Decline of CD4+ T-cell count before start of therapy and immunological response to treatment in antiretroviral-naive individuals
Mussini, Cristinaa; Cossarizza, Andreab; Sabin, Carolinec; Babiker, Abdeld; De Luca, Andreae; Bucher, Heiner Cf; Fisher, Marting; Rezza, Giovannih; Porter, Kholoudd; Dorrucci, Mariah; on behalf of CASCADE Collaboration
aClinic of Infectious Diseases, Modena University, Modena, Italy
bUniversity of Modena and Reggio Emilia School of Medicine, Modena, Italy
cUniversity College London (UCL) Medical School, Royal Free Campus, UK
dMedical Research Council Clinical Trials Unit, London, UK
eInstitute of Clinical Infectious Diseases, Catholic University ‘Sacro Cuore’, Rome, Italy
fBasel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland
gBrighton and Sussex University Hospitals NHS Trust, Brighton, UK
hDepartment of Infectious, Parasitic and Immunomediated Diseases, Istituto Superiore di Sanità, Rome, Italy.
Received 22 September, 2010
Revised 17 February, 2011
Accepted 28 February, 2011
Correspondence to Dr Maria Dorrucci, Istituto Superiore di Sanità, MIPI, Viale Regina Elena 299, 00161 Rome, Italy. Tel: +39 064990 2611; e-mail: firstname.lastname@example.org
Objective: Treatment guidelines recommend initiation of therapy for individuals experiencing rapid CD4 cell decline. It is not known, however, whether the rate of CD4 cell decline before combination antiretroviral therapy (cART) is related to immunological response following cART.
Methods: : We estimated precART and postcART CD4 cell slopes by mixed models and categorized patients into two groups according to whether estimated precART slopes were above or below the 75th percentile. We compared immunological responses of the two groups through both mixed models and survival techniques. Models were stratified by CD4 cell at baseline, adjusted for HIV RNA, age, sex, HIV transmission group, year of seroconversion, initiation during primary infection, hepatitis C virus and hepatitis B virus serostatus, and cART class.
Results: Of 2038 eligible patients, 1531 and 507 experienced median (interquartile range) precART CD4 cell slope of −105 (−471 to −61) and −42 (−62 to +80) cells/μl, respectively, over 2 years. After adjusting for potential confounders, individuals with shallower decline experienced a slower rate of CD4 cell recovery following cART initiation of +9.5 [95% confidence interval (CI) +6.6 to +12.2] compared to +13.9 (+13.0 to +14.8) cells/μl per month among those with steeper precART decline (P < 0.001). After stratifying by the baseline CD4 cell count, the adjusted relative hazard of an increase from baseline of more than 50 cells/μl was 0.70 (95% CI 0.62–0.79) for those with a shallower vs. steeper precART decline.
Conclusion: Findings highlight the existence of a subgroup of individuals with shallower precART CD4 cell decline who experience poorer CD4 cell increases after cART; new studies in this group may provide information to optimize responses to therapy.
For patients living with HIV, the primary goal of combination antiretroviral therapy (cART) is rapid and sustained viral suppression. This is generally followed by an increase in CD4 cell count and a subsequent reduction in the risk of HIV-related morbidity and mortality. Although treatment guidelines provide general guidance on the most appropriate CD4 cell count at which to initiate cART, it is an open question whether ensuing CD4 cell recovery is influenced by precART CD4 cell decline. This would provide the attending clinician with information on the CD4 cell gains that their patient might expect, given the observed rate of decline to date.
Although a proportion of patients who start cART will now achieve CD4 cell counts similar to those of the uninfected population [1–3], particularly if viral suppression can be maintained , the absolute CD4 cell level attained during the first 5 years on cART remains strongly dependent on the baseline CD4 cell count [4,5]. Moreover, baseline CD4 cell count is probably the strongest predictor of the short-term risk of AIDS or death [6,7] and so treatment guidelines generally recommend that the decision to start treatment is based mainly on this measurement, with some current guidelines recommending a threshold below 500 cells/μl for initiating cART [8,9].
Other factors are known to be associated with the speed and extent of CD4 cell count recovery after cART initiation, including antiretroviral naive status, viral pathogenicity, age, coinfection with hepatitis B or C, and some specific antiretroviral drugs [3,10].
