Abstract
Background
The proportion of people living with HIV/AIDS in the ageing population (>50 years) is increasing. We aim to explore the relationship between older age and treatment outcomes in HIV-positive persons from the Asia-Pacific region.
Methods
Patients from the Australian HIV Observational Database (AHOD) and the TREAT Asia HIV Observational Database (TAHOD) were included in the analysis. We used survival methods to assess the association between older age and all-cause mortality, as well as time-to treatment modification. We used regression analyses to evaluate changes in CD4 counts after combination antiretroviral therapy (cART) initiation and determined the odds of detectable viral load, up to 24 months of treatment.
Results
A total of 7142 patients were included in these analyses (60% TAHOD, 40% AHOD), of which, 25% were >50 years old. In multivariable analyses those aged >50 were at least twice as likely to die as those aged 30-39 years [HR (50-59 years): 2.27, 95% CI: 1.34-3.83; HR (>60years) 4.28, 95% CI: 2.42-7.55]. The effect of older age on CD4 count changes was insignificant (p-trend=0.06). The odds of detectable viral load after cART initiation decreased with age (p-trend=<0.0001). The effect of older age on time-to first treatment modification was insignificant (p-trend=0.21). We found no statistically significant differences in outcomes between AHOD and TAHOD participants for all endpoints examined.
Conclusion
The associations between older age and typical patient outcomes in HIV-positive patients from the Asia-Pacific region are similar in AHOD and TAHOD. Our data indicate that ‘age-effects’ traverse the resource-rich and resource-limited divide and indicate that future ageing-related findings might be applicable to each setting.
Keywords: HIV infection, ageing, Asia Pacific, outcomes, older patients, combination antiretroviral treatment
Introduction
After the scale-up of combination antiretroviral therapy (cART) in both resource-rich and resource-limited countries, most treated HIV-positive patients experience longer survival [1]. As the role out of frequent HIV-testing is expanded, an increasing proportion of new HIV diagnoses are found in older people [2,3]. Reasons for increases in new infections in older populations are multifactorial. Although HIV is transmitted at all ages, older individuals may be less inclined to be offered or request HIV testing as a consequence of both provider and patient perception of lack of risk [2]. In addition to HIV-positive patients surviving longer and a higher rate of diagnoses made in older individuals, there are now many older persons living with HIV/AIDS [4,5].
It has been suggested that HIV infection is associated with both physical and immunological changes commonly found in HIV-negative ageing populations. Desquilbet et al [6] reported that HIV-1 infection was associated with earlier occurrence of frailty (physical shrinking; unintentional weight loss; self-reported exhaustion; low physical activity; slowness; weakness of grip strength) and risk of exhibiting ‘frailty’ increased with duration of HIV infection. Exposure to HIV (including viral suppression) and long-term cART has been suggested to accelerate the body's natural ageing processes as a result of persistent immune activation and treatment induced pro-inflammatory effects leading to premature immunosenescence (ageing of the immune system) [7-12]. Furthermore, despite the clear success of cART, HIV-positive people appear to have an increased risk of serious ageing-related diseases including cardiovascular, liver and kidney disease, malignancies and bone disorders [13,14].
The additive effects of immunological changes associated with natural ageing and those resulting from HIV infection may affect the response to cART in ageing populations. Previous studies have shown that older populations have slower increases in CD4 after starting cART [15-18] even though older HIV-positive patients are more likely to achieve and maintain HIV-RNA viral load suppression compared to younger patients [16-21]. The population level response to cART and subsequent adherence and access to continual cART influence patterns of all-cause mortality. While it is expected that older aged HIV-positive persons would have a higher risk of mortality compared to younger population, comparing against general population level mortality rates, a notable excess risk differential exists across all age groups [22] [23]. There are minimal data published on directly comparing ageing associations for typical HIV treatment outcomes in resource-rich and resource-limited settings.
The objective of this analysis is to explore the relationship between older age and typical HIV treatment related outcomes. We aim to examine the associations between- older age and all-cause mortality; older age and mean CD4 cell count change in response to antiretroviral therapy; older age and the odds of detectable HIV-RNA viral load; older age and time-to first major treatment modification. The primary aim of this study is to establish and compare the patterns of older age associations in a cohort of patients from both resource-rich and resource-limited countries.
