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The Canadian HIV and aging cohort study - determinants of increased risk of cardio-vascular diseases… Durand, Madeleine; Chartrand-Lefebvre, Carl; Baril, Jean-Guy; Trottier, Sylvie; Trottier, Benoit; Harris, Marianne; Walmsley, Sharon; Conway, Brian; Wong, Alexander; Routy, Jean-Pierre; Kovacs, Colin; MacPherson, Paul A; Monteith, Kenneth M; Mansour, Samer; Thanassoulis, George; Abrahamowicz, Michal; Zhu, Zhitong; Tsoukas, Christos; Ancuta, Petronela; Bernard, Nicole; Tremblay, Cécile L Sep 11, 2017

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STUDY PROTOCOL Open AccessThe Canadian HIV and aging cohort study -determinants of increased risk of cardio-vascular diseases in HIV-infectedindividuals: rationale and study protocolMadeleine Durand1* , Carl Chartrand-Lefebvre2, Jean-Guy Baril3, Sylvie Trottier3, Benoit Trottier3, Marianne Harris4,Sharon Walmsley5, Brian Conway5, Alexander Wong6, Jean-Pierre Routy7, Colin Kovacs8, Paul A. MacPherson9,Kenneth Marc Monteith9, Samer Mansour9, George Thanassoulis10, Michal Abrahamowicz11, Zhitong Zhu11,Christos Tsoukas12, Petronela Ancuta13, Nicole Bernard14, Cécile L. Tremblay13 and For the investigators of theCanadian HIV and Aging Cohort StudyAbstractBackground: With potent antiretroviral drugs, HIV infection is becoming a chronic disease. Emergence of comorbidities,particularly cardiovascular disease (CVD) has become a leading concern for patients living with the infection. Wehypothesized that the chronic and persistent inflammation and immune activation associated with HIV disease leads toaccelerated aging, characterized by CVD. This will translate into higher incidence rates of CVD in HIV infected participants,when compared to HIV negative participants, after adjustment for traditional CVD risk factors. When characterized furtherusing cardiovascular imaging, biomarkers, immunological and genetic profiles, CVD associated with HIV will show differentcharacteristics compared to CVD in HIV-negative individuals.Methods/design: The Canadian HIV and Aging cohort is a prospective, controlled cohort study funded by the CanadianInstitutes of Health Research. It will recruit patients living with HIV who are aged 40 years or older or have lived with HIVfor 15 years or more. A control population, frequency matched for age, sex, and smoking status, will be recruited from thegeneral population. Patients will attend study visits at baseline, year 1, 2, 5 and 8. At each study visit, data on completemedical and pharmaceutical history will be captured, along with anthropometric measures, a complete physicalexamination, routine blood tests and electrocardiogram. Consenting participants will also contribute blood samples to aresearch biobank. The primary outcome is incidence of a composite of: myocardial infarction, coronary revascularization,stroke, hospitalization for angina or congestive heart failure, revascularization or amputation for peripheral artery disease,or cardiovascular death. Preplanned secondary outcomes are all-cause mortality, incidence of the metabolic syndrome,incidence of type 2 diabetes, incidence of renal failure, incidence of abnormal bone mineral density and body fatdistribution. Patients participating to the cohort will be eligible to be enrolled in four pre-planned sub-studies ofcardiovascular imaging, glucose metabolism, immunological and genetic risk profile.Discussion: The Canadian HIV and Aging Cohort will provide insights on pathophysiological pathways leading topremature CVD for patients living with HIV.Keywords: HIV, Aging, Cardiovascular, Prospective cohort, Study protocol* Correspondence: madeleine.durand.chum@ssss.gouv.qc.ca1Internal Medicine service, Centre de Recherche du CHUM, Montréal, QC H2J1T8, CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Durand et al. BMC Infectious Diseases  (2017) 17:611 DOI 10.1186/s12879-017-2692-2BackgroundAs a result of recent advances in antiretroviral therapy(ART), HIV-infected individuals live longer, with anestimated life expectancy that ranges from 20 to 50 yearsfollowing infection, and in some cases could reachnormal life expectancy [1–3]. Factors that impact onlongevity include: the age at infection, the nadir CD4 cellcount, the time spent with CD4 counts >500 cells/mm3,and other variables [1, 3–8]. In 2015, it was estimated that50% of HIV-infected persons were >50 years of age, mak-ing them susceptible to diseases related to aging [2, 3, 9].Furthermore, some age-related diseases may be overrepre-sented in the HIV population, and appear approximately 5to 10 years earlier than in the general population [10–12].This has led to the concept of “premature aging” in HIV-infected individuals [13, 14]. This term is controversial,since not all HIV-infected