Abstract
Introduction
HIV infection leads to a persistent expansion of terminally CD8T cells and CD8T suppressor-cells, a marker of chronic immune activation leading to a low CD4:CD8 ratio that may persist in the presence of potent ART and regained CD4 helper cells. It remains unclear whether a low CD4:CD8 ratio is associated with cardiovascular diseases (CVD).
Methods
We conducted an observational cohort study to investigate the association of immune depression and activation as characterized by the proxy of the CD4:CD8 ratio on the hazard of coronary heart disease (CHD) and stroke among treated individuals living with HIV, while accounting for viral load and known risk factors for CVD and exposure to abacavir or protease inhibitors. We used Cox proportional hazard models with time-dependent cumulative and lagged exposures to account for time-evolving risk factors and avoid reverse causality.
Results
CD4, CD8 and CD4:CD8 immunological markers were not associated with an increased hazard for CHD. CD8 cell count lagged at 12 months above 1000 cells per μl increased the hazard of stroke, after adjusting for socio-demographics, cardiovascular risk factors and exposure to specific types of antiretroviral drugs.
Conclusions
This analysis of treated HIV positive individuals within a large cohort with long-term follow-up does not provide evidence for a prognostic role of immune dysregulation regarding CHD. However, increased CD8 cell count may be a moderate risk factor for stroke. Early detection and treatment of HIV positive individuals are crucial for an optimal immune restoration and a limited CD8 cells expansion.
Keywords: HIV infection, cardiovascular diseases, immunosenescence, chronic inflammation markers
Introduction
Cardiovascular diseases (CVD) represent the second most important reason of death in antiretroviral therapy (ART) treated people living with HIV (PLWHIV)’ 1 2 Reasons relate to increasing age and exposure to common risk factors for CVD such as hypertension, dyslipidemia, diabetes, smoking and - of minor relevance - exposure to certain antiretroviral drugs such as protease inhibitors and abacavir3,4.
In HIV infection several factors are contributing to chronic immune activation in particular continuous viral replication and CD4 cell depletion which are associated with higher risk of CVD5–7. HIV infection leads to a persistent expansion of terminally differentiated effector memory CD8 T cells that is accompanied by a progressive decline of naïve and central memory CD8 T-cells and associated with a lower CD4:CD8cell ratio8. Quantitative and functional defects in CD8 T-cells remain even after long term effective ART9. Persistent elevation of CD8 T-cell count following long-term ART was found in several case control studies to be associated with overall mortality and mortality from non AIDS-defining events10,11.
Expansion of CD8 T-cells with the consequence of a low CD4:CD8 ratio in particular in well-treated and virologically suppressed individuals may characterize a subpopulation with distinct immunological abnormalities11 and may constitute a population at risk of „immunosenescence‟. In elderly uninfected individuals this immune phenotype is characterized by a low naïve/memory T-cell ratio, expansion of cytomegalovirus (CMV) specific CD8+ T-cells, enrichment of CD28- and PD-1+ T cells, increased CRP and IL-6 levels, reduced T cell telomere lengths and lower CD4:CD8 ratio11,12. Low CD4:CD8 cell ratio in the presence of viral suppression is associated with invariant natural killer T (iNKT) cell activation and pro-inflammatory marker production, which may promote a state of chronic immune activation and increased risk for cardiovascular disease and malignancies13,14.
Evidence from case control15,16 and cohort studies17 18 for an association of CD8 T-cells and CD4:CD8 ratio as proxies for immune-senescence and immune activation and risk of CVD is conflicting as many studies were too small and could not correct for important confounders. We investigated the independent association of CD8 T-cells and CD4:CD8 ratio and the risk of coronary heart disease (CHD) and stroke in the Swiss HIV Cohort Study (SHCS).
Methods
The SHCS is a collaborative study between various health partners and provide a research platform that promotes HIV-related research in Switzerland19–21. Since 1988, this observational study prospectively and routinely collects a host of data on HIV-positive individuals aged above 16 years every 6 months. Socio-demographic, behavioral data, laboratory results (including CD4 and CD8 cell counts and HIV-1 RNA viral load), ART regimen, co-medication, and clinical events are collected at enrollment and/or at each follow-up visits. Cardiovascular events and risk factors including cholesterol, blood glucose, weight, and smoking are also stored in a routine basis in the database since April 2000.
Baseline and time-updated exposure variables
We assessed the association of baseline and time-updated exposure variables on the risk of coronary heart disease (CHD) and ischemic stroke. CHD was defined as myocardial infarction, coronary angioplasty/stenting or coronary artery by-pass grafting while stroke includes cerebral infarction and carotid endarterectomy. Baseline patient characteristics included sex (male/female), education (compulsory school, vocational training, higher education and other/unknown), HIV transmission group (men having sex with men (MSM), injecting drug user (IDU) and other/unknown) and calendar year (<2010, 2010+). Time-updated variables were i) age, ii) CD4 cells counts (square root transformed and categorized as <200, 200-350, 350-500, 500+ cells per μl), iii) CD8 cells counts (square root transformed and categorized as <1000, 1000+ cells per μl), iv) CD4:CD8 ratio (log transformed and categorized as <0.5, 0.5-1.0, 1.0+), v) HIV RNA viral load (log10 transformed and categorized as <50, 50-500, 500+ copies/ml), vi) dyslipidemia (total cholesterol>6.2 mmol/l or high-density lipoprotein (HDL)<1.03 mmol/l or (triglycerides>2 mmol/l &fasting)), vii) diabetes (glucose >11.1 mmol/l or glucose ≥ 7 mmol/l if fasting or on antidiabetic drugs (oral or insulin)), viii) hypertension (systolic >140 mmHg or diastolic >90 mmHg or on anti-hypertensive drugs or (systolic >135 mmHg if diabetes or diastolic >85 mmHg if diabetes)), viii) obesity (body mass index (BMI)>30 kg/m2), ix) metabolic syndrome (having any three of the following conditions: abdominal obesity (waist circumference>102 cm in men, >88 cm in women), triglyderides≥1.69 mmol/L, low HDL cholesterol (<1.03 mmol/L in men, <1.29 mmol/L in women), blood pressure ≥130/≥85 mmHg or diabetes), x) smoking (no/yes), xi) protease inhibitor (PI) exposure (no/yes), xii) integrase inhibitor (INSTI) exposure (no/yes), xiii) didanosine (DDI) exposure (no/yes) and xiv) abacavir exposure (no/yes). Time- updated variables are updated at the end of each follow-up month and missing information is imputed using the last observation carried forward (LOCF).
Statistical analysis
We used time-dependent covariates Cox models to model time from enrolment to a first cardiovascular event, one year after the last clinical visit, death or cohort administrative censoring, whatever came first. The choice of the Cox models was justified by the prospective nature of our study and by the ability of such models to encompass covariates that change over time. So each follow-up month, subjects who have experienced an event are compared to those currently at risk. We included in our analysis all individuals in the SHCS followed between April 2000 (when routine collection of CVD started) and March 2021, with a minimum of one follow-up visit and one measurement of CD4, CD8 and RNA viral load at enrollment or in a time window of 3 months. We excluded individuals with a cardiovascular event record prior to April 2000, as well as individuals who had started ART before 2000. Follow-up times before April 2000 were left-truncated at April 1st 2000.
Risk factors for CHD and stroke were selected in two steps. First, we selected cardiovascular risk factors such as dyslipidemia, hypertension, diabetes, obesity, metabolic syndrome, smoking, positive cytomegalovirus serology, PI, INSTI22, DDI and abacavir exposures by modelling their association to the hazard of CHD and stroke, adjusted for patient characteristics including age, sex, educational status, transmission group and calendar year. Cardiovascular risk factors were considered at baseline and at three different lags (12, 24 and 36 months) to avoid the risk of detecting associations that reflect reverse causality. By lag, we understand an exposure observed at a defined number of months prior to a given follow-up. Among cardiovascular risk factors for which we considered having enough evidence of correlation with our outcome (p-value<0.05), we selected the representation that presented the better fit model (lowest Akaike criteria (AIC)). Second, we selected immunological and viral factors based on their significant association adjusted for patient characteristics and cardiovascular risk factors identified in the previous step. We considered lagged variables at 12, 24 and 36 months, as well as cumulative exposure using simple moving average (SMA) over the past 12 months and 24 months. Twelve months SMA were also lagged at 12 and 24 months, while 24 months SMA were lagged at 12 months. Nadir and lagged 12 months nadir were additionally considered for CD4 and CD4:CD8 ratio. Variables selected at the two aforementioned steps were carried into final multivariable Cox models for CHD and stroke.
Proportional assumption of the Cox models were assessed by testing the non-significance of the relationship between Schoenfeld residuals and time and log-linearity of the continuous covariates was assessed by visual inspection of smooth plots of the Martingale residuals. For the final models, missing baseline cardiovascular risk factors were imputed using multivariate imputation by chained equations and we pooled results over five imputed dataset. In sensitivity analyses, we compare results obtained with multiple imputation of baseline missing information to results from complete cases without imputation, as well as results with time-varying covariates updated with LOCF to results where LOCF is restricted to a maximum period of 12 months. All analyses were done in R Project for Statistical Computing (version 4.0.3) software23 using packages “survival” (version 3.2-7), “mice” (version 3.13.0) and “mitools” (version 2.4).
Results
A total of 15,303 HIV-positive individuals without record of CVD prior to April 2000 have been followed by the SHCS between April 2000 and March 2021 (median follow-up 11.1 years; interquartile range IQR: 5.2-18.1 years). Coronary heart disease was diagnosed in 563 HIV-positive individuals over 174,857 person-years (PY) (incidence rate (IR) 3.22 per 1000 PY, 95% confidence interval (CI) 2.96-3.50) and stroke was diagnosed in 275 HIV-positive individuals over 174,947 PY (IR 1.57 per 1000 PY; 95%CI 1.40-1.77). For the current analysis, 9,257 HIV-infected individuals had immunological and virological measurements 3 months from baseline and were selected (median follow-up 9.9 years; IQR: 5.1-15.6 years). Individuals who had started ART before April 2000 or never started ART were excluded (see Figure 1 for flow chart for HIV-infected individuals‟ selection). Baseline socio-demographic characteristics of HIV individuals included in our analysis were similar to the characteristics of HIV individuals followed overall during the study period with regard to age, sex and education (Table 1). However, the proportion of injecting drug users in the sample selected for analysis was half of the proportion of injecting drug users among the HIV individuals followed-up, reflecting the challenges in following-up for this particular population.
Figure 1. Flow chart of HIV-positive individual selection.
Table 1.
Socio-demographic characteristics and baseline cardiovascular, immunological and virological risk factors of HIV-positive individuals followed-up between April 2000 and March 2021, included in the study and with coronary heart disease and stroke.
| Overall | Selected for analysis |
Coronary heart disease patients analysed |
Stroke patients analysed |
|
|---|---|---|---|---|
| (n=15303) | (n=9257) | (n=199) | (n=124) | |
| Socio-demographic | ||||
| Age [year] | ||||
| < 50 | 13073 (85.4%) | 7837 (84.7%) | 109 (54.8%) | 68 (54.8%) |
| 50-65 | 1919 (12.5%) | 1223 (13.2%) | 76 (38.2%) | 41 (33.1%) |
| ≥ 65 | 311 (2.0%) | 197 (2.1%) | 14 (7.0%) | 15 (12.1%) |
| Female | 4301 (28.1%) | 2470 (26.7%) | 18 (9.0%) | 29 (23.4%) |
| Education | ||||
| Compulsory school | 3074 (20.1%) | 1631 (17.6%) | 26 (13.1%) | 18 (14.5%) |
| Vocational training | 6170 (40.3%) | 3537 (38.2%) | 98 (49.2%) | 69 (55.6%) |
| Higher education | 4780 (31.2%) | 3339 (36.1%) | 70 (35.2%) | 31 (25.0%) |
| Other / unknown | 1279 (8.4%) | 750 (8.1%) | 5 (2.5%) | 6 (4.8%) |
| Transmission group | ||||
| Men having sex with men | 6446 (42.1%) | 4333 (46.8%) | 98 (49.2%) | 46 (37.1%) |
| Injecting drug user | 2436 (15.9%) | 723 (7.8%) | 15 (7.5%) | 11 (8.9%) |
| Other / unknown | 6421 (42.0%) | 4201 (45.4%) | 86 (43.2%) | 67 (54.0%) |
| Calendar year | ||||
| ≥ 2010 | 4118 (26.9%) | 3876 (41.9%) | 35 (17.6%) | 20 (16.1%) |
| Cardiovascular risk factors | ||||
| Dyslipidemia | ||||
| No | 6375 (41.7%) | 3911 (42.2%) | 62 (31.2%) | 46 (37.1%) |
| Yes | 6181 (40.4%) | 3827 (41.3%) | 110 (55.3%) | 56 (45.2%) |
| Missing | 2747 (18.0%) | 1519 (16.4%) | 27 (13.6%) | 22 (17.7%) |
| Hypertension | ||||
| No | 8817 (57.6%) | 5539 (59.8%) | 97 (48.7%) | 50 (40.3%) |
| Yes | 3843 (25.1%) | 2200 (23.8%) | 81 (407%) | 57 (46.0%) |
| Missing | 2643 (17.3%) | 1518 (16.4%) | 21 (10.6%) | 17 (13.7%) |
| Diabetes | ||||
| No | 12333 (80.6%) | 7316 (82.2%) | 162 (81.4%) | 97 (78.2%) |
| Yes | 259 (1.7%) | 165 (1.8%) | 10 (5.0%) | 5 (4.0%) |
| Missing | 2711 (17.7%) | 1479 (16.0%) | 27 (13.6%) | 22 (17.7%) |
| Obesity | ||||
| No | 12055 (78.8%) | 7289 (78.7%) | 163 (81.9%) | 100 (80.6%) |
| Yes | 637 (4.2%) | 454 (4.9%) | 11 (5.5%) | 7 (5.6%) |
| Missing | 2611 (17.1%) | 1514 (16.4%) | 25 (12.6%) | 17 (13.7%) |
| Metabolic syndrome | ||||
| No | 10526 (68.8%) | 6604 (71.3%) | 130 (65.3%) | 79 (63.7%) |
| Yes | 1976 (12.9%) | 1123 (12.1%) | 41 (20.6%) | 23 (18.5%) |
| Missing | 2801 (18.3%) | 1530 (16.5%) | 28 (14.1%) | 22 (17.7%) |
| Smoking | ||||
| No | 6588 (43.1%) | 4428 (47.8%) | 86 (43.2%) | 54 (43.5%) |
| Yes | 6440 (42.1%) | 3261 (35.2%) | 86 (43.2%) | 52 (41.9%) |
| Missing | 2275 (14.9%) | 1568 (16.9%) | 27 (13.6%) | 18 (14.5%) |
| Cytomegalovirus status | ||||
| No | 945 (6.2%) | 914 (9.9%) | 21 (10.6%) | 17 (13.7%) |
| Yes | 6607 (43.2%) | 6503 (70.2%) | 133 (66.8%) | 79 (63.7%) |
| Missing | 7751 (50.7%) | 1840 (19.9%) | 45 (22.6%) | 28 (22.6%) |
| Immunological and viral factors | ||||
| CD4 cell counts (cells/μl) | ||||
| <200 | 3332 (21.8%) | 2421 (26.2%) | 58 (29.1%) | 43 (34.7%) |
| 200-350 | 3298 (21.6%) | 2069 (22.4%) | 45 (22.6%) | 31 (25.0%) |
| 350-500 | 3181 (20.8%) | 1991 (21.5%) | 38 (19.1%) | 18 (14.5%) |
| 500+ | 5061 (33.1%) | 2776 (30.0%) | 58 (29.1%) | 32 (25.8%) |
| Missing | 431 (2.8%) | |||
| CD8 cell counts (cells/μl) | ||||
| <1000 | 8912 (58.2%) | 5574 (60.2%) | 108 (54.3%) | 70 (56.5%) |
| 1000+ | 5068 (33.1%) | 3002 (32.4%) | 76 (38.2%) | 45 (36.3%) |
| Missing | 1323 (8.6%) | 681 (7.4%) | 15 (7.5%) | 9 (7.3%) |
| CD4:CD8 ratio | ||||
| <0.5 | 7985 (52.2%) | 5150 (55.6%) | 129 (64.8%) | 79 (63.7%) |
| 0.5-1 | 4447 (29.1%) | 2533 (27.4%) | 39 (19.6%) | 25 (20.2%) |
| 1+ | 1548 (10.1%) | 893 (9.6%) | 16 (8.0%) | 11 (8.9%) |
