Abstract
Objective
To describe changes in atherosclerotic cardiovascular disease (ASCVD) risk over time among people living with HIV (PLHIV).
Methods
We used data from the TREAT Asia HIV Observational Database (TAHOD) and the Australian HIV Observational Database (AHOD). Five-year ASCVD risk was calculated using the D:A:D equation. Individuals were eligible for inclusion if they were aged ≥18 years, had started ART, had no previous history of ASCVD and had complete ASCVD risk factor data available within the first 5 years of ART initiation.
Results
A total of 3368 adults contributed data, 3221 were from TAHOD and 147 were from AHOD. The median age at ART initiation was 36 [IQR 31–43] years for TAHOD participants, and 42 [IQR 35–50] years for AHOD participants. Most TAHOD (70.4%) and AHOD (91.8%) participants were male. Overall, ASCVD risk increased from 0.84% (95% CI 0.81%–0.87%) at ART initiation to 1.34% (95% CI 1.29%–1.39%) after 5 years on ART. After adjusting for traditional and HIV-associated ASCVD risk factors, ASCVD risk increased at a similar rate among sub-populations defined by HIV exposure (heterosexuals, men who have sex with men, people who inject drugs), race/ethnicity (Caucasian and Asian) and nadir CD4 at ART initiation (<200 and ≥200 cells/mm3).
Conclusions
These findings emphasize the growing burden of ASCVD risk among PLHIV and the need to develop interventions that are effective across a broad range of HIV sub-populations.
Introduction
People living with HIV (PLHIV) have an elevated risk of atherosclerotic cardiovascular disease (ASCVD) compared to HIV-negative people.1 Causes of excess ASCVD risk among PLHIV are not fully understood, with a complex interplay of conventional cardiovascular risk factors, HIV-associated chronic immune activation and inflammation, ART use, co-infections and lifestyle factors such as cigarette smoking, alcohol use and stress.2 These risk factors may be particularly concentrated within certain sub-populations with HIV such as people who inject drugs (PWID) or MSM.3
ASCVD risk assessment calculators can play a significant role in ASCVD prevention by identifying high-risk individuals and facilitating more intensive evaluation and management of reversible ASCVD risk factors. To date, the only HIV-specific ASCVD risk calculator is based on the Data Collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study, which incorporates both traditional and HIV-specific cardiovascular risk factors.4,5 Studies have confirmed the validity of the D:A:D model in various cohorts.6–13 Evidence suggests it performs better in estimating ASCVD risk for PLHIV than general population equations like the Framingham risk equation.7–9,11,13
Despite many studies investigating the prevalence of ASCVD risk factors in PLHIV, there is currently a lack of longitudinal studies on how ASCVD risk evolves over time. This study aims to describe changes in ASCVD risk over time among PLHIV and identify HIV sub-populations whose ASCVD risk increases rapidly. Such information may improve the design and implementation of interventions aiming to improve ASCVD prevention among PLHIV and as a result reduce the number of cardiovascular events in PLHIV.
Methods
Study population
The study population comprises PLHIV aged 18 years and over enrolled in the TREAT Asia HIV Observational Database (TAHOD) or the Australian HIV Observational Database (AHOD). Both TAHOD and AHOD cohorts have been described elsewise.14,15 Briefly, TAHOD involves 21 clinical sites across 12 Asian countries and territories.14 AHOD involves 30 clinical sites in Australia and two in New Zealand.15 Data collected includes information on participants demographics, mode of HIV exposure, CD4 cell counts, smoking status, other comorbidities, laboratory results, stage of disease and ART use. Data from both cohorts were merged.
Ethical approval
TAHOD has received approval from the human research ethics committees at participating sites, the coordination centre at TREAT Asia/amfAR and the Kirby Institute. Participating clinics follow local guidelines and regulations regarding patient consent.
AHOD was approved by St Vincent’s Hospital, Sydney Human Research Ethics Committee, University of New South Wales Human Research Ethics Committee and all other institutional review boards at participating sites as appropriate. Written informed consent was obtained from all participants at enrolment.
Inclusion criteria
PLHIV were included if they were aged ≥18 years, started ART, had no previous history of ASCVD and had complete ASCVD risk factor data available within the first 5 years of ART initiation. ASCVD risk factor data required for the D:A:D equation are age, sex, systolic blood pressure, smoking status, family history of CVD, diabetes, total cholesterol level, HDL-cholesterol level, CD4 lymphocyte count, current use of abacavir, cumulative exposure to protease inhibitors (PIs) and cumulative exposure to NRTIs.5 ASCVD scores were calculated at baseline and 6-monthly intervals thereafter using covariate data from the closest available date.
