In a three-arm randomized trial conducted among adult patients in HIV treatment centers in The Netherlands, Marlous Grijsen and colleagues examine the effects of temporary combination antiretroviral therapy during primary HIV infection. The objective of this study was to assess the benefit of temporary combination antiretroviral therapy (cART) during primary HIV infection (PHI).3 or symptomatic HIV disease. In total, 173 participants were randomized. The modified intention-to-treat analysis comprised 168 patients: 115 were randomized over the three study arms, and 53 randomized over the two treatment arms. Of the 115 patients randomized over the three study arms, mean viral set point was 4.8 (standard deviation 0.6) log10 copies/ml in the no treatment arm, and 4.0 (1.0) and 4.3 (0.9) log10 copies/ml in the 24- and 60-wk treatment arms (between groups: p<0.001). The median total time off therapy in the no treatment arm was 0.7 (95% CI 0.0–1.8) y compared to 3.0 (1.9–4.2) and 1.8 (0.5–3.0) y in the 24- and 60-wk treatment arms . In the adjusted Cox analysis, both 24 wk (hazard ratio 0.42 [95% CI 0.25–0.73]) and 60 wk of early treatment (hazard ratio 0.55 [0.32–0.95]) were associated with time to (re)start of cART.Adult patients with laboratory evidence of PHI were recruited in 13 HIV treatment centers in the Netherlands and randomly assigned to receive no treatment or 24 or 60 wk of cART ; if therapy was clinically indicated, participants were randomized over the two treatment arms . Primary end points were (1) viral set point, defined as the plasma viral load 36 wk after randomization in the no treatment arm and 36 wk after treatment interruption in the treatment arms, and (2) the total time that patients were off therapy, defined as the time between randomization and start of cART in the no treatment arm, and the time between treatment interruption and restart of cART in the treatment arms. cART was (re)started in case of confirmed CD4 cell count <350 cells/mmIn this trial, temporary cART during PHI was found to transiently lower the viral set point and defer the restart of cART during chronic HIV infection.ISRCTN59497461Current Controlled Trials Please see later in the article for the Editors' Summary Every year, nearly three million people become infected with HIV, the virus that causes AIDS. The first stage of HIV infection—primary HIV infection—lasts a few weeks and often goes undetected, although most individuals develop a short, flu-like illness. During this stage of infection, the immune system begins to make antibodies to HIV. The second stage of HIV infection, which lasts many years, also has no major symptoms but, during this stage, HIV slowly destroys immune system cells, including CD4 cells, a type of lymphocyte. Eventually, the immune system is unable to fight off other infections and patients enter the third phase of HIV infection—symptomatic HIV infection. The final stage—AIDS—is characterized by the occurrence of one or more AIDS-defining conditions, which include severe but unusual infections and several types of cancer. Early in the AIDS epidemic, most HIV-positive people died within ten years of infection. Nowadays, although there is still no cure for HIV infection, HIV has become a chronic disease because of the availability of combination antiretroviral therapy . This means that many HIV-positive people have a near-normal life span.3) of blood, when they develop severe constitutional symptoms such as fever lasting longer than a month, or when they develop an AIDS-defining condition. But could a short course of cART during primary HIV infection be clinically beneficial? Some, but not all, nonrandomized studies have shown that such treatment reduces the viral set point  and slows the decline of CD4 cell count in patients. In this randomized trial , the researchers assess the clinical benefit of temporary cART initiated during primary HIV infection by measuring its effects on the viral set point and on when patients have to restart cART during chronic HIV infection. In a randomized controlled trial, patients are assigned by the play of chance to receive different treatments and then followed to compare the effects of these treatments.It is currently recommended that people start cART when their CD4 count falls below 350 CD4 cells per cubic milliliter  and determined the time off therapy  for each patient. cART was (re)started following two consecutive CD4 counts below 350 cells/mmThese findings suggest that temporary cART during primary HIV infection can transiently lower the viral set point and can delay the need to restart cART during chromic HIV infection. They also suggest that 24 weeks of cART during primary HIV is as effective as 60 weeks of treatment. These findings need to be confirmed in other settings, and follow-up studies are needed to evaluate the long-term benefits of early temporary cART, but given the short time between cART interruption and treatment restart, the researchers suggest that not interrupting early cART, but instead continuing it for life, should be considered. However, they add, because patients are often physically and emotionally distressed at this stage of HIV infection, adherence to cART during primary HIV infection may be suboptimal, and so patients with primary HIV infection should be advised to start cART only when they feel ready to start treatment.http://dx.doi.org/10.1371/journal.pmed.1001196.Please access these web sites via the online version of this summary at HIV infection and AIDS, including information on the clinical progression of HIV infectionInformation is available from the US National Institute of Allergy and infectious diseases on NAM/aidsmap provides information about HIV/AIDS, including a factsheet on primary infectionAvert, an international AIDS charity on many aspects of HIV/AIDS, including detailed information on HIV treatment and care and on the stages of HIV infection (in English and Spanish)Information is available from 2010 antiretroviral therapy guidelines provide recommendations on when to initiate cARTThe World Health Organization's Primo-SHM is availableInformation about Avert and through the charity website HealthtalkonlinePatient stories about living with HIV/AIDS are available through  The optimal management of primary HIV infection (PHI) and the possible impact of temporary combination antiretroviral therapy (cART) on clinical outcome are controversial From a clinical perspective, an important question is whether patients who are treated during PHI remain off treatment longer than patients in whom treatment is deferred until indicated based on their CD4 cell count or clinical condition. A randomized controlled trial of 6 mo of zidovudine monotherapy in patients with PHI reported a reduction of minor opportunistic infections during the first year of follow-up We conducted the Primo-SHM trial, in which patients with PHI were randomized between no treatment and 24 and 60 wk of early cART. The aim of the study was to assess the clinical benefit of temporary cART initiated during PHI, measured by the time that patients could remain off therapy until subsequent (re)start of cART was indicated based on current treatment guidelines, and to assess the optimal duration of such early treatment.Inclusion criteria were age over 18 y and laboratory evidence of PHI infection, defined as a negative or indeterminate Western blot in combination with detectable plasma HIV-1 RNA (Fiebig stage I–IV) or, in case of a positive Western blot, a documented negative HIV screening test in the previous 180 d  with no treatment. Patients were recruited in 13 HIV treatment centers in the Netherlands. Participants were randomly assigned to receive no treatment or 24 or 60 wk of cART (three-way randomization). In cases where treatment was clinically indicated based on severe clinical symptoms  or the patient insisted on starting early cART, participants were randomized over the 24- and 60-wk treatment arms only (two-way randomization).The study was designed to evaluate (1) the effect of temporary treatment during PHI on the viral set point, defined as the plasma viral load (pVL) at 36 wk after randomization in the no treatment arm and at 36 wk after treatment interruption (TI) in the treatment arms, and (2) the total time that patients were off therapy after randomization (treatment-free period). The analyses of the viral set point and the total time off therapy were restricted to patients who were randomized over all three study arms. A second comparison evaluated the optimal duration of early cART by comparing all patients who were treated with 24 or 60 wk of cART, including patients from both the three- and two-way randomization.Recruitment started 1 May 2003 and continued until 31 March 2010. The present analysis includes follow-up data until 14 September 2011.Patients were allocated to one of the three study arms using a computerized minimization algorithm with stratification for the stage of PHI . The Dutch HIV Monitoring Foundation performed the randomization procedure, had no interaction with study participants, and was responsible for data management. Randomization results were sent by fax to the clinical investigators, who were unaware of the allocation procedure.Early cART consisted of a triple-class regimen of zidovudine/lamivudine (300/150 mg bid), efavirenz (600 mg qd), and lopinavir/ritonavir capsules (533/133 mg bid). The last was discontinued when the pVL dropped below 50 copies/ml. Changes to this regimen were allowed in case of transmitted drug resistance or if one of the drugs was contraindicated or not tolerated. After 28 January 2008, zidovudine/lamivudine was replaced by tenofovir/emtricitabine (245/200 mg qd), according to the Dutch standards of care, and lopinavir/ritonavir tablets (600/150 mg bid) replaced the capsules. Patients on early cART were required to reach viral suppression below 50 copies/ml in plasma before interrupting therapy.http://hivdb.stanford.edu/) Baseline evaluations included a medical history including the presence of symptoms compatible with an acute retroviral syndrome, a physical examination, and collection of blood for routine hematology and chemistry, CD4 and CD8 cell counts, pVL, hepatitis B and C serology, and storage of peripheral blood mononuclear cells. All study sites used a sensitive HIV RNA assay with comparable accuracy and a lower limit of detection of 40 or 50 copies/ml Randomization was performed at week 0, and patients randomized to receive cART started treatment. Patients were scheduled for follow-up visits at weeks 2, 4, 8, and 12, and every 12 wk thereafter for the duration of the study. Additional visits were scheduled at weeks 4, 8, and 12 following TI. During each follow-up visit, blood was collected for routine hematology and chemistry, CD4 and CD8 cell counts, pVL, and storage of peripheral blood mononuclear cells.3 on two consecutive occasions, severe constitutional symptoms, or the occurrence of an AIDS-defining event 3 or less on two consecutive occasions The primary efficacy end points were (1) the viral set point, defined as pVL at 36 wk after randomization in the no treatment arm and pVL at 36 wk after TI in the treatment arms, and (2) the total time that patients were off therapy, defined as the time between randomization and start of cART in the no treatment arm, and as the time between TI and restart of cART in the treatment arms. We chose the viral set point as the pVL at 36 wk to allow for stabilization of the