Persons living with HIV (PLWH) have a higher risk of cardiovascular disease (CVD) events [1–6] and an increased prevalence of certain risk factors [1,7–9] and comorbid conditions [10,11] than persons without HIV. Healthcare providers now must routinely consider these factors in the long-term care of PLWH [12,13]. CVD risk prediction tools are clinically useful and have long been used to guide recommendations for medication therapy or other preventive interventions [14–18]; however, they underestimate risk in PLWH [19–21]. ‘Heart age’ is an alternative and potentially easier concept to understand and interpret [22,23], using a patient's cardiovascular risk factor profile to estimate their vascular system's age [14,24].
This analysis provides the first estimates of heart age in a large, diverse cohort of PLWH in the United States (US). Our objectives were to calculate heart age for PLWH by select sociodemographic and clinical characteristics and to examine correlations between two different heart age model estimates.
We analyzed data from the HIV Outpatient Study (HOPS), an ongoing, prospective cohort study of PLWH aged at least 18 years receiving care at public and private HIV clinics in the United States, described in detail elsewhere [1,25]. Ethical review board at the Centers for Disease Control and Prevention approved the protocol and all study participants provided written informed consent.
From among 4358 HOPS participants seen during 1 January 2010 to 31 December 2017, we analyzed data for 3086 participants. We serially excluded 1272 (29.2%) for the following reasons: had fewer than two HOPS encounters (n = 13), lacked at least 1 data element required for calculation of heart age (n = 404), were pregnant (n = 3), not aged 30–74 years (heart age equations are validated only for persons in this age range, n = 540), or had prior stroke, myocardial infarction, or coronary heart disease (n = 312).
Outcome and predictor variables
Heart age was calculated as the ‘age of a person with the same predicted risk but with all other risk factors in the normal range’ . Excess heart age was calculated as the difference between an individual's heart and chronologic ages and represents the excess risk for CVD events . We used two models to calculate heart age. The cholesterol-based (‘laboratory-based’) model  included the following covariates: SBP, antihypertensive medication use, diabetes mellitus status, smoking status, age, sex, total blood cholesterol, and high-density lipoprotein (HDL) cholesterol. The BMI-based model (‘nonlaboratory’ or ‘office-based model’) included the same covariates except used BMI in place of total and HDL cholesterol [14,24].
We performed cross-sectional data analyses, defining the index visit for each patient as the most recent visit between 1 January 2010 and 31 December 2017 at which at least one total cholesterol value and at least 365 days of follow-up after the index visit were available. Sociodemographic characteristics, CVD risk factors, and HIV-related factors were collected from the index visit, or from medical encounter(s) that were within the window of observation (i.e. 1 year before through 1 year after the index visit).
Due to sociodemographic differences in men and women with HIV and sex-specific differences in CVD risk factors, all analyses were conducted separately for men and women [1,6,9,26]. We calculated age-adjusted and race/ethnicity-adjusted mean chronological age, heart age, excess heart age and the prevalence of excess heart age at least 10 years. Using the Pearson correlation coefficient (ρ), we examined linear correlations between estimates derived using the cholesterol- and BMI-based models. Repeating these analyses, we age-standardized the results to the US 2010 Census population to examine heart age among HOPS participants as though they had an age distribution similar to the U.S. population. Statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, North Carolina, USA), and P values less than 0.05 were considered statistically significant.
Among 2467 men and 619 women included in our analytic sample, the majority of men were non-Hispanic/Latino white (52.7%) and had more than a high school education (54.4%). The majority of women were non-Hispanic/Latino black (54.6%) and had a high school education or less (55.7%) (Table 1).
Heart age and excess heart age results
The mean chronological age in the cohort was 49.3 years in men (heart age: 60.8 years; excess heart age: 11.5 years; Table 2) and 49.1 years in women (heart age: 62.2 years; excess heart age: 13.1 years). Mean excess heart age was the lowest among the youngest age group (30–39 years), although it still exceeded chronological age by 7.8 years [95% confidence interval (CI: 6.9–8.8)] for men and 7.7 years (4.9–10.4) for women. Excess heart age was greatest among PLWH aged 50–59 years [men: 13.7 (13.0–14.4); women: 16.4 (14.8–18.0)], and those who reported cholesterol-lowering medication (i.e. statin) use [men: 15.1 (14.0–16.1); women: 18.6 (16.6–20.6)] or aspirin use [men: 16.1 (14.7–17.6); women: 19.4 (16.7–22.1)]; and men with hepitatis-C virus (HCV) co-infection [13.3 years (12.3–14.3)] or less than a high school education [13.9 (12.5–15.3)]. Heart age exceeded chronological age by at least 10 years among 53.1% of men and 59.3% of women studied.