| Missing | 1323 (8.6%) | 681 (7.4%) | 15 (7.5%) | 9 (7.3%) |
| HI viral load (copies/ml) | ||||
| <50 | 3760 (24.6%) | 1039 (11.2%) | 17 (8.5%) | 9 (7.3%) |
| 50-500 | 1089 (7.1%) | 455 (4.9%) | 11 (5.5%) | 7 (5.6%) |
| 500+ | 9879 (64.6%) | 7763 (83.9%) | 171 (85.9%) | 108 (87.1%) |
| Missing | 575 (3.8%) | |||
Dyslipidemia: total cholesterol>6.2 mmol/l or high-density lipoprotein (HDL)<1.03 mmol/l or (triglycerides>2 mmol/l &fasting), diabetes: glucose >11.1 mmol/l or glucose ≥ 7 mmol/l if fasting or on antidiabetic drugs (oral or insulin), hypertension: systolic >140 mmHg or diastolic >90 mmHg or on anti-hypertensive drugs or (systolic >135 mmHg if diabetes or diastolic >85 mmHg if diabetes), obesity: body mass index (BMI)>30 kg/m2, metabolic syndrome: having any three of the conditions abdominal obesity (waist circumference>102 cm in men, >88 cm in women) / triglyderides≥1.69 mmol/L / low HDL cholesterol (<1.03 mmol/L in men, <1.29 mmol/L in women) / blood pressure ≥130/≥85 mmHg / diabetes.
Important cardiovascular disease risk factors identified in preliminary analyses were dyslipidaemia, hypertension, metabolic syndrome, smoking, protease inhibitor exposure and abacavir exposure, all lagged at 36 months for CHD and hypertension lagged at 36 months and smoking lagged at 36 months for stroke (see supplementary appendix, Table S1). Since metabolic syndrome is a cluster of conditions that include hypertension and abnormal lipid levels, the variable was not carried forward for further analyses of CHD. The associations between the hazard of CHD and stroke and each considered functional form of time-updated CD4 and CD8 cell counts, CD4:CD8 ratio and HIV RNA viral load, after adjusting for socio-demographics and important cardiovascular risk factors are given in Tables S2-S7 of the supplementary appendix. None of the immunological and virological factors, neither as continuous nor as categorized variable, was associated with the hazard of CHD. CD8 lagged at 12 months as categorized variable was the only immunological factors associated in preliminary analyses and was therefore carried forward into a final model for stroke.
Results of mutually adjusted hazards for CHD and stroke, after imputation of missing baseline cardiovascular risk factors are shown in Table 2. Age, male, dyslipidaemia, hypertension, smoking, PI and abacavir exposures, all lagged at 36 months, were all positively associated to the hazard of CHD. For stroke, age, hypertension and smoking, both lagged at 36 months, and CD8 cell count above 1000 cells per μl lagged at 12 months were positively associated to the hazard of stroke. In HIV individuals with a 12-months lagged CD8 cell count above 1000 cells per μl, the risk of stroke after adjusting for socio-demographic and known cardiovascular risk factors was increased by more than 60% compared to individuals with CD8 below 1000 cells per μl (adjusted hazard ratio (aHR) 1.61, 95% CI 1.06-2.45).
Table 2. Results from multivariate Cox regression models for coronary heart disease and stroke.
| Coronary heart disease | Stroke | |
|---|---|---|
| aHR (95%CI) | aHR (95%CI) | |
| Age (time-varying) [year] | ||
| < 50 | 1 | 1 |
| 50-65 | 3.44 (2.29-5.16) | 4.27 (2.49-7.34) |
| ≥ 65 | 10.5 (6.5-16.94) | 13.05 (6.93-24.6) |
| Sex | ||
| Male | 1 | 1 |
| Female | 0.30 (0.17-0.53) | 0.81 (0.47-1.41) |
| Education | ||
| Compulsory school | 1 | 1 |
| Vocational training | 1.08 (0.67-1.75) | 1.85 (0.96-3.58) |
| Higher education | 0.98 (0.58-1.65) | 1.11 (0.52-2.36) |
| Other / unknown | 0.61 (0.23-1.61) | 1.00 (0.32-3.15) |
| Transmission group | ||
| Men having sex with men | 1 | 1 |
| Injecting drug user | 1.02 (0.56-1.88) | 1.15 (0.53-2.50) |
| Other /unkonwn | 1.19 (0.84-1.69) | 1.31 (0.81-2.12) |
| Calendar year | ||
| <2010 | 1 | 1 |
| >=2010 | 0.76 (0.45-1.29) | 0.52 (0.24-1.13) |
| Hypertension lagged 36 months | ||
| No | 1 | 1 |
| Yes | 1.85 (1.33-2.57) | 2.03 (1.32-3.12) |
| Smoking lagged 36 months | ||
| No | 1 | 1 |
| Yes | 1.71 (1.22-2.38) | 2.46 (1.57-3.86) |
| Dyslipidemia lagged 36 months | ||
| No | 1 | |
| Yes | 2.29 (1.63-3.21) | |
| Protease inhibitor exposure lagged 36 months | ||
| No | 1 | |
| Yes | 1.57 (1.14-2.16) | |
| Abacavir exposure lagged 36 months | ||
| No | 1 | |
| Yes | 1.83 (1.31-2.55) | |
| CD8 lagged 12 months [cells/μl] | ||
| <1000 | 1 | |
| 1000+ | 1.61 (1.06-2.45) |
aHR: adjusted hazard ratio ; CI : confidence interval.
Dyslipidemia: total cholesterol>6.2 mmol/l or high-density lipoprotein (HDL)<1.03 mmol/l or (triglycerides>2 mmol/l &fasting), hypertension: systolic >140 mmHg or diastolic >90 mmHg or on anti-hypertensive drugs or (systolic >135 mmHg if diabetes or diastolic >85 mmHg if diabetes).
Parameter estimates are pooled estimates from models fitted to 5 imputed datasets.
Results from HIV-positive individuals with complete baseline covariates, without imputation of missing cardiovascular risk factors dyslipidemia, hypertension, and smoking were similar (see supplementary appendix, Table S8). The selection of cardiovascular disease risk factors was robust with regard to the imputation of missing laboratory measurements; the effect estimates with missing baseline information imputed using multiple imputation on 5 datasets and with time-varying variables updated using LOCF restricted to a maximum of one year are presented in supplementary appendix (Table S9 and Table S10). Both final models for CHD and stroke satisfied the proportional assumption of the Cox regression model according to the test of temporal independence of the Schoenfeld residuals. Linearity of the relationships between the continuous covariates and estimated log hazard were satisfied according to a visual inspection of the Martingale residuals. CD4 and CD8 cell counts, CD4:CD8 ratio and HIV viral load were, however, still explored as variables categorized according to cut-off that are commonly used in other studies to distinguish between normal and abnormal conditions.
Discussion
Whether CD8 cell count and CD4:CD8 ratio as proxy variables for chronic immune stimulation by HIV plays a role in the elevated cardiovascular risk has been subject to contradicting results in the literature and motivated our work. In the present study of ART treated individuals the immunological markers CD4, CD8, CD4:CD8 ratio and HI viral load were not associated with significantly increased hazards for CHD in analyses with time lagged or cumulative exposure models showed and when adjusting for socio-demographics, cardiovascular risk factors and exposure to specific types of antiretroviral drugs. CD8 cell count lagged at 12 months above 1000 cells per μl, however, was associated with an increased hazard of stroke.
