Abstract
Objective:
To determine whether pitavastatin reduces enterocyte injury, assessed by intestinal fatty acid-binding protein (I-FABP), among antiretroviral therapy-treated people with HIV (PWH) enrolled in the REPRIEVE mechanistic substudy.
Design:
Randomized, double-blind, placebo-controlled trial.
Methods:
We analyzed fasting plasma I-FABP at entry and month 24 among participants randomized to pitavastatin 4 mg daily or placebo. Baseline characteristics were summarized across I-FABP quartiles. Associations between entry I-FABP and inflammatory and immune activation biomarkers were assessed using Spearman correlations. Longitudinal changes were evaluated using intention-to-treat and per-protocol analyses and generalized estimating equation models.
Results:
Among 710 participants with entry I-FABP measurements (median age 50 years; 18.5% female; 98% with undetectable viral load), median I-FABP levels were 2690 [Q1, Q3: 1888, 3770] (pg/mL). Entry I-FABP correlated modestly with markers of systemic inflammation and monocyte activation, including GDF-15 and soluble CD14. Over 24 months, I-FABP levels declined similarly in the pitavastatin and placebo arms, with no significant differences in absolute or fold change.
Conclusions:
In this well-treated HIV cohort, higher baseline I-FABP levels were associated with increased inflammatory markers at study entry. However, pitavastatin did not reduce I-FABP over follow-up. These findings suggest that the cardiovascular benefit of pitavastatin in REPRIEVE is unlikely to be mediated through reductions in enterocyte injury, although gut-barrier effects of other statins cannot be excluded.
Keywords: Biomarkers, Bacterial Translocation, Gut Permeability, HIV, Cardiovascular
Introduction
People with HIV (PWH) experience chronic immune activation and systemic inflammation despite effective antiretroviral therapy (ART), a phenomenon partly attributed to increased gut permeability. Disruption of the intestinal barrier permits translocation of microbial products such as lipopolysaccharide (LPS) from the intestinal lumen into systemic circulation.(1) This process, referred to as microbial translocation, perpetuates immune activation and contributes to disease progression and non-AIDS comorbidities including diabetes, obesity, cardiovascular disease, and stroke.(2-7)
To characterize this pathway, biomarkers reflecting distinct but related components of gut barrier dysfunction and microbial translocation are commonly used. Intestinal fatty acid-binding protein (I-FABP, also known as FABP2) is a cytosolic protein expressed in mature enterocytes of the small intestine and serves as a sensitive marker of enterocyte injury. In healthy individuals, I-FABP facilitates intracellular fatty acid trafficking, but elevated plasma concentrations indicate structural damage to the intestinal epithelium. Among PWH, I-FABP levels are consistently elevated, particularly in those with chronic infection compared with elite controllers,(8) and correlate with biomarkers of immune activation (e.g., soluble CD14, interleukin-6, D-dimer), microbial translocation, lower CD4+ T-cell counts, and increased mortality risk, even in individuals with durable ART-mediated viral suppression.(9) These findings support I-FABP as a biologically and clinically relevant biomarker for evaluating interventions that aim to restore gut integrity in PWH.
Statins have recently emerged as a promising therapeutic strategy beyond their lipid-lowering properties. In ART-treated PWH, statin therapy has been associated with reduced epithelial cell death within the colon.(10) Mechanistic studies suggest this effect is partly mediated through activation of peroxisome proliferator-activated receptor-γ (PPARγ) in colon-resident CD8+ memory T cells, which are implicated in epithelial injury through dysregulated lipid metabolism. Activation of PPARγ restores lipid homeostasis, thereby reducing T cell–mediated epithelial cell death and preserving gut barrier function.(10)
Here, we leveraged the REPRIEVE substudy to first assess the relationship of baseline levels of enterocyte injury to downstream markers of microbial translocation, inflammation and metabolic dysfunction and then to test the hypothesis that pitavastatin therapy modulates gut integrity, as reflected by reduced enterocyte injury (I-FABP).