Methodology
Study Population
The Asia-Pacific HIV Observational Database (APHOD) is part of the International Epidemiological Databases Evaluating AIDS (IeDEA) collaboration and consists of two adult cohorts, the Australian HIV Observational Database (AHOD), the TREAT Asia HIV Observational Database (TAHOD), as well as one paediatric cohort, the TREAT Asia Paediatric HIV Observational Database (TApHOD). This study includes the AHOD and TAHOD adult cohorts only. AHOD data are collected from 27 clinical sites throughout Australia, including hospitals, sexual health clinics and clinics of general practitioners. Prospective data collection commenced in 1999 and retrospective data are provided where available. Patients written and informed consent to participate is obtained at time of enrollment. TAHOD data are primarily collected from 17 tertiary clinical sites throughout Asia. Prospective data collection commenced in 2003 and retrospective data are provided where available. Patients written and informed consent is obtained at enrollment in sites where it is required by the local ethics committee, otherwise data at sites are collected anonymously. Ethics approval for APHOD was granted to all participating sites by relevant institutional review boards. All APHOD study procedures were developed in accordance with the revised 1975 Helsinki Declaration.
Twice-annually (March, September) data for AHOD and TAHOD are collected on a core set of demographic and clinical variables and transferred electronically to the Kirby Institute, UNSW Australia, Sydney, Australia. Detailed description of each cohort has been previously described elsewhere, and data are subjected to quality control and quality assurance standardized procedures [24,25]. All patients with at least one follow-up visit and who were recruited up to March 2010 for AHOD and September 2009 for TAHOD were included in the analysis. We further restricted our analysis population to patients initiating cART without any prior exposure to ART (treatment naïve only).
Statistical Analysis
We defined an older patient as calendar year age >50 years old. We tabulated demographics and clinical characteristics of the study population stratified by age groups- younger/older.
Cox proportional hazards (PH) models adjusted for fixed and time-updated covariates were used to estimate the hazard ratio (HR) of older age and the association with all-cause mortality (composite endpoint of AIDS-related and non-AIDS related deaths) and separately, the associate of older age with time-to first major treatment modification. We defined a treatment modification as a change from the original regimen of at least two drugs or by the addition (or subtraction) of a new class of antiretroviral. Treatment modifications due to any reason were include as endpoints, including modification due to toxicity/side-effects, virological failure, patient or physician decision, and unknown. Follow-up time was measured from the date of cART initiation or cohort enrollment for the mortality endpoint, whichever came later, and time-to first treatment modification was measured from initiation of cART. Patients were censored if not seen at the clinic for 12 months prior to the site administration censoring date of 30th September 2009 for TAHOD, or 31st March 2010 for AHOD.
Absolute differences in CD4 cell count (cells/μL) compared with pre-cART CD4 count levels were examined at 6, 12, 18 and 24 months duration of cART. We used Generalised Estimating Equations (GEE), assuming an exchangeable correlation structure to account for within patient variation of the data. A Logistic GEE with and exchangeable correlation structure was used to estimate the odds ratios (OR) associated with older age and the probability of having a detectable viral load (defined as plasma HIV-RNA >400 copies/ml) at 6, 12, 18 and 24 months duration of ART. We conducted sensitivity analyses to examine the robustness of our results. Based on previous analyses, we examined different model specifications for our CD4 count modeling [26-28] and evaluated the influence of reduced viral load testing some participating TAHOD clinics by restricting the data to complete-case analyses [29].