pVL in untreated and treated patients At the time the protocol was developed in 2002, insufficient data were available to make a reliable estimation of the sample size needed to detect a clinically significant difference between the study arms in viral set point and time to (re)start of cART. An estimated number of 30 patients were expected to be enrolled annually, and in the protocol amendment of 28 January 2008, the end of study inclusion was set at March 2010, when we expected to have enrolled approximately 200 patients.As defined in the study protocol, we conducted a modified intention-to-treat (mITT) analysis: participants who withdrew consent because they insisted on starting early cART while randomized to the no treatment arm or those who declined therapy after being randomized to one of the treatment arms were excluded from the analysis. In a standard intention-to-treat analysis, patients who are randomized to the no treatment arm but insist on starting treatment immediately have 0 d of survival since they reach the study end point immediately at initiation of treatment, and patients who are randomized to one of the treatment arms but refuse to start early treatment reach the study end point only at the moment they eventually start cART. This results in a strong and unnecessary bias in favor of the treatment arms. Therefore, we report the mITT analysis, in which patients who did not start with the allocated treatment plan were excluded.Participants who were lost to follow-up while still on early cART were not included in the analyses. Patients who discontinued early cART before completing the scheduled period of 24 or 60 wk remained in the mITT analysis. Participants who did not discontinue early cART at the scheduled TI date or were lost to follow-up after randomization/TI were considered having reached the (re)start end point. Per protocol analyses were performed excluding patients who discontinued cART earlier than planned and patients who did not discontinue cART at the scheduled TI date but instead elected to continue cART for reasons other than a low CD4 count or symptomatic disease. Patients who had not yet (re)started cART were censored at the last follow-up visit.Reported results concern data from the three-way randomized patients unless stated otherwise. Viral set point and the CD4 cell count measured at viral set point were compared using one-way ANOVA. The evolution of pVL and CD4+ T cell count following randomization/TI in the no treatment/treatment arms were analyzed using linear mixed models incorporating repeated measurements. Both the pVL and CD4 evolution showed a tri-phasic pattern with distinct slopes from week 0 to 8, week 8 to 36, and week 36 to 144. The total time off therapy for the no treatment versus the treatment arms was analyzed using the mITT population of the three-way randomized patients. For the second comparison, which evaluated the optimal duration of early cART, we combined the data of all treated patients, including those participants who were randomized over the two treatment arms. In both analyses, the total time off therapy was compared across the study arms using Kaplan-Meier plots and log rank tests.We fitted a series of multivariable Cox regression models of time to (re)start of cART using the mITT population and the per protocol population of the three-way randomized patients separately and of the three- and two-way randomized patients combined. Because the sample size of our study was relatively small, the study arms might by chance be imbalanced with regard to certain prognostic factors. To reduce potential bias, we weighted all Cox regression analyses using propensity score weights p-values were two-sided and considered statistically significant when less than 0.05.We hypothesized that the proposed mechanism by which early cART may result in a clinical benefit was by lowering of the viral set point and/or by increasing the CD4 cell count during the treatment period. Therefore, we also fitted Cox models that included the viral set point at 36 wk after randomization/TI in the no treatment/treatment arms and the CD4 cell count measured at viral set point. To further explore which factors might be associated with time to (re)start of cART, other than temporary early cART, viral set point, and CD4 cell count at viral set point, we performed a final sub-group analysis for which we selected all patients who had been randomized to one of the early treatment arms and fitted a multivariable Cox model using a stepwise selection process using all aforementioned variables. Data were analyzed using SPSS version 18.0 (SPSS) and SAS version 9.1.3 (SAS Institute). All reported The patient enrollment is summarized in 3 in the no treatment arm, and 584 (230) cells/mm3 and 483 (241) cells/mm3 in the 24- and 60-wk treatment arms. The multivariable Cox regression analyses were adjusted for this baseline difference. Nine of 99 patients (9%) for whom HIV genotyping was available harbored transmitted drug-resistance mutations: in eight patients a single mutation in reverse transcriptase or protease was present, and one patient harbored a mutation in both reverse transcriptase and protease. All participants were treated with at least three active drugs during early cART. The presence of CXCR4-using viruses at baseline, CCR5 Δ32 heterozygosity, and HLA B27 or B57 were equally distributed between the three study arms. One untreated patient had a chronic hepatitis B infection, and none had hepatitis C infection. Patients on early cART had a median delay of 2 (IQR 0–6) d between randomization and start of early cART. The study drugs were well tolerated and caused no serious adverse events.Demographic and baseline characteristics of the three-way randomized patients are summarized in No differences in baseline characteristics were seen between the three- and two-way randomized treated patients, with the exception of the nucleoside backbone (zidovudine/lamivudine versus tenofovir/emtricitabine) used during early cART .Of the three-way randomized patients, 66/76 treated participants (87%) had a pVL below 50 copies/ml at the time of TI. Five patients interrupted early cART on the scheduled TI date even though their pVL was not below 50 copies/ml (median pVL 161 [range 74–262] copies/ml). The remaining five participants had interrupted early cART before the scheduled TI date and had a median pVL of 977  copies/ml.10 copies/ml/wk in the 24- and 60-wk treatment arms, respectively (p = 0.05). From week 8 to 36 after randomization/TI the pVL did not change significantly in the no treatment and 24-wk treatment arms, but increased by 0.01 log10 copies/ml/wk in the 60-wk treatment arm  and 4.3 (0.9) log10 copies/ml in the 24- and 60-wk treatment arms, respectively . Seven patients in the 24- and 60-wk treatment arms had a pVL<1,000 copies/ml at viral set point, of whom four had a pVL below 100 copies/ml, as compared to none in the no treatment arm. From week 36 to 144 the pVL increased by 0.18 and 0.21 log10 copies/ml/y (p = 0.9) in the 24- and 60-wk treatment arms until the pVL in the three study arms slowly converged 2–3 y after TI.Twelve weeks after TI, 69/71 participants (97%) for whom a pVL was available had a viral rebound. Following TI, the pVL rebounded during the first 8 wk, with an estimated increase of 0.29 and 0.23 log3, respectively (p = 0.3). After TI the CD4 cell counts showed an initial rapid decline during the first 8 wk, with an estimated loss of 6.3 and 10.7 cells/mm3/wk in the 24- and 60-wk treatment arms, respectively (p = 0.3). From week 8 to 36 and week 36 to 144 after randomization/TI, the CD4 count decline was similar between all three study arms  and 503 (254) cells/mm3 in the 24- and 60-wk treatment arms, respectively .The mean CD4 cell count at TI in the 24- and 60-wk treatment arms was 737 (SD 245) and 672 (245) cells/mmp = 0.008). 54/78 participants (69%) (re)started cART according to the CD4 count criteria, four patients (5%) because of severe symptoms/AIDS diagnosis, and 20 (26%) patients because of a preference by physician or patient to restart cART. Of note, 14 of these 20 patients had already had one CD4 count <350 cells/mm3. The mean CD4 cell count at (re)start was 294 (SD 126) cells/mm3 in the no treatment arm, and 322 (114) and 317 (127) cells/mm3 in the 24- and 60-wk treatment arms (p = 0.7). The median time off therapy after randomization/TI was 0.7  y in the no treatment arm, and 3.0 (1.9–4.2) and 1.8 (0.5–3.0) y in the 24- and 60-wk treatment arms, respectively start of cART, 34/36 (94%) patients in the no treatment arm, 26/38 (68%) in the 24-wk treatment arm, and 27/38 (71%) in the 60-wk treatment arm would have (re)initiated cART (p = 0.01). The median time off therapy after randomization/TI would have been 0.5 (95% CI 0.4–0.6) y in the no treatment arm, and 2.0 (1.2–2.9) and 0.7 (0.3–1.0) y in the 24- and 60-wk treatment arms, respectively .At the time of analysis, 32/36 patients (89%) in the no treatment arm had initiated cART, while 22/38 (58%) in the 24-wk treatment arm and 24/38 (63%) in the 60-wk treatment arm had restarted cART (n = 128), including those participants who were randomized over the two treatment arms, the median time off therapy was 3.1 (95% CI 2.1–4.0) and 2.1 (1.1–3.1) y in the patients treated for 24- and 60-wk, respectively (p = 0.03). However, 6/128 patients discontinued cART earlier than planned, and 5/128 did not discontinue cART at the scheduled TI date even though their CD4 cell counts were high . Repeating the analysis for the per protocol population of the three-way randomized patients yielded similar results (Model 2). Then we pooled the three-way and two-way randomized patients and performed Cox analyses for both the mITT population and the per protocol population, adjusting both analyses for the randomization scheme of each patient. The hazard ratio for time to (re)start of cART was significantly smaller in both treatment arms as compared to the no treatment arm (Models 3 and 4), and there were no significant differences between the two treatment arms .We fitted six Cox regression models to compare the time to (re)start of cART between the patients in the no treatment and 24- and 60-wk treatment arms . To accoTo explore the contribution of the lowering of the viral set point and the increase in CD4 cell count during early treatment to the timing of (re)start of cART, we added these two parameters to the Cox models. Both the viral set point and the CD4 count at set point were strong and independent predictors of time to (re)start of cART in both the three-way randomized patients and the three- and two-way randomized patients combined (Models 5 and 6). In these two models, early cART was no longer significantly associated with time to restart of cART, suggesting that the lowering of the viral set point and the increase of the CD4 cell count during the early treatment period explained for the most part why early cART resulted in a clinical benefit.In an attempt to identify further factors that predict a longer time to restart of cART, we performed a final sub-group analysis of all patients who had been treated with temporary early cART. This model included both the viral set point and CD4 count at set point. We found that the stage of PHI, the self-reported occurrence of an acute retroviral syndrome, baseline pVL, and all other investigated factors were not statistically significantly associated with time to (re)start of cART.10 copies/ml, increased the CD4 