Abstract
Background:
Few studies have evaluated physical activity patterns or their association with vascular inflammation among youth living with perinatally-acquired HIV (YPHIV).
Methods:
We assessed YPHIV and youth perinatally HIV-exposed but uninfected (YPHEU) in the PHACS Adolescent Master Protocol with at least one Block physical activity questionnaire (PAQ) completed between ages 7-19 years. Physical activity metrics were: 1) daily total energy expenditure (TEE); 2) physical activity duration (PAD) defined as the minutes of daily moderate and vigorous activity. In a subgroup, we measured serum biomarkers of coagulation (fibrinogen, P-selectin) and endothelial dysfunction (sICAM, sVCAM, E-selectin) obtained within 3 months of a single PAQ. Repeated measures linear regression models were used to compare the trajectories of log-transformed TEE and PAD by HIV status, adjusting for confounders. Spearman correlations were calculated to assess the relationship of TEE and PAD with vascular biomarkers.
Results:
596 youth (387 YPHIV, 209 YPHEU) completed 1552 PAQs (median PAQs completed=3). Median age at enrollment (Q1, Q3) was 11 (9, 13) years. TEE and PAD increased with age in both YPHIV and YPHEU. However, even after adjusting for confounders, YPHIV had significantly less increase per year than YPHEU for TEE (5.7% [95% Confidence Interval (CI): −9.9%, −1.4%, p=0.010] less) and PAD (5.2% [95%CI: −9.2%, −1.1%, p=0.016] less). Among 302 youth with biomarker measures (187 YPHIV, 114 YPHEU), we observed little correlation with TEE or PAD.
Conclusions:
Both groups had increases in physical activity levels as they aged, but YPHIV had smaller increases throughout adolescence compared to YPHEU, which may impact long-term health.
Keywords: exercise, pediatric, cardiovascular, inflammation
INTRODUCTION
Physical activity has gained renewed attention as a strategy to minimize the risk of cardiovascular disease (CVD) in adults living with HIV. Several studies have evaluated and reviewed the effects of exercise on health in adults living with HIV and have shown benefits in body composition, lipid profile, inflammatory biomarkers and vascular disease[1–3],. Low physical activity levels decrease total life expectancy in uninfected adults[4], and as such physical inactivity is comparable to other modifiable risk factors such as smoking and obesity. There are limited data describing physical activity patterns and barriers to exercise in youth living with perinatally acquired HIV (YPHIV). Several small cross-sectional studies suggest that YPHIV have lower physical activity duration and intensity compared to uninfected youth [5–8]. However, few studies have evaluated longitudinal changes in physical activity in the pediatric HIV population or its relationship with vascular inflammation[5, 6, 9]. We evaluated participants enrolled in a Pediatric HIV/AIDS Cohort Study (PHACS) network study for the purposes of (1) assessing physical activity patterns across age in YPHIV compared to youth perinatally HIV-exposed but uninfected (YPHEU) and (2) investigating the association between physical activity measures and biomarkers of vascular dysfunction. These data will be useful in obtaining information about general activity levels and patterns in YPHIV and may inform development of targeted strategies designed to promote exercise as a non-pharmacological tool to decrease morbidity related to CVD.
METHODS
The Adolescent Master Protocol (AMP) is a prospective cohort study conducted by the PHACS network at 14 sites across the United States (US), including Puerto Rico[10]. YPHEU with similar ages and sociodemographic backgrounds were also enrolled at the same research sites. For this analysis, we considered physical activity assessments completed when participants were between 7-19 years of age. Our analysis was limited to the first 5 physical activity assessments conducted in YPHIV and the first 3 assessments in YPHEU, to prevent undue influence from participants who may have had more assessments than expected. We excluded participants with congenital cardiovascular malformation, pregnancy, HIV encephalopathy, cerebral palsy, or cognitive limitations, and those taking cholesterol-lowering agents, anti-depressants, anti-psychotics, or treatment for diabetes. Biomarkers of coagulation and endothelial dysfunction were collected at a single time point. Participants who completed a physical activity questionnaire within 3 months of biomarker measurement were included for the biomarker analysis (Supplemental Figure 1).