Multivariable models (both Cox proportional hazards and GEE) were adjusted for sex; likely mode of HIV exposure (homosexual, heterosexual, injecting drug user, other, missing); AIDS illness (Yes, No); cohort (AHOD, TAHOD); calendar year (<1999, 1999-2001, 2002-2004, 2005-2007, 2008-2010); and if appropriate time-updated CD4 count (less than 50, 50-99, 100-199, 200-349, 350-500, 500+, missing); viral load (HIV RNA ≤400, RNA >400 copies/ml, missing) and duration of cART (6, 12, 18, 24 months). Multivariable models covariates were added to the model a priori and no form of model selection was considered. For each endpoint an interaction term between age and cohort was fitted to statistically assess any significant differences in cohort ageing effects. We assumed “intention-to-continue-treatment” and ignored any changes, interruptions or the termination of treatment after initiation of cART. All statistical analyses were computed using SAS software, Version 9.1.3 of the SAS System for Windows.
Results
Clinical and demographic characteristics
The proportion of patients under the age of fifty years was 75% in the combined cohort, or 60% and 84% in AHOD and TAHOD respectively (Table 1a). Of the younger population, approximately 90% (N=4725) were aged between 30-49 and the remaining 10% (N=608) were under the age of 30. In the older population, 67% were aged 50-59 (N=1 210) and 33% (N=599) were aged 60 years and over. The older population predominately consisted of AHOD patients (62%). Males represented the majority in both the younger and older populations (77% and 89%, respectively). The predominant likely mode of HIV exposure was homosexual contact in AHOD participants, and heterosexual contact in the TAHOD participants. The proportions of hepatitis B, or hepatitis C co-infection were similar in both the older and younger groups in both AHOD and TAHOD. Older patients had a longer time since HIV diagnosis than younger patients and similarly, the average duration of cART was greater in the older group (Table 1b). The proportion of patients on their 4th (or more) cART regimen was higher in older patients compared to younger patients (36% vs. 15% respectively). The anchor agents used in the patient's most recent cART regimen for older and younger patients were proportionally similar. Immunological differences determined from the patients most recent clinical visit were similar in older and younger patients, and the proportion of patients with detectable HIV RNA viral load was lower in the older population (10% vs. 20% in the younger group).
Table 1a. Characteristics of older and younger HIV-positive patients in APHOD, AHOD and TAHOD.
| APHOD | AHOD | TAHOD | ||||
|---|---|---|---|---|---|---|
| <50 years | ≥50 years | <50 years | ≥50 years | <50 years | ≥50 years | |
|
| ||||||
| N (%) | N (%) | N (%) | N (%) | N (%) | N (%) | |
| Total | 5333 (75) | 1809 (25) | 1697 (60) | 1127 (40) | 3636 (84) | 682 (16) |
| Sex | ||||||
| Female | 1240 (23) | 192 (11) | 130 (8) | 37 (3) | 1110 (31) | 155 (23) |
| Male | 4093 (77) | 1617 (89) | 1567 (92) | 1090 (97) | 2526 (69) | 527 (77) |
| Likely mode of HIV exposure | ||||||
| Unknown | 61 (1) | 10 (1) | 11 (1) | 7 (1) | 50 (1) | 3 (0) |
| Homosexual contact | 1912 (36) | 1001 (55) | 1219 (72) | 879 (78) | 693 (19) | 122 (18) |
| Injecting drug user | 390 (7) | 41 (2) | 134 (8) | 31 (3) | 256 (7) | 10 (1) |
| Heterosexual contact | 2544 (48) | 581 (32) | 170 (10) | 96 (9) | 2374 (65) | 485 (71) |
| Other | 426 (8) | 176 (10) | 163 (10) | 114 (10) | 263 (7) | 62 (9) |
| Ethnicity | ||||||
| Caucasian | 1697 (32) | 1127 (62) | 1697 (100) | 1127 (100) | - | - |
| Chinese | 953 (18) | 270 (15) | - | - | 953 (26) | 270 (40) |
| Indian | 545 (10) | 57 (3) | - | - | 545 (15) | 57 (8) |
| Thai | 871 (16) | 149 (8) | - | - | 871 (24) | 149 (22) |
| Other | 1267 (24) | 206 (11) | - | - | 1267 (35) | 206 (30) |
| Age (years)1 | ||||||
| Less 30 | 608 (11) | - | 87 (5) | - | 521 (14) | - |
| 30 - 39 | 2256 (42) | - | 544 (32) | - | 1712 (47) | - |
| 40 - 49 | 2469 (46) | - | 1066 (63) | - | 1403 (39) | - |
| 50 - 59 | - | 1210 (67) | - | 735 (65) | - | 475 (70) |
| 60+ | - | 599 (33) | - | 392 (35) | - | 207 (30) |
| Hepatitis B | ||||||
| Positive | 314 (8) | 97 (7) | 87 (6) | 45 (5) | 227 (10) | 52 (11) |
| Negative | 3405 (92) | 1354 (93) | 1286 (94) | 940 (95) | 2119 (90) | 414 (89) |
| Not Tested | 1614 | 358 | 324 | 142 | 1290 | 216 |
| Hepatitis C | ||||||
| Positive | 498 (14) | 116 (8) | 216 (14) | 84 (8) | 282 (14) | 32 (8) |
| Negative | 3035 (86) | 1325 (92) | 1277 (86) | 931 (92) | 1758 (86) | 394 (92) |
| Not Tested | 1800 | 368 | 204 | 112 | 1596 | 256 |
At patients' most recent clinic visit.
