated to be under 4.5%, even in the group of patients receiving all three classes of ARV drugs, indicating that the risk of MI is unlikely to accelerate very rapidly.
Based on individual baseline data, we used a conventional cardiovascular risk equation to estimate the 3-year risk of MI among the 17 600 people enrolled in the DAD study. We estimate that the 3-year risk of MI among DAD patients is modest, at worst, in men receiving all three classes of ARV drug under worst scenario assumptions, around 2%. The reduction in MI absolute risk over 3 years if people receiving ARVs were to stop treatment is less than 1.5%. This compares with 3-year risks of AIDS or death of around 5–10% in people receiving ARVs, 25–35% in people not receiving ARVs, and 22–29% in people receiving ARVs if they were to cease treatment. These analyses suggest that in all but those people at the highest risk of MI, the benefits of ARV treatment in reducing AIDS and death far outweigh the possible risks in terms of increased rates of MI. Although this does, of course, assume that the effects of HAART do not accumulate further with additional exposure to HAART.
As far as we are aware, our study is the first to publish estimates of cardiovascular risk based on individual patient data in an international cohort of HIV-positive people receiving various combinations of ARV treatment. Many studies have identified raised lipid values in people receiving ARV drugs, which are indicative of a raised risk of clinical cardiovascular disease, including baseline data from the DAD study . One smaller study has been conducted that modelled the extent of PI-associated risk . One other paper has estimated the risk of cardiovascular disease in HIV-positive patients, but based on various scenarios of age, sex, smoking status and treatment rather than individual patient data . These analyses were in substantial agreement with our results, suggesting that the increased risk of cardiovascular disease through ARV treatment was modest, and clearly outweighed by decreased AIDS or deaths, except in scenarios of people at very high baseline risk of cardiovascular disease (men, aged 50, who smoked, and had developed lipodystrophy).
The estimates of risk of MI presented here need to be interpreted with extreme caution. Estimates were based on an essentially naive application of conventional risk equations, designed and validated for use in the US general population, to people with HIV across the US, Europe and Australia. The extent to which these risk equations apply to people with HIV in general, or the extent to which changes in cardiovascular risk factors which occur following ARV treatment immediately confer increased risks of the same magnitude predicted by these models are currently unknown. Furthermore, although we attempted to allow for different background rates of MI in different countries based on WHO MI mortality rates, there may be racial, geographical and temporal factors which we are not able to allow for. To capture some of the uncertainty, we calculated upper and lower limits for our estimates corresponding to worst and optimistic case scenarios, giving limits on the likely magnitude of estimates. The optimistic case scenario reflects the situation if indeed there is a substantial time lag of more than 5 years from the induction of dyslipidaemia by anti-HIV drugs until clinical CHD. The worst case scenario on the other hand depicts the situation if the lipodystrophy syndrome, and associated insulin resistance and hypertriglyceridaemia, are important independent predictors of CHD in HIV-infected subjects. In the background population, a clustering of central adiposity, insulin resistance and dyslipidemia is known as ‘the metabolic syndrome’, a syndrome conferring high risk of CHD . We also performed a series of sensitivity analyses to assess the effects of data assumptions, which suggested that our estimates were reasonably robust. A detailed assessment of the possible increased rate of cardiovascular disease will be available from the DAD study, with rates of cardiovascular disease endpoints, including MI, which will become available in early 2003.
The estimates of risk of AIDS presented here also need to be interpreted cautiously. The projected risk of AIDS, assuming that patients remain on therapy is based on algorithms developed and validated for short-term prognosis over a 3–6-month period . In the present analyses, we have extrapolated these short-term-risks to provide estimates over a 3-year period. The projections of the risk of AIDS if patients receiving ARVs were to stop treatment were obtained by assuming that, on cessation of treatment, viral load levels would increase to levels in untreated patients, and that CD4 counts would decline from on treatment levels at a similar rate to untreated patients. There are data to suggest that this latter assumption is not correct, and that on cessation of ARVs a patients CD4 count declines quite rapidly to pretreatment levels [19, 20]. If this is the case, then our projections of the 3-year risk of AIDS if patients stop their ARVs will be too low. However, as the increased 3-year risk of AIDS on stopping ARVs is projected to be around 10-fold greater than the decreased risk of MI, our conclusions are highly likely to be robust. Furthermore, because those patients receiving ARVs are probably the most at risk of AIDS, the benefits from ARVs are likely to be greatest in those patients at most risk of harm from ARVs.