Methods
This study included individuals with HIV that participated in the mechanistic substudy of the REPRIEVE randomized clinical trial. In brief, 31 REPRIEVE sites, primarily from the ACTG, were selected to participate in the mechanistic substudy based on capabilities for performing coronary Computed Tomographic Angiography and blood biomarker acquisition. The trial was designed by study principal investigators in consultation with the National Heart, Lung, and Blood Institute, the National Institute of Allergy and Infectious Diseases, and the ACTG. Institutional review board/ethics committee approval and any other applicable regulatory entity approvals were obtained from the Mass General Brigham Institutional Review Board and each clinical research site. Participants were provided with study information, including discussion of risks and benefits, and signed the approved declaration of informed consent.(11)
I-FABP was measured at two time points (entry and month 24) using fasting plasma samples, from substudy participants at Rutgers University. Quantification was performed with the Human FABP2/I-FABP Quantikine ELISA (R&D Systems), which demonstrated acceptable analytical precision. Intra-assay coefficients of variation (CVs) ranged from 2.9% to 4.1%, and inter-assay CVs ranged from 6.0% to 11.1%, consistent with expected performance for immunoassays.(12) Additional biomarkers and flow cytometry–derived measures were assessed at entry; detailed laboratory methods have been previously published.(13, 14)
Baseline demographics and clinical characteristics were summarized across quartiles of I-FABP using medians (Q1–Q3) for continuous variables and proportions for categorical variables. Both intention-to-treat and per-protocol analyses were performed to evaluate the effect of statin therapy on changes in I-FABP levels over the follow-up period. Changes across I-FABP quartiles were also examined. In addition, to account for within-participant correlation and evaluate longitudinal change, we fit GEE model with an exchangeable correlation structure including treatment arm, time, and their interaction. Spearman correlation coefficients were used to assess associations between entry I-FABP levels and other circulating biomarkers, as well as flow cytometry markers of monocytes (overall and by subset) and CD4+ and CD8+ T cells.
All statistical inferences used a two-sided α level of 0.05, with no adjustments for multiple comparisons. Analyses were conducted using RStudio (R Foundation for Statistical Computing).
Results
A total of 804 participants from 31 US sites were enrolled between April 2015 to February 2018. Of these, 710 had available entry I-FABP measurements, and 582 had complete I-FABP data to evaluate change over the follow-up period (Supplemental Figure 1). The treatment groups were well-balanced with respect to baseline characteristics (Supplemental Table 1). Among the 710 participants, the median age was 50 years (Q1, Q3: 46–55), 18.5% were female, 51.4% were White, 37.2% were Black or African American, and 24.9% were Hispanic or Latino. Participants had good virologic control (98% undetectable viral load), and CD4 cell count of 607 [Q1, Q3: 436, 784] (Table 1). I-FABP entry levels were 2690 [Q1, Q3: 1888, 3770] (pg/mL). Fasting triglyceride levels were lower in participants with higher I-FABP concentrations (median 120.5 mg/dL [Q1, Q3: 91.0–168.8] mg/dL in the lowest quartile vs. 102.5 [Q1, Q3: 76.2–158.2] mg/dL in the highest quartile). BMI showed a similar pattern, with obesity prevalence decreasing from 31.5% in the lowest quartile to 21.3% in the highest quartile. Median ASCVD risk scores and CD4:CD8 ratios did not differ across I-FABP quartiles (Table 1).
Table 1:
Baseline Characteristics of the Substudy Population at Entry.
