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Cumulative HIV Viremia and Non–AIDS-Defining Malignancies Among a Sample of HIV-Infected Male Veterans

Kowalkowski, Marc A. PhD*,†; Day, Rena S. PhD; Du, Xianglin L. MD, PhD; Chan, Wenyaw PhD§; Chiao, Elizabeth Y. MD, MPH‖,¶

JAIDS Journal of Acquired Immune Deficiency Syndromes: October 1st, 2014 - Volume 67 - Issue 2 - p 204–211
doi: 10.1097/QAI.0000000000000289
Epidemiology and Prevention
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SDC

Background: Research suggests that cumulative measurement of HIV exposure is associated with mortality, AIDS, and AIDS-defining malignancies. However, the relationship between cumulative HIV and non–AIDS-defining malignancies (NADMs) remains unclear. The aim of this study was to evaluate the effect of different HIV measures on NADM hazard among HIV-infected male veterans.

Methods: We performed a retrospective cohort study using Veterans Affairs HIV Clinical Case Registry data from 1985 to 2010. We analyzed the relationship between HIV exposure (recent HIV RNA, % undetectable HIV RNA, and HIV copy-years viremia) and NADM. To evaluate the effect of HIV, we calculated hazard ratios for 3 common virally associated NADM [ie, hepatocarcinoma (HCC), Hodgkin lymphoma (HL), and squamous cell carcinoma of the anus (SCCA)] in multivariable Cox regression models.

Results: Among 31,576 HIV-infected male veterans, 383 HCC, 211 HL, and 373 SCCA cases were identified. In multivariable regression models, cross-sectional HIV measurement was not associated with NADM. However, compared with <20% undetectable HIV, individuals with ≥80% had decreased HL [adjusted hazard ratio (aHR) = 0.62; 95% confidence interval (CI): 0.37 to 1.02] and SCCA (aHR = 0.64; 95% CI: 0.44 to 0.93). Conversely, each log10 increase in HIV copy-years was associated with elevated HL (aHR = 1.22; 95% CI: 1.06 to 1.40) and SCCA (aHR = 1.36; 95% CI: 1.21 to 1.52). Model fit was best with HIV copy-years. Cumulative HIV was not associated with HCC.

Conclusions: Cumulative HIV was associated with certain virally associated NADM (ie, HL and SCCA), independent of measured covariates. Findings underline the importance of early treatment initiation and durable medication adherence to reduce cumulative HIV burden. Future research should prioritize how to best apply cumulative HIV measures in screening for these cancers.

Supplemental Digital Content is Available in the Text.

*Levine Cancer Institute, Carolinas Healthcare System, Charlotte, NC;

College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, NC;

Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health, Houston, TX;

§Department of Biostatistics, University of Texas School of Public Health, Houston, TX;

Department of Medicine, Baylor College of Medicine, Houston, TX; and

Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX.

Correspondence to: Marc A. Kowalkowski, PhD, Levine Cancer Institute, Carolinas Healthcare System, 1021 Morehead Medical Drive, Charlotte, NC 28204 (e-mail: marc.kowalkowski@carolinashealthcare.org).

At the time of the research, Dr. Kowalkowski was on staff at Baylor College of Medicine, Department of Medicine and conducted his research at the Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX.

Supported in part by resources and the use of facilities at the Center for Innovations in Quality, Effectiveness and Safety (#CIN 13-413), Michael E. DeBakey Veterans Affairs Medical Center, and the Baylor College of Medicine Dan L. Duncan Cancer Center (P30CA125123-04S1). This research was also supported by the Baylor-UTHouston Center for AIDS Research (CFAR), an NIH-funded program (AI036211).

The funders had no role in study design, data collection and analysis, or preparation of this report.

E.C. (R01CA163103) received support from the NCI. The remaining authors have no conflicts of interest to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).

Received March 13, 2014

Accepted June 18, 2014

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INTRODUCTION

Combination antiretroviral therapy (cART) dynamically transformed the epidemiology of HIV in the United States. Since cART, the incidences of opportunistic infections and AIDS-defining malignancies (ADM) have declined.1 However, over the same interval, non–AIDS-defining malignancies (NADM) have increased, and now, NADM collectively represent over one-half of cancers diagnosed in HIV-infected individuals.2–5

Several NADM [eg, hepatocarcinoma (HCC), Hodgkin lymphoma (HL), and squamous cell carcinoma of the anus (SCCA)] are mediated by oncogenic viruses.6–8 In vitro data suggest that HIV interacts with other oncogenic viruses and may facilitate viral activity and proliferation.9,10 Thus, in the current HIV era defined by increased life expectancy and longer duration of cancer susceptibility, cumulative HIV may be a valuable measure to classify future cancer risk.

