Abstract
Background:
HIV infection is associated with cardiovascular events in adults. We compared mean BP obtained at study visits between youth with/without perinatally acquired HIV infection and evaluated whether HIV disease severity associated with BP.
Methods:
BP was compared between participants with/without HIV in the “Adolescent Master Protocol of the Pediatric HIV/AIDS Cohort Study”. Marginal repeated measures analyses using generalized estimating equations evaluated the association of HIV disease severity with BP index (mean BP/95th percentile BP) and abnormal BP.
Results:
447 youth with HIV and 226 youth without HIV were included. Youth with HIV were more often Black non-Hispanic (66% vs. 54%), had greater household income (54% vs. 35%), and lower measures of adiposity than those without. Systolic BP was similar between groups, but mean diastolic BP was lower for pre-adolescents (63.3mmHg (95% CI 59.0, 67.0) vs. 65.0 (61.5, 68.7)) with HIV. While youth with HIV had lower diastolic BP index [−0.011 (−0.021, −0.001)] and lower prevalence of abnormal BP [OR 0.78 (0.62, 0.97)] at study visits in initial adjusted models, these associations were attenuated after adjustment for BMI [−0.007 (95% CI −0.017, 0.003), OR 0.94 (0.76, 1.17), respectively]. HIV disease severity was not associated with systolic or diastolic BP.
Conclusion:
Youth with HIV had lower adiposity and BP than youth without HIV during study visits. While youth with HIV had lower risk of abnormal BP, this association did not persist after adjustment for adiposity. Prevention and treatment of other traditional CVD risk factors remain important among youth living with HIV.
Keywords: Pediatric, cardiovascular disease, acquired immunodeficiency syndrome, children, adolescents, blood pressure, hypertension
Graphical Abstract
Introduction:
Human immunodeficiency virus (HIV) infection in adults is associated with an increased risk for cardiovascular disease (CVD) and CVD risk factors. Adults living with HIV infection have a higher prevalence of hypertension, diabetes mellitus, and dyslipidemia and have a significantly greater odds of coronary heart disease than those who are uninfected.1–4
Children with HIV are also thought to be at greater CVD risk, with previous studies demonstrating a higher than expected prevalence of risk factors such as dyslipidemia, adiposity, insulin resistance, elevated inflammatory markers, and more frequent exposure to medications with cardiovascular toxicities.5–10 Prior work in the Pediatric HIV/AIDS Cohort Study (PHACS) demonstrated that adolescents with perinatal HIV infection not only had a notable CVD risk factor burden, but that markers of disease severity were associated with greater risk for atherosclerotic CVD.11 In 2011, the National Heart, Lung, and Blood Institute added HIV infection to the list of moderate risk conditions for accelerated atherosclerosis prior to 30 years of age.12
What remains unclear is if the 1.5–1.7-fold increase in cardiovascular events among adults with HIV-infection13–16 is due to the infection itself, antiretroviral therapy (ART), or co-morbid CVD risk factors unrelated to HIV. It is also unclear how HIV infection impacts blood pressure (BP) in children and how HIV infection may associate with hypertension in youth. With the antecedents to adult CVD often manifesting in youth, studying children with HIV, who are free of many co-morbid CVD risk factors that confound studies in adults, offers a unique opportunity to investigate the potential effects of HIV infection on CVD risk factors such as hypertension.
Therefore, our main study objective was to investigate the association of perinatal HIV infection (PHIV) with BP among children and adolescents. We specifically aimed to (1) compare systolic and diastolic BP and the prevalence of BP in the hypertensive range between children and adolescents living with PHIV and those who were perinatally HIV-exposed but uninfected (PHEU), and (2) determine if HIV disease severity is associated with BP among PHIV children and adolescents. We also aimed to determine if obesity and/or age group modified how HIV and HIV disease severity associated with BP.
Methods:
Data Availability
All data and materials have been made publicly available at the NICHD Data and Specimen Hub (DASH) and can be accessed at https://dash.nichd.nih.gov/study/17510.
Setting, Study Design, and Sample
The PHACS network conducts prospective cohort studies that aim to determine the outcomes of HIV infection and exposure to antiretroviral treatment in youth. The PHACS Adolescent Master Protocol (AMP) included children and youth from 15 academic centers in the United States who were either living with perinatal HIV infection or perinatally exposed to HIV but uninfected. Children in the AMP cohort were enrolled between 7–16 years of age from March 2007-December 2009 with the final AMP Study population consisting of 451 perinatally HIV-infected children and 227 HIV-exposed but uninfected children of a similar age and socio-demographic status. The study closed to follow-up in 2020. Enrolled participants underwent annual assessments during which information regarding their medical history, co-morbidities, current medications, and laboratory values were abstracted from the medical record and anthropometrics and BP were measured. For this study we included AMP participants with ≥1 study visits with BP measurements recorded before October 1, 2019. The protocol was approved by the institutional review board at each participating site, as well as by the Harvard T.H. Chan School of Public Health. Written informed consent was obtained from each child’s parent or legal guardian, and assent was obtained from participating children in accordance with the guidelines of the local institutional review boards.