Table 1b. Clinical characteristics of older and younger HIV-positive patients in APHOD, AHOD and TAHOD.
| APHOD | AHOD | TAHOD | ||||
|---|---|---|---|---|---|---|
| <50 years | ≥50 years | <50 years | ≥50 years | <50 years | ≥50 years | |
|
| ||||||
| N (%) | N (%) | N (%) | N (%) | N (%) | N (%) | |
| Time since first positive HIV test (years) | ||||||
| Mean (std) | 8 (5) | 12 (7) | 12 (6) | 15 (7) | 6 (4) | 7 (4) |
| Median (IQR) | 6 (4-11) | 11 (6-18) | 11 (7-16) | 15 (0-21) | 5 (3-8) | 7 (4-9) |
| AIDS Illness Status1 | ||||||
| Yes | 2577 (48) | 735 (41) | 339 (20) | 298 (26) | 2238 (62) | 437 (64) |
| No | 2756 (52) | 1074 (59) | 1358 (80) | 829 (74) | 1398 (38) | 245 (36) |
| Treatment Regimen Number3 | ||||||
| Naïve | 806 (15) | 130 (7) | 260 (15) | 78 (7) | 546 (15) | 52 (8) |
| 1st | 1653 (31) | 353 (20) | 341 (20) | 148 (13) | 1312 (36) | 205 (30) |
| 2nd | 1329 (25) | 369 (20) | 327 (19) | 175 (16) | 1002 (28) | 194 (28) |
| 3rd | 719 (13) | 308 (17) | 247 (15) | 179 (16) | 472 (13) | 129 (19) |
| 4th plus | 826 (15) | 649 (36) | 522 (31) | 547 (49) | 304 (8) | 102 (15) |
| Duration of cART (months)4 | ||||||
| Mean (std) | 57 (37) | 82 (42) | 68 (44) | 91 (44) | 51 (32) | 66 (34) |
| Median (IQR) | 52 (27-79) | 80 (48-118) | 63 (30-104) | 97 (55-131) | 49 (26-73) | 66 (41-86) |
| Treatment Regimen Type5 | ||||||
| Off treatment/Naïve | 667 (13) | 82 (5) | 213 (13) | 48 (4) | 454 (12) | 34 (5) |
| 3+ II, ±NRTI, ±NNRTI, ±PI | 87 (2) | 123 (7) | 81 (5) | 118 (10) | 6 (0) | 5 (1) |
| 3+ NNRTI+PI, ±NRTI | 115 (2) | 87 (5) | 91 (5) | 77 (7) | 24 (1) | 10 (1) |
| 3+ NRTI+NNRTI | 2876 (54) | 809 (45) | 606 (36) | 409 (36) | 2270 (62) | 400 (59) |
| 3+ NRTI+PI | 1198 (22) | 490 (27) | 496 (29) | 302 (27) | 702 (19) | 188 (28) |
| 3+ NRTI | 71 (1) | 37 (2) | 55 (3) | 31 (3) | 16 (0) | 6 (1) |
| Mono/Duo/Other | 319 (6) | 181 (10) | 155 (9) | 142 (12) | 164 (5) | 39 (6) |
| CD4 cell count (cells/μL)2 | ||||||
| 0 – 50 | 147 (3) | 28 (2) | 44 (3) | 13 (1) | 103 (3) | 15 (2) |
| 50 – 100 | 109 (2) | 32 (2) | 22 (1) | 17 (2) | 87 (2) | 15 (2) |
| 100 – 200 | 334 (6) | 105 (6) | 55 (3) | 44 (4) | 279 (8) | 61 (9) |
| 200 – 350 | 854 (16) | 301 (17) | 187 (11) | 148 (13) | 667 (18) | 153 (22) |
| >350 | 2813 (53) | 1085 (60) | 1100 (65) | 768 (68) | 1713 (47) | 317 (46) |
| Missing | 1076 (20) | 258 (14) | 289 (17) | 137 (12) | 787 (22) | 121 (18) |
| HIV RNA viral load (copies/ml)2 | ||||||
| ≤400 | 2339 (44) | 1231 (68) | 1010 (60) | 865 (77) | 1329 (37) | 366 (54) |
| >400 | 639 (12) | 142 (8) | 380 (22) | 108 (10) | 259 (7) | 34 (5) |
| Missing | 2355 (44) | 436 (24) | 307 (18) | 154 (14) | 2048 (56) | 282 (41) |
At patients' most recent clinical visit.
Measurement closest to the most recent clinic visit date. Missing assigned if last test date is outside a 6-month window.