Several cohort studies have investigated the association between CD4:CD8 ratio and risk of different non AIDS-defining events that were combined. Results are conflicting due to the chosen approaches, model definitions, and different examined endpoints. In the large ART-CC cohort including 49,865 patients CD4:CD8 cell ratio was after adjustment for other factors (in particular CD4 cell count) not prognostic for overall mortality or non-AIDS defining mortality. Information on smoking was not collected in ART-CC. CD8 cell count had a U-shaped association with non-AIDS mortality but was not prognostic in adjusted analysis24. In the Italian ICONA cohort a CD4:CD8 ratio <0.3 was independently from CD4 cell count and other relevant confounders associated with an increased risk of non AIDS-defining events but in the time up-dated analysis this association was no longer statistically significant. There were 71 non AIDS-defining events in 3236 individuals 25. Current CD4:CD8 ratio <0.3 was associated with increased risk of the composite endpoint of non-AIDS defining events (including cardiovascular events and chronic kidney disease) in a Thai cohort with a median period of viral load suppression of 6.1 years26. In the French APROCO cohort study of 1227 patients that were followed over a median of 9.2 years CD4:CD8 ratio was in the analysis with adjustment for CD4 cell count not associated with increased risk of non AIDS-defining events (which included bacterial infections, cardiovascular disease and malignancies). Only few cohorts were able to look more specifically into these associations with an explicit focus on CVD and NADM. In the US Vanderbilt Cohort of 2006 PLWHIV CD4:CD8 was inversely related to the risk of coronary heart disease events (CHD) independent of CD4 cell count, and known CHD risk factors (HR 0.87, 95% CI: 0.76–0.99)17. This association was not confirmed for other non AIDS-defining events. In the French APROCO/COPILOTE cohort study which included 1206 PLWHIV CD4:CD8 ratio was no longer independently associated with CHD once accounting for CD4 cell count18. None of these cohorts including the present study collected data on additional inflammation markers, which is a limitation. Several nested case control studies most of them recruiting patients from existing cohorts also found an association between CD4:CD8 cell count and CHD, but the design of these studies has known limits15,16. Others found CD4:CD8 ratio to be inversely to surrogate markers for CHD such as carotid intima thickening27,28. These inconclusive findings may relate to low event rates from small cohorts, and the use of composite endpoints including a large proportion of non-AIDS defining events other than CVD. Several studies did not use cumulative exposure models for studying the independent contribution of CD8 T-cells and CD4:CD8 ratio for CVD prediction. Most studies limited their analysis to the time points following full virological suppression, and did not model pre-ART virological exposure or immunosuppression or lagged single or cumulative time exposures to minimize reverse causality bias.
High CD8 counts or low CD4:CD8 ratio among virollogically suppressed PLWHIV reflect immunoactivation and immunosenescence29. Association between inflammation, chronic infections and atherosclerosis or stroke has been established, with multiple pathways of action30. However, it is difficult to say if an increased CD8 cell count contributes directly to the occurrence of stroke or whether it is a consequence of other events that drive this clinical outcome. We note that neither CD4 nor viral load showed to be strongly association to the hazard of both stroke and CHD. Importantly, exposure to protease inhibitor and abacavir (lagged by 36 months) were both associated to the risk of CHD in the mutually-adjusted model. This is an important result that confirms previous studies31–33 and stresses the necessity to balance risks and benefits while introducing an abacavir and PI-based regimens, in particular with patients with a high CHD risk.
Our results are important for ART treated individuals and their treating clinicians in an era where HIV has become a chronic condition and cardiovascular disease an important cause of morbidity. Evidence from our large cohort with long-term follow-up indicates that in well-treated individuals traditionally monitored immune markers do not seem to have relevant prognostic value for CHD and stroke. Management of known risk factors for CHD and stroke and the switch from PIs and abacavir to alternative regimens if possible are the likely most efficient strategies to prevent CHD and stroke. Whether additional treatment of inflammation markers will further benefit PLWHIV and risk for CHD has to be shown in ongoing trials34.
The choice of all time-dependent covariates was done among several functional forms that include lagged and cumulative lagged exposures captured through single moving average. Lagging covariates avoid bias due to reverse causality that might happen when covariate is more a marker of an event rather than a predictor. Lagging a covariate can be seen as the length of the delay until the recorded covariate starts to affect the hazard. This might imply a loss of power since observations in the first months of follow-up, where past information is not available, are discarded. However, observed times to event in this study were mostly larger than 36 months for both CHD (median time 105 months; IQR: 52-156 months) and stroke (median time 99 months; IQR: 42-155 months).
With the exception of baseline immunological and virological variables for which the availability was part of the inclusion criteria into our study, missing baseline covariates were imputed using multivariate imputation by chained equations. Time-varying covariates were updated at the beginning of each follow-up month using LOCF, a widely-used popular imputation method in longitudinal data analysis. Despite this method is often seen as conservative and has been criticized, we preferred it to a linear interpolation or a carry back and forward method between the follow-up visits that make use of the future to predict the present.
