Abstract
Objectives
To determine the time to CD4 : CD8 ratio normalization among Canadian adults living with HIV in the modern ART era. To identify characteristics associated with ratio normalization.
Patients and methods
Retrospective analysis of the Canadian Observational Cohort (CANOC), an interprovincial cohort of ART-naive adults living with HIV, recruited from 11 treatment centres across Canada. We studied participants initiating ART between 1 January 2011 and 31 December 2016 with baseline CD4 : CD8 ratio <1.0 and ≥2 follow-up measurements. Normalization was defined as two consecutive CD4 : CD8 ratios ≥1.0. Kaplan–Meier estimates and log-rank tests described time to normalization. Univariable and multivariable proportional hazards (PH) models identified factors associated with ratio normalization.
Results
Among 3218 participants, 909 (28%) normalized during a median 2.6 years of follow-up. Participants with higher baseline CD4+ T-cell count were more likely to achieve normalization; the probability of normalization by 5 years was 0.68 (95% CI 0.62–0.74) for those with baseline CD4+ T-cell count >500 cells/mm3 compared with 0.16 (95% CI 0.11–0.21) for those with ≤200 cells/mm3 (P < 0.0001). In a multivariable PH model, baseline CD4+ T-cell count was associated with a higher likelihood of achieving ratio normalization (adjusted HR = 1.5, 95% CI 1.5–1.6 per 100 cells/mm3, P < 0.0001). After adjusting for baseline characteristics, time-dependent ART class was not associated with ratio normalization.
Conclusions
Early ART initiation, at higher baseline CD4+ T-cell counts, has the greatest impact on CD4 : CD8 ratio normalization. Our study supports current treatment guidelines recommending immediate ART start, with no difference in ratio normalization observed based on ART class used.
Introduction
With the use of modern ART, the life expectancy of people living with HIV continues to increase and is approaching that of the general population.1,2 However, a significant gap in the number of comorbidity-free years still exists.2 As people live longer with HIV, they may face challenges related to ongoing immune dysfunction, which often persists despite achieving virological suppression.3,4 This immune dysfunction is distinct from immune deficiency and contributes to increased non-AIDS-related morbidity.3–5 One of the clinically relevant biomarkers of immune reconstitution is the CD4 : CD8 ratio. HIV infection leads to selective depletion of CD4+ T-cells and an increase in CD8+ T-cells through CD8+ T-cell activation, senescence and exhaustion, leading to an inverted CD4 : CD8 ratio. The CD4 : CD8 ratio often remains low with initiation of effective ART, even when the CD4+ T-cell count recovers, and this has been associated with increased risk of morbidity and mortality.3–6
People living with HIV are at a higher risk of developing comorbidities such as cardiovascular disease, liver and renal disease (including faster progression to end-stages of these diseases) as well as non-AIDS-related malignancies.7–10 A low CD4 : CD8 ratio is associated with greater risk of developing these non-AIDS-related morbidities3,5 and therefore can be used to identify patients that could benefit from additional screening and preventative measures. Furthermore, the CD4 : CD8 ratio can serve as an additional target for monitoring treatment efficacy.
In a number of large cohort studies, only a minority of patients normalized their CD4 : CD8 ratio with initiation of ART.11–14 Studies looking to characterize which participants are more likely to achieve normalization yielded a number of observations. One study reported that women were more likely to achieve ratio normalization,4 while men who reported sex with other men as their risk factor for HIV acquisition were less likely to normalize their ratio.11 Participants starting ART at the time of acute HIV infection had shorter time to ratio normalization than those with chronic infection.15 Across studies, baseline CD4+ T-cell count >350 cells/mm3 at the time of ART initiation has been associated with higher likelihood of normalization. However, these studies were conducted during an era when early ART initiation was not the standard of care.
The type of ART may also be an important determinant of CD4 : CD8 ratio normalization. Studies have reported on the relationship between raltegravir-containing regimens, and increased likelihood of normalization of the CD4 : CD8 ratio, as well as faster rate of normalization when compared with other ARTs.13,16 There is limited data looking at the likelihood and rate of CD4 : CD8 ratio normalization across the integrase stand transfer inhibitor (INSTI) class to which raltegravir belongs.17,18 INSTIs are now recommended as first-line agents in the treatment of HIV and thus warrant further investigation.
The aim of this study was to describe CD4 : CD8 ratio normalization in the modern ART era and compare ratio normalization by ART class and other covariates. We report on participant characteristics associated with greater likelihood of ratio normalization with the goal of identifying factors predictive of worse outcomes. As treatment guidelines emphasize early ART initiation with patients starting treatment at significantly higher CD4+ T-cell counts, we hypothesize that there will be higher rates of ratio normalization relative to historical values. Furthermore, we hypothesize that those participants that receive INSTI-containing regimens are more likely to normalize their CD4 : CD8 ratio. By identifying the clinical factors associated with ratio normalization in the modern era of HIV treatment, we may be able to achieve greater reversal of immunological dysfunction and further narrow the morbidity and mortality gap for patients.
Patients and methods
Participants
This retrospective study was conducted using data from the Canadian Observational Cohort (CANOC), an interprovincial cohort of ART-naive adults living with HIV across Canada, who initiated ART on or after 1 January 2000. CANOC includes data from patients attending 11 participating cohorts in British Columbia, Saskatchewan, Ontario, Quebec, and Newfoundland and Labrador. Complete details of the CANOC design and data collection have been described elsewhere.11
To be included in the current analysis, participants had to have initiated ART with two NRTIs and either an INSTI, NNRTI or PI on or after 1 January 2011, had a pre-treatment CD4 : CD8 ratio of <1.0 within 2 years prior to treatment initiation, and had ≥2 follow-up CD4 : CD8 ratio measures within 2 years of treatment initiation.
Ethics
The human subjects activities of the CANOC have been approved by the harmonized University of British Columbia-Simon Fraser University Research Ethics Board at Providence Health Care Research Institute (H07-02684) as well as the local institutional review boards at each of the participating cohorts.
Statistical analysis
Demographic and clinical characteristics at baseline were summarized with median and IQR or frequency and percentage and compared by initial ART class using Kruskal–Wallis Rank Sum tests or Chi-squared tests.
Normalization was defined as a CD4 : CD8 ratio ≥1.0 on two consecutive measures ≥30 days apart. Time to CD4 : CD8 ratio normalization was summarized using Kaplan–Meier estimates and compared by initial ART class and baseline CD4+ T-cell category using log-rank tests. Participants were censored if they: (1) switched to a multi-class regimen, fusion inhibitor or CCR5 inhibitor-based regimen; (2) did not achieve viral suppression (defined as two consecutive viral loads ≤50 copies/mL measured >30 days apart) within 1 year of ART initiation; (3) had virological failure (defined as two detectable viral loads >1000 copies/mL on consecutive measures >30 days apart) following initial period of suppression; or (4) on the date of their last ratio measurement.
Univariable and multivariable proportional hazards models were used to examine the association of demographic and clinical variables with CD4 : CD8 normalization. The following variables were selected a priori to be examined and included in multivariable models: age, sex, risk factor, time-dependent ART class, baseline CD4+ T-cell count, baseline viral load and calendar year of first ART. Baseline CD8+ T-cell count did not meet the proportional hazards assumption and so multivariable models were stratified according to baseline CD8+ T-cell count.
As a sensitivity analysis of the effect of initial ART class, we also calculated the Kaplan–Meier estimates, log-rank test, and univariable and multivariable proportional hazards models after censoring participants when they switched from their initial regimen class to a different class. In this analysis we also considered the initial NRTI backbone (emtricitabine/tenofovir versus abacavir/lamivudine) as a separate covariate. A second sensitivity analysis calculated the Kaplan–Meier estimates and log-rank test by initial ART class when patients who did not achieve virological suppression or experienced virological failure were not censored; in this analysis patients were only censored either if they switched to a different class, or on the date of their last ratio measurement.
Results
A total of 5024 individuals enrolled in CANOC initiated ART between 1 January 2011 and 31 December 2016. A total of 3218 individuals met the inclusion criteria for the current analysis. Participants were hierarchically excluded for the following reasons: 283 did not start on two NRTIs with one INSTI, NNRTI or PI; 767 did not have a baseline CD4 : CD8 ratio documented within 2 years prior to treatment initiation; 251 had a baseline CD4 : CD8 ratio >1.0; and 505 had less than two follow up measured ratios within 2 years of ART initiation (Figure 1).
Figure 1.
Study inclusion. CANOC, Canadian Observational Cohort.
The demographic and clinical characteristics of participants are compared by first ART class in Table 1 with similar numbers of participants in each group. Most participants with INSTI-based regimens were in Ontario (37%) and Quebec (30%) whereas most PI-based regimens were in British Columbia (56%). Participants on PI-based regimens were more likely to have injection drug use (IDU) as their sole risk factor and to have a history of hepatitis C compared with those starting on INSTI- or NNRTI-based regimens. Compared with INSTIs and NNRTIs, participants starting on PIs had slightly lower absolute CD8+ T-cell count (850 versus 900 and 932 cells/mm3), absolute CD4+ T-cell count (320 versus 362 and 350 cells/mm3) and CD4 : CD8 ratio (0.34 versus 0.38 and 0.37). The most common initial INSTI regimens were elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate (35%), dolutegravir/abacavir/lamivudine (31%), emtricitabine/tenofovir disoproxil fumarate with raltegravir (16%) and emtricitabine/tenofovir disoproxil fumarate with dolutegravir (10%). Most NNRTI regimens were either efavirenz/emtricitabine/tenofovir disoproxil fumarate (74%) or rilpivirine/emtricitabine/tenofovir disoproxil fumarate (17%). For those starting with PIs, the most common combinations were emtricitabine/tenofovir disoproxil fumarate with either ritonavir- or cobicistat-boosted atazanavir (39%) or boosted darunavir (33%) or abacavir/lamivudine with boosted atazanavir (12%). The majority of participants maintained their first ART class throughout follow up (n = 2583, 80.3%) with very few having more than one ART class switch during follow up (n = 89, 2.7%). Of those who switched ART classes, those starting on NNRTIs or PIs were most likely to switch to INSTIs (63% and 74% respectively) and those starting on INSTIs switched to NNRTIs (51%) or PIs (49%) evenly. Patients were followed for a median [IQR] of 2.6 [1.3–4.1] years and had a median of 4 [3–5] CD4 : CD8 ratios measured per year. Baseline characteristics of individuals excluded from the analysis are summarized in Table S1 (available as Supplementary data at JAC Online).