In this paper, we have examined the possible risks of MI in patients without a prior MI enrolled in the DAD study. However, this does not capture all the possible adverse events associated with ARV treatments. We excluded 252 patients with a prior MI, a group with a known increased risk of subsequent MI, because the risk equations used do not apply to this subgroup. The latter carry a substantial risk for major coronary events of more than 20% per 10 years . Hence we have probably underestimated the total number of MIs in the entire DAD cohort. There may also be increased risks of other cardiovascular disease, such as angina pectoris, claudication and stroke. ARV treatments are also associated with other toxicities, including hepatitis, lactic acidosis, pancreatitis, neuropathy, gastrointestinal problems and a syndrome of lipodystrophy . The decision whether to initiate or continue ARVs in a particular patient needs to be based on a comparison of the known benefits of ARV treatment against all the possible risks.
We concentrated analyses on estimates of risks of MI in patients enrolled in the DAD study. The reason for choosing risks of MI to be estimated, rather than other combined cardiovascular endpoints, was because assessing rates of MI was the principal objectives of the DAD study at its conception. Indeed, estimates of the risks of MI developed and presented here will be compared with observed rates in follow-up in the DAD study when they become available. An area for future work will be, in due course, to use the follow-up data from the DAD study to validate, adjust or perhaps re-derive more appropriate and accurate cardiovascular risk models for people with HIV, either receiving or not receiving ARV drugs.
The ATHENA study was supported by a grant (CURE/97–46486) from the Health Insurance Fund Council, Amstelveen, the Netherlands. The Aquitaine Cohort was supported by a grant from the Agence Nationale de Recherches sur le SIDA (ANRS, Action Coordonnée no. 7, Cohortes). The BASS study was supported by grants from the Fondo de Investigación Sanitaria (FIS 99/0887) and Fundación para la Investigación y la Prevención del SIDA en Espanã (FIPSE 3171/00). The EuroSIDA study was supported by grants from the European Commission BIOMED 1 (CT94-1637) and BIOMED 2 (CT97-2713) programs, from Pharmacia & Upjohn, GlaxoSmithKline, Roche and Merck. The ICONA network was supported by an unrestricted educational grant from Glaxo Wellcome, Italy. The Swiss HIV Cohort Study was supported by a grant (3345–062041) from the Swiss National Science Foundation.
Support for the DAD study was provided by the Oversight Committee for The Evaluation of Metabolic Complications of HAART, a collaborative committee with representation from academic institutions, the EMEA, the FDA and all pharmaceutical companies with licensed anti-HIV drugs in the US marked, i.e. Abbott, Agouron, Boehringer Ingelheim, Bristol-Myers Squibb, GlaxoSmithKline, Merck, Pfizer, Pharmacia & Upjohn, Hoffman-La Roche.
The members of the 11 cohorts are as follows:
ATHENA (AIDS Therapy Evaluation Project Netherlands): Coordinating centre: F de Wolf, J Lange, E van der Ven, H Tissing, T Hantke, R Meester.
Participating physicians (city): W Bronsveld (Alkmaar); H Weigel, K Brinkman, P Frissen, J Veen, M Hillebrand, P van Dam, S Schieveld, J Mulder, E van Gorp, P Meenhorst, A van Eeden, S Danner, F Claessen, R Perenboom, D Blanckenberg, S Blank, JK Eeftinck Schattenkerk, M Godfried, S Lowe, J van der Meer, F Nellen, K Pogany, T van der Poll, J Prins, P Reiss*, T Ruys, M van der Valk, A Verbon, F Wit (Amsterdam); C Richter, R van Leusen (Arnhem); R Vriesendorp, F Jeurissen (Den Haag); R Kauffmann, E Koger (Den Haag); B Bravenboer (Eindhoven); C 10 Napel (Enschede); HG Sprenger, G Law (Groningen); RW 10 Kate (Haarlem); M Leemhuis (Leeuwarden); F Kroon, E Schippers (Leiden); G Schrey, S van der Geest, A van der Ven (Maastricht); P Koopmans, M Keuter, D Telgt (Nijmegen); M van der Ende, I Gyssens, S de Marie (Rotterdam); J Juttmann, C van der Heul (Tilburg); M Schneider, J Borleffs, l Hoepelman, C Jaspers, A Matute, C Schurink (Utrecht); W Blok (Vlissingen).