A recent study has shown that, among patients re-initiating cART after a single episode of treatment interruption, those with shallower CD4 cell declines during the period off treatment tended to have a smaller initial CD4 cell increase after re-initiation. This effect was independent of the CD4 cell count at the time when patients re-initiated cART, which tended to be higher in patients with less rapid CD4 cell declines .
The aim of the present study is to assess whether this phenomenon is also present in patients starting cART for the first time. The analyses were performed using data from the CASCADE Collaboration of cohorts of individuals with well estimated dates of HIV seroconversion. As all participants have been followed since HIV seroconversion, complete information is available on each individual's CD4 cell count history prior to starting cART. This information is often incomplete in cohorts of seroprevalent individuals, who may start cART soon after diagnosis.
Patients and methods
We analysed data from 23 cohorts of individuals with HIV infection from Europe, Australia and Canada participating in the CASCADE Collaboration. Details of CASCADE have been published elsewhere ; briefly, all cohorts include persons infected with HIV-1 for whom the date of seroconversion could be estimated (in most cases as the midpoint between the last HIV-negative and the first HIV-positive test dates).
Participants in this analysis were ART-naive and initiated cART, defined as either a single or boosted protease inhibitor-based regimen or a NNRTI (nonnucleoside reverse transcriptase inhibitor)-based regimen in combination with at least two NRTIs (nucleoside or nucleotide reverse transcriptase inhibitors), or a triple NRTI regimen. All patients were required to have at least two CD4 cell measurements before cART and at least two in the year after cART initiation. CD4+ T-cell count was performed in each clinical centre by flow cytometry, following well established standard procedures.
We evaluated the longitudinal pattern of CD4 cell count change before initiation of cART by applying mixed effects regression models. In particular, we accounted for dependencies between the repeated CD4 cell measurements within a participant using a random regression line approach with an unstructured covariance matrix . We assessed both a linear and a piecewise linear trend over time, with a change point from 1 to 5 years before starting cART. The piecewise linear model with a change point at 2 years before cART was found to provide the best fit to the data (assessed via log-likelihood values and Akaike's information criterion) and, thus, we generated a graph showing the estimated pattern of CD4 cell decline over time before cART initiation. On the basis of these findings, our analyses then focused on the rate of CD4 cell decline in the 2 years prior to cART initiation (‘precART slope’) for each individual.
We categorized individuals on the basis of the distribution of the estimated precART slopes: individuals with a CD4 cell slope that was below the 75th percentile of the distribution as the reference group, and those with a CD4 slope above the 75th percentile as the comparator group. We also applied random coefficient models to evaluate the effect of this precART slope (dichotomized as above) on the rate of CD4 cell increase in the first year after initiation of cART. These analyses were adjusted for other factors potentially associated with immunological response, including the precART CD4 cell count and viral load (the most recent values measured within the 3 months prior to starting cART, i.e. the ‘baseline CD4 cell’ and ‘baseline viral load’), age at cART, sex, HIV-transmission group, year of seroconversion, AIDS diagnosis at cART, hepatitis C virus (HCV) and hepatitis B virus (HBV) status, class of antiretrovirals received and whether or not cART was started during primary HIV infection (defined as any initiation of cART between the date of the last negative HIV test and 3 months after the first positive HIV test).
To highlight the clinical relevance of our findings and to simplify their presentation, we also investigated changes in CD4 cell count after starting cART by considering the time to a confirmed (measured on two consecutive occasions) CD4 cell increase from precART levels of at least 50 cells/μl. Follow-up for this analysis was right-censored at the earliest of the following: 1 year after starting cART; death; or the date of last clinical visit. Given the importance of the baseline CD4 cell count, analyses were performed after stratifying by the baseline CD4 cell count (in increments of 100 cells/μl) and applying Cox models, both separately and after stratification, with the same covariates as for the mixed linear models described above.