Although serial collection of HIV RNA is available, cross-sectional values have been more commonly used.11 Recently, research has shown a stronger association between cumulative HIV and increased risk of AIDS and mortality, compared with cross-sectional HIV measurements.12 Furthermore, Zoufaly et al13 examined 6022 HIV-infected patients receiving cART and observed an association between cumulative HIV and ADM (ie, non-HL). Although HIV-infected individuals have been shown to have an excess of ADM and NADM, the effect of cumulative HIV on NADM risk has not been adequately evaluated.14 If associated with NADM risk, cumulative HIV may be a functional instrument to identify high-risk individuals for cancer screening. The aim of our study was to evaluate the association between 3 different cross-sectional and cumulative HIV measures and virally associated NADM hazard (ie, HCC, HL, and SCCA, representing the 3 most prevalent virally associated NADM15) among the US male veterans diagnosed with HIV infection between 1985 and 2010.

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METHODS

This study was approved by the Institutional Review Board of Baylor College of Medicine (Houston, TX).

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Study Population

The Department of Veterans Affairs (VA) HIV Clinical Case Registry (CCR) is a nationwide registry containing health-related information on all known HIV-infected VA users.16,17 The registry draws upon the electronic medical records of over 65,000 HIV-infected patients cared for by the VA since the registry's inception. After the identification and registration of HIV-infected veterans, all past clinical data are electronically retrieved. The database is automatically updated electronically. On-site HIV coordinators provide maintenance and verification. The registry includes demographic, laboratory, pharmacy, outpatient and hospitalization data, and vital status.

About 66,991 HIV-infected adult veterans were enrolled in the VA HIV CCR between 1985 and 2010. Figure 1 describes criteria used to define the final sample. The study population was restricted to HIV-infected veterans older than 18 years with documented CD4 and HIV measurement. Inclusion required a confirmed HIV diagnosis date based on (1) the presence of multiple International Classification of Diseases, Ninth Revision (ICD-9) code for HIV (042 or V08) or (2) a combination of ICD-9 code for HIV, positive HIV-related test (eg, enzyme-linked immunosorbent assay, Western blot, and quantifiable HIV RNA measurement), or prescription delivery of cART. To avoid inclusion of individuals erroneously added to the registry, individuals without adequate HIV diagnostics (ie, only a single ICD code for HIV and no laboratory or pharmacy records) or vital statistics were removed (n = 6769). HIV index date was defined as the earliest ICD-9 code, positive test, or prescription delivery. Because of limited number of females in the population (<2%), only male veterans were included in our analyses. Additionally, we removed individuals whose death or censor date was the same as their HIV index. To analyze the effect of cumulative HIV on individuals treated in the modern treatment era, we conducted our analyses using only individuals ever receiving cART, defined as any combination of 2 nucleoside reverse-transcriptase inhibitor classes and 1 of either nonnucleoside reverse-transcriptase inhibitor or protease inhibitor classes, integrase inhibitors, or CCR5 inhibitors, and any combination of 2 classes. About 31,576 HIV-infected veterans were included in the final sample.

FIGURE 1

FIGURE 1

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Virally Associated NADM

The primary outcome was diagnosis of incident virally associated NADM (ie, HCC, HL, and SCCA), identified from inpatient and outpatient ICD-9 codes (HCC = 155.0, HL = 201.4−9, and SCCA = 154.2−3). The follow-up interval for longitudinal analyses spanned from the index visit to NADM diagnosis, death, or December 31, 2010 (the final date of the current CCR iteration), whichever occurred first. To minimize inclusion of patients with prevalent NADM, individuals diagnosed with NADM before or within 6 months after the initial HIV diagnosis date were excluded.

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Data Management and Calculating HIV RNA Load

To account for potential differences in follow-up visit frequency, each individual's follow-up duration was divided into 7-day intervals. Each interval had a unique beginning and end date. Laboratory values were updated at the beginning of each interval, with the last observation carried forward when no new measurement was available.

HIV RNA load was modeled using 3 different strategies. Recent viral load was captured as a time-updated (7-day intervals), noncumulative HIV measurement. Recent viral load was modeled as the log copies per milliliter.

Two different cumulative HIV measurements were also generated. The first was a time-updated measurement of the cumulative percentage of follow-up HIV RNA was in the undetectable range, as shown in Equation 1 below. For continuity across study years and standardization of operational procedures at different contributing VA facilities, the threshold for undetectable HIV RNA was established as <500 copies per milliliter.

where ti(j−1) and ti(j) represent the beginning and end date, respectively, of each follow-up interval, and Uj represents whether or not HIV RNA was in the undetectable range over the referred interval.