cell count, and deferred the need for initiation of cART during chronic HIV infection by 1.1–2.3 y. Both viral set point and CD4 cell count measured at viral set point were associated with time to (re)start of cART. The duration of temporary early cART was not predictive, suggesting that 24 wk of cART would be sufficient. The time off therapy was longer in the 24-wk treatment arm than in the 60-wk treatment arm, but in the per protocol analysis, and in the Cox models, which were adjusted for possible confounders, there was no statistically significant difference between the 24- and 60-wk treatment arms.The present randomized trial provides the strongest evidence to date of a clinical benefit of temporary cART initiated during PHI. Early cART transiently lowered the viral set point by 0.5–0.8 logOverall, these findings are in agreement with the data of the SETPOINT study and SPARTAC trial, in which, respectively, 36 and 48 wk of cART during early HIV infection modestly delayed the need for subsequent initiation of cART An important strength of our study is that it showed a significant benefit of temporary early cART as compared to deferred therapy even using conservative mITT analyses, in which patients who discontinued cART earlier than planned remained in the analyses and those who did not discontinue cART at the scheduled TI date were considered as having reached the study end point.p = 0.02). More importantly, after adjusting for baseline CD4 cell count in the Cox models, temporary early cART remained associated with a longer time to subsequent reinitiation of cART. Second, by providing the option of randomizing patients over the two treatment arms only, we might have introduced selection bias, as patients with more severe symptoms might have been less likely to be enrolled in the three-way randomization. However, we observed that the baseline characteristics, viral set point, CD4 cell count measured at viral set point, and the time to (re)start of cART did not differ between the three- and two-way randomized treated patients. Additionally, the adjusted Cox models also showed that the time to (re)start of cART was not different for the treated patients randomized over three or two study arms. Third, according to current treatment guidelines zidovudine/lamivudine was replaced by tenofovir/emtricitabine halfway through the trial, which is a potential source of variability in baseline characteristics between treatment groups. However, the proportion of PHI patients treated with both drugs was comparable in both treatment arms start of cART. This is in contrast with findings from two previous cohort studies. In one study, initiation of cART within 2 wk of antibody seroconversion was associated with viral and immunological benefits during at least 24 wk after TI as compared to starting therapy between 2 wk and 6 mo after HIV seroconversion Temporary cART initiated during PHI deferred the subsequent restart of cART, which, according to the Cox models, was most likely caused by the effects of the CD4 gain during treatment and the transient lowering of the viral set point. The question remains how the latter might be explained. We know that two critical events in PHI are the massive destruction of CD4 T cells in the gastrointestinal tract and the establishment of latent HIV reservoirs. Early treatment may result in viral suppression and immune restoration in gut-associated lymphoid tissue The gain in treatment-free years must be weighed against potential disadvantages of starting cART in the acute stage of the disease, a period in which patients are often physically and emotionally distressed and adherence may be suboptimal. In addition, early cART is often initiated before baseline resistance test results are available and therefore may require “overtreatment” to ensure an effective drug regimen, which may increase the risk of drug toxicity. In our study, patients were started on a triple-class therapy. In any case, if early treatment is considered, it should at least include a boosted protease inhibitor until resistance testing results are available Structured TI studies in chronic HIV infection have fallen into disfavor after the SMART trial demonstrated that CD4-guided TI was associated with adverse outcomes and a rapid CD4 cell count decline as compared to continuous therapy Nonetheless, even in the patients in our study who received temporary early cART, the total time off therapy was relatively short. The lowering of the viral set point was transient, suggesting loss of protective immune functions and the emergence of viral escape mutants. Therefore, a reasonable question is whether early cART should not be interrupted but continued for life, given the concern that uncontrolled HIV replication and chronic immune activation carry an increased risk of morbidity and mortality at all stages of HIV infection In conclusion, this randomized study demonstrates a clear clinical benefit of temporary cART initiated during PHI. Early cART transiently lowered the viral set point and deferred the need for restart of cART during chronic HIV infection. Although extended follow-up studies are needed to evaluate the long-term benefits of such early treatment, starting cART when the patient is ready to do so seems the most reasonable advice for patients with PHI.Table S1Baseline characteristics, viral set point, and CD4 cell count measured at viral set point of the three- and two-way randomized treated patients.(DOC)Click here for additional data file.Text S1Trial protocol.(DOC)Click here for additional data file.Text S2CONSORT checklist.(DOC)Click here for additional data file.


The degree of immune reconstitution achieved in response to suppressive ART is associated with baseline individual characteristics, such as pre-treatment CD4 count, levels of viral replication, cellular activation, choice of treatment regimen and gender. However, the combined effect of these variables on long-term CD4 recovery remains elusive, and no single variable predicts treatment response. We sought to determine if adiposity and molecules associated with lipid metabolism may affect the response to ART and the degree of subsequent immune reconstitution, and to assess their ability to predict CD4 recovery.We studied a cohort of 69  HIV-infected, treatment-naïve South African subjects initiating antiretroviral treatment . We collected information at baseline and six months after viral suppression, assessing anthropometric parameters, dual energy X-ray absorptiometry and magnetic resonance imaging scans, serum-based clinical laboratory tests and whole blood-based flow cytometry, and determined their role in predicting the increase in CD4 count in response to ART.+ T cell count, viral load, CD8+ T cell activation (CD95 expression) and metabolic and anthropometric parameters linked to adiposity (LDL/HDL cholesterol ratio and waist/hip ratio) significantly contribute to variability in the extent of CD4 reconstitution (ΔCD4) after six months of continuous ART.We present evidence that baseline CD4+ T cell recovery in virally suppressed individuals, representing a workable predictive model of immune reconstitution.Our final model accounts for 44% of the variability in CD4 Defectrecovery ; however32CCR5 [Baseline CD4+ T cell count remains the most relevant predictor of clinical progression and survival in subjects on antiretroviral therapy (ART) , but by 32CCR5 , antiret32CCR5  and, in 32CCR5 .+ T cells), alterations of memory T cell subsets and depletion of innate immune subsets  are associated with advanced HIV infection [Immune activation of the T cell compartment  is associated with chronic inflammation and increased immune activation, with alteration of metabolic parameters associated with lipid metabolism and increased atherogenic risk (as assessed by increased carotid intima-media thickness) in subjects of both sexes ,19. A nuThe existence of a relationship between metabolic markers, viremia and immune activation is also suggested by the observation that ART-mediated suppression of HIV replication results in a rapid normalization of a number of markers linked to cardiovascular risk .While these observations highlight the negative effects of HIV infection on lipid metabolism and overall atherogenic risk, it is of note that cohort-based observations indicate that high adiposity  might be beneficial for HIV-infected individuals, contributing to lower steady state viral replication and slower disease progression ,26. Alto+ T cell count and early CD4 response [+ T cell immune reconstitution. To our knowledge, adiposity-associated metabolic markers , have not used in these models, and their predictive role remains unclear.In a reported multivariate analysis, subject age, nadir and baseline CD4 count and initial viral load were found to be inversely associated with early CD4 response to suppressive ART ; importaresponse ; however+ T cell counts. The present study followed a cohort of ART-naïve, HIV-infected South African subjects. We demonstrate that metabolic parameters measured before ART have a significant effect on the degree of immune reconstitution attained after six months of continuous ART and contribute significantly to a predictive model of CD4+ T cell immune reconstitution.Based on the reported association of viremia and CD4 counts with body mass index (BMI) and serum lipid levels, we sought to determine: (1) if adiposity and markers associated with lipid metabolism can affect the degree of early (six months ) immune We assessed 69 ART-naïve, HIV-infected subjects initiating ART  at the Clinical HIV Research Unit of the Themba Lethu Clinic, Johannesburg, South Africa . Medical history was obtained from the clinic record and by interview. Written informed consent was obtained from all participants as per University of the Witwatersrand Ethics Committee- and Wistar Institute Institutional Review Board-approved study protocol.2. Dual energy X-ray absorptiometry (DEXA) scans were performed using a Hologic QDR-2000 scanner, assessing limb and trunk fat and lean mass. Magnetic resonance imaging (MRI) scans were performed using a Toshiba Flexart 0.5 T; a single L4-L5 axial section was used to determine sagittal diameter, visceral, subcutaneous, total abdominal and peri-renal fat. The analysis was conducted using V3.51*R553 software.Baseline height, weight and anthropometric measurements were obtained pre-ART by trained study personnel; BMI was calculated as weight (kg) divided by height (m)END), using the single platform method described by Scott and Glencross [CD4 counts were assessed at baseline  and approximately 36 weeks from ART initiation . The following antibody combinations were used for the specified target populations: T cell activation/differentiation: CD8, CD28, CD3, CD38; and T cell activation: CD8, CD95, CD3, HLA-DR. After RBC lysis, sample fluorescence data were acquired with a FACScalibur flow cytometer and analyzed using CellQuest software (BD Biosciences). Isotype-matched control antibodies were used as negative controls for gate positioning.Summary statistics  are reported for each independent variable (listed in Table BL) was 243 cells/mm3, with a median log10VL (log10VLBL) of 4.7. Median BMI was 26.8kg/m2, with 70% of the cohort being overweight or obese (48 of 69 subjects with BMI > 25); median LDL/HDL ratio was 1.8, and median serum fasting glucose was 4.2 mmol/l. According to the Adult Treatment Panel III guidelines [The baseline characteristics of our cohort are summarized in Table idelines , 65% of END) was 421 cells/mm3 (interquartile range: 355-505), with a median gain (ΔCD4) of 172 (IQR 92-247) CD4+ T cells; five subjects (5.2%) failed to gain CD4 on ART in the presence of viral suppression . As expected, the spread of the distribution in CD4 gain after ART supports the hypothesis that, in addition to viral suppression alone, other factors may determine the extent of immune reconstitution on ART.After 24 weeks of ART, the median endpoint CD4 count , suggesting that subjects with high levels of viral replication had the most benefit from pharmacological suppression in terms of CD4 recovery. Conversely, lower baseline CD4BL correlated with higher ΔCD4 , indicating a greater benefit of therapy in these subjects.The unadjusted effects of baseline characteristics on ART-mediated immune reconstitution, as measured by ΔCD4 count, are summarized in Table + CD8+ T cells, an immune activation parameter previously shown to predict pDC recovery on ART [2 = 0.13). We did not detect a significant association of CD38 or HLA-DR expression on CD4+ or CD8+ T cells with CD4 outcomes (not shown).Baseline levels of CD95y on ART , had a s2 = 0.072); no association was observed for BMI or gender, suggesting that the relationship is limited to central adiposity, as assessed by waist/hip ratio. LDL/HDL cholesterol ratio  was also associated with ΔCD4, unlike other lipid measures (not shown).As summarized in Table To assess if the observed negative effect of central adiposity  and lipid indicators could be associated with incomplete or delayed suppression of viral load below 50 copies/ml, we compared the proportion of individuals achieving viral suppression (VL < 400 c/ml) over time between normal/underweight, overweight and obese subjects, using a Kaplan-Meier analysis. The survival curves were not significantly different Figure . In addi2 of each model tested is reported in Table BL and log10VLBL accounted for approximately 18% of the variability in ΔCD4 (adj. R2 = 0.1828). We also observed a significant interaction between CD4BL and log10VLBL . As CD8+ T cell activation has been associated with clinical outcomes in past studies, we tested whether including in this model the frequency of CD95+ CD8+ T cells, the only activation term individually associated with the ΔCD4 outcome, would improve the predictivity of CD4BL and VLBL: our results indicate an adj. R2 of 0.2751 for the combined model, supporting the use of an activation term.We used a multivariable approach to estimate the combined effect of multiple baseline variables on CD4 recovery on ART. The adjusted R2 = 0.1122, similar to CD4BL alone); when both metabolic parameters were added to CD4BL and VLBL, the model accounted for almost 37% of ΔCD4 variability (adj. R2 = 0.3673), confirming the role of these metabolic terms as outcome predictors.The metabolic terms, LDL/HDL cholesterol ratio and waist/hip ratio, together accounted for 11% of ΔCD4 variability , which is approximately twice as much as the best performing CD4BL and log10VLBL-based model, and 1.6 times greater than the model including CD4BL, log10VLBL and CD95 expression. The addition of an interaction term between CD4BL and CD95+ CD8+ T cells resulted in a further increase of the model predictivity , but as the effect of the interaction term per se was not significant (p = 0.057), it was not included in the final model.The final model, selected for best fit by assessing the models' -2 log likelihood  in response to ART-mediated viral suppression, confirmed in our cohort, suggests that a number of factors, in addition to successful viral suppression, might affect the extent of immune recovery. Pre-treatment CD4 counts, viral load and immune activation are recognized to play a role in determining the levels of immune recovery ,12,34-36+ T cell recovery outcome, explaining only 21% of its variability. The effect of baseline CD4 on ΔCD4 was negative, confirming a prior report [+ T cell levels on CD4 immune reconstitution, which was found to be positive, as we considered ΔCD4 (a measure incorporating CD4BL) more relevant to assessing an immune reconstitution response. Prior studies have reported an effect of age and gender on CD4 outcomes of treatment [Our results confirm that pre-ART VL, CD4 count and cellular activation , alor report ; unlike reatment ,27; whil+ T cell recovery. While this finding is novel, associations of lipid levels and viral replication have been reported [We found a meaningful negative association between LDL/HDL ratio and CD4reported -43, suggreported -46. Morereported -49). A rreported , and stureported -56.Adiposity has generally been associated with better viral control and slower disease progression in ART-naïve, HIV-positive subjects ,26,57,582). Based on these observations, as well as the present contribution, further studies in larger cohorts will be necessary to determine if metabolic parameters play the same role in low-central adiposity individuals, and to further explore the relationship between lipids and viral control.While we did not evidence significant differences in time to viral suppression to < 50 c/ml between normal, overweight and obese subjects Figure , we cannAltogether our data indicate that metabolic parameters contribute to predicting the degree of immune reconstitution achieved upon viral suppression. While our study does not address the pathophysiologic mechanisms underlying this relationship, prior reports indicate that fat accumulation promotes low-level inflammation, which, in turn, has been shown to be associated with lack of immunologic reconstitution , suggest+ T cell gain in response to viral suppression, representing, to our knowledge, the best predictive model on immune reconstitution to date, and represents a marked improvement over more conventional assessments .By including pre-ART metabolic parameters in conjunction with baseline CD4, viral load and immune activation, our final model accounts for 44% of the variability in CD4While not designed to support clinical interventions, our results, if supported by validation in a larger cohort, suggest the testable hypothesis that clinical and behavioural interventions aimed at reducing weight in subjects with central adiposity, as well as pharmacological intervention aimed at improving LDL/HDL ratios , might improve the immunological outcomes or ART, at least in the short term.As with all modeling techniques, there are limitations to our findings. In the first place, we modeled the effect of the assessed variables on the change in CD4 between baseline and six months on ART: it remains to be determined if incorporating multiple early CD4 measurements would improve the predictivity of the model. Moreover, the predictive value of the model will have to be validated in a larger independent cohort.In addition, due to the relatively small size of the study, we did not assess the effect of clinical conditions that could affect some of the parameters studies here .As we gain a more accurate estimate of response to ART, it remains to be determined, through further studies, how each variable impacts CD4 recovery mechanistically and whether additional predictors may improve the reliability of the prediction.We report for the first time that metabolic markers can contribute significantly to the variability of immune reconstitution outcomes following ART initiation in a cohort of HIV-1-infected South African subjects. While the current study clearly establishes the predictive potential for metabolic markers, further studies will be required to determine the cost effectiveness of this predictive approach, and to determine whether additional longitudinal measurement would further improve the model performance.The authors declare that they have no competing interests.LA was responsible for study design, data management, data analysis, and manuscript and illustration preparation. ASF supervised the statistical analysis, and contributed to data discussion and manuscript preparation. CF was responsible for clinical coordination and patient interaction, and contributed to data discussion and manuscript revision. XY was responsible for statistical analysis, and contributed to data discussion and manuscript revision. NJC was responsible for lipid assessment, and contributed to critical analysis, data discussion and manuscript preparation. DG was responsible for flow cytometry supervision, and contributed to data discussion and manuscript revision. DL was responsible for flow cytometry analysis and CD4 assessment, and contributed to manuscript revision. WS was responsible for clinical laboratory supervision, and contributed to data discussion and manuscript preparation. EP contributed to data discussion and manuscript revision. IS was responsible for supervising clinical coordination and patient interaction, and contributed to data discussion and manuscript preparation. LJM was responsible for supervising immunology laboratory assessments, and contributed to study design, critical analysis and manuscript preparation.All authors read and approved the final manuscript.


MVC is the first-in-class CCR5 antagonist approved for use in treatment of CCR5-tropic (R5) HIV-1 infection; however, there is limited experience with MVC in regimens containing newer PIs and other new agents. This open-label 96-week multi-center study evaluates MVC in a variety of regimens to obtain additional safety and efficacy data in TE patients with limited options due to intolerance or resistance in Brazil.Adult TE patients with R5 HIV-1 only (HIV-1 RNA >1000 cp/mL) received MVC 150-600mg (based on concomitant ARV) twice daily, combined with optimized background therapy (OBT). Every 12 weeks, safety parameters (primary endpoint), HIV RNA, and CD4 counts were assessed; we report data at 48 weeks.10 copies/mL and 185 cells/mm3, respectively. OBT comprised PI+NRTI (67%), PI+NRTI+NNRTI (7.8%), PI+NRTI+other (14.6%), or other regimens (10.2%). The most frequently used NRTIs were TDF (82%), 3TC (76%) and AZT (23%); the most common PIs (most boosted with RTV) were DRV (45%), LPV (41%) and ATV (15%); 16% received RAL. OBTs contained ≤1 drug (1% of patients), 2 drugs (7.3%), 3 drugs (24.3%), 4 drugs (35.4%), 5 drugs (23.8%), or ≥6 drugs (8.2%). Sixty-five patients (31.6%) discontinued; reasons included death (6 patients), adverse events (5), insufficient clinical response (36), lost to follow-up (4), or other (14). There were 238 treatment-related adverse events in 103 patients; 16 treatment-related serious adverse events in 9 subjects; 10 category C events, none treatment-related; and 2 of the 4 malignancies  were considered treatment-related. The most common grade 3/4 lab test abnormalities were GGT elevation (11% of patients), hyperbilirubinemia (11%) and serum amylase elevation (6%). Median CD4 count increased persistently through week 48 , with similar responses comparing quartiles of baseline CD4; the median increase from baseline at week 48 was 164.5 cells/mm3. HIV-1 RNA decreases were maintained regardless of baseline viral load; overall 41.1% of patients had HIV-1 RNA <400 copies/mL at week 48.Of treated 206 patients, 70% were male (mean age 43 yrs) and 80% Caucasian; 2.9% and 5.8% were seropositive for HBV or HCV infection, respectively. Median baseline HIV-1 RNA and CD4 counts were 4.9 logIn highly TE patients, regimens combining MVC with different agents from multiple classes were well tolerated and provided marked antiviral and immunologic responses.