Of the 11 683 individuals who started cART after January 1996 in CASCADE, 7862 were ART-naive and aged at least 16 years. Of these, 2393 had CD4 cell and viral load measurements at the start of cART (i.e. at the baseline visit performed within 3 months before initiation of cART) and at least two CD4 cell assessments in the 2 years before this baseline visit; for 2038 of these individuals, at least two CD4 cell measurements were available within the first year after starting cART. Out of a total of 20 281 CD4 cell measurements from these individuals, 10 678 were collected within the 2 years prior to cART initiation and 9603 in the year after cART. Over the first year after starting cART, only seven (0.3%) deaths were observed.
Pattern of CD4 cell decline before start of combination antiretroviral therapy
The pattern of CD4 cell decline for the 2038 patients in the 8 years prior to starting cART is shown in Fig. 1. Allowing for a change in slope at 2 years before cART, CD4 cell count was estimated to decrease by 39.3 cells/μl per year [95% confidence interval (CI) from 34.3 to 44.3] from 8 to 2 years before cART, and by 96.6 cells/μl per year (95% CI from 92.3 to 100.9) over the 2 years prior to cART. Using the parameters from this model, the precART slope was estimated for each patient in the 2 years prior to cART with a median of −91.5 cells/μl per year (interquartile range: −124.7, −61.3; range: −471.4 to +80.0). Of the study participants, 1531 had an estimated annual CD4 cell loss of more than 61.0 cells/μl per year and were categorized as having steeper (ST) CD4 cell decline; the remaining 507 had an estimated annual CD4 cell loss of less than 61.0 cells/μl per year and were categorized as having shallower (SH) decline. Box-plots demonstrating the CD4 cell counts of the participants in the two selected groups over the 2 years prior to starting cART are shown in Fig. 2.
Descriptive characteristics of the two groups according to their rate of CD4 cell decline prior to initiation of cART are reported in Table 1. We did not observe any difference in demographic variables between the two groups nor between the class of ART study participants received. Of note, those in the SH group had a higher CD4 cell count and a lower viral load at the start of cART when compared to those in the ST group. Further, whereas the median year of seroconversion among the SH group was 1996, it was 1999 among those in the ST group.
Precombination antiretroviral therapy CD4 cell decline and immunological response in the first year after starting combination antiretroviral therapy
A median of four (range: 2–32) CD4 cell count determinations was available from each individual in the first year after starting cART; the number of determinations per individual was almost the same in the two groups [median (range) of 4 (2–32) and 4 (2–23) in the SH and ST groups, respectively]. The median time between consecutive determinations was similar between the two groups: median (range) of 2.1 (0.1–10.6) and 2.0 (0.1–10.5) months among the SH and ST groups, respectively.
Applying a random coefficients model, we estimated a lower mean increase in CD4 cell level in the first year of cART (by −45.5 cells, P < 0.001) for those in the SH vs. the ST group. The mean CD4 cell slope in the first year was +9.5 cells/μl per month (95% CI 6.7–12.2) in the SH group and +13.9 cells/μl per month (95% CI 12.9–14.8) in the ST group (P value for test of difference in slopes: < 0.001). After adjusting for other variables that were statistically significant in univariable models (with P values ≤0.05), including the precART CD4 cell count and viral load, HIV-transmission group, year of seroconversion, start of cART during primary infection, class of antiretrovirals and a diagnosis of AIDS before cART (Table 2), those in the SH group continued to demonstrate a smaller increase in CD4 cell counts in the first year after starting cART compared with individuals in the ST group (by −4.4 cells/μl per month, P < 0.001; see Table 2). Note that the model reported in Table 2 does not include adjustment for HCV, as information on HCV status was not available for all participants. However, when the analyses were repeated in this subgroup (n = 1 858; 91%), and after adjustment for HCV status, results were similar (data not shown).
A formal test of interaction between the baseline CD4 cell count (as a continuous covariate, per 100 cell/μl increment) and the precART slope group was statistically significant (P < 0.001). For this reason, the multivariable analysis was repeated stratifying the study population by baseline CD4 cell count and the differences in the monthly rate of CD4 cell count over the first year between the ST and SH groups were by −3.5 (P = 0.28), −5.4 (P = 0.02), −3.5 (P = 0.04), −4.2 (P = 0.02), −4.0 (P = 0.11), and −7.4 (P = 0.05) cells/μl among those with baseline CD4 cell counts of 100 or less, 101–200, 201–300, 301–400, 401–500 and more than 500/cells/μl, respectively.