Based on the evidence from research in cART medication adherence and the association between adherence and virologic failure, the %time undetectable HIV RNA was modeled as a categorical variable <20%, 20%–39%, 40%–59%, 60%–79%, and ≥80%.18,19

The final method of measurement, HIV copy-years viremia, has been previously developed and used to predict all-cause mortality among HIV-infected individuals.11 The measurement represents an area-under-the-curve estimate, analogous to pack-years smoking. A complete description can be found elsewhere.12

Briefly, each HIV RNA measurement was attributed to its respective 7-day intervals. Subsequently, using Equation 2 shown below, we calculated cumulative HIV exposure, where ti(j−1) and ti(j) represent the beginning and end date, respectively, of each follow-up interval and VLj represents the HIV RNA load over the referred interval. HIV copy-years is expressed in number of HIV copies multiplied by years (per milliliter of plasma). For example, 100,000 copy-years can represent having 10,000 HIV copies every day for 10 years or 100,000 HIV copies every day for 1 year.

Similar to previous studies, HIV copy-years was modeled as log copy-years per milliliter.11,12

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Statistical Analysis

Descriptive statistics were calculated to determine the distributions of sociodemographic and clinical variables in the study population. Characteristics were described in the overall cohort and separately in individuals diagnosed with HL, SCCA, and HCC. Spearman rank correlation coefficients were used to evaluate the correlation among the raw and transformed HIV measures. Additionally, we calculated incidence rates (IRs) of HL, SCCA, and HCC.

Cox proportional hazards regression models were constructed to determine the individual effects of each of 3 different HIV measurements on the time to incident HL, SCCA, and HCC. Separate crude and multivariable regression models were fit for each different HIV measure and NADM, respectively. To adjust for potential confounding, covariates were selected for multivariable regression models based on clinical relevance. These variables were age at HIV diagnosis, race/ethnicity, illicit drug use (defined by ICD-9 code), Deyo modification of the Charlson comorbidity index (excluding points allotted for HIV diagnosis), and the HIV diagnosis era (eg, pre-cART 1985–1995, early-cART 1996–2001, and late-cART 2002–2010). Initial immune function was estimated using nadir CD4 count before cART initiation. For individuals who received cART concurrent with the earliest HIV diagnosis, the first CD4 count was captured as the nadir. Time-updated CD4 has been shown to be associated with cancer risk and was included to describe fluctuating immune status throughout the follow-up period. Additional variables representing diagnoses of well-documented risk factors were included in models evaluating HCC (ie, hepatitis C virus and cirrhosis) and SCCA (ie, condyloma). Akaike information criterion assessed model fit.20 Analyses were performed using SAS version 9.1.

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RESULTS

Sample Characteristics

Participant characteristics for 31,576 HIV-infected veterans who met inclusion criteria (Fig. 1) and for strata defined by NADM diagnosis are provided in Table 1. Mean age at HIV diagnosis was 45 years (SD = 10). Over one-half were racial/ethnic minorities. Thirty-five percent had used illicit drugs. HIV diagnoses were captured from 1985 to 2010. Most HIV diagnoses occurred in the late cART era, but a greater proportion of individuals with NADM were diagnosed with HIV before 1996 (43%–48%; P < 0.01).

TABLE 1

TABLE 1

Overall, 45% of individuals, but 66% of SCCA cases (P < 0.01), had a CD4 nadir before cART initiation <200 cells per microliter. Over the study interval, mean recent CD4 was 427 cells per microliter (SD = 284). At the end of follow-up (eg, censoring/death, NADM diagnosis), 55% of the cohort had CD4 >350. The mean number of HIV measurements collected annually per individual was 3.2 (SD = 3.1), and on average, each individual contributed 9 years (SD = 5) of follow-up. At the time of NADM or censoring, the mean recent HIV RNA was 40,687 copies per milliliter (SD = 185,182), highest among HL cases (mean = 48,719; SD = 123,896). At the final study observation, the mean %time undetectable HIV RNA was 49% (SD = 34%). Overall, 27% had % ime undetectable HIV <20%, but 52% of HL cases and 43% of SCCA cases experienced this low level of viral load control (P < 0.01). The mean HIV copy-years was 212,743 (SD = 467,984), overall, and was highest among SCCA (mean = 328,641; SD = 654,596).

Table 2 describes rank correlations between the different HIV measurements used in the current analyses. All correlations were in the projected directions (eg, recent HIV and HIV copy-years were directly correlated with one another and inversely correlated with %time undetectable HIV). Although correlations between different measures varied in strength, all correlations were statistically significant (P < 0.01).

TABLE 2

TABLE 2

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Association Between HIV RNA Load Measures and NADM Incidence

During the study interval, 383 HCC [IR = 133/100,000 person-years; 95% confidence interval (CI): 124 to 142/100,000], 211 HL (IR = 70/100,000 person-years; 95% CI: 59 to 79/100,000), and 373 SCCA (IR = 129/100,000 person-years; 95% CI: 119 to 138/100,000) were diagnosed. The unadjusted associations of the 3 HIV RNA measures with HCC, SCCA, and HL incidence are provided in Table 3. HIV copy-years was a significant predictor of NADM hazard. Per log10 copy-years per milliliter increase in cumulative HIV, there was a significant increase in HL (HR = 1.26; 95% CI: 1.11 to 1.42), SCCA (HR = 1.48; 95% CI: 1.33 to 1.66), and HCC (HR = 1.15; 95% CI: 1.05 to 1.26).

TABLE 3

TABLE 3

The results from separate Cox regression models for each HIV RNA measure, adjusted for age, race/ethnicity, illicit drug use, era of HIV diagnosis, comorbidity, and pretreatment nadir and recent CD4 are described in Table 4 [additional covariates included in SCCA (ie, condyloma) and HCC (ie, cirrhosis, hepatitis C) models]. In the adjusted models, the relationship between NADM hazard and recent HIV RNA was not statistically significant. However, the association between the 2 cumulative HIV RNA measures and HL and SCCA hazard was robust to multivariate adjustment. Compared with individuals with <20% time undetectable HIV RNA, individuals with ≥80% time undetectable had a decreased hazard for HL [adjusted hazard ratio (aHR) = 0.62; 95% CI: 0.37 to 1.02] and SCCA (aHR = 0.64; 95% CI: 0.44 to 0.93). The relationship between HIV copy-years and HL and SCCA was also significant. HL (aHR = 1.22; 95% CI: 1.06 to 1.40) and SCCA (aHR = 1.36; 95% CI: 1.21 to 1.52) hazard increased per log10 increase in HIV copy-years. The results from analyses of HCC did not support a relationship between HIV RNA exposure and HCC risk. A comprehensive table of results from each of the adjusted models, complete with all covariates, has been provided as an online supplement (see Appendix 1, Supplemental Digital Content,http://links.lww.com/QAI/A553).

TABLE 4

TABLE 4

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Comparison of Model Fit Between Different HIV RNA Metrics

Model fit was evaluated using Akaike information criterion. The difference between models incorporating each distinct HIV exposure variable was small. However, in each scenario, the HIV copy-years model was slightly superior, suggesting improved prognostic significance (data not shown).

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DISCUSSION

To the best of our knowledge, this study is the first to compare the relationship between cross-sectional and cumulative HIV metrics and the time to incidence of individual virally associated NADM. Principally, results indicated elevated cumulative HIV exposure was associated with HL and SCCA incidence among our sample. The relationship with HCC was less stable. Additionally, compared with noncumulative recent HIV RNA level, cumulative HIV copy-years provided better-fitting models to predict time to NADM incidence. Our findings suggest that not all NADM are the same, but provide initial evidence to support a potential role for cumulative HIV in monitoring the development of certain virally associated NADM.

Research has shown cumulative HIV measures are associated with mortality11 and ADM diagnosis.13,21 Mugavero et al11 examined the association between cumulative HIV and all-cause mortality. The authors reported higher cumulative HIV copy-years increased the risk of death. Additionally, another study evaluated the effect of cumulative HIV, as the duration in years of HIV exposure >500 copies per milliliter, on any ADM or NADM diagnosis, respectively.21 The authors observed longer HIV exposure was associated with increased ADM incidence but not NADM. However, there were few NADM events and the study was not powered to investigate virally associated or individual NADM diagnoses separately. In another recent study of cART and non-cART users, we observed higher %time undetectable HIV RNA was associated with decreased SCCA risk.22 However, this study did not assess different HIV measures or other NADM.

In this study, HL and SCCA risk increased with higher cumulative HIV copy-years and decreased with better %time undetectable HIV. The underlying mechanisms explaining the association between HIV and NADM remain unclear. However, HIV may directly affect oncogenesis through tissue, cellular, or genetic mechanisms.23–25 Additionally, persistent replication, as noted by cumulative HIV, is associated with immunodeficiency and dampened clearance of latent viral coinfections [eg, Epstein–Barr virus (EBV), human papillomavirus (HPV), and human herpesvirus 8 (HHV-8)], potentially predisposing individuals to malignancies caused by other oncogenic viruses.26

HIV-related HL is known to be associated with EBV coinfection.27 Previous research has suggested EBV levels may remain high under conditions of insufficient HIV control.28 Therefore, cumulative HIV may indicate underlying EBV burden. Friis et al29 suggested that even after cART initiation, it may take 1–2 years or longer to achieve effective EBV control. Additionally, HIV antigens are known to drive B-cell stimulation. In turn, chronic stimulation leads to uncontrolled B-cell division, possibly boosting the opportunity for malignant clone development and expansion.30

HIV-related SCCA is also associated with persistent oncogenic viral infection, namely HPV.31,32 HIV-infected individuals are more likely than HIV-uninfected individuals to have anal HPV infection, including infection with multiple and high-risk oncogenic HPV genotypes, even in the absence of anal intercourse.33–35 HIV-infected individuals also experience longer durations of persistent HPV infection.33 HPV proliferation involves a coordinated and complex interaction between HPV oncoproteins and Rb, p53 and other targets.36 Similar to EBV, persistent HPV infection has been observed to be associated with HIV proliferation and cellular immune deficit.37 Additional evidence suggests HIV and HPV genes reciprocally interact, either directly or through cytokine-mediated mechanisms, resulting in increasing amounts of HPV and enhancing the potential for malignant progression.10

HIV impacted HCC development differently than other virally associated NADM studied. HIV has been shown to accelerate cirrhosis development.38 However, in this study, there was not a significant relationship between HIV and HCC, independent of cirrhosis. It is possible that the interface between HIV and HCV at the cellular level may be less direct than what occurs between HIV and EBV or HPV. Additionally, there may be effects of cART on HCV and HBV burden not captured here.

Our results highlight the effects of cumulative HIV on HL and SCCA incidence, independent of measured covariates, and support previous hypotheses that HL and SCCA may be different than other NADM.39 Additionally, our results coincide with previous population-based findings12,40 and support previous molecular observations regarding the relationships between HIV and the proliferation of other oncogenic viruses.10,28 The 2 cumulative measurements evaluated in the current study provide information about the overall impact of intermittently controlled or continuous HIV replication using opposing methodologies. Specifically, 1 approach captures HIV directly, whereas the other illustrates the absence of viremia. The relationships between each measure and cancer incidence were representative of this pattern. However, HIV copy-years provided improved model fit. One difference and potential advantage of HIV copy-years is the measure's ability to more exactly quantify the duration of exposure over time. For future studies, this level of precision may be the best measure of cumulative HIV to incorporate into risk stratification for ADM, HL, and SCCA.

The findings from this study should be viewed within the context of the study design. The retrospective cohort study design used may be subject to unmeasured confounders (eg, smoking). However, lifestyle factors such as smoking have not been shown to effect changes in HIV load.41 Additionally, all individuals included in the current sample had received cART, but the type and duration of treatment regimens received were not measured. Data for this study were extracted from a national, system-wide VA registry of HIV-infected veterans. A principal strength was this resource provides one of the largest data repositories of HIV-infected individuals. However, certain limitations are inherent in such large registry databases. Most relevant to our analyses were potential variations in follow-up visit frequency. We attempted to reduce the potential for information bias by segmenting the follow-up period into 1-week intervals, establishing equivalent visit frequencies per individual. However, some individuals may have experienced longer intervals without updated HIV measurements. In our data, the average number of HIV measurements collected annually was over 3 and was not different between NADM cases and noncases. Finally, this study was conducted exclusively on male veterans, which may have implications on the generalizability of its findings.

In the current sample of HIV-infected male veterans, uncontrolled HIV replication, as indicated by cumulative HIV copy-years and %time undetectable HIV RNA, was associated with HL and SCCA development, independent of measured covariates. Cumulative HIV copy-years demonstrated the most robust association with the cancers studied and may provide the most sensitive metric to better describe the impact of HIV viremia and underlying viral coinfection. Thus, copy-years may be the most appropriate measure of HIV viral load control for future epidemiologic research assessing HIV-associated outcomes. Additional research is needed to define implementation strategies for cumulative HIV measurements as a screening tool for virally associated cancers associated with chronic HIV infection.

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Keywords:

HIV; viral load measurement; non–AIDS-defining cancer; Hodgkin lymphoma; squamous cell carcinoma of the anus

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