Impact of CD4 and CD8 dynamics and viral rebounds on loss of virological control in HIV controllers

Objective: HIV controllers (HICs) spontaneously maintain HIV viral replication at low level without antiretroviral therapy (ART), a small number of whom will eventually lose this ability to control HIV viremia. The objective was to identify factors associated with loss of virological control. Methods: HICs were identified in COHERE on the basis of \ue2\u89\ua55 consecutive viral loads (VL) \ue2\u89\ua4500 copies/mL over \ue2\u89\ua51 year whilst ART-naive, with the last VL \ue2\u89\ua4500 copies/mL measured \ue2\u89\ua55 years after HIV diagnosis. Loss of virological control was defined as 2 consecutive VL >2000 copies/mL. Duration of HIV control was described using cumulative incidence method, considering loss of virological control, ART initiation and death during virological control as competing outcomes. Factors associated with loss of virological control were identified using Cox models. CD4 and CD8 dynamics were described using mixed-effect linear models. Results: We identified 1067 HICs; 86 lost virological control, 293 initiated ART, and 13 died during virological control. Six years after confirmation of HIC status, the probability of losing virological control, initiating ART and dying were 13%, 37%, and 2%. Current lower CD4/CD8 ratio and a history of transient viral rebounds were associated with an increased risk of losing virological control. CD4 declined and CD8 increased before loss of virological control, and before viral rebounds. Discussion: Expansion of CD8 and decline of CD4 during HIV control may result from repeated low-level viremia. Our findings suggest that in addition to superinfection, other mechanisms, such as low grade viral replication, can lead to loss of virological control in HICs

CD4 cell count response to first-line combination ART in HIV-2+ patients compared with HIV-1+ patients: A multinational, multicohort European study

Background: CD4 cell recovery following first-line combination ART (cART) is poorer in HIV-2+ than in HIV-1+ patients. Only large comparisons may allow adjustments for demographic and pretreatment plasma viral load (pVL). Methods: ART-naive HIV+ adults from two European multicohort collaborations, COHERE (HIV-1 alone) and ACHIeV2e (HIV-2 alone), were included, if they started first-line cART (without NNRTIs or fusion inhibitors) between 1997 and 2011. Patients without at least one CD4 cell count before start of cART, without a pretreatment pVL and with missing a priori-defined covariables were excluded. Evolution of CD4 cell count was studied using adjusted linear mixed models. Results: We included 185 HIV-2+ and 30321 HIV-1+ patients with median age of 46 years (IQR 36-52) and 37 years (IQR 31-44), respectively. Median observed pretreatment CD4 cell counts/mm3 were 203 (95% CI 100-290) in HIV-2+ patients and 223 (95% CI 100-353) in HIV-1+ patients. Mean observed CD4 cell count changes from start of cART to 12months were +105 (95% CI 77-134) in HIV-2+ patients and +202 (95% CI 199-205) in HIV-1+ patients, an observed difference of 97 cells/mm3in 1 year. In adjusted analysis, the mean CD4 cell increase was overall 25 CD4 cells/mm3/year lower (95% CI 5-44; P=0.0127) in HIV-2+ patients compared with HIV-1+ patients. Conclusions: A poorer CD4 cell increase during first-line cART was observed in HIV-2+patients, even after adjusting for pretreatment pVL and other potential confounders. Our results underline the need to identify more potent therapeutic regimens or strategies against HIV-2

CD4/CD8 Ratio and the Risk of Kaposi Sarcoma or Non-Hodgkin Lymphoma in the Context of Efficiently Treated Human Immunodeficiency Virus (HIV) Infection: A Collaborative Analysis of 20 European Cohort Studies