| Overall | Q1 (395.2, 1886.5)1 |
Q2 (1891.6, 2688.6) |
Q3 (2691.8, 3766.2) |
Q4 (3771.6, 17744.3) |
P- value |
|
|---|---|---|---|---|---|---|
| (N=710) | (N=178) | (N=177) | (N=177) | (N=178) | ||
| Age (years) | 50 (46, 55) | 50 (46, 55) | 50 (46, 55) | 50 (47, 54) | 51 (46, 55) | 0.855 |
| Natal sex | ||||||
| Male | 579 (81.5%) | 149 (83.7%) | 149 (84.2%) | 136 (76.8%) | 145 (81.5%) | 0.264 |
| Female | 131 (18.5%) | 29 (16.3%) | 28 (15.8%) | 41 (23.2%) | 33 (18.5%) | |
| Race | ||||||
| White | 365 (51.4%) | 88 (49.4%) | 97 (54.8%) | 88 (49.7%) | 92 (51.7%) | 0.762 |
| Black or African American | 264 (37.2%) | 69 (38.8%) | 64 (36.2%) | 70 (39.5%) | 61 (34.3%) | |
| Asian | 10 (1.4%) | 4 (2.2%) | 1 (0.6%) | 1 (0.6%) | 4 (2.2%) | |
| Other | 71 (10.0%) | 17 (9.6%) | 15 (8.5%) | 18 (10.2%) | 21 (11.8%) | |
| Ethnicity | ||||||
| Hispanic or Latino | 177 (24.9%) | 39 (21.9%) | 47 (26.6%) | 43 (24.3%) | 48 (27.0%) | 0.089 |
| Not Hispanic or Latino | 522 (73.5%) | 132 (74.2%) | 127 (71.8%) | 133 (75.1%) | 130 (73.0%) | |
| Unknown | 11 (1.5%) | 7 (3.9%) | 3 (1.7%) | 1 (0.6%) | 0 (0%) | |
| ASCVD risk score (%) | 4.6 (2.6, 7) | 4.8 (2.5, 7) | 4.6 (2.5, 7.1) | 4.2 (2.6, 6.8) | 4.7 (2.8, 7.1) | 0.847 |
| ASCVD risk score (%) | ||||||
| 0-<2.5 | 167 (23.5%) | 41 (23.0%) | 44 (24.9%) | 42 (23.7%) | 40 (22.5%) | 0.743 |
| 2.5-<5 | 217 (30.6%) | 50 (28.1%) | 53 (29.9%) | 61 (34.5%) | 53 (29.8%) | |
| 5-<7.5 | 175 (24.6%) | 49 (27.5%) | 40 (22.6%) | 38 (21.5%) | 48 (27.0%) | |
| 7.5–10 | 101 (14.2%) | 20 (11.2%) | 28 (15.8%) | 26 (14.7%) | 27 (15.2%) | |
| >10 | 50 (7.0%) | 18 (10.1%) | 12 (6.8%) | 10 (5.6%) | 10 (5.6%) | |
| Smoking status | ||||||
| Never | 316 (44.5%) | 83 (46.6%) | 85 (48.0%) | 71 (40.1%) | 77 (43.3%) | 0.643 |
| Current | 173 (24.4%) | 39 (21.9%) | 37 (20.9%) | 50 (28.2%) | 47 (26.4%) | |
| Former | 219 (30.8%) | 56 (31.5%) | 55 (31.1%) | 54 (30.5%) | 54 (30.3%) | |
| Substance use | ||||||
| Never | 351 (49.4%) | 86 (48.3%) | 96 (54.2%) | 89 (50.3%) | 80 (44.9%) | 0.355 |
| Current/Former | 356 (50.1%) | 91 (51.1%) | 81 (45.8%) | 86 (48.6%) | 98 (55.1%) | |
| Systolic blood pressure (mmHg) | 122 (114, 132) | 125.5 (114, 133.8) | 122 (112, 131) | 122 (114, 132) | 122 (114, 130) | 0.309 |
| Fasting Derived LDL Cholesterol (mg/dL) at Baseline | 106 (88, 127) | 107 (89, 128) | 108 (94, 127) | 106 (85.8, 124.2) | 103 (82, 127) | 0.089 |
| Fasting HDL Cholesterol (mg/dL) at Baseline | 47 (38, 59) | 47 (38.2, 58) | 46 (35, 57) | 48 (40, 59) | 47 (37, 61) | 0.348 |
| Fasting Triglycerides (mg/dL) at Baseline | 109 (81, 161.5) | 120.5 (91, 168.8) | 120 (81, 171) | 101 (76, 149) | 102.5 (76.2, 158.2) | 0.006 |
| BMI (race-specific) (kg/m2) | ||||||
| Low/Normal | 227 (32.0%) | 42 (23.6%) | 61 (34.5%) | 53 (29.9%) | 71 (39.9%) | 0.038 |
| Overweight | 286 (40.3%) | 80 (44.9%) | 65 (36.7%) | 72 (40.7%) | 69 (38.8%) | |
| Obese | 197 (27.7%) | 56 (31.5%) | 51 (28.8%) | 52 (29.4%) | 38 (21.3%) | |
| Time since HIV diagnosis (years) | 15 (9, 22) | 16 (10, 22) | 16 (10, 22) | 16 (10, 22) | 14 (7, 20.8) | 0.125 |
| Total ART use (years) | 11.2 (6.5, 16.8) | 12 (6.6, 17) | 12 (7, 17.6) | 11.6 (7, 16.5) | 10 (6, 16.2) | 0.310 |
