Abstract
Background.
The efficacy of integrase strand transfer inhibitors (INSTIs) among adults with HIV is well-established, though adverse effects, particularly weight gain, are common. Comparable data for treatment-experienced adolescents and young adults with perinatally-acquired HIV (AYAPHIV) are limited.
Methods.
AYAPHIV in the US-based Pediatric HIV/AIDS Cohort Study who switched to bictegravir (BIC), dolutegravir (DTG), elvitegravir (EVG), or raltegravir (RAL) from any prior regimen were eligible. Using mixed-effects models, viral load, CD4 count, weight, and body mass index were described through 2 years after switch for each INSTI.
Results.
Among 556 AYAPHIV, there were 167 switches to BIC, 282 to DTG, 189 to EVG, and 151 to RAL. Viral suppression (<200 copies/mL) at 1 and 2 years after switch was 74% and 69% for BIC, 62% and 60% for DTG, 76% and 68% for EVG, and 58% and 52% for RAL. Mean CD4 counts were above 500 cells/mm3 after switch through 2 years for all INSTIs. Average weight gain in the first year after switch to BIC, DTG, EVG, and RAL was 0.2, 2.5, 3.8, and −0.2 kilograms for females and 2.3, 4.8, 2.9, and 2.6 kilograms for males. Among previously underweight/healthy individuals, 13%, 18%, 36%, and 12% of females and 6%, 8%, 12%, and 11% of males switching to BIC, DTG, EVG, and RAL were overweight/obese by 2 years after switch.
Conclusions.
Individual INSTI-based regimens among treatment-experienced AYAPHIV had moderate effectiveness with respect to viral suppression. Continued average weight gain across INSTIs raises concerns about long-term cardiometabolic sequalae.
Keywords: integrase strand transfer inhibitors, perinatal HIV, HIV viral load, CD4 count, weight gain
Among treatment-naive adults with HIV-1, integrase strand transfer inhibitor (INSTI)-based regimens including bictegravir (BIC), dolutegravir (DTG), elvitegravir (EVG), and raltegravir (RAL) have been shown to be effective, tolerable, have a high barrier to resistance, and have few drug interactions [1-6]. Studies of these regimens among antiretroviral treatment (ART)-experienced adults are heterogeneous between specific INSTIs but cumulatively suggest a benefit of switching to INSTIs, with higher effectiveness among virologically suppressed versus viremic adults at the time of switch [7-17]. While these studies help inform treatment for older adolescents and young adults with perinatally acquired HIV (AYAPHIV), they primarily include mature adults who acquired HIV later in life and none have reported outcomes separately for this unique population with HIV ageing into adulthood.
Although AYAPHIV share some similarities with ART-experienced adults, their life long exposure to HIV through gestation, early infancy, childhood, and puberty makes them distinct in ways that may impact response to different HIV treatment strategies [18]. Additionally, given the ART era in which they were born and the often-prolonged delays in approving new, more effective treatments in children, ART-experienced AYAPHIV often have complex resistance profiles, which may differ from ART-experienced adults because of archived resistance over a lifetime of exposure to ART and potential non-adherence [19, 20]. They also differ significantly from adults with regard to age-related differences in psychosocial stressors, and immunologic, metabolic, functional, and comorbidity profiles at the time of initiation of particular ART regimens [18]. Data are, therefore, needed that specifically evaluate effectiveness of INSTI-based regimens among treatment-experienced AYAPHIV.
Further, given reports about weight gain with INSTI use among adults [21-26], there is a need to evaluate weight changes specifically among AYAPHIV. There is mounting evidence of high rates of obesity-related early comorbidities including hypertension and insulin resistance in this population [27, 28]. Additionally, overweight and obesity in adolescence and young adulthood are often associated with depression, anxiety, social stigma, and other psychosocial consequences [29]. The few studies that evaluated weight gain among youth with HIV focused on younger children and adolescents initiating INSTIs, predominantly DTG [30-36]. One study focused on 66 older AYAPHIV in Italy and did not observe excessive weight gain with switching to an INSTI-based regimen [37].
We provide comprehensive data on HIV plasma viral load, CD4 count, weight, body mass index (BMI), and treatment discontinuation at 1 and 2 years after switch to BIC-, DTG-, EVG, and RAL-based regimens in a large, diverse cohort of AYAPHIV in the United States.
METHODS
Study Population
The Pediatric HIV/AIDS Cohort Study Adolescent Master Protocol (AMP) and AMP Up studies were designed to evaluate the long-term health effects of PHIV and ART among children, adolescents, and young adults. In March 2007, 15 sites in the US including Puerto Rico began enrolling children and adolescents between 7 and <16 years of age with PHIV and a comparison cohort exposed to but without HIV in AMP with follow-up ending in February 2020. In May 2014, 14 sites began enrolling young adults 18 years of age and older with and without PHIV into AMP Up, including those who aged up from AMP, with follow-up ending in March 2025. Both the AMP and AMP Up protocols were approved by the Institutional Review Board at each participating site and the Harvard T.H. Chan School of Public Health. Written informed consent was obtained from each participant or their authorized representative.
We evaluated participants with PHIV enrolled in the AMP and AMP Up studies through 1 October 2023, who had a recorded treatment switch to a regimen including BIC, DTG, EVG, and/or RAL.
Primary Exposures and Outcomes
ART history was abstracted from medical charts. Lifetime treatment history was used to identify the first recorded switch to BIC, DTG, EVG, and/or RAL for participants in AMP and treatment history from entry onwards was used to identify the first study-recorded switch to BIC, DTG, EVG, and/or RAL for participants in AMP Up who were not previously enrolled in AMP. Participants who switched from 1 INSTI to another (eg, RAL to DTG) were allowed to contribute to outcomes for each INSTI they received from the time of switch to either discontinuation or end of follow-up. Outcomes of interest included time to treatment discontinuation as well as viral suppression (<200 copies/mL), CD4 count (cells/mm3), and BMI (kg/m2) at the time of first switch to a particular INSTI and at 1 and 2 years after switch. BMI was categorized as underweight (<18.5 kg/m2), healthy weight (18.5 to <25.0 kg/m2), overweight (25.0 to <30.0 kg/m2), or obese (≥30.0 kg/m2). Additional outcomes included changes in weight (kg) and BMI from 1 year prior to first recorded switch to 1 year after switch to a particular INSTI.
Statistical Methods
Characteristics at time of first recorded switch to BIC, DTG, EVG, and/or RAL and at time of INSTI discontinuation (if observed) were summarized for all participants who had a recorded switch. Kaplan–Meier methods were used to estimate median time to discontinuation of each INSTI.
Given that age at regimen switch varied widely between INSTIs, all further analyses were restricted to participants who switched to an INSTI between 15 and 29 years of age to allow for comparability. Follow-up periods during which participants experienced a pregnancy in the year prior to switch to a particular INSTI or during follow-up after switch were also excluded. Piece-wise linear mixed effects models with random intercepts and slopes were used to estimate HIV plasma viral loads, CD4 counts, weight, and BMI during the year prior to switch and through 2 years after switch for each INSTI. Participants were required to have at least 2 outcome measurements with at least one after switch to a particular INSTI to be included. We a priori chose 3 times at which slopes were allowed to change (ie, knots): at 1 year before switch, at time of switch, and at 1 year of follow-up after switch to a particular INSTI. We applied a nested correlation structure to account for participants who contributed more than 1 longitudinal series of data points by having been prescribed different INSTIs throughout their study follow-up. The most flexible covariance structure that resulted in model convergence was chosen. Mixed effects models for weight and BMI were adjusted for age and CD4 count at time of switch to a particular INSTI using inverse probability weighting to further account for differences between INSTIs in age at time of switch and disease severity at time of switch (ie, potential for a return to health in the setting of more advanced HIV). Ninety-five percent percentile-based bootstrap confidence intervals (CIs) with a minimum of 1000 bootstrap samples were reported for estimated slopes. Analyses were conducted using SAS version 9.4.
RESULTS
Among 806 AYAPHIV enrolled in the AMP and AMP Up studies through 1 October 2023, 556 (69%) had at least 1 recorded treatment switch to an INSTI-based regimen (Table 1). Sixty-two percent of the study participants were female sex at birth; 61% self-reported as Black non-Hispanic, and 29% as Hispanic (regardless of race). There were 167 regimen switches to BIC, 282 to DTG, 189 to EVG, and 151 to RAL. As the newest INSTI, treatment switches to BIC occurred at older ages (median: 24 years) and were more likely to be from prior INSTI-based regimens. Median viral load at time of switch to RAL and median CD4 count at time of switch to EVG were higher than those at time of switch to the other INSTIs. A higher proportion of switches to DTG occurred at a CDC class C clinical stage (33%) compared to the other INSTIs (26–30%).
Table 1.
