Abstract
Background
We previously identified anti-CD4 autoantibodies in antiretroviral therapy (ART)-naive people with Human Immunodeficiency Virus (HIV) (PWH) with CD4 < 100 cells/µL using luciferase immunoprecipitation systems (LIPSs). The influence of ART initiation on anti-CD4 autoantibodies and their impact on CD4 recovery in PWH with higher CD4 counts are unclear.
Methods
In the Strategic Timing of Antiretroviral Treatment (START) trial, ART-naive PWH with CD4 > 500 cells/µL were randomized to start ART immediately (immediate) or delay until CD4 < 350 cells/µL or Acquired Immunodeficiency Syndrome (AIDS) (deferred). In a subset of participants, we measured anti-CD4 autoantibodies using LIPS from plasma samples collected at baseline and month 24. We used logistic regression models to evaluate associations with the presence of anti-CD4 autoantibodies and to compare month 24 anti-CD4 autoantibodies between treatment groups. In the immediate group, the mean change in CD4 counts from baseline to month 24 was compared by baseline anti-CD4 autoantibody serostatus.
Results
Baseline anti-CD4 autoantibody prevalence in 1199 participants (600 immediate, 599 deferred) was 41%; anti-CD4 autoantibodies were higher with recent HIV infection and among participants in Africa. At month 24, anti-CD4 autoantibody prevalence had decreased to 26% in the immediate and 31% in the deferred group (odds ratio [OR] = 2.04; P < .001). Participants with recent HIV infection had larger decreases in anti-CD4 autoantibodies (P < .001). There was no difference in CD4 recovery from baseline to month 24 by baseline anti-CD4 autoantibodies within the immediate group.
Conclusions
Anti-CD4 autoantibodies are common in untreated PWH with high CD4 counts; however, with ART use, their prevalence decreases. Anti-CD4 autoantibodies are not associated with CD4 recovery in PWH with CD4 > 500 cells/µL initiating ART.
Keywords: CD4 T cells, anti-CD4 autoantibodies, HIV, antiretroviral therapy, immune reconstitution
Anti-CD4 autoantibodies often develop shortly after HIV infection. Antiretroviral therapy reduces these autoantibodies, especially when started soon after HIV infection. However, the pathogenic significance of these autoantibodies in individuals who begin antiretroviral therapy (ART) with high CD4 counts remains uncertain.
Although modern suppressive antiretroviral therapy (ART) is highly effective at limiting Human Immunodeficiency Virus-1 (HIV-1) viral replication and most people with HIV (PWH) initiating ART with CD4 counts of >200 cells/µL will attain CD4 counts of >500 cells/µL within 2 years [1], there is a subset of 15%–30% of PWH that exhibit a limited CD4 recovery after initiating ART [2]. Despite virologic suppression, these individuals continue to be at increased risk of poor outcomes due to low CD4 counts including Acquired Immunodeficiency Syndrome (AIDS)-defining events, cardiovascular disease, non–AIDS-defining diseases, and increased all-cause mortality even after 3 years of viral control [3–5].
Several factors have been associated with impaired CD4 recovery in virally suppressed PWH [2] Among these, anti-CD4 receptor autoantibodies were first identified in PWH in the pre-ART era [6–8] and were found to be inversely correlated with CD4 count in 1 study [9]. Subsequent studies have sought to further characterize this relationship. One study demonstrated an association between anti-CD4 autoantibodies and impaired CD4 recovery in PWH who had been on ART for greater than 2 years [10]. Another found that early ART initiation at the acute HIV stage was associated with reduced anti-CD4 autoantibody levels at weeks 48 and 96, which in turn correlated with CD4 count at those timepoints [11]. However, other studies did not find an association of anti-CD4 autoantibodies with absolute CD4 count [12–14]. Recently, we identified anti-CD4 autoantibodies in 29% of people with advanced HIV (CD4 < 100 cells/µL) initiating ART and found that these individuals had a slower rate and extent of CD4 reconstitution after 4 years of suppressive ART [15]. In the present study, we sought to (1) estimate the seroprevalence of anti-CD4 autoantibodies in ART-naive individuals with CD4 cell counts of >500 cells/µL, (2) estimate the effect of immediate versus delayed ART initiation on the prevalence of anti-CD4 autoantibodies in a randomized comparison, and (iii) assess the effect of anti-CD4 autoantibodies on CD4 recovery in PWH initiating ART with high pre-ART CD4 counts.
