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Rate and predictors of progression in elite and viremic HIV-1 controllers

Leon, Agathe; Perez, Ignacio; Ruiz-Mateos, Ezequiel; Benito, Jose Miguel; Leal, Manuel; Lopez-Galindez, Cecilio; Rallon, Norma; Alcami, Jose; Lopez-Aldeguer, Jose; Viciana, Pompeyo; Rodriguez, Carmen; Grau, Eulalia; Iribarren, Jose; Gatell, Jose Maria; Garcia, Felipeon behalf of the EC and Immune Pathogenesis Working group of the Spanish AIDS Research Network

doi: 10.1097/QAD.0000000000001050

Background: The proportion of HIV controllers developing virologic, immunological or clinical progression and the baseline predictors of these outcomes have not been assessed in large cohorts.

Methods: A multicenter cohort of HIV controllers was followed from baseline (the first of the three HIV-1 RNA levels < 50 in elite controller or from 50 to 2000 copies/ml in viremic controllers) up to August 2011, to the development of a progression event (loss of viral load control, CD4+ decline, AIDS or death) or to the censoring date (lost to follow-up or initiation of antiretroviral therapy). Predictive models of progression at baseline and a risk score for the combined HIV-1 progression end point were calculated.

Results: Four hundred and seventy-five HIV-1 controllers of whom 204 (42.9%) were elite controller with 2972 person-years of follow-up were identified. One hundred and forty-one (29.7%) patients lost viral load control. CD4+ cell count declined in 229 (48.2%) patients. Thirteen patients developed an AIDS event and four died. Two hundred and eighty-seven (60.4%) developed a combined HIV-1 progression. Baseline predictors for the progression end points and for elite and viremic controller patients were very similar: risk for HIV-1 acquisition, baseline calendar year, CD4+ nadir, viral load before baseline and hepatitis C virus coinfection. The probability of a combined HIV-1 progression at 5 years was 70% for elite controllers with the highest score compared with 13% for those with the lowest.

Conclusion: HIV-1 disease progression in elite and viremic controllers is frequent. We propose a baseline clinical score to easily classify these patients according to risk of progression. This score could be instrumental for taking clinical decisions and performing pathogenic studies.

Supplemental Digital Content is available in the text

aHospital Clinic-Fundació Clinic, HIVACAT, Universidad de Barcelona, Barcelona

bLaboratory of Immunovirology, Biomedicine Institute of Seville, Infectious Disease Unit, Virgen del Rocio University Hospital, University of Seville, Seville

cIIS-Fundación Jiménez Diaz, UAM, Madrid

dHospital Universitario rey Juan Carlos, Móstoles

eCentro Nacional de Microbiología ISCIII

fAIDS Immunopathology Laboratory, ISCIII, Madrid

gHospital la Fe, Valencia

hCentro Sanitario Sandoval, Madrid

iHospital Trias i Pujol de Can Ruti, Badalona

jHospital Donostia, San Sebastian, Spain.

*Members of the EC and Immune Pathogenesis Working Group of the Spanish AIDS Research Network are listed in the Acknowledgements.

Correspondence to Dr Agathe Leon, Infectious Diseases Service, Hospital Clinic, Villarroel, 170. 08036 Barcelona. Spain. Tel: +34 93 2275586; fax: +34 93 4514438; e-mail:

Received 27 September, 2015

Revised 16 December, 2015

Accepted 23 December, 2015

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 Website (

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Understanding the mechanisms leading to the spontaneous control of HIV-1 infection is of great interest because they could serve as a good pathogenic model of functional cure of HIV-1 infection [1,2]. However the heterogeneity in criteria used to define these individuals can introduce inconsistencies in results from research studies making difficult the identification of the biological mechanisms underlying these phenotypes [3–5].

Moreover the assessment of HIV-1 disease progression has been reported partially and only in small cohorts (the largest has 140 elite controllers) [6] with different requirements and definitions of duration of virologic control [7–13].

A systematic review of definitions of extreme phenotypes of HIV-1 control identified that the most frequent HIV-1 RNA threshold used to define elite controllers was 50 copies/ml and 2000 HIV RNA copies/ml in the case of viremic controllers, with a minimum duration threshold of 1 year [14]. According with this operational definition of controllers, we described the virologic, immunologic and clinical progression outcomes of a multicenter cohort of 475 HIV-1 controllers and we estimated in predictive models independent variables associated with these events. We developed also a risk score for evaluating the probability of achieving a combined HIV-1 disease progression end point based on easily available baseline parameters.

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Patients and data collection

HIV-1 infected adults (≥18 years old) fulfilling predefined criteria of HIV-1 controllers and a minimum of 12 months of follow-up from the baseline time point, were included in this observational longitudinal multicenter study. In absence of any combination of antiretroviral therapy (cART) a patient was considered an elite controller if during the follow-up had at least three consecutive plasma viral load determinations not more than 50 HIV-1 RNA copies/ml maintained at least 12 months. The same criteria were applied to define viremic controllers but in this case plasma viral load should have been between 50 and 2000 HIV-1 RNA copies/ml. [1,3,7]. The date of the first of these three consecutive measurements was considered the baseline time point (month 0). HIV-1 RNA in elite controllers was quantified most of the time (86.1%) using techniques with a detection limit below 50 copies/ml (1.7 and 12.2% of viral load assessments were performed in elite controllers with a detection level below 500 and 200 copies/ml, respectively. In these cases, it was required to remain below these cutoff points).