Precombination antiretroviral therapy CD4 cell rate decline as possible determinant of progression to an increase from baseline of at least 50 cells/μl
Separate Kaplan–Meier curves and Cox models were applied for the different levels of baseline CD4 cell count (Fig. 3). Those in the SH group experienced slower attainment of an increase from baseline in CD4 cell count of at least 50 cells/μl; this difference was apparent in every CD4 cell count strata. Crude and adjusted relative hazards of achieving a CD4 cell count increase of at least 50 cells/μl are also shown (Fig. 3). The adjusted relative hazard for this immunological end-point was below 1 in each of the CD4 cell strata, although this was not always statistically significant (Fig. 3). Further, when applying the multivariable Cox model after stratifying by baseline CD4 cell count (same strata of Fig. 3), the relative hazard of achieving an increase of more than 50 cells/μl was 0.70 (95% CI 0.62–0.79) for those in the SH group compared to the ST group.
To our knowledge, this is the first study to consider whether precART CD4 cell decline is associated with immune recovery on cART among ART-naive patients. We found that patients with a steeper precART decline tended to experience faster immune reconstitution once they started cART, independently of baseline CD4 cell count and HIV RNA value.
It is well known that, upon initiation of cART, the reconstitution of the CD4+ T-cell pool characteristically exhibits a biphasic pattern. The initial increase is very rapid, usually observed in the first 4–6 months following treatment [5,14] and likely reflects redistribution of memory cells from lymphoid tissue, as suppression of viral replication causes a reduction in immune activation [15–17]. Previous observations, not including the pretreatment CD4 cell slope, showed that the first phase of CD4 cell increase was predicted only by higher pretreatment viral load, whereas a slower but longer second phase increase was associated with younger age, lower pretreatment CD4 cell count and higher pretreatment viral load . Although immune activation and its reduction are key factors, a high baseline viral load, which determines a high immune activation, is also related to a faster decline of CD4+ T cells probably migrating into the lymphoid tissue. This migration is not only related to a steeper pretreatment CD4 cell slope but also strongly influences the initial phase of the CD4 cell response to cART, consistent with the suggestion that relatively more lymphocytes, including recent thymic emigrants, are sequestered in lymphoid tissue in persons with higher viral loads, leading to greater and faster CD4 cell redistribution after viral suppression [16–18]. Thus, patients with high immune activation (i.e. high viral load and a steep pretreatment CD4 cell slope) may be expected to have more rapid CD4 cell responses on cART.
Progressive CD4 cell depletion, with a disproportionate decline in naive CD4 and CD8 cells, characterizes untreated HIV-1 disease [18,19]. The mechanisms that underlie these changes, and the processes responsible for T-cell reconstitution after cART, remain incompletely understood. Until now, precART CD4 cell declines and cART-induced immune reconstitution have been studied separately [20,21]. Here we show that the dynamics of these two phenomena are correlated. During infection, the immune system and the virus coexist in a precarious balance. The continuous loss of CD4+ T lymphocytes has to be compensated by the production of new cells, in a scenario that is not only extremely dynamic within a given patient, but also influenced by several host and viral factors [22–24]. We considered the slope of CD4 cell in the 2 years before treatment initiation, as this period is likely to be of most relevance to clinicians caring for patients, the majority of whom will not have been diagnosed with HIV at the time of seroconversion. In patients with a steeper precART CD4 decline, immune redistribution could be the leading factor for immune reconstitution. In patients with a lower CD4 cell decline, the dynamic could be completely different, and rather than redistribution, there could be a slow but continuous killing of CD4 cells that, in the first period of treatment, could negatively impact immune reconstitution.
The present study poses several questions about the pathogenesis of HIV infection that cannot be answered with current knowledge. We were not able to evaluate different patterns of qualitative immune reconstitution among the two groups of patients, which may be seen as a limitation. Robbins et al.  have shown that only patients who started cART with a CD4 cell count more than 350 cells/μl are likely to regain normal naive-memory cell ratios. Thus, it is important to study in greater depth, among patients starting treatment, the different populations of CD4+ T cells that are involved in immune recovery. Furthermore, it is crucial to analyse not only the changes in the CD4+ T-cell pool present in peripheral blood, but also those in lymphoid tissues and in key sites such as the gastrointestinal tract.