Comparative results using two methods for heart age estimation
There was a strong linear correlation between excess heart age estimates using the cholesterol-based and BMI-based models (men, ρ = 0.87; women, ρ = 0.84), with similar estimates of mean excess heart age (men: 11.5 and 11.6 years; women: 13.1 and 13.8 years; cholesterol-based and BMI-based models, respectively). Results were modestly attenuated when age-standardized to the 2010 US Census population (men, 11.3 and 11.5 years; women, 12.6 and 13.2 years; cholesterol-based and BMI-based models, respectively), but heart age still exceeded chronological age in all categories for men and women (results not shown).
Among PLWH, heart age exceeded chronological age by a mean of 11.5 years for men and 13.1 years for women and was at least 10 years among more than half of all participants. We identified a high correlation of excess heart age estimates between the cholesterol-based and BMI-based models, which may represent the first study to do so.
Our excess heart age estimates exceeded those of the general US population [men: 7.8 years; women: 5.4 years using 2011–2013 Behavioral Risk Factor Surveillance Survey (BRFSS) data], and those reported from a small sample of New York PLWH (7.2 years) with clinical visits between 2004 and 2009 . Our findings were consistent with estimates of excess heart age in some subpopulations, for example, non-Hispanic black men (11.0 years) and women (11.1 years) , men and women with diabetes , and populations with chronic HCV, or HIV/HCV co-infection (12.5 and 9.6 years, respectively) , all calculated using the BMI-based model. Excess heart age emerged at younger ages among PLWH compared with the general US population (e.g. among 30–39 year olds: 7.8 and 7.7 years for men and women in the HOPS compared with 3.8 and −0.3 years for men and women in the general US population) but was consistent with increased estimates of excess heart age among similarly aged adults with diabetes (men: 4 years; women: 8 years) . Excess heart age was particularly high among women with HIV where it consistently exceeded that of men with HIV across key sociodemographic and clinical characteristics. Compared with men, women in the HOPS had higher prevalence of certain CVD risk factors, more advanced HIV disease stage, and lower prevalence of HIV viral suppression. Factors, such as early menopause, increased inflammation and immune activation, and differing plaque morphology [26–29] may have contributed to the higher excess heart age among women in our study; however, the etiological basis for increased CVD risk in women living with HIV is unclear.
Despite having greater heart age than the general population, PLWH may have been less likely than patients without HIV to be prescribed medication for CVD risk factor management . Guidelines for CVD risk factor management are well established for the general population , but there is evolving guidance around recommended medication therapy for chronic disease management among PLWH, which recommends blood pressure and lipid management, whenever clinically feasible [32,33]. These recommendations were based on previously published national guidelines and are not specific for the PLWH population [31,34,35]. Updated national guidelines for cholesterol management acknowledged HIV as an atherosclerotic CVD ‘risk enhancer’ for the first time in 2018 . Vascular aging may occur earlier among PLWH , atherosclerotic plaque morphology may differ [38,39], and sex-specific risk may present differently compared with the general population; therefore, differences in recommendations, such as lower age thresholds for statin therapy initiation may be needed.
Statin use appears to reduce mortality among PLWH [40,41], but a dearth of clinical trial data related to the safety and efficacy of statins in this population has left clinicians without clear guidance on the type and dose of statin to prescribe . Two ongoing multicenter studies, the Elite Controller and ART-Treated HIV+ Statin Versus ASA Treatment Intervention Study (https://clinicaltrials.gov/ct2/show/NCT02081638) and the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) , may inform CVD prevention in PLWH. Future guidelines will need to consider available evidence as well as national guidelines for hypertension, high blood cholesterol, and lifestyle management to reduce CVD risk, and their implications for HIV-treatment algorithms . Furthermore, they could emphasize use of team-based models of care, previously shown to be effective for the support of blood pressure control and for integrated HIV care [44,45].
