1161
Predicting Clinical Progression or Death in Subjects with Early-Stage Human
Immunodeficiency Virus (HIV) Infection: A Comparative Analysis of
Quantification of HIV RNA, Soluble Tumor Necrosis Factor Type II Receptors,
Neopterin, and b2-Microglobulin
Daniel S. Stein, Robert H. Lyles, Neil M. H. Graham,*
Charles J. Tassoni,* Joseph B. Margolick, John P. Phair,
Charles Rinaldo, Roger Detels, Alfred Saah,*
and John Bilello, for the Multicenter AIDS Cohort Study†
Departments of Medicine and Pharmacology, Albany Medical College,
Albany, New York; Johns Hopkins University School of Hygiene and
Public Health, Baltimore, Maryland; Department of Medicine,
Northwestern University, Chicago, Illinois; University of Pittsburgh,
Pittsburgh, Pennsylvania; and Department of Epidemiology, School of
Public Health, University of California, Los Angeles
Quantification of human immunodeficiency virus (HIV) RNA by branched-chain DNA signal
amplification, measurement of soluble tumor necrosis factor type II receptors (sTNFR-II), neopterin,
b2-microglobulin, or CD4 cell counts can be used to predict the risk of clinical progression or death
in HIV infection but have not been compared in the same study. Ninety subjects were categorized
into progression groups by their rate of CD4 cell decline and matched into triplets by initial CD4
cell count, age, race, and calendar time. By matched logistic regression, only the sTNFR-II and
HIV RNA values were predictive of outcome across the progression groups. Categorization of
baseline HIV RNA and sTNFR-II resulted in differences in progression to several clinical outcomes.
sTNFR-II concentrations were the only immune marker examined that increased the prognostic
utility of HIV RNA determination in early-stage subjects. Further studies in later stages of disease
or after therapy are indicated.
Since the beginning of the human immunodeficiency virus
(HIV) epidemic, clinicians have tried to predict the risk of
progression and survival of their patients at the time of presentation in order to get an accurate prognosis and to best target
therapy. The earliest markers used included symptoms and CD4
lymphocyte counts [1]. Unfortunately, CD4 lymphocyte counts
are not very discriminating of prognosis, especially in earlystage disease, and are quite variable. This led to the evaluation
of several additional laboratory tests, either alone or in conjunction with the use of CD4 lymphocyte counts [1 – 12]. Among
the soluble immune activation markers that have been most
useful are neopterin (a breakdown product of pteridine metabolism induced by interferon-g), b2-microglobulin (one of the
protein chains of the class 1 HLA complex), and tumor necrosis
factor (TNF) type II receptors (sTNFR-II). The 55-kDa type
Received 18 February 1997; revised 17 June 1997.
All subjects gave written informed consent, and the MACS protocol was
approved by the Institutional Review Board of each institution in accordance
with the guidelines of the US Department of Health and Human Services.
Financial support: NIH (AI-15104, AI-35042, AI-35043, AI-35040, AI35041, AI-37984, AI-35039, AI-37613, and RR-00722).
Reprints or correspondence: Dr. Daniel S. Stein, Clinical Pharmacology
Studies Unit, A-142, Albany Medical College, 47 New Scotland Ave., Albany,
NY 12208.
* Present affiliations: Glaxo-Wellcome, Inc., Research Triangle Park, North
Carolina (N.M.H.G.); RW Johnson Pharmaceutical Research Institute, Raritan,
New Jersey (C.J.T.); Merck & Co., Inc., West Point, Pennsylvania (A.S.).
†
Multicenter AIDS Cohort Study investigators are listed after text.
The Journal of Infectious Diseases 1997;176:1161–7
q 1997 by The University of Chicago. All rights reserved.
0022–1899/97/7605–0006$02.00
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I (sTNFR-I) and 75-kDa type II TNF receptors are the cell
membrane – bound receptors for TNF-a, secreted predominately by macrophages, and lymphotoxin-a, secreted exclusively by T lymphocytes and NK cells [13]. Proteolytic cleavage yields the extracellular domains of the receptors, which
are soluble, stable, and more easily measured than are the
cytokines that bind to them. The levels of sTNFR-I and sTNFRII correlate with their induction by TNF-a, interleukin-1, interleukin-6, and interleukin-2, which have been implicated in
the immunopathogenesis of HIV infection [14 – 16]. In a previous comparative case-control trial of neopterin, b2-microglobulin, CD4 lymphocyte count, sTNFR-I, and sTNFR-II for predicting clinical course, sTNFR-II was the single strongest
predictive marker of outcome [4].