Although some current and previous treatment guidelines for initiation of cART [7,8] incorporate a rapidly declining CD4 cell count (e.g. >100 cells/μl per year) as one of the conditions that may indicate the need for therapy among ART-naive patients, to our knowledge, no recent clinical trials or cohort studies are available that support this guideline .
It should be noted that the strict inclusion criteria for our analysis (at least two CD4 cell count determinations in the 2 years prior to cART and at least two determinations in the year after starting cART) resulted in the exclusion of a large proportion of patients in CASCADE. Thus, our analyses will be most generalizable to patients who are under very close clinical care.
In conclusion, we unexpectedly found that individuals with a faster rate of CD4 cell loss precART tended to experience better immunological responses when they started cART than those with slower precART CD4 cell declines. The clinical relevance of this finding deserves further investigation.
The authors thank the reviewers for careful comments about statistical methods and interpretations of the results. They thank also Fenicia Vescio and Flavia Chiarotti for helpful discussion.
C.M. ideated the study and contributed to the manuscript. A.C. contributed to the manuscript. C.S. discussed statistical approach and contributed to the manuscript. A.B. discussed statistical approach and contributed to the manuscript. A.DeL. contributed to the manuscript. H.B. contributed to the manuscript. M.F. contributed to the manuscript. K.P. coordinated CASCADE, discussed statistical approach and contributed to the manuscript. G.R. contributed to the manuscript. M.D. discussed statistical approach, performed all statistical analyses and contributed to the manuscript. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2011) under EuroCoord grant agreement no. 260694. H.C.B. received funding from santésuisse and the Gottfried and Julia Bangerter-Rhyner-Foundation. H.C.B. has received travel grants, honoraria or unrestricted research grants from GlaxoSmithKline, Bristol-Myers-Squibb, Gilead, Janssen, Roche, Abbott, Tibotec, Boehringer-Ingelheim and viiv Healthcare. These companies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The other authors declare no conflict of interest.
Steering Committee: Julia Del Amo (Chair), Laurence Meyer (Vice Chair), Heiner C. Bucher, Geneviève Chêne, Deenan Pillay, Maria Prins, Magda Rosinska, Caroline Sabin, Giota Touloumi. Co-ordinating Centre: Kholoud Porter (Project Leader), Sara Lodi, Kate Coughlin, Sarah Walker, Abdel Babiker, Janet Darbyshire. Clinical Advisory Board: Heiner Bucher, Andrea De Luca, Martin Fisher, Roberto Muga.
Collaborators: Australia: Sydney AIDS Prospective Study and Sydney Primary HIV Infection cohort (John Kaldor, Tony Kelleher, Tim Ramacciotti, Linda Gelgor, David Cooper, Don Smith); Canada: South Alberta clinic (John Gill); Denmark: Copenhagen HIV Seroconverter Cohort (Louise Bruun Jørgensen, Claus Nielsen, Court Pedersen); Estonia: Tartu Ülikool (Irja Lutsar); France: Aquitaine cohort (Geneviève Chêne, Francois Dabis, Rodolphe Thiebaut, Bernard Masquelier), French Hospital Database (Dominique Costagliola, Marguerite Guiguet), Lyon Primary Infection cohort (Philippe Vanhems), French PRIMO cohort (Marie-Laure Chaix, Jade Ghosn), SEROCO cohort (Laurence Meyer, Faroudy Boufassa); Germany: German cohort (Osamah Hamouda, Claudia Kucherer); Greece Greek Haemophilia cohort (Giota Touloumi, Nikos Pantazis, Angelos Hatzakis, Dimitrios Paraskevis, Anastasia Karafoulidou); Italy: Italian Seroconversion Study (Giovanni Rezza, Maria Dorrucci, Claudia Balotta), ICONA cohort (Antonella d'Arminio Monforte, Alessandro Cozzi-Lepri, Andrea De Luca.) The Netherlands: Amsterdam Cohort Studies among homosexual men and drug users (Maria Prins, Jannie van der Helm, Anneke Krol, Hanneke Schuitemaker); Norway: Oslo and Ulleval Hospital cohorts (Mette Sannes, Oddbjorn Brubakk, Anne Eskild, Johan N Bruun); Poland: National Institute of Hygiene (Magdalena Rosinska, Joanna Gniewosz); Portugal: Universidade Nova de Lisboa (Ricardo Camacho); Russia: Pasteur Institute (Tatyana Smolskaya); Spain: Badalona IDU hospital cohort (Roberto Muga, Jordi Tor), Barcelona IDU Cohort (Patricia Garcia de Olalla, Joan Cayla), Madrid cohort (Julia Del Amo, Jorge del Romero), Valencia IDU cohort (Santiago Pérez-Hoyos, Ildefonso Hernandez Aguado); Switzerland: Swiss HIV Cohort Study (Heiner C. Bucher, Martin Rickenbach, Patrick Francioli); Ukraine: Perinatal Prevention of AIDS Initiative (Ruslan Malyuta); United Kingdom: Edinburgh Hospital cohort (Ray Brettle), Health Protection Agency (Valerie Delpech, Sam Lattimore, Gary Murphy, John Parry, Noel Gill), Royal Free Haemophilia cohort (Caroline Sabin, Christine Lee), UK Register of HIV Seroconverters (Kholoud Porter, Anne Johnson, Andrew Phillips, Abdel Babiker, Janet Darbyshire, Valerie Delpech), University College London (Deenan Pillay), University of Oxford (Harold Jaffe).
1. Maini MK, Gilson RJC, Chavda N, Gill S, Fakoya A, Ross EJ, et al
. Reference ranges and sources of variability of CD4 counts in HIV-seronegative women and men. Genitourin Med 1996; 72:27–31.
2. Bofill M, Janossy G, Lee CA, MacDonald-Burns D, Phillips AN, Sabin C, et al
. Laboratory control values for CD4 and CD8 T lymphocytes: implications for HIV-1 diagnosis. Clin Exp Immunol 1992; 88:243–252.
3. Mocroft A, Phillips AN, Gatell J, Ledergerber B, Fisher M, Clumeck N. Normalization of CD4 counts in patients with HIV-1 infection and maximum virological suppression who are taking combination antiretroviral therapy: an observational cohort-study. Lancet 2007; 370:407–413.
4. Kaufmann GR, Perrin L, Pantaleo G, Opravil M, Furrer H, Telenti A, et al
. CD4 T-lymphocyte recovery in individuals with advanced HIV-1 infection receiving potent antiretroviral therapy for 4 years: the Swiss HIV cohort Study. Arch Intern Med 2003; 163:2187–2195.
5. Hunt PW, Deeks SG, Rodriguez B, Valdez H, Shade SB, Abrams DI, et al
. Continued CD4 cell count increase in HIV-infected adults experiencing 4 years of viral suppression on antiretroviral therapy. AIDS 2003; 17:1907–1915.
6. Kaplan JE, Hanson DL, Jones JL, Dworkin MS. Viral load as an independent risk factor for opportunistic infections in HIV-infected adults and adolescents. AIDS 2001; 15:1831–1836.
7. Williams P, Swindell S, Currier JS. Joint effects of HIV-1 RNA levels and CD4 lymphocyte cells on the risk of specific opportunistic infections. AIDS 1999; 13:1035–1044.
8. Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. http://www.aidsinfo.nih.gov
. [Accessed 2 December 2009]
9. Thompson MA, Aberg JA, Cahn P, Montaner JS, Rizzardini G, Telenti A, et al
. Antiretroviral treatment of adult HIV Infection: 2010 recommendations of the International AIDS Society-USA Panel. JAMA 2010; 304:321–333.
10. Wood E, Yip B, Hogg RS, Sherlock CH, Jahnke N, Harrigan RP, et al
. Full suppression of viral load is needed to achieve an optimal CD4 cell count response among patients on triple drug antiretroviral therapy. AIDS 2000; 14:1955–1960.