The HIV-1 controllers included in the study (n = 475) compose the HIV controllers cohort of the Spanish AIDS Research Network (ECRIS). The patients included in this cohort were selected after a systematic search of five already existing cohorts for different purposes. These cohorts were: the HIV-1 infected adult Cohort of the Spanish AIDS Research Network (CoRIS) with 6000 patients [15], the RIS Cohort of HIV-1 Long-Term Non-Progressors with 271 patients [16], the cohort of Hospital Clinic of Barcelona with 3800 patients, the cohort of the Hospital Virgen del Rocío of Sevilla with 2000 patients and the cohort of Hospital Carlos III of Madrid with 1300 patients. These five cohorts were prospectively and routinely collecting written consent and all necessary data using predefined questionnaires to allow the identification of HIV-1 controllers.

HIV-1 controllers were followed from the baseline time point (month 0) up to August 2011, to the development of a progression event (see definition below) or to the censoring date (lost to follow-up or initiation of cART by decision of treating physician following the country guidelines at any given moment).

Progression events could be as follows: virological progression defined by Lambotte O et al. [11,13] as a failure to suppress more than 90% of HIV-1-RNA determinations below the aforementioned thresholds of viral load used to define controllers; immunological progression, defined as a decrease of more than 25% of CD4+ cell count with respect to the CD4+ baseline value [17–19]; clinical progression, defined as a new AIDS event (according to the Centre for Disease Control and Prevention grade C event) or death; HIV disease progression combined end point was defined as at least one of the virologic, immunological or clinical progression outcomes mentioned previously, whichever came first.

A description of the observed events and their crude incidence rate per 100 person-years of follow-up (PYFU) was calculated for elite controllers (n = 204) and viremic controllers (n = 271).

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Statistical methodology

Random intercept regression models were used to study the dynamics of viral load and CD4+ T-cell count. Variations in viral load and CD4+ were expressed as percentage of change relative to the initial value for each patient. For both variables, the percentage of change was calculated as the difference between each follow-up CD4+ or viral load value and the baseline one, divided by the latter and multiplied by 100. The influence of sex, age at inclusion, group of controllers (elite controller or viremic controller), the existence of virologic progression, the time from HIV-1 diagnosis and the interaction between group and years of follow-up was tested using the likelihood ratio test.

Models to predict baseline markers associated with virologic progression, CD4+ progression and or the combined HIV-1 disease progression outcomes were performed for elite and viremic controllers. All the available baseline characteristics of the patients were the variables included in the models: age, sex, risk for HIV-1 acquisition at diagnosis, time of follow-up from HIV-1 diagnosis to month 0, calendar year of month 0, mean CD4+ T-cell count from HIV-1 diagnosis to month 0, median viral load from HIV-1 diagnosis to month 0, CD4+ nadir from HIV-1 diagnosis to month 0; mean CD4+ T-cell count at month 0, median viral load at month 0, hepatitis C virus (HCV) serology and hepatitis B virus (HBV) serology.

For building the models, data for each patient were entered into the Cox proportional hazards regression model [20] in which each patient is assigned a risk score (R): R = β 1 X 1 + β 2 X 2 + β 3 X 3 + .......+ β k X k in which X 1 ,X 2 ,X 3,. .. .. X k are the levels of k prognostic variables (risk factors) and β 1, β 2, β 3,… β k are regression coefficients. The regression coefficients are estimated by the method of maximum likelihood estimation applied to the Cox partial likelihood. Model estimation denotes the probability that a patient with risk factor values x {X 1 , X 2 , X 3,. . .X k} and risk score R will be free of event t years later. The predictive accuracy of the models (between the observed and the estimated data) was quantified by the concordance index (C-index) [21,22], which was interpreted considering that its performance depends on the sample size and the proportion of patients who experienced the event [23].

An internal validation of the full sample using bootstrapping (100 random samples of equal size) was undertaken [21,24] allowing the estimation of C-index value distribution through the repeated resampling of data and the mean of the 100 C-index values from the bootstrap samples and the bias from the original value of C-index.

A score of risk for the combined HIV-1 disease progression end point in elite and viremic controllers was performed. To classify samples into specific risk groups with a higher or a lower risk of developing the event within 5–10 years from baseline, we chose thresholds from the full set of elite controllers (n = 204) and viremic controllers (n = 271) according to the tertiles of risk score [22]. We conducted all analyses using version 11 of Stata for Windows (Stata Corp, College Station, Texas, USA).

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Characteristics of participants

Among 13 371 HIV-1-infected adults followed in the five cohorts, 475 HIV controllers were identified. Because of the unavailability of HIV-1 RNA determinations during the early 1990s, the median delay between the date of HIV-1 diagnosis and the baseline time point (month 0) was 4.9 years [interquartile (IQR) 1.5–10.8] but in 137 individuals (28.8%) the time from the HIV-1 diagnosis to baseline (the date of the first three consecutive viral load measurements below the detection limit) was less than 2 years. From baseline time point, HIV-1 controllers had been followed up a median of 6.0 years (IQR 2.9–10.1).

Main characteristics of the elite and viremic controllers are summarized in Table 1. Two hundred and four patients (43%) were elite controllers {prevalence 1.53% [95% confidence interval (CI) 1.3–1.7]} contributing 1469 PYFU and 271 were viremic controllers [prevalence 2.03% (95%CI 1.8–2.3)] contributing 1510 PYFU. We did not observe any difference in sex or age at diagnosis between groups, but sexual risk for HIV-1 acquisition (heterosexual and homosexual) was less frequent among elite controllers than among viremic controllers (28 vs. 55%, respectively; P < 0.0001). More elite controllers were former intravenous drug users with positive hepatitis C serology than viremic controllers (74 vs. 45%; P < 0.0001). HIV-1 infection occurred earlier in elite controllers than in viremic controllers, consequently, the time from HIV-1 diagnosis to month 0 (median 7 vs. 3.5 years; P = 0.0007) and the time of follow-up (7.6 vs. 5 years; P < 0.0001) was much longer in elite controllers.