Of the virus load markers that have been used, quantification
of circulating levels of HIV RNA by polymerase chain reaction
or branched-chain DNA signal amplification methods (bDNA)
have been superior to other methods and strong independent predictors of clinical outcome compared with CD4 lymphocyte
counts in most [5, 6, 8, 11, 12] but not all studies [7, 9, 17, 18].
sTNFR-II concentrations correlate with CD4 lymphocyte count,
neopterin, b2-microglobulin, and HIV RNA by polymerase chain
reaction [3, 19–22], although the correlations of sTNFR-II and
HIV RNA with CD4 lymphocyte count became nonsignificant
after multivariate analysis in the one study that performed this
analysis [22]. This implies that sTNFR-II receptors are reflecting
the immune response to the virus load and that they are not simply
a reflection of the number of lymphocytes.
To date, measurement of circulating sTNFR-II concentrations, neopterin, and b2-microglobulin and quantification of the
UC: J Infect
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Stein et al.
number of circulating HIV RNA copies per milliliter have not
been directly compared for their independent predictive value
for clinical progression and death. We therefore compared these
three soluble immune activation markers and HIV RNA copies
per milliliter in subjects selected from the Multicenter AIDS
Cohort Study who were matched by baseline criteria into
groups with different slopes of CD4 lymphocyte decline.
Methods
Population. The Multicenter AIDS Cohort Study (MACS) has
been previously described [23]. Briefly, from April 1984 to March
1985, the MACS enrolled 4954 homosexual and bisexual men
from four metropolitan areas—Baltimore plus Washington, DC,
Chicago, Pittsburgh, and Los Angeles. At enrollment, 1809 of
these men were HIV-seropositive. Subjects are followed at 6month intervals, at which time physical examinations and study
questionnaires are completed, and blood is drawn and saved for
hematologic, virologic, and immunologic testing. The 90 subjects
for this study were based on the matched triplet design of Muñoz
et al. [24]. HIV-infected participants were matched according to
their slope of CD4 lymphocyte decline, baseline CD4 cell count
({50 cells/mL), race, age ({5 years), and calendar time into
matched triplets. Secondary to lack of frozen material being available for analysis from the original late progression group of Muñoz
et al. [24], rematching was done with the above criteria but based
on a CD4 cell decline slope between 010 and 0 cells/6-month
visit, to yield the rapid progression (£ 053 cells/6-month visit),
moderate progression (õ 010 cells/6-month visit), and late (or
slow) progression (§ 010 cells/6-month visit) groups selected for
this study (30 triplets, 90 subjects). These groups represent, with
the aforementioned restrictions, approximately the 25th, 50th, and
85th percentiles of observed CD4 cell decline, respectively. Frozen
samples were selected from the baseline visit, the next visit 6
months later, and the time closest to death, AIDS, or end of followup (in that order of selection). In addition, for the moderate and
late progression groups, a sample was also tested at a visit chronologically closest to that of the clinical progression in the rapid
progression group. While CD4 lymphocyte determinations and
immune activation markers were, in general, available from all of
these visits, HIV RNA quantification was obtainable only from the
baseline sample. Overall follow-up extends to 10 years (through 1
July 1995).
Assays. sTNFR-II concentrations were determined in frozen
serum samples by a commercial ELISA (Quantikine; R&D Systems, Minneapolis) as previously described [22]. The mean value
for normal subjects according to the manufacturer is 1.725 ng/
mL (95% confidence interval [CI], 1.003–3.17 ng/mL). Normal
controls were included in each run, and values were within the
expected range. Neopterin and b2-microglobulin concentrations
were also measured according to the manufacturer’s instructions
by standard RIA (Kabi Pharmacia Diagnostics, Uppala, Sweden;
and Hennings, Berlin), with upper limits of normal of 9.9 mmol/
L and 2.4 mg/mL, respectively. HIV RNA was quantified on frozen
plasma according to the manufacturer’s instructions by the bDNA
method [25]. The lower limit of detection is 500 copies/mL, and
samples that did not yield a detectable measurement were considered to have 100 copies/mL for the analysis.
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JID 1997;176 (November)
Statistical analysis. Data were analyzed by use of SAS [26].
