Download Recent Thymus Emigrant CD4+ T Cells Predict HIV Disease

Clinical Infectious Diseases
MAJOR ARTICLE
HIV/AIDS
Recent Thymus Emigrant CD4+ T Cells Predict HIV
Disease Progression in Patients With Perinatally Acquired
HIV
Ramia Zakhour, Dat Q. Tran, Guenet Degaffe, Cynthia S. Bell, Elizabeth Donnachie, Weihe Zhang, Norma Pérez, Laura J. Benjamins, Gabriela Del Bianco,
Gilhen Rodriguez, James R. Murphy, and Gloria P. Heresi
Department of Pediatrics, University of Texas Health Center, Houston, Texas
Background. Robust immune restoration in human immunodeficiency virus (HIV)–positive patients is dependent on thymic
function. However, few studies have investigated thymic function and its correlation with disease progression over time in HIVpositive patients.
Methods. In this longitudinal prospective study, we followed 69 HIV-positive patients who were perinatally infected. Peripheral
blood mononuclear cells were stained with monoclonal anti-CD4 and anti-CD31 and recent thymic emigrants (CD4+recently
emigrated from the thymus (RTE), CD4+CD31+) quantified by flow cytometry. Statistical analysis used Wilcoxon rank sum test,
Kruskal–Wallis, Spearman correlation, and Kaplan–Meier estimates; Cox regression models were performed for the longitudinal
analysis.
Results. Median age of HIV positive patients enrolled was 13 years (interquartile range [IQR], 8.6). CD4+RTE% decreased with
age and was higher in females. Median CD4+RTE% was 53.5%, IQR, 22.9. CD4+RTE% was closely related to CD4+% and absolute
counts but independent of viral load and CD8+CD38+%. Antiretroviral compliance as well as higher nadir CD4+% were associated
with higher CD4+RTE%. Low CD4+RTE% predicted poor progression of VL and CD4+% over time.
Conclusions. CD4+RTE% predicts disease progression and may reflect history of disease in HIV-positive patients and adolescents. They are easy to measure in the clinical setting and may be helpful markers in guiding treatment decisions.
Keywords. HIV; thymus; CD31; patients.
Human immunodeficiency virus (HIV) infection can cause
marked compromise of immune capacities that are, in part, consequences of diminished thymus production of T cells [1–4].
Central goals of clinical management of HIV patients include
maintaining or restoring CD4+ T cells. The clinical success
of this arm of management is most often evaluated as fraction
of blood lymphocytes that are CD4+. However, this measurement does not discriminate whether the enumerated CD4+
cells are of peripheral or thymic origin; cells of thymic origin
are required for more robust immunological capacities, providing a larger T-cell receptor repertoire [5]. To address
this, studies have been conducted to directly measure T cells
recently emigrated from the thymus (RTE). Results demonstrate that HIV infection can cause notable reductions in
RTE and that in some circumstances therapy can restore
RTE production. However, few studies have evaluated RTE
in patients at sites of primary care delivery, especially in
Received 16 October 2015; accepted 18 January 2016; published online 21 February 2016.
Correspondence: G. P. Heresi, Department of Pediatrics, University of Texas, 6431 Fannin
MSB 3.126, Houston, TX 77030 ([email protected]).
Clinical Infectious Diseases® 2016;62(8):1029–35
© The Author 2016. Published by Oxford University Press for the Infectious Diseases Society
of America. All rights reserved. For permissions, e-mail [email protected].
DOI: 10.1093/cid/ciw030
patients who acquired HIV perinatally when their immune
systems were immature.
Although CD4 and viral load (VL) have long been shown to
be good markers of disease control in HIV, multiple studies
have proven the additive role of other markers as prognostic determinants for HIV disease progression, in particular, markers
of immune activation [6]. However, there continues to be a need
for easy-to-measure markers of disease progression [7], especially ones that can reflect successful therapeutic interventions.
Determining the role of RTE in predicting clinical outcome
in HIV-positive patients deserves further investigation. This
could be especially important for patients who have seemingly
well controlled disease while on and compliant with combined
antiretroviral therapy (cART), but a fraction of whom will subsequently deteriorate.
RTE measurements using T-cell receptor excision circles
(TREC) have proven difficult to apply in clinical settings [3, 5].