Characteristics at Time of INSTI-based Regimen Switch
| Alla (N = 556) |
Bictegravir (N = 167) |
Dolutegravir (N = 282) |
Elvitegravir (N = 189) |
Raltegravir (N = 151) |
|
|---|---|---|---|---|---|
| Age (y) | 20.7 (17.4, 24.6) | 24.0 (21.2, 26.3) | 21.9 (18.9, 26.1) | 21.2 (18.4, 24.1) | 17.4 (14.6, 21.6) |
| Female sex at birth | 347 (62%) | 112 (67%) | 195 (69%) | 117 (62%) | 91 (60%) |
| Self-reported race/ethnicity | |||||
| Black non-Hispanic | 339 (61%) | 108 (65%) | 163 (58%) | 136 (72%) | 86 (57%) |
| White non-Hispanic | 39 (7%) | 12 (7%) | 20 (7%) | 11 (6%) | 8 (5%) |
| Hispanic (regardless of race) | 159 (29%) | 43 (26%) | 86 (30%) | 40 (21%) | 53 (35%) |
| More than one race/other race | 15 (3%) | 4 (2%) | 10 (4%) | 2 (1%) | 3 (2%) |
| Missing | 4 (1%) | 0 (0%) | 3 (1%) | 0 (0%) | 1 (1%) |
| HIV viral load (log10 copies/mL)b | 2.22 (1.30, 3.76) | 1.88 (1.30, 3.37) | 2.05 (1.30, 3.71) | 1.62 (1.30, 3.19) | 3.43 (1.88, 4.29) |
| Missing | 15 (3%) | 10 (6%) | 8 (3%) | 6 (3%) | 5 (3%) |
| CD4 count (cells/mm3)b | 526 (283, 744) | 478 (303, 772) | 480 (217, 727) | 567 (302, 777) | 462 (268, 668) |
| Missing | 14 (3%) | 7 (4%) | 10 (4%) | 4 (2%) | 4 (3%) |
| CDC class C clinical stage | 157 (28%) | 43 (26%) | 94 (33%) | 50 (26%) | 46 (30%) |
| Prior ARV regimenc | |||||
| ART with INSTI/EI/FI | 12 (2%) | 79 (47%) | 53 (19%) | 31 (16%) | 16 (11%) |
| ART with PI | 165 (30%) | 17 (10%) | 57 (20%) | 46 (24%) | 57 (38%) |
| ART with NNRTI | 66 (12%) | 14 (8%) | 22 (8%) | 23 (12%) | 12 (8%) |
| Other ARV | 28 (5%) | 9 (5%) | 14 (5%) | 5 (3%) | 12 (8%) |
| No ARV | 63 (11%) | 24 (14%) | 27 (10%) | 30 (16%) | 20 (13%) |
| Missing | 222 (40%) | 24 (14%) | 109 (39%) | 54 (29%) | 34 (23%) |
| INSTI regimen included TAF | 194 (35%) | 167 (100%) | 69 (24%) | 115 (61%) | 4 (3%) |
Summary statistics presented as median (Q1, Q3) or N (%).
Abbreviations: ART, antiretroviral treatment; ARV, antiretroviral; BMI, body mass index; CDC, Centers for Disease Control and Prevention; EI, entry inhibitor; FI, fusion inhibitor; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; TAF, tenofovir alafenamide fumarate.
The “All” column represents the first time a participant was on any specific INSTI regimen, whereas the 4 specific INSTI columns represent the time that participants first initiated those specific INSTI regimens; therefore, the “All” column does not represent a total of the subsequent 4 columns.
Nearest measure up to +/−14 d for HIV viral load and +/−30 d for CD4, weight, and BMI. If no measure available within these windows, linear interpolation was performed using the closest available pre- and post-INSTI initiation measures.
Prior ARV regimen was generally unavailable for participants who enrolled in AMP Up already on an INSTI-based regimen.
Over follow-up, the median observed duration of time on the INSTI regimen was 2.4 years for BIC, 3.6 years for DTG, 3.2 years for EVG, and 3.1 years for RAL. We observed 26/167 (16%) discontinuations of BIC, 127/282 (45%) discontinuations of DTG, 99/189 (52%) discontinuations of EVG, and 109/151 (72%) discontinuations of RAL. Characteristics at the time of INSTI discontinuation are presented in Table 2. Median years to discontinuation were 6.1, 4.6, and 4.0 years for DTG, EVG, and RAL, respectively (Figure 1); this could not be calculated for BIC due to limited follow-up on this most recently approved INSTI.
Table 2.