Treatment is defined as duration of treatment >14 days, number of ARVs ≥3 and start of treatment >1996.
Time receiving cART (in months), does not include structured treatment breaks or time off treatment.
Based on the last treatment record available for patient.
All-cause Mortality
Risk of all-cause mortality increased with age in both univariate and multivariate Cox models (Table 2). Relative to persons aged 30-39 years old, patients aged 50-59 years had a 2-fold increase (95% CI: 1.4-3.8) risk of all-cause mortality and patient's aged 60+ had a 4-fold increase (95% CI: 2.4-7.5) in risk of all-cause mortality. Within each age stratum, there were different proportions of IDU mode of HIV exposure potentially confounding our results. To adjust for this, we fitted an interaction between younger patients and IDU mode of HIV exposure and we found the interaction term to be insignificant (p-value=0.38, HR not shown). We found no differences for the association of older age and risk of all-cause mortality between the two cohorts (Figure 1). Cox Model assumptions, including the validity of proportional hazards were not violated.
Table 2. Age associations of all-cause mortality, time-to first treatment modification, mean change CD4 cell count response, and odds of detectable HIV-RNA viral load.
| Age Association | Univariate Model | Multivariable Model1 | ||||
|---|---|---|---|---|---|---|
| Estimate | p-value | global p-value | Estimate | p-values | global p-value | |
| All-cause mortality (Hazard Ratio) | ||||||
| <30 | 1.55 (0.81 to 2.96) | 0.19 | 0.001 | 1.58 (0.81 to 3.06) | 0.18 | <0.0001 |
| 30-39 (reference) | 1.00 | - | 1.00 | - | ||
| 40-49 | 1.50 (0.99 to 2.28) | 0.06 | 1.61 (1.05 to 2.45) | 0.03 | ||
| 50-59 | 1.75 (1.05 to 2.93) | 0.03 | 2.27 (1.34 to 3.83) | <0.0001 | ||
| ≥60 | 3.17 (1.84 to 5.47) | <0.0001 | 4.28 (2.42 to 7.55) | <0.0001 | ||
| Mean change CD4 cell count (absolute mean difference) | ||||||
| <30 | -16 (-35 to 4) | 0.11 | 0.3 | -5 (-23 to 13) | 0.57 | 0.06 |
| 30-39 (reference) | 0.0 | - | 0.0 | - | ||
| 40-49 | -7 (-25 to 11) | 0.46 | -13 (-31 to 4) | 0.14 | ||
| 50-59 | -9 (-33 to 15) | 0.45 | -16 (-39 to 7) | 0.18 | ||
| ≥60 | -30 (-61 to 2) | 0.07 | -45 (-75 to -14) | <0.0001 | ||
| Probability of detectable viral load (Odds Ratio) | ||||||
| <30 | 1.42 (1.12 to 1.8) | 0.01 | <0.0001 | 1.36 (1.07 to 1.73) | 0.01 | <0.0001 |
| 30-39 (reference) | 1.00 | - | 1.00 | - | ||
| 40-49 | 0.73 (0.57 to 0.93) | 0.01 | 0.69 (0.54 to 0.89) | <0.0001 | ||
| 50-59 | 0.61 (0.42 to 0.88) | <0.0001 | 0.53 (0.36 to 0.78) | <0.0001 | ||
| ≥60 | 0.52 (0.29 to 0.91) | 0.05 | 0.55 (0.31 to 0.97) | 0.04 | ||
| Time until treatment change (Hazard Ratio) | ||||||
| <30 | 0.89 (0.77 to 1.03) | 0.13 | 0.55 | 0.89 (0.77 to 1.03) | 0.13 | 0.55 |
| 30-39 (reference) | 1.00 | - | 1.00 | - | ||