Outcomes and definitions
Baseline was defined as the date of ART initiation. Diabetes mellitus was defined as a clinical diagnosis, two consecutive measurements of fasting plasma glucose >7 mmol/L or treatment with antidiabetic drugs. Baseline nadir CD4 cell count was defined as the lowest CD4 cell count documented before ART initiation. The primary endpoint, the ASCVD risk score, was defined as the probability of an ASCVD event in the next 5 years calculated using the D:A:D equation5 at baseline and every 6 months thereafter. For patients who were lost to follow-up (not having a clinic visit in the last 12 months) or died during the first 5 years of ART, data were included up to the date of loss-to-follow-up or death.
Statistical analyses
Beta-distributed generalized linear mixed models were used as the ASCVD risk scores are a percentage [and therefore bound to the interval of (0, 1)], and were highly skewed.16 The exploratory data analysis indicated a potential non-linear association between time and ASCVD risk scores. To reflect this, we tested a piecewise function of time over the linear regression and it showed a better model fit. The preferred model with the best goodness-of-fit statistics was a piecewise linear model at an inflection point of 3 years. The random effects included intercept and slope (time), allowing the intercept and the linear effect of time to vary across subjects. A logit link function was used to map the bounded outcome response at (0, 1), the ASCVD scores, to the real numbers.
We stratified the study population into sub-groups of interest and added these as fixed effects interacted with time in our model. These sub-groups were defined by HIV exposure (PWID, MSM, heterosexual and other), race/ethnicity (Asian, Caucasian and other) and nadir CD4 count at ART initiation (<200 and ≥200 cells/mm3). Sub-population models were additionally adjusted for age, sex, BMI, smoking status and family history of ASCVD.
Results
Study population
Our study databases contained information on 11 280 potentially eligible patient records, 10 789 from the TAHOD and 491 from the AHOD. Although AHOD contains data on approximately 4000 PLHIV, numbers were limited in this analysis as the ASCVD risk factor data required are only collected at AHOD sites that also participate in the International Cohort Consortium of Infectious Diseases (RESPOND) study.17 Figure S1 (available as Supplementary data at JAC Online) describes the reasons for patient data being excluded from our main analyses and Table S1 provides a comparison of included and excluded participants. The final number of PLHIV included was 3368 (3221 from the TAHOD, 147 from the AHOD). Table 1 presents the study participants’ baseline characteristics at ART initiation. The median age at ART initiation was 36 [IQR 31–43] years for TAHOD participants, and 42 [IQR 35–50] years for AHOD participants. Most TAHOD (70.4%) and AHOD (91.8%) participants were male. Almost all (99.2%) TAHOD participants were of Asian ethnicity and most (87.1%) AHOD participants were of Caucasian ethnicity. The main modes of HIV acquisition for TAHOD participants were heterosexual sex (63.3%) and MSM sex (29.6%). A higher proportion of participants indicated MSM sex as their mode of HIV acquisition in AHOD (77.6%). Most of the study population had a baseline ASCVD risk score of >5% (95.5%), while 0.6% had a baseline ASCVD risk score of ≥10%. The median duration of PIs during the first 5 years of ART initiation was 0 years [IQR 0–0.3 years] for TAHOD participants and 0.5 years [IQR 0–4.5 years] for AHOD participants. The median durations of non-nucleotide reverse transcriptase inhibitors were 5 years [IQR 0–5 years] for both TAHOD participants and AHOD participants.
Table 1.
TAHOD and AHOD participant baseline characteristics at ART initiation
| Characteristics | TAHOD (n = 3221) | AHOD (n = 147) | Total (n = 3368) | |
|---|---|---|---|---|
| Sex | Male | 2267 (70.4) | 135 (91.8) | 2402 (71.3) |
| Female | 954 (29.6) | 12 (8.2) | 966 (28.7) | |
| Age (years) | Median (IQR) | 36 (31, 43) | 42 (35, 50) | 37 (31, 43) |
| BMI (kg/m2) | Median (IQR) | 21.2 (19.1, 23.6) | 24.2 (21.7, 26.6) | 21.3 (19.1, 23.7) |
| Unknown | 784 (24.3) | 75 (51) | 859 (25.5) | |
| Race | Caucasian | 7 (0.2) | 128 (87.1) | 135 (4.0) |
| Asian | 3194 (99.2) | 9 (6.1) | 3203 (95.1) | |
| Others | 20 (0.6) | 1 (0.7) | 21 (0.6) | |
| Unknown | 0 (0) | 9 (6.1) | 9 (0.3) | |
| Mode of HIV acquisition | Heterosexual | 2039 (63.3) | 19 (12.9) | 2058 (61.1) |
| MSM | 953 (29.6) | 114 (77.6) | 1067 (31.7) | |
| PWID | 100 (3.1) | 8 (5.5) | 108 (3.2) | |
| Other | 129 (4.0) | 3 (2.0) | 132 (3.9) | |
| Unknown | 0 (0) | 3 (2.0) | 3 (0.1) | |
| Family history of ASCVD | Yes | 212 (6.6) | 13 (8.8) | 225 (6.7) |
| Smoking status | Never | 1997 (62.0) | 59 (40.1) | 2056 (61.0) |
| Former | 555 (17.2) | 37 (25.2) | 592 (17.6) | |
| Current | 669 (20.8) | 51 (34.7) | 720 (21.4) | |
| Diabetes mellitus | Yes | 40 (1.2) | 2 (1.4) | 42 (1.2) |
| CD4 cell count (cells/μL) | Median (IQR) | 222 (110, 359) | 380 (245, 542) | 228 (112, 369) |
| Systolic blood pressure (mmHg) | Median (IQR) | 120 (110, 130) | 124 (114, 130) | 120 (110, 130) |
| Fasting total cholesterol (mmol/L) | Median (IQR) | 4.8 (4.0, 5.5) | 4.9 (4.2, 5.6) | 4.8 (4.0, 5.6) |
| HDL-cholesterol (mmol/L) | Median (IQR) | 1.2 (1.0, 1.4) | 1.1 (1.0, 1.4) | 1.2 (1.0, 1.4) |
| Current PI use | Yes | 378 (11.7) | 55 (37.4) | 433 (12.9) |
| Atazanavir | Yes | 114 (3.5) | 11 (7.5) | 125 (3.7) |
| Lopinavir | Yes | 136 (4.2) | 4 (2.7) | 140 (4.2) |
| Current NRTI use | Yes | 3156 (98.0) | 132 (89.8) | 3288 (97.6) |
| Tenofovir | Yes | 912 (28.3) | 46 (31.3) | 958 (28.4) |
| Abacavir | Yes | 310 (9.6) | 16 (10.9) | 326 (9.7) |
| Current INSTI use | Yes | 53 (1.6) | 19 (12.9) | 72 (2.1) |
| Raltegravir | Yes | 26 (0.8) | 4 (2.7) | 30 (0.9) |
| Dolutegravir | Yes | 16 (0.5) | 11 (7.5) | 27 (0.8) |
| Current NNRTI use | Yes | 2607 (80.9) | 54 (36.7) | 2661 (79.0) |
| Efavirenz | Yes | 1496 (46.4) | 30 (20.4) | 1526 (45.3) |
| Nevirapine | Yes | 1100 (34.2) | 20 (13.6) | 1120 (33.3) |
| Five-year primary ASCVD riska | <5% | 3079 (95.6) | 139 (94.5) | 3218 (95.6) |
| 5%–9.9% | 123 (3.8) | 6 (4.1) | 129 (3.8) | |
| ≥10% | 19 (0.6) | 2 (1.4) | 21 (0.6) | |
| HIV RNA viral load (copies/mL) | Median (IQR) | 84 315 (23 700, 252 045) | 27 943 (807, 125 541) | 82 564 (21 400, 245 995) |
| Unknown | 1173 (36.4) | 27 (18.4) | 1194 (35.5) |
Values are n (% total) unless otherwise indicated.
aBased on the D:A:D risk equation.
Changes in ASCVD risk over time among PLHIV
Out of the participants, 76 (2.4%) had ASCVD risk increase from <5% to >7.5% by the fifth year post ART initiation. They were generally older than the overall cohort, with a median age of 49 years (IQR 45–56 years) at baseline. Among these 76 participants, 39 (51.3%) remained or became a smoker during follow-up. The overall predicted mean ASCVD score at ART initiation was 0.84% (95% CI 0.81%–0.87%), which increased to 0.91% (0.88%–0.94%) in the first year, 1.0% (0.96%–1.03%) in the second year, 1.09% (1.05%–1.13%) in the third year, 1.21% (1.16%–1.25%) in the fourth year and 1.34% (1.29%–1.39%) in the fifth year (Figure 1). The ASCVD risk factors that changed most between baseline and year 5 of ART were median age, proportion diabetic and median CD4 cell count (Table S2).