METHODS
Study Participants
In the Strategic Timing of Antiretroviral Treatment (START) trial (NCT00867048) [16], ART-naive PWH with CD4 > 500 cells/µL were randomized to immediate versus deferred ART initiation (until CD4 < 350 cells/µL or development of AIDS). The START trial was designed and conducted by the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT). People with HIV who were ≥18 years of age and who had never used ART, had no history of AIDS, and were in generally good health were eligible for the study if they had had 2 CD4 counts of >500 cells/µL at least 2 weeks apart within 60 days before enrollment.
The study was approved by the institutional review board or ethics committee at each participating site, and written informed consent was obtained from all participants. Additional consent was obtained for collection and storage of specimens for future use.
A subset of START study participants was selected for this study in 2 stages. First, 400 participants (200 from each study group) were selected to represent a range of baseline CD4 values in order to determine whether the prevalence of anti-CD4 autoantibodies was sufficiently high (5% or more) to warrant further study; here, 50 participants each were selected with baseline CD4 counts of 500–599, 600–699, 700–799, and >800 cells/µL from the immediate and deferred groups. In a second stage, the study was expanded to 800 more participants, without restrictions on baseline CD4 counts, for a total of 1200 participants (600 from each study group). We refer to the first 400 participants as “Batch 1” and the next 800 as “Batch 2” to acknowledge the time difference in the laboratory analyses. For each participant, anti-CD4 autoantibodies were measured from stored plasma collected at the baseline and month 24 visits.
Anti-CD4 Autoantibody Measurement
Anti-CD4 autoantibodies were measured from stored plasma using the fluid-phase luciferase immunoprecipitation system (LIPS) immunoassay, which has been previously shown to have high diagnostic performance for detecting autoantibodies in many different autoimmune diseases [17]. Fluid-phase immunoassays generally show the highest sensitivity, specificity, and dynamic range of detection of autoantibodies [18]. We previously identified antibodies against CD4 in 29% of ART-naive people with advanced HIV using LIPS, without evidence of antibodies against other potential extracellular autoantigens [15]. In this study, LIPS was performed at the National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health (NIH) laboratory as previously described [15, 17, 19]. Briefly, the complete extracellular region corresponding to D1–D4 domains of the CD4 receptor was generated as a recombinant chimeric Gaussia luciferase fusion protein and transfected into mammalian Cos1 cells. The resulting crude lysate was used in LIPS in a 96-well format, and serum antibody-bound antigen was measured by addition of coelenterazine substrate. A cutoff of 25 000 relative light units (RLUs) based on mean plus 3 SDs of people without HIV was used to determine seropositivity. Each participant's baseline and month 24 samples were run on the same plate. The laboratory was blinded to study group. Plasma had been stored at a central laboratory at −80° C.
Batch Effect Adjustment
Samples were tested in 2 separate batches. In batch 1, we tested 400 participant samples with a range of baseline CD4 counts for the presence of anti-CD4 autoantibodies. In batch 2, anti-CD4 autoantibody levels tended to be higher compared to batch 1 (Supplementary Figure 1). This batch effect was consistent across CD4 count, treatment group, and study visit (Supplementary Figure 2). To account for this batch effect, we included an indicator variable for batch in our regression models.
Statistical Analysis
We compared baseline seroprevalence of anti-CD4 autoantibodies according to baseline CD4 stratum using chi-squared tests stratified by batch. We used multivariate logistic regression models to assess predictors of baseline anti-CD4 autoantibody seroprevalence; a binary variable was classified as 1 if the anti-CD4 autoantibody level was >25 000 RLUs. Models considered associations with age, sex, region of enrollment, baseline CD4 and HIV RNA, and recent HIV infection and were adjusted for batch. Recent HIV infection was defined as HIV diagnosis within 6 months prior to randomization based on classification using a previously described multiassay algorithm and participant self-report [20]. Recent HIV infection was included as a covariate in statistical models because the duration of HIV infection was hypothesized to correlate with the development of anti-CD4 autoantibodies.
Anti-CD4 autoantibody seropositivity at month 24 was compared between randomization groups in multivariate models adjusting for the same factors as above, with the addition of baseline seropositivity. In a sensitivity analysis, we replaced region of enrollment with HIV subtype (B, non-B, unknown) in a multivariate model. Predictors of incident anti-CD4 autoantibody seropositivity at month 24 within the deferred group were explored using similar multivariate logistic regression models. To assess the effect of anti-CD4 autoantibodies on CD4 recovery following ART initiation, we compared the mean change in CD4 count at month 24 between participants in the immediate group classified as baseline anti-CD4 autoantibodies seropositive versus seronegative using linear regression models adjusting for the same factors as the logistic regression models.
RESULTS
Baseline Characteristics
Anti-CD4 autoantibodies were measured for a total of 1199 participants (600 immediate, 599 deferred); 1 participant in the deferred group was excluded from the study due to insufficient specimen volume for the assay. Baseline characteristics are shown in Table 1. Characteristics were similar between treatment groups; median age was 35 years, 27% female in the immediate and 23% in the deferred groups (overall 25%), the racial distribution was comparable, and the median CD4 count was 645 cells/µL in the immediate and 649 cells/µL in the deferred groups (overall 648 cells/µL). Participant characteristics within batches 1 and 2 are presented in Supplementary Table 1.
Table 1.
Participant Characteristics by Treatment Group
| Immediate | Deferred | Overall | |
|---|---|---|---|
| Median [IQR] or N (%) | Median [IQR] or N (%) | Median [IQR] or N (%) | |
| No. of participants | 600 | 599 | 1199 |
| Demographics | |||
| Age (years) | 35 [28, 43] | 35 [28, 43] | 35 [28, 43] |
| Female sex | 160 (26.7) | 138 (23.0) | 298 (24.9) |
| Race | |||
| Black | 164 (27.3) | 163 (27.2) | 327 (27.3) |
| White | 256 (42.7) | 286 (47.7) | 542 (45.2) |
| Other (Latino, Asian, Other) | 180 (30.0) | 150 (25.0) | 330 (27.5) |
| Geographic location | |||
| Africa | 108 (18.0) | 104 (17.4) | 212 (17.7) |
| Asia | 60 (10.0) | 51 (8.5) | 111 (9.3) |
| Europe/Israel/Australia | 212 (35.3) | 229 (38.2) | 441 (36.8) |
| Latin America | 145 (24.2) | 144 (24.0) | 289 (24.1) |
| USA | 75 (12.5) | 71 (11.9) | 146 (12.2) |
| HIV history | |||
| Likely mode of HIV infection | |||
| Sexual contact with same sex | 339 (56.5) | 355 (59.3) | 694 (57.9) |
| Sexual contact with opposite sex | 217 (36.2) | 202 (33.7) | 419 (34.9) |
| Injection drug use | 9 (1.5) | 4 (0.7) | 13 (1.1) |
| Blood products/other/unknown | 35 (5.8) | 38 (6.3) | 73 (6.1) |
| Years known HIV positive | 0.9 [0.4, 2.9] | 1.2 [0.4, 3.0] | 1.0 [0.4, 3.0] |
| Recent infection (within 6 months) | 46 (7.7) | 46 (7.7) | 92 (7.7) |
| HIV subtype | |||
| B | 285 (47.5) | 295 (49.2) | 580 (48.4) |
| Other (non-B) | 176 (29.3) | 195 (32.6) | 371 (30.9) |
| Unknown | 139 (23.2) | 109 (18.2) | 248 (20.7) |
| Baseline CD4/HIV RNA | |||
| CD4a cell count (cells/μL) | 645 [583, 752] | 649 [578, 759] | 648 [580, 756] |
| Nadir CD4b cell count (cells/μL) | 549 [480, 643] | 545 [474, 643] | 547 [477, 643] |
| CD8 cell count (cells/μL) | 1040 [790, 1376] | 1105 [796, 1453] | 1072 [795, 1429] |
| CD4/CD8 ratio | 0.6 [0.5, 0.8] | 0.6 [0.4, 0.8] | 0.6 [0.5, 0.8] |
| HIV RNA (copies/mL) | 14 299 [4571, 48192] | 16 000 [5056, 43100] | 15 000 [4707, 45924] |
| HIV RNA < 3000 cp/mL | 113 (18.8) | 112 (18.7) | 225 (18.8) |
| Baseline medical history | |||
| Hepatitis B/C | 37 (6.3) | 33 (5.6) | 70 (5.9) |
| Prior CVD (stroke, MI, revascularization) | 5 (0.8) | 2 (0.3) | 7 (0.6) |
| Baseline CVD risk factors | |||
| Body mass index (kg/m2) | 24.1 [22.0, 27.5] | 24.4 [22.2, 27.6] | 24.3 [22.1, 27.5] |
| Current smoking | 182 (30.3) | 210 (35.1) | 392 (32.7) |
| Baseline biomarkers | |||
| IL-6 (pg/mL) | 1.39 [0.96, 2.22] | 1.45 [0.98, 2.23] | 1.42 [0.97, 2.23] |
| D-dimer (μg/mL) | 0.33 [0.22, 0.52] | 0.31 [0.22, 0.48] | 0.32 [0.22, 0.50] |
| Baseline anti-CD4 autoantibodies (AAB) | |||
| Anti-CD4 AAB (RLU) | 19 436 [10980, 295600] | 18 363 [10458, 168678] | 18 801 [10761, 205635] |
| Log10 anti-CD4 AAB | 4.3 [4.0, 5.5] | 4.3 [4.0, 5.2] | 4.3 [4.0, 5.3] |
| Post randomization | |||
| Initiated ART before M24 specimen | 600 (100.0) | 196 (32.7) | 196 (32.7) |
| Cumulative ART use before M24 specimen (months) | 23.5 [22.8, 24.0] | 0.0 [0.0, 6.9] | 20.6 [0.0, 23.5] |
| CD4 cell count at ART startc (cells/μL) | 642 [580, 745] | 456 [345, 614] | … |
Abbreviations: ART, antiretroviral therapy; CVD, cardiovascular; MI, myocardial infarction.
aMean of screening/baseline values.
bDocumented in participant record.
cAmong participants who initiated ART before the M24 specimen.
Baseline Anti-CD4 Autoantibody Prevalence and Associations With Baseline Factors
Among the full cohort of 1199 participants, the prevalence of anti-CD4 autoantibodies at baseline was 40.5% (41.2% in the immediate and 39.7% in the deferred groups). Overall, there was no difference in baseline anti-CD4 autoantibody prevalence by baseline CD4 cell count (P = .39) (Figure 1; Supplementary Table 2). From multivariate logistic regression models adjusted for age, sex, enrollment region, baseline CD4, baseline HIV RNA level, recent HIV infection (≤6 months), and batch (Figure 2), baseline anti-CD4 autoantibody prevalence was independently associated with (1) global region, where the highest prevalence was among participants enrolled at sites in Africa (53.8%), compared to other regions (range of 29.5%–45%, P = .001 for test across regions), and (2) recent HIV infection (≤6 months) compared to longer duration of infection (65.2% vs 38.4%; adjusted odds ratio [aOR] 3.24, 95% CI 2.05–5.13, P < .001). In a sensitivity analysis including HIV subtype (B, non-B, or unknown) instead of geographic region of enrollment in the multivariate model, subtype B was associated with a lower baseline anti-CD4 autoantibody prevalence (aOR 0.45, 95% CI .34–.60, P < .001) (Supplementary Table 3).
Figure 1.
Distribution of baseline anti-CD4 autoantibody levels by baseline CD4 count, n = 1199. A cutoff value of ≥25 000 relative light units (RLUs) represented by the dashed line defines anti-CD4 autoantibody seropositive (AAb+) versus seronegative (AAb−). Number in each strata: 500–599, n = 399; 600–699, n = 374, 700–799, n = 212, 800+, n = 214.
Figure 2.
Associations* of baseline factors with anti-CD4 autoantibody prevalence at baseline. *Multivariate regression model adjusted for all factors listed above and assay batch.
Month 24 Anti-CD4 Autoantibody Prevalence
By month 24, all 600 participants in the immediate and 196 (32.7%) in the deferred groups had initiated ART (median time on ART: 23.5 vs 0.0 months). The overall prevalence of anti-CD4 autoantibodies at month 24 was 30.7% in the deferred compared to 25.8% in the immediate groups (aOR deferred vs immediate: 2.04, 95% CI 1.38–3.01, P < .001) (Figure 3), indicating that early initiation of continuous ART decreased the prevalence of anti-CD4 autoantibodies at 24 months compared to deferring ART. When adjusting only for batch and baseline anti-CD4 autoantibody prevalence, this aOR was 1.77 (95% CI 1.24–2.54; P = .002). Participant characteristics by month 24 anti-CD4 autoantibody seropositivity are shown in Supplementary Table 4.
Figure 3.
Anti-CD4 autoantibody prevalence by treatment group and visit. aOR: Odds ratio comparing month 24 anti-CD4 autoantibody prevalence in the deferred versus immediate treatment groups after adjustment for baseline anti-CD4 autoantibody seropositivity, sex, age, baseline CD4, baseline HIV RNA, recent HIV infection (within 6 months), and assay batch.
The cross-tabulation between anti-CD4 autoantibody prevalence at baseline and month 24 by study group is shown in Table 2. Among those seropositive at baseline, 37.7% in the immediate and 29.4% in the deferred groups became seronegative at month 24. In contrast, among those who were seronegative at baseline, 0.3% (N = 1) in the immediate group and 4.4% (N = 16) in the deferred group became anti-CD4 autoantibody seropositive at month 24.
Table 2.
Baseline vs Month 24 Cross-tabulation for Anti-CD4 Autoantibody Seropositivity by Treatment Group
| Month 24 | |||||||
|---|---|---|---|---|---|---|---|
| Immediate Group | Deferred Group | ||||||
| N in Grp | Anti-CD4 autoantibody positive, N (%) | Anti-CD4 autoantibody negative, N (%) | N in Grp | Anti-CD4 autoantibody positive, N (%) | Anti-CD4 autoantibody negative, N (%) | ||
| Baseline | Anti-CD4 autoantibody positive | 247 | 154 (62.3) | 93 (37.7) | 238 | 168 (70.6) | 70 (29.4) |
| Anti-CD4 autoantibody negative | 353 | 1 (0.3) | 352 (99.7) | 361 | 16 (4.4) | 345 (95.6) | |
| Total | 600 | 155 | 445 | 599 | 184 | 415 | |
Among deferred group participants classified as anti-CD4 autoantibody seronegative at baseline, those who became seropositive at month 24 had lower odds of having started ART before month 24 (OR 0.21, 95% CI .05–.96, P = .044 in univariate model with batch adjustment) (Supplementary Table 5). In multivariate models, after adjusting for sample batch, there was no association between anti-CD4 autoantibody seroconversion from negative at baseline to positive at month 24 and age, sex, region of enrollment, baseline CD4 count, HIV RNA, ART initiation status, or length of ART. There were no incident cases of anti-CD4 autoantibody seropositivity at month 24 among the 13 participants who had recent HIV infection at entry.
Participants With Recent HIV Infection
Among the 92 participants with recent infection (≤6 months) across both groups, anti-CD4 autoantibody seropositivity decreased from 65.2% at baseline to 19.6% at month 24 (delta 45.6%). In comparison, among the 1107 participants with HIV infection of >6 months, seropositivity decreased from 38.4% to 29.0%, (delta 9.4%) representing a 4.9 times higher rate of conversion to seronegative status by month 24 for those with recent infections (P < .001 for difference). Findings were consistent across treatment groups.
CD4. Recovery
We next assessed the potential impact of anti-CD4 autoantibody seropositivity on the rate of CD4 recovery after ART initiation within the immediate group. The median baseline CD4 count in the immediate group was 645 cells/µL. When comparing participants who had anti-CD4 autoantibodies at baseline to those without, there was no difference in CD4 recovery from baseline to month 24 (Figure 4). The mean absolute CD4 count at month 24 in the anti-CD4 autoantibody seropositive group was 897 cells/µL, with a mean increase of 202 cells/µL from baseline. In comparison, the mean absolute CD4 count at month 24 in the seronegative group was 882 cells/µL, with a mean increase of 203 cells/µL (P = .79 for difference). Supplementary Figure 3 shows the mean changes in CD4 levels from baseline through month 24 by anti-CD4 autoantibody seropositivity assessed at month 24; there was no evidence for a difference in mean change in CD4 levels at month 24 (P = .90).
Figure 4.
Mean change in CD4 cell counts from baseline in the immediate group following randomization (mean +/− 2 SE) by baseline anti-CD4 autoantibody seropositivity (≥25 000 RLUs).
DISCUSSION
In this study, we found a high prevalence of anti-CD4 autoantibodies in PWH with pre-ART CD4 counts of >500 cells/µL, particularly in people with recent HIV infection, as well as in study participants in Africa. We also found that immediate ART initiation decreased the prevalence of anti-CD4 autoantibodies by month 24 compared with the deferred ART group where two-thirds of participants had remained ART naive by month 24. Anti-CD4 autoantibodies have been previously associated with immunologic nonresponse [9, 10, 21] and linked to a decreased rate and extent of CD4 recovery in individuals with low pre-ART CD4 counts [15]. However, we did not find an association between anti-CD4 autoantibodies and the rate of CD4 recovery among participants in the immediate group. Notably, the CD4 counts at ART initiation were relatively high in this group (median 642 cells/µL); the lack of an effect on CD4 recovery seen in this study in contrast to others may be due to a “ceiling effect,” where CD4 count recovery may have been limited by a relatively high CD4 count before starting ART. The clinical impact of anti-CD4 autoantibodies on PWH who have high CD4 cell counts is unclear, but within our study population, we found no evidence of a role for anti-CD4 autoantibodies in identifying PWH at risk for poor CD4 recovery.
Anti-CD4 autoantibody seropositivity was associated with several participant characteristics, which may be important factors in the pathogenesis of these autoantibodies. In multivariate analyses, we did not find evidence of a difference in baseline seropositivity between men and women. We previously reported a strong female predominance of these autoantibodies in people with advanced HIV [15]; given this difference, it is possible that females tend to retain the autoantibodies to a greater extent than males in longer, untreated disease. We also found that the prevalence of autoantibodies was associated with geographic site of enrollment, with the highest prevalence in participants from Africa, and among those with recent (≤6 months) HIV infection. It is possible that the increased prevalence of anti-CD4 autoantibodies in participants in Africa could be due to differences in envelope protein structure from the higher rate of non-B subtypes of HIV-1 among this participant group, as we found that non-B subtype was independently associated with baseline anti-CD4 autoantibodies. However, the subtype was unknown for 21% of participants. Further research is needed to discern whether the geographic differences in anti-CD4 autoantibody prevalence are due to viral differences, differences in the immune response among these participants, or other factors.
Contrary to our original hypothesis, a higher prevalence of anti-CD4 autoantibodies was seen in participants with recent HIV infection compared to those with longer duration of infection. While the mechanisms behind this are unclear, we hypothesize that the high HIV RNA and increased CD4 cell destruction in early HIV infection may lead to the exposure of epitopes that generate anti-CD4 autoantibodies and that these autoantibodies may wane over time, albeit not completely in all individuals, during clinical latency. In support of this hypothesis, we noted both an increased rate of conversion to anti-CD4 autoantibody seronegativity in individuals with recent HIV infection who were randomized to immediate ART compared to those who had more longstanding infection and that these autoantibodies were also lost among some individuals with recent HIV infection in the deferred group.
In conclusion, our findings suggest that (1) anti-CD4 autoantibodies often develop shortly after HIV infection and potentially more frequently with non-B subtypes and (2) ART initiation reduces these autoantibodies, especially when ART is started soon after HIV infection. The potential pathogenic role of these autoantibodies in individuals who begin ART with high CD4 counts remains uncertain.
Supplementary Material
Notes
Acknowledgments. We thank the START study participants, investigators, and staff.
Author contributions. The INSIGHT START Study Group conducted the original START trial. For this study, B. P. E., A. L., S. M., B. G., J. V. B., P. D. B., and I. S. contributed to the conceptualization. B. P. E., A. L., S. M., B. G., J. V. B., P. D. B., and I. S. contributed to the methodology. All authors contributed to the investigation. S. M. and B. G. verified the data and performed the statistical analyses. P. D. B. and I. S. contributed to the supervision of this study. P. D. B., I. S., and the INSIGHT START Study Group contributed to the resources of this study. B. P. E. wrote the original draft of the manuscript. All authors contributed to reviewing and editing of the manuscript and were responsible for the final decision to submit for publication.
Disclaimer . The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the US Government. However, the findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services.
Financial support. Funding for START includes the U.S. National Institutes of Health grants UM1-AI068641, UM1-AI120197, and 1U01-AI36780 (ClinicalTrials.gov ID: NCT00867048). The START study was supported by the National Institute of Allergy and Infectious Diseases; National Institutes of Health Clinical Center; National Cancer Institute; National Heart, Lung, and Blood Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Agence Nationale de Recherches sur le SIDA et les Hépatites Virales (France); National Health and Medical Research Council (Australia); National Research Foundation (Denmark); Bundesministerium für Bildung und Forschung (Germany); European AIDS Treatment Network; Medical Research Council (United Kingdom); National Institute for Health Research; National Health Service (United Kingdom); and University of Minnesota. Antiretroviral drugs were donated to the START central drug repository by AbbVie, Bristol-Myers Squibb, Gilead Sciences, GlaxoSmithKline/ViiV Healthcare, Janssen Scientific Affairs, and Merck. Deidentified data and supporting documentation will be made available to researchers after approval of a proposal for use of the data. Proposals for data use should be submitted by means of the research proposal form on the INSIGHT website (http://www.insight-trials.org). This research was supported in part by the Intramural Research Program of the NIH.
Contributor Information
Brian P Epling, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Shweta Mistry, Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.
Birgit Grund, Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.
Jason V Baker, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA; Division of Infectious Diseases, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA.
Carlos Brites, Department of Medicine, Universidade Federal da Bahia, Salvador, Brazil.
Julien Gras, Infectious Diseases Department, APHP-Saint-Louis Hospital, Paris, France.
Sandy Pillay, Enhancing Care Foundation, Durban, South Africa.
Blessing Uche, Institute of Human Virology, Abuja, Nigeria.
John A Chiorini, Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA.
Andrea Lisco, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Peter D Burbelo, Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA.
Irini Sereti, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Supplementary Data
Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.
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