Table 1

Table 1

One hundred and fifty-eight of the 475 controllers (33.2%) were lost during follow-up (75 elite controllers and 83 viremic controllers) and 91 (19.1%) patients started cART (23 elite controllers and 68 viremic controllers) and were censored for the analysis after a median follow-up of 5.7 years (IQR 2.6–9.4). Calendar year of M0, sex, type of controller and prevalence of positive hepatitis C serology was similar between censored and noncensored patients (data not shown).

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Viral load and CD4+ dynamics

During follow-up, 6509 (2852 elite controller) and 6596 (3039 elite controller) measurements of HIV-1 RNA and CD4+ T cells, respectively, were available for analysis. Sex, age at inclusion, the existence of a virologic progression event and years from HIV-1 diagnosis did not influenced significantly the evolution of CD4+ cell count and viral load. However, the CD4+ cell count and viral load dynamics was different in elite controllers compared with viremic controllers. A smaller decline of CD4+ cell count per year was observed in the 204 elite controllers vs. the 271 viremic controllers [–1.18 and –2.56, respectively (P = 0.001)], as well as a lower increase in viral load percentage change [0.34 and 2.25, respectively, P = 0.013]. This means that for every additional year of follow-up the percentage change in CD4+ cell count was expected to decrease by an average of 1.18% in elite controller and 2.56% in viremic controller patients. Similarly, for every additional year of follow-up the percentage change in viral load was expected to increase by an average of 0.34% in elite controller and 2.25% in viremic controller patients.

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Progression of HIV-1 disease

Virological progression was observed in 141 (29.7%) of the 475 patients (48 were elite and 93 viremic controllers). This crude incidence rate represents four events per 100 PYFU (95%CI 3–5) and 7 (95%CI 5–8) in elite controllers and viremic controllers, respectively (P = 0.001). Among the 141 patients who lost virologic control, a concomitant immunological progression was observed in 89 (63.1%; 30 elite and 59 viremic controllers). The median duration of follow-up until the development of a virologic failure was 6.2 years (IQR 3.5–10.4) in elite controllers vs. 3.9 (2.3–8.4) in viremic controllers, P = 0.0008 (see Table 2).

Table 2

Table 2

Two hundred and twenty-nine out of the 475 patients (48.2%) had a CD4 + progression defined as a decrease of more than 25% of baseline CD4+ T-cell count. Ninety patients were elite and 139 viremic controllers. Crude incidence rate was 6 events per 100 PYFU (95%CI 5–8) and 10 (95%CI 8–12) in elite and viremic controllers, respectively (P < 0.0001). Only 89 (38.9%; 35 elite and 54 viremic controllers) of the 229 who presented an immunological progression, developed also a virological progression. Median interval between baseline and CD4+ decline was lower in elite controllers [5.4 years (IQR 3.1–9.3) vs. 3.8 (2.2–7.4), respectively, P = 0.0009] (see Table 2).

Clinical progression (defined as new AIDS or death events) was detected in 16 of the 475 patients (3.4%). Crude incidence rate was 0.4 events per 100 PYFU and 0.6 in elite and viremic controllers, respectively (P = 0.46). Thirteen controllers had AIDS events and four died (one patient suffered both). AIDS events were: six pulmonary and three extra-pulmonary tuberculosis, two recurrent bacterial pneumonia, and two cervical cancers. Causes of death were: cirrhosis, hypoxic encephalopathy, bacterial pneumonia and one patient died for an unknown cause. Five and two elite controller and eight and two viremic controller patients, developed AIDS and death, respectively (P = 0.57). The median time duration to clinical progression was 8.1 (IQR 4.5–10.9) years in elite vs. 5.2 (2.9–9.5) in viremic controllers, P = 0.42.

A combined HIV-1 disease progression end point (virological, immunological or clinical progression) was observed in 287 of the 475 patients (60.4%) of whom 108 of 204 were elite controllers (52.9%) and 179 of 271 viremic controllers (66.0%). Crude incidence rate was 9 events per 100 PYFU (95%CI 7–11) and 15 (95%CI 7–11) in elite and viremic controllers, respectively (P < 0.0001). Median time to the combined HIV-1 disease end point was longer in the elite controller group [4.9 years (IQR 3.0–8.7) vs. 3.3 (IQR 2.0–6.3), respectively, P < 0.0001] (see Table 2).

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Predictive models

Based on the prognostic variables observed in the Cox regression for elite (n = 204) and viremic controller patients (n = 271) (observed data. See univariate analysis for different end points in Table 2) and the estimating regression coefficients applied to each variable, models (with estimated data) to predict virological progression, immunological progression and the combined HIV disease progression end point at baseline, were performed and are summarized in Table 3. The number of clinical events was too low to create a progression model. Models were adjusted for the following variables: age, sex, risk for HIV-1 acquisition, time from HIV-1 diagnosis, calendar year of month 0, mean CD4+ T-cell count, viral load and nadir CD4+ from the HIV-1 diagnosis to month 0, hepatitis C and hepatitis B virus serology. Only significant variables are displayed in Table 3. Because the number of intravenous drug users coinfected by hepatitis C affected an important rate of the population, Supplementary Table 1, reanalyzed the models for all endpoints without considering either hepatitis C coinfection, or risk of HIV-1 acquisition.

Table 3

Table 3

Calendar year of month 0 and nadir CD4+ T-cell count were the baseline predictors for most of the aforementioned progression events considered individually or combined both, for elite and viremic controller patients. Data are shown in Table 3. Although risk for HIV-1 infection was a factor associated with progression (virologic and immunologic) only in elite controllers and baseline viral load and hepatitis C co-infection were factors influencing only virologic progression, both in elite and viremic controller patients. Overall, all (elite and viremic) controllers with a sexual risk of HIV-1 acquisition, with a more recent HIV-1 diagnosis, with a lower nadir, higher baseline viral load and a positive hepatitis C serology had a worse prognosis.

C-index and percentile approach of the internal validation with the 100 random bootstrap samples for the different models, showed good discrimination ability with values ranging between 0.66 and 0.79 (see Table 3) and similar values (observed bias of ≤0.001) to the observed C-index.

Score of risk for combined HIV-1 disease progression was calculated as the sum of the regression coefficients of the significant variables, risk for HIV-1 acquisition, calendar year and nadir CD4+ in the case of elite controllers and as the sum of the last two variables in the case of viremic controller patients (see right column of Table 3). For example, an elite controller, MSM (coefficient of 1.54) in 2010 (coefficient 0.88) with a nadir of 600 CD4+ T cells [as the nadir coefficient is per an increase of 50 CD4+ cells/μl, the result would be obtained by multiplying –0.07 per 12 (600/50 CD4+ cells) = –0.84] would have a final score of 1.58 (1.54+0.88–0.84).

Tertiles of risk score were evaluated and displayed in Table 4. The probability of a combined HIV-1 disease progression at 5 years would be 70% for those elite controllers with the highest progression score (as it would be the case of the elite patients of our example) compared with 13% for the lowest, whereas in viremic controllers, it would be 100 and 41%, respectively. Survival free of combined HIV-1 disease progression end point stratified by risk score and for both elite and viremic controller patients is shown in Fig. 1a and b.

Table 4

Table 4

Fig. 1

Fig. 1

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We are reporting the incidence rate of virologic progression, CD4+ decline and a combined HIV-1 progression outcome in a large cohort of HIV-1 controllers. Moreover predictive models for these events and a risk score for the combined HIV-1 progression end point based on easily available baseline parameters when the status of HIV-1 controller was established were also identified.

One of the main findings of our study is that considering the most frequent definition of controllers, a high proportion of them experienced virological progression (23.7% of elite controllers), CD4+ decline (44.5% of elite controllers) and even clinical events, such as AIDS or death (3.5% of elite controllers). Similar to our data other groups using the same definition of controllers in small series have also shown that a significant proportion of them progress due to an increase in viral load or a decrease in CD4+ T-cell count. Depending on the definition of the end point, rates of virologic progression varies from 34.5 to 66.6% or from 45 to 53% in the case of CD4+ decline. Conversely, the development of AIDS events or death has been described to be low in these patients [7,11,25]. Taking into account that the definition of controllers used in our cohort lasting one year of follow-up might be considered not stringent enough, we have applied more strict definitions of controllers previously reported defining a follow-up period of 5 [26] and 10 years [11,25]. In Supplementary Table 2 and Table 3, we describe the observed events and their crude incidence rate per 100 PYFU and the combined HIV-1 disease progression end point considering these two additional definitions of HIV-1 controllers for elite and viremic controllers and we have observed only small variations in the predictive variables.

Accordingly to this important proportion of progression, if the aim of studying controllers is considering them as a good pathogenic model of HIV-1 remission without antiretroviral treatment, other components should be added to the operational definition of controllers to improve the value of this population. Recently an evaluation of HIV-1 elite controller definition within the CASCADE seroconversion cohort [14] suggested elite controller definitions with 90% of measurements below 400 copies/ml over at least 10-year follow-up were less likely to progress to a composite endpoint of AIDS, death, ART, or CD4+ less than 350 cells/μl than nonelite controller individuals. A recent article evaluating the inmmunologic and virologic progression in a population of 217 HIV-1 controllers with a different definition (patients cART-naive with a diagnosis at least 5 years before enrollment and a viral load below 400 copies/ml in the five preceding consecutive measurements), during a follow-up of 4 years found only 10 patients with immunologic progression and five with virologic progression [26].

The second finding is the development for the first time, of predictive models and a risk score for HIV-1 progression in elite and viremic controllers according to the values of variables easily obtained at baseline. We think that the existence of predictive models and a score of progression could help to improve patient clinical and research outcomes by enabling comparison and outcomes across different settings and populations and can be used as objective adjuncts to clinical judgment to evaluate for example the time at which cART should be initiated. Recently the START study evidenced that antiretroviral therapy should be recommended for patients in whom HIV has been diagnosed regardless of the CD4+ count; however, a net benefit of starting ART in HIV-positive low-risk patients (patients with baseline CD4+ above 800 cells/ml or baseline HIV RNA under 5000 copies/ml) was unclear [27]. Then, we believe that the score of risk would be useful to start cART in those elite and viremic controller patients with a higher score of risk, as well as to compare the pathogenic mechanisms leading the different risk score levels. We have found a number of factors associated with disease progression. Length of follow-up [1,6,14,28–30], nadir CD4+ T cells, low level viremia [7,13,31,32], sexual risk [33–39] and HCV coinfection [9,11,26,30,40–42] have all been previously described. Length of follow-up was associated with a worse prognosis, this finding is in agreement with several reports showing that adding to the operational virologic definition of controllers an enlarge period of sustained virologic suppression, clearly decreases the progression [14,26]. We have added as supplementary data two tables (Tables B and C, showing the proportion of patients, the crude incidence rate, the time to events and the variables related to combined HIV-1 disease progression according to three definitions of duration threshold of HIV-1 controllers. According to our results, in small series, viremic controller show higher progression rates than elite controller [7,13,31], underlying the implications of a low level of viremia in a chronic inflammatory state and treatment outcomes [32]. However, 61% of our elite controller with immunologic progression did not have any previous virological progression. These data emphasize the central role of immune activation and bacterial translocation [43,44] in HIV-1 disease progression suggesting a potential benefit of antiretroviral therapy or immunomodulatory therapy at least in part of this population [45,46].