Psychoactive substances are known to be an important driver of cardiovascular damages and it has been reported that consumption of those drugs is more important within IDU and MSM transmission groups35. Since SHCS started to collect data on recreational drugs only from 2007 onwards, we were not able to further control for addiction in the current analysis and this can be seen as a limitation. Also, CD4 and CD8 cell counts are the two markers used to routinely monitor immune system in SHCS. Data on T cell activation or exhaustion were not available, as well as cardiovascular disease biomarkers such as the high-sensitivity C-reactive protein The well-established SHCS provides a high-quality dataset with regular biannual follow-up visits, a vast amount of information on associated risk factors over more than 20 years and a substantial amount of cardiovascular events. Despite our study population was relatively young with a median age of 37 years, we had considerable statistical power to assess the association of the identified most important time-evolving immunological, viral and cardiovascular risk factors to the hazard of both CHD and stroke. Also, SHCS enabled to estimate incidence rates of stroke and CHD within the HIV infected population of Switzerland. In US, HIV infection has been associated with an increased risk of acute myocardial infarction36 and stroke37. The global burden disease (GBD)38 estimated a ischemic stroke incidence below 0.413/1000 for 2019, suggesting that the risk of stroke might be at least 3 times higher in the HIV population in Switzerland. However our incidence rates remain difficult to compare to the overall Swiss population since stroke and CHD data are lacking at Swiss population level. The shortage of population-based information, together with the lack of personal identifier that makes difficult to link different databases are reported as the main obstacles to get those statistics39.
Conclusions
Immune dysregulation as indexed by CD8 cell count and the CD4:CD8 cell ratio does not appear to be prognostic markers for CHD. An increased CD8 cell count may be a moderate risk factor for stroke. If confirmed by other studies this adds to our knowledge on the importance of early HIV diagnosis and treatment for optimal immune restoration and limitation of terminally differentiated memory CD8 T cells expansion as induced by unopposed HIV replication.
Supplementary Material
Acknowledgements
We are grateful to patients, study nurses and care providers that participate in the SHCS. Members of the Swiss HIV Cohort Study are: Aebi-Popp K, Anagnostopoulos A, Battegay M, Bernasconi E, Böni J, Braun DL, Bucher HC, Calmy A, Cavassini M, Ciuffi A, Dollenmaier G, Egger M, Elzi L, Fehr J, Fellay J, Furrer H, Fux CA, Günthard HF (President of the SHCS), Haerry D (deputy of "Positive Council"), Hasse B, Hirsch HH, Hoffmann M, Hösli I, Huber M, Kahlert CR (Chairman of the Mother & Child Substudy), Kaiser L, Keiser O, Klimkait T, Kouyos RD, Kovari H, Ledergerber B, Martinetti G, Martinez de Tejada B, Marzolini C, Metzner KJ, Müller N, Nicca D, Paioni P, Pantaleo G, Perreau M, Rauch A (Chairman of the Scientific Board), Rudin C, Scherrer AU (Head of Data Centre), Schmid P, Speck R, Stöckle M (Chairman of the Clinical and Laboratory Committee), Tarr P, Trkola A, Vernazza P, Wandeler G, Weber R, Yerly S.
Financial support
Swiss National Science Foundation (grant #177499), SHCS research foundation and Swiss Federal Office for Public Health.
Footnotes
Potential conflicts of interest:
HCB has received in the 36 months prior to the submission of this manuscript grants, support for travelling, consultancy fees and honorarium from Gilead, BMS, Viiv Healthcare, Roche and Pfizer that were not related to this project. He serves as the president of the association contre le HIV et autres infections transmissibles. In this function he has received support for the Swiss HIV Cohort Study from ViiV Healthcare, Gilead, BMS, and MSD. The institution of HF received educational grants not related to the publication from ViiV, Gilead, MSD, Abbvie and Sandoz. All other authors declare no competing interests. The institution of MH has received consultancy fees from ViiV and Gilead not related to the manuscript.
Author contributions:
FC and HB conceived and designed the study. FC did the statistical analysis. FC and HB verified the underlying data. FC and HB drafted the manuscript and all authors critically revised the manuscript and consented to final publication. All authors have full access to the data included in the study and accept responsibility for the decision to submit for publication.
Disclaimer:
The content is solely the responsibility of the authors
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