Several studies have shown CD31 expression on CD4+ cells to
decrease with maturation and correlate well with TREC content,
making it an acceptable marker of CD4+RTE cells [8–10]. Because CD31 is expressed on the surface of CD4 cells and methodology to measure surface markers on CD4 cells, including
CD4, are routine components of clinical management of HIV
patients, it is possible that the addition of a single reagent to
HIV/AIDS
•
CID 2016:62 (15 April)
•
1029
a routine standard of care assay could provide quantitative information on CD4+RTE cells for pediatric patients in clinical
settings [5].
We hypothesized that CD4+RTE% measured by flow cytometry will predict the subsequent clinical course with respect to
HIV disease for perinatally HIV-infected patients. To this purpose, we prospectively measured CD4+RTE% in a population of
perinatally infected patients and adolescents attending a pediatric HIV specialty clinic.
METHODS
Patients
The study involved 69 HIV-positive patients, perinatally infected,
receiving care through the Pediatric HIV Clinic of UTHealth,
Houston, Texas. During the study period, the clinic provided
services to 96 HIV-positive patients and adolescents (all were
offered participation in the study; 1 declined), of whom 84 attended the clinical laboratory that collaborated in providing
samples for this study (blood samples either could not be obtained or were of poor quality for 15 of the patients who attended the collaborating clinical laboratory). The Institutional
Review Board of UTHealth approved the study. Blood samples
were collected at each routinely scheduled clinic visit between
January 2010 and September 2012. Clinical and laboratory
data were retrieved from medical records.
Lymphocyte Phenotyping
Peripheral blood mononuclear cells were isolated from EDTA
anticoagulated fresh whole blood using a commercial lymphocyte separation media (MP Biomedical, LLC). A total of 50 000
cells were surface stained for 20 minutes at 4°C in the dark with
PerCP-Cy5.5–conjugated anti-CD4 (Clone RPA-T4, BioLegend, San Diego, California) and fluorescein isothiocyanate–
conjugated anti-CD31 (Clone MBC 78.2 [R-PECAM1.2],
Invitrogen, Carlsbad, California), then washed and resuspended
in phosphate-buffered solution. A FACSCalibur (BD Biosciences) was used for data acquisition. Analysis was performed using
FlowJo software, version 7.6 (Tree Star, Inc.). Supplementary
Figure 1 presents the gating strategy.
Analyses
Patients were classified into groups representing their clinical
status with respect to HIV disease at baseline ( point of enrollment and collection of first blood sample). Patients classified
into clinically good status (Cgood) had CD4 ≥25% and undetectable HIV RNA in plasma (<50 HIV RNA copies/mL). Patients in clinically poor status (Cpoor) had CD4 <25% and
≥50 HIV RNA copies/mL. Patients in clinically intermediate
status (Cint) had CD4 ≥ 25% and ≥50 HIV RNA copies/mL.
One patient had CD4 <25% and <50 HIV RNA copies/mL.
This individual was excluded from group analysis.
Patients were considered to be on treatment if they were receiving cART defined as dual-nucleoside–nucleotide reverse
1030
•
CID 2016:62 (15 April)
•
HIV/AIDS
transcriptase inhibitors with either a nonnucleoside reverse
transcriptase inhibitor or a protease inhibitor without interruption for at least 3 months prior to baseline CD4+RTE measurement. Duration of overall antiretroviral exposure was expressed
as cumulative duration in days of receipt of any 1 or more antiretroviral drugs. Patients were considered ART naive if they
had not received any cART in the past. All others were considered as having interrupted treatment.
The data were initially analyzed cross-sectionally using historical findings and clinical measures made on baseline blood
samples. Spearman ρ was used to calculate correlations, and
Mann–Whitney U, Kruskal–Wallis, and Wilcoxon signed ranks
tests were used for comparisons. A linear regression model was
used to determine independent predictors of CD4+RTE%. Likelihood ratio tests were used to compare models and determine
best fit in a backward stepwise method.
To further investigate the capacity of CD4+RTE% to predict
clinical course, Kaplan–Meier estimates, univariate log-rank
tests, and multivariate Cox proportional hazards regression models were calculated for 2 outcomes: deterioration of clinical status
and improvement of clinical status. A deteriorating clinical event
was defined among baseline Cgood and Cint patients as any
follow-up visit with CD4 <25% and ≥50 HIV RNA copies/mL
(Cpoor). An improving clinical event was defined among baseline
Cpoor and Cint patients as any follow-up visit with CD4 ≥25%
and <50 HIV RNA copies/mL (Cgood). Assumptions of proportional hazards and time-varying covariate effects were graphically
assessed. A P value <.05 was considered as statistically significant.