Correlation of the markers was estimated by Spearman’s method,
given the lack of normality of the distributions and the skewness
of the distributions of some of the soluble immune activation markers into the normal range. Friedman’s test [27] was applied to
compare the baseline marker distributions across the 3 matched
groups. Conditional logistic regression was used to model the odds
of being a case (defined in separate analyses as either membership
within the rapid progression group or death) while maintaining the
matching. Dichotomous predictor variables were constructed for
each assay, distinguishing a subject with a value greater than the
median for that assay from a subject with a value less than or
equal to the baseline median for that assay. Further models were
constructed to assess the risk per unit change above normal range
for sTNFR-II, neopterin, or b2- microglobulin and per 1 log change
in HIV RNA. The reason for the use of the change above the
upper limit of normal is that for an immune activation marker such
as sTNFR-II, unlike HIV RNA, there is a range of concentrations
that are normally detectable that would indicate no risk of progression. Only variables that were significant in univariate analysis
were entered into multivariate models. Interaction terms of HIV
RNA and sTNFR-II were tested in the models and were not significant. All P values are two-sided.
Results
Table 1 shows baseline characteristics of the subjects in each
progression group. The HIV RNA, sTNFR-II, neopterin, and
b2-microglobulin values (median, interquartile range) increased from the slowest progression group to the fastest progression group (HIV RNA and sTNFR-II both P õ .001 for
trend, Friedman’s test). For neopterin and b2-microglobulin
values, however, the trends across the progression groups were
not significant. The subjects within a triplet were matched for
CD4 lymphocyte count at baseline, and therefore, as expected,
the CD4 lymphocyte counts for the groups overall are not
significantly different (P Å .8). Stepdown tests adjusting for
multiple comparisons revealed that significant pairwise differences between the rapid and moderate and the rapid and late
(but not the moderate and late) CD4 cell decline groups contributed to the significant overall differences for both sTNFR-II
and HIV RNA. The greatest clinical difference between the
groups is the percentage of subjects who died of AIDS (13.3%
vs. 43.3% vs. 90%), with somewhat less of a difference for
the intermediate end point of the development of a clinical
AIDS diagnosis (33.3% vs. 70% vs. 93.3%).
In table 2, the subjects have been classified according to
whether they eventually developed AIDS (by the 1993 case
definition revision excluding CD4 lymphocyte count õ200
cells/mL [28]) or died. While this descriptive summary ignores
the matching, the same trends are observed for each of the
soluble immune activation markers and HIV RNA for these
clinically defined end points.
At baseline, HIV RNA copies/mL correlated with sTNFRII (r Å .51, P õ .001), b2-microglobulin (r Å .45, P õ .001),
UC: J Infect
JID 1997;176 (November)
Predictive Value of HIV RNA and Immune Markers
1163
Table 1. Descriptive baseline statistics for 3 progression groups.
HIV RNA, copies/mL
sTNFRII, ng/mL
CD4 cell count/mm3
Neopterin, nmol/L
b2-microglobulin, mg/L
Antiretrovirals‡
Acyclovir‡
PCP prophylaxis‡
AIDS§
AIDS death§
Total*
(n Å 90)
Late progression
(n Å 30)
Moderate progression
(n Å 30)
Rapid progression
(n Å 30)
6985 (1356 – 25,890)
3.70 (3.10 – 4.65)
590.50 (447 – 788)
10.40 (8.70 – 15.20)
2.13 (1.62 – 2.80)
66.7
22.2
47.8
65.6
48.9
1683 (855 – 8280)
3.13 (2.92 – 4.08)
625.50 (445 – 789)
10.29 (8.24 – 14.60)
1.90 (1.55 – 2.75)
43.3
10.0
33.3
33.3
13.3
6816 (1894 – 12,245)
3.64 (3.33 – 4.42)
590.50 (447 – 829)
10.35 (8.72 – 15.40)
2.09 (1.60 – 2.53)
70.0
33.3
66.7
70
43.3
17,830† (6985 – 57,930)
4.11† (3.62 – 4.93)
534 (456 – 772)
11.17 (8.70 – 16.68)
2.52 (1.87 – 3.00)
86.7
23.3
43.3
93.3
90.0
NOTE. Data are median (interquartile range) or %, as appropriate. PCP, Pneumocystis carinii pneumonia.
* Missing HIV RNA (n Å 3); neopterin (n Å 8); b2-microglobulin (n Å 7).