In April 2003, Médecins Sans Frontières launched an HIV/AIDS programme to provide free HAART to HIV-infected patients in Laos. Although HIV prevalence is estimated as low in this country, it has been increasing in the last years. This work reports the first results of an observational cohort study and it aims to identify the principal determinants of the CD4 cells evolution and to assess mortality among patients on HAART.We performed a retrospective database analysis on patients initiated on HAART between 2003 and 2009 (CD4<200cells/μL or WHO stage 4). We excluded from the analysis patients who were less than 16 years old and pregnant women. To explore the determinants of the CD4 reconstitution, a linear mixed model was adjusted. To identify typical trajectories of the CD4 cells, a latent trajectory analysis was carried out. Finally, a Cox proportional-hazards model was used to reveal predictors of mortality on HAART including appointment delay greater than 1 day.A total of 1365 patients entered the programme and 913 (66.9%) received an HAART with a median CD4 of 49 cells/μL [IQR 15–148]. High baseline CD4 cell count and female gender were associated with a higher CD4 level over time. In addition, this gender difference increased over time. Two typical latent CD4 trajectories were revealed showing that 31% of women against 22% of men followed a high CD4 trajectory. In the long-term, women were more likely to attend appointments without delay. Mortality reached 6.2% (95% CI 4.8-8.0%) at 4 months and 9.1% (95% CI 7.3-11.3%) at 1 year. Female gender  and high CD4 trajectory  were independently associated with a lower death rate.Patients who initiated HAART were severely immunocompromised yielding to a high early mortality. In the long-term on HAART, women achieved a better CD4 cells reconstitution than men and were less likely to die. This study highlights important differences between men and women regarding response to HAART and medical care, and questions men’s compliance to treatment. Highly active antiretroviral therapy (HAART) has clearly reduced morbidity and mortality of HIV-infected patients both in industrialized and developing countries -3. The mIn Asia, the first case of AIDS was reported in 1984. The actual HIV prevalence is low but several countries show growing HIV epidemics ,8. In LaIn July 2001, Médecins Sans Frontières (MSF) opened a HIV/AIDS project in Savannakhet Provincial Hospital in Lao PDR to provide prophylaxis and treatment for opportunistic infections for HIV-infected patients. In April 2003, MSF started a programme of free antiretroviral distribution in Savannakhet Hospital which became at that time the only place in the country to provide HAART to HIV/AIDS-infected patients.Several studies on cohorts of HIV-infected patients receiving HAART in Thailand and Cambodia have been published but to date, there is no equivalent publication on the outcomes of patients on-HAART in Lao PDR -20. To fPatients included in this retrospective analysis entered the MoH-MSF programme of free HAART distribution between April 2003 and June 2009. We excluded from analysis patients who were less than 16 years old and pregnant women. Patients started on HAART if they met one of the following criteria: (1) CD4 cell count < 200 cells/μL irrespective of WHO clinical stage; (2) WHO clinical stage 4 irrespective of CD4 cell count. Date of HAART initiation for eligible patients ranged between September 2003 and June 2009.Main initial regimens were triple-drug combinations made of two nucleoside reverse transcriptase inhibitor + one non-nucleoside reverse transcriptase inhibitor.HIV testing, HAART, treatment of opportunistic infections (including tuberculosis), laboratory monitoring and transportations of patients were free of charge for eligible patients.After a first visit at HAART initiation, patients were followed-up at week 2 and 4, and then, once a month at Savannakhet Provincial Hospital. After 6 months on HAART and if there were no complaints from the patients, the follow-up took place every 3 months. The CD4 cell count was monitored every 6 months, but no measurement of viral load was available. A paper-based system was used by the medical team to allow the clinical follow-up of the patients. Some key data on patients and follow-up were collected to produce activity reports using FUCHIA 1.6.1 software .In October 2006, MSF opened a second treatment centre in capital Vientiane allowing the transfer of pre-HAART and on-HAART patients who originally lived in or around Vientiane. No information on patients transferred to Vientiane could be retrieved so they were censored at date of transfer. We considered a patient as lost to follow-up if their last visit preceded the closure date of the database by 6 months and if he was not dead and had not been transferred to Vientiane.th June 2009. The CD4 cell counts not available at HAART initiation (D0) were retrieved from 15 days before to 15 days after D0, and in a median time of 34 days before D0 for those with still missing CD4 cell count.We performed a retrospective analysis on anonymized data, captured with Fuchia, made available to us by MoH-MSF. An internal unique key was generated by the system to link the patients’ visits so that it was impossible to identify a patient. The database was locked on 30Baseline characteristics of men and women were compared using Fisher’s exact test for categorical variables and Wilcoxon test for continuous variables.We estimated the median progression of CD4 cells on HAART. We fitted a random-intercept linear mixed model for repeated measures using all available CD4 measurements at each time point to explore the effect of several characteristics of patients at HAART initiation: age, gender, Body mass index (BMI), WHO clinical stage, CD4 cell count and haemoglobin level. We also fitted a latent trajectory model, both for men and women, with a latent variable characterizing different patterns of CD4 cells over time . This meWe also analyzed the punctuality of scheduled appointments, defined as attending the visit the exact scheduled day (or before), over time on HAART using mixed-effect logistic regression.0 and the date of last visit, the date of transfer or the date of death. We described the mortality on HAART using Kaplan-Meier curves and log-rank tests. A Cox proportional-hazards model was adjusted to explore the link between typical latent CD4 trajectories and mortality, thus baseline CD4 count was not included because its effect was captured by the latent trajectories. Other possible risk factors at HAART initiation such as age, gender, Body mass index (BMI), WHO clinical stage and haemoglobin level were considered in the analysis. Punctuality of scheduled appointments was also investigated. Proportional hazards (PH) assumption was checked by testing the Schoenfeld residuals. Because gender appeared to violate the PH assumption, time was split at 9 months and the effect of gender was thus estimated independently before and after 9 months on HAART.The participation time on HAART was calculated as the time between DStatistical analyses were performed with Stata 10.1  software using the generalized linear latent and mixed model (GLLAMM) framework .This research followed the principles of the Helsinki Declaration, 2004 amendment and analysis plan was approved by the Ministry of Health – Centre for HIV, AIDS and STIs (MoH/CHAS) of Lao PDR which granted access to the anonymized data. This retrospective data analysis was performed on routinely collected data which were fully anonymized and involved no risk for participants. Verbal informed consent was obtained from patients during the first visit, and therefore, their first inclusion into the database.A total of 1365 patients were registered in the database. Among them, 3 were excluded because there were no data recorded for them and 134 were excluded because they were less than 16 years old. Before receiving HAART, 127 patients died, 30 were transferred to Vientiane and 71 were lost to follow-up. Among the 1000 patients alive and followed, 913 (91.3%) were started on HAART and included in the analyses where as 87 were not initiated on ART because they were not eligible at the closing date of the database.P=0.002). The median BMI  and the median haemoglobin level  did not strongly differ by gender (P = 0.07). The two main initial regimens were: stavudine/lamivudine/neviparine (Triomune®) (65.1%) and stavudine/lamivudine/efavirenz (33.1%).Patients’ characteristics at HAART initiation are presented in Table During HAART, the median CD4 cell gain increased strongly in the first year and attained +156 cells/μL [IQR 104–231] at 12 months (n=335). Then, it increased slowly from +191 cells/μL [IQR 121–269] at 18 months (n=336) to +294 cells/μL [IQR 162.5-405] at 42 months (n=152) before stabilizing.The results of the random-intercept linear mixed model are reported in Table The latent trajectory analysis reveals both for men and women two typical latent trajectories of CD4 cells over time Figure . The firAnalysis of appointments revealed that 85.6% of visits took place without delay and that the median delay was 2 days [IQR 1–7]. A significant interaction between gender and time (in years) indicated a better punctuality of appointments for women compared to men in the long-term on HAART .P=0.007) and BMI  at baseline, and by the typical latent CD4 trajectories , showing a higher mortality among patients with a low CD4 trajectory. In addition, mortality seems higher for men after 9 months on HAART  were independently associated with a lower death rate , 98 were transferred to Vientiane (10.7%) and 107 died (11.7%). Cumulative probability of being lost to follow-up on HAART was 2.42% (95% CI 1.16-3.19%) at 12 months and 5.33% (95% CI 3.72-7.63%) at 30 months. Kaplan-Meier estimates of mortality attained 6.2% (95% CI 4.8-8.0%), 7.7% (95% CI 6.1-9.7%) and 9.1% (95% CI 7.3-11.3%) at 4, 9 and 12 months, respectively. Mortality differed by the following factors: CD4 cell count  states that, on average, 21% of the patients are lost to follow-up in the first 6 months after HAART initiation in resource-limited settings . In Sub-As it has been reported in other studies conducted in Southeast Asia, patients who start HAART were severely immunocompromised ,16,18. WAs in other studies, the baseline CD4 cell count is a strong predictor of immune reconstitution, indicating that patients with higher baseline CD4 cell count achieve a better immune recovery ,30. In aSeveral studies on large cohorts and long follow-up have failed to associate gender and immune reconstitution ,32. ThisThe latent trajectory analysis has identified two patterns of the CD4 evolution both for men and women. Overall, about a quarter of the patients followed a similar high CD4 trajectory, and about three quarters a lower one. About a third of women achieved a high CD4 trajectory whereas about a fifth of men achieved it. These results are in line with the mixed model.The prevalence of the two latent trajectories reflects the distribution of baseline CD4 cell count. Indeed, patients with a low CD4 cell count at HAART initiation are more likely to follow a low CD4 trajectory. This explains why the prevalence of the first latent trajectory is high. Strength of this approach is that it describes the typical evolution of the CD4 cells in that cohort and it captures the effect of the low baseline CD4 count. The high membership probability observed even for patients with a low number of CD4 measurements allows us to assign patients to a unique latent trajectory with confidence. This is a new approach to explore different patterns of the CD4 evolution and it could be generalized to other longitudinal studies to reveal different patients’ behaviour over time.Half of the deaths occur in the first four months after HAART initiation, showing a higher early mortality than the one generally observed in low-income countries. A recent scale-up of national antiretroviral therapy programmes in the Asia Pacific region shows that 50% of mortality occurs in the first 6 months of therapy . High eaMortality differs by patients’ baseline characteristics such as CD4 cell count and BMI. These relationships have already been largely described both in low-income and high-income countries ,47,53-55As shown in an observational cohort in South Africa, mortality is strongly associated with immune reconstitution during HAART . Indeed,Our study also reveals a higher mortality among men. This result is also reported in other studies -63. Men This study presents some limitations. First, we have no information about outcomes of patients lost to follow-up and transferred to Vientiane, which could bias the estimated immune recovery and mortality in the cohort. Adjusting mortality for loss to follow-up could correct the estimates, but due to the low lost to follow-up rate in this cohort, we did not use this method . Then, bThis MSF programme where 913 patients initiated HAART with a very severe immuno-suppression reveals an important heterogeneity between patients and better outcomes for women. Indeed, a quarter of the patients present a high CD4 trajectory and women experience a better immune recovery and lower mortality than men. This study provides additional evidence for differences between men and women, once on HAART and, in particular, a difficulty for men to maintain a long-term compliance to treatment. Moreover, this study recommends that clinicians and healthcare providers have to pay special attention to men during treatment.The authors declare that they have no competing interests.MB designed the analyses, analyzed the data and interpreted the results, wrote the paper and submitted the final version. KS designed the study and collected the data. PP designed the study, interpreted the results and reviewed the manuscript. AHS designed the study and collected the data. LC designed the study, interpreted the results and reviewed the manuscript. AC designed the study, collected the data, interpreted the results and reviewed the manuscript. CP designed the study, interpreted the results and reviewed the manuscript. RE designed the analyses, interpreted the results and reviewed the manuscript. JFE coordinated the research, designed the analyses, interpreted the results and reviewed the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/13/27/prepub


HIV infection is a disease associated with chronic inflammation and immune activation. Antiretroviral therapy reduces inflammation, but not to levels in comparable HIV-negative individuals. The HMG-coenzyme A reductase inhibitors (statins) inhibit several pro-inflammatory processes and suppress immune activation, and are a logical therapy to assess for a possible salutary effect on HIV disease progression and outcomes.Eligible patients were patients enrolled in the Johns Hopkins HIV Clinical Cohort who achieved virologic suppression within 180 days of starting a new highly active antiretroviral therapy (HAART) regimen after January 1, 1998. Assessment was continued until death in patients who maintained a virologic suppression, with right-censoring of their follow-up time if they had an HIV RNA > 500 copies/ml. Cox proportional hazards regression was used to assess statin use as a time-varying covariate, as well as other demographic and clinical factors.A total of 1538 HIV-infected patients fulfilled eligibility criteria, of whom 238 (15.5%) received a statin while taking HAART. There were 85 deaths . By multivariate Cox regression, statin use was associated with a relative hazard of 0.33  after adjusting for CD4, HIV-1 RNA, hemoglobin and cholesterol levels at the start of HAART, age, race, HIV risk group, prior use of ART, year of HAART start, NNRTI vs. PI-based ART, prior AIDS-defining illness, and viral hepatitis coinfection. Malignancy, non-AIDS-defining infection and liver failure were particularly prominent causes of death.Statin use was associated with significantly lower hazard of dying in these HIV-infected patients who were being effectively treated with HAART as determined by virologic suppression. Our results suggest the need for confirmation in other observational cohorts, and if confirmed, the need for a clinical trial of statin use in HIV infection. It is now well-recognized that HIV infection causes chronic inflammation and immune activation. Untreated HIV infection is associated with persistently high levels of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-alpha, coagulation biomarkers and acute phase proteins such as fibrinogen, D-dimer and high-sensitivity C-reactive protein Given this scenario, it is possible that anti-inflammatory treatments might improve HIV outcomes. ART is not sufficient, and other measures that reduce inflammation might provide additional benefit. The 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (“statins”) are a class of drugs that have been found to inhibit several pro-inflammatory processes independent of their cholesterol-lowering effects. They decrease pro-inflammatory cytokine levels and acute phase proteins, and also appear to repress the activation of T lymphocytes The relatively widespread use of statins in HIV-infected individuals make them a logical drug class to examine in an observational cohort of HIV-infected patients receiving ART. Since ART is the cornerstone of HIV therapy, our analysis focused on the association of statin use with clinical outcomes in patients who maintained a full virologic suppression on ART.This study was approved by the Johns Hopkins University School of Medicine institutional review board. All subjects gave written informed consent to have their clinical data collected longitudinally for research purposes. The clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki.This analysis was conducted using data from patients in the Johns Hopkins HIV Clinical Cohort. Briefly, this is a longitudinal cohort study that enrolls patients who present for HIV care at the Johns Hopkins HIV/AIDS Clinics. The refusal rate for participation is < 0.5%. Starting with their first clinic visit, demographic data, all medication use, clinical diagnoses (HIV and non-HIV), and comprehensive laboratory data are collected This analysis examined survival after the initiation of a new HAART regimen after cohort enrollment from January 1, 1998 through December 31, 2009. We further limited the analysis to patients who achieved suppression of HIV-1 RNA level to undetectable levels within 6 months of starting HAART (< =  400 copies/ml by first generation PCR assay or <50 copies/ml by ultrasensitive PCR assay). HAART was defined as use of a new non-nucleoside reverse transcriptase inhibitor (NNRTI), protease inhibitor (PI) or integrase strand transfer inhibitor (InSTI) with two nucleoside analog reverse transcriptase inhibitors (NRTIs), or use of an NNRTI plus PI and/or InSTI. The time origin was the day of initiation of the new HAART regimen, and patients had to have received at least 90 days of this regimen to qualify for analysis. Statin use was coded as present if the statin was being received by the patient at the time of, or after initiation of HAART. A patient had to receive a statin for at least 30 days to be considered as a statin user. Use of antiretroviral agents and statins was determined based on prescribing, not filling of prescriptions, so adherence was not directly assessed.Mortality data, including date and cause of death, are obtained by regular assessment of National Death Index and the National Social Security Death Index. In addition, we routinely determine deaths reported in the medical records from our institutions and other facilities, by telephone or other communication from relatives, friends, and other medical providers.Cox proportional hazards regression was used to examine the association of statin use with death. For this analysis, the initiation of the HAART regimen was the time origin. The patient contributed follow-up time only as long as s/he maintained a suppressed viral load. Follow-up was right-censored if the patient developed a viral load of greater than 500 copies/ml, or 90 days after the last undetectable viral load measurement if no further values were obtained. We examined statin use as a time-varying variable, since patients who received statins were not always receiving them at the time that the HAART regimen was started. Analyzing statin use as time-varying minimizes immortal-time bias  that would occur if statin use was analyzed simply as a fixed covariate. The risk sets include whether a statin is being used or not at that particular time after starting HAART, irrespective of whether the patient received a statin at some other time during follow-up. Statin use was also lagged by 90 days if it was discontinued so that a death would attributed to a statin user if the statin had been used at least 90 days prior to the event.Other variables that were examined included age, sex, race, HIV transmission risk group, hemoglobin level at HAART initiation, CD4+ cell count at HAART initiation, HIV-1 RNA level at HAART initiation, specific HAART anchor drug , calendar date of initiation of the HAART regimen (< 2004 vs. ≥ 2004), prior ART use before starting this HAART regimen prior AIDS-defining illness (ADI), and a random total cholesterol level at HAART initiation. We compared these patient characteristics of the sample by receipt of a statin using the Wilcoxon rank-sum test for continuous variables or the chi-squared test for categorical variables. Each of these variables were also analyzed as covariates in a multivariate Cox regression.Finally, we conducted a similar analysis to that described above for the use of antihypertensive therapy. Since antihypertensive therapy is often used in patients who are prescribed statins, we wished to see if this therapy was associated with survival as a method to assess for potential bias in the prescribing of these drugs to individuals who were believed by their providers to be more adherent or otherwise better candidates for cardiovascular risk intervention.We analyzed the data from 1538 HIV-infected patients who achieved virologic suppression within 6 months of starting HAART and who subsequently maintained suppression until death or until their data were censored. The median time to censoring was 570 days (interquartile range: 268–1286 days), with no significant difference in time to censoring between those receiving and not receiving statins. The demographic and clinical characteristics of the sample are shown in There were 85 deaths . By multivariate Cox regression, statin use was associated with a relative hazard (RH) of 0.33 , adjusting for CD4, HIV-1 RNA, hemoglobin and cholesterol levels at the start of HAART, age, race, HIV risk group, prior use of ART, year of HAART start, NNRTI vs. PI-based ART, prior AIDS-defining illness, and viral hepatitis coinfection . Of thesThe causes of death are shown in A total of 407 of the patients received antihypertensive therapy. There was substantial overlap among users of statins and antihypertensives, with 46% of those on statins also receiving antihypertensive therapy. An analysis of antihypertensive therapy, done similarly to the statin use analysis, showed no association with survival .We found that statin use was associated with a significantly decreased hazard of dying. Statins have anti-inflammatory and cellular immunological effects It is notable that a lower baseline hemoglobin was also strongly associated with survival in these patients, as others have also found No observational analysis can provide definitive evidence for the efficacy of drug therapy, since selection bias can affect the associations found. Since statins could have been selectively prescribed to individuals thought to be more adherent or at lower risk of dying, we adjusted for both other prognostic variables (variables associated with death) and several variables such as MSM that were associated with use of statins. There were no significant interactions between statin use and survival and HIV risk group or several other variables. We also analyzed the association of antihypertensive drugs, another class of therapy used to manage cardiovascular disease risk, and that also tends to also be used in patients who receive statins (60% overlap in our population). There was no association between antihypertensive therapy and survival. We were unable to assess other anti-inflammatory and immunosuppressive drugs either because of infrequent use , or over the counter use that we did not capture . Since all of the patients in this analysis maintained suppressed viral loads, we believe that differential adherence to therapy was less likely to be a significant issue. Nevertheless, it is certainly possible that statins were used selectively in patients with a better survival prognosis and that there are unmeasured confounders that would explain the association we found. Even if statin use is clinically causing a reduced mortality, it could be due to reasons other than an effect on inflammation and immune activation.In summary, we found that patients who maintained virologic suppression on effective HAART appeared to derive additional survival benefit from the use of a statin. If additional observational data support this finding, a randomized clinical trial would be warranted to confirm this association.


Although CD4 cell count monitoring is used to decide when to start antiretroviral therapy in patients with HIV-1 infection, there are no evidence-based recommendations regarding its optimal frequency. It is common practice to monitor every 3 to 6 months, often coupled with viral load monitoring. We developed rules to guide frequency of CD4 cell count monitoring in HIV infection before starting antiretroviral therapy, which we validated retrospectively in patients from the Swiss HIV Cohort Study.6/L, respectively. The Track-shot rule was only marginally superior to the Snap-shot rule. Both rules lose usefulness for CD4 counts coming near to treatment threshold.We built up two prediction rules (“Snap-shot rule” for a single sample and “Track-shot rule” for multiple determinations) based on a systematic review of published longitudinal analyses of CD4 cell count trajectories. We applied the rules in 2608 untreated patients to classify their 18 061 CD4 counts as either justifiable or superfluous, according to their prior ≥5% or <5% chance of meeting predetermined thresholds for starting treatment. The percentage of measurements that both rules falsely deemed superfluous never exceeded 5%. Superfluous CD4 determinations represented 4%, 11%, and 39% of all actual determinations for treatment thresholds of 500, 350, and 200×106/L, respectively. When CD4 counts fall below these limits, increased monitoring frequency becomes advisable. These rules offer guidance for efficient CD4 monitoring, particularly in resource-limited settings.Frequent CD4 count monitoring of patients with CD4 counts well above the threshold for initiating therapy is unlikely to identify patients who require therapy. It appears sufficient to measure CD4 cell count 1 year after a count >650 for a threshold of 200, >900 for 350, or >1150 for 500×10 In Western countries therapy for HIV-1 infection is recommended when the CD4 count falls below 350×10frequency with which such monitoring should be undertaken is not currently clear, and monitoring strategies have not been evaluated in clinical trials. Guidelines do not give explicit recommendations about monitoring frequency during the pre-treatment phase While a patient's CD4 count is above whatever threshold at which treatment will be started, repeated monitoring of the count is necessary. However, the Our interest in evidence-based monitoring in chronic medical conditions We conducted a systematic review of the natural evolution and variability in CD4 cell count and viral load, to base our decision rules on the best evidence available from observational studies. Our aim was to summarize suitable descriptors of the longitudinal evolution of both these markers in HIV-infected patients followed up in prospective cohorts while receiving no antiretroviral treatments. We searched Medline and EMBASE for “CD4 OR viraemia OR viral load”, associated with “regression OR longitudinal OR slope OR monitoring”. We also examined the bibliographies of all relevant papers. The literature search was conducted in 2008 and updated in 2010. From among various statistical approaches to longitudinal analysis of HIV-1 biomarkers, we chose the most widely used and readily applicable, based on mixed-effects (multilevel) linear modelling. For CD4 counts, a majority of analyses used square-root transformation and thus only those ones were included; for viral load all used logarithmic transformation. The population parameters describing the evolution of biomarkers of HIV-1 were extracted, averaged, and rounded. Their prognostic value for the development of AIDS and prediction of death has been addressed in a large meta-analysis i) and is characterized by a subject-specific slope (decline rate bi); actual CD4 counts depart from this line because of random fluctuations, laboratory imprecision, and model incomplete accuracy. The subject-specific baseline (ai) and slope (bi) are considered as random variables normally distributed around average population values (α and β), while the deviations of actual CD4 counts from the subject-specific line are considered to be independently and normally distributed around zero. The subject's specific slope and intercept represent the “signal” hidden by the “noise” of within-subject fluctuations. As the fall in CD4 count depends on the individual slope and the time interval, measurements made close together capture only short-term variability and contain little information about the true slope. On the other hand, multiple determinations will refine evaluation of the subject's true current state, which may be advantageous in making therapeutic decisions, especially near the threshold for starting treatment. Monitoring decisions will therefore vary according to the distance of the patient from the threshold for antiretroviral treatment. The definition of such a threshold represents a peculiar aspect of CD4 monitoring Our literature review confirmed that a mixed-effects linear model of square-root transformed CD4 counts was most often used to describe individual trajectories in untreated patients, beyond the acute changes observed during the few months after primary infection. According to this model, the square root of the CD4 count falls along a linear mean trajectory starting from a subject-specific baseline . Detailed clinical data are obtained from the subjects on recruitment and then at visits scheduled at least 6-monthly; all CD4 and viral load determinations are entered into the database. All patients give their written consent to epidemiological analysis of their follow up data. This validation study was approved by the SHCS scientific board.To test their performance with actual clinical data, we applied both decision rules to an unselected series of HIV-1 infected patients. The multi-centre Swiss HIV Cohort Study (SHCS), established in 1988, includes about a half of all HIV-positive individuals in Switzerland . We also tested both rules with P set to 0.1. Subsequent CD4 determinations performed before the time indicated by the rule (tnext) were counted, compared with the threshold, and discarded as superfluous. The Snap-shot rule was then reiterated on the next justifiable (i.e. non-superfluous) measurement, done after tnext, and so on. The Track-shot rule took into account only previous CD4 values not discarded. Treatment thresholds were set at CD4 counts of 200, 350, and 500×106/L.Of the 9570 subjects recruited during the past 12 years (1 January 1996 to 31 May 2008), 5551 have undergone CD4 and viral load monitoring before starting antiretroviral therapy. The data from patients with at least P level of 0.05; or not more than 10% using a P of 0.1. Calculations were performed using Excel  and STATA software .The main objective of the study was to verify the adequacy of the classification of CD4 measures by both rules. Concretely, we aimed to check that not more than 5% of the CD4 results deemed superfluous would actually reach the threshold, using a predefined Most published descriptions of CD4 cell counts and viral load evolution in untreated HIV infection come from cohort data. We screened 149 abstracts addressing the topic of the rate of fall in CD4 counts in untreated HIV-1 infection, and we identified 40 publications that described mathematically the natural evolution of CD4 counts in untreated HIV-1 infected patients. Among them, 11 did not provide usable parameters, while 19 used longitudinal models, data transformations, or parameterizations that were not relevant to our approach see . Thus, wThere was a moderate degree of heterogeneity across the estimates shown in Snap-shot rule to determine the suitable time for CD4 remeasurement is illustrated in P, for a given result. The essence of this rule can be represented in a nomogram .The rule loses its usefulness in the presence of low to intermediate CD4 counts . An Excel spreadsheet implementing both the Snap-shot rule and the Track-shot rule is available as supporting information  of 20 (6.2) (cell count)0.5. Two thirds of the patients started antiretroviral therapy before May 2008.In total, 2608 patients from the Swiss HIV Cohort Study were entered into the validation study . They haCompared with the study patients, those who could not be included because they lacked repeated CD4 testing were slightly more often women , older , and intravenous drug users . Above all, they had significantly lower initial CD4 counts, with a median of 250 , and higher initial viral loads, with a median of 51 800 . Among them, 2689 (91%) started antiretroviral therapy soon after their single off-treatment CD4 determination, which presumably often represented a main criterion for treatment decision.Snap-shot rule and the Track-shot rule give similar results.Application of the monitoring rules, illustrated in 6/L). The Snap-shot rule declared fewer measurements superfluous than the Track-shot rule for a threshold of 200×106/L, an equal amount for 350×106/L, and more for 500×106/L. However, the corresponding absolute counts fell dramatically on increasing the threshold, as expected. The analysis confirmed that the percentage of CD4 results that were actually below the threshold value among determinations deemed superfluous was always lower than the preset probability level P used to run the rules, moderately so for the 350 and 500×106/L thresholds but markedly for the 200×106/L threshold. There were 4905 superfluous tests in 1024 patients, taken 322 (IQR 139–605) days before the time indicated by the Snap-shot rule run at P = 0.05 for the 200×106/L threshold; for the 350×106/L threshold, there were 1724 superfluous tests in 421 patients, requested 287 (122–555) days in advance; and for the 500 cells/µL threshold, 625 superfluous tests in 192 patients, requested 256 (102–482) days in advance. When we restricted the analysis to the CD4 results obtained within ±3 months of the date indicated as suitable for re-measurement by the rules, the absolute numbers of values became smaller (219–2393), while the percentages of falsely superfluous CD4 results remained below the preset probability . Very similar results emerged for the Track-shot rule (not detailed).We applied both rules iteratively to CD4 counts in the patients in the validation study. Track-shot rule evaluated average (SD) posterior slope estimates of 1.67 (0.51) (cell count)0.5/year, and intercept estimates of 26 (4.3) (cell count)0.5. Its use with a non-informative prior intercept gave very similar results; conversely, a non-informative prior slope severely compromised the validity (not detailed).There was no advantage in using the rules with individualized slopes modified according to age, initial viral load, or HIV infection route (not detailed). The Snap-shot rule, a 25% reduction in the population average slope (β) translated into a few more measurements being declared superfluous ; conversely, increasing β by 25% slightly reduced this percentage ; these manipulations minimally affected the rate of falsely superfluous results . Increasing or decreasing the slope dispersion (σb) by 25% had little effect on the number of either superfluous measurements (absolute changes <1.3%), or falsely superfluous results (absolute changes <0.4%). Modifying intra-patient variability (σe) had more dramatic effects: a 25% reduction in σe increased both the number of superfluous measurements  and the rate of falsely superfluous results , while a 25% increase in σe had the opposite effect. Despite this, none of the actual percentages of falsely superfluous rates, calculated with a preset P value of 0.05, exceeded 5.7%. The Track-shot rule was not more sensitive to changes in the choice of parameter values (not detailed).Both rules were robust towards changes in parameter values, which did not markedly affect the outcomes. For the Snap-shot rule applies to a single measurement. The Track-shot rule takes into account a series of CD4 results in a given patient. We validated both rules using CD4 counts collected before the start of therapy in a large cohort of HIV-1 infected patients. The rules proved reliable and robust and can therefore be used to guide the management of asymptomatic patients.Monitoring CD4 cell counts in asymptomatic HIV-1 infected patients to decide when to start antiretroviral therapy is unanimously recommended and is cost-effective We used a similar approach to evaluate the performance of repeated viral load determination based on a systematic literature review see . It confVarious approaches have been used to describe the average trend of CD4 or viral load and their inter- and intra-patient variability Track-shot rule would outperform the Snap-shot rule, since it adduces more information. However, except for patients starting from high CD4 levels and targeting a low treatment threshold (200×106/L), the Track-shot rule was not superior (e), which combines biological fluctuation, laboratory imprecision, and model inaccuracy; inter-individual slope variability plays the second role. This is further reflected by the results of our sensitivity analysis.We initially expected that the superior . RelaxinP), in particular for the low target threshold of 200×106/L  for 421 patients. The percentage would increase to almost 40% with the Track-shot rule at a treatment threshold of 200×106/L, which was commonly used in developing countries until very recently. Although there is no indication that our rules would perform differently in resource-poor settings, they should be validated in populations differing from Swiss HIV patients.We found that roughly 11% of all CD4 cell counts performed in untreated patients were of questionable clinical usefulness, as they had less than a 5% chance of being under 350×10e). The Snap-shot rule ceases to be applicable once the variogram meets the treatment threshold , shows the highest load that a subsequent measurement can be expected to reach, with a probability of 5% (dashed line) or 10% (dotted line). The continuous line indicates the viral load trajectory predicted in an average patient, taking about 12 years to increase by 1 log unit. After 6.7 years one patient in 20, and after 9.2 years one patient in 10, can be expected to have a 2 log unit increase .Variogram for viral load monitoring. This variogram, based on the (TIF)Click here for additional data file.Appendix S1snap-shot rule and the track-shot rule.Construction of the monitoring rules: statistical assumptions and mathematical derivations underlying the (PDF)Click here for additional data file.Appendix S2Evaluation of viral load monitoring: literature review and application of the rules to viral load.(PDF)Click here for additional data file.Excel Tool S1snap-shot rule and the track-shot rule to guide CD4 determination frequency.An easy-to-use computer tool to assist CD4 cell count monitoring in HIV infection before starting antiretroviral therapy. It implements both the (XLS)Click here for additional data file.PRISMA Flow Diagram S1Outline of the literature search.(PDF)Click here for additional data file.


The clinical relevance is still inconclusive. To bridge this gap, we aim to study the role of these hypermutations in evolution of drug resistance; we characterised hA3G/F-mediated hypermutations in the RT region of the pol gene of patients with or without antiretroviral therapy (ART).Human APOBEC3G/F (hA3G/F) restricts retroviral replication through G-to-A hypermutations, which can generate drug-resistant progenies In 88 HIV-1-positive individuals, drug resistance genotyping was carried out in plasma virus and provirus by population sequencing. Hypermutations were determined by three different approaches using Hypermut 2.0 software, cluster analysis and APOBEC3G-mediated defectives indices. Clinical and demographic characteristics of these individuals were studied in relation to these hypermutations.p=0.03). In therapy failure patients, proviral hypermutation were associated with greater intra-compartmental genetic diversity (p<0.001). In therapy-naïve individuals, hypermutated proviral DNA with M184I and M230I mutations due to the editing of hA3G, had stop codons in the open reading frames and the same mutations were absent in the plasma virus. Only a limited concordance was found between the drug resistance mutations in plasma RNA and proviral DNA.hA3G/F-mediated hypermutated sequences in proviral DNA, but not in plasma virus, were identified in 11.4% (10/88) subjects. Proviral hypermutations were observed more frequently in patients with ART failure than in ART-naïve individuals (in vivo.hA3G lethal hypermutation was significantly associated with ART failure in Indian HIV-1 subtype C patients. It is unlikely that viral variants, which exhibit hypermutated sequences and M184I and/or M230I, will mature and expand  However, sub-lethal hypermutations have been suggested to contribute to the HIV-1 genetic diversity [The human apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3G (APOBEC3G or hA3G) belongs to a family of at least 10 other proteins including hA3D, hA3F, hA3H which acts as a potent host restriction factor of retroviral replication through cytidine deaminase activity . HIV-1 aactivity . In the nded DNA , consequnded DNA . Generaliversity  and the iversity .in vitro [in vivo consequences of hA3G/F are not well understood. Computer prediction of pol sequences has identified potential target sites for hA3G/F, but the role of hA3G in HIV-1 drug resistance in vivo is unknown and considered to be low [in vivo studies used HIV-1 genes gag, env, vpu, and vif to identify hA3G induced hypermutations in proviral sequences [pol gene, which is a major target in antiretroviral therapy (ART) [Cytidine deamination in the proviral sequences can generate drug-resistant progenies in vitro , though o be low . Also, mequences –13. Onlypy (ART) –16.in vitro observations and the limited knowledge about the in vivo consequences, we aimed to characterise the nature of hA3G/F-mediated hypermutations in the RT region of the pol gene in a population of Indian HIV-1-positive patients, and its correlation with clinical and demographic parameters as well as with drug resistance.To bridge the gap between the n=88) were included in the study. Among these patients, 56 were ART naive and 32 were ART experienced at inclusion in the study  from 102 HIV-1-positive individuals who were participants of on-going studies in southern India between November 2009 and October 2011. Among the 102 patients, both RNA virus and provirus were amplified in 86.2% (88/102), only RNA virus in 5.9% (6/102) and only provirus in 7.8% (8/102). Only paired sequences from both RNA virus and provirus .HIV-1 subtyping was carried out using maximum likelihood phylogenetic analysis with best-fitted model for the dataset in MEGA 5.0 software . RecombiTo estimate G-to-A substitutions, proviral DNA sequences were aligned against the consensus Indian subtype C sequence . The hA3Hypermut software was used to determine hA3G/F-mediated hypermutated sequences . FurtherThe World Health Organization (WHO) recommendations for surveillance of drug resistance mutations updated in the 2009 (SDRM_2009) list were used to define the transmitted drug resistance mutations in therapy-naïve patients . Drug reDescriptive statistics were used to describe the characteristics of the patients. The demographic, clinical and viral genetic differences between hypermutated and non-hypermutated groups were evaluated by Mann–Whitney U Test and Fisher's exact test. Spearman rank co-relation was used to find associations between different factors. The statistical analysis was calculated in SPSS software version 16.The study was approved by Institutional Ethical Review Board, St. John's Medical College Hospital, Bangalore, India (IERB Study No. 153/2010). Written informed consent was obtained from all the adult participants and the caregivers of the children prior to recruitment, and a verbal assent was obtained from children older than nine years.Subtype C was identified in 98.9% (87/88) of the patients along with one A1C recombinant strain. Phylogenetic analysis using the sequences from both compartments verified the common origin of the strains in each individual.p=0.03; Fisher's exact test). Patients failing therapy had a 4.96-fold  higher risk of lethal hypermutations than therapy-naive patients. Among the six therapy-experienced patients whose samples were available before therapy, two had hypermutated proviral sequences at failure but not when they were therapy-naïve.hA3G mediated hypermutated sequences in proviral DNA were identified in 11.4% (10/88) of the patients . Howeverp<0.001) and the intra-compartmental genetic diversity (p<0.001), but not in the hA3F-specific G-to-A score , but not with plasma viral divergence. This correlation was found in the therapy-experienced patients only , but not in the therapy-naive individuals.When all of the patients were analysed, the consolidated hA3G specific G-to-A score was weakly associated with proviral divergence  were observed in both compartments of one patient giving the prevalence of transmitted drug resistance as 1.8% (1/56). One patient had D67DN and K70KE mutations in the proviral sequence. In one patient with hypermutated sequence, M184I was observed in the proviral sequence but not in plasma. Therefore, a high level of concordance in DR Genotyping  was observed in the therapy-naïve individuals.DRM were found in 84.4% (27/32) of the patients failing ART . ExactlyImportant differences were found between the plasma RNA and the proviral DNA compartments. M184V and T215Y/F were observed in plasma only in six and three patients, respectively, while M41L and K65R were observed in one patient each in provirus only. Additional NRTI mutations were observed in 25% (8/32) of proviral and plasma viral sequences, respectively. Additional NNRTI mutations were found mainly in plasma  and to a lesser extent in proviral DNA . In two proviral sequences only, the E138K mutation was observed.pol gene of proviral DNA from a minority (11.4%) of 88 Indian HIV-1 subtype C-infected patients. The hypermutations occurred more frequently in patients failing therapy than in therapy-naïve patients. There was a correlation between their presence and the proviral divergence, which is in line with the view that hA3G contributes to viral evolution.In the present study, hA3G mediated lethal hypermutations were identified in the clinically important In vitro studies and computer predictions have suggested a role of hA3G in the evolution of HIV drug resistance [Vif mutants, the HIV drug resistance mutations M184I [in vitro without any drug exposure. Also, one study has reported the co-presence of M184I and E138K in 24% of hypermutated sequences of treated patients [sistance , 10. Thuns M184I  and E138ns M184I  may be ipatients . In contin vitro results, differences have been reported on how often hA3G induced mutations can be found in vivo. When analysing HIV clones, hypermutated proviral DNA was detected in resting T-cells of all nine treated patients who had undetectable viremia, suggesting that the mutated viral genomes were able to integrate and persist in these cells [In addition to these se cells . In contse cells . Furtherse cells . In our se cells , we obsese cells , thus thse cells .in vivo. The presence of an increased number of hypermutated sequences in the therapy failure patients might be associated with decreased fitness of the virus due to the DRMs [No hypermutation in plasma HIV RNA was found. This is in line with the finding that the viruses released into plasma at low levels in nine patients on successful ART were devoid of hypermutated sequences . This dathe DRMs  as theseIn addition to our analysis of hA3G mutations, we compared the sequences with regard to DRM. Transmitted drug resistance was uncommon which is concordant with previous findings , 27. ForThe presence of drug resistance mutations like M184V, T215Y, K103N, and Y181C in plasma virus but not in the provirus in our study indicates that routine HIV RNA monitoring every six months and subsequent plasma viral genotyping at failure identifies the most recent viral populations circulating, although different sources of the two viral populations cannot be excluded. Therefore, in such a setting, testing of proviral DNA could possibly underestimate the true burden of recently developed resistant virus. However, the presence of key mutations in the proviral DNA which was not present in plasma is of importance for the selection of future ART regimen. World Health Organisation recommends the use of dried blood spot for transmitted drug resistance surveillance but not in patients undergoing therapy . High coOur study has some inadvertent caveats. First, the analysis is based on population sequencing and therefore does not detect any hypermutations in the minor quasi-species. Second, the number of therapy failure patients is low compared to the treatment naïve individuals. This is due to a low number of treatment failures in our settings because of high (>95%) adherence. We have thus observed only 2.8% 9/323) of viral treatment failure with a median duration of four years of therapy who had initially suppressed viremia following the initiation of the therapy [3 of virain vivo which is supported by the absence of those mutations in plasma viral populations. However, further studies are required to validate the idea with appropriate in vitro cell culture models and with analysis of a large number of patients to gain the mechanistic view of the role of hA3G in the evolution of drug resistance and its clinical consequences. The evidence from this study also suggests the possible use of proviral drug resistance genotyping as an alternate to plasma viral genotyping for surveillance of transmitted drug resistance in resource-limited settings, specifically from the rural and remote part of the country where the logistics challenges remain.In conclusion, our study showed for the first time that hA3G lethal-hypermutation was associated with the use of treatment in Indian patients who failed ART. Though DRM were present in the hypermutated proviral compartment, all strains had a stop codon in its ORF. Therefore, it is unlikely that such viral variants will mature and expand