| 40-49 | 0.99 (0.89 to 1.1) | 0.87 | 0.99 (0.89 to 1.1) | 0.87 | ||
| 50-59 | 1.02 (0.88 to 1.17) | 0.82 | 1.02 (0.88 to 1.17) | 0.82 | ||
| ≥60 | 1.06 (0.86 to 1.3) | 0.61 | 1.06 (0.86 to 1.3) | 0.61 | ||
Multivariable model adjusted for sex, cohort, HIV exposure, AIDS illness, calendar year, duration of cART, and where appropriate time-updated CD4 cell count, time-updated HIV-RNA viral load.
Figure 1. The association of the hazard ratio for all cause mortality with age stratified by cohort.
AHOD, Australian HIV Observational Database; CI confidence interval; TAHOD, TREAT Asia HIV Observational Database.
Immunological Responses
CD4 cell count responses to antiretroviral treatment varied over time and were largely determined by CD4 cell count at cART initiation (Table 2). The effect of older age on CD4 cell response was statistically insignificant (p-trend=0.06). However, patient's aged 60+ had statistically poorer immunological responses. Patients in this age group had a mean difference in absolute CD4 count gain of -45 cells/μl (95% CI: -75 to -14 cells/microliter) relative to 30-39 year olds patients. The interaction term between older age and cohort was statistically insignificant (p-value=0.72) and sensitivity analyses showed the estimated age effects were robust under different model specifications adjusting for time and pre-cART CD4 cell count (data not shown).
Detectable Viral Load
The odds of detectable viral load were associated with age (Table 2). Older age groups relative to the reference group (30-39 years) had significantly lower odds of detectable viral load. In the multivariable model, 40-49 years old patients had a relative Odds Ratio (OR) of 0.69 (95% CI: 0.54-0.89), and patients aged 50-59 and 60+ years had respectively an OR of 0.53 (95% CI: 0.36-0.78) and 0.55 (95% CI: 0.31-0.97). Patients <30 years old had an increased OR of detectable viral load relative to patients aged 30-39 years. We found no differences in the measured age association across the two cohorts, AHOD and TAHOD (Figure 2). Sensitivity analyses were performed to assess the impact of patients who were lost to follow-up and missing records. Resulting age associations were qualitatively similar.
Figure 2. Association of odds ratio of detectable HIV RNA viral load with age stratified by cohort.
AHOD, Australian HIV Observational Database; CI, confidence interval; TAHOD, TREAT Asia Observational Database.
Time to cART Regimen Change
We found no significant age effect for the time-to major treatment modification (across all competing risks of switching), p=0.21 (Table 2). The interaction term between age and cohort was insignificant. A strong cohort effect was identified. TAHOD patients were half as likely, relative to AHOD, to change treatment (HR=0.59; 95% CI: 0.5-0.64) during our observed study period.
Discussion
In this study we found that ageing in HIV-positive APHOD patients was statistically significantly associated with increased all-cause mortality and a decreased likelihood of detectable viral load after cART initiation. Ageing was not significantly associated with a reduced response in CD4 counts after treatment initiation, except in patients older than 60 years old. We found that age was not associated with time-to first major modification of cART, and importantly, we show that the patterns of the association between age and all-cause mortality were consistent in HIV-patients from both resource-rich and resource-limited countries.
Many studies have shown an association between older age and increased risk of all-cause mortality [5,16,17,30,34]. Our results are consistent with these ageing trends in terms of magnitude and direction of risk. Additionally, our results are consistent with previous reports that have shown that older HIV populations initiating cART have higher odds of maintaining an undetectable HIV-RNA viral load during follow-up [16-21]. In a prospective cohort study conducted by Nogueras et al., it was hypothesised that this observation is related to better demonstrated treatment adherence in older patients, whom may have more stable lifestyles than younger patients [16]. Others have shown that increases in CD4 cell count following initiation of cART may be blunted in older patients [15-18]. Our data support this finding, though primarily in older age patients (>60 years old). However the clinical significance of this finding and how it traslates into an risk of all-cause mortality remains unclear. The rates of time-to first major modification of cART are similar in older and younger HIV-positive patients which is consistent with other studies that evaluated predictors of early cART modification [20,29,31]. Older age is generally not associated with treatment modification.
In this study, our key finding was the lack of statistically significant different patterns in the association between older age and HIV related treatment outcomes in resource-rich (AHOD) and resource-limited (TAHOD) countries. The patient demographics; clinical characteristics; availability of cART; clinical care setting; and availability of medical resources in AHOD and TAHOD are vastly different [32,33]. Nevertheless, we report markedly similar ageing associations for all typical HIV-related treatment outcomes and suggest that some results or findings from future ageing studies might be applicable to both resource-rich and resource-limited settings.
There are limitations to our analyses. We do not report any associations between older age and the risk of serious non-AIDS events (SNE) or other known biomarkers associated with ageing related morbidity (CD4/CD8 ratios, d-dimer, IL-6, etc). We do not collect these data routinely in AHOD and in TAHOD, however collection of SNE's data has recently commenced in TAHOD. The two cohorts are substantially different and we have attempted to adjusted for this by fitting an interaction terms in all of our models. However, within TAHOD there are many different ethnicities, from low, middle and high income countries, which might confound our results. Previous APHOD studies that specifically aimed to compare outcomes between high- and low-income countries or between ethnicities found little difference [26,29,34-37].
In conclusion, this study on HIV and ageing in the Asia-Pacific region has shown that older patients on cART maintained better virological control than younger patients. Older patients had marginally poorer CD4 cell response and a two-fold higher risk of all-cause mortality. We found no differences in the time-to first major modification of cART. Importantly, we did not find any significant difference in the ageing associations of typical HIV-related outcomes between a resource-rich and a predominately resource-limited cohort. As the number of ageing HIV-positive patients increases in the coming years, many will experience typical ageing related morbidity, perhaps earlier and further complicated by their HIV infection. The general burden of disease in the ageing HIV-population and the affect on financial resources is yet to be determined and warrants further investigation.
Acknowledgments
Funding Source: The TREAT Asia HIV Observational Database and the Australian HIV Observational Database are initiatives of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the U.S. National Institutes of Health's National Institute of Allergy and Infectious Diseases, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and National Cancer Institute, as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, The University of New South Wales. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above.
The TREAT Asia HIV Observational Database: Cambodia: CV Mean, V Saphonn* and K Vohith, National Center for HIV/AIDS, Dermatology & STDs, Phnom Penh. China: FJ Zhang*, HX Zhao and N Han, Beijing Ditan Hospital, Capital Medical University, Beijing; PCK Li* and MP Lee, Queen Elizabeth Hospital, Hong Kong. India: N Kumarasamy*, S Saghayam and C Ezhilarasi, YRG Centre for AIDS Research and Education, Chennai; S Pujari*, K Joshi and A Makane, Institute of Infectious Diseases, Pune. Indonesia: TP Merati* ