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CLINICAL SCIENCE

Multimorbidity, age-related comorbidities and mortality

association of activation, senescence and inflammation markers in HIV adults

Duffau, Pierrea,b,*; Ozanne, Alexandrac,*; Bonnet, Fabricec,d; Lazaro, Estibaliza,e; Cazanave, Charlesf; Blanco, Patricka,g; Rivière, Etiennee; Desclaux, Arnaudf; Hyernard, Carolinea; Gensous, Noemieb; Pellegrin, I.c,g,*; Wittkop, L.c,h,*

Author Information
doi: 10.1097/QAD.0000000000001875

Abstract

Introduction

Combination antiretroviral therapy (cART) has increased survival of HIV-infected patients [1]. However, the prevalence of age-related comorbidities remains higher than that of the general population, suggesting that individuals with HIV suffer from accelerated aging [2,3].

Aging and HIV infection have been associated with immunosenescence, represented by a gradual deterioration of the immune system marked especially by T-cell dysfunction, through a change of their activation status and their secretory profile towards pro-inflammatory molecules [4–10]. This persistent inflammatory state, defined as ‘inflammaging,’ has been linked to an increased risk of subsequent morbidity and mortality in HIV patients who experience premature aging, and has emerged as an important target for intervention in the modern treatment era [11–13]. Senescence may, therefore, play a role in the development of various comorbidities experienced even if little is known about what drives senescence in the context of HIV infection [5]. Previously, the association between markers representative of T-cell activation, senescence and inflammation with age-related comorbidities has been assessed [12,14–16], however, none of these studies considered potential additive or interactive effects neither the possible different immune phenotype according to different specific comorbidities.

We assessed the association of a combination of biomarkers related to immune-activation, senescence and inflammation summarized in two scores with the presence of multimorbidity, the risk of age-related comorbidities, and mortality over a 3-year follow-up period in cART-treated HIV patients in an exploratory analysis.

Materials and methods

Study population

Patients included in the ANRS CO3 Aquitaine Cohort, a hospital-based cohort with a prospective data collection of HIV-1 infected patients in France were consecutively enrolled in the CIADIS sub-study between October 2011 and May 2013 for cellular immune activation or senescence and biomarkers of inflammation measurement (inclusion) [17,18]. Only patients with an HIV RNA less than 40 copies/ml at inclusion, and with at least one follow-up visit after inclusion in the CIADIS sub-study, were included in the present study. The follow-up period covers the inclusion between October 2011 and May 2013, until December 2016.

Definition of age-related comorbidities

Age-related comorbidities [kidney disease, diabetes, dyslipidemia, cardiovascular events, hypertension, degenerative central nervous system (CNS) disorders and cancer] were defined by an active group of clinicians using International Classification of Diseases-10 (ICD-10) codes [17] and biological parameters if deemed necessary. In addition to ICD-10 codes, renal dysfunction was assessed using the estimated glomerular filtration rate (eGFR) as calculated by the simplified Modification of Diet in Renal Disease (MDRD) formula. Diabetes was defined based on use of insulin or hypoglycemic drugs, or two consecutive blood glucose results of at least 7 mmol/l. Dyslipidemia was defined by use of statins or fibrates, or pathological lipid levels. Cardiovascular events were defined based on history of bypass surgery, angioplasty or endarterectomy. Hypertension was defined based on two consecutive measurements of SBP of at least 140 mmHg or DBP of at least 90 mmHg (within 2 years before inclusion). CNS disorders and cancer were defined by ICD-10 codes.

Measurement of immune activation, senescence and inflammation markers

Human samples were obtained from the processing of biological samples through the Centre de Ressources Biologiques (CRB) Santé of Bordeaux - Bordeaux Biothèques Santé BB-0033-00094. The research protocol was conducted under French legal guidelines and fulfilled the requirements of the local institutional ethics committee. Blood samples were analyzed at the Bordeaux University Hospital by flow cytometry using a NAVIOS flow cytometer (Beckman-Coulter, France). The assessment of activation and senescence markers was performed as previously described [17], and 12 soluble markers were measured by ELISA and cytometric bead arrays at inclusion: D-dimer (coagulation), BAFF/BLYSS (B lymphocytes), sCD163, sCD14 (monocytes/macrophages), MPO (neutrophils), sCD40L (T lymphocytes and platelets), CRP, IL-18, IP-10/CXCL10, IL-6, TNFR1 (systemic inflammation) and CD54 (ICAM). Serum was stored at −80 °C until assayed (see Supplementary Methods A, https://links.lww.com/QAD/B282).