Figure 1.
Predicted mean ASCVD score (%) in the first 5 years of ART initiation from the preferred piecewise model. The ASCVD scores were calculated from the D:A:D equation. The dot line represents the geometric mean value of the observed ASCVD scores at each follow-up time point. N represents the number of PLHIV at each follow-up time point.
Changes in ASCVD risk over time among HIV sub-populations
The rate of increase in ASCVD risk did not differ significantly among HIV sub-populations categorized by HIV exposure, race/ethnicity and nadir CD4 count at ART initiation (Figure 2).
Figure 2.
Adjusted mean ASCVD score (%) predictions in the first 5 years of ART by (a) mode of HIV acquisition, (b) race/ethnicity and (c) nadir CD4 count at ART initiation. The ASCVD scores were calculated from the D:A:D equation. Mean scores are reflective of the core reference categories: male sex, Asian ethnicity, heterosexual mode of HIV exposure, age >55 years, baseline nadir CD4 count ≥200 cells/mm3, BMI between 18.5–24.9 kg/m2, non-smoker and no family history of ASCVD.
Discussion
We modelled changes in ASCVD risk over time among PLHIV. Overall, we observed that ASCVD risk increased consistently over time. The rate of increase did not differ among sub-populations defined by HIV exposure, race/ethnicity and nadir CD4 count at ART initiation.
Overall, our study population had a low ASCVD risk compared to previously described cohorts.12,18–20 At baseline, the prevalence of moderate-to-high 5-year ASCVD risk in our study was lower than in other studies (≥5%: 4.5% versus 8.8%–25.8%,12,19–21 ≥10%: 0.5% versus 2.1%–4.4%).12,19 This was related to the younger age of our study cohort18,19 and the lower prevalence of ASCVD risk factors such as hyperlipidaemia,12,21 diabetes mellitus diagnosis,18,19,21 current smoking,18–20 and family history of ASCVD.18–20 In addition, these studies were cross-sectional studies12,19,21 or defined baseline differently from our analysis.18,20 These studies also typically included a high proportion of participants exposed to ART before the baseline.12,18,20,21
Studies have suggested that some HIV sub-populations, particularly PWID and those with low nadir CD4 count, have a high risk of ASCVD.22–25 However, our findings show that, despite differences in baseline risk, the rates at which ASCVD risk change over time do not appear to differ substantially among the HIV sub-populations assessed. This may be indicative of many major drivers of ASCVD risk being non-modifiable (e.g. age, sex, family history of ASCVD). In a sub-analysis of participants whose 5-year ASCVD risk increased from <5% at baseline to >7.5% during follow-up, we found that this population was substantially older than the main cohort. Importantly, the prevalence of smoking in this group was >50% suggesting smoking cessation interventions in older PLHIV could be most impactful in reducing population level ASCVD risk if intervention resources are limited.
The present analysis has several limitations. First, there were a substantial percentage of study participants excluded from this study as they did not have all the ASCVD risk factor data required to calculate ASCVD risk scores. We assumed these data were missing at random. However, it cannot be ruled out that this assumption may have introduced some immeasurable bias to our findings. Second, although the D:A:D equation has been well validated in Asian populations,9 it should be acknowledged that others have found it may underestimate ASCVD risk in low-predicted ASCVD risk groups.7 Finally, information on smoking status and mode of HIV acquisition in our cohorts relied on self-report, which may introduce reporting bias. It is possible that people might have underreported male-to-male sex, intravenous drug use and smoking due to fear of stigma.
ASCVD risk increased consistently among PLHIV on ART. The rate of increase did not differ among sub-populations defined by HIV exposure, race/ethnicity or nadir CD4 count at ART initiation. These findings emphasize the growing burden of ASCVD risk among PLHIV and the need to develop interventions that are effective across a broad range of HIV sub-populations.
Supplementary Material
Acknowledgements
We thank all participants who contribute data to the TREAT Asia HIV Observational Database (TAHOD) and the Australian HIV Observational Database (AHOD).