Data collection:
At each visit we measured anthropometrics including weight (kg), height (cm), waist circumference (cm) and used height and weight to calculate body mass index (kg/m2). Each measure was converted to a Z-score based on normative US data.17
For youth with PHIV, we also measured fasting lipids (mg/dL), fasting glucose (mg/dL), fasting insulin (mcU/mL), serum creatinine (mg/dL), and abstracted data on CD4 (cells/μl) and HIV RNA (copies/mL). We used these measures when obtained within one year of each BP measurement.
Blood Pressure Measurement:
BP was obtained during clinical assessments and recorded in the medical record. Most BP measurements were obtained with an automated, oscillometric device according to a standardized protocol, including resting for 5 minutes, ensuring back/arm/feet support and use of an appropriately sized cuff. Two measures of systolic and diastolic BP were obtained at each visit; if these BPs differed by >5 mmHg then a third measure was obtained. The mean of all systolic and diastolic BP measures at each visit was calculated. BP outcomes were defined as follows:
1) BP index (defined separately for systolic and diastolic BP): mean BP/age-sex-height specific 95th percentile BP. A BP index ≥1 indicates a BP ≥95th percentile.
2) Abnormal BP: mean BP in the hypertensive range18 (systolic and/or diastolic BP ≥age-sex-height specific 95th percentile for children <13 years; systolic BP ≥130 mmHg and/or diastolic BP ≥80 mmHg for youth ≥13 years).
Exposure:
Perinatal HIV status was obtained from the medical record and was characterized as PHIV or PHEU. Amongst the PHIV group, HIV severity until the time of each BP measurement was determined by: 1) nadir CD4 count (<200 vs ≥200 cells/mm3); 2) lifetime viral load (VL) burden, defined as i) percentage of time with suppressed VL (<400 copies/mL) and ii) time-averaged area under the VL curve [(AUC/individual time span)]. Both measures of VL burden included multiple imputation of missing VL values.
Covariates:
We considered the following covariates in our analyses: age groups [childhood (7–10.9 years), pre-adolescent (11–12.9 years), adolescent (13–17.9 years), and young adult (18 years+)], race (White vs. non-White) and ethnicity (Hispanic vs. non-Hispanic) as proxies for social determinants of health, sex at birth (male vs. female), household income (≤$10,000 vs. >$10,000) and adiposity [exploring BMI as a continuous (BMI Z-score) and categorical (<85th vs. ≥85th percentile) variable in separate analyses]. For our analyses, we used BMI measured within one year of each BP measurement.
To be included in an age group category, participants had to have at least one record in that age group during follow-up. Some, but not all, participants had more than one visit with BP measurements recorded within each age group. For the tables describing the characteristics of PHIV and PHEU within each age group, one visit was randomly selected as representative for each person in each age category. For the multivariable analyses, all available visits with BP measurements were included.
Antiretroviral use [categorized as combination antiretroviral therapy (cART), other ART, and none] at the time of each BP measure was also recorded. Kidney disease was defined as ever having a prior kidney disease diagnosis (based on reported diagnoses as classified by MedDRA®) or estimated glomerular filtration rate (eGFR) <60 ml/min/1.73m2. eGFR was calculated using the equation 0.413 × [height (cm)/serum creatinine (mg/dL)].
Statistical Analyses:
Sociodemographic and clinical characteristics, as well as the distribution of exposure and outcome measures, were described for youth with PHIV and PHEU, with BP measures compared using Wilcoxon rank sum testing.
We assessed the association between each systolic and diastolic BP index and HIV status by fitting linear regression models using generalized estimating equations (GEE) for repeated measures (≥1 clinic visits/person) with robust variance, specifying the distribution as normal and the identity link, unadjusted and adjusted for covariates. We also conducted a secondary analysis of youth 13 years of age and older in order to estimate the association between mean systolic and diastolic BP in mmHg with HIV status.
To estimate the prevalence of abnormal BP in each age group, we analyzed repeated measures (≥1 clinic visits/person) using modified Poisson regression models,19 unadjusted and adjusted for covariates. We also considered potential effect modification of the association of perinatal HIV status with BP index and abnormal BP by adiposity (BMI >85th %ile) and pediatric age group, separately.
An imputation model using a longitudinal linear mixed-effects model with a random intercept and random slope for age and ARV use was fit to impute viral load from birth to the last clinic visit. The model included variables known to be associated with viral load: year of birth, year of viral load measurement, ARV use, age, sex, race, ethnicity, and imputed income level. Income was imputed based on the known distribution of household income level among our participants with non-missing data. To account for missing CD4 count and income, chained equations were used. Chained equations were cycled through 10 times to create 25 imputation-based datasets.
To explore the association of HIV severity with outcomes, we utilized marginal repeated measures analysis models using generalized estimating equations with robust variance for BP index outcomes and a modified Poisson regression model for abnormal BP, both unadjusted and adjusted for covariates. Secondary analysis of HIV disease severity with BP outcomes in mmHg among those 13 years of age and older was also conducted.
Statistical analyses were performed using SAS® 9.4 (SAS Institute, Cary, NC) and R (Version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were two-sided; emphasis was placed on consistency of results across analyses under various assumptions. DJ and WY had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
RESULTS
Study Population:
The study population included 447 PHIV and 226 PHEU unique youth who had at least one clinic visit with BP measured. These youth contributed multiple BP measurements throughout the course of the study, with varying numbers of unique participants contributing BP measurements during each age category as depicted in Table 1 and Figure 1.
Table 1:
Demographics and anthropometrics of youth with PHIV and youth with PHEU at a randomly selected time point within each age group
| Age Group1–2 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Childhood (7–10.9 years) | Pre-adolescent (11–12.9 years) | Adolescent (13–17.9 years) | Young Adult (18+ years) | ||||||||||
| Characteristics | YLPHIV (N=156) | YLPHEU (N=139) | P-value | YLPHIV (N=249) | YLPHEU (N=174) | P-value | YLPHIV (N=407) | YLPHEU (N=207) | P-value | YLPHIV (N=143) | YLPHEU (N=26) | P-value | |
| Age at BP measurement (years) | Median (Q1, Q3) | 10.0 (9.1, 10.6) | 9.9 (8.8, 10.5) | 0.17* | 12.1 (11.6, 12.6) | 12.1 (11.6, 12.4) | 0.19* | 15.3 (14.2, 16.4) | 15.1 (13.8, 16.4) | 0.22* | 18.8 (18.4, 19.4) | 18.5 (18.2, 18.8) | 0.019* |
| Sex | M | 69 (44%) | 74 (53%) | 0.12** | 111 (45%) | 92 (53%) | 0.093** | 190 (47%) | 107 (52%) | 0.24** | 73 (51%) | 9 (35%) | 0.12** |
| F | 87 (56%) | 65 (47%) | 138 (55%) | 82 (47%) | 217 (53%) | 100 (48%) | 70 (49%) | 17 (65%) | |||||
| Race | White | 35 (22%) | 42 (30%) | 0.47** | 57 (23%) | 58 (33%) | 0.076** | 91 (22%) | 67 (32%) | 0.035** | 26 (18%) | 9 (35%) | 0.27** |
| Black | 113 (72%) | 89 (64%) | 179 (72%) | 107 (61%) | 291 (71%) | 130 (63%) | 106 (74%) | 16 (62%) | |||||
| Other | 3 (2%) | 3 (2%) | 3 (1%) | 4 (2%) | 5 (1%) | 4 (2%) | 2 (1%) | 0 (0%) | |||||
| Not known/not reported | 5 (3%) | 5 (4%) | 10 (4%) | 5 (3%) | 20 (5%) | 6 (3%) | 9 (6%) | 1 (4%) | |||||
| Ethnicity | Hispanic or Latino | 36 (23%) | 48 (35%) | 0.025** | 60 (24%) | 62 (36%) | 0.003** | 101 (25%) | 73 (35%) | 0.004** | 33 (23%) | 9 (35%) | 0.43** |
| Not Hispanic or Latino | 120 (77%) | 89 (64%) | 189 (76%) | 109 (63%) | 305 (75%) | 131 (63%) | 109 (76%) | 17 (65%) | |||||
| Unknown | 0 (0%) | 2 (1%) | 0 (0%) | 3 (2%) | 1 (0%) | 3 (1%) | 1 (1%) | 0 (0%) | |||||
| Household income | Less than $10,000 | 31 (21%) | 50 (37%) | 0.039** | 46 (19%) | 54 (32%) | 0.023** | 75 (19%) | 67 (33%) | <0.001** | 12 (19%) | 9 (53%) | 0.24** |
| $10,001-$20,000 | 36 (25%) | 36 (26%) | 65 (28%) | 49 (29%) | 93 (24%) | 62 (31%) | 16 (25%) | 3 (18%) | |||||
| $20,001-$30,000 | 22 (15%) | 23 (17%) | 42 (18%) | 33 (20%) | 72 (19%) | 23 (11%) | 9 (14%) | 2 (12%) | |||||
| $30,001-$40,000 | 19 (13%) | 9 (7%) | 27 (11%) | 9 (5%) | 41 (11%) | 15 (7%) | 10 (16%) | 1 (6%) | |||||
| $40,001-$50,000 | 12 (8%) | 7 (5%) | 18 (8%) | 8 (5%) | 35 (9%) | 17 (8%) | 4 (6%) | 1 (6%) | |||||
| $50,001-$70,000 | 9 (6%) | 5 (4%) | 17 (7%) | 9 (5%) | 32 (8%) | 9 (4%) | 5 (8%) | 0 (0%) | |||||
| $70,001-$100,000 | 10 (7%) | 4 (3%) | 11 (5%) | 5 (3%) | 30 (8%) | 6 (3%) | 5 (8%) | 1 (6%) | |||||
| Greater than $100,000 | 6 (4%) | 2 (1%) | 10 (4%) | 2 (1%) | 10 (3%) | 3 (1%) | 2 (3%) | 0 (0%) | |||||
| . | 11 | 3 | 13 | 5 | 19 | 5 | 80 | 9 | |||||
| Weight (kg) | Median (Q1, Q3) | 32.55 (27.15, 39.50) | 37.30 (30.00, 50.68) | <0.001* | 44.20 (35.53, 52.83) | 48.04 (39.53, 62.70) | <0.001* | 56.14 (47.85, 67.80) | 65.30 (53.31, 80.27) | <0.001* | 62.60 (54.00, 74.32) | 76.19 (58.60, 85.80) | 0.027* |
| Weight Z-score | Mean (s.d.) | 0.2 (1.1) | 1.0 (1.4) | <0.001*** | 0.2 (1.3) | 0.8 (1.4) | <0.001*** | 0.1 (1.3) | 0.8 (1.3) | <0.001*** | −0.2 (1.7) | 0.7 (1.3) | 0.003*** |