Statistical analysis

Determination of cellular-Chronic Immune Activation anD Senescence and soluble-Chronic Immune Activation anD Senescence-weighted scores

We performed a principal component analysis (PCA) as previously described [17,18]. We included eight cellular activation and senescence markers (percentages of activated, senescent, naive and terminally differentiated CD4+ and CD8+ T cells) and the 12 soluble markers. Development of the PCA allowed for the selection of two independent-weighted scores: the ‘cellular-CIADIS-weighted score’ [Principal Component 1 (PC1)] and the ‘soluble-CIADIS-weighted score’ (PC2; see Supplementary Figure 1, https://links.lww.com/QAD/B282). The two weighted CIADIS-scores were categorized by the interquartile range: low profile (≤Q1), medium profile (Q1−Q3) and high profile (>Q3).

In order to determine, which biomarker made the largest contribution to each weighted CIADIS-score, the relative contribution of each marker was calculated.

Main outcome variables

Multimorbidity at inclusion was defined as at least three comorbidities among those described above. For the longitudinal analysis, the main outcome variables were the time to the occurrence of a new age-related comorbidity and the time to all-cause death over a 3-year follow-up period. Patients were censored at their last follow-up date. For the primary outcome, patients who died were censored at their death date.

As it has been hypothesized in the published literature that different markers could be involved depending on the comorbidity of interest, we performed several supplementary analyses in which the main outcome variable ‘the presence of multimorbidity’ or ‘the time to the first novel age-related comorbidity’ was replaced separately by each of the above listed age-related comorbidities (see Supplementary Methods B, https://links.lww.com/QAD/B282).

Factors associated with multimorbidity at inclusion in the Chronic Immune Activation anD Senescence sub-study

Logistic regression models were used to study the association of the two weighted scores with multimorbidity at inclusion.

Our primary multivariable model included both the cellular-CIADIS and soluble-CIADIS scores (Model 1). In subsequent multivariable models (Models 2–5), cellular and soluble markers with a P value less than 0.05 in univariable analysis were used instead of the cellular- CIADIS and soluble-CIADIS score, respectively, as some markers could be considered a therapeutic target.

The percentages of CD8+DR T cells and levels of TNFR1 were strongly associated with multimorbidity in univariable analysis and had a higher relative contribution to the cellular-CIADIS and soluble-CIADIS score, respectively (see Supplementary Table 1, https://links.lww.com/QAD/B282).

All models were adjusted for identified confounding factors [age, sex, Centers for Disease Control and Prevention (CDC) classification and cumulative duration on cART], and defined as: Model 1 integrated both the cellular-CIADIS and the soluble-CIADIS-weighted score; Model 2: cellular-CIADIS-weighted score with TNFR1; Model 3: CD8+DR with soluble-CIADIS-weighted score and Model 4: CD8+DR with TNFR1. As previous studies have demonstrated that the CD4+/CD8+ ratio was associated with age-related comorbidities and could serve as a surrogate marker of immune activation, senescence and inflammation [19], we performed an additional multivariable model that included the CD4+/CD8+ ratio (Model 5).

Factors associated with the risk of a new age-related comorbidity or mortality within the 3-year follow-up

Cox proportional hazards models with delayed entry were used to determine factors associated with age-related comorbidities and all-cause mortality.

Concerning the association between biomarkers and the risk of a novel age-related comorbidity, we performed the same five adjusted models as described above but adjusted for age, sex, CDC classification, number of other age-related comorbidities at inclusion and CD4+ cell count at inclusion.

Regarding the association between biomarkers and mortality, as we found that the TNFR1 and IL-6 levels were strongly associated with mortality in univariable analyses, we performed multivariable analyses with these markers separately instead of the soluble CIADIS score. All models were adjusted for identified confounding factors (age, sex, CDC classification and cumulative duration on cART), and defined as: Model 1 integrated both the cellular-CIADIS and the soluble-CIADIS-weighted score; Model 2: cellular-CIADIS-weighted score with TNFR1; Model 3: cellular-CIADIS-weighted score with IL-6 and Model 4: the CD4+/CD8+ ratio. In order to accurately compare our results with scores from previous studies on mortality prediction, we performed additional multivariable models with the Veterans Aging Cohort Study (VACS) index (Model 5) and the immune risk profile (IRP) score (Model 6) in place of the CIADIS scores. The reason to use these additional models is that the VACS index was specifically designed to indicate increasing risk of all-cause mortality in HIV patients with increasing score [20], and the IRP score represents a cluster of factors that were strongly predictive of earlier mortality among an aged Swedish cohort, as well as in younger individuals [21].

Analyses were performed using SAS 9.4 (SAS Institute, Inc, Cary, North Carolina, USA) and the free software R 3.1.2 using the R package ‘FactoMineR.’