Characteristics at Time of INSTI-based Regimen Discontinuation
| Did Not Discontinuea (N = 428) |
Bictegravir (N = 26) |
Dolutegravir (N = 127) |
Elvitegravir (N = 99) |
Raltegravir (N = 109) |
|
|---|---|---|---|---|---|
| Age (y) | 26.5 (24.0, 30.5) | 26.0 (23.5, 30.7) | 23.9 (20.7, 27.6) | 22.5 (20.9, 25.7) | 20.1 (16.6, 24.2) |
| Female sex at birth | 264 (62%) | 21 (81%) | 95 (75%) | 70 (71%) | 65 (60%) |
| Pregnancy within prior 40 wks | 1 (0%) | 2 (10%) | 5 (5%) | 11 (16%) | 7 (11%) |
| Pregnancy within prior 13 wks | 0 (0%) | 0 (0%) | 4 (4%) | 7 (10%) | 1 (2%) |
| Self-reported race/ethnicity | |||||
| Black non-Hispanic | 254 (59%) | 17 (65%) | 79 (62%) | 73 (74%) | 70 (64%) |
| White non-Hispanic | 31 (7%) | 1 (4%) | 9 (7%) | 5 (5%) | 5 (5%) |
| Hispanic (regardless of race) | 128 (30%) | 7 (27%) | 34 (27%) | 21 (21%) | 32 (29%) |
| More than one race/other race | 13 (3) | 1 (4%) | 3 (2%) | 0 (0%) | 2 (2%) |
| Missing | 2 (0%) | 0 (0%) | 2 (2%) | 0 (0%) | 0 (0%) |
| HIV viral load (log10 copies/mL)b | 1.5 (1.3, 2.4) | 2.7 (1.3, 3.7) | 2.2 (1.3, 3.8) | 2.5 (1.5, 3.9) | 2.6 (1.6, 4.1) |
| Missing | 246 (57%) | 3 (12%) | 6 (5%) | 5 (5%) | 4 (4%) |
| CD4 count (cells/mm3)b | 558 (364, 746) | 464 (242, 677) | 427 (229, 646) | 447 (255, 725) | 455 (164, 686) |
| Missing | 247 (58%) | 1 (4%) | 7 (6%) | 4 (4%) | 4 (4%) |
| Weight (kg)b | 66.1 (57.6, 80.3) | 64.9 (54.1, 75.4) | 61.2 (54.9, 73.3) | 67.3 (56.3, 78.3) | 59.1 (51.7, 78.2) |
| Missing | 193 (45%) | 4 (15%) | 20 (16%) | 10 (10%) | 9 (8%) |
| BMI (kg/m2)b | |||||
| Underweight (<18.5) | 10 (2%) | 2 (8%) | 7 (6%) | 3 (3%) | 9 (8%) |
| Healthy weight (18.5 to <25) | 99 (23%) | 9 (35%) | 55 (43%) | 38 (38%) | 49 (45%) |
| Overweight (25.0 to <30) | 55 (13%) | 6 (23%) | 20 (16%) | 20 (20%) | 19 (17%) |
| Obese (≥30) | 42 (10%) | 4 (15%) | 21 (17%) | 22 (22%) | 18 (17%) |
| Missing | 222 (52%) | 5 (19%) | 24 (19%) | 16 (16%) | 14 (13%) |
Summary statistics presented as Median (Q1, Q3) or N (%). For pregnancy, denominator only included females for calculation of %.
Abbreviations: BMI, body mass index; INSTI, integrase strand transfer inhibitor.
Characteristics at end of follow-up instead of at time of INSTI-regimen discontinuation.
Nearest measure up to −90/+7 d for VL and −90/+30 d for CD4, weight, and BMI. If no measure available within these windows, linear interpolation was performed using the closest available pre- and post-INSTI discontinuation measures.
Figure 1.
Kaplan–Meier survival curves for time to regimen discontinuation.
Note: Censoring events are expressed as closed circles overlaid atop Kaplan-Meier curves.
Mixed-effects model–based results showed fewer participants to be virally suppressed at the time of switch to RAL (26%) relative to BIC (57%), DTG (49%), and EVG (65%) (Figure 2A). Subsequently, there was a larger average decrease in HIV viral load in the first year after switch to RAL (−0.7 log10 copies/mL, 95% confidence interval [CI]: −1.0, −.4) relative to the other INSTIs (Figure 2B). Viral suppression at 1 and 2 years after switch was 74% and 69% for BIC, 62% and 60% for DTG, 76% and 68% for EVG, and 58% and 52% for RAL. Fewer females than males were virally suppressed from time of switch to BIC, DTG, or EVG through 2 years after switch. However, average CD4 counts were generally maintained above 500 cells/mm3 after switch through 2 years after switch for both females and males across all INSTIs (Figure 3).
Figure 2.
Predicted HIV viral loads through 2 y after switch to INSTI-based regimen, by sex at birth.
Figure 3.
Predicted mean CD4 count (cells/mm3) with confidence bands through 2 y after switch to INSTI-based regimen, by sex at birth.
Mixed-effects model–based results for weight showed increases among females in the first year after switch to DTG (2.5 kg, 95% CI: 0.8, 4.1) or EVG (3.8 kg, 95% CI: 1.0, 6.4), though these trajectories did not differ substantially from weight increases in the year prior to switch to these regimens (DTG: 1.7 kg, 95% CI: −0.3, 4.0; EVG: 2.0 kg, 95% CI: 0.2, 3.6; Figure 4). Similarly, among males, there were increases in weight in the first year after switch to BIC, DTG, EVG, or RAL, though these trajectories did not differ substantially from those in the year prior to switch to these regimens.
Figure 4.
Predicted mean weight (kg) with confidence bands through 2 y after switch to INSTI-based regimen, by sex at birth.