International audienceAbstract Background A persistently low CD4/CD8 ratio has been reported to inversely correlate with the risk of non-AIDS defining cancer in people living with human immunodeficiency virus (HIV; PLWH) efficiently treated by combination antiretroviral therapy (cART). We evaluated the impact of the CD4/CD8 ratio on the risk of Kaposi sarcoma (KS) or non-Hodgkin lymphoma (NHL), still among the most frequent cancers in treated PLWH. Methods PLWH from the Collaboration of Observational HIV Epidemiological Research Europe (COHERE) were included if they achieved virological control (viral load ≤ 500 copies/mL) within 9 months following cART and without previous KS/LNH diagnosis. Cox models were used to identify factors associated with KS or NHL risk, in all participants and those with CD4 ≥ 500/mm3 at virological control. We analyzed the CD4/CD8 ratio, CD4 count and CD8 count as time-dependent variables, using spline transformations. Results We included 56 708 PLWH, enrolled between 2000 and 2014. At virological control, the median (interquartile range [IQR]) CD4 count, CD8 count, and CD4/CD8 ratio were 414 (296–552)/mm3, 936 (670–1304)/mm3, and 0.43 (0.28–0.65), respectively. Overall, 221 KS and 187 NHL were diagnosed 9 (2–37) and 18 (7–42) months after virological control. Low CD4/CD8 ratios were associated with KS risk (hazard ratio [HR] = 2.02 [95% confidence interval {CI } = 1.23–3.31]) when comparing CD4/CD8 = 0.3 to CD4/CD8 = 1) but not with NHL risk. High CD8 counts were associated with higher NHL risk (HR = 3.14 [95% CI = 1.58–6.22]) when comparing CD8 = 3000/mm3 to CD8 = 1000/mm3). Similar results with increased associations were found in PLWH with CD4 ≥ 500/mm3 at virological control (HR = 3.27 [95% CI = 1.60–6.56] for KS; HR = 5.28 [95% CI = 2.17–12.83] for NHL). Conclusions Low CD4/CD8 ratios and high CD8 counts despite effective cART were associated with increased KS/NHL risks respectively, especially when CD4 ≥ 500/mm3

Mortality in migrants living with HIV in western Europe (1997-2013): A collaborative cohort study

Background: Many migrants face adverse socioeconomic conditions and barriers to health services that can impair timely HIV diagnosis and access to life-saving treatments. We aimed to assess the differences in overall mortality by geographical origin in HIV-positive men and women using data from COHERE, a large European collaboration of HIV cohorts from 1997 to 2013. Methods: In this observational cohort study, we included HIV-positive, antiretroviral-naive people accessing care in western Europe from COHERE. Individuals were eligible if enrolled in a cohort that collected information on geographical origin or ethnic origin from Jan 1, 1997, to March 19, 2013, aged 18-75 years, they had available information about sex, they were not infected perinatally or after the receipt of clotting factor concentrates, and were naive to combination antiretroviral therapy at cohort entry. Migrants' origins were grouped into seven regions: western Europe and similar countries (Australia, Canada, New Zealand, and the USA); eastern Europe; North Africa and the Middle East; sub-Saharan Africa; Latin America; the Caribbean; and Asia and the rest of Oceania (excluding Australia and New Zealand). Crude and adjusted mortality rate ratios were calculated by use of Poisson regression stratified by sex, comparing each group with the native population. Multiple imputation with chained equations was used to account for missing values. Findings: Between Oct 25, 1979, and March 19, 2013, we recruited 279 659 individuals to the COHERE collaboration in EuroCoord. Of these 123 344 men and 45 877 women met the inclusion criteria. Our data suggested effect modification by transmission route (pinteraction=0·12 for men; pinteraction=0·002 for women). No significant difference in mortality was identified by geographical origin in men who have sex with men. In heterosexual populations, most migrant men had mortality lower than or equal to that of native men, whereas no group of migrant women had mortality lower than that in native women. High mortality was identified in heterosexual men from Latin America (rate ratio [RR] 1·46, 95% CI 1·00-2·12, p=0·049) and heterosexual women from the Caribbean (1·48, 1·29-1·70, p<0·0001). Compared with that in the native population, mortality in injecting drug users was similar or low for all migrant groups. Interpretation: Characteristics of and risks faced by migrant populations with HIV differ for men and women and for populations infected heterosexually, by sex between men, or by injecting drug use. Further research is needed to understand how inequalities are generated and maintained for the groups with higher mortality identified in this study. Funding: EuroCoord

Immunological and virological response to antiretroviral treatment in migrant and native men and women in Western Europe; is benefit equal for all?

Objectives: The aim of the study was to evaluate differences in immunovirological response to combination antiretroviral therapy (cART) in migrant and native men and women within a European collaboration of HIV cohorts Collaboration of Observational HIV Epidemiological Research in Europ (COHERE) in EuroCoord, 2004\ue2\u80\u932013. Methods: Migrants were defined as those with geographical origin (GO) different from the reporting country and were grouped as originating from Western Europe and Western Countries (WEWC), Eastern Europe (EE), North Africa and the Middle East (NAME), sub-Saharan Africa (SSA), Latin America (LA), Caribbean (CRB) and Asia/Oceania (ASIA/OCE). Native (NAT) individuals were defined as those originating from the reporting country. CD4 cell counts were modelled using piecewise linear mixed-effects models with two slopes, whereas models to estimate subdistribution hazard ratios (sHRs) were used for time to virological response (VR) (i.e. time from cART initiation to the first of two successive HIV RNA measurements &lt; 400 HIV-1 RNA copies/ml). Results: Of 32 817 individuals, 25 799 (78.6%) were men. The percentage of migrants was higher in women (48.9%) than in men (21.2%) and migrants from SSA accounted for the largest migrant group (29.9% in men and 63.3% in women). Migrant men and women from SSA started at lower CD4 cell counts than NAT individuals, which remained lower over time. VR was \ue2\u89\ua5 85% at 12 months for all groups except CRB women (77.7%). Compared with NAT men and women, lower VR was experienced by NAME [sHR 0.91; 95% confidence interval (CI) 0.86\ue2\u80\u930.97] and SSA (sHR 0.88; 95% CI 0.82\ue2\u80\u930.95) men and CRB (sHR 0.77; 85% CI 0.67\ue2\u80\u930.89) women, respectively. Conclusions: Immunovirological response to cART in Western Europe varies by GO and sex of patients. ART benefits are not equal for all, underlining the point that efforts need to prioritize those most in need

Reference curves for CD4 T-cell count response to combination antiretroviral therapy in HIV-1-infected treatment-naïve patients

Objectives: The aim of this work was to provide a reference for the CD4 T-cell count response in the early months after the initiation of combination antiretroviral therapy (cART) in HIV-1-infected patients. Methods: All patients in the Collaboration of Observational HIV Epidemiological Research Europe (COHERE) cohort who were aged ≥ 18 years and started cART for the first time between 1 January 2005 and 1 January 2010 and who had at least one available measurement of CD4 count and a viral load ≤ 50 HIV-1 RNA copies/mL at 6 months (± 3 months) after cART initiation were included in the study. Unadjusted and adjusted references curves and predictions were obtained using quantile regressions. Results: A total of 28 992 patients were included in the study. The median CD4 T-cell count at treatment initiation was 249 [interquartile range (IQR) 150, 336] cells/μL. The median observed CD4 counts at 6, 9 and 12 months were 382 (IQR 256, 515), 402 (IQR 274, 543) and 420 (IQR 293, 565) cells/μL. The two main factors explaining the variation of CD4 count at 6 months were AIDS stage and CD4 count at cART initiation. A CD4 count increase of ≥ 100 cells/mL is generally required in order that patients stay ‘on track’ (i.e. with a CD4 count at the same percentile as when they started), with slightly higher gains required for those starting with CD4 counts in the higher percentiles. Individual predictions adjusted for factors influencing CD4 count were more precise. Conclusions: Reference curves aid the evaluation of the immune response early after antiretroviral therapy initiation that leads to viral control

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen

Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen

Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir