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Association of CD4+ T-cell Count, HIV-1 RNA Viral Load, and Antiretroviral Therapy With Kaposi Sarcoma Risk Among HIV-infected Persons in the United States and Canada

Dubrow, Robert MD, PhD*; Qin, Li PhD; Lin, Haiqun MD, PhD; Hernández-Ramírez, Raúl U. MSc*; Neugebauer, Romain S. PhD§; Leyden, Wendy MPH§; Althoff, Keri N. PhD, MPH; Achenbach, Chad J. MD, MPH; Hessol, Nancy A. MSPH#; Modur, Sharada P. PhD; D'Souza, Gypsyamber PhD, MPH, MS; Bosch, Ronald J. PhD**; Grover, Surbhi MD, MPH††,‡‡; Horberg, Michael A. MD, MAS§§; Kitahata, Mari M. MD, MPH‖‖; Mayor, Angel M. MD, MSc¶¶; Novak, Richard M. MD##; Rabkin, Charles S. MD***; Sterling, Timothy R. MD†††; Goedert, James J. MD***; Justice, Amy C. MD, PhD†,‡‡‡,§§§; Engels, Eric A. MD, MPH***; Moore, Richard D. MD, MHSc‖‖‖; Silverberg, Michael J. PhD, MPH§for the North American AIDS Cohort Collaboration on Research and Design of the International Epidemiologic Databases to Evaluate AIDS

JAIDS Journal of Acquired Immune Deficiency Syndromes: August 1st, 2017 - Volume 75 - Issue 4 - p 382–390
doi: 10.1097/QAI.0000000000001394
Epidemiology

Background: Kaposi sarcoma (KS) remains common among HIV-infected persons. To better understand KS etiology and to help target prevention efforts, we comprehensively examined a variety of CD4+ T-cell count and HIV-1 RNA viral load (VL) measures, as well as antiretroviral therapy (ART) use, to determine independent predictors of KS risk.

Setting: North American AIDS Cohort Collaboration on Research and Design.

Methods: We followed HIV-infected persons during 1996–2009 from 18 cohorts. We used time-updated Cox regression to model relationships between KS risk and recent, lagged, trajectory, and cumulative CD4 count or VL measures, as well as ART use. We used Akaike's information criterion and global P values to derive a final model.

Results: In separate models, the relationship between each measure and KS risk was highly significant (P < 0.0001). Our final mutually adjusted model included recent CD4 count [hazard ratio (HR) for <50 vs. ≥500 cells/μL = 12.4; 95% confidence interval (CI): 6.5 to 23.8], recent VL (HR for ≥100,000 vs. ≤500 copies/mL = 3.8; 95% CI: 2.0 to 7.3), and cumulative (time-weighted mean) VL (HR for ≥100,000 vs. ≤500 copies/mL = 2.5; 95% CI: 1.0 to 5.9). Each P-trend was <0.0001. After adjusting for these measures, we did not detect an independent association between ART use and KS risk.

Conclusions: Our results suggested a multifactorial etiology for KS, with early and late phases of development. The cumulative VL effect suggested that controlling HIV replication promptly after HIV diagnosis is important for KS prevention. We observed no evidence for direct anti-KS activity of ART, independent of CD4 count and VL.

Supplemental Digital Content is Available in the Text.

*Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale School of Medicine, New Haven, CT;

Department of Internal Medicine, Yale School of Medicine, New Haven, CT;

Department of Biostatistics, Yale School of Public Health, Yale School of Medicine, New Haven, CT;

§Division of Research, Kaiser Permanente, Oakland, CA;

Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD;

Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL;

#Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA;

**Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;

††Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA;

‡‡Department of Oncology, Princess Marina Hospital, Gaborone, Botswana;

§§Mid-Atlantic Permanente Research Institute, Kaiser Permanente Mid-Atlantic States, Rockville, MD;

‖‖Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA;

¶¶Department of Internal Medicine, Universidad Central del Caribe School of Medicine, Bayamon, Puerto Rico;

##Division of Infectious Diseases, University of Illinois College of Medicine, Chicago, IL;

***Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD;

†††Department of Medicine, Vanderbilt University Medical Center, Nashville, TN;

‡‡‡Department of Health Policy and Management, Yale School of Public Health, New Haven, CT;

§§§Research Service, Veterans Affairs Connecticut Healthcare System, West Haven, CT; and

‖‖‖Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.

Correspondence to: Robert Dubrow, MD, PhD, Yale School of Public Health, P.O. Box 208034, New Haven, CT 06520-8034 (e-mail: robert.dubrow@yale.edu).

Supported by National Institutes of Health grants U01AI069918, F31DA037788, G12MD007583, K01AI093197, K23EY013707, K24AI065298, K24AI118591, K24DA000432, KL2TR000421, M01RR000052, N01CP01004, N02CP055504, N02CP91027, P30AI027757, P30AI027763, P30AI027767, P30AI036219, P30AI050410, P30AI094189, P30AI110527, P30MH62246, R01AA016893, R01CA165937, R01DA011602, R01DA012568, R24AI067039, U01AA013566, U01AA020790, U01AI031834, U01AI034989, U01AI034993, U01AI034994, U01AI035004, U01AI035039, U01AI035040, U01AI035041, U01AI035042, U01AI037613, U01AI037984, U01AI038855, U01AI038858, U01AI042590, U01AI068634, U01AI068636, U01AI069432, U01AI069434, U01AI103390, U01AI103397, U01AI103401, U01AI103408, U01DA03629, U01DA036935, U01HD032632, U10EY008057, U10EY008052, U10EY008067, U24AA020794, U54MD007587, UL1RR024131, UL1TR000004, UL1TR000083, UL1TR000454, UM1AI035043, Z01CP010214, and Z01CP010176; contracts CDC-200-2006-18797 and CDC-200-2015-63931 from the Centers for Disease Control and Prevention, USA; contract 90047713 from the Agency for Healthcare Research and Quality, USA; contract 90051652 from the Health Resources and Services Administration, USA; Grants CBR-86906, CBR-94036, HCP-97105, and TGF-96118 from the Canadian Institutes of Health Research, Canada; Ontario Ministry of Health and Long Term Care; and the Government of Alberta, Canada. Additional support was provided by the National Cancer Institute (including the intramural research program), National Institute for Mental Health, and National Institute on Drug Abuse.

A portion of this work was presented as a poster at the 14th International Conference on Malignancies in AIDS and Other Acquired Immunodeficiencies; November 12–13, 2013; Bethesda, MD.

The authors have no conflicts of interest to disclose.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Department of Veterans Affairs.

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 December 15, 2016

Accepted March 30, 2017

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INTRODUCTION

After the introduction of combination antiretroviral therapy (ART) in 1996, Kaposi sarcoma (KS) incidence among HIV-infected persons sharply declined,1 with a continuing, slower decline since 2000.2–4 Nevertheless, KS accounted for 12% of cancer diagnoses among HIV-infected persons in the United States in 2010,5 with the cumulative incidence estimated to be 4% by the age of 75 years in the United States and Canada during 2005–2009.6

KS-associated herpesvirus (KSHV) infection is a necessary but not solely sufficient cause of KS.7,8 Among HIV-infected persons, a strong inverse association between CD4+ T-cell count (CD4) and KS risk is well established.9 Nevertheless, KS occurrence is not uncommon among HIV-infected persons with relatively high CD4 and/or sustained suppression of HIV replication by ART,10–18 indicating that KS etiology among HIV-infected persons is more complex than a simple interplay between KSHV infection and low CD4 and that KS can occur without severe immunosuppression, as observed in classical19,20 and African endemic KS.21

Although many studies have examined relationships between KS risk and CD4, HIV-1 RNA viral load (VL), and ART use, several questions remain unanswered. First, over the course of HIV infection, when is CD4 most predictive of KS risk? KS risk has been observed to be inversely associated with baseline,22–24 recent/current,11,25–30 and nadir31 CD4, and positively associated with time duration with CD4 <200 cells/μL,32 with inconclusive evidence that recent/current CD4 is the best predictor.11,26

Second, is VL associated with KS risk independent of CD4, and if so, which VL measure is the best predictor? A direct independent association between recent/current VL and KS risk has been observed in some studies,11,26,29 but not all.25,27 Few studies have examined cumulative VL in relation to KS risk.26,32

Third, is the protective effect of ART on KS risk entirely mediated by its effects on CD4 and VL? KS risk is clearly lower in ART users vs. nonusers1,24,26,30,31 and is inversely associated with time since ART initiation.27,28,33,34 However, the literature is inconsistent about ART effects, such as direct anticancer activity,35,36 independent of CD4 and VL.11,25–27,30,31

Few studies have comprehensively examined CD4 and VL measures, as well as ART use, to determine independent predictors of KS risk.26 In this investigation, we used data from a large, North American HIV-cohort consortium, which provided the sample size and requisite data necessary to perform this analysis, with the goals of elucidating potential contributors to KS development and helping to target KS prevention.

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METHODS

Study Sample

The study sample included adults (≥18 years) followed during 1996–2009 from 18 United States and Canadian cohorts participating in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD; see Table S1, Supplemental Digital Content, http://links.lww.com/QAI/B8).37 Cohorts submitted demographic and clinical data using standardized collection methods. Cancer diagnoses, including KS, were collected by manual review of medical records and pathology reports or by cancer registry linkage.6 Institutional Review Board approval was obtained for each participating cohort.

For our analytic data set, we defined entry date (ie, baseline) for each person as the latest of the following dates: January 1, 1996, cohort-specific start for reporting cancer diagnoses, 18th birthday, or 360 days before the later of the first CD4 or VL laboratory measurement. We defined exit date as the earliest of cohort-specific end for reporting cancer diagnoses, death, KS diagnosis, or the earlier of the last CD4 or VL laboratory measurement plus 540 days. We then excluded persons with <2 CD4 or <2 VL measurements or with <180 days of follow-up.