| Height (cm) | Median (Q1, Q3) | 137.37 (130.39, 144.28) | 139.00 (133.00, 145.47) | 0.071* | 149.43 (143.27, 156.50) | 153.42 (147.37, 159.33) | <0.001* | 161.40 (155.23, 168.25) | 165.00 (159.23, 172.00) | <0.001* | 164.47 (157.50, 173.00) | 167.25 (161.00, 173.50) | 0.33* |
| Height Z-score | Mean (s.d.) | −0.0 (1.1) | 0.5 (1.1) | <0.001*** | −0.1 (1.2) | 0.4 (1.1) | <0.001*** | −0.5 (1.2) | 0.0 (1.0) | <0.001*** | −0.7 (1.3) | −0.1 (1.1) | 0.007*** |
| BMI (kg/m2) | Median (Q1, Q3) | 17.01 (15.58, 19.91) | 19.32 (16.28, 23.90) | <0.001* | 19.05 (17.06, 22.01) | 20.97 (17.60, 26.59) | <0.001* | 21.25 (18.79, 25.17) | 22.94 (19.62, 29.11) | <0.001* | 22.55 (20.16, 26.66) | 25.48 (20.83, 31.09) | 0.049* |
| BMI Z-score | Mean (s.d.) | 0.3 (1.1) | 0.9 (1.3) | <0.001*** | 0.3 (1.2) | 0.8 (1.3) | <0.001*** | 0.4 (1.1) | 0.8 (1.3) | <0.001*** | 0.1 (1.4) | 0.8 (1.3) | 0.018*** |
| BMI categories | Underweight | 8 (5%) | 5 (4%) | <0.001** | 11 (4%) | 10 (6%) | <0.001** | 13 (3%) | 8 (4%) | <0.001** | 15 (10%) | 1 (4%) | 0.056** |
| Healthy weight | 102 (65%) | 67 (48%) | 165 (66%) | 84 (48%) | 268 (66%) | 104 (50%) | 93 (65%) | 12 (46%) | |||||
| Overweight | 28 (18%) | 20 (14%) | 37 (15%) | 26 (15%) | 68 (17%) | 32 (15%) | 14 (10%) | 6 (23%) | |||||
| Obese | 18 (12%) | 47 (34%) | 36 (14%) | 54 (31%) | 58 (14%) | 63 (30%) | 21 (15%) | 7 (27%) | |||||
| Waist circumference (cm) | Median (Q1, Q3) | 62.10 (57.33, 68.00) | 68.00 (58.27, 81.00) | <0.001* | 69.00 (62.90, 77.57) | 73.23 (65.13, 88.13) | <0.001* | 75.40 (68.75, 85.47) | 79.17 (71.00, 94.17) | <0.001* | 79.30 (73.33, 90.60) | 86.50 (74.03, 102.00) | 0.094* |
| # missing | 3 | 0 | 2 | 1 | 4 | 2 | 8 | 0 | |||||
| Waist circumference Z-score | Mean (s.d.) | 0.1 (0.9) | 0.7 (1.2) | <0.001*** | 0.3 (1.0) | 0.7 (1.1) | <0.001*** | 0.3 (1.0) | 0.6 (1.0) | <0.001*** | 0.2 (1.0) | 0.6 (1.1) | 0.089*** |
Wilcoxon Test
Chi-SquareTest
T-Test
To be included in an age group category, participants had to have at least one record in that age group during follow-up. If a participant had multiple records within an age group, only one record was randomly selected for inclusion in the table. YPHEU: youth perinatally HIV exposed but uninfected; YPHIV: youth with perinatally acquired HIV
Figure 1 –
consort diagram
Sociodemographic, Anthropometric, and Clinical Characteristics:
The sociodemographic and anthropometric characteristics of youth with PHIV vs. PHEU are displayed by age category in Table 1 for a randomly selected data point in each age category and the overall demographic characteristics of all youth at their first visit is displayed in Table S1. In general, when compared with PHEU, youth with PHIV were more often Black and not Hispanic/Latino (66% vs. 54%) and had greater household income (54% vs. 35% with income >$20K). Compared to PHEU, PHIV had lower mean measures of adiposity across all age groups and a higher percentage had healthy weight during all age groups.
Six percent of youth with PHIV had kidney disease in childhood, increasing to 7% in pre-adolescents, 11% in adolescence, and 15% in adulthood (Table S2). Serum creatinine data was only available for a small number of PHEU participants at a single visit, precluding our ability to report on kidney disease prevalence in this group.
Table 2 displays the BP measures of all youth and Figure 2 describes the prevalence of abnormal BP categories in each age group. Forty-five percent of PHIV and 48% of PHEU had three BP measurements taken at the visit; the rest had two BP measurements taken. Systolic BP (mmHg and index) and proportion with abnormal BP at a given visit were similar between groups, but diastolic BP (mmHg and index) was lower for preadolescents with PHIV vs. PHEU: 63.3mmHg (95% CI 59.0, 67.0) vs. 65.0 (61.5, 68.7) and 0.80 (0.76, 0.85) vs. 0.83 (0.78, 0.88), respectively. Similarly, the proportion of youth with BP in the elevated or hypertensive range at a study visit was similar in most age groups but was significantly higher among preadolescents with PHEU (35%) than PHIV (24%), p=0.014. There was no effect modification by adiposity status or age group in any of the models, therefore these variables were maintained in the final adjusted models as covariates. While PHIV had lower DBP index [mean difference −0.011 (−0.021, −0.001)] and lower adjusted prevalence for abnormal BP [OR 0.78 (0.62, 0.97)] than PHEU, these associations were attenuated after further adjustment for BMI Z-score [mean difference −0.007 (95% CI −0.017, 0.003), OR 0.94 (0.76, 1.17), respectively] (Table 3). These findings were consistent when exploring BP in mmHg among youth 13 years of age or older (Table S4). The point estimates obtained in a fully adjusted model incorporating BMI as a categorical variable to indicate overweight/obesity status vs. not (BMI ≥85th percentile vs. < 85th percentile) instead of a linear variable (BMI Z-score) were similar (data not shown).
Table 2:
Blood pressure measures in youth with PHIV compared with youth with PHEU at a randomly selected time point within each age group
| Age Group1–2 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Childhood (7–10.9 years) | Pre-adolescent (11–12.9 years) | Adolescent (13–17.9 years) | Young Adult (18+ years) | ||||||||||
| Blood pressure measures | YLPHIV (N=156) | YLPHEU (N=139) | P-Value* | YLPHIV (N=249) | YLPHEU (N=174) | P-Value* | YLPHIV (N=407) | YLPHEU (N=207) | P-Value* | YLPHIV (N=143) | YLPHEU (N=26) | P-Value* | |
| SBP (mmHg) | Median (Q1, Q3) | 106 (101, 111) | 107.3 (99.5, 114.5) | 0.31 | 110.0 (102.5, 116.0) | 110.6 (105.0, 119.3) | 0.051 | 112.7 (104.5, 120.7) | 114.0 (108.0, 121.5) | 0.031 | 117.3 (109.7, 125.5) | 115 (105, 121) | 0.15 |
| SBP index | Median (Q1, Q3) | 0.93 (0.88, 0.96) | 0.93 (0.87, 0.98) | 0.91 | 0.91 (0.85, 0.96) | 0.90 (0.86, 0.97) | 0.50 | 0.88 (0.83, 0.94) | 0.88 (0.84, 0.94) | 0.38 | 0.89 (0.84, 0.94) | 0.88 (0.80, 0.93) | 0.24 |
| DBP (mmHg) | Median (Q1, Q3) | 62.3 (57.7, 66.3) | 63.3 (58.5, 67.0) | 0.34 | 63.3 (59.0, 67.0) | 65.0 (61.5, 68.7) | 0.004 | 66 (61, 71) | 66.7 (62.3, 71.0) | 0.16 | 69 (65, 75) | 67.3 (61.7, 73.3) | 0.26 |
| DBP index | Median (Q1, Q3) | 0.81 (0.76, 0.88) | 0.83 (0.77, 0.88) | 0.44 | 0.80 (0.76, 0.85) | 0.83 (0.78, 0.88) | 0.006 | 0.81 (0.75, 0.87) | 0.82 (0.76, 0.87) | 0.52 | 0.83 (0.78, 0.91) | 0.81 (0.73, 0.90) | 0.23 |
| Mean arterial pressure | Median (Q1, Q3) | 76.9 (72.7, 81.4) | 78.0 (73.7, 81.9) | 0.31 | 78.7 (74.2, 82.9) | 80.7 (76.2, 85.8) | 0.004 | 81.6 (76.3, 86.2) | 83.0 (78.3, 87.0) | 0.045 | 85.3 (80.8, 90.2) | 83.1 (76.1, 89.0) | 0.12 |
| Pulse pressure | Median (Q1, Q3) | 43.8 (39.0, 49.3) | 44.0 (37.5, 50.5) | 0.88 | 46.0 (39.3, 52.5) | 45.7 (40.5, 53.0) | 0.86 | 46.3 (40.0, 53.5) | 47.3 (41.7, 55.0) | 0.11 | 47.7 (40.0, 53.5) | 47.5 (39.7, 52.0) | 0.65 |
Wilcoxon Test
To be included in an age group category, participants had to have at least one record in that age group during follow-up. If a participant had multiple records within an age group, only one record was randomly selected for inclusion in the table. YPHEU: youth perinatally HIV exposed but uninfected; YPHIV: youth with perinatally acquired HIV
Figure 2:
Proportion of youth with blood pressure in the elevated or hypertensive range
Table 3:
Estimated mean differences (95% CI) in BP index or prevalence ratios (95% CI) for abnormal BP in youth with PHIV compared to youth with PHEU
| SBP index | DBP index | Abnormal BP vs not | ||||
|---|---|---|---|---|---|---|
| Model | β (95% CI)1 | P-value | β (95% CI)1 | P-value | Prevalence ratio (95% CI)2 | P-value |
| Unadjusted model | −0.007 (−0.017, 0.003) | 0.16 | −0.007 (−0.017, 0.003) | 0.16 | 0.76 (0.61, 0.96) | 0.021 |
| Model adjusted for sex, race/ethnicity, and age category 3 | −0.006 (−0.016, 0.004) | 0.21 | −0.011 (−0.021, −0.001) | 0.038 | 0.78 (0.62, 0.97) | 0.028 |
| Model further adjusting for BMI Z-score | 0.004 (−0.005, 0.013) | 0.42 | −0.007 (−0.017, 0.003) | 0.19 | 0.94 (0.76, 1.17) | 0.58 |
Abnormal BP: blood pressure in the hypertensive range
Estimates (95% CI) from linear regression models specifying normal distribution.
Prevalence ratios (95% CI) from a modified Poisson model using GEE
Age categories: Childhood (7–10.9 years); Pre-adolescent (11–12.9 years); Adolescent (13–17.9 years); Young Adult (18+ years). Within each age group, a participant could contribute multiple BP measures.
YPHEU: youth perinatally HIV exposed but uninfected; YPHIV: youth with perinatally-acquired HIV
At baseline, 23% of PHIV had a nadir CD4 <200 cells/mm3, the overall percent time with viral suppression (<400 copies/mL) was 35%, and the mean time-averaged AUC viral load was 3.57 log10 copies/mL (standard deviation 0.7) (Table S3). 91% were on cART with 71% on a protease inhibitor. 7% were on no ART at a random timepoint. Among youth with PHIV, lipid values decreased sequentially from the youngest to oldest age category, while measures of glucose metabolism increased across the same categories as did percent of youth on cART (Table S5).
Table 4 shows the association of nadir CD4 (<200 vs. ≥200 cells/mm³), percent time HIV VL<400 copies/mL (per one percent increase), and time-averaged AUC VL (per one unit increase) with each BP outcome. No measure of HIV disease severity was associated with SBP or DBP after adjustment for age, sex, race, ethnicity, income, and ART categories. Data exploring BP as mmHg among youth 13 years of age and older are displayed in Table S6.
Table 4:
Association of HIV Disease burden with Abnormal Blood Pressure and Blood Pressure Index
| Unadjusted | Adjusted2 | ||||
|---|---|---|---|---|---|
| Outcome | Measure of HIV Disease Burden1 | Estimates or Prevalence Ratios1 (95% CI) | P-value | Estimates or Prevalence Ratios1 (95% CI) | P-value |
| SBP index | Nadir CD4 <200 vs ≥200 cells/mm³ | −0.014 (−0.027, −0.000) | 0.046 | −0.009 (−0.022, 0.003) | 0.16 |
| DBP index | Nadir CD4 <200 vs ≥200 cells/mm³ | 0.004 (−0.009, 0.018) | 0.53 | −0.007 (−0.020, 0.006) | 0.28 |
| BP in Stage 1/2range vs Normal range | Nadir CD4 <200 vs ≥200 cells/mm³ | 0.87 (0.65, 1.18) | 0.38 | 0.84 (0.60, 1.17) | 0.30 |
| SBP index | Percent time HIV VL<400 copies/mL | 0.0000 (−0.0002, 0.0003) | 0.71 | 0.0002 (−0.0000, 0.0004) | 0.084 |
| Time-averaged AUC VL | 0.0005 (−0.0070, 0.0081) | 0.89 | −0.0069 (−0.0140, 0.0002) | 0.056 | |
| DBP index | Percent time HIV VL<400 copies/mL | 0.0002 (−0.0001, 0.0004) | 0.16 | −0.0000 (−0.0002, 0.0002) | 0.97 |
| Time-averaged AUC VL | −0.0121 (−0.0208, −0.0033) | 0.007 | −0.0053 (−0.0139, 0.0033) | 0.23 | |
| BP in Stage 1/2 range vs Normal range | Percent time HIV VL<400 copies/mL | 1.00 (1.00, 1.01) | 0.47 | 1.00 (1.00, 1.01) | 0.86 |
| Time-averaged AUC VL | 0.93 (0.78, 1.10) | 0.41 | 0.98 (0.82, 1.17) | 0.83 | |
Abnormal BP: blood pressure in the hypertensive range.
Estimates (95% CI) from linear regression models specifying normal distribution; prevalence ratios (95% CI) from modified Poisson regression models.
Adjusted for age (years), sex, race (White vs non-White), ethnicity (Hispanic or Latino vs not), income (≤$10,000 vs. >$10,000), and antiretroviral (ARV) use (combined ART, no ARV, vs. non-suppressive ART).
AUC: area under the curve; DBP: diastolic blood pressure; SBP: systolic blood pressure; VL = viral load; BP index = mean BP/age-sex-height specific 95th percentile BP; BP index ≥1 indicates a BP ≥95th percentile.