Important among our findings was the high correlation in excess heart age estimates between the two models used, suggesting that either the cholesterol-based or BMI-based method could be used to estimate heart age in PLWH. This finding may be especially important for resource-constrained medical settings where BMI may be easier to routinely measure than blood cholesterol levels. One study reported high correlation in results from Framingham equations using BMI or blood cholesterol to estimate cardiovascular risk in PLWH from Cote d’Ivoire .
Despite giving unique insight to CVD risk among PLWH, our analysis is subject to several limitations. HOPS participants included in this analysis were older, had more advanced HIV, and more chronic comorbidities than excluded participants (data not shown). We excluded 24.8% of participants with prior CVD from this analysis, compared with 7.6% of participants excluded in a similar study in the general US population  underscoring the greater risk for developing CVD among PLWH. On the basis of on established CVD risk functions [14,47], which were not validated for use in PLWH, heart age calculations may underestimate CVD risk in this population [19–21,48]. Despite limitations and in the absence of tools validated for use in PLWH, the heart age calculator may give healthcare providers an easy-to-understand and readily available tool (https://www.cdc.gov/vitalsigns/cardiovasculardisease/heartage.html) with which to discuss heart health with PLWH.
The burden of excess heart age is common among PLWH, begins in early adulthood, and impacts both men and women. High correlation of results between the cholesterol-based and BMI-based heart age estimation models suggests that either method could be used for care management of PLWH. Among PLWH, CVD risk factors should be addressed early and proactively. Routine use of the heart age calculator may help optimize CVD risk stratification and facilitate interventions for aging PLWH.
Authors’ contributions: All authors contributed to the writing of this article. A.M.T., M.D.R., F.L., and K.B. conceived the idea. F.J.P. and K.L. contributed to the study design and collection of data. N.R. and A.M.T. were responsible for analysis and interpretation of the data under the guidance of C.G., Q.Y., M.D.R., and K.B. A.M.T. wrote all major drafts of the article with critical input from F.J.P., M.D.R., K.L., D.P., Q.Y., F.L., P.P., and K.B. All authors have reviewed and approved the article.
Funding: This work was supported by the Centers for Disease Control and Prevention (contract nos. 200-2001-00133, 200-2006-18797 and 200-2011-41872).
Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
The HIV Outpatient Study (HOPS) Investigators currently include the following persons and sites:
Kate Buchacz, Marcus D. Durham, Jun Li, Division of HIV/AIDS Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA; Cheryl Akridge, Stacey Purinton, Nabil Rayeed, Selom Agbobil-Nuwoaty, Kalliope Chagaris, Kimberly Carlson, Carl Armon, Linda Battalora, Jonathan Mahnken Cerner Corporation, Kansas City, Missouri, USA; Frank J. Palella, Saira Jahangir, Conor Daniel Flaherty, Patricia Bustamante, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA; John Hammer, Kenneth S. Greenberg, Barbara Widick, Rosa Franklin, Rocky Mountain Cares, Denver, CO; Douglas J. Ward, Troy Thomas, Cheryl Stewart, Dupont Circle Physicians Group, Washington, DC, USA; Jack Fuhrer, Linda Ording-Bauer, Rita Kelly, Jane Esteves, State University of New York (SUNY), Stony Brook, New York, USA; Ellen M. Tedaldi, Ramona A. Christian, Faye Ruley, Dania Beadle, Princess Davenport, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA; Richard M. Novak, Andrea Wendrow, Stockton Mayer. University of Illinois at Chicago, Chicago, Illinois, USA; Mia Scott, Billie Thomas, Loraine Van Slyke APEX Family Medicine, Denver, Colorado, USA; Cynthia Mayer, Terry Beitler, Karen Maroney, Denise Franklin, SJH Comprehensive Research Institute, Tampa, Florida, USA.
Conflicts of interest
F.P. has served on the Advisory Board for Gilead Sciences and Janssen Pharmaceuticals; speakers bureau for Gilead Sciences, Janssen Pharmaceuticals, Merck, and ViiV. K.L. has served on the Advisory Board for Gilead Sciences, received research support from AbbVie and Gilead Sciences, and has given CME Lectures for Simply Speaking and Integrity. All other authors declare that no competing interests exist.
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* Investigators listed in the Appendix.