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Estimation of Time-updated CD4 and VL Values

The timing and frequency of observed CD4 and VL laboratory measurements varied by person. To standardize, for each person we estimated time-updated CD4 or log10(VL) values at 30-day intervals using linear interpolation between consecutive pairs of CD4 or VL laboratory measurements, respectively. Moreover, we carried the first CD4 or VL laboratory measurement backward 360 days or to the entry date, whichever was reached first. Similarly, we carried the last laboratory measurement forward 360 days or to the exit date (see Figure S1, Supplemental Digital Content, http://links.lww.com/QAI/B8).

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Statistical Methods

We sought to systematically evaluate a variety of CD4 and VL measures (ie, recent, lagged, trajectory, and cumulative) to develop a final model that included the most robust CD4 and/or VL predictors of KS risk. We modeled relationships between KS risk and CD4 or VL measures using proportional hazards regression. Our time metric was months (30 days) from baseline; thus, all time metrics were 30-day multiples.

CD4 and VL measures were updated at the beginning of each 30-day interval from baseline but were lagged by 180 days (defined as “recent”) to reduce the possibility that the presence of KS influenced CD4 or VL (ie, reverse causality). We also considered lag intervals of 540, 900, or 1260 days. These analyses were, by necessity, restricted to persons with at least the amount of follow-up corresponding with the lag. For example, it is impossible to assess VL lagged by 900 days among persons with <900 days of follow-up.

Among persons with at least 1260 days of follow-up, we then examined the following time-updated measures during a 1080-day time window (approximately 3 years) lagged by 180 days: CD4 or log10(VL) trajectory (ie, linear regression slope); proportion of time CD4 <200 cells/μL or VL >500 copies/mL; and CD4 or VL time-weighted mean (see Figure S2, Supplemental Digital Content, http://links.lww.com/QAI/B8). The latter 2 measures represented different measures of cumulative exposure (time-weighted mean is a cumulative measure normalized according to time).

We modeled measures as categorical variables to allow for the possibility of nonlinear relationships with KS risk. We also modeled measures as continuous variables, calculating P values for trend. We adjusted all models for sex, race/ethnicity, cohort, and baseline age and calendar period.

Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC).

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Model Selection to Develop Final Model

For model selection, we used Akaike's information criterion (AIC), a quantity commonly used to compare models. AIC takes into account both goodness of fit (ie, discrepancy between observed values and modeled values) and model parsimony. A lower AIC indicates the preferred model, with an AIC decrease of >10 between 2 models considered meaningful.38 Global P values for categorical variables were also taken into account for model selection.

To develop our final model, we first fit a separate Cox model for each CD4 and VL measure (1 measure per model). Second, we selected the best CD4 measures by comparing models that included 2 or more CD4 measures in the same model. Third, we selected the best VL measures in the same manner. Finally, we chose our final model by comparing models that included 2 or more of the best CD4 and VL measures together in the same model.

In the final model, we estimated the percent of the association between cumulative (time-weighted mean) VL and KS risk mediated by recent CD4 by calculating the quantity (βunadj − βadj)/βunadj, where βunadj is the coefficient for cumulative VL unadjusted for recent CD4 and βadj is the coefficient adjusted for recent CD4.39

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ART Models

Among persons ART naive at baseline, we examined the relationship between KS risk and time-updated proportion of time on ART during the 1080-day time window lagged by 180 days. We expected to observe confounding by indication because it was the standard clinical practice during the study period to base ART initiation on CD4.40 Furthermore, CD4 likely would be a mediator of any ART effect on KS risk. To adjust for confounding by indication by CD4 without removing its mediation effect, we included the following time-updated variable in the ART models: CD4 at the start of the time window if the person was ART naive at the start of the time window or CD4 at ART initiation if the person initiated ART before the start of the time window. Thus, at any given time point, we adjusted for the CD4 value that the decision to initiate ART would be based on if the person was ART naive at the start of the time window or was based on if the person had initiated ART before the start of the time window. In either case, this CD4 variable could not be a mediator because it was measured before or at the start of the time window of the ART measure. To determine whether the ART effect was mediated by the CD4 and VL measures in our final model, we added these measures to the ART model, still adjusting for confounding by CD4.

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Sensitivity Analyses

For the primary analyses, we imputed race/ethnicity for the 5.5% of persons with unknown values using cohort-specific probability weights according to sex and baseline age and calendar year. We did not adjust for HIV risk group or smoking because of large numbers of unknowns. However, for sensitivity analyses, we examined the effect of including each of these variables as a covariate either using multiple imputation or by including unknowns as a category.

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RESULTS

The 18 NA-ACCORD cohorts included 97,323 persons. We then excluded persons with no follow-up time according to our entry/exit date criteria (N = 6061), with <2 CD4 or <2 VL laboratory measurements (N = 10,678), or with <180 days of follow-up (N = 2888). The remaining 77,696 persons were 85% male, 44% white, and 41% black (Table 1). At baseline, most persons were aged 30–49 years (69%) and ART naive (72%), with CD4 ≥200 cells/μL (67%) and VL >500 copies/mL (73%). Persons who eventually developed KS (N = 396) were more likely to be male, white, aged 30–39 years, have a baseline calendar period of 1996–1998, have lower CD4, and higher VL, and to be a man who has sex with men (Table 1). Median follow-up was 5.0 years (interquartile range: 2.6–8.8 years). Median number of observed CD4 and VL laboratory measurements per person was the same: 14 (interquartile range: 6–26).

TABLE 1

TABLE 1

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Recent and Lagged Measures

We examined the relationship between CD4 or VL, respectively, and KS risk according to number of days lagged (Table 2). The strength of the association between CD4 and KS risk declined progressively with increasing lag, with a hazard ratio (HR) for the 180-day lag of 33.1 [95% confidence interval (CI): 22.9 to 47.8] for CD4 <50 vs. ≥500 cells/μL. The AIC comparison among lags confirmed that the model with the 180-day lag was the preferred model.

TABLE 2

TABLE 2

The strength of the association between VL and KS risk was similar for the 180- and 540-day lags and then declined with increasing lag, with a HR for the 180-day lag of 16.1 (95% CI: 12.0 to 21.5) for VL ≥100,000 vs. ≤500 copies/mL (Table 2). The AIC for the 180- and 540-day lags did not appreciably differ.

However, valid comparison of AICs across the 4 lags required that each AIC be calculated from a model using the same data set (ie, persons with at least 1260 days of follow-up; Table 2). Consequently, to further adjudicate the AIC comparison between the 180- and 540-day lags, we derived AICs for these 2 models among persons with at least 540 days of follow-up (which provided a larger N and more KS cases) and found 180 days to be the optimal VL lag (AIC of 6180 for the 180-day lag vs. 6240 for the 540-day lag).

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Trajectory and Cumulative Measures

Then we examined the relationship between KS risk and CD4 or VL measures (ie, slope, proportion of time below/above a threshold, or time-weighted mean), respectively, during the 1080-day time window lagged by 180 days and found the relationship to be highly significant (P < 0.0001) for each measure (Table 3). The 26,476 persons excluded from these analyses (those with <1260 days of follow-up) had a later baseline calendar period and a higher proportion with unknown smoking status compared with included persons, but other baseline characteristics did not meaningfully differ (see Table S2, Supplemental Digital Content, http://links.lww.com/QAI/B8).

TABLE 3-a

TABLE 3-a

TABLE 3-b

TABLE 3-b

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Selection of Best CD4 Predictors

Next, we examined models that included 2 or more CD4 measures (Table 4). The best CD4 model according to the AIC included CD4 lagged by 180 days and CD4 slope. Furthermore, in models including these measures and proportion of time CD4 <200 cells/μL or CD4 time-weighted mean, the global P values for the latter measures were no longer significant.

TABLE 4-a

TABLE 4-a

TABLE 4-b

TABLE 4-b

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Selection of Best VL Predictors

We also examined models that included 2 or more VL measures (Table 4). The 2 best VL models according to the AIC included VL lagged by 180 days and VL time-weighted mean or these measures plus proportion of time VL >500 copies/mL. Because the global P value for the latter measure was not significant (P = 0.11) in the latter model, we chose the more parsimonious model as the best VL model.

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Selection of Final Model

To derive a final model, we examined models including 2 or more of the best CD4 and VL predictors determined above: CD4 lagged by 180 days, CD4 slope, VL lagged by 180 days, and VL time-weighted mean (Table 4). According to the AIC, the 2 best models included CD4 and VL, each lagged by 180 days, and VL time-weighted mean, or these measures plus CD4 slope. Because the global P value for CD4 slope was only borderline significant (P = 0.055) in the latter model, we chose the more parsimonious model as our final model (Table 5). P-trend for each measure in the final model was highly significant (P < 0.0001). In this model, with VL time-weighted mean modeled as a continuous variable, we estimated that CD4 lagged by 180 days mediated 38% of the association between VL time-weighted mean and KS risk.

TABLE 5

TABLE 5

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ART Models

Among persons who were ART naive at baseline (N = 35,804; KS cases = 119), the HR per 20% of time on ART during the 1080-day time window lagged by 180 days was 0.77 (95% CI: 0.70 to 0.85) with adjustment for baseline covariates; 0.71 (95% CI: 0.64 to 0.79) with further adjustment for confounding by CD4; and 0.96 (95% CI: 0.85 to 1.09) with further adjustment for the CD4 and VL measures in the final model (see Table S3, Supplemental Digital Content, http://links.lww.com/QAI/B8).