Physical Activity measures
Physical Activity attributes were collected using the Block Physical Activity Questionnaire (PAQ)[11], relying on self-report or caregiver report. PAQ is designed for school-age children (ages 8-17). This questionnaire asks information over the last seven days on frequency (times/week) and duration (minutes or hours/day) of nine activities ranging from vigorous physical activity to more sedentary activities such as watching television and playing video games. Specifically, it asks about walking, inside chores, outside chores, part-time work, sports games with friends, aerobic/weight training, physical education (frequency only), and television/computer use (hours per day only). The possible responses on the questionnaire for frequency and duration of each activity are ordinal categories. Outcomes obtained from PAQ were: 1) daily total energy expenditure (TEE) in kilocalories (kcal) per day; 2) recreational activity excluding chores, part-time work and walking; 3) physical activity duration (PAD) defined as the minutes of daily moderate and vigorous activity. Sufficient daily physical activity was defined as PAD ≥ 60 minutes/day, as recommended by the US Department of Health and Human Services[12].
Biomarkers of endothelial dysfunction
Markers of coagulation (fibrinogen and P-selectin) and endothelial dysfunction [soluble intracellular cell adhesion molecule-1 (sICAM), soluble vascular cell adhesion molecule-1 (sVCAM), and E-selectin] were measured as previously described[13]. Fibrinogen was measured by nephelometry (Dade-Behring, Deerfield, IL); sICAM, sVCAM, P-selectin and E-selectin were measured by ELISA (R&D Systems, Minneapolis, MN).
Potential Confounders
Variables considered as potential confounders of the relationship between HIV status and physical activity outcomes included: sex assigned at birth, race (white or other vs Black/African American), ethnicity (Hispanic/Latino vs non-Hispanic/Latino), geographic region (Midwest, South, West, or Puerto Rico vs Northeast), caregiver education (high school vs less than high school), annual household income (≤ vs > $20,000) as reported at study entry, season (spring, summer, or fall vs winter), and body mass index (BMI) z- score at or nearest to each PAQ.
Statistical Methods
Energy expenditure variables were natural log-transformed for analyses. We evaluated each continuous physical activity outcome across age by fitting a repeated measures linear regression model using generalized estimating equations (GEEs) with the robust variance to account for correlation in measurements within participants, adjusting for confounders. From each model, we calculated the predicted mean physical activity outcome for each one-year increase in age and evaluated effect modification of age by HIV status. Among YPHIV only, we fit similar models to assess the association of nadir CD4% and peak viral load at entry, as well as current viral load and CD4 at each PAQ assessment, with longitudinal patterns of physical activity. For the binary outcome of sufficient daily physical activity, we fit a modified Poisson model using GEEs to estimate prevalence ratios (PRs). We calculated Spearman correlations to assess the relationship between each physical activity measure and each biomarker of coagulation and endothelial dysfunction overall and by HIV status. In YPHIV, the correlations were also computed separately by viral load at the time of the PAQ (≤ or >400 copies/mL).
RESULTS
Baseline characteristics
For the longitudinal physical activity analysis, we included 596 participants (n=387 YPHIV, n=209 YPHEU) with 1552 completed PAQs. The YPHIV and YPHEU cohorts did not differ substantially with respect to sex, race, or caregiver education. At the time of their first completed PAQ, 78% of YPHIV participants had a CD4 count > 500 cells/mm3 and 67% had a suppressed viral load (≤ 400 copies/mL). The most common regimen at the time of the first PAQ was a protease inhibitor (PI)-based regimen (Table 1).
Table 1.