11. Mussini C, Touloumi G, Bakoyannis G, Sabin C, Castagna A, Sighinolfi L, et al
. Magnitude and determinants of CD4 recovery following HAART resumption after one cycle of treatment interruption. J Acquir Immune Defic Syndr 2009; 52:588–594.
12. CASCADE Collaboration. Changes in the uptake of antiretroviral therapy and survival in persons with known duration of HIV infection in Europe
. HIV Med 2000
13. Littell RC, Miliken GA, Stroup WW, Wolfinger RD. SAS system for mixed models.
Cary, NC: SAS Institute; 1996.
14. Battegay M, Nüesch R, Hirschel B, Kaufmann GR. Immunological recovery and antiretroviral therapy in HIV-1 infection. Lancet Infect Dis 2006; 6:280–287.
15. Pakker NG, Notermans DW, de Boer RJ, Roos MT, de Wolf F, Hill A, et al
. Biphasic kinetics of peripheral blood T cells after triple combination therapy in HIV-1 infection: a composite of redistribution and proliferation. Nat Med 1998; 4:208–214.
16. Bucy RP, Hockett RD, Derdeyn CA, Saag MS, Squires K, Sillers M, et al
. Initial increase in blood CD4(+) lymphocytes after HIV antiretroviral therapy reflects redistribution from lymphoid tissues. J Clin Invest 1999; 103:1391–1398.
17. Diaz M, Douek DC, Valdez H, Hill BJ, Peterson D, Sanne I, et al
. T cells containing T cell receptor excision circles are inversely related to HIV replication and are selectively and rapidly released into circulation with antiretroviral treatment. AIDS 2003; 17:1145–1149.
18. Rabin RL, Roederer M, Maldonado Y, Petru A, Herzenberg LA. Altered representation of naive and memory CD8 subsets in HIV-infected children. J Clin Invest 1995; 95:2054–2060.
19. Kalayjian RC, Spritzler J, Pu M, Landay A, Pollard RB, Stocker V, et al
. Distinct mechanisms of T cell reconstitution can be identified by estimating thymic volume in adult HIV-1 disease. J Infect Dis 2005; 192:1577–1587.
20. Moore RD, Keruly JC. CD4+ cell count 6 years after commencement of highly active antiretroviral therapy in persons with sustained virologic suppression. Clin Infect Dis 2008; 46:149–150.
21. Waters L, Mandalia S, Randell P, Wildfire A, Gazzard B, Moyle G. The impact of HIV tropism on decreases in CD4 cell count, clinical progression, and subsequent response to a first antiretroviral therapy regimen. Clin Infect Dis 2008; 46:1617–1623.
22. Ariyoshi K, Jaffar S, Alabi AS, Berry N, Schim van der Loeff M, Sabally S, et al
. Plasma RNA viral load predicts the rate of CD4 T cell decline and death in HIV-2-infected patients in west Africa. AIDS 2000; 14:339–344.
23. Leng Q, Borkow G, Weisman Z, Stein M, Kalinkovich A, Bentwich Z. Immune activation correlates better than HIV plasma viral load with CD4 T-cell decline during HIV infection. J Acquir Immune Defic Syndr 2001; 27:389–397.
24. Ding M, Tarwater P, Rodriguez M, Chatterjee R, Ratner D, Yamamura Y, et al
. Estimation of the predictive role of plasma viral load on CD4 decline in HIV-1 subtype C-infected subjects in India. J Acquir Immune Defic Syndr 2009; 50:119–125.
25. Robbins GK, Spritzler JG, Chan ES, Asmuth DM, Gandhi RT, Rodriguez BA, et al
. Incomplete reconstitution of T cell subsetsconcombination antiretroviral therapy in the AIDS Clinical Trials Group protocol 384. Clin Infect Dis 2009; 48:350–356.
26. Wolbers M, Babiker A, Sabin C, Young J, Dorrucci M, Chêne G, et al
, on behalf of the CASCADE Collaboration. Pretreatment CD4 cell slope and progression to AIDS or death in HIV-infected patients initiating antiretroviral therapy: the CASCADE Collaboration. PLoS Med 2010; 7:e1000239.
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combination antiretroviral therapy response; CD4 cell decline; immune reconstitution; seroconverters
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