Results

Patient characteristics

Of 3500 patients followed in the ANRS CO3 Aquitaine Cohort, 1010 patients were included in the CIADIS study, of whom 828 (82%) had available cellular and soluble immune biomarkers and an HIV RNA less than 40 copies/ml (see Supplementary Figure 2, https://links.lww.com/QAD/B282).

Characteristics at inclusion are shown in Table 1. The median age was 51 years [interquartile range (IQR) 45–57] and 75% were men. Patients received cART for a median of 13 years (IQR: 8–16 years) and the median duration of HIV viral suppression was 5 years (IQR: 2–9). The median CD4+ T-cell count was 579 cells/μl (IQR: 431–760 cells/μl) and 23% of patients were CDC stage C. Hepatitis B (HBV) or hepatitis C viral (HCV) co-infections were reported in 23 and 7% of patients, respectively. A positive CMV serology was found in 89% of patients. Hypertension and dyslipidemia were the most frequent comorbidities representing 45 and 43% of patients, respectively. Diabetes, cardiovascular disease, cancers and neurocognitive disorders accounted for 13, 10, 14 and 3% of patients, respectively.

T1-11
Table 1:
Relationship of baseline characteristics to comorbidities.

Patients with multimorbidity were older, more frequently men, presented more often with a CDC stage C, a longer duration of cART, and a significantly longer CD4+ T-cell count nadir measurement compared with patients with less than three comorbidities at inclusion (P < 0.01). There was no difference in the CD4+ T-cell count, HBV or HCV co-infections, or positive CMV serology among patients having less than three or at least three comorbidities.

Association between the Chronic Immune Activation anD Senescence-weighted scores and multimorbidity at inclusion

The cellular-CIADIS and soluble-CIADIS-weighted scores were all significantly higher in patients with multimorbidity compared with patients having less than three age-related comorbidities (P < 0.01).

Percentages of CD4+ and CD8+ T-cell expressing activation markers (HLA-DR, P < 0.01) and senescence markers (CD57+CD28, P < 0.01; TEMRA, P < 0.01) were significantly higher in patients having multimorbidity compared with patients having less than three comorbidities. Percentages of naive CD4+ and CD8+ T cells were significantly lower in patients with multimorbidity compared with patients having less than three comorbidities (P < 0.01; Table 2).

T2-11
Table 2:
Relationship of baseline immune scores and immune markers to comorbidities.

Regarding levels of soluble markers, D-Dimer (P < 0.01), sCD163 (P < 0.01), sCD14 (P = 0.04), TNFR1 (P < 0.01) and CRP (P = 0.02) were significantly higher in patients with multimorbidity compared with patients having less than three comorbidities. No difference was found for levels of BAFF/BLYSS, MPO, sCD54, IL-18, IP10 or IL-6. The CD4+/CD8+ ratio was not different between patients with multimorbidity and patients having less than three comorbidities (Table 2).

In multivariable analysis (Fig. 1), the cellular-CIADIS-weighted score remained significantly associated with multimorbidity, independent of the soluble-CIADIS-weighted score (OR 1.3; 95% CI 1.0–1.6; P < 0.02). The soluble-CIADIS-weighted score and CD4+/CD8+ ratio were not associated with multimorbidity. TNFR1 remained significantly associated with multimorbidity, independent of the cellular-CIADIS-weighted score (OR 1.3; 95% CI 1.1–1.6; P < 0.01) and also after adjustment for percentages of CD8+DR T cells (OR = 1.3; 95% CI 1.1–1.6; P < 0.01; Fig. 1). In contrast to the cellular CIADIS score, percentages of CD8+DR T cells, adjusted on the soluble CIADIS-weighted score or on TNFR1, were not found to be independently associated with multimorbidity (see univariable analysis in Supplementary Table 2, https://links.lww.com/QAD/B282).

F1-11
Fig. 1:
Multivariable logistic regression models* of the association between CIADIS markers, CIADIS scores and multimorbidity (N = 828).A cellular-CIADIS-weighted score above 0 represents an immune phenotype with higher T-cell activation, and expression of terminal differentiation and senescence markers, whereas a negative cellular-CIADIS-weighted score represents a less activated and less senescent profile. All biomarkers were studied by univariable analysis but only biomarkers with a P-value less than 0.05 in univariable analyses are included in multivariable models. P-value in bold are significantly lower than 0.05. OR, odds ratio; TNFR1, tumor necrosis factor receptor-1; 95% CI: 95% confidence intervals. cART, combination antiretroviral therapy; CDC, Center for Disease Control; CIADIS, Chronic Immune Activation anD Senescence. *All models were adjusted for age, sex, CDC classification and cumulative duration on cART.

In supplementary analyses, each of the above-listed age-related comorbidities was studied separately (see Supplementary Figure 3, https://links.lww.com/QAD/B282) demonstrating that the soluble-CIADIS-weighted score and TNFR1, as well as CD4+ T-cell activation and senescence markers (OR = 1.3; 95% CI 1.1–1.7; P < 0.01 and OR = 1.2; 95% CI 1.0–1.5; P = 0.03, respectively) were significantly associated with the presence of cardiovascular disease in adjusted analyses. CD4+ T-cell activation was significantly associated with dyslipidemias, independent of the soluble-CIADIS-weighted score and also independent of TNFR1. Higher TNFR1 levels remained significantly associated with the presence of diabetes in adjusted analyses. None of the other cellular or soluble markers, nor the CD4+/CD8+ ratio was associated with any age-related comorbidity, considered separately.

Effect of the Chronic Immune Activation anD Senescence-weighted scores on the occurrence of a new age-related comorbidity at 3-year follow-up

The median follow-up was 3 years (IQR 1–4). The incidence of a new age-related comorbidity was 17 per 100 person-years (95% CI [15–18]).

In adjusted analyses, a higher cellular-CIADIS-weighted score remained significantly associated with a higher risk of an age-related comorbidity, independent of the soluble-CIADIS-weighted score (hazard ratio = 2.2; 95% CI 1.6–3.1; P < 0.01), sex, CDC classification, number of age-related comorbidities and CD4+ T-cell count at inclusion. Similar results were found after adjustment for TNFR1 levels (hazard ratio = 2.3; 95% CI 1.6–3.2; P < 0.01). A higher percentage of CD8+DR T cells were significantly associated with a higher risk for a new age-related comorbidity, independent of the soluble-CIADIS-weighted score (hazard ratio = 2.0; 95% CI 1.4–2.7; P < 0.01) and also in a model adjusted for TNFR1 levels (hazard ratio = 2.1; 95% CI 1.5–2.8; P < 0.01; Fig. 2). A lower CD4+/CD8+ ratio (hazard ratio = 0.6; 95% CI 0.4–0.7; P < 0.01) was associated with a higher risk for a new age-related comorbidity (Fig. 2). Neither the soluble-CIADIS-weighted score nor TNFR1 levels were associated with the occurrence of a new comorbidity (see univariable analysis in Supplementary Table 3, https://links.lww.com/QAD/B282).

F2-11
Fig. 2:
Hazard ratios with 95% confidence intervals estimated by the Cox proportional hazards models* with delayed entry for the association between CIADIS scores and the risk of the first age-related comorbidities over a 3-year follow-up (N = 828).The two weighted CIADIS scores, TNFR1 and CD8+DR were used as categorical variables in the models. Each variable was categorized by their interquartile range: low profile (≤Q1), medium profile (Q1–Q3) and high profile (>Q3). For each categorical variable in these analyses, the reference category is the low category. All biomarkers were studied by univariable analysis, however, only biomarkers with a P value less than 0.05 in univariable analyses are included in multivariable models. P values in bold are significantly lower than 0.05. HR, hazard ratio; TNFR1: tumor necrosis factor receptor-1; 95% CI, 95% confidence intervals. *All models were adjusted for age, sex, CDC classification, number of other age-related comorbidities at inclusion, and CD4+ cell count at inclusion.

When replacing the main outcome variable ‘occurrence of new age-related comorbidity’ by separately analyzing each of the above listed age-related comorbidities, none of the cellular and soluble biomarkers was associated with a higher risk for diabetes, degenerative disorders, cancer and hypertension (see Supplementary Figure 4, https://links.lww.com/QAD/B282). Higher CD4+ DR percentages and higher cellular-CIADIS-weighted score were significantly associated with cardiovascular disease (P = 0.02) or dyslipidemia (P < 0.01), respectively. A lower CD4+/CD8+ ratio was associated with a higher risk for hypertension and dyslipidemia.

Effect of the Chronic Immune Activation anD Senescence-weighted scores on all-cause mortality

There were 24 deaths during follow-up. The mortality rate was 0.8 per 100 person-years (95% CI 0.5–1.2).

In univariable analysis (see Supplementary Table 4, https://links.lww.com/QAD/B282), higher soluble CIADIS-weighted score as well as higher TNFR1 and BAFF/BLYSS levels were significantly associated with a higher risk of all-cause mortality (P < 0.01). Neither the cellular CIADIS-weighted score nor any of the T-cell subsets studied, including CD8+DR, were associated with a higher risk of all-cause mortality. In adjusted analysis, higher levels of TNFR1 and BAFF/BLYSS were significantly associated with a higher risk of all-cause mortality (hazard ratio = 1.6; 95% CI 1.2–2.1; P < 0.01; Fig. 3). The cellular-CIADIS-weighted score, soluble-CIADIS-weighted score, IL-6 levels, VACS index, IRP score and CD4+/CD8+ ratio were not found to be associated with the occurrence of death.

