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Serum Neopterin, an Immune Activation Marker, Independently Predicts Disease Progression in Advanced HIV-1 Infection

  1. Donna Mildvan1,
  2. John Spritzler2,
  3. Sidney E. Grossberg3,
  4. John L. Fahey4,
  5. David M. Johnston5,
  6. Barbara R. Schock6, and
  7. Jonathan Kagan7
  1. 1Beth Israel Medical Center, New York, New York
  2. 2Harvard School of Public Health, Boston, Massachusetts
  3. 3Medical College of Wisconsin, Milwaukee, Wisconsin
  4. 4University of California at Los Angeles School of Medicine, California
  5. 5LabCorp, Research Triangle Park, North Carolina
  6. 6Frontier Science & Technology Research Foundation, Inc., Amherst, New York
  7. 7Division of AIDS, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland
  1. Reprints or correspondence: Donna Mildvan, Beth Israel Medical Center, First Ave. at 16th St., New York, NY 10003 (mildvan{at}ix.netcom.com).
 
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Abstract

Background. CD4+ T lymphocyte (CD4) counts and plasma human immunodeficiency virus (HIV) type 1 RNA concentrations predict clinical outcome in HIV-1 infection. Our objective was to assess the independent prognostic value for disease progression of soluble markers of immune system activation.

Methods. This retrospective marker-validation study utilized previously obtained clinical and laboratory data, including CD4+ cell counts, and made use of stored frozen serum samples to assay for levels of β-microglobulin, neopterin, endogenous interferon, triglycerides, interleukin-6, soluble tumor necrosis factor-α receptor II, and HIV-1 RNA, and to determine HIV genotypic reverse-transcriptase inhibitor resistance. The 152 patients who participated in this study represented a subsample of participants in AIDS Clinical Trials Group (ACTG) 116B/117, a randomized trial that demonstrated the clinical benefit of didanosine over zidovudine monotherapy in persons with advanced HIV-1 infection. Marker data were analyzed in relation to protocol-defined clinical disease progression, using Cox proportional hazards models.

Results. The median duration of follow-up was 344 days. Elevated baseline values for neopterin (P =.0009), endogenous interferon (P =.00039) and interleukin-6 (P =.0007) were each associated with greater subsequent risk of clinical disease progression. In a head-to-head comparison that was adjusted for CD4+ cell count (P =.0165) and HIV-1 RNA level (P =.1220), we found that elevated values for neopterin (P =.0002) and, to a lesser extent, endogenous interferon (P =.0053) were the strongest predictors of increased risk of clinical disease progression 6 months later.

Conclusions. Soluble markers of immune activation add prognostic information to CD4 counts and viral load for risk of disease progression in advanced HIV-1 infection. The robust performance of neopterin, an inexpensive and reliably measured serum marker, supports its potential suitability for patient monitoring, particularly in resource-limited settings.

As biomarkers for HIV-1 infection, plasma HIV-1 RNA concentration reflects the magnitude of viral replication and the CD4+ T lymphocyte count represents the resulting end-organ damage or immunodeficiency. An additional major component of HIV-1 pathogenesis is immune system activation, reflecting a dysregulated host immune response to HIV-1 infection and its sequelae [1, 2]. To assess the independent prognostic value of each of these pathogenetic elements for disease progression, we conducted a comprehensive retrospective marker validation study. Included were longitudinal clinical and laboratory data derived from the clinical endpoint trial of nucleoside analogue monotherapies, AIDS Clinical Trials Group (ACTG) 116B/117 [3]. Using stored frozen serum samples from a subset of study participants, we assayed levels of a series of virologic and soluble immune activation markers identified as promising in earlier studies in which the markers were examined individually. The markers were then compared head-to-head to assess their relative ability to predict subsequent protocol-defined clinical events.

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Methods

Patients and laboratory evaluations. The 152 subjects in this study (New Works Concept Sheet 032) include those who were among the first 10 subjects enrolled at each of the 36 participating sites into ACTG 116B/117 who had available stored serum specimens for subsequent HIV-1 RNA and resistance substudies [4–6], and who also had available stored serum samples frozen at -70°C in the Adult ACTG Data Management Center inventory. ACTG 116B/117 was a controlled trial in subjects who had tolerated zidovudine therapy for ⩾16 weeks and had AIDS, AIDS-related complex with CD4+ T cell counts of ⩽300 cells/mL (symptomatic HIV infection), or were asymptomatic with ⩽200 CD4+ cells/mL. The trial compared continued zidovudine therapy (600 mg daily) with didanosine therapy (500 or 750 mg daily) [3]. Results demonstrated a statistically significant beneficial effect of didanosine therapy on time to clinical progression (defined as progression to an AIDS-defining event or death for those who had entered the trial with symptomatic or asymptomatic infection and as progression to a new, nonrecurrent AIDS-defining event or death for those who had entered the trial with AIDS) [3]. The serum specimens from our subsample of 152 subjects were assayed from baseline to the time of the primary clinical progression endpoint of the parent study (n = 77 such endpoints in our sample) or, if censored, to the time of study discontinuation, for levels of the following markers: β-microglobulin, by an automated microparticle EIA (Imx System, Abbott Laboratories); neopterin, by a commercial EIA (ELItest Neopterin EIA, BRAHMS); endogenous interferon, primarily interferon-α, by a sensitive bioassay described elsewhere [7]; triglycerides, by enzymic reaction on chemistry profiles; IL-6, by EIA (R & D Systems); and soluble tumor necrosis factor-α receptor II (sTNFR-II), by EIA (R & D Systems). In addition, PBMCs were cocultivated for isolation of HIV-1 virus, and phenotypic drug susceptibility testing was performed in real time during the parent trial in Adult ACTG virology laboratories according to consensus methodologies [5]. In a subset of 93 subjects with sufficient sample volumes, serum HIV-1 RNA levels were measured (LabCorp) by quantitative RT-PCR using the Amplicor HIV-1 Monitor Test, version 1.0 (Roche Molecular Systems). Population-based sequence analysis of the HIV-1 reverse-transcriptase gene to identify mutations associated with nucleoside reverse-transcriptase inhibitor drug resistance was performed (LabCorp) using the Affymetrix GeneChip system (GeneChip 2.0 software; Affymetrix).

Four groups of variables were used in the analysis: (1) categorical drug/dose level (didanosine 750 mg, didanosine 500 mg, zidovudine); received either dose of didanosine or received zidovudine; baseline diagnosis (AIDS, symptomatic HIV disease, asymptomatic HIV disease); risk category; race; sex; (2) the categorical time-varying indicator variables for reverse-transcriptase gene mutations known to confer resistance to zidovudine or didanosine [8]; (3) the continuous baseline variables (age, hemoglobin, baseline WBC count, concentration of zidovudine or didanosine necessary to inhibit virus replication by 50% [IC50]); and (4) the continuous time-varying variables for the levels of immune markers, CD4+ T cell count and log10 HIV-1 RNA.

Statistical methods. To assess the value of early marker evaluations (levels at baseline and change to week 8) as predictors of subsequent risk of disease progression, Cox proportional hazards models were fitted separately for data for each marker (linear and quadratic components). A multiple-marker Cox proportional hazards model of the risk of disease progression 6 months in the future was fitted (using a forward and backward stepwise model selection procedure [9], forcing the inclusion of age and sex variables in all models), with baseline and time-varying covariates. For a given risk time, covariate values were taken from the most recent evaluation that was ⩾6 months in the past; for a risk time of <6 months, covariate values at baseline were used.

Missing data (presumed missing at random) were handled as follows. In the stepwise model selection procedure, the “dummy variable adjustment” method [10] was used to determine which covariates to include in the model, and then that model was fitted using a multiple imputation (MI) method [11]. Results for the multiple-marker model are based on this method; the P value for the model itself (rather than the specific covariates) is the largest of the M = 5 likelihood ratio test P values based on each individual MI data set.

All model fitting was done both with and without adjustment by treatment assignment using the “coxph” function of S-PLUS software, version 3.4 (MathSoft), and (for the MI) the MI and MIANALYZE procedures in SAS software, version 8.2 (SAS).

Statistical significance of single-marker models selected without a stepwise procedure was based on a 5%-level likelihood ratio test. The criterion for statistical significance of the model selected by the stepwise procedure was the need for the dummy variable adjustment P value for the likelihood ratio test of this model (vs. the null model with only age and sex) to be smaller than at least 95% of the corresponding P values obtained when the same stepwise procedure was applied to 100 simulated (null) data sets generated from the observed data by randomly permuting the assignment of covariate vectors to the time-to-progression outcomes. Wald P values for individual covariates from the selected multiple-marker model are presented only as benchmarks of relative predictive value.

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Results

Of the 152 subjects in our study, 52 had a diagnosis of AIDS, 91 had symptomatic HIV-1 infection, and 9 had asymptomatic HIV-1 infection; 49 subjects were treated with zidovudine, 44 with didanosine at a dosage of 750 mg/day, and 59 with didanosine at a dosage of 500 mg/day. The median (25th percentile, 75th percentile) duration of follow-up of subjects was 344 (211, 543) days. At baseline, the median CD4+ T cell count was 75 (31, 172) cells/mL, log10 RNA was 4.86 (4.58, 5.34) copies/mL, and neopterin concentration was 16.03 (11.43, 21.12) nmol/L. Our subsample was similar to the remaining 761 subjects in the parent study, who had a median baseline CD4+ cell count of 97 cells/mL.

Association of baseline and week 8 marker values with risk of disease progression

Neopterin. We did regression analysis of risk using only the baseline neopterin value (n = 68 subjects with baseline neopterin evaluations) and found that a higher level of this marker was associated with greater risk of disease progression (P =.0009; hazard ratio [HR] = 2.94 [95% CI, 1.48–5.87], comparing subjects with 21.1 versus 11.4 nmol/L [the 75th and 25th percentiles, respectively, in the sample]). When the change in neopterin level from baseline to week 8 was added to the model (n = 50 subjects with neopterin evaluations whose disease had not progressed by week 8), a lesser decrease in neopterin level was associated with greater risk, after adjustment for baseline level (model P =.0004; HR = 2.68 [1.19–6.02], comparing subjects with an increase of 4.5 nmol/L vs. those with a decrease of 2.3 nmol/L [the 75th and 25th percentiles, respectively, in the sample]).

Endogenous interferon. We did regression analysis of risk using only the baseline endogenous interferon value (n = 120 subjects) and found that a higher level of this marker was associated with greater risk (P =.00039; HR = 1.4 [1.2–1.64], comparing subjects with 26 vs. 10 International Units [IU]/mL [the 75th and 25th percentiles, respectively, in the sample]). When the change in interferon level from baseline to week 8 was added to the model (n = 89 subjects with interferon evaluations whose disease had not progressed by week 8), change in interferon level was not significantly associated with risk after adjustment for baseline level.

IL-6. We did regression analysis of risk using only the baseline IL-6 value (n = 92 subjects) and found that a higher level of this marker was associated with greater risk (P =.0007; HR = 2.07 [1.42–3.03], comparing subjects with 74.8 vs. 16.0 nmol/L [the 75th and 25th percentiles, respectively, in the sample]). When the change in IL-6 level from baseline to week 8 was added to the model (n = 55 with IL-6 evaluations whose disease had not progressed by week 8), a lesser decrease in IL-6 was associated with greater risk after adjustment for baseline level (model P =.00003; HR = 1.22 [1.04–1.42], comparing subjects with no change vs. those with a decrease of 37 nmol/L [the 75th and 25th percentiles, respectively, in the sample]).

CD4, RNA, and the remaining markers. A greater baseline CD4+ cell count (P =.000004; HR = 0.31 [0.19–0.51], comparing subjects with 172 vs. 31 cells/mL [the 75th and 25th percentiles, respectively, in the sample]) and a greater change in CD4+ cell count from baseline to week 8 (model P =.00002; HR = 0.74 [0.58–0.94], comparing subjects with an increase of 19.5 cells/mL vs. those with a decrease of 18.0 cells/mL [the 75th and 25th percentiles, respectively, in the sample]) were associated with reduced risk of disease progression. A greater baseline RNA level (P =.00319; HR = 1.87 [1.3–2.67], comparing subjects with 5.344 log10 copies/mL vs. those with 4.576 log10 copies/mL [the 75th and 25th percentiles, respectively, in the sample]) and (marginally) lesser decreases from baseline to week 8 in RNA level (model P =.0005; HR = 1.6 [1.01–2.52], comparing subjects with an increase of 0.146 log10 copies/mL vs. those with a decrease of 0.244 log10 copies/mL [the 75th and 25th percentiles, respectively, in the sample]) were associated with increased risk of subsequent disease progression. No significant associations were found for any of the other markers examined.

Benchmark values for CD4 and neopterin. Based on univariate Cox models, the risk of disease progression in the first year for patients with baseline CD4+ cell counts of 50, 100, and 200 cells/mL is equivalent to the risk of disease progression in the first year for patients with baseline neopterin values of 20.4, 16.9, and 9.9 nmol/L, respectively. In addition, Kaplan-Meier plots for subjects with baseline CD4+ counts of ⩽200 cells/mL and for subjects with baseline neopterin levels of ⩾9.9 nmol/L are virtually the same (data not shown).

Predictors of risk of progression 6 months in the future

In a head-to-head comparison of all the candidate predictors, including genotypic and phenotypic resistance assays, the stepwise model-fitting procedure selected the statistically significant model with the covariates presented in table 1 in the order of their selection, after adjustment for age and sex. The significant marker variables that were selected include time-varying CD4 count (P =.0155), time-varying neopterin level (P =.0089), and time-varying endogenous interferon level (P =.0241). When time-varying HIV-1 RNA level was added to this model, the overall model was significant (MI method, P <.0001), and lower CD4 counts (P =.0165) and greater neopterin and endogenous interferon levels remained associated with greater risk (P =.0002 and P =.0053, respectively), but HIV-1 RNA level was not significant (P =.122).

Table 1
View larger version:
Table 1

Predictors of risk of HIV-1 disease progression 6 months in the future.

We also fit the following univariate models to determine whether the selected serum markers as well as CD4 count and RNA level are by themselves useful in predicting risk of disease progression 6 months in the future. When we did regression analysis of risk using the neopterin level alone (n = 95), a higher level of neopterin was associated with greater risk (P <.00001; HR = 2.233 [1.632–3.058], comparing subjects with 20.0 vs. 10.9 nmol/L [the 75th and 25th percentiles, respectively, in the sample]). The corresponding results from regression analysis of risk using endogenous interferon alone (n = 143) are as follows: P =.0013; HR = 1.040 (1.017–1.063), comparing subjects with 16 versus 10 IU/mL (the 75th and 25th percentiles, respectively, in the sample). When we did regression analysis of risk using CD4+ T cell count alone (n = 152), a lower count was associated with increased risk (P <.00001; HR = 3.322 [2.041–5.405], comparing subjects with 39 vs. 179 cells/mL [the 25th and 75th percentiles, respectively, in the sample]). The corresponding results from regression analysis of risk using HIV-1 RNA level alone (n = 114) are as follows: P =.0006; HR = 1.812 (1.308–2.510), comparing subjects with 5.32 versus 4.47 log10 copies/mL (the 75th and 25th percentiles, respectively, in the sample).

Influence of treatment assignment

Approximately two-thirds of subjects in each regression analysis sample were receiving one of the two didanosine dosages used in ACTG 116B/117, a distribution that mirrored that of the parent study. When treatment assignment was included in the regression models reported here, the results (not shown) remained virtually unchanged.

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Discussion

In a head-to-head comparison of all the candidate prognostic markers considered in this study, serum neopterin and, to a lesser extent, circulating endogenous interferon proved to be the best independent predictors of risk of clinical progression (as defined by the primary endpoint of ACTG 116B/117) 6 months in the future, after adjustment for CD4+ T cell count and HIV-1 RNA concentration, with higher levels predicting greater risk. Of note, these markers outperformed HIV-1 RNA as well as in vitro phenotypic and genotypic resistance assays, each of which had been shown to predict clinical outcome when examined individually in ACTG 116/117B [4–6], although possible differences could be accounted for by our use of serum rather than plasma for this study. Our data also indicate that higher baseline levels and smaller declines from baseline to week 8 in levels of neopterin and IL-6 are independent early predictors of disease progression and that a higher baseline level of endogenous interferon also predicts greater risk.

Serum of healthy individuals does not contain significant levels of neopterin (an interferon-γ-inducible product of macrophages [12]), endogenous interferon (primarily interferon-α [7]), or the pro-inflammatory cytokine IL-6 [13]. Elevated levels detected in the circulation represent the expression of immune activation and cytokine dysregulation characteristic of HIV-1 infection [1, 2, 14, 15].