individuals show signs ofaccelerated aging, and not all components of the agingphenotype have been observed prematurely in the HIV-population. Co-morbid conditions of particular concerninclude cardiovascular diseases (CVD), the metabolicsyndrome (MetS), early onset of osteoporosis, and renalimpairment. The pathophysiological processes leading tothese phenotypes are complex and not completelyunderstood.Several hypotheses have been put forward to try toexplain premature aging: mitochondrial toxicity, im-munodeficiency, antiretroviral toxicity, lifestyle, and achronic state of immune activation [15–18]. The conse-quence of chronic immune activation is the developmentof a senescent immune phenotype, with impaired thymicfunction, reduced T-cell repertoire and regenerationpotential and T cell exhaustion, similarly to what is ob-served in aging [19, 20].Large cohort studies have shown an increased risk ofCVD in HIV-infected individuals. Compared to age andsex matched controls, we found a hazard ratio for myo-cardial infarction associated with HIV infection of 2.13(95% confidence interval [CI] 1.69–2.63) in local datafrom a Québec administrative database [21]. In the inter-national Data Collection on Adverse Events of anti-HIVDrugs (D:A:D) cohort including 33,347 HIV positivepatients, an overall rate of myocardial infarction of 3.48/1000 person-years was reported [22, 23]. A meta-analysis of observational and randomized controlled tri-als reporting CVD showed that the relative risk of CVDwas 1.61 (95% CI 1.43–1.81) among ART-naïve HIV-infected compared with HIV-uninfected people [24, 25].The overrepresentation of traditional cardiovascular riskfactors, particularly smoking, in HIV-infection populationcan explain part of the excess CVD risk [26]. However,traditional risk factors may not explain the totality of theexcess risk. We wish to further characterize the causativepathways and identify predictors of this premature CVDassociated with HIV infection. Through better under-standing of the particular physiopathology underlyingCVD in HIV, our ultimate aim is to prevent negativeoutcomes and optimize primary care interventions. Thisstudy will focus on identifying the type, frequency anddeterminants of premature CVD in HIV-infectedindividuals.Study hypothesisThe chronic and persistent inflammation associated withHIV disease leads to accelerated aging, characterized bypremature CVD, altered metabolism and immunesenescence. This will translate into higher incidencerates of CVD in HIV infected participants, when com-pared to HIV negative participants, after adjustment fortraditional CVD risk factors. When characterized furtherusing cardiovascular imaging, biomarkers, immuno-logical and genetic profiles, CVD associated to HIV willshow different characteristics compared to CVD in HIV-uninfected individuals.Methods/designStudy designThis is a controlled, prospective cohort study. Pre-planned sub-studies will be nested within the cohort.The exposure of interest for the main cohort and allsub-studies is HIV infection.SettingThis is a multicenter study, conducted in 10 Canadiancenters.Primary outcomeIncidence of CVD, defined as incidence of any of the fol-lowing conditions: MI, coronary revascularization, stroke,transient ischemic attack, hospitalization for angina orcongestive heart failure, revascularization or amputationfor peripheral artery disease, or cardiovascular death.Secondary outcomesSecondary outcomes are: i)incidence of all-cause mortal-ity, ii)incidence of major adverse cardiac and cerebrovas-cular events (MACE - composite outcome composed ofcardiovascular death, MI, coronary revascularization orstroke), iii)incidence of individual components of theprimary outcome, iv)incidence of the metS, v)incidenceof type 2 diabetes, vi)incidence of renal failure, vii)inci-dence of abnormal bone mineral density and body fatdistribution.Complete definitions of outcomes are available inAdditional file 1.Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 2 of 10Study participantsHIV-infected: All individuals living with HIV (as definedby a positive serology for HIV) followed in the HIVclinics of the participating centers, as well as all newlyinfected or newly diagnosed participants meeting inclu-sion criteria will be offered to take part into the cohort.HIV-uninfected: HIV-seronegative individuals will beselected from among i)healthy individuals from thegeneral population, reached through publicity campaigns,HIV prevention clinics and participating communitymembers. ii) HIV-uninfected individuals followed at theparticipating clinics and hospitals. The HIV-negativeparticipants will be frequency matched for age, sex andsmoking status, but no individual matching will be done.This will be achieved through periodic comparisons of ageand sex distributions of the HIV-positive and HIV-negative participants (every 6 months during the enroll-ment period) and adjustments in study publicity targetingthe HIV-negative group.Inclusion criteriaHIV-infected participants must be ≥40 years of age orhave HIV infection lasting for at least 15 years (provenby anti-HIV antibody test) at the time of enrollment, beable to provide informed consent and have a life expect-ancy of at least 1 year. Control group participants mustbe ≥40 years of age, be able to provide informed consentand have a life expectancy of at least 1 year.Interventions and comparisonsThis is an observational study, so no study interventionis planned. Participants will receive usual care from theirtreating physicians throughout the study period.Study visitsPatients will be followed yearly for 3 years (baseline, year1 and year 2), then have additional visits at years 5 and,depending on their recruitment date, at year 8. At eachstudy visit, participants will complete study question-naires, have a complete medical history and physicalexamination by a physician, have a panel of blood testsand an electrocardiogram. Twice during the study (year1 and final year), participants will also undergo a dualenergy X-ray absorptiometry to measure bone mineraldensity. In addition, all participants will be invited tocontribute to a research blood bank, to which they willcontribute serum and cells. Depending on availability atresearch sites, participants will also be invited toundergo leukapheresis, to maximize the number of whiteblood cells available in the biobank. Throughout follow-up, participants’ eligibility to the pre-planned sub-studieswill be assessed, and patients will be offered participa-tion in the sub-studies (presented below). Additionalfiles 2 and 3 present tasks performed at each study visitand list of blood tests obtained, respectively.Covariates and data collectionAt the baseline visit, data will be collected (and enteredinto the electronical case report form) on socio-demographic factors, all past and current medication, allpast and current medical conditions (including highblood pressure, diabetes, dyslipidemia, family history ofpremature CVD), presence of risk factors for HIV trans-mission, and lifestyle habits (smoking, alcohol consump-tion, illicit drug use and level of physical activity). Forparticipants with HIV, date of diagnosis and presumedmode of transmission will also be recorded. A completephysical examination will be performed by the treatingphysician, and the study nurse will record vital signs,height, weight, and waist circumference.At the subsequent yearly visits, data on medicalhistory, medication history and lifestyle habits will beupdated. Anthropometric measures and physicalexamination will be repeated. Data on primary andsecondary outcomes will be recorded using specific casereport forms. Additional file 4 presents the full contentsof the case report form that will be filled out at eachstudy visit.Statistical considerationsPower calculationBased on data from both our local HIV database andour pilot study [27, 28], we expect the HIV positivepopulation to be 80% male, with a mean age of50 years at the beginning of the study. Based on datafrom the Framingham study, expected rates of ourprimary outcome in HIV-negative participants are 10/1000 person-years for men and 4/1000 person-yearsfor women in the 45–54 age group [29]. The ratesmore than double for the 55–64 age group [29]. Ac-cordingly, we assume the overall CVD incidence ratein HIV-negative controls will range between 8/1000PY and 12/1000 PY. Analysis for the primary outcomewill be limited to subjects who are free of CVD atbaseline. We expect that, at baseline, up 10% of HIV-and 15% of HIV+ subjects will have prevalent CVD[29]. Finally, we assume that an average CVD-freesubject will contribute 4 years of follow-up (of themaximum potential follow-up of 5 years). Under theseassumptions, the recruitment of 1000 subjects in eachgroup (implying that 900 HIV- and 850 HIV+ sub-jects will be free of CVD at baseline, contributing3600 and 3400 PY of follow-up) will yield 80% powerwith alpha = 0.05 to detect hazard ratios of 1.72 to1.90 for the primary composite outcomeof CVD.These risk increases are clinically plausible and areactually lower than the more than 2-fold (HR > 2.0)Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 3 of 10increases in the risk of major CVD events associatedwith HIV infection [30]. Some studies have reportedhigher HRs for specific sub-populations or CVDevents, e.g. HR 4.0 for risk of stroke among youngersubjects [31]. All sample size calculations were donewith the PASS software [32].Statistical analysisThe analyses will focus on comparing the incidence ofCVD in HIV+ subjects and comparing it to the inci-dence of CVD in HIV- controls.Primary outcomeLongitudinal analyses of the CVD incidence/prospect-ive analysis: This analysis will use prospective data andwill focus on time until occurrence of the first incidentCVD event, as defined by the composite outcomedescribed above. It will be limited to subjects without ahistory of CVD at cohort entry. Analyses will employtime-to-event statistical methods including Cox propor-tional hazards (PH) model [33] and its flexible exten-sions. Time 0 will be defined as the cohort entry andsubjects who do not develop CVD during the follow-upwill be right-censored at the earliest of times of: loss tofollow-up, administrative end of the study or a non-CVD death. In all models, the binary indicator of HIVinfection will be the main ‘exposure’ and its effect onthe risk (hazard) of incident CVD will be adjusted for“standard confounders”: age, sex, ethnicity, education,income, and illicit drug use. However, two differenttypes of models will handle differently the CVD riskfactors, to provide a better understanding of the mech-anisms through which HIV infection may affect futureCVD risks. The first model will adjust the effect of HIVfor only the baseline values of CVD risk factors, inaddition to “standard confounders”. This will allow usto estimate and test the potential differences in CVDincidence between HIV+ vs. HIV- individuals who hadthe same initial values of common CVD risk factors, atcohort entry. In contrast, the second model will adjustfor the up-dated values of CVD risk factors, modeledby time-varying covariates, representing the mostrecent value observed until a given date during thefollow-up. This model will permit testing if there is a‘residual’ impact of HIV infection that is not mediatedthrough longitudinal changes in common CVD riskfactors. The independent association between HIV andCVD incidence will be then estimated through theadjusted HR, with 95% confidence intervals (CI). Inboth analyses the proportional hazards assumption wewill be tested using a flexible extension of the Coxmodel [34, 35].Sensitivity analysisRetrospective and prospective data analysis: In anothermodel, we will not exclude patients with presence ofCVD at baseline. The rationale for this is as follows: ifHIV patients experience premature CVD, as wehypothesize, and since HIV patients enter the cohortafter varying durations of HIV infection, the risk ofCVD associated with HIV might be underestimated ifpatients with prevalent CVD are excluded from theanalysis, i.e.: we would “miss” the earlier events occur-ring in the HIV infected patients before their entry intothe cohort.In this model, HIV infection will remain the main ‘ex-posure’, but time 0 will be defined as birth date. HIVinfection will be coded in a time-dependent manner: “0”for the time before HIV infection in HIV-infected partic-ipants and for HIV-negative controls throughout follow-up, and “1” following first HIV diagnosis in HIV-infectedindividuals. Incidence of CVD will be modeled usingtime-to-event statistical methods, as described above,with the first incidence of CVD occurring before cohortentry for patients with prevalent CVD at baseline. Datesof CVD occurrence for patients with prevalent CVD atbaseline will be obtained retrospectively (at the baselinevisit, through pre-planned data collection for completemedical history). Patients will be right-censored at theearliest of times of: loss to follow-up, administrative endof the study or a non-CVD death.Effect of HIV on the risk (hazard) of incident CVDwill be adjusted for “standard confounders”: sex, ethni-city, education level, and income (coded as constant overtime), as well as smoking, illicit drug use, sedentary life-style, and BMI (coded as time dependent variables).Period of prospective cohort participation (coded in atime-dependent fashion as 0/1) will also be added as apotential confounding variable, as being prospectivelyfollowed and assessed for CVD will be likely to increaseCVD detection.Secondary outcomesIncidence rates of secondary outcomes will be modeledas described for the primary outcome, through adjustedmultivariable survival models.Analyses restricted to HIV+ cohortEffect of antiretroviral therapyTo estimate the effect of antiretroviral medication onCVD risk, supplementary analysis will be performed inthe cohort of HIV-infected individuals. Incidence ratesof the primary outcome will be modeled using survivalmodels similar to those described for the primary out-come, but with drug exposure as the main exposurevariable. Drug exposure will be captured as time-dependent variables capturing cumulative exposure,Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 4 of 10updated for each month of exposure. Models of CVDrisk according to exposure to individual drugs will be fit-ted for abacavir, tenofovir, efavirenz, atazanavir, lopinavir,darunavir, raltegravir, dolutegravir and elvitegravir. Thesedrugs will be analyzed individually due to past signals ofcardiovascular toxicity, prevalence of use in our cohortor insufficient data on cardiovascular toxicity available inthe literature [21, 36–41]. Exposure to other antiretrovir-als will be examined by drug class, as opposed to indi-vidually, because we would have insufficient power todetect effect of individual drugs.Nadir CD4 and HIV viral loadLymphocyte CD4 nadir is an important variable inHIV, as it reflects the depth of the immune systemsuppression by the virus. It can also be used as an in-direct proxy for unknown HIV infection date [36]. Asurvival model will be fitted with nadir CD4 (codedas a continuous variable) as the exposure variable andincidence of CVD as the outcome. Similarly, the cu-mulative effect of unsuppressed viral replication willbe analysed, as it has been shown to be correlatedwith mortality in HIV infection [42]. Unsuppressedviral load leads to immune activation and inflamma-tion, which, according to our research hypothesis,lead to CVD. In a survival model, the time-dependent, cumulative area under the curve of viralload measurements will be used as the exposure vari-able, with incidence of CVD as the outcome.Pre-planned sub-studiesSub study 1 - characterization of atherosclerotic plaqueLo reported increased prevalence of subclinical coron-ary atherosclerotic disease in HIV [43]. It is yet un-clear if the composition of the atherosclerotic plaquein HIV is similar to that seen in the general popula-tion [36, 43–45]. More data are needed tocharacterize the atherosclerotic process, with possibleimplications for screening and treatment. In this sub-sub-study, 5 cardiac imaging modalities will be ap-plied to characterize the atherosclerosis in HIV posi-tive patients and compare it to that in HIV-negativecontrols: 1) positron emitting tomography (PET) scanof the ascending aorta and carotid arteries 2)carotidarteries ultrasound with measurement of intima-mediathickness 3) cardiac computed tomography scan with-out injection of contrast media, 4) cardiac computedtomography scan with injection of contrast media,and 5) intravascular coronary ultrasound (in patientsfor whom coronary angiogram is clinically indicated).Most imaging modalities will be cross-sectional, butcardiac computed tomography scan and carotid arter-ies ultrasound will be repeated to assess progressionof coronary artery disease.HypothesisAtherosclerotic plaque has a different composition inHIV infected individuals, which can be described byimaging modalities.OutcomesFor each of the imaging modalities, the primary outcomeis difference in atherosclerotic plaque presence andcomposition between HIV+ and HIV-. For repeated mea-sures (cardiac scan with contrast media), the outcomes in-clude rate of progression of total plaque volume.Population and settingParticipants recruited in the Montreal study sites will beeligible to this sub-study. Participants with a moderateFramingham risk score (ranging from 5 to 20%) will beasked to take part in this sub-study. Computed tomog-raphy scans, carotid ultrasound and vascular PET scanswill be done at the Centre Hospitalier de l’Université deMontréal radiology core lab. Coronary angiograms andintravascular ultrasound will be done at the CHUM cor-onary catheter laboratory. Projected sample sizes havebeen calculated individually for each imaging modalitiesto ensure sufficient power to detect clinically meaningfuldifferences. We expect to perform 30 cardiac PET scans,200 intima-media measurements, 200 non-contrastmedia cardiac scans, 190 contrast-media cardiac scans,and 100 intravascular ultrasounds.Sub-study 2-characterization of dysglycemiaThere are conflicting results in the literature regard-ing relative prevalence and determinants of dysglyce-mia (defined as impaired fasting glucose, impairedglucose tolerance or diabetes) in HIV-infected patientscompared to the HIV-negative population [46–49].Antiretrovirals, lipodystrophy, and altered metabolismdue to long-lasting inflammation could contribute todevelopment of dysglycemia in this population [49,50]. In this sub-study, oral glucose tolerance tests willbe used to assess patients at risk of or with knowndysglycemia in a prospective fashion, with levels ofbiomarkers to further characterize the nature of HIV-associated dysglycemia.HypothesisDysglycemia in HIV infection is associated with a differ-ent metabolic profile. Precisely, we hypothesize therewill be more endothelial dysfunction (as measured byincreased ICAM1, VCAM1, and E-selectin), morecoagulation activation (as measured by increased PAI-1and fibrinogen), more oxidative stress (as measured byoxidized-LDL), and more inflammation (as measured byIL-6, TNF-α, CRP) in HIV+ than in HIV- patients withdysglycemia.Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 5 of 10OutcomesThe primary outcome is difference in area under thecurve for the biomarkers throughout the oral glucosetolerance test between HIV+ and HIV- patients.Population and settingsAll patients from the main cohort recruited in the Mon-tréal sites with fasting plasma glucose > = to 5.6 mmol/lor HbA1c > = 5.6% will be asked to undergo an oral glu-cose tolerance test to screen for dysglycemia. Serum willbe banked during the oral glucose tolerance tests, andpatients with abnormalities in glucose metabolism willhave their sample analyzed for the biomarkers of interest(see hypothesis). Measurements will be done in the me-tabolism core lab of the CHUM. According to the inci-dence of dysglycemia, we aim to perform about 150 oralglucose tolerance tests, and to fully analyse serum forbiomarkers in 1/3 (50). This will grant 90% power atalpha 0.05 to detect a 10% difference in oxidized LDLbetween the HIV + and HIV- patients, with similarpower for the other biomarkers.Sub-study 3-characterization of immune profile in CVDGlobally impaired immunity secondary to HIV infectionmight contribute to the risk of CVD in HIV infected in-dividuals via several pathways. Of particular interest isthe depletion of Th17 lymphocytes, leading to microbialtranslocation from the gut, sustained immune activation,and eventually immune senescence [51–55]. In this sub-study, we will perform cross sectional immunologicalstudies to better characterize the contribution of variousimmune profiles on development of CVD, and howthose differ between HIV+ and HIV- individuals.HypothesisCVD associated with HIV is immunologically distinctfrom CVD in HIV uninfected patients: impaired immunityinduced by HIV infection leads to an immune risk profilethat contributes to development of premature CVD.OutcomesImmunological analyses will focus on Th17 paucity,monocyte activation, Cytomegalovirus infection, andpresence of an immune risk phenotype characterised byT-cell senescence and immune activation. The primaryoutcomes are specific to each of those three aspects.They involve measures of correlations between immuneprofiles and total coronary plaque volume, in partici-pants with and without HIV.Population and settingFor this sub study, the presence of CVD will be mea-sured as total coronary plaque volume, which is mea-sured on injected cardiac Computed tomography (CT)scan. Banked samples from all patients who undergoinjected cardiac CT scan will be analyzed. The plannedsample size is 190 patients in total.Sub-study 4 - characterization of genetic profile in CVDSeveral studies have reported that a genetic risk score isassociated with incident and prevalent CVD [56–58]. Itremains unknown whether a genetic predisposition foratherosclerosis could also potentiate HIV-specificmechanisms for CVD. We propose to evaluate whethera genetic risk score comprised of 30 single nucleotidepolymorphisms (already validated in genome-wideassociation studies) can predict the presence of CVD,and whether the risk score is more strongly associatedwith CVD in HIV+ as compared to HIV- individuals.HypothesisA genetic risk score will be more strongly associated withsubclinical CVD in HIV+ than in HIV- participants.OutcomesThe primary outcome is the difference in risk scoresbetween HIV+ and HIV- patients with CVD.Population and settingsThe genetic risk score will be measured in all patientsfrom the prospective cohort.For sub-studies 1–4, details of the planned statisticalanalysis are presented in Additional file 5.DiscussionThe Canadian HIV and Aging Cohort Study is alarge, controlled, prospective cohort. It aims to recruit1000 participants living with HIV, and 1000 partici-pants unexposed to HIV. While its primary endpointsare significant clinical events for patients living withHIV, the pre-planned sub-studies will increase ourbasic understanding of the complex interplay betweenthe immune alterations caused by chronic HIV infec-tion and CVD. The cohort will provide a strong basefor future studies of comorbidities associated withaging with HIV.Several methodological aspects of this cohort warrantfurther discussion. First, the age at enrolment of the par-ticipants living with HIV was carefully considered. Asour main focus was cardiovascular health, recruiting par-ticipants who were too young would have led to lowevent rates and low power. Yet recruiting patients withHIV infection that is long-lasting might result in missingthe early alterations of the cardiovascular system thatare driven by the chronic state of inflammation found inthose patients. We settled for an enrolment age of40 years or older, but planned that patients with veryDurand et al. BMC Infectious Diseases  (2017) 17:611 Page 6 of 10long-lasting HIV infection (15 years or more) could berecruited even if younger than 40.Selection of the HIV uninfected patients was an-other key aspect of our design. One strategy wouldhave been to simply recruit patients from the generalpopulation through publicity campaigns, with a fre-quency matching for age and sex only. However, verylarge differences in traditional cardiovascular risk fac-tors, particularly smoking, may have obscured the riskdue to HIV, so we decided to frequency match the se-lection on smoking status as well. Also, as HIV infec-tion is associated with certain lifestyles, which in turncould impact on the outcomes of our study, we electedto recruit our HIV uninfected cohort as well fromclinics that treated our patients living with HIV. Thosechoices imply that our results will not be generalizableto the general population, but we felt that they suitedour general aim to isolate the effect of HIV infection asmuch as possible.Power calculations were based on event rates from theFramingham cohort [29]. These rates may not apply tothe Canadian population, and may be lower now that ag-gressive treatment of cardiovascular risk factors is wide-spread. As such, we may lack power to detectdifferences in the occurrence of the primary endpoint.However, our power calculations were conservative, aim-ing to be able to detect HRs for our composite endpointof 1.72 to 1.90, which is lower than the 2.13 95%CI[1.69–2.63] we observed in our own population [59].Our study encompasses the entire research spectrum,from collection and analysis of epidemiological data onlifestyle, quality of life and medical co-morbidities toadvanced fundamental imaging and immunologicalresearch. We have access to detailed epidemiological data,and a biobank with stored cells and plasma. Our team ofexperts also spans the whole research continuum fromclinical to basic science, including community members,health practitioners, specialists in cardiology, immunology,virology, genetics, radiology, and internal medicine, with abalance between clinical and basic scientists. This studystructure will allow rapid and in-depth investigations intonovel immuno-metabolic pathways that lead to CVD inthe setting of HIV infection.Another important advantage of our cohort setting isthe universal health care system that is available to allparticipants of our study. This will greatly contribute toalleviate discrepancies in healthcare that would be dueto socioeconomic status. Central data collection andmanagement is another strength. Overall, flexibility ofthe study structure, with possible addition of furthersub-studies to the main cohort, is a major capacity-building aspect of this cohort.We believe the Canadian HIV and Aging Cohort Studywill be a powerful research tool to offer rapid responsesto emerging clinical questions for people living with HIVand their healthcare providers.Additional filesAdditional File 1: Study Outcomes definitions. (DOCX 34 kb)Additional File 2: Flow chart of tasks performed at each study visits.(DOCX 23 kb)Additional File 3: Blood tests to be drawn at each study visit. (DOCX 26 kb)Additional file 4: Detailed data collection form for the Canadian HIVand Aging Study. (PDF 830 kb)Additional File 5: Supplementary Statistical Analysis for sub studies.(DOCX 24 kb)AbbreviationsAIDS: Acquired Immunodeficiency Syndrome; CVD: Cardiovascular Disease;HIV: Human Immunodeficiency Virus; MetS: Metabolic Syndrome; MI: MyocardialInfarctionAcknowledgementsWe acknowledge the support of the Canadian’s Institutes of Health Research’s(CIHR) HIV Clinical Trials Network (CTN), for supporting the study in kind byproviding data management and hosting the central database, as well as forlogistical support. We acknowledge the immense work done by CIHR’s CTNCommunity Advisory Committee, who reviewed and gave feedback on thisprotocol.We would like to acknowledge all study staff, and all study participants, fortheir hard work and time devoted to this study.In addition, we acknowledge the contribution of the following people fortheir participation in design and conduct of the study:Jean-Claude Tardif, Jean-Louis Chiasson, Claude Fortin, Daniel Kaufmann,Gilles Soulez, Guy Cloutier, John Gill, Josée Côté, Julian Falutz, KennethRosenthal, Sophie Bernard, Bastien Lamontagne, Marc Leclerc, StéphanieMatte, Annie Chamberland, Sylla Mohamed.FundingThis study is funded by the Canadian Institutes of Health Research, through ateam grant initiative (grant number 284512). This study also received supportin kind (design, hosting and maintenance of central database) by theCanadian Institutes of Health Research HIV Clinical Trial Network (Studyidentification CTN 272).Availability of data and materialsThe datasets generated during the current study will be available from thecorresponding author on reasonable request.Authors’ contributionsMD has written this manuscript. All co-authors have revised and ap-proved the final manuscript. All authors participated in Study Designand protocol development, and are actively participating in conductingthe study.Ethics approval and consent to participateThis study protocol has been approved by the research ethics board of theCentre Hospitalier de l’Université de Montréal (CHUM) and by research ethicboards of all participating centers. Participants will sign distinct informedconsent forms to participate to the main cohort, the research biobank, andthe sub-studies.Consent for publicationNot applicable.Competing interestsMD has no competing interests to disclose. This project was funded by theCanadian Institutes of Health Research (Grant id: 284,512).Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 7 of 10Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Internal Medicine service, Centre de Recherche du CHUM, Montréal, QC H2J1T8, Canada. 2Department of Radiology, Centre hospitalier de l’Université deMontréal, Montréal, Canada. 3Clinique médicale urbaine du Quartier latin,Montreal, Canada. 4BC Centre for Excellence in HIV/AIDS, Vancouver, Canada.5Division of Infectious Diseases, University Health Network, Toronto, Canada.6Infectious Diseases Clinic, Regina Qu’Appelle Health Region, Regina, Canada.7Chronic viral infection service and Division of Hematology, McGill UniversityHealth Centre, Montreal, Canada. 8Maple Leaf Medical HIV ResearchCollaborative Inc., Toronto, Canada. 9The Ottawa Hospital Research Instituteand the University of Ottawa, Ottawa, Canada. 10Preventive and GenomicCardiology, McGill University Health Center and Research Institute, Montreal,Canada. 11Department of Epidemiology and Biostatistics, McGill University,Montreal, Canada. 12McGill University, Immunology service, Montreal GeneralHospital, Montreal, Canada. 13Centre de recherche du CHUM, Montreal,Canada. 14Research Institute of the McGill University Health Center, Divisionof Experimental Medicine, McGill University, Division of Clinical Immunology,McGill University health Center (MUHC), Chronic Viral Illness Service,Montreal, Canada.Received: 20 June 2017 Accepted: 17 August 2017References1. 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Top HIV Med. 2009;17(4):118–23.Name of co-author Declaration of competing interestCarl ChartrandLefebvreReceives Equipment support from Philips HealthcareCanada and Bayer, Research Collaboration: TeraRecon,Siemens HealthineersJean-Guy Baril Have been consultant or speaker in conferencessupported by: ViiV healthcare, Merk Frosst et Gilead.Is member of institution having received researchgrants from Glaxo Smith Kline, ViiV healthcare, MerkFrosst and Gilead.Sylvie Trottier No competing interestsBenoit Trottier No competing interestsMarianne Harris MH has received honoraria for consultancy and/orspeaking engagements from Gilead Sciences CanadaInc., Merck Canada Inc., ViiV Healthcare, andTheratechnologies Inc.Sharon Walmsley Dr. Walmsley has served on advisory boards, speakingengagements, meetings, symposiums, and clinicalstudies for ViiV Health Care, GSK, Merck, Janssen,Abbvie, BMS, and Gilead Sciences.Brian Conway, No competing interestsAlexander Wong No competing interestsRouty Jean-Pierre No competing interestsColin Kovacs No relationships/conditions/circumstancesthat present potential conflict of InterestPaul A.MacPhersonPM has served on advisory boards and receivedhonoraria for consultancy and/or speakingengagements from Gilead Sciences CanadaInc., Merck Canada Inc., ViiV Healthcare, andJanssen CanadaKenneth MarcMonteithNo competing interestsSamer Mansour No competing interestsGoergesThanassoulisNo competing interestsMichalAbrahamoviczNo competing interestsZhitong Zhu No competing interestsChristos Tsoukas, No competing interestsPetronela Ancuta, No competing interestsNicole Bernard, No competing interestsCécile L Tremblay Received research grant from Merck Frosst, Gilead,VIIV. 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BMC Infectious Diseases  (2017) 17:611 Page 9 of 10for coronary heart disease: case-control and prospective cohort analyses.Lancet. 2010;376(9750):1393–400.59. Durand M, Sheehy O, Baril JG, Lelorier J, Tremblay CL. Association betweenHIV infection, antiretroviral therapy and risk of acute myocardial infarction: acohort and nested case-control study using Quebec's public healthinsurance database (RAMQ). J Acquir Immune Defic Syndr. 2011;•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Durand et al. BMC Infectious Diseases  (2017) 17:611 Page 10 of 10


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