†
Trends overall are significant (P õ .001) as are pairwise comparisons of rapid vs. moderate progression group and rapid vs. late progression group.
‡
Ever used by end of study period (1 July 1995).
§
By end of study.
and neopterin (r Å .24, P Å .04). sTNFR-II correlated with
b2-microglobulin (r Å .32, P Å .003) and neopterin (r Å .27,
P Å .02) in addition to HIV RNA. CD4 lymphocyte count
correlated inversely with sTNFR-II (r Å 0.29, P Å .006), HIV
RNA (r Å 0.26, P Å .01), and neopterin (r Å 0.30, P Å
.006). Neopterin had a weak inverse correlation with CD4
lymphocyte count (r Å 0.21, P Å .06) and no significant
correlation with b2-microglobulin (r Å .16, P Å .15).
Since there are few data on the time course of changes in
sTNFR-II, we investigated this issue. Figure 1 compares the
time course of changes in sTNFR-II to the changes in CD4
lymphocyte count that defined each progression group. sTNFRII values increased slightly over the first 6 months, rising markedly at the point closest to death or AIDS as noted in the rapid
progression group (a change in median values from 4.21 to
6.31) and at the last follow-up point of the moderate progression group. These changes are not simply a reflection of the
time a subject has been infected, since both the moderate and
slow progression groups had medians below the rapid progression group at a similar length of follow-up.
Using the baseline data, we fit univariate and multivariate
matched conditional logistic regression models to estimate the predictive value of each marker with respect to the separate outcomes
of being in the rapid CD4 cell decline group and death from AIDS
during the study period. Table 3 shows results of the models in
which we dichotomized each marker at its median value. Only
sTNFR-II (odds ratio [OR], 5.85; 95% CI, 1.63–20.96) and HIV
RNA (OR, 4.83; 95% CI, 1.6–14.62) were significant predictors,
both univariately and multivariately, of rapid progression, with similar adjusted odds ratios for each marker (OR, 4.07; 95% CI, 1.08–
15.36; and OR, 3.95; 95% CI, 1.26–12.41, respectively). Both
sTNFR-II and HIV RNA were univariate predictors of death, but in
multivariate analysis, sTNFR-II became of borderline significance,
although the adjusted ORs were similar.
In the second set of models (table 4), we analyzed the risk
per unit change of each immune activation marker above their
Table 2. Descriptive baseline statistics based on eventual development of clinical AIDS event or AIDS death.
HIV RNA, copies/mL
sTNFRII, ng/mL
CD4 cell count/mm3
Neopterin, nmol/L
b2-microglobulin, mg/L
Thrush or fever
Follow-up time to AIDS, years*
Follow-up time to AIDS death, years*
AIDS
(n Å 59)
No AIDS
(n Å 31)
AIDS death
(n Å 44)
No AIDS death
(n Å 46)
11,160 (5909 – 36,290)
4.00 (3.30 – 4.73)
535 (434 – 721)
11.83 (9.00 – 16.68)
2.25 (1.62 – 2.97)
5.1
6.30 (3.07 – 8.37)
7.82 (4.57 – 9.53)
2148 (679 – 4768)
3.49 (3.01 – 4.04)
725 (497 – 863)
10.02 (6.51 – 13.91)
1.90 (1.75 – 2.42)
0
9.75 (9.46 – 9.94)†
9.75 (9.46 – 9.94)†
15,795 (6931 – 53,350)
4.00 (3.32 – 4.88)
524 (437 – 682)
10.33 (8.66 – 16.68)
2.34 (1.69 – 3.00)
6.8
4.26 (2.73 – 6.81)
6.41 (3.97 – 8.46)
2469 (972 – 8280)
3.58 (3.04 – 4.19)
717.5 (496 – 863)
10.43 (9.00 – 15.08)
1.95 (1.59 – 2.53)
0
9.55 (8.97 – 9.92)†
9.74 (9.53 – 9.93)†
NOTE. Data are median (interquartile range) or %, as appropriate.
* Patients are censored at end of follow-up (Ç10 years).
†
All subjects were censored at end of follow-up.
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UC: J Infect
1164
Stein et al.
JID 1997;176 (November)
Figure 1. Changes in median
CD4 lymphocyte count (A) and
changes in median sTNFR-II (B)
over time (Ç8 years) for 3 matched
progression groups (late, mod, rapid
Å late, moderate, and rapid progression).
respective upper limit of normal and per log10 copies per milliliter change in HIV RNA. As in the prior analysis, only sTNFRII and HIV RNA were univariate predictors of rapid progression or death. However, in contrast to the dichotomous analysis,
on multivariate analysis only the risk per log10 copies per milliliter change in HIV RNA remained significant. If sTNFR-II
was analyzed as a dichotomous value (as in the first set of
analyses) in a model with per log10 copies per milliliter change
in HIV RNA, the multivariate model also resulted in only log10
copies per milliliter change in HIV RNA being significant (for
death as outcome, sTNFR-II, adjusted OR, 2.24; 95% CI, 0.39 –
12.77; HIV RNA, adjusted OR, 4.86; 95% CI, 1.62 – 14.55).
To quantify further the interaction of sTNFR-II and HIV
RNA on the risk of being a rapid progressor, development of
AIDS, or death, and their impact on the times to progression
observed in our study population, we classified the subjects by
whether they had both HIV RNA and sTNFR-II values above
the median for the population (6985 copies/mL and 3.7 ng/
Table 4. Conditional logistic regression analysis of matched triplets
using per unit change to predict rapid progression or death.
Multivariate
Univariate
Outcome, marker
Table 3. Conditional logistic regression analysis of matched triplets
using above versus below or equal to median value to predict rapid
progression or death.
Multivariate
Univariate
Outcome, marker
Rapid progression
HIV RNA
sTNFRII
Neopterin
b2-microglobulin
AIDS death
HIV RNA
sTNFRII
Neopterin
b2-microglobulin
OR (95% CI)
P
Adusted OR
(95% CI)
P
4.83
5.85
1.77
2.03
(1.6 – 14.62)
(1.63 – 20.96)
(0.57 – 5.46)
(0.79 – 5.2)
.005
.007
.32
.14
3.95 (1.26 – 12.41)
4.07 (1.08 – 15.36)
—
—
.019
.038
6.33
6.72
1.34
1.96
(1.81 – 22.18)
(1.45 – 31.02)
(0.41 – 4.33)
(0.67 – 5.74)
.004
.015
.63
.22
5.57 (1.46 – 21.17)
4.99 (0.87 – 28.63)
—
—
.012
.072
09-03-97 13:10:27
P
Adjusted OR
(95% CI)
P
5.71 (2.09 – 15.61)
.001
5.25 (1.78 – 15.53)
.003
1.95 (1.13 – 3.38)
.017
1.12 (0.61 – 2.05)
.71
1.03 (0.93 – 1.13)
.57
—
1.09 (0.58 – 2.03)
.79
—
5.8 (2.03 – 16.59)
2.54 (1.18 – 5.47)
1.02 (0.92 – 1.13)
1.11 (0.58 – 2.12)
.001
.017
.70
.76
5.05 (1.67 – 15.24)
1.59 (0.76 – 3.34)
—
—
.004
.22
NOTE. OR, odds ratio; CI, confidence interval. ULN, upper limit of normal.
NOTE. OR, odds ratio; CI, confidence interval.
/ 9d39$$no33
Rapid progression
HIV RNA per
log10 copies/
mL
sTNFRII per ng/
mL ú ULN
Neopterin per
nmol/L ú
ULN
b2-microglobulin
per mg/L ú
ULN
AIDS death
HIV RNA
sTNFRII
Neopterin
b2-microglobulin
OR (95% CI)
jinfa
UC: J Infect
JID 1997;176 (November)
Predictive Value of HIV RNA and Immune Markers
1165
Table 5. Classification of clinical progression by categories of HIV RNA and sTNFRII.
Marker value
Both sTNFRII and HIV RNA ú median
value* (n Å 28)
One assay ú median value (n Å 30)
Only HIV RNA ú median (n Å 16)†
Only sTNFRII ú median (n Å 14)†
Neither ú median value (n Å 29)
% rapid
progression
% clinical
AIDS
diagnosis
% AIDS
death
60.7
26.7
31.3
21.4
13.8
89.3
73.3
87.5
57.1
34.5
75.0
46.7
62.5
28.6
24.1
Median
time to
AIDS
diagnosis
(years)
Median
time to
AIDS death
(years)
3.8
8.0
7.08
9.14
ú9.5‡
5.1
9.1
8.71
9.54
ú9.7‡
* Median values at baseline were 3.7 ng/mL for sTNFRII and 6985 copies/mL for HIV RNA.