| Nadir CD4 (cells/mm3) | ||||||
| <50 | 156 (22.0%) | 36 (20.2%) | 37 (20.9%) | 36 (20.3%) | 47 (26.4%) | 0.445 |
| 50-199 | 197 (27.7%) | 45 (25.3%) | 54 (30.5%) | 54 (30.5%) | 44 (24.7%) | |
| 200-349 | 188 (26.5%) | 49 (27.5%) | 48 (27.1%) | 38 (21.5%) | 53 (29.8%) | |
| 350+ | 143 (20.1%) | 42 (23.6%) | 32 (18.1%) | 39 (22.0%) | 30 (16.9%) | |
| Unknown | 26 (3.7%) | 6 (3.4%) | 6 (3.4%) | 10 (5.6%) | 4 (2.2%) | |
| HIV-1 RNA (copies/mL) | ||||||
| <LLQ | 616 (86.8%) | 159 (89.3%) | 151 (85.3%) | 149 (84.2%) | 157 (88.2%) | 0.581 |
| LLQ -< 400 | 70 (9.9%) | 14 (7.9%) | 20 (11.3%) | 20 (11.3%) | 16 (9.0%) | |
| 400+ | 13 (1.8%) | 1 (0.6%) | 5 (2.8%) | 3 (1.7%) | 4 (2.2%) | |
| CD4 count (cells/mm3) | 607 (436, 784) | 604 (445.5, 764.8) | 595 (425, 805) | 634 (436, 810) | 602 (422.2, 739) | 0.476 |
| CD4:CD8 ratio | 0.9 (0.6, 1.2) | 0.9 (0.6, 1.2) | 0.8 (0.5, 1.2) | 0.9 (0.7, 1.2) | 0.9 (0.5, 1.3) | 0.537 |
| Entry ART regimen class | ||||||
| NRTI + INSTI | 320 (45.1%) | 99 (55.6%) | 84 (47.5%) | 70 (39.5%) | 67 (37.6%) | <0.001 |
| NRTI + NNRTI | 175 (24.6%) | 34 (19.1%) | 30 (16.9%) | 57 (32.2%) | 54 (30.3%) | |
| NRTI + PI | 117 (16.5%) | 21 (11.8%) | 32 (18.1%) | 24 (13.6%) | 40 (22.5%) | |
| NRTI-sparing | 25 (3.5%) | 11 (6.2%) | 7 (4.0%) | 4 (2.3%) | 3 (1.7%) | |
| Other NRTI-containing | 73 (10.3%) | 13 (7.3%) | 24 (13.6%) | 22 (12.4%) | 14 (7.9%) | |
| Baseline Value of I-FABP (pg/mL) | 2690.2 (1887.8, 3770.2) | 1405.3 (1127, 1685) | 2300.4 (2105.6, 2480.4) | 3212.3 (2977.6, 3483) | 4659.2 (4216.8, 5606.4) |
The range of I-FABP values within each quartile.
Among the available entry biomarkers, I-FABP demonstrated the strongest positive correlations with Growth/Differentiation Factor-15 (GDF-15) and soluble CD14 (sCD14) (Spearman ρ = 0.24 and 0.12, respectively; both P < 0.01), and a negative correlation with oxLDL (Spearman ρ = −0.16, P < 0.001) (Supplemental Figure 2), though these correlations were modest. In addition, entry I-FABP levels correlated positively with CD169 and HLA-DR expression on all-monocytes and across each monocyte subset as well (Supplemental Figure 3).
At entry, median I-FABP levels were 2,864 pg/mL (Q1–Q3: 1,916–3,989) in the pitavastatin arm (N=292) and 2,643 pg/mL (Q1–Q3: 1,867–3,665) in the placebo arm (N=290) (Supplemental Table 1). Over the follow-up period, I-FABP levels declined comparably in both groups. Median absolute changes were −183 pg/mL (Q1, Q3: −1,294 to 715) in the pitavastatin arm and −296 pg/mL (Q1, Q3: −1,337 to 492) in the placebo arm (P = 0.49). When expressed as fold change from baseline, I-FABP levels were 9% lower in the pitavastatin arm (median fold change 0.91; Q1–Q3: 0.63–1.37) and 14% lower in the placebo arm (median fold change 0.86; Q1–Q3: 0.58–1.26) (P = 0.30). Per-protocol analyses restricted to participants who completed treatment as randomized, yielded similar results (Supplemental Table 2). In the GEE model, none of the treatment, time, or treatment-by-time interaction terms were statistically significant. Pitavastatin effect did not differ across I-FABP quartiles (Figure 1).