Characteristics of YPHIV and YPHEU Participants in PHACS AMP Study
| Perinatal HIV status | ||||
|---|---|---|---|---|
| Participants | YPHIV (N=387) | YPHEU (N=209) | P-Value* | |
| Age at enrollment (years) | Median (Q1, Q3) | 12 (9, 14) | 10 (8, 11) | <0.01 |
| Biological sex at birth | Male | 183 (47%) | 110 (53%) | 0.21 |
| Female | 204 (53%) | 99 (47%) | ||
| Race | Black or African American | 274 (71%) | 136 (65%) | 0.12 |
| White | 90 (23%) | 65(31%) | ||
| Other | 5 (1%) | 4 (2%) | ||
| Hispanic/Latino Ethnicity | 95 (25%) | 70 (33%) | 0.01 | |
| Geographic region of enrolling clinical site | Northeast | 139 (36%) | 56 (27%) | <0.01 |
| Midwest | 60 (16%) | 21 (10%) | ||
| South | 132 (34%) | 76 (36%) | ||
| West | 35 (9%) | 33 (16%) | ||
| Puerto Rico | 21 (5%) | 23 (11%) | ||
| Caregiver education | Less than High School | 99 (26%) | 64 (31%) | 0.21 |
| At least High School | 284 (73%) | 145 (69%) | ||
| Annual household income ≤ $20,000 | 165 (43%) | 132 (63%) | <0.01 | |
| BMI z-score at first PAQ | Median (Q1, Q3) | 0.25 (−0.34, 1.23) | 0.82 (−0.14, 1.88) | <0.01 |
| Total energy expenditure (kcal/day) at first PAQ | Median (Q1, Q3) | 257 (114, 550) | 242 (130, 546) | 0.92 |
| Sum of moderate to vigorous activity (min/day) at first PAQ | Median (Q1, Q3) | 68.4 (26.7, 132.6) | 68.4 (32.1, 133.8) | 0.83 |
| Sufficient daily physical activity | 210 (54%) | 115 (55%) | 0.859 | |
| Nadir CD4 percent ≥15% at study entry | 276 (71%) | |||
| Peak viral load at study entry | <10K copies/mL | 23 (6%) | ||
| 10K-75K copies/mL | 88 (23%) | |||
| >75K copies/mL | 275 (71%) | |||
| CD4 cell count (cells/mm3) at first PAQ | < 200 | 10 (3%) | ||
| 200-350 | 26 (7%) | |||
| 350-500 | 48 (12%) | |||
| > 500 | 302 (78%) | |||
| Viral load ≤ 400 copies/mL at first PAQ | 258 (67%) | |||
| Type of ARV regimen at first PAQ | NNRTI-based cART | 59 (15%) | ||
| PI-based cART | 228 (59%) | |||
| INSTI-based cART | 4 (1%) | |||
| More than 2 classes | 44 (11%) | |||
| Not on cART | 45 (12%) | |||
| Type of ARV regimen at last PAQ | NNRTI-based cART | 76 (20%) | ||
| PI-based cART | 157 (41%) | |||
| INSTI-based cART | 23 (6%) | |||
| More than 2 classes | 71 (18%) | |||
| Not on cART | 47 (12%) | |||
| Percent of lifetime on cART at first PAQ | Median (Q1, Q3) | 69.0 (51.0, 86.0) | ||
| PAQs | YPHIV (N=1116) | YPHEU (N=436) | ||
| Season when PAQ administered | Winter | 156 (14%) | 111 (25%) | |
| Spring | 303 (27%) | 118 (27%) | ||
| Summer | 441 (40%) | 152 (35%) | ||
| Fall | 216 (19%) | 55 (13%) | ||
P-value by Wilcoxon rank sum test for continuous measures, Chi-Square Test for categorical measures and Fisher’s Exact Test for binary measures.
ARV: antiretroviral, BMI: body mass index, cART: combination antiretroviral therapy; INSTI: integrase strand transfer inhibitor, NNRTI: non-nucleotide reverse transcriptase inhibitor, PAQ: physical activity questionnaire, PI: protease inhibitor, Q1,Q3: first and third interquartile range
Physical Activity Measures in YPHIV vs YPHEU
TEE:
In adjusted models, log TEE increased with age in both YPHIV and YPHEU. On average, TEE increased by about 5.7% less per year in YPHIV vs. YPHEU (95% confidence interval (CI): −9.9%, −1.4%, p=0.010, Figure 1a, Supplemental Table 1).
Figure 1:
Predicted means from repeated measures linear regression adjusted for sex, race and ethnicity, caregiver’s education, annual household income and geographic region as reported at enrolment, as well as body mass index z-score obtained within 6 months of each physical activity questionnaire and season at the time of each physical activity questionnaire.