Soluble markers of immune activation have been studied extensively in natural history cohorts and as response indicators of antiretroviral treatment [14–17]. To date, however, no single additional marker of immune system activation has been validated as a surrogate for clinical outcome across trials, populations, and stages of HIV disease, as have CD4 and RNA. Early studies that identified the promise of immune activation markers did not include HIV-1 RNA [14, 18–23], with which they have been subsequently shown to correlate [24–26]. Later investigations that did examine RNA and evaluated selected individual markers yielded contradictory results with respect to which marker, if any, added independent prognostic value to the CD4+ cell count and RNA level [25–28]. The interpretation that markers perform differently at different stages of HIV disease [28, 29] may resolve some of the apparent differences among these investigations, including our own. Taken together, RNA and sTNFR-II are strong independent predictors in early infection [25, 26], and, as we and others demonstrate [25, 28], CD4 and neopterin are strong predictors in late-stage disease. Soluble TNFR-II, shown elsewhere to be of predictive value in advanced disease [25, 28, 30], was outperformed in our study by neopterin and endogenous interferon, as well as IL-6, which is known to synergize with TNF-α to upregulate HIV-1 [31, 32]. On the basis of these results, additional head-to-head investigations will be required to define the comparative role of sTNFR-II in late-stage HIV-1 infection.

Because the treatment regimens used in this retrospective study were limited to nucleoside analogue monotherapies, a question might arise about the applicability of our results to modern highly active antiretroviral therapy (HAART) combinations. It is worthwhile to emphasize here that a marker's value is determined by how closely it relates to primary disease pathogenesis, how well it reflects stage of disease and predicts outcome, and how sensitively it displays the influence of treatment [16]. The currently employed surrogate markers for clinical endpoints, CD4 and RNA, were validated retrospectively in natural history cohorts of patients who had received little or no therapy and in positive-outcome trials similar to ACTG 116B/117. All such studies were conducted during the pre-HAART era [33–36], when ample numbers of clinical endpoints were observed, rather than during the current HAART era, when studies capturing large numbers of clinical endpoints are rare [37, 38]. The value of CD4 and RNA has been established in the HAART era by extrapolation of these earlier validation studies on the basis of the rationale that both potent therapy and monotherapy influence marker responses and clinical outcomes by the same mechanism (i.e., inhibition of viral replication), only that HAART is more effective. Thus, our study, which demonstrates the usefulness of neopterin as a prognostic marker, is similarly applicable in the current HAART era, when more potent therapy is expected to, and does, display a greater effect than does dual-nucleoside therapy on CD4, RNA, as well as neopterin [39], consistent with the greater beneficial effect such therapy has on clinical progression. Moreover, our findings are relevant because the markers we examined were selected retrospectively, allowing study of a contemporary and comprehensive panel of markers that included evaluation of viral resistance. This study underscores the advantage of retrospective validation studies for biomarkers that can be reliably assayed years after samples are collected, as was persuasively accomplished for HIV-1 RNA [33], and lends support for the establishment and maintenance of specimen repositories for future marker validation studies.

For clinical monitoring, the CD4+ T cell count has been a remarkably consistent and useful tool for defining stage of disease and as the indication for when to initiate treatment, and HIV-1 RNA has proven to be an exquisitely sensitive indicator of response to antiretroviral treatment. The serum neopterin assay is reproducible [40] and inexpensive; it is estimated to cost half of the CD4+ cell count determination and a fifth of the viral load assay, and it requires only minimal laboratory infrastructure to perform by commercial EIA method. For these reasons, and because neopterin can add significant independent prognostic information, our results provide a rationale for its further study and potential application in defining populations at greatest need for antiretroviral therapy, either as a supplement or possibly alone, in areas of the world where measurement of CD4+ T cell counts and HIV-1 RNA levels may not be as readily available.

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Acknowledgments

We acknowledge the statistical assistance of Ellen Chan and thank Drs. Robert Coombs, Richard D'Aquila, Paul Palumbo, Brooks Jackson, James Kahn, Victor DeGruttola, Seth Welles, Carol Hooper (deceased), and Patricia Reichelderfer for their overall support of this project. We gratefully acknowledge the help of Amy Pruitt in manuscript preparation and the technical assistance of Monika Casey, Christine Schoenherr (deceased), and Susan Stehn. We are indebted to all the trial participants for their contribution to this study.

Financial support. This work was supported in part by the Adult ACTG, funded by the National Institute of Allergy and Infectious Diseases (grants AI-38858, AI-46370, AI-38855, and AI36086).

Potential conflicts of interest. All authors: no conflicts.

  • Received June 30, 2004.
  • Accepted October 22, 2004.
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  1. Clin Infect Dis. (2005) 40 (6): 853-858. doi: 10.1086/427877
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