Figure 1:
Change in I-FABP levels from entry to month 24 stratified by entry level I-FABP quartiles and treatment arm.
Individual participant trajectories for intestinal fatty acid–binding protein (I-FABP) levels from entry to Month 24 are shown, stratified by baseline I-FABP quartiles (Q1–Q4) and treatment arm (placebo vs. pitavastatin). Colored lines represent within-participant changes, and red squares represent median values at each time point. Across all quartiles, I-FABP levels demonstrated broad inter-individual variability with no pitavastatin over 24 months.
Discussion:
In this substudy of REPRIEVE, among 582 participants with complete I-FABP data, I-FABP, a marker of enterocyte function, correlated with other inflammatory markers, but was not reduced by pitavastatin over 2-years of follow-up. These results suggest no effect on gut barrier function in REPRIEVE but are specific to pitavastatin and do not exclude the possibility of gut-barrier-mediated effects for other statins.
The observed levels of I-FABP at entry in REPRIEVE appear higher than the reported levels in the general population, and consistent with levels reported in other chronic HIV cohorts.(8, 9) Moreover, the pattern of elevated I-FABP and its associations with lower BMI and triglycerides is consistent with prior reports in ART-treated PWH,(8) possibly reflecting persistent intestinal dysfunction affecting nutrient absorption and lipid metabolism.
Entry I-FABP levels showed the strongest correlations with GDF-15 and sCD14, both of which are markers of systemic inflammation. GDF-15 is a divergent member of the transforming growth factor β superfamily, and is often induced under stress conditions, seemingly to maintain cell and tissue homeostasis; and was linked to pathological conditions including inflammation and myocardial ischemia.(15, 16) sCD14 reflects monocyte activation and is a well-established surrogate of microbial translocation in treated HIV infection.(9) Consistent with that, I-FABP correlated with monocyte markers of systemic inflammation and macrophage activation (CD169, HLA-DR).(17)
We initially hypothesized that pitavastatin might modulate gut integrity in part through PPAR-γ–dependent pathways, as has been previously demonstrated from PWH derived colon.(10) However, mixed data is reported regarding the effect of pitavastatin on PPAR-γ. In adipocyte and macrophage models, pitavastatin has been shown to suppress PPAR-γ expression and activity, inhibiting adipocyte differentiation and downregulating CD36 via PPAR-γ–dependent mechanisms, which is more consistent with PPAR-γ inhibition than agonism.(18) By contrast, several studies of other statins report PPAR-γ activation: atorvastatin has been shown to increase PPAR-γ activity and attenuate inflammatory responses in peripheral monocytes,(19, 20) while rosuvastatin has been associated with upregulation of PPAR-γ expression and PPAR-γ- linked vascular or myocardial effects in experimental models.(21) This evidence suggests that unlike other statins, pitavastatin is not generally regarded as a direct PPAR-γ agonist, with cell-type and context-specific effects on PPAR-γ signaling. Such in-class differences in PPAR-γ modulation may therefore explain why rosuvastatin (10 mg/day) showed modest decreases in IFABP in the SATURN-HIV study, a randomized, double-blind, placebo-controlled 48 week trial (n=147) (22), in contrast to data obtained in REPRIEVE using pitavastatin. Of note, other markers of gut integrity, such as zonulin-1, were not affected by rosuvastatin, in SATURN HIV suggesting a limited overall effect. Similarly, we have previously reported no effects of pitavastatin on sCD14, a binding protein for lipopolysaccharide, which can indicate gut leakiness.(13)
Participants in this substudy of REPRIEVE had well-controlled HIV and relatively low levels of systemic inflammation at entry, which may limit the modifiability of gut-barrier injury in this population. Furthermore, we only had I-FABP as a marker of gut permeability, which predominantly reflects small-intestinal injury. We did not have colonic tissues available to assess directly for epithelial cell death.(10) Taken together, our findings do not exclude gut-barrier effects of other statins, effects in individuals with higher baseline inflammatory activity, or other gut-effects of pitavastatin.