Based on mixed-effects models for BMI, 46%, 28%, 35%, and 45% of females were overweight/obese at the time of switch to BIC, DTG, EVG, or RAL, respectively (Figure 5A). In comparison, 35%, 25%, 32%, and 24% of males were overweight/obese at the time of switch to BIC, DTG, EVG, or RAL, respectively. Figure 5B shows the trajectories of BMI in the 2 years prior to switch through 2 years after switch for each INSTI, by sex at birth. Similar to results for weight, there were increases in BMI in the first year after switch to DTG (1.0 kg/m2, 95% CI: 0.4, 1.6) and EVG (1.3 kg/m2, 95% CI: 0.3, 2.4) among females, though these slopes were similar to those prior to switch to these INSTIs. Among males, there were increases in BMI in the first year after switch to DTG (1.4 kg/m2, 95% CI: 0.7, 2.2) and RAL (1.0 kg/m2, 95% CI: −0.1, 1.9), though again these increases did not differ substantially from those in the year prior to switch to these INSTIs. Among those underweight or healthy weight at time of switch, 13% (95% CI: 5%, 29%), 18% (95% CI: 11%, 29%), 36% (95% CI: 21%, 54%), and 12% (95% CI: 3%, 34%) of females and 6% (95% CI: 1%, 27%), 8% (95% CI: 3%, 21%), 12% (95% CI: 4%, 29%), and 11% (95% CI: 3%, 31%) of males switching to BIC, DTG, EVG, and RAL were overweight/obese by 2 years after switch, respectively.
Figure 5.
Predicted body mass index (BMI) through 2 y after switch to INSTI-based regimen, by sex at birth.
DISCUSSION
In a relatively large cohort of treatment-experienced AYAPHIV in the United States, the majority maintained or achieved viral suppression after switch to an INSTI-based regimen. Though not substantially different from weight gain prior to switch, increases in weight and BMI through 2 years after switch to BIC, DTG, EVG, or RAL were observed at varying levels among females and males.
Despite decreases in average HIV viral loads in the first year after switch to all INSTIs, the proportion of AYAPHIV virally suppressed at <200 copies/mL after switch to an INSTI-based regimen was moderate, with 74% and 69% virally suppressed 1 and 2 years after switch to BIC and 62% and 60% virally suppressed 1 and 2 years after switch to DTG. Though only 43% and 51% of switches to BIC and DTG occurred while viremic, these results are comparable to those observed in trials of INSTIs among viremic treatment–experienced adults [12, 13]. Specifically, the SAILING trial reported viral suppression at <50 copies/mL to be 71% at 48 weeks among viremic treatment-experienced adults who received DTG [12]. Our results are also similar to the IMPAACT 1093 study of 23 young adolescents with detectable HIV viral load, which reported viral suppression at <400 copies/mL to be 74% at 48 weeks after receipt of DTG [38]. For this study, we could not assess reasons for virologic failure such as adherence issues or accumulated resistance during follow-up. This is relevant as most currently available long-acting injectable strategies include cabotegravir, an INSTI whose activity could be compromised with development of resistance on other INSTI-based regimens.
Among our population of treatment-experienced AYAPHIV, we observed increases in weight of about 3 kg in the first year after switch to DTG or EVG, with no substantial differences between females and males. This increase is comparable to what was reported in a pooled analysis of weight gain in 8 randomized controlled clinical trials of treatment-naive adults with HIV, which estimated an increase of 2.3 kg at 48 weeks after initiation of any INSTI and 2.8 kg at 48 weeks after initiation of DTG [24]. Though the study also reported weight gain of 3.1 kg at 48 weeks after initiation of BIC, we did not observe substantial weight gain overall in the 1 year after switch to BIC in our population. While weight gain after switch to particular INSTIs did not substantially differ from weight increases prior to switch, the continued weight gain on INSTI-based regimens is concerning, particularly for AYAPHIV who are also more likely to have been exposed in childhood to thymidine analogues such as stavudine and/or didanosine, which have been associated with visceral fat accumulation [39].
Over a third of our study population was overweight/obese at the time of switch to an INSTI-based regimen and ~15% of those underweight/healthy weight at time of switch were overweight/ obese by 2 years after switch to an INSTI. The proportion over-weight/obese also increased over time in the pooled analysis of weight gain among treatment-naive adults with HIV from 47.7% at baseline to 53.4% at 48 weeks [33]. That study did not observe a significant clinical impact of weight gain on cardiometabolic parameters in their study population due to short duration of follow-up. However, becoming overweight/obese will likely exacerbate existing cardiovascular disease risk among AYAPHIV as they age into adulthood with a life long history of and ongoing exposure to HIV and ART [40]. It will be important to monitor the clinical consequences of weight gain on the long-term cardiometabolic health of AYAPHIV.