RESULTS
Demographic, clinical, and laboratory data from baseline are
presented in Table 1. As expected, CD4+RTE% correlated negatively with age (ρ = −0.48 [P < .001]). Values of CD4+RTE%
were somewhat higher in females compared with males; the difference approached statistical significance (medians 57.8 [interquartile range = 21.0] and 47.8 [interquartile range = 25.4],
respectively, P = .055).
Associations of Baseline CD4+RTE% With Patient History and Selected
Baseline Laboratory Outcomes
We used correlation analysis to test for univariate relationships
between CD4 + RTE% at baseline, other outcomes measured on
the same blood sample, and outcomes selected as indicators of
each patient’s HIV disease history.
CD4+RTE% Correlates Positively With CD4 and Is Independent
of Viral Load
The findings demonstrated strongly significant positive correlations between CD4+RTE% and other contemporary measures of
CD4+ cells, including CD4+% (r = 0.44; P < .001) and CD4:CD8
ratio (r = 0.37; P = .002). Baseline CD4+RTE% showed equally
strong positive correlations with nadir CD4 count (r = 0.41;
P < .001); this is a value that, in most instances, occurred many
Table 1.
Demographic Clinical and Laboratory Data at Baseline
HIV Clinical Status Group
Outcome
N
All HIV Positive
69
Cpoor
9
Cint
P1 a
Cgood
34
P2a
P3 a
25
14.1 [11.1]
.002
.012
.668
18 (52.9)
12 (48)
.461
.336
.708
26 (76.5)
14 (56)
.688
.187
.127
13.2b [9.7]
.011
Age (y)
13 [8.6]
17.7 [5.3]
Female
37 (53.6)
6 (66.7)
Black
49 (71)
8 (88.9)
11.7 [7.6]
CD4
33 [5.48]
38.6 [15.1]
<.001
<.001
CD4+Abs cell/µL
792 [718]
228 [267]
803 [680]
897 [574]
<.001
<.001
.382
CD4+RTE%
53.5 [22.9]
46.1 [21.8]
54.4 [19.3]
57.8 [27.6]
.066
.188
.933
378.1 [489.6]
100.8 [118.7]
447.9 [453]
394.4 [610.9]
<.001
<.001
.771
18 [12]
10 [5]
20 [7]
20 [20]
<.001
.011
.920
CD4+%
CD4+RTEAbs cell/µL
CD4+%Nadir
33 [12]
CD4+AbsNadir cell/µL
308 [493.5]
92 [140]
411 [331.25]
333 [599]
<.001
.014
.471
CD4/CD8 ratio
0.8 [0.6]
0.2 [0.2]
0.7 [0.5]
1.2 [1.2]
<.001
<.001
.003
CD8
CD8+%
40.9 [21.7]
61 [26.7]
43 [19.6]
32.4 [15.3]
.002
<.001
.003
CD8+Abs cell/µL
931 [637]
1200 [832]
1042 [686.5]
806 [461]
.758
.102
.032
CD8+CD38+%
21 [20]
38 [28]
23.5 [18.3]
15 [17.5]
.057
.001
.002
CD8+HLA-DR+%
10 [11.5]
26 [13]
11.5 [10.8]
6 [5.5]
.002
<.001
.015
HIV VL
Log10VL
2.2 [1.5]
4.5 [2.6]
2.8 [1.1]
Undetectable
.061
<.001
<.001
Average Daily VLc
3.4 [1.0]
3.8 [0.7]
3.4 [1.1]
2.87 [1.3]
.060
.010
.063
0.8 [1.9]
1.9 [5.2]
0.61 [2.0]
0.44 [1.0]
.125
.020
.840
.445
.046
.040
Treatment
Age at initiation (y)
cART
Yes
56 (81.2)
7 (77.8)
24 (70.6)
24 (96)
No, ART Interrupted
7 (10.1)
2 (22.2)
5 (14.7)
0 (0)
No, ART naive
6 (8.7)
0 (0)
5 (14.7)
1 (4)
Values are medians (interquartile ranges) or counts (%).