†
Subgroup of ‘‘one assay ú median’’ group.
‡
Subjects censored at end of follow-up (10 years) without median time to event being reached.
mL) or whether they had only one or neither above the median,
which yielded 3 groups of approximately equal size. As seen
in table 5, consistent with the logistic regression analysis, the
highest-risk group for subjects developing AIDS, being in the
rapid progression group, or dying and of progressing to that
end point in the shortest median interval has both markers
above the median, rather than one or neither above the median.
The 2 smaller subgroups (only HIV RNA or only sTNFR-II)
of the group with one assay above the median are included for
comparison but are limited by the smaller number of subjects.
The follow-up for the ‘‘neither’’ group is ú9.5 years secondary
to the few end points in these categories and the censoring of
subjects at end of follow-up (10 years) without reaching the
median time to event. The selection criteria used do not allow
statistical survival analysis to be performed. In addition, since
a minimum of four visits (2 years) were required to calculate
a CD4 cell slope, all of the survival times may be biased to
longer intervals than in the absence of these criteria.
Discussion
This is the first study to compare multiple immune activation
markers and HIV RNA together for their predictive value for
the outcomes of rapid progression or death. We found that of
these assays, only sTNFR-II and HIV RNA were predictive of
rapid progression or death in separate matched logistic regression analyses. The multivariate estimation of this increased risk
(table 3) was Ç4- to 5-fold for the group in each assay that
had values above the medians for our study population for the
end points of rapid progression or death. These increased risks
were statistically significant except in the death analysis, in
which P for sTNFR-II was borderline at .07. As seen in table
5, the use of both markers on a dichotomous basis demonstrated
descriptive differences in prognosis and relative median survival time. This is the first indication that a marker of immune
activation can add to quantification of HIV RNA in predicting
prognosis. Since the groups were matched by baseline CD4
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lymphocyte count, after being classified by their CD4 lymphocyte decline, these markers were assessed independently of the
baseline CD4 lymphocyte count. This resembles the common
clinical dilemma of prognosticating patients’ clinical course
after their presentation with similar CD4 lymphocyte counts.
The baseline measurements were also unaffected by antiretroviral therapy, since none was available at the time the samples
were initially obtained. Antiretroviral therapy and prophylaxis
against opportunistic infection could have influenced the progression to AIDS or death that occurred during follow-up.
However, any such effect would have been conservative —
causing a bias toward not finding a difference in clinical progression or death — since the sickest subjects would have been
the first ones to receive therapy once it became available.
The findings of the models in which we dichotomized each
marker at their baseline medians indicated independence of
sTNFR-II and HIV RNA, while the continuous models showed
them to be univariately predictive but not independent predictors after multivariate analysis (i.e., only HIV RNA remained significant). Using classification of the groups by the
median values in table 5, we showed differences in clinical
outcome. Immune activation markers such as sTNFR-II reflect
lymphocyte function, as they are linked to cytokine stimulation.
Whether models treating sTNFR-II continuously are appropriate is unclear, since there may be a threshold effect involved
for the underlying cytokine activities. The expression patterns
of cytokines and the interrelationship between cytokines, their
antagonists, their receptors, and HIV regulation is a temporally
complex process. Present evidence indicates that the process
is a multifactorial interaction involving a number of different
cell types and mediators. In response to cytokine stimulation,
many receptors are down-regulated, and increasing amounts of
cytokine do not necessarily result in increasing effect [29, 30].
There is, of course, a normal range for a soluble immune
activation marker, while there is not for HIV RNA. In addition,
there is a large difference in the magnitude of the range of
values being compared when each marker is on a continuous
UC: J Infect
1166
Stein et al.
scale — Ç4- to 5-fold for sTNFR-II compared with 10,000fold for HIV RNA. Such large differences in magnitude allow,
in comparison to sTNFR-II, random and biologic variability to
be a lesser problem for the HIV RNA determinations.
Future studies using a larger sample size or subjects in laterstage disease will be important to further compare these markers and to more accurately determine the predictive utility of
sTNFR-II. Three studies in late-stage disease subjects (õ150
CD4 lymphocytes/mL) have found only very high levels of HIV
RNA (ú100,000 copies/mL) to be predictive of progression [6,
7, 10], and CD4 lymphocyte count remained an independent
predictor of progression. This was also found in one of the two
large studies of moderately advanced (CD4 lymphocyte count,
200 – 500) subjects [9, 12]. In subjects with ú500 CD4 lymphocytes/mL, HIV RNA has been an independent predictor of outcome, while CD4 lymphocyte count generally has not been [5,
8, 18]. Therefore, the fact that any threshold of sTNFR-II could
independently add to the predictive accuracy of HIV RNA
quantification in an early-HIV-disease-stage population such
as ours is remarkable.