While our study population was relatively large, after subsetting by INSTI and sex at birth, our sample size became limited to further stratify by other risk factors for weight gain such as age, use of tenofovir alafenamide (TAF), and race/ethnicity. Weight gain may differ for adolescents and young adults compared to adults due to differences in pubertal and sexual maturation. Prior literature among adults with HIV has also shown larger weight gain with use of TAF and among females who self-report as Black [24]. We were also interested in estimating return-to-health after switch to an INSTI regimen but were limited in the sample size of participants with low CD4 counts within each INSTI and sex at birth analysis subset. We were also unable to stratify our analyses based on ART received prior to switch due to incomplete histories of lifetime ART for AMP Up participants. As this was primarily a descriptive study to quantify HIV viral load, CD4 counts, weight, and BMI after switch to specific INSTIs, further studies are warranted to identify risk factors for weight gain, particularly for those who became overweight/obese after switch to an INSTI.
In summary, this study of a large, diverse cohort of AYAPHIV switching to an INSTI-based regimen shows moderate effectiveness of the newer INSTIs, BIC, and DTG. These data may help providers develop realistic viral suppression targets for this heavily treatment-experienced population. This study also shows variable continued average weight gain after switch to specific INSTIs, which has implications for the long-term cardiometabolic health of this population.
Acknowledgments.
We thank the participants and families for their participation in PHACS and the individuals and institutions involved in the conduct of PHACS. 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, Principal Investigators: Ellen Chadwick, Sonia Hernandez-Diaz, Jennifer Jao, Paige Williams; Program Director: Liz Salomon and HD052102: Principal Investigator: George R Seage III; Program Director: Liz Salomon) and with 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 (Data Management Center Director: Suzanne Siminski), and regulatory services and logistical support were provided by Westat, Inc (Project Director: Tracy Wolbach). The following institutions, clinical site investigators and staff participated in conducting PHACS AMP and AMP Up in 2022, in alphabetical order: Ann & Robert H. Lurie Children’s Hospital of Chicago: Ellen Chadwick, Emanuela (Lela) Lartey, Rohit Kalra, Kathleen Malee; Baylor College of Medicine: Mary Paul, Shelley Buschur, Chivon McMullen-Jackson, Lynnette Harris; BronxCare Health System: Murli Purswani, Martha Cavallo, Mahboobullah Mirza Baig, Alma Villegas; Children’s Diagnostic & Treatment Center: Lisa- Gaye Robinson, Alan Bernegger, Patricia Garvie; Boston Children’s Hospital: Sandra K. Burchett, Michelle E. Anderson, Christine M. Salois; Jacobi Medical Center: Andrew Wiznia, Marlene Burey, Ray Shaw; Rutgers—New Jersey Medical School: Arry Dieudonne, Juliette Johnson, Karen Surowiec; St. Christopher’s Hospital for Children: Janet S. Chen, Taesha White, Mitzie Grant; St. Jude Children’s Research Hospital: Katherine Knapp, Erick Odero, Megan Wilkins; San Juan Hospital Research Unit/Department of Pediatrics, San Juan Puerto Rico: Nicolas Rosario, 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, Christine Kwon, Robin McEnvoy; 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.
Financial support.
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).
Footnotes
Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
References
- 1.Molina JM, Clotet B, van Lunzen J, et al. Once-daily dolutegravir versus darunavir plus ritonavir for treatment-naive adults with HIV-1 infection (FLAMINGO): 96 week results from a randomised, open-label, phase 3b study. Lancet HIV 2015; 2: e127–36. [DOI] [PubMed] [Google Scholar]
- 2.Walmsley SL, Antela A, Clumeck N, et al. Dolutegravir plus Abacavir–lamivudine for the treatment of HIV-1 infection. N Engl J Med 2013; 369:1807–18. [DOI] [PubMed] [Google Scholar]
- 3.Sax PE, DeJesus E, Mills A. Co-formulated elvitegravir, cobicistat, emtricitabine, and tenofovir versus co-formulated efavirenz, emtricitabine, and tenofovir for initial treatment of HIV-1 infection: a randomised, double-blind, phase 3 trial, analysis of results after 48 weeks. Lancet 2012; 379:2439–48. [DOI] [PubMed] [Google Scholar]
- 4.DeJesus E, Rockstroh JK, Henry K, et al. Co-formulated elvitegravir, cobicistat, emtricitabine, and tenofovir disoproxil fumarate versus ritonavir-boosted atazanavir plus co-formulated emtricitabine and tenofovir disoproxil fumarate for initial treatment of HIV-1 infection: a randomised, double-blind, phase 3, non-inferiority trial. Lancet 2012; 379:2429–38. [DOI] [PubMed] [Google Scholar]