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Sensitivity Analyses

Our sensitivity analyses revealed our final model to be highly robust, with no confounding by HIV risk group or smoking (data not shown).

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DISCUSSION

Using data from a large North American HIV-cohort consortium, we found each of a wide variety of CD4 and VL measures (recent, lagged, trajectory, and cumulative) to be highly significantly associated with KS risk. However, we found recent CD4, recent VL, and cumulative VL to be the most robust independent predictors. Furthermore, the protective effect of cumulative ART exposure seemed to be completely explained by these factors, meaning we did not detect an independent anti-KS effect of ART itself.

Our results suggested a multifactorial etiology for KS, with early and late phases of development. Although the recent VL and CD4 effects suggested that HIV and immunosuppression are involved in late phases of KS development, the cumulative VL effect suggested that ongoing HIV exposure may promote earlier phases of development. Of course, KSHV acquisition represents an additional requirement for KS development that we were unable to evaluate.

The cumulative VL effect, in particular, suggested that to prevent KS it is important to control HIV replication promptly after HIV diagnosis, no matter how high the CD4 count, consistent with current recommendations41 and with the Strategic Timing of Antiretroviral Treatment trial in which KS incidence was significantly lower in persons with CD4 >500 cells/μL who initiated ART immediately compared with those who deferred initiation.16

The first of 3 questions we posed in the Introduction was when is CD4 most predictive of KS risk over the course of HIV infection. An inverse association between recent/current CD4 and KS risk has been consistently observed by others. However, most previous studies measured current CD4,11,25–28,30 whereas we lagged our recent CD4 (and our recent VL) measure by 180 days to reduce the possibility of reverse causality. We found recent CD4 to be a better predictor of KS risk than CD4 with longer lags. Each of our 2 cumulative CD4 measures was not significant when included in models with recent CD4 and CD4 slope, the latter dropping out in the presence of VL measures. Thus, we established recent CD4 as the most robust CD4 predictor of KS risk, suggesting an etiologic role for increasingly severe immunodeficiency late in KS development.

Our second question was whether VL is associated with KS risk independent of CD4, and if so, which VL measure is the best predictor. We found a slower decline in HRs with increasing lag observed for VL than for CD4 (Table 2), suggesting a role for past, and possibly cumulative, VL exposure. We then found that both VL lagged by 180 days and VL time-weighted mean were independent predictors of KS risk. Moreover, only 38% of the association between VL time-weighted mean and KS risk was mediated by recent CD4, suggesting that most of the cumulative VL effect acted through an independent mechanism.

The independent effect of recent VL was consistent with some,11,26,29 but not all,25,27 previous studies. The 2 studies that did not demonstrate an independent effect, each with fewer than 45 KS cases,25,27 may have had limited statistical power to detect this association. Several studies have found recent/current VL to be associated with risk for non-Hodgkin lymphoma, the other main AIDS-defining cancer,26,32,42 and for overall AIDS-defining illness,43 independent of CD4.

We are aware of only 2 studies that examined the relationship between cumulative VL exposure and KS risk. One study with only 39 KS cases found that cumulative time with VL >500 copies/mL was not associated with KS risk after adjusting for cumulative time with CD4 <200 cells/μL.32 In the second study, which included current CD4, current VL, and ART exposure in its final model, time with VL >100,000 copies/mL was the only cumulative measure tested.26 Several studies have found cumulative VL measures to be associated with risk for non-Hodgkin lymphoma.32,44,45

An effect of recent VL independent of recent CD4 suggests that recent VL provides independent information about recent immune dysfunction,43 possibly HIV-induced inflammation.8,46–48 Alternatively, this effect may be due to prooncogenic effects of secreted HIV-1-encoded proteins, most notably Tat or Nef.49–56 Possible mechanisms for the cumulative VL effect include the aforementioned mechanisms and HIV-induced immunosenescence.57

Our final question was whether the protective effect of ART on KS risk is entirely mediated by its effects on CD4 and VL. Our observed association between higher proportion of time on ART and reduced KS risk was expected.1,24,26,30,31 Also expected was the strengthening of the association after adjusting for confounding by indication by CD4, presumably because of delayed ART initiation by persons with higher CD4, consistent with evolving HIV treatment guidelines during the study period.40 We found no independent effect of ART after adjusting for the CD4 and VL measures in the final model. Most previous studies, which adjusted only for current25,30 or nadir31 CD4 or for both current CD4 and VL,26,27 observed an independent ART effect, although 1 study that adjusted for both current CD4 and VL did not observe an independent effect.11 No previous studies took cumulative VL into account.