The TREAT Asia HIV Observational Database : V Khol, V Ouk, C Pov, National Center for HIV/AIDS, Dermatology & STDs, Phnom Penh, Cambodia; FJ Zhang, HX Zhao, N Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China; MP Lee, PCK Li, TS Kwong, TH Li, Queen Elizabeth Hospital, Hong Kong SAR, China; N Kumarasamy, C Ezhilarasi, Chennai Antiviral Research and Treatment Clinical Research Site (CART CRS), VHS-Infectious Diseases Medical Centre, VHS, Chennai, India; S Pujari, K Joshi, S Gaikwad, A Chitalikar, Institute of Infectious Diseases, Pune, India; RT Borse, V Mave, I Marbaniang, S Nimkar, BJ Government Medical College and Sassoon General Hospital, Pune, India; IKA Somia, TP Merati, AAS Sawitri, F Yuliana, Faculty of Medicine Udayana University—Prof. Dr IGNG Ngoerah Hospital, Bali, Indonesia; E Yunihastuti, A Widhani, S Maria, TH Karjadi, Faculty of Medicine Universitas Indonesia—Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia; J Tanuma, S Oka, T Nishijima, National Center for Global Health and Medicine, Tokyo, Japan; JY Choi, Na S, JM Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea; YM Gani, NB Rudi, Hospital Sungai Buloh, Sungai Buloh, Malaysia; I Azwa, A Kamarulzaman, SF Syed Omar, S Ponnampalavanar, University Malaya Medical Centre, Kuala Lumpur, Malaysia; R Ditangco, MK Pasayan, ML Mationg, Research Institute for Tropical Medicine, Muntinlupa City, Philippines; HP Chen, YJ Chan, PF Wu, E Ke, Taipei Veterans General Hospital, Taipei, Taiwan; OT Ng, PL Lim, LS Lee, T Yap, Tan Tock Seng Hospital, Singapore; A Avihingsanon, S Gatechompol, P Phanuphak, C Phadungphon, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand; S Kiertiburanakul, A Phuphuakrat, L Chumla, N Sanmeema, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; R Chaiwarith, T Sirisanthana, J Praparattanapan, K Nuket, Faculty of Medicine and Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand; S Khuwuwan, P Kambua, S Pongrapass, J Limlertchareonwanit, Chiangrai Prachanukroh Hospital, Chiang Rai, Thailand; TN Pham, KV Nguyen, DTH Nguyen, DT Nguyen, National Hospital for Tropical Diseases, Hanoi, Vietnam; CD Do, AV Ngo, LT Nguyen, Bach Mai Hospital, Hanoi, Vietnam; AH Sohn, JL Ross, B Petersen, TREAT Asia, amfAR—The Foundation for AIDS Research, Bangkok, Thailand; MG Law, A Jiamsakul, D Rupasinghe, The Kirby Institute, UNSW Sydney, NSW, Australia.
Australian HIV Observational Database : New South Wales: D Ellis, Plaza Medical Centre, Coffs Harbour^; M Bloch, Holdsworth House Medical Practice, Sydney; D Allen, Holden Street Clinic, Gosford^; L Burton, Lismore Sexual Health & AIDS Services, Lismore; D Baker*, R Mousavi, H Farlow, E Byrne, East Sydney Doctors, Surry Hills; DJ Templeton*, L Garton, T Doyle, RPA Sexual Health, Camperdown; Eva Jackson, Nepean and Blue Mountains Sexual Health and HIV Clinic, Penrith^; N Ryder, G Sweeney, B Moran, Clinic 468, HNE Sexual Health, Tamworth; A Carr, K Hesse, A Hawkes, St Vincent’s Hospital, Darlinghurst; R Finlayson, M Shields, R Burdon, P Calleia, Taylor Square Private Clinic, Darlinghurst; K Brown, Illawarra Sexual Health Service, Warrawong^; R Varma, Sydney Sexual Health Centre^, Sydney; R Bopage, J Walsh, S Varghese, C Chung, Western Sydney Sexual Health Clinic; DE Smith, Albion Street Centre^; A Cogle*, National Association of People living with HIV/AIDS; C Lawrence*, National Aboriginal Community Controlled Health Organisation; B Mulhall, Department of Public Health and Community Medicine, University of Sydney; M Law*, K Petoumenos*, J Hutchinson*, N Rose, T Dougherty, D Byonanebye, A Han, D Rupasinghe, The Kirby Institute, University of NSW. Northern Territory: M Gunathilake*, S Hall, Centre for Disease Control, Darwin. Queensland: C Thng*, Gold Coast Sexual Health Clinic, Southport; D Russell*, M Rodriguez, Cairns Sexual Health Service, Cairns; D Sowden, K Taing, J Broom, S Dennien, Clinic 87, Sunshine Coast Hospital and Health Service, Nambour; D Orth, D Youds, Gladstone Road Medical Centre, Highgate Hill^; E Priscott, S Benn, E Griggs, Sexual Health and HIV Service in Metro North, Brisbane. South Australia: W Donohue, O’Brien Street General Practice, Adelaide^. Victoria: R Moore, Northside Clinic, North Fitzroy^; NJ Roth*, H Lau, Prahran Market Clinic, South Yarra; R Teague, J Silvers, W Zeng, A Levey, Melbourne Sexual Health Centre, Melbourne; J Hoy*, M Giles, M Bryant, S Price, P Rawson Harris*, The Alfred Hospital, Melbourne; I Woolley*, T Korman, J O’Bryan*, K Cisera, Monash Medical Centre, Clayton. Western Australia: D Nolan, Department of Clinical Immunology, Royal Perth Hospital, Perth^. New Zealand: G Mills, Waikato District Hospital Hamilton^; N Raymond, Wellington Hospital, Wellington^. *Indicates steering committee members in 2023; ^Indicates no longer participating in the AHOD study.