- 5.Lennox JL, Dejesus E, Lazzarin A, et al. Safety and efficacy of raltegravir-based versus efavirenz-based combination therapy in treatment-naive patients with HIV-1 infection: a multicentre, double-blind randomised controlled trial. Lancet 2009; 374:796–806. [DOI] [PubMed] [Google Scholar]
- 6.Sax PE, Pozniak A, Montes ML, et al. Coformulated bictegravir, emtricitabine, and tenofovir alafenamide versus dolutegravir with emtricitabine and tenofovir alafenamide, for initial treatment of HIV-1 infection (GS-US-380–1490): a randomised, double-blind, multicentre, phase 3, non-inferiority trial. Lancet 2017; 390:2073–82. [DOI] [PubMed] [Google Scholar]
- 7.Yang LL, Li Q, Zhou LB, Chen SQ. Meta-analysis and systematic review of the efficacy and resistance for human immunodeficiency virus type 1 integrase strand transfer inhibitors. Int J Antimicrob Agents 2019; 54:547–55. [DOI] [PubMed] [Google Scholar]
- 8.Daar ES, DeJesus E, Ruane P, et al. Efficacy and safety of switching to fixed-dose bictegravir, emtricitabine, and tenofovir alafenamide from boosted protease inhibitor-based regimens in virologically suppressed adults with HIV-1: 48 week results of a randomised, open-label, multicentre, phase 3, non-inferiority trial. Lancet HIV 2018; 5:e347–56. [DOI] [PubMed] [Google Scholar]
- 9.Arribas JR, Pialoux G, Gathe J, et al. Simplification to coformulated elvitegravir, cobicistat, emtricitabine, and tenofovir versus continuation of ritonavir-boosted protease inhibitor with emtricitabine and tenofovir in adults with virologically suppressed HIV (STRATEGY-PI): 48 week results of a randomised, open-label, phase 3b, non-inferiority trial. Lancet Infect Dis 2014; 14:581–9. [DOI] [PubMed] [Google Scholar]
- 10.Eron JJ, Young B, Cooper DA, et al. Switch to a raltegravir-based regimen versus continuation of a lopinavir–ritonavir-based regimen in stable HIV-infected patients with suppressed viraemia (SWITCHMRK 1 and 2): two multicentre, double-blind, randomised controlled trials. Lancet 2010; 375:396–407. [DOI] [PubMed] [Google Scholar]
- 11.Lake JE, Trottier B, Garcia-Diaz J, et al. STRIIVING: switching to Abacavir/dolutegravir/lamivudine fixed dose combination (ABC/DTG/3TC FDC) from a PI, INI or NNRTI based regimen maintains HIV suppression at week 48. J Int AIDS Soc 2016; 19:80. [Google Scholar]
- 12.Cahn P, Pozniak AL, Mingrone H, et al. Dolutegravir versus raltegravir in antiretroviral-experienced, integrase-inhibitor-naive adults with HIV: week 48 results from the randomised, double-blind, non-inferiority SAILING study. Lancet 2013; 382:700–8. [DOI] [PubMed] [Google Scholar]
- 13.Molina J-M, LaMarca A, Andrade-Villanueva J, et al. Efficacy and safety of once daily elvitegravir versus twice daily raltegravir in treatment-experienced patients with HIV-1 receiving a ritonavir-boosted protease inhibitor: randomised, double-blind, phase 3, non-inferiority study. Lancet Infect Dis 2012; 12:27–35. [DOI] [PubMed] [Google Scholar]
- 14.Davy-Mendez T, Napravnik S, Zakharova O, Wohl DA, Farel CE, Eron JJ. Effectiveness of integrase strand transfer inhibitors among treatment-experienced patients in a clinical setting. AIDS 2019; 33(7):1187–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Rossetti B, Fabbiani M, Di Carlo D, et al. Effectiveness of integrase strand transfer inhibitors in HIV-infected treatment-experienced individuals across Europe. HIV Med 2022; 23(7)):774–89. [DOI] [PubMed] [Google Scholar]
- 16.Squires KE, Bekker L-G, Eron JJ, et al. Safety, tolerability, and efficacy of raltegravir in a diverse cohort of HIV-infected patients: 48-week results from the REALMRK study. AIDS Res Hum Retroviruses 2013; 29:859–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Naumann U, Moll A, Schleehauf D, et al. Similar efficacy and tolerability of raltegravir-based antiretroviral therapy in HIV-infected patients, irrespective of age group, burden of comorbidities and concomitant medication: real-life analysis of the German ‘WIP’ cohort. Int J STD AIDS 2017; 28:893–901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Agwu AL, Fairlie L. Antiretroviral treatment, management challenges and outcomes in perinatally HIV-infected adolescents. J Int AIDS Soc 2013; 16:18579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Van Dyke RB, Patel K, Kagan RM, Karalius B, Traite S. Meyer WA 3rd, et al; pediatric HIV/AIDS cohort study (PHACS); antiretroviral drug resistance among children and youth in the United States with perinatal HIV. Clin Infect Dis 2016; 63:133–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Contreras GA, Bell CS, Del Bianco G, et al. Incidence and predictors of antiretroviral resistance in perinatally HIV-1 infected children and adolescents. J Infect 2016; 72:353–61. [DOI] [PubMed] [Google Scholar]