In conclusion, pitavastatin did not reduce plasma I-FABP over 2-years in REPRIEVE, suggesting that its cardiovascular benefit in this cohort is unlikely to be mediated through reductions in enterocyte injury. However, these results do not preclude gut-barrier effects for other members of the statin class.
Supplementary Material
Acknowledgements
The study investigators thank the study participants, site staff, and study-associated personnel for their ongoing participation in the trial. In addition, we thank the following: the ACTG for clinical site support; ACTG Clinical Trials Specialists (Laura Moran, MPH, and Jhoanna Roa, MD) for protocol development and implementation support; the data management center, Frontier Science Foundation, for data support; the Center for Biostatistics in AIDS Research for statistical support; and the Community Advisory Board for input for the community.
Funding Statement
This study is supported through NIH grants U01HL123336 and 1UG3HL164285, to the Clinical Coordinating Center, and U01HL123339 and 1U24HL164284, to the Data Coordinating Center, as well as funding from Kowa Pharmaceuticals America, Inc., Gilead Sciences, and ViiV Healthcare. The NIAID supported this study through grants UM1 AI068636, which supports the Advancing Clinical Therapeutics Globally (ACTG) Leadership and Operations Center; and UM1 AI106701, which supports the ACTG Laboratory Center. This work was also supported by the Nutrition Obesity Research Center at Harvard (P30DK040561 to SKG).
NHLBI/NIH Grants Policy Statement
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute or the National Institute of Allergy and Infectious Diseases; the National Institutes of Health; or the U.S. Department of Health and Human Services. This manuscript is the result of funding in whole or in part by the National Institutes of Health and is subject to the NIH Public Access Policy.
Disclosures
AA reports no relevant disclosures.
UDA reports grant support through her K99/R00 Pathway to Independence NIH/NIDDK award.
THB reports grant from Massachusetts General Hospital during the conduct of the study THB was also a member of the scientific advisory board and reports equity in Excision BioTherapeutics, unrelated to this work.
CJF reports research grant support through his institution from Gilead Sciences, ViiV Healthcare, Merck and Pfizer unrelated to this work.
MVZ reports grant support through her institution from NIH/NIAID and Gilead Sciences, Inc., relevant to the conduct of the study, as well as grants from NIH/NIAID and NIH/NHLBI; support for attending CROI and International Workshop for HIV and Women from conference organizing committee when abstract reviewer and/or speaker; and participation in DSMB for NIH funded studies, outside the submitted work.
JAA reports grants from Massachusetts General Hospital during the conduct of the study; institutional research support for clinical trials from Gilead Sciences, Glaxo Smith Kline, Janssen, Macrogenics, Merck, Pfizer, Regeneron, and ViiV Healthcare and personal fees for advisory boards from Glaxo Smith Kline/ViiV, Invivyd, Merck and Regeneron; and participation on DSMB for Kintor Pharmaceuticals, all outside the submitted work.
JSC reports consulting fees from Merck and Company outside the submitted work.
GSB reports no relevant disclosures.
SMC reports no relevant disclosures
ABL reports no relevant disclosures.
PSD reports no relevant disclosures.
HJR reports grants from Kowa Pharmaceuticals during the conduct of the study, as well as grants from NIH/NIAID, NIH/NHLBI, NIH/NIDDK, and NIH/NIA, outside of the submitted work.
DSK reports grants from NIH/NIAID outside of the submitted work and NIH/NIDDK related to the work.
SKG reports grant support through his institution from NIH, Kowa Pharmaceuticals America, Inc., Gilead Sciences, Inc., and ViiV Healthcare for the conduct of the study; personal fees from Theratechnologies and ViiV; and service on the Scientific Advisory Board of Marathon Asset Management, all outside the submitted work.
Data Sharing Statement
Data are available from the REPRIEVE Trial (mghreprievetrial@mgb.org) upon reasonable request. In this case, shared data may include individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices). To gain access, data requestors will need to sign a data access agreement. Study protocol is available on clinicaltrials.gov. Data will be available following publication, pending approval by the parent study data sharing committee and the ACTG.
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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
Data are available from the REPRIEVE Trial (mghreprievetrial@mgb.org) upon reasonable request. In this case, shared data may include individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices). To gain access, data requestors will need to sign a data access agreement. Study protocol is available on clinicaltrials.gov. Data will be available following publication, pending approval by the parent study data sharing committee and the ACTG.