PAD:
Overall, log minutes of moderate to vigorous activity increased with age; however, the estimated difference per year was approximately 5.2% less for YPHIV compared to YPHEU in adjusted models (95% CI: −9.2%, −1.1% p=0.016, Figure 1b, Supplemental Table 1).
Sufficient physical activity:
There was no overall difference in the proportion meeting sufficient physical activity throughout adolescence by HIV status, nor did the yearly increase in sufficient physical activity differ between YPHIV and YPHEU in adjusted models.
Recreational activity:
Recreational caloric expenditure increased with age, however, the increase per year of age was about 14% less in YPHIV than in YPHEU (95% CI −21% – −5.5%, p=0.002).
In multivariable models for each energy expenditure measure, female sex was associated with lower physical activity measures and geographic region differences were also noted (Supplemental Table 1). BMI z-score, race, ethnicity, household income, caregiver education and season were not associated with physical activity outcomes.
Physical activity measures and associations with HIV variables
Among YPHIV, in adjusted models, nadir CD4% ≤15% at entry was associated with lower increase in TEE as youth aged (percent difference: −7.4%, 95% CI −14%, −0.6%, p=0.038). CD4 count and viral load measured at each PAQ and peak viral load at entry were not associated with physical activity.
Physical activity measures and biomarkers
In the sub-group cross-sectional analysis of 301 participants, vascular biomarkers showed no correlations with any of the physical activity measures, overall or within any sub-groups (YPHEU, YPHIV, YPHIV with viral load ≤ or >400 copies/mL), with correlations ranging from −0.11 to 0.17.
DISCUSSION
In this large US cohort, we observed increases in physical activity levels through adolescence in both YPHIV and YPHEU, but these increases were smaller in YPHIV compared to YPHEU. By late adolescence, YPHIV had significantly lower physical activity levels than YPHEU.
Systematic reviews of longitudinal studies have found a decline in physical activity levels during adolescence in youth without HIV [14, 15]. There are, however, limited data describing physical activity patterns in YPHIV. Cross-sectional studies suggest that YPHIV have lower physical activity duration and intensity compared to youth without HIV [5–8].
In our analysis, YPHIV showed a more blunted increase in physical activity measures as they aged through adolescence compared to YPHEU. Although the difference over time between the groups is small, it may have clinical significance as children who are active but become less active as they age have a higher risk of becoming obese in young adulthood when compared to individuals who are consistently active throughout childhood and adolescence[16]. Among YPHIV, those with less severe history of HIV disease (e.g. higher nadir CD4%) had steeper increases in physical activity duration and intensity, suggesting not surprisingly, that better childhood HIV health status and potentially overall health status, contributes to improved physical activity even later in adolescence. YPHIV have a dampened increase in vigorous physical activity as they age compared with YPHEU, but this did not translate to a difference in the proportion meeting sufficient physical activity. This lack of difference between groups may be because HIV status does not influence an underlying phenomenon to fall short of meeting national guidelines when one already has little activity. The design of our study did not allow us to investigate the reasons why lower levels of activities have been found in the context of HIV. We can hypothesize these are likely multifactorial and may include: fewer opportunities for physical activity that require space and are costly (gyms, membership, clubs), social support from parents and friends, social isolation and stigma, physical activities included as part of their activities of daily living (public transportation, occupation-related)[17] which may not be captured adequately by self-reporting are all possible explanations. Data from studies in adults living with HIV suggest that some of the barriers to engaging in physical activity participation that could be extrapolated to YPHIV include bodily pain, and depression. Potential facilitators include higher cardiorespiratory fitness, and motivation[18], as well as sustained self-efficacy, the belief in one’s capability to participate in physical activity and to choose activity over existing barriers[19].
Interventions designed to increase aerobic exercise in adults living with HIV have proven to be safe and beneficial[20], however interventions using self-determination, the internet or gaming platforms and school-based programs to promote self-efficacy and social support may be more appropriate and sustainable for YPHIV[21–23].
Physical activity achieves its benefits through several hypothesized mechanisms [24, 25]. Pathways that are of particular interest in HIV and CVD include physical activity’s anti-inflammatory effects[25]. In this analysis, we did not identify a relationship between physical activity and plasma biomarkers of coagulation or endothelial dysfunction. These findings could be secondary to our cross-sectional study design, or the fact that the relationship between physical activity and cardiometabolic benefits at a young age may be independent of vascular inflammation and different than what has been observed in adults. More research is needed in this area.