Aquitaine (France): Scientific committee: R Salamon (chair), J Beylot, M Dupon, M Le Bras, JL Pellegrin, JM Ragnaud; Coordinating centre staff: F Dabis*, G Chêne, N Bernard, D Lacoste, D Malvy, D Neau, M Dupon, J-F Moreau, P Morlat, P Mercié, JL Pellegrin, JM Ragnaud, D Commenges, H Jacqmin-Gadda, R Thiébaut, S Lawson-Ayayi, V Lavignolle, MJ Blaizeau, M Decoin, AM Formaggio, S Delveaux, S Labarerre, B Uwamaliya, E Vimard, L Merchadou, G Palmer, D Touchard, D Dutoit, F Pereira, B Boulant; Participating physicians (city): J Beylot, P Morlat, N Bernard, M Bonarek, F Bonnet, B Coadou, P Gelie, D Jaubert, C Nouts, D Lacoste, M Dupon, H Dutronc, G Cipriano, S Lafarie, JY Lacut, JL Pellegrin, P Mercie, JF Viallard, I Faure, P Rispal, C Cipriano, B Leng, M Le Bras, F Djossou, D Malvy, JP Pivetaud, JM Ragnaud, C De La Taille, D Neau, T Galperine, A Ochoa, D Chambon (Bordeaux).
AHOD (Australian HIV Observational Database, Australia): Coordinating centre: M Law*, K Petoumenos (Sydney, New South Wales). Participating sites (city, state): J Anderson, J Bal (Melbourne, Victoria), D Austin, A Gowers, D Baker, R McFarlane, A Carr, D Cooper (Sydney, New South Wales), J Chuah, W Fankhauser (Gold Coast, Queensland), S Mallal, J Skett (Perth, Western Australia), A Mijch, K Watson (Melbourne, Victoria), N Roth, H Wood (Melbourne, Victoria).
BASS (Spain): Coordinating centre: G Calvo*, F Torres, S Mateu (Barcelona). Participating physicians: P Domingo, MA Sambeat, J Gatell, E Del Cacho (Barcelona), G Sirera, G Viñas (Badalona).
The Brussels St. Pierre Cohort (Belgium): N Clumeck, S De Wit*, M Gerard, P Hermans, M Hildebrand, K Kabeya, D Konopnicki, MC Payen, B Sommereijns, Y Van Laethem.
CPCRA (USA): Central coordination: J Neaton, G Bartsch*, W El-Sadr, E Krum, D Wentworth. Participating physicians (city, state): R Luskin-Hawk (Chicago, Illinois), E Telzak (Bronx, New York), DI Abrams (San Francisco, California), D Cohn (Denver, Colorado), N Markowitz (Detroit, Michigan), R Arduino (Houston, Texas), D Mushatt (New Orleans, Louisiana), G Friedland (New Haven, Connecticut), G Perez (Newark, New Jersey), E Tedaldi (Philadelphia, Pennsylvania), E Fisher (Richmond, Virginia), F Gordin (Washington, DC), LR Crane (Detroit, Michigan), J Sampson (Portland, Oregon), J Baxter (Camden, New Jersey).
EuroSIDA Study Group (multinational): Central coordination: O Kirk*, A Mocroft, AN Phillips*, JD Lundgren*†. Participating countries and physicians (city): Austria N Vetter (Vienna); Belgium N Clumeck, P Hermans (Brussels), R Colebunders (Antwerp); Czech Republic L Machala (Prague); Denmark J Nielsen, T Benfield, J Gerstoft, T Katzenstein, B Røge, P Skinhøj (Copenhagen), C Pedersen (Odense); France C Katlama, J-P Viard (Paris), T Saint-Marc, P Vanhems (Lyon); Germany M Dietrich, C Manegold, J van Lunzen (Hamburg); V Miller, S Staszewski, M Bieckel (Frankfurt), FD Goebel (Munich), B Salzberger (Cologne), J Rockstroh (Bonn); Greece J Kosmidis, P Gargalianos, H Sambatakou, J Perdios, G Panos, I Karydis, A Filandras (Athens); Hungary D Banhegyi (Budapest); Ireland F Mulcahy (Dublin); Israel I Yust, D Turner (Tel Aviv), S Pollack, Z Ben-Ishai (Haifa), Z Bentwich (Rehovot), S Maayan (Jerusalem); Italy S Vella, A Chiesi (Rome), C Arici (Bergamo), R Pristerá (Bolzano), F Mazzotta, A Gabbuti (Florence), R Esposito, A Bedini (Modena), A Chirianni, E Montesarchio (Naples), V Vullo, P Santopadre, P Narciso, A Antinori, P Franci, M Zaccarelli (Rome), R Finazzi (Milan); Luxembourg R Hemmer, T Staub (Luxembourg); Norway J Bruun, A Maeland, V Ormaasen (Oslo); Poland B Knysz, J Gasiorowski (Wroclaw), A Horban (Warsaw), D Prokopowicz (Bialystok), A Boron-Kaczmarska, M Pynka (Szczecin), M Beniowski (Chorzow), H Trocha (Gdansk); Portugal F Antunes, K Mansinho, R Proenca (Lisbon); Spain J González-Lahoz, B Diaz, T García-Benayas, L Martin-Carbonero, V Soriano (Madrid), B Clotet, A Jou, J Conejero, C Tural (Badalona), JM Miró (Barcelona); Sweden A Blaxhult, B Heidemann, P Pehrson (Stockholm); United Kingdom M Fisher (Brighton), R Brettle (Edinburgh), S Barton, AM Johnson, D Mercey, C Loveday, MA Johnson, A Pinching, J Parkin, J Weber, G Scullard (London).
HivBivus (Sweden): Central coordination: L Morfeldt*, G Thulin, A Sundström. Participating physicians (city): B Åkerlund (Huddinge), K Koppel, A Karlsson (Stockholm), L Flamholc, C Håkangård (Malmö).
ICONA (Italy): Central coordination: A D'Arminio Monforte*, P Pezzotti. Participarting physicians: M Moroni, A d'Arminio Monforte, A Cargnel, S Merli, GM Vigevani, C Pastecchia, A Lazzarin, R Novati, L Caggese, C Moioli (Milano), MS Mura, G Madeddu (Sassari), F Suter, C Arici (Bergamo), PE Manconi (Cagliari), F Mazzotta (Firenze), A Poggio, G Bottari (Verbania), G Pagano, A Alessandrini (Genova), A Scasso, A Vincenti (Lucca),V Abbadesse, S Mancuso (Palermo), F Alberici, M Sisti (Piacenza), M Arlotti, P Ortolani (Rimini), F De Lalla, G Tositti (Vicenza), N Piersantelli, R Piscopo (Genova), E Raise, S Pasquinucci (Venezia), F Soscia, L Tacconi (Latina), U Tirelli, G Nasti (Aviano) E Rinaldi, L Pusterla (Como), G Carosi, F Castelli (Brescia), G Cadeo, D Vangi (Brescia), G Carnevale, D Galloni (Cremona), G Filice, R Bruno (Pavia), A Sinicco, M Sciandra, P Caramello, L Gennero, ML Soranzo, A Macor (Torino), G Rizzardini, C Abeli (Busto Arsizio), F Chiodo, V Colangeli (Bologna), L Bonazzi, M Ursitti (Reggio Emilia), F Menichetti, A Smorfa (Pisa), R Esposito, C Mussini (Modena), F Ghinelli, L Sighinolfi (Ferrara), F Gritti, O Coronado (Bologna),T Zauli, G Ballardini (Ravenna), M Montroni, A Costantini (Ancona), E Petrelli, A Cioppi (Pesaro), L Ortona, A De Luca, N Petrosillo, P Noto, P Narciso, G D'Offizi, A Antinori, P De Longis, V Vullo, M Lichtner (Roma), G Pastore, ML Perulli (Bari), A Chirianni, L Loiacono, M Piazza, S Nappa, N Abrescia, M De Marco (Napoli), A Colomba, T Prestileo (Palermo), C De Stefano, A La Gala (Potenza), T Ferraro, A Scerbo (Catanzaro), P Grima, P Tundo (Lecce), E Pizzigallo, F Ricci (Chieti), B Grisorio, S Ferrara (Foggia).
Nice Cohort (France): Central coordination: C Pradier*, E Fontas, C Caissotti. Participating physicians: P Dellamonica, P Pugliese.
SHCS (The Swiss HIV Cohort Study, Switzerland): Scientific Committee: R Amiet, M Battegay (chair), E Bernasconi, H Bucher, P Bürgisser, M Egger, P Erb, W Fierz, M Flepp, P Francioli, HJ Furrer, M Gorgievski, H Günthard, P Grob, B Hirschel, C Kind, T Klimkait, B Ledergerber, U Lauper, M Opravil, F Paccaud, G Pantaleo,L Perrin, W Pichler, JC Piffaretti, M Rickenbach, C Rudin, P Sudre, V Schiffer, J Schupbach, A Telenti, P Vernazza, R Weber*. Participating physicians (city): HC Bucher, M Battegay (Basel), HJ Furrer, M Egger (Bern), A Calmy, B Hirschel (Geneve), A Telenti (Lausanne), E Bernasconi, L Magenta (Lugano), T Wagels, P Vernazza (St.Gall), M Flepp, R Weber (Zürich).
DAD Steering Committee: F Houyez, T Mertenskoetter, I Weller, persons with * above (†chair) DAD Central Coordination: N Friis-Møller, P Ricks, C Sabin.