- 21.Norwood J, Turner M, Bofill C, et al. Brief report: weight gain in persons with HIV switched from efavirenz-based to integrase strand transfer inhibitor-based regimens. J Acquir Immune Defic Syndr 2017; 76:527–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.NAMSAL ANRS 12313 Study Group; Kouanfack C, Mpoudi-Etame M, et al. Dolutegravir-based or low-dose efavirenz-based regimen for the treatment of HIV-1. N Engl J Med 2019; 381:816–26. [DOI] [PubMed] [Google Scholar]
- 23.Venter WDF, Moorhouse M, Sokhela S, et al. Dolutegravir plus two different prodrugs of tenofovir to treat HIV. N Engl J Med 2019; 381:803–15. [DOI] [PubMed] [Google Scholar]
- 24.Sax PE, Erlandson KM, Lake JE, et al. Weight gain following initiation of antiretroviral therapy: risk factors in randomized comparative clinical trials. Clin Infect Dis 2020; 71:1379–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kanters S, Renaud F, Rangaraj A, et al. Evidence synthesis evaluating body weight gain among people treating HIV with antiretroviral therapy—a systematic literature review and network meta-analysis. EClinicalMedicine 2022; 48:101412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Bai R, Lv S, Wu H, Dai L. Effects of different integrase strand transfer inhibitors on body weight in patients with HIV/AIDS: a network meta-analysis. BMC Infect Dis 2022; 22:118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Yusuf HE, Griffith D, Agwu AL. Preventing and diagnosing HIV-related comorbidities in adolescents. Top Antivir Med 2022; 30:537–44. [PMC free article] [PubMed] [Google Scholar]
- 28.Haw NJL, Lesko CR, Ng DK, et al. Agwu A; North American AIDS cohort collaboration on research and design (NA-ACCORD). incidence of non-AIDS defining comorbidities among young adults with perinatally acquired HIV in North America. AIDS 2024; 38:1366–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Cheng HL, Medlow S, Steinbeck K. The health consequences of obesity in young adulthood. Curr Obes Rep 2016; 5:30–7. [DOI] [PubMed] [Google Scholar]
- 30.Riscart J, Soria-Alcaide E, Prieto L, et al. Weight gain among adolescents on dolutegravir: a real concern? CROI 2022. (Abstract 731) https://www.croiconference.org/abstract/weight-gain-amongadolescents-on-dolutegravir-a-real-concern/. [Google Scholar]
- 31.Yeoh DK, Campbell AJ, Bowen AC. Increase in body mass Index in children with HIV, switched to tenofovir alafenamide fumarate or dolutegravir containing antiretroviral regimens. Pediatr Infect Dis J 2021; 40:e215–6. [DOI] [PubMed] [Google Scholar]
- 32.Frange P, Avettand-Fenoel V, Veber F, Blanche S. No overall impact on body mass index for age change after dolutegravir initiation in a French paediatric cohort. HIV Med 2022; 23:1019–24. [DOI] [PubMed] [Google Scholar]
- 33.Rose PC, De la Rey Nel E, Cotton MF, et al. Decreased hepatic steatosis in South African adolescents with perinatal HIV switching to dolutegravir-containing regimens. Pediatr Infect Dis J 2023; 42:564–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Koay WLA, Dirajlal-Fargo S, Levy ME, Kulie P, Monroe A, Castel AD. Rakhmanina NY; DC cohort executive committee. Integrase strand transfer inhibitors and weight gain in children and youth with perinatal human immunodeficiency virus in the DC cohort. Open Forum Infect Dis 2021; 8:ofab308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Thivalapill N, Simelane T, Mthethwa N, et al. Transition to dolutegravir is associated with an increase in the rate of body mass Index change in a cohort of virally suppressed adolescents. Clin Infect Dis 2021; 73:e580–e586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Belfrage E, Soeria-Atmadja S, Growth NL. Weight gain and BMI in virally suppressed children on antiretroviral therapy with specific reference to dolutegravir. BMC Pediatr 2023; 23:339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Taramasso L, Di Biagio A, Bovis F, et al. Switching to integrase inhibitors unlinked to weight increase in perinatally HIV-infected young adults and adolescents: a 10-year observational study. Microorganisms 2020; 8:864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Viani RM, Alvero C, Fenton T, et al. P1093 study team. Safety, pharmacokinetics and efficacy of dolutegravir in treatment-experienced HIV-1 infected adolescents: forty-eight-week results from IMPAACT P1093. Pediatr Infect Dis J 2015; 34:1207–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Gelpi M, Afzal S, Fuchs A, et al. Prior exposure to thymidine analogs and didanosine is associated with long-lasting alterations in adipose tissue distribution and cardiovascular risk factors. AIDS 2019; 33:675–83. [DOI] [PubMed] [Google Scholar]
- 40.Patel K, Wang J, Jacobson DL, et al. Pediatric HIV/AIDS Cohort Study (PHACS). Aggregate risk of cardiovascular disease among adolescents perinatally infected with the human immunodeficiency virus. Circulation 2014; 129:1204–12. [DOI] [PMC free article] [PubMed] [Google Scholar]