Consistent with prior studies, we found sTNFR-II to be significantly correlated with HIV RNA copies per milliliter [22]
and to be predictive of outcome [4, 20]. Interestingly, despite
the significant correlation found between b2-microglobulin and
HIV RNA, b2-microglobulin had no predictive value of clinical
outcome in any model evaluated. The correlation we found of
sTNFR-II with CD4 lymphocyte count was consistent with our
earlier study [22] but was weaker than that reported by Godfried
et al. [4]. In contrast to Godfried et al. [4], we had a longer
follow-up time (up to 10 years compared with 3.2 years) and
earlier-stage subjects and used a different ELISA method for
determining sTNFR-II, which apparently yields a lower normal
range. Use of other assays may also yield different results, and
extrapolation to our results should be done with caution. Our
data on the changes in sTNFR-II values extend the findings
reported previously [3, 16] over a much longer time frame and
larger group of subjects.
Our study used a matched triplet design, in which subjects
were classified by slope of CD4 cell decline and then matched
for those known confounders that can influence progression
[1]. This allowed increased statistical power while using 90
subjects over an unmatched study of similar size. However,
the subjects were not matched for other unknown potential
factors that could influence rates of progression, such as virus
strain [7, 31, 32] and other aspects of the immune response
[11, 14]. Therefore, while the use of both sTNFR-II and HIV
RNA values had increased predictive value over their use separately, these other factors are likely reasons for the inability to
completely predict the subsequent clinical course. Since all of
the markers we examined change over time and in response to
antiretroviral therapy [3, 9, 12, 33 – 35], their prognostic utility
for events occurring within a shorter time frame will likely be
higher. The differential utility of HIV RNA and sTNFR-II, the
two strongest prognostic predictors found, may also be different
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JID 1997;176 (November)
in later-stage subjects. Last, since the determination of sTNFRII does not require special processing and is cheaper to perform
than HIV RNA quantification, our data indicating its prognostic
utility could make its use valuable in those areas with significant processing or cost restrictions, such as the developing
world, where HIV RNA determinations are unavailable.
Acknowledgments
We thank Alvaro Muñoz for his helpful discussions and suggestions regarding this investigation and Cynthia Kleeberger for coordinating the study.
MACS Investigators
Johns Hopkins University School of Hygiene and Public Health
(Baltimore): Alfred J. Saah, Principal Investigator; Haroutune Armenian, Homayoon Farzadegan, Donald R. Hoover, Nancy Kass,
Joseph Margolick, Justin McArthur, Ellen Taylor. Howard Brown
Health Center and Northwestern University Medical School (Chicago): John P. Phair, Principal Investigator; Joan S. Chmiel, Bruce
Cohen, Maurice O’Gorman, Daina Variakojis, Jerry Wesch, Steven
M. Wolinsky. University of California, Los Angeles, Schools of
Public Health and Medicine: Roger Detels, Principal Investigator;
Barbara R. Visscher, Janice P. Dudley, John L. Fahey, Janis V.
Giorgi, Oto Martı́nez-Maza, Eric N. Miller, Hal Morgenstern, Parunag Nishanian, John Oishi, Jeremy Taylor, Harry Vinters. University of Pittsburgh Graduate School of Public Health (Pittsburgh):
Charles R. Rinaldo, Principal Investigator; Roger Anderson, James
T. Becker, Phalguni Gupta, Monto Ho, Lawrence Kingsley, John
Mellors, Oliver Ndimbie, Sharon Riddler, Anthony Silvestri,
Sharon Zucconi. Data Coordinating Center, Johns Hopkins University School of Hygiene and Public Health: Alvaro Muñoz, Principal
Investigator; Baibai Chen, Clara Chu, Cheryl Enger, Stephen
Gange, Lisa P. Jacobson, Cynthia Kleeberger, Robert Lyles, Steven Piantadosi, Sol Su. NIH (Bethesda, MD): National Institute of
Allergy and Infectious Diseases: Lewis Schrager, Project Officer;
National Cancer Institute: Sandra Melnick.
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