Abbreviations: Abs, absolute; ART, antiretroviral therapy; cART, combined antiretroviral therapy; Cgood, clinically good status; Cint, clinically intermediate status; Cpoor, clinically poor status;
HIV, human immunodeficiency virus; RTE, recently emigrated from the thymus; VL, viral load.
a
P1, P value for comparisons between groups Cpoor and Cint; P2, Cpoor and Cgood; P3, Cint and Cgood. Significant P values are in bold.
b
Values in italics are outside of clinical laboratory normal ranges.
c
To estimate cumulative plasma HIV RNA, a geometric mean daily VL is calculated. The log10 of each reported VL (from each patient’s first VL test until the baseline test) is determined, and the
trapezoidal rule is used to estimate daily VL between measurements [11]. The sum of these daily log values is divided by the cumulative number of days between first and most recent VL
measurement.
years before the reference CD4+RTE% measurement. Associations of CD4+RTE% with contemporary measurements of
CD8+ cells were not as strong but reached significance for a negative relationship with CD8+% (r = −0.25; P = .040) and the subset CD8+HLA-DR+% (r = −0.24; P = .046). In contrast with
CD8+HLA-DR+%, a second marker of immune system activation, CD8+CD38+%, showed no relationship with CD4+RTE%
(r = 0.003; P = .982). Analysis did not reveal any significant correlations between CD4+RTE% and contemporary HIV viremia
(r = −0.07; P = .560), cumulative historical VL (r = 0.01;
P = .900), or peak historical VL (r = −0.07; P = .594).
on average, ART-naive patients had lower CD4+RTE% when
compared with patients with interrupted treatment and patients
on cART. The differences between ART-naive and cART groups
approached statistical significance (P = .064) in spite of the small
number of patients in the ART-naive group (Table 2).
Table 2.
CD4+RTE% and Age by Treatment Group
ART History
Outcome
N
CD4+RTE% in HIV Perinatally Infected Children Is Predicted by
Treatment Status and, to a Lesser Extent, to Nadir CD4+%
Fifty-six (81%) patients were on cART and 13 were not on cART
at baseline. Of the 13 not on cART, 6 had never received treatment
(ART naive, slow progressors) and 7 had been on cART but had
compliance issues (interrupted treatment). Despite being younger
Naive
6
Interrupted
7
cART
P1 a
P2 a
P3 a
56
NA
NA
NA
CD4+RTE%
37.6 [11.6]
47.8 [25.8]
54.9 [22.1]
.366
.064
.338
Age
11.6 [10.6]
15.4 [6.7]
13.0 [9.0]
.534
.719
.327
Values are medians and interquartile ranges.
Abbreviations: ART, antiretroviral therapy; cART, combined antiretroviral therapy; NA, not
applicable; RTE, recently emigrated from the thymus.
a
P1 lists P values for comparisons between ART naive and ART interrupted; P2 is for ART
naive and cART; and P3 is for ART interrupted and cART.
HIV/AIDS
•
CID 2016:62 (15 April)
•
1031
To further resolve the relationship with treatment status, a
multivariate linear regression model with CD4+RTE% as a dependent variable was constructed. After controlling for associations between CD4+RTE% by age and gender, the model
confirmed the significant reduction in CD4+RTE% among
treatment-naive patients and a minor reduction among patients
with interrupted treatment compared with those on cART
(Table 3). Though only approaching significance, CD4+RTE%
remained positively associated with nadir CD4+% as the next
most influential variable in predicting CD4+RTE%. Ethnicity,
HIV clinical status group, CD8+%, CD8+CD38+%, CD8+HLADR+%, and CD4:CD8 ratio did not show a significant association
with CD4+RTE% after adjusting for age, gender, treatment status,
and nadir CD4+%.
Longitudinal Studies of CD4+RTE Cells
CD4+RTE% at Baseline Predicts Change Over Time in HIV
Disease Clinical Status Group
To identify values of CD4+RTE% that associate with improving
or deteriorating HIV disease clinical status, the 33 patients with
HIV clinical status Cpoor or Cgood and with at least 1 additional blood sample after baseline were evaluated for clinical change
over the duration of observation. Three (38%) of the 8 patients
initially classified as Cpoor at baseline improved to Cint or
Cgood clinical status at least once during their follow-up. The
patients who showed improvement had a median baseline CD4+
RTE% of 49.5%, which is 14.4% higher than for those who
remained in the Cpoor classification. Reciprocally, of the
25 patients classified at baseline as Cgood, 11 (44%) showed
deteriorating clinical status, with at least 1 subsequent blood
sample indicating less than Cgood classification. The patients
who showed clinical deterioration had a median CD4+RTE%
Table 3.