Discussion
In this United States cohort of youth 7–22 years of age born to mothers living with HIV, youth with PHIV had no clinically significant difference in BP and were no more likely to have abnormal BP at study visits than youth with PHEU. Further, among the children and adolescents with PHIV, no measure of HIV disease severity was associated with systolic or diastolic BP. While youth with PHIV had a more favorable CVD risk profile when considering their degree of adiposity, they had a significant HIV disease burden as determined by CD4 nadir and VL and a large proportion had co-morbid kidney disease, characteristics typically associated with greater CVD risk.
These results were surprising, particularly when considering that young US adults 18–24 years of age living with HIV have a more than 6.5 times greater risk for CV events such as myocardial infarction and ischemic heart disease than their non-infected counterparts.3 With hypertension the leading cause of CV events worldwide, we expected to see a greater BP burden among youth infected with HIV to potentially explain this greater risk for CV events. In fact, earlier work in the AMP cohort demonstrated that almost 50% of adolescents with PHIV had a coronary artery PDAY risk score ≥1, a score that includes presence of hypertension and indicates increased CVD risk.11 In addition to hypertension, the PDAY score is calculated as a weighted combination of traditional and modifiable risk factors associated with increased CV morbidity and mortality, such as dyslipidemia, cigarette smoking, obesity, and hyperglycemia. Despite this, in our study, we found decreasing, not increasing, levels of cholesterol from childhood to young adulthood in youth with PHIV and we also found lower levels of adiposity – lower weight, BMI, waist circumference and prevalence of overweight/obesity – in youth with PHIV than those with PHEU. However, we found increasing levels of fasting glucose and insulin among youth PHIV in that same timeframe, along with HOMA-IR levels that could be considered indicative of early insulin resistance starting in preadolescence.
These findings contrast with the CVD risk profile of adults living with HIV, which may explain why HIV is considered a moderate risk condition for accelerated atherosclerosis before 30 years of age and why it is associated with increased risk for CV events in adults. Adults living with HIV have a 2-fold greater prevalence of CVD risk factors – such as hypertension and dyslipidemia - than adults in the general population.20 Data from adults living with HIV suggest that large arteries are impacted, with elevated pulse pressure observed, a finding that can predict aortic remodeling and future hypertension. We did not find a significant difference in pulse pressure between PHIV and PHEU, nor did we find a clinically significant difference in mean arterial pressure between groups. While gaps remain regarding the etiology for this increased risk in CVD and related risk factors, metabolic complications related to long-term use of ART and adverse lifestyle behaviors have been thought to contribute.21 Notably, while the overall prevalence of cART use was high (>90%) in our population, we did observe that the percent of children taking ART decreased sequentially with increasing age category, along with mean total cholesterol, LDL cholesterol and HDL cholesterol. This observational finding should be explored further in future studies. Another potential explanation for the discrepancy between our findings and that described in the adult literature is that the adverse CV effects of ART may take a longer period to manifest than the period our participants were observed.
It is also possible that these more effective antiretroviral therapies have allowed adults with HIV to live longer than in decades past. A consequence of greater life expectancy enjoyed by many adults living with HIV is that contemporary studies in this population are now confounded by the sequelae of decades of adverse lifestyle habits such as tobacco smoking, unhealthy diets, and sedentary behavior, similar to studies in the general population.13,22 Therapeutic lifestyle changes in adults living with HIV appear to improve BP, suggesting that traditional risk factor prevention and treatment may aid efforts to reduce CVD risk in this group.23 Our results revealed an attenuation of association between HIV and BP after inclusion of BMI to our multivariable model. This suggests that, in youth with perinatal HIV infection, HIV may influence BP via its influence on nutritional status. While this repeated cross-sectional analysis of longitudinal data cannot infer causality, it does suggest that interventions to improve nutrition status in these youth may lead to optimized BP. It also lends further support to the importance of standard primary preventive strategies to promote cardiovascular health in those living with HIV in the United States. In Sub-Saharan Africa, the CVD risk profile of adults living with HIV are quite different than in the US, with obesity, diabetes, and dysglycemia emerging but not yet predominant concerns. This disparate distribution of CVD risk factors reflects differences in demographics, lifestyle, and healthcare access and suggests ideal treatment approaches should vary by region.24–26
Another pathway by which HIV might lead to increased BP and CVD risk is via the development of chronic kidney disease (CKD). CKD confers substantial CVD risk in both children and adults. In fact, sudden cardiac death occurs in 40% of all youth with end stage kidney disease and is the leading cause of mortality in this population.27 Early into the AIDS epidemic, kidney disease developed in youth with PHIV as early as 2–3 years of age.28 In the absence of ART, 40% of children with PHIV developed CKD, with 10–15% developing HIV associated nephropathy.29,30 While the majority of youth with PHIV in this cohort were on ART, they still demonstrated a significant and increasing prevalence of CKD (6% prevalence in childhood, 15% prevalence in young adulthood) that surpassed the prevalence of CKD in the general adolescent population, which is estimated as <1%.31 The proportion of youth with PHIV and BP in the elevated or hypertensive range also increased over the same timeframe, from 25% in childhood to almost 45% in young adulthood. While these individuals didn’t have hypertension per se, but rather an abnormal BP based on mean BP at one visit, the trend aligns with the increase in CKD prevalence and the proportion is in line with known prevalence of hypertension in children with CKD (25–50%).32 Greater exploration into the potential association of CKD with BP and hypertension in this cohort is needed.