Our study has several strengths including the detailed analysis of the physical activity patterns in YPHIV. This study is limited by the self-report nature of the physical activity, subject to recall bias. However, questionnaires such as ours have wide applicability, low participant burden, and the capability to broadly assess different physical activity intensities. The design of the AMP study, which only enrolls YPHIV and YPHEU, precluded inclusion of a comparator group of youth who were HIV unexposed uninfected. However, our large sample size among YPHIV with a comparison group of YPHEU provide one of the first published data on longitudinal changes in physical activity in this important population. Lastly, we were not able to directly measure subclinical vascular changes in our study.
Conclusion
YPHIV have lower increases in physical activity as they age compared to YPHEU, and this results in lower activity levels by their late teens. Early interventions to attenuate this decline could be beneficial for long-term cardiometabolic health. Further research is warranted to understand this change and how other social determinants can modify the biological tendency of activity to decline with age in this population.
Supplementary Material
Acknowledgement:
We thank the participants and families for their participation in PHACS, and the individuals and institutions involved in the conduct of PHACS.
The following institutions, clinical site investigators and staff participated in conducting PHACS AMP and AMP Up in 2018, in alphabetical order: Ann & Robert H. Lurie Children’s Hospital of Chicago: Ellen Chadwick, Margaret Ann Sanders, Kathleen Malee, Yoonsun Pyun; Baylor College of Medicine: William Shearer, Mary Paul, Chivon McMullen-Jackson, Mandi Speer, Lynnette Harris; Bronx Lebanon Hospital Center: Murli Purswani, Mahboobullah Mirza Baig, Alma Villegas; Children’s Diagnostic & Treatment Center: Lisa Gaye-Robinson, Sandra Navarro, Patricia Garvie; Boston Children’s Hospital: Sandra K. Burchett, Michelle E. Anderson, Adam R. Cassidy; Jacobi Medical Center: Andrew Wiznia, Marlene Burey, Ray Shaw, Raphaelle Auguste; Rutgers - New Jersey Medical School: Arry Dieudonne, Linda Bettica, Juliette Johnson, Karen Surowiec; St. Christopher’s Hospital for Children: Janet S. Chen, Maria Garcia Bulkley, Taesha White, Mitzie Grant; St. Jude Children’s Research Hospital: Katherine Knapp, Kim Allison, Megan Wilkins, Jamie Russell-Bell; San Juan Hospital/Department of Pediatrics: Midnela Acevedo-Flores, Heida Rios, Vivian Olivera; Tulane University School of Medicine: Margarita Silio, Medea Gabriel, Patricia Sirois; University of California, San Diego: Stephen A. Spector, Megan Loughran, Veronica Figueroa, Sharon Nichols; University of Colorado Denver Health Sciences Center: Elizabeth McFarland, Carrie Chambers, Emily Barr, Mary Glidden; University of Miami: Gwendolyn Scott, Grace Alvarez, Juan Caffroni, Anai Cuadra
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.
Funding: The study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development with co-funding from the National Institute on Drug Abuse; the National Institute of Allergy and Infectious Diseases; the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke; the National Institute on Deafness and Other Communication Disorders; the National Institute of Dental and Craniofacial Research; the National Cancer Institute; the National Institute on Alcohol Abuse and Alcoholism; the Office of AIDS Research; and the National Heart, Lung, and Blood Institute through cooperative agreements with the Harvard T.H. Chan School of Public Health (HD052102) (Principal Investigator: George R Seage III; Program Director: Liz Salomon) and the Tulane University School of Medicine (HD052104) (Principal Investigator: Russell Van Dyke; Co-Principal Investigator: Ellen Chadwick; Project Director: Patrick Davis). Data management services were provided by Frontier Science and Technology Research Foundation (PI: Suzanne Siminski), and regulatory services and logistical support were provided by Westat, Inc (PI: Julie Davidson).
Footnotes
Meetings: Parts of the data were presented at AIDS 2020: virtual in July 2020.
Conflicts of interest:
There are no conflicts of interest.
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