Summary of Linear Regression Analysis Predicting CD4+RTE%
SE
β
P Value
Variable
B
Female
9.057
2.970
0.312
.003
Age (y)
−1.057
0.326
−0.405
.002
Treatment group
Interrupted
Naive
Nadir CD4+%
Constant
−4.366
4.695
−0.091
.356
−12.300
5.354
−0.241
.025
0.309
0.174
0.221
55.490
6.367
NA
.080
<.001
Initial independent variables included were age, gender, ethnicity, human immunodeficiency
virus clinical status group, CD8+%, CD8+CD38+%, CD8+HLA-DR+%, CD4:CD8 ratio,
treatment status, and nadir CD4+%. Variables that were found to be nonsignificant were
removed from the model in a stepwise fashion and log likelihood ratios were calculated to
compare models before and after removal of each variable to ensure that no significant
change to the model was occurring (P value <.05 for likelihood ratio test). We retained all
variables with a P value <.1 in the model. Statistically significant (P < .05) associations
with CD4+RTE% are bolded.
Treatment groups were coded as categorical variables with baseline category being
combined antiretroviral therapy. Baseline category for gender was male. Nadir CD4+%
and age were used as continuous variables.
Abbreviations: β, standardized regression coefficients; B, unstandardized regression
coefficient; NA, not applicable; RTE, recently emigrated from the thymus; SE, standard
errors of unstandardized coefficients.
1032
•
CID 2016:62 (15 April)
•
HIV/AIDS
of 45.8% at baseline, which is 18.1% lower than for those who
remained in Cgood classification. These findings suggest that
patients with CD4+RTE% greater or equal to approximately
50% in a blood sample are more likely to show subsequent clinical improvement, while those with less than approximately
50% CD4+RTE% are more likely to clinically deteriorate.
CD4+RTE <50% Independently Predicts Deterioration of HIV
Clinical Status
Baseline clinical variables predictive of longitudinal disease outcomes were identified using multivariate Cox regression models
comprising 59 baseline Cgood and Cint patients (to evaluate for
deterioration) and 42 baseline Cpoor and Cint patients (to evaluate for improvement). Low CD4+RTE% <50% and high CD8+
HLA-DR+ >10% significantly predicted deteriorating clinical
course in Cgood and Cint patients at baseline (Figure 1A and
1B). Alternatively, treatment status completely predicted improving clinical course (Figure 1C) such that, of 17 (40%) baseline Cpoor and Cint patients who improved to Cgood status
during follow-up, all were actively receiving cART. Within
these Cpoor and Cint patients receiving cART at baseline, CD8+
HLA-DR+ <10% significantly predicted improvement of clinical
status (Figure 1D). High CD4+RTE% (≥50%) and low CD8+%
(<36%) at baseline showed univariate association to improvement
in clinical status during follow-up but did not remain significant
in the multivariate model.
DISCUSSION
In this study, CD4+RTE% measured as CD31 expressed on CD4+
T cells predicted HIV disease progression (mainly deterioration)
in perinatally HIV-infected patients and adolescents. CD4+RTE%
in our population decreased with age and was somewhat higher in
females, as previously described [12, 13]. We did not find correlations between CD4+RTE% and VL or between CD4+RTE% and
average daily VL, which reflects overall exposure to virus. Similar
to our findings, De Rossi et al showed that increases in RTE measured as TREC do not correlate with changes in VL and that there
are no differences over time in TRECs between patients with or
without virologic response to cART [14]. Nikolic-Djokic et al
showed that TREC levels correlate with immunologic response
but not with virologic response to treatment [15]. These findings
may be due to the dissociation between virologic and immune responses frequently described in patients and/or to the fact that resistant viruses are less virulent and less likely to cause immune
suppression despite persistent viremia [16]. Thymocytes have
been described to be resistant to entry of protease inhibitor–resistant
HIV virus [17]. It has also been previously described that TREC
do not correlate with VL in patients on treatment for a prolonged
period of time [18]. This might explain the finding in our
population since most had been on cART for years at study entry.