An important study limitation was lack of information on KSHV infection status. Not only is KSHV infection necessary for KS development, but among persons infected with both KSHV and HIV, KSHV viremia is strongly associated with subsequent development of KS, independent of CD4 and VL.58 Other limitations included the possibility of selection bias because of exclusion criteria, the inherent imperfection of estimating time-updated CD4 and VL values, the arbitrary 1080-day time window, limited median follow-up of 5 years, follow-up only through 2009, not considering KS stage at diagnosis or whether diagnosis was clinical or pathological, and the relatively low proportion of women and Hispanics. Furthermore, the CI estimates in the final model did not take into account multiple testing in the model selection process. Finally, although NA-ACCORD is representative of HIV-infected persons in the United States and Canada,37 our results may not be generalizable to resource-limited settings.1,59

Strengths of our study included its large size, validated KS diagnoses,6 comprehensive assessment of alternative CD4 and VL measures, lagged analyses to reduce the possibility of reverse causality, and use of a time window of fixed duration (ie, 1080 days) for trajectory and cumulative measures. The latter approach was superior to using simple cumulative measures (eg, time duration with CD4 below a threshold), which vary by person according to amount of follow-up time, although we did not capture effects that might originate >1260 days in the past. Finally, we developed a methodologic approach to examine the overlapping roles of CD4 and VL in cancer risk among HIV-infected persons that can be applied to other cancer types.

In summary, we found that recent CD4, recent VL, and cumulative VL were robust independent predictors of KS risk, consistent with a multifactorial etiology for KS, with early and late phases of development. The novel finding of an independent association between cumulative VL and KS risk provided a possible explanation for the elevated KS risk among HIV-infected persons on ART and suggested the importance of controlling HIV replication promptly after HIV diagnosis to prevent KS. However, this finding requires confirmation and further characterization, including how long the cumulative VL effect lasts after viral suppression by ART. Finally, we found no evidence for direct anti-KS activity of ART, independent of CD4 and VL.

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ACKNOWLEDGMENTS

The authors would like to thank all individuals involved with the NA-ACCORD collaboration, including staff, investigators, and patients, for their invaluable contributions to this work.