Table 1.
Baseline demographic and clinical characteristics of participants by initial ART regimen class
| Characteristic | Missing (%) | INSTI | NNRTI | PI |
|---|---|---|---|---|
| Participants (n) | 1057 | 1098 | 1063 | |
| Age, years | 38 [30–48] | 39 [31–47] | 37 [30–46] | |
| Male | 0.1 | 938 (88.8) | 977 (89.1) | 833 (78.5) |
| Race | 29.9 | |||
| White | 450 (58.3) | 392 (52.3) | 384 (52.4) | |
| Black | 103 (13.3) | 113 (15.1) | 78 (10.6) | |
| Indigenous | 56 (7.3) | 89 (11.9) | 149 (20.3) | |
| Asian | 70 (9.1) | 77 (10.3) | 73 (10.0) | |
| Hispanic | 42 (5.4) | 46 (6.1) | 28 (3.8) | |
| Mixed | 25 (3.2) | 22 (2.9) | 18 (2.5) | |
| Other | 26 (3.4) | 11 (1.5) | 3 (0.4) | |
| Province | ||||
| British Columbia | 280 (26.5) | 478 (43.5) | 591 (55.6) | |
| Saskatchewan | 59 (5.6) | 42 (3.8) | 98 (9.2) | |
| Ontario | 387 (36.6) | 425 (38.7) | 206 (19.4) | |
| Quebec | 314 (29.7) | 139 (12.7) | 150 (14.1) | |
| Newfoundland and Labrador | 17 (1.6) | 14 (1.3) | 18 (1.7) | |
| Risk Factor | 19.2 | |||
| MSM Only | 587 (66.6) | 535 (63.2) | 408 (46.7) | |
| IDU Only | 86 (9.8) | 113 (13.4) | 218 (25.0) | |
| MSM and IDU | 27 (3.1) | 29 (3.4) | 52 (6.0) | |
| Heterosexual | 135 (15.3) | 129 (15.2) | 157 (18.0) | |
| Other | 46 (5.2) | 40 (4.7) | 38 (4.4) | |
| Hepatitis C | 4.4 | 136 (13.5) | 168 (16.0) | 269 (26.5) |
| Hepatitis B | 17.5 | 117 (12.9) | 96 (10.9) | 74 (8.5) |
| Calendar year of ART initiation | 2014 [2013–2015] | 2012 [2011–2013] | 2012 [2011–2014] | |
| CD4 : CD8 ratio | 0.38 [0.23–0.57] | 0.37 [0.23–0.54] | 0.34 [0.18–0.52] | |
| CD4 count (cells/mm3) | 362 [230–510] | 350 [230–480] | 320 [160–460] | |
| CD4 count category (cells/mm3) | ||||
| ≤200 | 234 (22.1) | 234 (21.3) | 336 (31.6) | |
| 201–350 | 268 (25.4) | 316 (28.8) | 268 (25.2) | |
| 351–500 | 274 (25.9) | 308 (28.1) | 242 (22.8) | |
| >500 | 281 (26.6) | 240 (21.9) | 217 (20.4) | |
| CD8 count | 900 [640–1260] | 932 [624–1300] | 850 [610–1200] | |
| Viral load (log10 copies/mL) | 4.72 [4.21–5.18] | 4.61 [4.15–5.08] | 4.81 [4.31–5.32] | |
| Follow-up (years) | 1.45 [0.79–2.39] | 3.59 [2.34–4.66] | 3.08 [1.62–4.34] | |
| Number of CD4 : CD8 ratios per year | 4.22 [3.18–5.43] | 3.75 [2.86–4.70] | 4.21 [3.08–5.42] | |
| Number of regimen switches during follow-up | ||||
| 0 | 1008 (95.4) | 807 (73.5) | 768 (72.2) | |
| 1 | 32 (3.0) | 252 (23.0) | 262 (24.6) | |
| 2 | 17 (1.6) | 26 (2.4) | 26 (2.4) | |
| 3–6 | 0 (0.0) | 13 (1.2) | 7 (0.7) | |
| First regimen switch | 80.3 | |||
| INSTI | NA | 182 (62.5) | 217 (73.6) | |
| NNRTI | 25 (51.0) | NA | 78 (26.4) | |
| PI | 24 (49.0) | 109 (37.5) | NA |
IDU, injection drug use; NA, not applicable.