Klein et al found no preferential loss of CD31+ cells among
naive CD4 cells after planned treatment interruptions that
Figure 1. A and B, Significant predictors of deterioration: patients transitioning from better than clinically poor status (Cpoor) at baseline to Cpoor during follow-up. (C and D),
Significant predictors of improvement: patients transitioning from less than clinically good status (Cgood) at baseline to Cgood during follow-up. Abbreviations: ART, antiretroviral therapy; cART, combined antiretroviral therapy; RTE, recently emigrated from the thymus.
resulted in a decrease in CD4 counts in HIV-positive patients,
concluding that immune activation must contribute to this decrease in addition to possibly decreased thymic function [19]. In
our study, thymic function measured as CD4+RTE% was independent of immune activation measured as CD8+CD38+% but
not as measured by CD8+HLA-DR+%. CD38 measured on
CD8+ cells has been suggested to independently predict response to therapy and disease progression in patients and adults
[20–22]. Interestingly, CD4+RTE% did not follow the same pattern of change as CD8+CD38+% when the clinical groups
changed over time and did not correlate with CD8+CD38+%
overall in our study. Discrepancy between CD38 expression
and HLA-DR expression on CD8+ T cells has been described,
with the existence of CD8+CD38−HLA-DR+, CD8+CD38+
HLA-DR−, and CD8+CD38+HLA-DR+ cells [23]. CD38 and
HLA-DR seem to be expressed on CD8+ T cells at different
but partially overlapping stages of their development and maturation [24]. CD38 is expressed on both immature naive CD8
cells as well as activated memory cells [25], whereas HLA-DR is
solely expressed on stimulated and activated CD8+ T cells [26].
HLA-DR expression has been shown to correlate negatively
with TREC content of CD8+ T cells [26]. This could explain
the negative correlation that we found between CD8+HLA-
DR+% and CD4+RTE%. CD8+HLA-DR+% was recently described to be significantly higher than CD8+CD38+% in HIV
controller patients and to have higher expression on mildly
stimulated CD8+ T cells, whereas there was more coexpression
of HLA-DR and CD38 on highly activated CD8+ T cells (higher
antigen exposure) [23]. These findings combined with ours suggest that HLA-DR has some particularity/superiority over
CD38 in predicting disease progression, which might be related
to HIV disease pathogenesis and its influence on the generation
of cells expressing 1 or both of these markers.
In this study, CD8+HLA-DR+% and CD4+RTE% both predicted deterioration of HIV disease as determined by change in
CD4 and VL. However, CD4+RTE% was the single most potent
prognostic marker to predict such deterioration (P = .0004).
CD4+RTE% in HIV-positive patients in our study was mainly
dependent on treatment status and nadir CD4+%. This highlights the importance of treatment in thymic function maintenance and recovery as even patients not on treatment at time
of study entry but with a history of treatment had higher CD4+RTE% than ART-naive patients despite preserving relatively
good CD4 counts and VL. This may be an additional reason to
support the start of therapy in patients in whom no other obvious
indication for treatment is present. Another finding that supports
HIV/AIDS
•
CID 2016:62 (15 April)
•
1033
early initiation of treatment is that patients in the Cgood group
were significantly younger at initiation of cART compared with
those in the Cpoor group. Similar to Blanche et al, we found
nadir CD4+% to correlate with CD4+RTE% and to have a
close to significant role in predicting its value [27]. The importance of nadir CD4+% and treatment history in predicting CD4+RTE% highlights that RTE reflect disease history.
The remainder of our results, particularly what pertains to
differences in RTE between viremic and aviremic patients and
changes with age, do not concur with findings by Blanche et al
[27]. This discordance is partially due to differences in the populations. However, an important difference is that our study and
most previously published literature expressed RTE as a subpopulation of CD4+ cells, whereas Blanche et al expressed RTE as
a percentage of naive CD4 cells, making their data difficult to
compare with ours and those of others [28, 29].
We found CD4+RTE% to predict the evolution of virologic
and immunologic status of patients. This is based, in part, on
the results of the survival analysis but also shown by trends of
CD4+RTE% when broken down by clinical groups and stability
of those groups over time. Small group size may have accounted
for the nonstatistical significance of some of these observations.