F3-11
Fig. 3:
Hazard ratios with 95% confidence intervals estimated by the Cox proportional hazards models* with delayed entry for the association between Chronic Immune Activation anD Senescence scores and mortality over a 3-year follow-up (N = 828).The two weighted CIADIS-scores were used as categorical variables in the models. Each variable was categorized by their interquartile range: low profile (≤Q1), medium profile (Q1–Q3) and high profile (>Q3). For each categorical variable in these analyses, the reference category is the low category. TNFR1 and IL-6 were used as quantitative variables. All biomarkers were studied by univariable analysis, however, only biomarkers with a P value less than 0.05 in univariable analyses are included in multivariable models. P values in bold are significantly lower than 0.05. CDC, Center for Disease Control; CIADIS, Chronic Immune Activation anD Senescence; HR, hazard ratio; TNFR1, tumor necrosis factor receptor-1; IL-6, interleukin-6; 95% CI, 95% confidence intervals. *All models were adjusted for age, sex, CDC classification, number of other age-related comorbidities at inclusion, and CD4+ cell count at inclusion.

Discussion

In an exhaustive assessment of immune activation and inflammation markers in 828 successfully treated patients, we found a significant association between the cellular-CIADIS-weighted score and multimorbidity as well as the risk of a new age-related comorbidity. Interestingly, CD8+ T-cell activation was also found to be strongly associated with the occurrence of a new comorbidity. Our results are in accordance with the work published by Erlandson et al.[22], where higher CD8+ T-cell activation was found to be associated with functional impairment, but our work partly contrasts published work by Tenorio et al. [12], where T-cell activation was not related to a higher risk of age-defining events. The difference in findings might be explained by differences in patient characteristics, especially regarding the duration since diagnosis and the duration of viral load suppression, both significantly longer as reported in the current study.

Several studies have demonstrated that persistent innate immune activation could predict age-related morbidities. The first such study was an analysis of IL-6 and D-dimer data from the SMART trial, which linked inflammation and coagulation to subsequent comorbidities in HIV infection [23]. Since then, many studies have been published demonstrating heterogeneous results concerning the association between inflammation markers and age-related comorbidities. Some studies presented data with small patient sample sizes, younger patients, different timing of measurements, or considered only a few comorbidities with limited number of biomarkers [12,24–26]. We studied the soluble-CIADIS-weighted score composed of 12 soluble biomarkers, of which five were included in the SASP phenotype. We found that the soluble-CIADIS-weighted score was not associated with multimorbidity, nor with the occurrence of a new age-related comorbidity. Interestingly, we showed a singularity for TNFR1, which was found to be associated with higher risk of multimorbidity and mortality.

Most previous studies showed that patients on cART often fail to normalize the CD4+/CD8+ ratio. In our study, we found that the CD4+/CD8+ ratio predicts the risk of age-related comorbiditiy but was not associated with multimorbidity in the cross-sectional study. Hema et al.[27] recently published findings from the APROCO/COPILOTE cohort showing that the CD4+/CD8+ ratio was associated with age-related cancers but did not add supplementary prognostic information when added to the CD4+ cell count. Of note, in comparison to the CIADIS cohort, the median age of the APROCO/COPILOTE cohort was much older, the median CD4+/CD8+ ratio was lower, and all patients were not virologically suppressed. Interestingly, Menozzi et al.[28] showed that the CD4+/CD8+ ratio was not associated with multimorbidity in a study population comparable with the CIADIS cohort.

Among the VACS index, IRP, CD4+/CD8+ ratio, CIADIS-weighted scores, CD8+DR expression, and TNFR1 levels, only increased TNFR1 levels were significantly associated with mortality. These data underscore that the VACS index as well as IRP are perhaps not suitable in well controlled and suppressed patients.

It is important to identify new biomarkers to implement in clinical practice in order to identify patients at increased risk of developing comorbidities or death. Our data suggest that TNFR1 might be one of these factors. At all stages of HIV infection, increased amounts of TNF-alpha and TNF-alpha receptors can be detected in the plasma [29,30]. In controlled HIV-infected patients, increased levels of TNFR1 have been associated with most age-related comorbidities, frailty, and mortality. Therefore, tumor necrosis factor (TNF)-blocking agents and/or TNF-inhibitor therapy could be useful in the context of HIV. A considerable advantage in favor of using anti-TNF therapy in HIV-1 infected patients is its safety, as no increase in the mortality rate has been observed [31]. Recently, it has been shown that the TNF-alpha blocker, adalimumab, could induce epigenetic modifications in cells undergoing senescence in a model of breast cancer, thus contributing to the attenuation of the Senescence-Associated Secretory Phenotype (SASP)-promoting effects [32].