Categorical variables are presented as frequency (%). Continuous variables are presented as median [IQR].
Overall, 909 participants (28%) normalized their CD4 : CD8 ratio with a 0.18 probability (95% CI 0.17–0.19) of doing so by 1 year and a 0.44 probability (95% CI 0.42–0.47) of doing so by 5 years. CD4 : CD8 ratios by patient from treatment initiation to normalization according to ART class are shown in Figure S1. The majority of participants who did not achieve normalization were censored at their last available CD4 : CD8 ratio (77%) while others were censored due to failure to suppress within 1 year (17%), virological failure after suppression (3%), or a switch to a multi-class, fusion inhibitor- or CCR5 inhibitor-based regimen (3%). Although more participants normalized their CD4 : CD8 ratio on an NNRTI-based regimen (32.7%) relative to PI- (28.2%) and INSTI- (23.7%) based regimens, time to normalization was not associated with initial ART class (P = 0.9) (Figure 2). The probability (95% CI) of achieving normalization by 5 years from treatment initiation was 0.49 (0.41–0.57), 0.43 (0.39–0.47), and 0.45 (0.4–0.5) for INSTI-, NNRTI-, and PI-based regimens, respectively. Similarly, time to normalization was not associated with initial ART class (P=0.5) when individuals were not censored for virological failure (Figure S2).
Figure 2.
Cumulative distribution of time to CD4 : CD8 ratio normalization by initial ART regimen.
The probability of achieving ratio normalization varied according to baseline CD4+ T-cell count (Figure 3). Individuals starting treatment with a baseline CD4+ T-cell count of ≤200 cells/mm3 had a 5 year probability (95% CI) of achieving normalization of 0.16 (0.11–0.21), compared with 0.37 (0.32–0.42) among those with baseline CD4+ T-cell count between 201–350 cells/mm3, 0.56 (0.51–0.61) among those with baseline CD4+ T-cell count between 351–500 cells/mm3 and 0.68 (0.62–0.74) among those with baseline CD4+ T-cell count of >500 cells/mm3 (P < 0.0001).
Figure 3.
Cumulative distribution of time to CD4 : CD8 ratio normalization by baseline CD4+ T-cell count.
Hazard ratios (HR) and 95% CI from univariable and multivariable proportional hazards models are presented in Table 2. Time-dependent ART class was not associated with ratio normalization in a univariable model or when baseline characteristics were taken into account in a multivariable analysis. In a sensitivity analysis that censored participants who switched to a different class, initial ART class and initial NRTI backbone were not associated with ratio normalization in univariable or multivariable models (Table S2).
Table 2.
Univariable and multivariable proportional hazards models of time to CD4 : CD8 ratio normalization
| Univariable |
Multivariablea (n = 2597) |
|||
|---|---|---|---|---|
| Characteristic | HR (95% CI) | P value | aHR (95% CI) | P value |
| Time-dependent ART Class | ||||
| INSTI based | Ref. | Ref. | ||
| NNRTI based | 1 (0.86–1.17) | 0.98 | 1.14 (0.93–1.39) | 0.21 |
| PI based | 1.03 (0.88–1.22) | 0.7 | 1.13 (0.92–1.39) | 0.23 |
| Age, years | ||||
| ≤30 | Ref. | Ref. | ||
| 31-40 | 0.84 (0.72–1) | 0.04 | 0.98 (0.8–1.18) | 0.81 |
| 41-50 | 0.65 (0.55–0.79) | <0.0001 | 0.85 (0.69–1.06) | 0.16 |
| >50 | 0.72 (0.59–0.89) | <0.01 | 1.1 (0.87–1.4) | 0.44 |
| Female | 1.23 (1.02–1.47) | 0.03 | 1.37 (1.04–1.78) | 0.02 |
| Risk factor | ||||
| MSM only | Ref. | Ref. | ||
| IDU only | 0.93 (0.75–1.17) | 0.55 | 0.82 (0.64–1.06) | 0.13 |
| MSM + IDU | 1.03 (0.71–1.48) | 0.9 | 1.04 (0.71–1.51) | 0.85 |
| Heterosexual | 0.98 (0.8–1.22) | 0.88 | 0.86 (0.67–1.11) | 0.26 |
| Other | 0.83 (0.57–1.2) | 0.32 | 0.85 (0.56–1.28) | 0.43 |
| Baseline CD4 per 100 cells/mm3 | 1.3 (1.27–1.33) | <0.0001 | 1.55 (1.5–1.6) | <0.0001 |
| Baseline viral load >100 000 copies/mL | 0.63 (0.54–0.73) | <0.0001 | 1.01 (0.84–1.21) | 0.92 |
| Calendar year of ART initiation | 1.02 (0.97–1.07) | 0.41 | 1.01 (0.95–1.07) | 0.81 |
HR, hazard ratio; aHR, adjusted hazard ratio; IDU, injection drug use.
Multivariable model stratified by baseline CD8 count.
Higher baseline CD4+ T-cell counts were strongly associated with an increased likelihood of ratio normalization, with an HR of 1.55 (95% CI 1.5–1.6, P < 0.0001) per 100 cell/mm3 increase in baseline CD4+ T-cell count after adjusting for other covariates. Women were more likely to normalize compared with men [adjusted HR (aHR)=1.37, 95% CI 1.04–1.78, P = 0.02]. Age did not have a linear effect and so was included as a categorical variable. Age, risk factor, baseline viral load, and calendar year of ART initiation were not associated with ratio normalization.
Discussion
In this large Canadian cohort of 3218 HIV-treatment-naive individuals starting ART, 909 (28%) achieved ratio normalization during a median follow up of 2.6 years. The probability of achieving CD4 : CD8 ratio normalization at 5 years was 0.44. Among those participants who initiated ART with a baseline CD4+ T-cell count of >500 cells/mm3, the probability of normalization was 0.50 by 2 years and 0.68 by 5 years. These findings suggest that in the modern ART era, the probability of achieving ratio normalization is good and is even better for those starting treatment at high baseline CD4+ T-cell counts.
Our findings contrast with several earlier studies that found only a minority of individuals achieve CD4 : CD8 ratio normalization, with reversal of immunological dysfunction rarely observed despite virological suppression and recovery of the CD4+ T-cell count. Indeed, in an earlier analysis of the CANOC data, which included participants recruited between 2000 and 2010, only 7.2% of participants achieved ratio normalization during a median 2.77 years of follow up.11 A higher cut off of >1.2 was used to define ratio normalization for this earlier analysis, however, similar results were observed in studies using the more common cut off of >1.0. In 2015, Mussini et al.12 reported that among 3236 participants from an Italian cohort, 14% normalized their ratio to >1.0 with a 29.4% probability of normalization at 5 years of treatment. In a Thai cohort of 800 participants, probability of ratio normalization to >1.0 at 5 years was only 18.6%.14 A notable difference between these reports and the current study is the baseline CD4+ T-cell count among participants at the time of ART initiation. The median baseline CD4+ T-cell count in the current study was 348 cells/mm3, which is significantly higher than in the initial CANOC analysis (190 cells/mm3), in the Italian cohort (223 cells/mm3) and in the Thai cohort (206 cells/mm3). This fundamental difference is a reflection of the changing practice of immediate rather than delayed ART start.19 Our study provides further support to the current treatment guidelines, emphasizing early treatment initiation.20,21 In addition to reducing overall mortality with reduced risk of AIDS-related complications, early ART initiation is a vital factor when it comes to immune reconstitution and reduced risk of non-AIDS related morbidity.
Another evolving aspect of HIV treatment has been the ART regimens. Although all currently recommended regimens are effective in achieving virological suppression and CD4+ T-cell recovery in the absence of underlying mutations, less is known about adequacy of immune reconstitution across ART classes. INSTIs are a potent and well-tolerated class of antiretroviral drugs that are now considered first line agents for treatment of HIV infection.21 Few studies have compared CD4 : CD8 ratio normalization by ART class to date, especially after the uptake of INSTI-based regimens increased. In the French cohort, INSTI-containing regimens were associated with greater likelihood of CD4 : CD8 ratio normalization during the initial 1 year of treatment, relative to other regimens.13 Although raltegravir was the only INSTI-based regimen in the study, accounting for only 8% of study participants (n = 45), other studies provided some support for higher rates of ratio normalization with raltegravir.16 In contrast, Masiá et al.17 found higher mean CD4 : CD8 ratio increase with NNRTI-based regimens compared with PI-based regimens, with no significant difference relative to INSTI-based regimens. That study was also limited by small number of participants taking INSTI-based regimens (n = 71). In our study one-third of participants started treatment with INSTI-based regimens (n = 1057), and an additional 399 individuals switched from an initial regimen of NNRTIs or PIs to an INSTI-based regimen. We did not observe a difference in CD4 : CD8 ratio normalization based on ART class in a multivariable analysis accounting for baseline differences between groups, although indication bias could have confounded this finding. This suggests that timing of ART initiation influences ratio normalization and in turn immune reconstitution, more so than choice of ART.
Women were more likely to achieve ratio normalization in our cohort (aHR 1.37, 95% CI 1.04–1.78; P = 0.02). A similar observation was made in the Thai cohort and might be indicative of underlying immunological differences between sexes.14 Immunological profiling across ages and sexes conducted in the general Swedish population, showed greater prevalence of inverted CD4 : CD8 ratio among men across the life span.22 Older individuals were also more likely to have an inverted ratio and this was accompanied by fewer naive T-cells and increased numbers of senescent CD8+ T-cell populations. However, we did not observe an age-related effect in the multivariable analysis when baseline CD4+ T-cell count was accounted for, suggesting that older adults are able to achieve ratio normalization and early treatment initiation is especially beneficial in this age group.
There were several strengths to our study. Firstly, study participants were taking a wide variety of ART regimens with relatively equal representation from each ART class, including INSTI-based regimens. Secondly, we accounted for switches in ART classes by using a time-dependent variable for regimen class in univariable and multivariable proportional hazards models. Limitations of our study included lack of analysis on switches and outcomes within ART classes to account for possible drug-specific effects. Furthermore, we did not account for time-dependent differences in NRTI backbone among ART regimens. Lastly, our period of follow up was relatively short in duration, especially in the INSTI group.
In conclusion, there is mounting evidence that an inverted CD4 : CD8 ratio is associated with persistent immune dysfunction, even among patients that have recovered their CD4+ T-cell counts and achieved virological suppression on ART. Our study demonstrates that with the uptake of current treatment guidelines, early ART initiation at higher baseline CD4+ T-cell counts has resulted in a greater number of individuals achieving CD4 : CD8 ratio normalization relative to historical values. This observation was independent of ART-class used, indicating that timing, more so than choice, of ART is important for immune reconstitution. It is unclear whether improved ratio normalization will translate into increased survival, lower rates of comorbidity, or increased comorbidity-free years among people living with HIV, which warrants further study.
Supplementary Material
Acknowledgements
Members of the Canadian Observational Cohort (CANOC) Collaboration
The CANOC Collaborative Research Centre: Principal Investigator: Robert Hogg (British Columbia Centre for Excellence in HIV/AIDS; Simon Fraser University); Site Principal Investigators: Zabrina Brumme (British Columbia Centre for Excellence in HIV/AIDS; Simon Fraser University), Ann N. Burchell [Ontario HIV Treatment Network (OHTN); University of Toronto; OHTN Cohort Study (OCS)], Curtis Cooper (University of Ottawa; OCS), Deborah Kelly (Memorial University of Newfoundland), Marina Klein (Montreal Chest Institute Immunodeficiency Service Cohort; McGill University), Abigail Kroch (Ontario HIV Treatment Network; University of Toronto), Mona Loutfy (University of Toronto; Maple Leaf Medical Clinic; OCS), Nimâ Machouf (Clinique Medicale l’Actuel; Université de Montreal), Julio Montaner (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), Kate Salters (BC Centre for Excellence in HIV/AIDS; Simon Fraser University), Janet Raboud (University of Toronto; University Health Network; OCS), Chris Tsoukas (McGill University), Stephen Sanche (University of Saskatchewan), Réjean Thomas (Clinique Médicale l’Actuel), Sharon Walmsley (University Health Network; University of Toronto), Alexander Wong (University of Saskatchewan); Co-Principal Investigators: Tony Antoniou (St Michael’s Hospital; University of Toronto; Institute for Clinical Evaluative Sciences), Ahmed Bayoumi (St Michael’s Hospital; University of Toronto), Mark Hull (British Columbia Centre for Excellence in HIV/AIDS), Bohdan Nosyk (British Columbia Centre for Excellence in HIV/AIDS; Simon Fraser University); Co-Investigators: Angela Cescon (Northern Ontario School of Medicine), Michelle Cotterchio (Cancer Care Ontario; University of Toronto), Charlie Goldsmith (Simon Fraser University), Silvia Guillemi (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), P. Richard Harrigan (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), Marianne Harris (St Paul’s Hospital), Sean Hosein (Community AIDS Treatment Information Exchange (CATIE)), Sharon Johnston (Bruyere Research Institute; University of Ottawa), Claire Kendall (Bruyere Research Institute; University of Ottawa), Clare Liddy (Bruyere Research Institute; University of Ottawa), Viviane Lima (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), David Moore (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), Alexis Palmer (British Columbia Centre for Excellence in HIV/AIDS; Simon Fraser University), Sophie Patterson (British Columbia Centre for Excellence in HIV/AIDS; Simon Fraser University), Peter Phillips (British Columbia Centre for Excellence in HIV/AIDS; University of British Columbia), Anita Rachlis (University of Toronto; OCS), Sean B. Rourke (University of Toronto; OCS), Hasina Samji (British Columbia Centre for Excellence in HIV/AIDS), Marek Smieja (McMaster University), Benoit Trottier (Clinique Medicale l’Actuel, Université de Montreal), Mark Wainberg (McGill University; Lady Davis Institute for Medical Research); Collaborators: Chris Archibald (Public Health Agency of Canada Centre for Communicable Diseases and Infection Control), Ken Clement (Canadian Aboriginal AIDS Network), Monique Doolittle-Romas (Canadian AIDS Society), Laurie Edmiston (Canadian Treatment Action Council), Sandra Gardner (OHTN; University of Toronto; OCS), Brian Huskins (Canadian Treatment Action Council), Jerry Lawless (University of Waterloo), Douglas Lee [University Health Network; University of Toronto; Institute for Clinical Evaluative Sciences (ICES)], Renee Masching (Canadian Aboriginal AIDS Network), Stephen Tattle (Canadian Working Group on HIV & Rehabilitation), Alireza Zahirieh (Sunnybrook Health Sciences Centre); Analysts and Staff: Claire Allen (Regina General Hospital), Stryker Calvez [Saskatoon HIV/AIDS Research Endeavour (SHARE)], Guillaume Colley (British Columbia Centre for Excellence in HIV/AIDS), Jason Chia (British Columbia Centre for Excellence in HIV/AIDS), Daniel Corsi (The Ottawa Hospital Immunodeficiency Clinic; Ottawa Hospital Research Institute), Louise Gilbert (Immune Deficiency Treatment Centre), Nada Gataric (British Columbia Centre for Excellence in HIV/AIDS), Lucia Light (OHTN), David Mackie (The Ottawa Hospital), Costa Pexos (McGill University), Susan Shurgold (British Columbia Centre for Excellence in HIV/AIDS), Leah Szadkowski (University Health Network), Chrissi Galanakis (Clinique Médicale L’Actuel), Benita Yip (British Columbia Centre for Excellence in HIV/AIDS), Jaime Younger (University Health Network), and Julia Zhu (British Columbia Centre for Excellence in HIV/AIDS).
The authors are grateful to the participants of the CANOC collaboration, whose willingness to participate in research and make their data available made this work possible. The authors value the support of the administrative teams who collect and maintain the data at individual sites and at the data coordinating center.
Funding
CANOC is supported by the Canadian Institutes of Health Research (CIHR) and by the CIHR Canadian HIV Trials Network: Centres Grant—Centres for HIV/AIDS Population Health and Health Services Research [CIHR #02684], two Operating Grants—HIV/AIDS Priority Announcement [CIHR #134047], Population and Public Health Grant [CIHR #136882], Foundation Grant - Expansion of Antiretroviral Therapy and its Impact on Vulnerable Populations in Canada and Global Settings [CIHR #143342], and CIHR Canadian HIV Trials Network [CTN #242].
Transparency declarations
S.W. receives salary support from the Ontario HIV Treatment Network. S.W conducts clinical trials, serves on advisory boards and speaks at CME events for ViiV, Merck, and Gilead. C.C. served on the advisory board for ViiV with regards to HIV vaccine development. All other authors: none to declare.
Author contributions
A.Z. and S.L.W. conceived the project. L.S. performed statistical analysis. A.Z., L.S., and S.L.W. planned the analyses, interpreted results, and wrote the manuscript. C.C., M.L., A.W., A.M. and R.S.H. provided critical feedback and edits to the final version of the manuscript. All of the CANOC investigators approved the study, contributed data and reviewed the final manuscript.
Supplementary data
Tables S1 and S2 and Figures S1 and S2 are available as Supplementary data at JAC Online.
Contributor Information
the Canadian Observational Cohort (CANOC) Collaboration:
Robert Hogg, Zabrina Brumme, Ann N Burchell, Curtis Cooper, Deborah Kelly, Marina Klein, Abigail Kroch, Mona Loutfy, Nimâ Machouf, Julio Montaner, Kate Salters, Janet Raboud, Chris Tsoukas, Stephen Sanche, Réjean Thomas, Sharon Walmsley, Alexander Wong, Tony Antoniou, Ahmed Bayoumi, Mark Hull, Bohdan Nosyk, Angela Cescon, Michelle Cotterchio, Charlie Goldsmith, Silvia Guillemi, P Richard Harrigan, Marianne Harris, Sean Hosein, Sharon Johnston, Claire Kendall, Clare Liddy, Viviane Lima, David Moore, Alexis Palmer, Sophie Patterson, Peter Phillips, Anita Rachlis, Sean B Rourke, Hasina Samji, Marek Smieja, Benoit Trottier, Mark Wainberg, Chris Archibald, Ken Clement, Monique Doolittle-Romas, Laurie Edmiston, Sandra Gardner, Brian Huskins, Jerry Lawless, Douglas Lee, Renee Masching, Stephen Tattle, Alireza Zahirieh, Claire Allen, Stryker Calvez, Guillaume Colley, Jason Chia, Daniel Corsi, Louise Gilbert, Nada Gataric, Lucia Light, David Mackie, Costa Pexos, Susan Shurgold, Leah Szadkowski, Chrissi Galanakis, Benita Yip, Jaime Younger, and Julia Zhu
References
- 1. Wandeler G, Johnson LF, Egger M.. Trends in life expectancy of HIV-positive adults on antiretroviral therapy across the globe: comparisons with general population. Curr Opin HIV AIDS 2016; 11: 492–500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Marcus JL, Leyden W, Alexeeff SE. et al. Comparison of Overall and Comorbidity-Free Life Expectancy Between Insured Adults With and Without HIV Infection, 2000-2016. JAMA Netw Open 2020; 3: e207954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Serrano-Villar S, Sainz T, Lee SA. et al. HIV-infected individuals with low CD4/CD8 ratio despite effective antiretroviral therapy exhibit altered T cell subsets, heightened CD8+ T cell activation, and increased risk of non-AIDS morbidity and mortality. PLoS Pathog 2014; 10: e1004078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Serrano-Villar S, Gutierrez C, Vallejo A. et al. The CD4/CD8 ratio in HIV-infected subjects is independently associated with T-cell activation despite long-term viral suppression. J Infect 2013; 66: 57–66. [DOI] [PubMed] [Google Scholar]
- 5. Serrano-Villar S, Perez-Elias MJ, Dronda F. et al. Increased risk of serious non-AIDS-related events in HIV-infected subjects on antiretroviral therapy associated with a low CD4/CD8 ratio. PLoS One 2014; 9: e85798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Serrano-Villar S, Moreno S, Fuentes-Ferrer M. et al. The CD4: CD8 ratio is associated with markers of age-associated disease in virally suppressed HIV-infected patients with immunological recovery. HIV Med 2014; 15: 40–39. [DOI] [PubMed] [Google Scholar]
- 7. Feinstein MJ, Hsue PY, Benjamin LA. et al. Characteristics, prevention, and management of cardiovascular disease in people living with HIV: a scientific statement from the American Heart Association. Circulation 2019; 140: e98–e124. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Guaraldi G, Orlando G, Zona S. et al. Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis 2011; 53: 1120–6. [DOI] [PubMed] [Google Scholar]
- 9. Sherman KE, Rockstroh J, Thomas D.. HIV and liver disease: an Update. Hepatology 2015; 62: 1871–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Castilho JL, Bian A, Jenkins C. et al. Increased cancer risk with lower CD4/CD8 ratio among adults with HIV in NA-ACCORD. 27th Conference on Retroviruses and Opportunistic Infections, Boston, MA, 2020. Abstract 71.
- 11. Leung V, Gillis J, Raboud J. et al. Predictors of CD4: CD8 ratio normalization and its effect on health outcomes in the era of combination antiretroviral therapy. PLoS One 2013; 8: e77665. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Mussini C, Lorenzini P, Cozzi-Lepri A. et al. CD4/CD8 ratio normalisation and non-AIDS-related events in individuals with HIV who achieve viral load suppression with antiretroviral therapy: an observational cohort study. Lancet HIV 2015; 2: e98–e106. [DOI] [PubMed] [Google Scholar]
- 13. De Salvador-Guillouët F, Sakarovitch C, Durant J. et al. Antiretroviral regimens and CD4/CD8 ratio normalization in HIV-infected patients during the initial year of treatment: a cohort study. PLoS One 2015; 10: e0140519. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Han WM, Apornpong T, Kerr SJ. et al. CD4/CD8 ratio normalization rates and low ratio as prognostic marker for non-AIDS defining events among long-term virologically suppressed people living with HIV. AIDS Res Ther 2018; 15: 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Davy-Mendez T, Napravnik S, Zakharova O. et al. Acute HIV infection and CD4/CD8 ratio normalization after antiretroviral therapy initiation. J Acquir Immune Defic Syndr 2018; 79: 510–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Serrano-Villar S, Zhou Y, Rodgers AJ. et al. Different impact of raltegravir versus efavirenz on CD4/CD8 ratio recovery in HIV-infected patients. J Antimicrob Chemother 2017; 72: 235–9. [DOI] [PubMed] [Google Scholar]
- 17. Masiá M, Padilla S, Barber X. et al. Comparative impact of suppressive antiretroviral regimens on the CD4/CD8 T-cell ratio: a cohort study. Medicine 2016; 95: e3108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Serrano-Villar S, Martínez-Sanz J, Ron R. et al. Effects of first-line antiretroviral therapy on the CD4/CD8 ratio and CD8 cell counts in CoRIS: a prospective multicentre cohort study. Lancet HIV 2020; 7: e565–e573. [DOI] [PubMed] [Google Scholar]
- 19. Lundgren JD, Babiker AG, Gordin F. et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med 2015; 373: 795–807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Panel on Antiretroviral Guidelines for Adults and Adolescents, Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. https://aidsinfo.nih.gov/contentfiles/lvguidelines/adultandadolescentgl.pdf.
- 21. Saag MS, Benson CA, Gandhi RT. et al. Antiretroviral drugs for treatment and prevention of HIV infection in adults: 2018 recommendations of the International Antiviral Society–USA Panel. Jama 2018; 320: 1–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Wikby A, Månsson IA, Johansson B. et al. The immune risk profile is associated with age and gender: findings from three Swedish population studies of individuals 20-100 years of age. Biogerontology 2008; 9: 299–308. [DOI] [PubMed] [Google Scholar]
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