Contributor Information
Weisi Chen, Kirby Institute, UNSW Sydney, Sydney, Australia.
Kathy Petoumenos, Kirby Institute, UNSW Sydney, Sydney, Australia.
Agus Somia, Department of Tropical and Infectious Diseases, Udayana University, Denpasar, Indonesia.
Natalie Edmiston, School of Medicine, Rural Research, Western Sydney University, Sydney, Australia.
Romanee Chaiwarith, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Ian Woolley, Monash Infectious Diseases, Monash Health and Monash University, Melbourne, Australia.
Jeremy Ross, TREAT Asia, amfAR—The Foundation for AIDS Research, Bangkok, Thailand.
Sanjay Pujari, Institute of Infectious Diseases, Pune, India.
David C Boettiger, Kirby Institute, UNSW Sydney, Sydney, Australia.
the International Epidemiology Databases to Evaluate AIDS—Asia Pacific:
V Khol, V Ouk, C Pov, Phnom Penh, F J Zhang, H X Zhao, N Han, M P Lee, P C K Li, T S Kwong, T H Li, N Kumarasamy, C Ezhilarasi, S Pujari, K Joshi, S Gaikwad, A Chitalikar, R T Borse, V Mave, I Marbaniang, S Nimkar, I K A Somia, T P Merati, A A S Sawitri, F Yuliana, E Yunihastuti, A Widhani, S Maria, T H Karjadi, J Tanuma, S Oka, T Nishijima, J Y Choi, N a S, J M Kim, Y M Gani, N B Rudi, I Azwa, A Kamarulzaman, S F Syed Omar, S Ponnampalavanar, R Ditangco, M K Pasayan, M L Mationg, H P Chen, Y J Chan, P F Wu, E Ke, O T Ng, P L Lim, L S Lee, T Yap, A Avihingsanon, S Gatechompol, P Phanuphak, C Phadungphon, S Kiertiburanakul, A Phuphuakrat, L Chumla, N Sanmeema, R Chaiwarith, T Sirisanthana, J Praparattanapan, K Nuket, S Khuwuwan, P Kambua, S Pongrapass, J Limlertchareonwanit, T N Pham, K V Nguyen, D T H Nguyen, D T Nguyen, C D Do, A V Ngo, L T Nguyen, A H Sohn, J L Ross, B Petersen, M G Law, A Jiamsakul, D Rupasinghe, D Ellis, M Bloch, D Allen, L Burton, D Baker, R Mousavi, H Farlow, E Byrne, D J Templeton, L Garton, T Doyle, Eva Jackson, N Ryder, G Sweeney, B Moran, A Carr, K Hesse, A Hawkes, R Finlayson, M Shields, R Burdon, P Calleia, K Brown, R Varma, R Bopage, J Walsh, S Varghese, C Chung, D E Smith, A Cogle, C Lawrence, B Mulhall, M Law, K Petoumenos, J Hutchinson, N Rose, T Dougherty, D Byonanebye, A Han, D Rupasinghe, D Russell, M Rodriguez, D Sowden, K Taing, J Broom, S Dennien, D Orth, D Youds, E Priscott, S Benn, E Griggs, N J Roth, H Lau, R Teague, J Silvers, W Zeng, A Levey, J Hoy, M Giles, M Bryant, S Price, P Rawson Harris, I Woolley, T Korman, J O’Bryan, K Cisera, and N Raymond
Funding
The TREAT Asia HIV Observational Database and the Australian HIV Observational Database are initiatives of TREAT Asia, a programme of amfAR, The Foundation for AIDS Research, with support from the US National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, the National Institute on Drug Abuse, the National Heart, Lung and Blood Institute, the National Institute on Alcohol Abuse and Alcoholism, the National Institute of Diabetes and Digestive and Kidney Diseases and the Fogarty International Center, as part of the International Epidemiology Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Department of Health and Ageing Australian Government. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above.