There are several limitations in our study. Regarding the risk of each specific comorbidity, the low number of incident cases for some age-related comorbidities and death could have limited the power of the longitudinal analysis. These results need to be confirmed by further analyses. The construction of both CIADIS scores was not specifically oriented for a specific comorbidity and was an explorative analysis for considering ‘immune and inflammation’ profiles. Considering the large number of biomarkers, many statistical tests and models were performed. This may have increased the number of significant associations. Our study is exploratory in nature and therefore, P values need to be interpreted with caution, given the high number of models and statistical tests. Finally, as the majority of patients were men in our study, the results may differ if the same analyses would be performed in a large cohort of women.

Nonetheless, strengths of our study included the study design (consecutive enrollment of study participants in the ANRS CO13 Aquitaine cohort), prospective standardized data collection and the consideration of activation and inflammation markers at the same time (allowing for the study of specific patient profiles).

Owing to the complexity of the aging process and its multifactorial components, it could be difficult to identify pertinent ‘universal’ biomarkers. Future studies could be expanded to include more focused biomarkers linked to different mechanisms, tissue dysfunctions and/or comorbidities. Current strategies to limit immune activation and inflammation in order to prevent the occurrence of age-related comorbidities are only marginally successful [33–36]. As HIV eradication is currently impossible, intensive studies are needed to determine if and how immune activation can be silenced in HIV infection.

Acknowledgements

The ANRS CO3 Aquitaine cohort study group: Coordination: F. Bonnet.

Scientific committee: F. Bonnet, S. Bouchet, D. Breilh, M. Dupon, H. Fleury, V. Gaborieau, D. Lacoste, D. Malvy, P. Mercié, P. Morlat, D. Neau, I. Pellegrin, JL. Pellegrin, S. Tchamgoué, L. Wittkop. Epidemiology and Methodology: S. Lawson-Ayayi, L. Richert, R. Thiébaut, L. Wittkop. Infectious Diseases and Internal Medicine: K. André, F. Bonnet, N. Bernard, N. Berthol, P. Biscay, O. Caubet, L. Caunègre, C. Cazanave, I. Chossat, B. Cougoul, C. Courtault, FA. Dauchy, S. De Witte, D. Dondia, M. Dupon, P. Duffau, H. Dutronc, S. Farbos, I. Faure, H. Ferrand, V. Gaborieau, Y. Gerard, C. Greib, M. Hessamfar-Joseph, Y. Imbert, D. Lacoste, P. Lataste, E. Lazaro, D. Malvy, J. Marie, C. Martell, M. Mechain, P. Mercié, E. Monlun, P. Morlat, D. Neau, A. Ochoa, F. Paccalin, JL. Pellegrin, MC. Pertusa, T. Pistone, I. Raymond, MC. Receveur, E. Riebero, P. Rispal, N. Rouanes, A. Saunier, L. Sorin, S. Tchamgoué, C. Valette, MA. Vandenhende, MO. Vareil, JF. Viallard, H. Wille, G. Wirth.

Immunology: I. Pellegrin, P. Blanco. Virology: H. Fleury, ME. Lafon, C. Tumiotto, P. Trimoulet, P. Bellecave. Pharmacology: S. Bouchet, D. Breilh, F. Haramburu, G. Miremont-Salamé. Data collection, Project Management and Statistical Analyses: MJ. Blaizeau, I. Crespel, M. Decoin, S. Delveaux, F. Diarra, C. D’Ivernois, C. Hanappier, S. Lawson-Ayayi, O. Leleux, F. Le Marec, E. Lenaud, A. Perrier, A. Pougetoux, B. Uwamaliya-Nziyumvira.

IT department and eCRF development: V. Conte, O. Leleux, V. Sapparrart.

The authors acknowledge the Centre de Resources Biologiques-Bordeaux Biothèques Santé (CRB-BBS) biobank for managing patient samples and Celine Cognet in the Translational Research Analytic Platform (PARS) for excellent technical assistance. The authors also thank JetPub Scientific Communications for English language editing and helpful comments.

Author contributions: L.W. and I.P. designed the study. A.O., P.D., L.W. and I.P. drafted the manuscript. Additionally, A.O. and L.W. were responsible for performing all analyses, act as guarantor for the analyses and have full access to the data set. All authors participated in the discussion and interpretation of the findings, and were involved in the preparation and review of the final manuscript for submission.

Funding/support: This study was supported by a grant from the Agence Nationale de Recherches sur le Sida et Les Hépatites Virales (ANRS, Action Coordonnée no.7, Cohortes). The CIADIS sub-study was supported by ViiV, Janssen, MSD and from Inserm Aviesan.

Conflicts of interest

There are no conflicts of interest.

Parts of this work have been presented at the 20th International Workshop on HIV and Hepatitis Observational Databases (IWHOD), 7–9 April 2016, Budapest, Hungary.

References

1. Antiretroviral Therapy Cohort CollaborationSurvival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. Lancet HIV 2017; 4:e349–e356.
2. Guaraldi G, Orlando G, Zona S, Menozzi M, Carli F, Garlassi E, et al. Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis 2011; 53:1120–1126.
3. Schouten J, Wit FW, Stolte IG, Kootstra NA, van der Valk M, Geerlings SE, et al. Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV cohort study. Clin Infect Dis 2014; 59:1787–1797.
4. van Deursen JM. The role of senescent cells in ageing. Nature 2014; 509:439–446.
5. Cohen J, Torres C. HIV-associated cellular senescence: a contributor to accelerated aging. Ageing Res Rev 2017; 36:117–124.
6. Baar MP, Brandt RMC, Putavet DA, Klein JDD, Derks KWJ, Bourgeois BRM, et al. Targeted apoptosis of senescent cells restores tissue homeostasis in response to chemotoxicity and aging. Cell 2017; 169:132–147. e16.
7. de Keizer PL. The fountain of youth by targeting senescent cells?. Trends Mol Med 2017; 23:6–17.
8. Zanni F, Vescovini R, Biasini C, Fagnoni F, Zanlari L, Telera A, et al. Marked increase with age of type 1 cytokines within memory and effector/cytotoxic CD8+ T cells in humans: a contribution to understand the relationship between inflammation and immunosenescence. Exp Gerontol 2003; 38:981–987.
9. Hunt PW, Martin JN, Sinclair E, Bredt B, Hagos E, Lampiris H, et al. T cell activation is associated with lower CD4+ T cell gains in human immunodeficiency virus-infected patients with sustained viral suppression during antiretroviral therapy. J Infect Dis 2003; 187:1534–1543.
10. Appay V, Kelleher AD. Immune activation and immune aging in HIV infection. Curr Opin HIV AIDS 2016; 11:242–249.
11. Deeks SG, Tracy R, Douek DC. Systemic effects of inflammation on health during chronic HIV infection. Immunity 2013; 39:633–645.
12. Tenorio AR, Zheng Y, Bosch RJ, Krishnan S, Rodriguez B, Hunt PW, et al. Soluble markers of inflammation and coagulation but not T-cell activation predict non-AIDS-defining morbid events during suppressive antiretroviral treatment. J Infect Dis 2014; 210:1248–1259.
13. Bandera A, Colella E, Rizzardini G, Gori A, Clerici M. Strategies to limit immune-activation in HIV patients. Expert Rev Anti Infect Ther 2017; 15:43–54.
14. Justice AC, McGinnis KA, Skanderson M, Chang CC, Gibert CL, Goetz MB, et al. VACS Project TeamTowards a combined prognostic index for survival in HIV infection: the role of ‘non-HIV’ biomarkers. HIV Med 2010; 11:143–151.
15. Lang S, Mary-Krause M, Simon A, Partisani M, Gilquin J, Cotte L, et al. HIV replication and immune status are independent predictors of the risk of myocardial infarction in HIV-infected individuals. Clin Infect Dis 2012; 55:600–607.
16. Papagno L, Spina CA, Marchant A, Salio M, Rufer N, Little S, et al. Immune activation and CD8+ T-cell differentiation towards senescence in HIV-1 infection. PLoS Biol 2004; 2:E20.
17. Duffau P, Wittkop L, Lazaro E, le Marec F, Cognet C, Blanco P, et al. Association of immune-activation and senescence markers with non-AIDS-defining comorbidities in HIV-suppressed patients. AIDS 2015; 29:2099–2108.
18. Ozanne A, Duffau P, Dauchy F-A, Rigothier C, Terrien C, Lazaro E, et al. CIADIS sub-study in the ANRS CO3 Aquitaine cohort study groupActivation, senescence and inflammation markers in HIV patients: association with renal function. AIDS 2017; 31:1119–1128.
19. Hunt PW, Lee SA, Siedner MJ. Immunologic biomarkers, morbidity, and mortality in treated HIV infection. J Infect Dis 2016; 214 (suppl 2):S44–S50.
20. Justice AC, Modur SP, Tate JP, Althoff KN, Jacobson LP, Gebo KA, et al. NA-ACCORD and VACS Project TeamsPredictive accuracy of the Veterans Aging Cohort Study index for mortality with HIV infection: a North American cross cohort analysis. J Acquir Immune Defic Syndr 2013; 62:149–163.
21. Ferguson FG, Wikby A, Maxson P, Olsson J, Johansson B. Immune parameters in a longitudinal study of a very old population of Swedish people: a comparison between survivors and nonsurvivors. J Gerontol A Biol Sci Med Sci 1995; 50:B378–B382.