Another unexpected finding in this study was the lack of an independent association between HIV infection and HIV disease severity with BP. A cascade of biological events occurs after infection with HIV that leads to a proinflammatory and hypercoagulable state. A combination of direct viral effects on the vasculature and myocardium, persistent immune activation from viremia and co-infections, exposure to combined antiretroviral therapy, and typical lifestyle factors resulting in metabolic dysfunction all contribute to CVD in those infected with HIV.33,34 ART, particularly protease inhibitors, while extremely effective at viral suppression can cause hypertension and more than double the risk for CHD in those with HIV.3 Almost all (90%) of the youth with PHIV in this study were on ART, with the majority (71%) taking a protease inhibitor. Despite this, there was no increase in BP or prevalence of abnormal BP during study visits among those with PHIV after adjusting for known comorbidities, nor was BP increased with measures of increased HIV severity.
Our study has several limitations. This was a marginal analysis of repeated measures exploring associations of exposure and outcomes in each age group and therefore we cannot infer causality. While the BPs were obtained according to a standardized protocol, they were all automated and from one visit, with visits generally one year apart. Therefore, we can only comment on abnormal BP and not the prevalence of hypertension, which would require replicate auscultatory BP measurements at multiple visits. We also used one randomly selected visit for blood pressure data for each participant in each age category in our descriptive analyses, and approximately half of those visits had 3 measurements and the other half had two measurements included in the visit average; it is possible that this approach may have under- or over-estimated the BP index and burden of abnormal BP. Further, we did not assess for other markers of CVD such as arterial stiffness and endothelial dysfunction which can precede the development of overt hypertension. Despite these limitations, our study has notable strengths. We present data from a large longitudinal cohort of youth with perinatal HIV infection with repeated measures across childhood into adulthood. This diverse, national sample of youth had robust measures of CVD risk factors collected allowing us to explore the independent association of HIV infection with BP in youth.
Perspectives:
In this cohort of US youth, those living with perinatal HIV infection did not have higher BP or greater prevalence of abnormal BP at study visits than youth who were not infected with HIV despite being exposed perinatally to the virus. Further, while there were small differences in BP indices by HIV disease severity, these were not clinically meaningful and did not persist after adjusting for traditional and non-traditional CVD risk factors. These findings warrant further exploration, particularly given the different CVD risk profile in this group compared to the general population regarding prevalence of obesity and kidney disease. Specifically, future studies to understand whether other HIV-related factors are associated with BP or hypertension in youth with PHIV, or if traditional CVD risk factors confer greater CVD risk than HIV infection alone could help target treatment efforts.33 Long-term follow up of youth with PHIV is also key to determine the prevalence of hypertension over time, and studies in regions where CVD risk profiles differ may be key to understanding the pathophysiology of increased risk among individuals living with HIV.
Supplementary Material
Novelty and Relevance:
What Is New?
Unlike adults, youth living with HIV do not have higher blood pressure or greater odds of having blood pressure in the hypertensive range than their uninfected counterparts, nor does HIV disease severity appear to independently contribute to blood pressure levels.
What Is Relevant?
This study suggests an important role of traditional risk factors for cardiovascular disease such as adiposity and related co-morbidities such as insulin resistance on youth with and without HIV infection, lending insight into potential mechanisms of cardiovascular disease in adults living with HIV.
Clinical/Pathophysiological Implications?
With HIV-infected individuals living longer, determining how exposures unique to those living with the disease contributes to their cardiovascular disease risk is essential for preventive efforts. Studying youth, who are free from decades of exposure to traditional cardiovascular disease risk factors, offers a window into potential mechanisms of increased risk in this high-risk group.
Acknowledgments:
We thank the participants and families for their participation in PHACS, and the individuals and institutions involved in the conduct of the PHACS Adolescent Master Protocol (AMP) study. A complete list of the institutions, clinical site investigators and staff which participated in AMP can be found at: https://phacsstudy.org/publications/amp-up-series-acknowledgements/
Sources of Funding:
The Pediatric HIV/AIDS Cohort Study (PHACS) network was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), Office of The Director, National Institutes of Health (OD), National Institute of Dental & Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), and the National Heart, Lung, and Blood Institute (NHLBI) through grants to the Harvard T.H. Chan School of Public Health (P01HD103133, and HD052102) and with Tulane University School of Medicine (HD052104).
Non-standard Abbreviations and Acronyms:
- AMP
Adolescent Master Protocol
- ART
antiretroviral therapy
- PDAY
Pathobiological Determinants of Atherosclerosis in Youth
- PHACS
Pediatric HIV/AIDS Cohort Study
- PHEU
youth with perinatal HIV exposure but uninfected
- PHIV
youth with perinatally acquired HIV
- VL
viral load
Footnotes
Disclosures: None
Note: The conclusions and opinions expressed in this article are those of the authors and do not necessarily reflect those of the National Institutes of Health or U.S. Department of Health and Human Services.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data and materials have been made publicly available at the NICHD Data and Specimen Hub (DASH) and can be accessed at https://dash.nichd.nih.gov/study/17510.