Transparency declarations
All authors report no potential competing interests.
Data availability
Data were collected as part of a regional cohort collaboration. The cohort collaboration has data-sharing policies that were approved by the corresponding IRB and specify that both internal and external investigators are subject to a formal process to request access to the data through submission of a concept sheet that adheres to these policies. This study was conducted under these policies, and data will only be available upon request for researchers who meet the criteria for access to confidential data. Interested individuals should contact Boondarika Petersen (tor.nakornsri@treatasia.org).
Supplementary data
Figure S1 and Tables S1 and S2 are available as Supplementary data at JAC Online.
References
- 1. Shah ASV, Stelzle D, Lee KK et al. Global burden of atherosclerotic cardiovascular disease in people living with HIV: systematic review and meta-analysis. Circulation 2018; 138: 1100–12. 10.1161/CIRCULATIONAHA.117.033369 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Freiberg MS, So-Armah K. HIV and cardiovascular disease: we need a mechanism, and we need a plan. J Am Heart Assoc 2016; 4: e003411. 10.1161/JAHA.116.003411 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. McGettrick P, Mallon PWG, Sabin CA. Cardiovascular disease in HIV patients: recent advances in predicting and managing risk. Expert Rev Anti Infect Ther 2020; 18: 677–88. 10.1080/14787210.2020.1757430 [DOI] [PubMed] [Google Scholar]
- 4. Friis-Moller N, Thiebaut R, Reiss P et al. Predicting the risk of cardiovascular disease in HIV-infected patients: the data collection on adverse effects of anti-HIV drugs study. Eur J Cardiovasc Prev Rehabil 2010; 17: 491–501. 10.1097/HJR.0b013e328336a150 [DOI] [PubMed] [Google Scholar]
- 5. Friis-Moller N, Ryom L, Smith C et al. An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons: the data-collection on adverse effects of anti-HIV drugs (D:A:D) study. Eur J Prev Cardiol 2016; 23: 214–23. 10.1177/2047487315579291 [DOI] [PubMed] [Google Scholar]
- 6. Anikpo I, Agovi AM, Cvitanovich MJ et al. The data-collection on adverse effects of anti-HIV drugs (D:A:D) model for predicting cardiovascular events: external validation in a diverse cohort of people living with HIV. HIV Med 2021; 22: 936–43. 10.1111/hiv.13147 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. van Zoest RA, Law M, Sabin CA et al. Predictive performance of cardiovascular disease risk prediction algorithms in people living with HIV. J Acquir Immune Defic Syndr 2019; 81: 562–71. 10.1097/QAI.0000000000002069 [DOI] [PubMed] [Google Scholar]
- 8. Krikke M, Hoogeveen RC, Hoepelman AI et al. Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), systematic coronary risk evaluation for the Netherlands (SCORE-NL) and data collection on adverse events of anti-HIV drugs (D:A:D) risk prediction models. HIV Med 2016; 17: 289–97. 10.1111/hiv.12300 [DOI] [PubMed] [Google Scholar]
- 9. Edwards-Jackson N, Kerr S, Tieu H et al. Cardiovascular risk assessment in persons with HIV infection in the developing world: comparing three risk equations in a cohort of HIV-infected Thais. HIV Med 2011; 12: 510–5. 10.1111/j.1468-1293.2011.00916.x [DOI] [PubMed] [Google Scholar]
- 10. Dhillon S, Sabin CA, Alagaratnam J et al. Level of agreement between frequently used cardiovascular risk calculators in people living with HIV. HIV Med 2019; 20: 347–52. 10.1111/hiv.12731 [DOI] [PubMed] [Google Scholar]
- 11. Serrano-Villar S, Estrada V, Gómez-Garre D et al. Diagnosis of subclinical atherosclerosis in HIV-infected patients: higher accuracy of the D:A:D risk equation over Framingham and SCORE algorithms. Eur J Prev Cardiol 2014; 21: 739–48. 10.1177/2047487312452964 [DOI] [PubMed] [Google Scholar]