Our study has several limitations that deserve a comment. First, the date of seroconversion is not known for all patients. However, this is the real scenario which we usually find in HIV-1 infection, whereby individuals present at clinics without available plasma viremia measurements or complete past viral load history. Recently HIV-1 controllers were identified from an international dataset of seroconverted cohorts [14], but of the 140 HIV-controllers selected, only 64 patients (45.7%) had viral load available within 24 months from seroconversion. Notably in our cohort, in 137 individuals (28.8%) the time from the HIV-1 diagnosis and the establishment of status's controller was less than 2 years, so that very close to seroconversion. Second, because we performed the modelling with baseline available data, we cannot exclude the possibility that other factors like HLA typing, bacterial translocation or levels of immune activation factors should be considered for adjusting the follow-up in controllers. However, these other unmeasured factors are not available in clinical routine and are not usually present when the physician is trying to evaluate in a controller patient, the time at which cART should be initiated. Moreover, our data may not be generalizable to other cohorts with a lower rate of intravenous drug users and hepatitis C coinfected patients.

In conclusion, progression in HIV-1 controllers, both in elite and viremic controllers is frequent. Controllers with a shorter length of follow-up, a sexual risk for HIV-1 acquisition, a lower nadir, a higher viral load and co-infected by hepatitis C, present globally a higher risk of virological immunological progression or a combined HIV-1 disease progression end point. We propose a clinical score to easily classify these patients according to risk of progression. This score could be instrumental for taking clinical decisions (time to start cART) and performing pathogenic studies in these patients.

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In memoriam to Iñaki Pérez.

We are indebted to the participants in the study. Elisa de Lazzari for her participation in the statistical analysis. Mariola López for helping with the initial construction of the clinical cohort. The members of the elite controller working group of the Spanish AIDS Research Network include from the Centro Nacional de Microbiología ISCIII, Madrid, Spain, Concepción Casado and María Pernas; from Hospital Universitario Virgen del Rocio, Sevilla, Spain, Kawthar Machmach, Clara Restrepo; from Hospital Clinic de Barcelona-Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Nuria Climent, Montserrat Plana, Alberto Crespo Guardo, Mireia Arnedo, Sonsoles Sánchez, Nuria Climent, Gilles Mirambeau, Eloisa Juste and Victor Sanchez. The Spanish HIV BioBank for their support with biological processing, storing and providing distinct samples in controller patients.

Annex: centers and investigators involved in coris: Executive committee: Juan Berenguer, Julia del Amo, Federico García, Félix Gutiérrez, Pablo Labarga, Santiago Moreno y María Ángeles Muñoz.

Fieldwork, data management and analysis: Paz Sobrino-Vegas, Victoria Hernando Sebastián, Belén Alejos Ferreras, Débora Álvarez, Susana Monge, Inmaculada Jarrín, Yaiza Rivero, Cristina González Blázquez.

BioBank: M Ángeles Muñoz-Fernández, Isabel García-Merino, Coral Gómez Rico, Jorge Gallego de la Fuente y Almudena García Torre.

Participating centres of CoRIS: Hospital General Universitario de Alicante (Alicante): Joaquín Portilla Sogorb, Esperanza Merino de Lucas, Sergio Reus Bañuls, Vicente Boix Martínez, Livia Giner Oncina, Carmen Gadea Pastor, Irene Portilla Tamarit, Patricia Arcaina Toledo.

Hospital Universitario de Canarias (Santa Cruz de Tenerife): Juan Luis Gómez Sirvent, Patricia Rodríguez Fortúnez, María Remedios Alemán Valls, María del Mar Alonso Socas, Ana María López Lirola, María Inmaculada Hernández Hernández, Felicitas Díaz-Flores.

Hospital Carlos III (Madrid): Vicente Soriano, Pablo Labarga, Pablo Barreiro, Pablo Rivas, Francisco Blanco, Luz Martín Carbonero, Eugenia Vispo, Carmen Solera.

Hospital Universitario Central de Asturias (Oviedo): Victor Asensi, Eulalia Valle, José Antonio Cartón.

Hospital Clinic (Barcelona): José M. Miró, María López-Dieguez, Christian Manzardo, Laura Zamora, Iñaki Pérez, Ma Teresa García, Carmen Ligero, José Luis Blanco, Felipe García-Alcaide, Esteban Martínez, Josep Mallolas, José M. Gatell.

Hospital Doce de Octubre (Madrid): Rafael Rubio, Federico Pulido, Silvana Fiorante, Jara Llenas, Violeta Rodríguez, Mariano Matarranz.

Hospital Donostia (San Sebastián): José Antonio Iribarren, Julio Arrizabalaga, María José Aramburu, Xabier Camino, Francisco Rodríguez-Arrondo, Miguel Ángel von Wichmann, Lidia Pascual Tomé, Miguel Ángel Goenaga, Ma Jesús Bustinduy, Harkaitz Azkune Galparsoro.

Hospital General Universitario de Elche (Elche): Félix Gutiérrez, Mar Masiá, Cristina López Rodríguez, Sergio Padilla, Andrés Navarro, Fernando Montolio, Catalina Robledano García, Joan Gregori Colomé.

Hospital Germans Trías i Pujol (Badalona): Bonaventura Clotet, Cristina Tural, Lidia Ruiz, Cristina Miranda, Roberto Muga, Jordi Tor, Arantza Sanvisens.

Hospital General Universitario Gregorio Marañón (Madrid): Juan Berenguer, Juan Carlos López Bernaldo de Quirós, Pilar Miralles, Jaime Cosín Ochaíta, Isabel Gutiérrez Cuellar, Margarita Ramírez Schacke, Belén Padilla Ortega, Paloma Gijón Vidaurreta, Ana Carrero Gras, Teresa Aldamiz-Echevarría Lois y Francisco Tejerina Picado.

Hospital Universitari de Tarragona Joan XXIII, IISPV, Universitat Rovira i Virgili (Tarragona): Francesc Vidal, Joaquín Peraire, Consuelo Viladés, Sergio Veloso, Montserrat Vargas, Miguel López-Dupla, Montserrat Olona, Alba Aguilar, Joan Josep Sirvent, Verónica Alba, Olga Calavia.

Hospital Universitario La Fe (Valencia): José López Aldeguer, Marino Blanes Juliá, José Lacruz Rodrigo, Miguel Salavert, Marta Montero, Eva Calabuig, Sandra Cuéllar.

Hospital Universitario La Paz (Madrid): Juan González García, Ignacio Bernardino de la Serna, José Ramón Arribas López, María Luisa Montes Ramírez, Jose Ma Peña, Blanca Arribas, Juan Miguel Castro, Fco Javier Zamora Vargas, Ignacio Pérez Valero, Miriam Estébanez, Silvia García Bujalance, Marta Díaz.

Hospital de la Princesa (Madrid): Ignacio de los Santos, Jesús Sanz Sanz, Ana Salas Aparicio, Cristina Sarriá Cepeda.

Hospital San Pedro-CIBIR (Logroño): José Antonio Oteo, José Ramón Blanco, Valvanera Ibarra, Luis Metola, Mercedes Sanz, Laura Pérez-Martínez.

Hospital San Pedro II (Logroño): Javier Pinilla Moraza.

Hospital Universitario Mutua de Terrassa (Terrassa): David Dalmau, Angels Jaén Manzanera, Mireia Cairó Llobell, Daniel Irigoyen Puig, Laura Ibáñez, Queralt Jordano Montañez, Mariona Xercavins Valls, Javier Martinez-Lacasa, Pablo Velli, Roser Font.

Hospital de Navarra (Pamplona): María Rivero, Marina Itziar Casado, Jorge Alberto Díaz González, Javier Uriz, Jesús Repáraz, Carmen Irigoyen, María Jesús Arraiza.

Hospital Parc Taulí (Sabadell): Ferrán Segura, María José Amengual, Víctor Segura, Gemma Navarro, Montserrat Sala, Manuel Cervantes, Valentín Pineda.

Hospital Ramón y Cajal (Madrid): Santiago Moreno, José Luis Casado, Fernando Dronda, Ana Moreno, María Jesús Pérez Elías, Dolores López, Carolina Gutiérrez, Beatriz Hernández, María Pumares, Paloma Martí.

Hospital Reina Sofía (Murcia): Alfredo Cano Sánchez, Enrique Bernal Morell, Ángeles Muñoz Pérez.

Hospital San Cecilio (Granada): Federico García García, José Hernández Quero, Alejandro Peña Monje, Leopoldo Muñoz Medina, Jorge Parra Ruiz.

Centro Sanitario Sandoval (Madrid): Jorge Del Romero Guerrero, Carmen Rodríguez Martín, Teresa Puerta López, Juan Carlos Carrió Montiel, Cristina González, Mar Vera.

Hospital Universitario Santiago de Compostela (Santiago de Compostela): Antonio Antela, Arturo Prieto, Elena Losada.

Hospital Son Espases (Palma de Mallorca): Melchor Riera, Javier Murillas, Maria Peñaranda, Maria Leyes, Ma Angels Ribas, Antoni Campins, Concepcion Villalonga, Carmen Vidal.

Hospital Universitario de Valme (Sevilla): Juan Antonio Pineda, Eva Recio Sánchez, Fernando Lozano de León, Juan Macías, José del Valle, Jesús Gómez-Mateos.

Hospital Virgen de la Victoria (Málaga): Jesús Santos González, Manuel Márquez Solero, Isabel Viciana Ramos, Rosario Palacios Muñoz.

Hospital Universitario Virgen del Rocío (Sevilla): Pompeyo Viciana, Manuel Leal, Luis Fernando López-Cortés, Mónica Trastoy.

Author contributions: A.L., J.M.G. and F.G. contributed to the design of the clinical study. A.L., E.R.M, J.M.B, M.L., C.L.G., J.A., N.R., J.L.A., P.V., C.R., E.G., J.I. and F.G. contributed to the implementation of the clinical cohort. A.L., I.P., E.R.M., J.M.B, J.M.G. and F.G. contributed to the data analysis and interpretation of the results. A.L., J.M.G. and F.G. contributed to the manuscript writing. All authors approved the final manuscript.

Funding: F.G. is a recipient of a Research Grant from IDIBAPS, Barcelona, Spain. E.R.M. has a grant from Fondo de Investigaciones Sanitarias (FIS) (CP08/00172 and CPII14/00025). Work in CNM is supported by grant SAF 2007-61036 and 2010-17226 from MICINN Spain, by grants 36558/06, 36641/07, 36779/08, 360766/09 from FIPSE Spain and FIS (PI 13/02269) and in part by the RETIC de Investigación en SIDA of the FIS. This work was supported by Redes Telemáticas de Investigación Cooperativa en Salud (RETICS) (RD06/0006/0021, RD12/0017/0036), Consejería de Salud de la Junta de Andalucía (2008, PI-0270, 2009, PI-0066), FIS PS09-00120 and PI13/01912, and Fondo Europeo de Desarrollo Regional (FEDER).

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Conflicts of interest

The authors do not have a commercial or other association that might pose a conflict of interest for this manuscript.

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1. Walker BD. Elite control of HIV Infection: implications for vaccines and treatment. Top HIV Med 2007; 15:134–136.
2. Madec Y, Boufassa F, Porter K, Meyer L. Spontaneous control of viral load and CD4 cell count progression among HIV-1 seroconverters. AIDS 2005; 19:2001–2007.
3. Pereyra F, Jia X, McLaren PJ, Telenti A, de Bakker PI, Walker BD, et al. The major genetic determinants of HIV-1 control affect HLA class I peptide presentation. Science 2010; 330:1551–1557.
4. Saez-Cirion A, Sinet M, Shin SY, Urrutia A, Versmisse P, Lacabaratz C, et al. Heterogeneity in HIV suppression by CD8 T cells from HIV controllers: association with Gag-specific CD8 T cell responses. J Immunol 2009; 182:7828–7837.
5. Vigneault F, Woods M, Buzon MJ, Li C, Pereyra F, Crosby SD, et al. Transcriptional profiling of CD4 T cells identifies distinct subgroups of HIV-1 elite controllers. J Virol 2011; 85:3015–3019.
6. Madec Y, Boufassa F, Porter K, Prins M, Sabin C, d’Arminio Monforte A, et al. Natural history of HIV-control since seroconversion. AIDS 2013; 27:2451–2460.
7. Okulicz JF, Marconi VC, Landrum ML, Wegner S, Weintrob A, Ganesan A, et al. Clinical outcomes of elite controllers, viremic controllers, and long-term nonprogressors in the US Department of Defense HIV natural history study. J Infect Dis 2009; 200:1714–1723.
8. Migueles SA, Sabbaghian MS, Shupert WL, Bettinotti MP, Marincola FM, Martino L, et al. HLA B*5701 is highly associated with restriction of virus replication in a subgroup of HIV-infected long term nonprogressors. Proc Natl Acad Sci U S A 2000; 97:2709–2714.
9. Hunt PW, Brenchley J, Sinclair E, McCune JM, Roland M, Page-Shafer K, et al. Relationship between T cell activation and CD4+ T cell count in HIV-seropositive individuals with undetectable plasma HIV RNA levels in the absence of therapy. J Infect Dis 2008; 197:126–133.
10. Blankson JN, Bailey JR, Thayil S, Yang HC, Lassen K, Lai J, et al. Isolation and characterization of replication-competent human immunodeficiency virus type 1 from a subset of elite suppressors. J Virol 2007; 81:2508–2518.
11. Boufassa F, Saez-Cirion A, Lechenadec J, Zucman D, Avettand-Fenoel V, Venet A, et al. CD4 dynamics over a 15 year-period among HIV controllers enrolled in the ANRS French observatory. PLoS One 2011; 6:e18726.
12. Grabar S, Selinger-Leneman H, Abgrall S, Pialoux G, Weiss L, Costagliola D. Prevalence and comparative characteristics of long-term nonprogressors and HIV controller patients in the French Hospital Database on HIV. AIDS 2009; 23:1163–1169.
13. <CID2005,lambotte controllersmasde10años.pdf>.
14. Gurdasani D, Iles L, Dillon DG, Young EH, Olson AD, Naranbhai V, et al. A systematic review of definitions of extreme phenotypes of HIV control and progression. AIDS 2014; 28:149–162.
15. Sobrino-Vegas P, Gutierrez F, Berenguer J, Labarga P, Garcia F, Alejos-Ferreras B, et al. The Cohort of the Spanish HIV Research Network (CoRIS) and its associated biobank; organizational issues, main findings and losses to follow-up. Enferm Infecc Microbiol Clin 2011; 29:645–653.
16. Buchbinder SP, Katz MH, Hessol NA, O’Malley PM, Holmberg SD. Long-term HIV-1 infection without immunologic progression. AIDS 1994; 8:1123–1128.
17. Detels R, English PA, Giorgi JV, Visscher BR, Fahey JL, Taylor JM, et al. Patterns of CD4+ cell changes after HIV-1 infection indicate the existence of a codeterminant of AIDS. J Acquir Immune Defic Syndr 1988; 1:390–395.
18. Lang W, Perkins H, Anderson RE, Royce R, Jewell N, Winkelstein W Jr. Patterns of T lymphocyte changes with human immunodeficiency virus infection: from seroconversion to the development of AIDS. J Acquir Immune Defic Syndr 1989; 2:63–69.
19. Lyles RH, Munoz A, Yamashita TE, Bazmi H, Detels R, Rinaldo CR, et al. Natural history of human immunodeficiency virus type 1 viremia after seroconversion and proximal to AIDS in a large cohort of homosexual men. Multicenter AIDS Cohort Study. J Infect Dis 2000; 181:872–880.
20. Rosen HR, Prieto M, Casanovas-Taltavull T, Cuervas-Mons V, Guckelberger O, Muiesan P, et al. Validation and refinement of survival models for liver retransplantation. Hepatology 2003; 38:460–469.
21. Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54:774–781.
22. Fan C, Prat A, Parker JS, Liu Y, Carey LA, Troester MA, et al. Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Med Genomics 2011; 4:3.
23. Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004; 23:2109–2123.
24. Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012; 98:683–690.
25. Lambotte O, Boufassa F, Madec Y, Nguyen A, Goujard C, Meyer L, et al. HIV controllers: a homogeneous group of HIV-1-infected patients with spontaneous control of viral replication. Clin Infect Dis 2005; 41:1053–1056.
26. Noel N, Lerolle N, Lecuroux C, Goujard C, Venet A, Saez-Cirion A, et al. Immunologic and Virologic Progression in HIV Controllers: The Role of Viral “Blips” and Immune Activation in the ANRS CO21 CODEX Study. PLoS One 2015; 10:e0131922.
27. Lundgren JD, Babiker AG, Gordin F, Emery S, Grund B, Sharma S, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med 2015; 373:795–807.
28. Okulicz JF, Lambotte O. Epidemiology and clinical characteristics of elite controllers. Curr Opin HIV AIDS 2011; 6:163–168.
29. Miura T, Brockman MA, Brumme ZL, Brumme CJ, Pereyra F, Trocha A, et al. HLA-associated alterations in replication capacity of chimeric NL4-3 viruses carrying gag-protease from elite controllers of human immunodeficiency virus type 1. J Virol 2009; 83:140–149.
30. Sajadi MM, Constantine NT, Mann DL, Charurat M, Dadzan E, Kadlecik P, et al. Epidemiologic characteristics and natural history of HIV-1 natural viral suppressors. J Acquir Immune Defic Syndr 2009; 50:403–408.
31. Garcia F, Plana M, Soriano A, Vidal C, Arnedo M, Gil C, et al. Predictors of progression in chronically infected naive patients with plasma viraemia below 5000 copies/ml and CD4 T lymphocytes greater than 500 x 10(6)/I. AIDS 2001; 15:131–133.
32. Lucero C, Torres B, Leon A, Calvo M, Leal L, Perez I, et al. Rate and predictors of non-AIDS events in a cohort of HIV-infected patients with a CD4 T cell count above 500 cells/mm(3). AIDS Res Hum Retroviruses 2013; 29:1161–1167.
33. Beyrer C, Abdool Karim Q. The changing epidemiology of HIV in 2013. Curr Opin HIV AIDS 2013; 8:306–310.
34. Folch C, Fernandez-Davila P, Ferrer L, Soriano R, Diez M, Casabona J. High-risk sexual behaviour by partner type among men who have sex with men. Enferm Infecc Microbiol Clin 2014; 32:341–349.
35. Koedijk FD, van Bergen JE, Dukers-Muijrers NH, van Leeuwen AP, Hoebe CJ, van der Sande MA. The value of testing multiple anatomic sites for gonorrhoea and chlamydia in sexually transmitted infection centres in the Netherlands. Int J STD AIDS 2012; 23:626–631.
36. Kent CK, Chaw JK, Wong W, Liska S, Gibson S, Hubbard G, et al. Prevalence of rectal, urethral, and pharyngeal chlamydia and gonorrhea detected in 2 clinical settings among men who have sex with men: San Francisco, California. Clin Infect Dis 2005; 41:67–74.
37. Casado C, Pernas M, Sandonis V, Alvaro-Cifuentes T, Olivares I, Fuentes R, et al. Identification of a cluster of HIV-1 controllers infected with low replicating viruses. PLoS One 2013; 8:e77663.
38. Cornelissen M, Pasternak AO, Grijsen ML, Zorgdrager F, Bakker M, Blom P, et al. HIV-1 dual infection is associated with faster CD4+ T-cell decline in a cohort of men with primary HIV infection. Clin Infect Dis 2012; 54:539–547.
39. Collaboration TNHPWGft. (COHERE) oOHERE, EuroCoord i. Factors associated with short-term changes in HIV viral load and CD4(+) cell count in antiretroviral-naive individuals. AIDS 2014; 28:1351–1356.
40. Olson AD, Meyer L, Prins M, Thiebaut R, Gurdasani D, Guiguet M, et al. An evaluation of HIV elite controller definitions within a large seroconverter cohort collaboration. PLoS One 2014; 9:e86719.
41. Sobrino-Vegas P, Monge Corella S, Serrano-Villar S, Gutierrez F, Blanco JR, Santos I, et al. Incidence of hepatitis C virus (HCV) in a multicenter cohort of HIV-positive patients in Spain 2004–2011: increasing rates of HCV diagnosis but not of HCV seroconversions. PLoS One 2014; 9:e116226.
42. Peters L, Klein MB. Epidemiology of hepatitis C virus in HIV-infected patients. Curr Opin HIV AIDS 2015; 10:297–302.
43. Cortes FH, Passaes CP, Bello G, Teixeira SL, Vorsatz C, Babic D, et al. HIV controllers with different viral load cutoff levels have distinct virologic and immunologic profiles. J Acquir Immune Defic Syndr 2015; 68:377–385.
44. León A, Leal L, Torres B, Lucero C, Inciarte A, Arnedo M, et al. Association of microbial translocation biomarkers with clinical outcome in controllers HIV-infected patients. AIDS 2015; 29:675–681.
45. Klatt NR, Chomont N, Douek DC, Deeks SG. Immune activation and HIV persistence: implications for curative approaches to HIV infection. Immunol Rev 2013; 254:326–342.
46. Hatano H, Somsouk M, Sinclair E, Harvill K, Gilman L, Cohen M, et al. Comparison of HIV DNA and RNA in gut-associated lymphoid tissue of HIV-infected controllers and noncontrollers. AIDS 2013; 27:2255–2260.

controllers; HIV-1 progression; predictive model

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