In a study of adults, RTEs were deemed unsuitable as a marker of
CD4 recovery [30]. On the other hand, Li et al recently showed
CD31 expressed on naive CD4 cells at initiation of treatment to
predict rate of CD4 recovery in HIV-positive adults [28]. Our
finding that CD4+RTE% at any point in time can predict deterioration in clinical grouping in HIV-positive patients is new.
The dynamics and lack of homogeneity in our population
may have limited the significance of some of our observations.
Our results were not always significant due to small sample size
in some of our groups.
CONCLUSION
In our study, CD4+RTE% reflected HIV history including
cART exposure history and nadir CD4+%. Baseline CD4+
RTE% independently predicted CD4 and VL failure. This easily
measurable subset of CD4+ T cells may thus guide the clinician
in treatment related decision making, including introduction of an
adjuvant therapy to increase thymic output and in determining
patient prognosis. The information provided by CD4+RTE%
measurements seems to be independent/incremental to that provided by other routinely measured laboratory values.
Supplementary Data
Supplementary materials are available at http://cid.oxfordjournals.org.
Consisting of data provided by the author to benefit the reader, the posted
materials are not copyedited and are the sole responsibility of the author, so
questions or comments should be addressed to the author.
Notes
Acknowledgments. We thank our patients, without whom this work
would not have been possible. We also thank the clinical and laboratory
staff who made the blood collection possible.
1034
•
CID 2016:62 (15 April)
•
HIV/AIDS
Financial support. This work was supported by the Texas HIV
Initiative.
Potential conflicts of interest. All authors: No reported conflicts. All
authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content
of the manuscript have been disclosed.
References
1. Stebbing J, Gazzard B, Douek DC. Where does HIV live? N Engl J Med 2004;
350:1872–80.
2. Fernandez S, Nolan RC, Price P, et al. Thymic function in severely immunodeficient HIV type 1-infected patients receiving stable and effective antiretroviral therapy. AIDS Res Hum Retroviruses 2006; 22:163–70.
3. Sandgaard KS, Lewis J, Adams S, Klein N, Callard R. Antiretroviral therapy increases thymic output in children with HIV. AIDS 2014; 28:209–14.
4. Cotugno N, Douagi I, Rossi P, Palma P. Suboptimal immune reconstitution in vertically HIV infected children: a view on how HIV replication and timing of
HAART initiation can impact on T and B-cell compartment. Clin Dev Immunol
2012; doi:10.1155/2012/805151.
5. Kohler S, Thiel A. Life after the thymus: CD31+ and CD31- human naive CD4+
T-cell subsets. Blood 2009; 113:769–74.
6. Hazenberg MD, Otto SA, van Benthem BH, et al. Persistent immune activation
in HIV-1 infection is associated with progression to AIDS. AIDS 2003; 17:1881–8.
7. Langford SE, Ananworanich J, Cooper DA. Predictors of disease progression in
HIV infection: a review. AIDS Res Ther 2007; 4:11.
8. Junge S, Kloeckener-Gruissem B, Zufferey R, et al. Correlation between recent thymic emigrants and CD31+ (PECAM-1) CD4+ T cells in normal individuals during aging and in lymphopenic children. Eur J Immunol 2007; 37:3270–80.
9. Demeure CE, Byun DG, Yang LP, Vezzio N, Delespesse G. CD31 (PECAM-1) is a
differentiation antigen lost during human CD4 T-cell maturation into Th1 or Th2
effector cells. Immunology 1996; 88:110–5.
10. Tanaskovic S, Fernandez S, Price P, Lee S, French MA. CD31 (PECAM-1) is a
marker of recent thymic emigrants among CD4+ T-cells, but not CD8+ T-cells
or gammadelta T-cells, in HIV patients responding to ART. Immunol Cell Biol
2010; 88:321–7.
11. Cole SR, Napravnik S, Mugavero MJ, Lau B, Eron JJ Jr, Saag MS. Copy-years viremia as a measure of cumulative human immunodeficiency virus viral burden.
Am J Epidemiol 2010; 171:198–205.
12. Pido-Lopez J, Imami N, Aspinall R. Both age and gender affect thymic output:
more recent thymic migrants in females than males as they age. Clin Exp Immunol
2001; 125:409–13.
13. Kilpatrick RD, Rickabaugh T, Hultin LE, et al. Homeostasis of the naive CD4+ T
cell compartment during aging. J Immunol 2008; 180:1499–507.
14. De Rossi A, Walker AS, Klein N, De Forni D, King D, Gibb DM. Increased thymic
output after initiation of antiretroviral therapy in human immunodeficiency virus
type 1-infected children in the Paediatric European Network for Treatment of
AIDS (PENTA) 5 Trial. J Infect Dis 2002; 186:312–20.
15. Nikolic-Djokic D, Essajee S, Rigaud M, et al. Immunoreconstitution in children
receiving highly active antiretroviral therapy depends on the CD4 cell percentage
at baseline. J Infect Dis 2002; 185:290–8.
16. Ometto L, De Forni D, Patiri F, et al. Immune reconstitution in HIV-1-infected
children on antiretroviral therapy: role of thymic output and viral fitness. AIDS
2002; 16:839–49.
17. Haynes BF, Sempowski GD. Can the thymus win the battle against drug-resistant
HIV? Nat Med 2001; 7:661–2.
18. Saitoh A, Singh KK, Sandall S, et al. Association of CD4+ T-lymphocyte counts
and new thymic emigrants in HIV-infected children during successful highly active antiretroviral therapy. J Allergy Clin Immunol 2006; 117:909–15.
19. Klein N, Sefe D, Mosconi I, et al. The immunological and virological consequences
of planned treatment interruptions in children with HIV infection. PLoS One
2013; 8:e76582.
20. Rosso R, Fenoglio D, Terranova MP, et al. Relevance of CD38 expression on CD8T
cells to evaluate antiretroviral therapy response in HIV-1-infected youths. Scand J
Immunol 2010; 71:45–51.
21. Ruel T, Ssewanyana I, Achan J, et al. Dynamics of T cell activation accompanying
CD4 recovery in antiretroviral treated HIV-infected Ugandan children. Clin Immunol 2009; 131:410–4.
22. Karim R, Mack WJ, Stiller T, et al. Association of HIV clinical disease progression
with profiles of early immune activation: results from a cluster analysis approach.
AIDS 2013; 27:1473–81.
23. Hua S, Lecuroux C, Saez-Cirion A, et al. Potential role for HIV-specific CD38-/
HLA-DR+ CD8+ T cells in viral suppression and cytotoxicity in HIV controllers.
PLoS One 2014; 9:e101920.
24. Ramzaoui S, Jouen-Beades F, Gilbert D, et al. During HIV infection, CD4+ CD38+
T-cells are the predominant circulating CD4+ subset whose HLA-DR positivity
increases with disease progression and whose V beta repertoire is similar to that
of CD4+ CD38- T-cells. Clin Immunol Immunopathol 1995; 77:33–41.
25. Benito JM, Lopez M, Lozano S, et al. Differential upregulation of CD38 on different
T-cell subsets may influence the ability to reconstitute CD4+ T cells under successful
highly active antiretroviral therapy. J Acquir Immune Defic Syndr 2005; 38:373–81.
26. Imamichi H, Lempicki RA, Adelsberger JW, et al. The CD8+ HLA-DR+ T cells
expanded in HIV-1 infection are qualitatively identical to those from healthy controls. Eur J Immunol 2012; 42:2608–20.
27. Blanche S, Scott-Algara D, Le Chenadec J, et al. Naive T lymphocytes and recent
thymic emigrants are associated with HIV-1 disease history in French adolescents
and young adults infected in the perinatal period: the ANRS-EP38-IMMIP study.
Clin Infect Dis 2014; 58:573–87.
28. Li T, Wu N, Dai Y, et al. Reduced thymic output is a major mechanism of immune
reconstitution failure in HIV-infected patients after long-term antiretroviral therapy. Clin Infect Dis 2011; 53:944–51.
29. Lewis J, Walker AS, Klein N, Callard R. CD31+ cell percentage correlation with
speed of CD4+ T-cell count recovery in HIV-infected adults is reversed in children: higher thymic output may be responsible. Clin Infect Dis 2012; 55:304–7;
author reply 7.
30. Bofill M, Martinez-Picado J, Ruiz-Hernandez R, et al. Naive CD4(+) T cells and
recent thymic emigrant levels in treated individuals with HIV: clinical relevance.
AIDS Res Hum Retroviruses 2006; 22:893–6.
HIV/AIDS
•
CID 2016:62 (15 April)
•
1035