- 12. Nery MW, Martelli CM, Silveira EA et al. Cardiovascular risk assessment: a comparison of the Framingham, PROCAM, and DAD equations in HIV-infected persons. Scientific World J 2013; 2013: 969281. 10.1155/2013/969281 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Pirš M, Jug B, Eržen B et al. Cardiovascular risk assessment in HIV-infected male patients: a comparison of Framingham, SCORE, PROCAM and DAD risk equations. Acta Dermatovenerol Alp Pannonica Adriat 2014; 23: 43–7. 10.15570/actaapa.2014.11 [DOI] [PubMed] [Google Scholar]
- 14. Zhou J, Kumarasamy N, Ditangco R et al. The TREAT Asia HIV observational database: baseline and retrospective data. J Acquir Immune Defic Syndr 2005; 38: 174–9. 10.1097/01.qai.0000145351.96815.d5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Hughes C, Puhr R, Ojaimi S et al. Human immunodeficiency virus-infected young people in Australia: data from the Australian HIV observational database. Intern Med J 2018; 48: 1447–56. 10.1111/imj.14040 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Hunger M, Döring A, Holle R. Longitudinal beta regression models for analyzing health-related quality of life scores over time. BMC Med Res Methodol 2012; 12: 144. 10.1186/1471-2288-12-144 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. The Respond Study Group . How to RESPOND to modern challenges for people living with HIV: a profile for a new cohort consortium. Microorganisms 2020; 8: 1164. 10.3390/microorganisms8081164 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Boyd MA, Mocroft A, Ryom L et al. Cardiovascular disease (CVD) and chronic kidney disease (CKD) event rates in HIV-positive persons at high predicted CVD and CKD risk: a prospective analysis of the D:A:D observational study. PLoS Med 2017; 14: e1002424. 10.1371/journal.pmed.1002424 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wu PY, Chen MY, Sheng WH et al. Estimated risk of cardiovascular disease among the HIV-positive patients aged 40 years or older in Taiwan. J Microbiol Immunol Infect 2019; 52: 549–55. 10.1016/j.jmii.2019.03.006 [DOI] [PubMed] [Google Scholar]
- 20. Shahmanesh M, Schultze A, Burns F et al. The cardiovascular risk management for people living with HIV in Europe: how well are we doing? Aids 2016; 30: 2505–18. 10.1097/QAD.0000000000001207 [DOI] [PubMed] [Google Scholar]
- 21. Mallya SD, Reddy TS, Kamath A et al. Determinants of metabolic syndrome and 5-year cardiovascular risk estimates among HIV-positive individuals from an Indian tertiary care hospital. AIDS Res Treat 2020; 2020: 5019025. 10.1155/2020/5019025 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Hileman CO, McComsey GA. The opioid epidemic: impact on inflammation and cardiovascular disease risk in HIV. Curr HIV/AIDS Rep 2019; 16: 381–8. 10.1007/s11904-019-00463-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Moore RD, Keruly JC, Chaisson RE. Differences in HIV disease progression by injecting drug use in HIV-infected persons in care. J Acquir Immune Defic Syndr 2004; 35: 46–51. 10.1097/00126334-200401010-00006 [DOI] [PubMed] [Google Scholar]
- 24. Phillips AN, Carr A, Neuhaus J et al. Interruption of antiretroviral therapy and risk of cardiovascular disease in persons with HIV-1 infection: exploratory analyses from the SMART trial. Antivir Ther (Lond) 2008; 13: 177–87. 10.1177/135965350801300215 [DOI] [PubMed] [Google Scholar]
- 25. Liang Y, Ketchum NS, Turner BJ et al. Cardiovascular risk assessment varies widely by calculator and race/ethnicity in a majority Latinx cohort living with HIV. J Immigr Minor Health 2020; 22: 323–35. 10.1007/s10903-019-00890-w [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data were collected as part of a regional cohort collaboration. The cohort collaboration has data-sharing policies that were approved by the corresponding IRB and specify that both internal and external investigators are subject to a formal process to request access to the data through submission of a concept sheet that adheres to these policies. This study was conducted under these policies, and data will only be available upon request for researchers who meet the criteria for access to confidential data. Interested individuals should contact Boondarika Petersen (tor.nakornsri@treatasia.org).