22. Erlandson KM, Allshouse AA, Jankowski CM, Lee EJ, Rufner KM, Palmer BE, et al. Association of functional impairment with inflammation and immune activation in HIV type 1-infected adults receiving effective antiretroviral therapy. J Infect Dis 2013; 208:249–259.
23. Kuller LH, Tracy R, Belloso W, De Wit S, Drummond F, Lane HC, et al. INSIGHT SMART Study GroupInflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 2008; 5:e203.
24. Freiberg MS, Bebu I, Tracy R, So-Armah K, Okulicz J, Ganesan A, et al. Infectious Disease Clinical Research Program HIV Working GroupD-dimer levels before HIV seroconversion remain elevated even after viral suppression and are associated with an increased risk of non-AIDS events. PloS One 2016; 11:e0152588.
25. Borges ÁH, Silverberg MJ, Wentworth D, Grulich AE, Fätkenheuer G, Mitsuyasu R, et al. INSIGHT SMART; ESPRIT; SILCAAT Study GroupsPredicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers. AIDS Lond Engl 2013; 27:1433–1441.
26. Duprez DA, Neuhaus J, Kuller LH, Tracy R, Belloso W, De Wit S, et al. INSIGHT SMART Study GroupInflammation, coagulation and cardiovascular disease in HIV-infected individuals. PloS One 2012; 7:e44454.
27. Hema MN, Ferry T, Dupon M, Cuzin L, Verdon R, Thiébaut R, et al. ANRS CO 8 (APROCO/COPILOTE) study groupLow CD4/CD8 ratio is associated with non aids-defining cancers in patients on antiretroviral therapy: ANRS CO8 (Aproco/Copilote) Prospective Cohort Study. PloS One 2016; 11:e0161594.
28. Menozzi M, Zona S, Santoro A, Carli F, Stentarelli C, Mussini C, Guaraldi G, et al. CD4/CD8 ratio is not predictive of multimorbidity prevalence in HIV-infected patients but identify patients with higher CVD risk. J Int AIDS Soc 2014; 17:19709.
29. Sereti I, Krebs SJ, Phanuphak N, Fletcher JL, Slike B, Pinyakorn S, et al. RV254/SEARCH 010, RV304/SEARCH 013 and SEARCH 011 protocol teamsPersistent, albeit reduced, chronic inflammation in persons starting antiretroviral therapy in acute HIV infection. Clin Infect Dis 2017; 64:124–131.
30. Castillo-Mancilla JR, Brown TT, Erlandson KM, Erlandson KM, Palella FJ Jr, Gardner EM, et al. Suboptimal adherence to combination antiretroviral therapy is associated with higher levels of inflammation despite HIV suppression. Clin Infect Dis 2016; 63:1661–1667.
31. Ting PT, Koo JY. Use of etanercept in human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) patients. Int J Dermatol 2006; 45:689–692.
32. Prattichizzo F, Giuliani A, Recchioni R, Bonafè M, Marcheselli F, De Carolis S, et al. Anti-TNF-α treatment modulates SASP and SASP-related microRNAs in endothelial cells and in circulating angiogenic cells. Oncotarget 2016; 7:11945–11958.
33. Calza L, Vanino E, Salvadori C, Manfredi R, Colangeli V, Cascavilla A, et al. Tenofovir/emtricitabine/efavirenz plus rosuvastatin decrease serum levels of inflammatory markers more than antiretroviral drugs alone in antiretroviral therapy-naive HIV-infected patients. HIV Clin Trials 2014; 15:1–13.
34. O’Brien MP, Hunt PW, Kitch DW, Klingman K, Stein JH, Funderburg NT, et al. A Randomized placebo controlled trial of aspirin effects on immune activation in chronically human immunodeficiency virus-infected adults on virologically suppressive antiretroviral therapy. Open Forum Infect Dis 2017; 4:ofw278.
35. Piconi S, Parisotto S, Rizzardini G, Passerini S, Terzi R, Argenteri B, et al. Hydroxychloroquine drastically reduces immune activation in HIV-infected, antiretroviral therapy-treated immunologic nonresponders. Blood 2011; 118:3263–3272.
36. Routy JP, Angel JB, Patel M, Kanagaratham C, Radzioch D, Kema I, et al. Assessment of chloroquine as a modulator of immune activation to improve CD4 recovery in immune nonresponding HIV-infected patients receiving antiretroviral therapy. HIV Med 2015; 16:48–56.

* Pierre Duffau, Alexandra Ozanne, I. Pellegrin and L. Wittkop contributed equally to the writing of this article.

Keywords:

age-related comorbidities; antiretroviral therapy; cellular markers; chronic immune activation; HIV-infection; inflammation; senescence; soluble markers

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