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REFERENCES

1. Semeere AS, Busakhala N, Martin JN. Impact of antiretroviral therapy on the incidence of Kaposi's sarcoma in resource-rich and resource-limited settings. Curr Opin Oncol. 2012;24:522–530.
2. Robbins HA, Shiels MS, Pfeiffer RM, et al. Epidemiologic contributions to recent cancer trends among HIV-infected people in the United States. AIDS. 2014;28:881–890.
3. Franceschi S, Lise M, Clifford GM, et al. Changing patterns of cancer incidence in the early- and late-HAART periods: the Swiss HIV Cohort Study. Br J Cancer. 2010;103:416–422.
4. Park LS, Tate JP, Sigel K, et al. Time trends in cancer incidence in persons living with HIV/AIDS in the antiretroviral therapy era: 1997–2012. AIDS. 2016;30:1795–1806.
5. Robbins HA, Pfeiffer RM, Shiels MS, et al. Excess cancers among HIV-infected people in the United States. J Natl Cancer Inst. 2015;107:dju503.
6. Silverberg MJ, Lau B, Achenbach CJ, et al. Cumulative incidence of cancer among persons with HIV in North America: a cohort study. Ann Intern Med. 2015;163:507–518.
7. Uldrick TS, Whitby D. Update on KSHV epidemiology, Kaposi sarcoma pathogenesis, and treatment of Kaposi sarcoma. Cancer Lett. 2011;305:150–162.
8. Robey RC, Bower M. Facing up to the ongoing challenge of Kaposi's sarcoma. Curr Opin Infect Dis. 2015;28:31–40.
9. Clifford GM, Franceschi S. Cancer risk in HIV-infected persons: influence of CD4(+) count. Future Oncol. 2009;5:669–678.
10. Maurer T, Ponte M, Leslie K. HIV-associated Kaposi's sarcoma with a high CD4 count and a low viral load. N Engl J Med. 2007;357:1352–1353.
11. Lodi S, Guiguet M, Costagliola D, et al. Kaposi sarcoma incidence and survival among HIV-infected homosexual men after HIV seroconversion. J Natl Cancer Inst. 2010;102:784–792.
12. Hleyhel M, Belot A, Bouvier AM, et al. Risk of AIDS-defining cancers among HIV-1-infected patients in France between 1992 and 2009: results from the FHDH-ANRS CO4 cohort. Clin Infect Dis. 2013;57:1638–1647.
13. Crum-Cianflone NF, Hullsiek KH, Ganesan A, et al. Is Kaposi's sarcoma occurring at higher CD4 cell counts over the course of the HIV epidemic? AIDS. 2010;24:2881–2883.
14. Krown SE, Lee JY, Dittmer DP, et al. More on HIV-associated Kaposi's sarcoma. N Engl J Med. 2008;358:535–536.
15. Stebbing J, Powles T, Bower M. AIDS-associated Kaposi's sarcoma associated with a low viral load and a high CD4 cell count. AIDS. 2008;22:551–552.
16. Lundgren JD, Babiker AG, Gordin F, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;373:795–807.
17. Mocroft A, Furrer HJ, Miro JM, et al. The incidence of AIDS-defining illnesses at a current CD4 count ≥200 cells/μL in the post-combination antiretroviral therapy era. Clin Infect Dis. 2013;57:1038–1047.
18. Phillips AN, Gazzard B, Gilson R, et al. Rate of AIDS diseases or death in HIV-infected antiretroviral therapy-naive individuals with high CD4 cell count. AIDS. 2007;21:1717–1721.
19. Touloumi G, Hatzakis A, Potouridou I, et al. The role of immunosuppression and immune-activation in classic Kaposi's sarcoma. Int J Cancer. 1999;82:817–821.
20. Brown EE, Whitby D, Vitale F, et al. Virologic, hematologic, and immunologic risk factors for classic Kaposi sarcoma. Cancer. 2006;107:2282–2290.
21. Mwanda OW, Fu P, Collea R, et al. Kaposi's sarcoma in patients with and without human immunodeficiency virus infection, in a tertiary referral centre in Kenya. Ann Trop Med Parasitol. 2005;99:81–91.
22. Biggar RJ, Chaturvedi AK, Goedert JJ, et al. AIDS-related cancer and severity of immunosuppression in persons with AIDS. J Natl Cancer Inst. 2007;99:962–972.
23. Engels EA, Biggar RJ, Hall HI, et al. Cancer risk in people infected with human immunodeficiency virus in the United States. Int J Cancer. 2008;123:187–194.
24. Franceschi S, Maso LD, Rickenbach M, et al. Kaposi sarcoma incidence in the Swiss HIV Cohort Study before and after highly active antiretroviral therapy. Br J Cancer. 2008;99:800–804.
25. Bohlius J, Valeri F, Maskew M, et al. Kaposi's sarcoma in HIV-infected patients in South Africa: multicohort study in the antiretroviral therapy era. Int J Cancer. 2014;135:2644–2652.
26. Guiguet M, Boue F, Cadranel J, et al. Effect of immunodeficiency, HIV viral load, and antiretroviral therapy on the risk of individual malignancies (FHDH-ANRS CO4): a prospective cohort study. Lancet Oncol. 2009;10:1152–1159.
27. Mocroft A, Kirk O, Clumeck N, et al. The changing pattern of Kaposi sarcoma in patients with HIV, 1994-2003: the EuroSIDA Study. Cancer. 2004;100:2644–2654.
28. Rohner E, Valeri F, Maskew M, et al. Incidence rate of Kaposi sarcoma in HIV-infected patients on antiretroviral therapy in Southern Africa: a prospective multicohort study. J Acquir Immune Defic Syndr. 2014;67:547–554.
29. Silverberg MJ, Chao C, Leyden WA, et al. HIV infection, immunodeficiency, viral replication, and the risk of cancer. Cancer Epidemiol Biomarkers Prev. 2011;20:2551–2559.
30. Serraino D, Angeletti C, Carrieri MP, et al. Kaposi's sarcoma in transplant and HIV-infected patients: an epidemiologic study in Italy and France. Transplantation. 2005;80:1699–1704.
31. Patel P, Hanson DL, Sullivan PS, et al. Incidence of types of cancer among HIV-infected persons compared with the general population in the United States, 1992–2003. Ann Intern Med. 2008;148:728–736.
32. Bruyand M, Thiebaut R, Lawson-Ayayi S, et al. Role of uncontrolled HIV RNA level and immunodeficiency in the occurrence of malignancy in HIV-infected patients during the combination antiretroviral therapy era: agence Nationale de Recherche sur le Sida (ANRS) CO3 Aquitaine Cohort. Clin Infect Dis. 2009;49:1109–1116.
33. Boettiger DC, Sabin CA, Grulich A, et al. Is nelfinavir exposure associated with cancer incidence in HIV-positive individuals? AIDS. 2016;30:1629–1637.
34. Bruyand M, Ryom L, Shepherd L, et al. Cancer risk and use of protease inhibitor or nonnucleoside reverse transcriptase inhibitor-based combination antiretroviral therapy: the D: A: D study. J Acquir Immune Defic Syndr. 2015;68:568–577.
35. Landriscina M, Spadafora C, Cignarelli M, et al. Anti-tumor activity of non-nucleosidic reverse transcriptase inhibitors. Curr Pharm Des. 2007;13:737–747.
36. Sgadari C, Monini P, Barillari G, et al. Use of HIV protease inhibitors to block Kaposi's sarcoma and tumour growth. Lancet Oncol. 2003;4:537–547.
37. Gange SJ, Kitahata MM, Saag MS, et al. Cohort profile: the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). Int J Epidemiol. 2007;36:294–301.
38. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach. 2nd ed. New York, NY: Springer-Verlag; 2002.
39. VanderWeele TJ. Mediation analysis: a practitioner's guide. Annu Rev Public Health. 2016;37:17–32.
40. Richardson ET, Grant PM, Zolopa AR. Evolution of HIV treatment guidelines in high- and low-income countries: converging recommendations. Antivir Res. 2014;103:88–93.
41. Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the Use of Antiretroviral Agents in HIV-1-infected Adults and Adolescents. Department of Health and Human Services. Available at: https://aidsinfo.nih.gov/guidelines. Accessed December 11, 2016.
42. Bower M, Fisher M, Hill T, et al. CD4 counts and the risk of systemic non-Hodgkin's lymphoma in individuals with HIV in the UK. Haematologica. 2009;94:875–880.
43. Engels EA, Rosenberg PS, O'Brien TR, et al. Plasma HIV viral load in patients with hemophilia and late-stage HIV disease: a measure of current immune suppression. Multicenter Hemophilia Cohort Study. Ann Intern Med. 1999;131:256–264.
44. Engels EA, Pfeiffer RM, Landgren O, et al. Immunologic and virologic predictors of AIDS-related non-Hodgkin lymphoma in the highly active antiretroviral therapy era. J Acquir Immune Defic Syndr. 2010;54:78–84.
45. Zoufaly A, Stellbrink HJ, Heiden MA, et al. Cumulative HIV viremia during highly active antiretroviral therapy is a strong predictor of AIDS-related lymphoma. J Infect Dis. 2009;200:79–87.
46. Borges AH, Dubrow R, Silverberg MJ. Factors contributing to risk for cancer among HIV-infected individuals, and evidence that earlier combination antiretroviral therapy will alter this risk. Curr Opin HIV AIDS. 2014;9:34–40.
47. Borges AH, Silverberg MJ, Wentworth D, et al. Predicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers. AIDS. 2013;27:1433–1441.
48. Dubrow R, Silverberg MJ, Park LS, et al. HIV infection, aging, and immune function: implications for cancer risk and prevention. Curr Opin Oncol. 2012;24:506–516.
49. Barillari G, Ensoli B. Angiogenic effects of extracellular human immunodeficiency virus type 1 Tat protein and its role in the pathogenesis of AIDS-associated Kaposi's sarcoma. Clin Microbiol Rev. 2002;15:310–326.
50. Barillari G, Franzese O, Comandini A, et al. Spindle cells from AIDS-associated Kaposi's sarcoma lesions express telomerase activity that is enhanced by Kaposi's sarcoma progression factors. Oncol Rep. 2010;24:219–223.
51. Chen X, Cheng L, Jia X, et al. Human immunodeficiency virus type 1 Tat accelerates Kaposi sarcoma-associated herpesvirus Kaposin A-mediated tumorigenesis of transformed fibroblasts in vitro as well as in nude and immunocompetent mice. Neoplasia. 2009;11:1272–1284.
52. Nunnari G, Smith JA, Daniel R. HIV-1 Tat and AIDS-associated cancer: targeting the cellular anti-cancer barrier? J Exp Clin Cancer Res. 2008;27:3.
53. Rusnati M, Presta M. HIV-1 Tat protein and endothelium: from protein/cell interaction to AIDS-associated pathologies. Angiogenesis. 2002;5:141–151.
54. Zeng Y, Zhang X, Huang Z, et al. Intracellular tat of human immunodeficiency virus type 1 activates lytic cycle replication of Kaposi's sarcoma-associated herpesvirus: role of JAK/STAT signaling. J Virol. 2007;81:2401–2417.
55. Zhou F, Xue M, Qin D, et al. HIV-1 Tat promotes Kaposi's sarcoma-associated herpesvirus (KSHV) vIL-6-induced angiogenesis and tumorigenesis by regulating PI3K/PTEN/AKT/GSK-3beta signaling pathway. PLoS One. 2013;8:e53145.
56. Zhu X, Guo Y, Yao S, et al. Synergy between Kaposi's sarcoma-associated herpesvirus (KSHV) vIL-6 and HIV-1 Nef protein in promotion of angiogenesis and oncogenesis: role of the AKT signaling pathway. Oncogene. 2014;33:1986–1996.
57. Unemori P, Leslie KS, Hunt PW, et al. Immunosenescence is associated with presence of Kaposi's sarcoma in antiretroviral treated HIV infection. AIDS. 2013;27:1735–1742.
58. Engels EA, Biggar RJ, Marshall VA, et al. Detection and quantification of Kaposi's sarcoma-associated herpesvirus to predict AIDS-associated Kaposi's sarcoma. AIDS. 2003;17:1847–1851.
59. Mesri EA, Cesarman E, Boshoff C. Kaposi's sarcoma and its associated herpesvirus. Nat Rev Cancer. 2010;10:707–719.
Keywords:

Kaposi sarcoma; HIV infection; acquired immunodeficiency syndrome; CD4+ T-cell count; HIV-1 RNA viral load; antiretroviral therapy

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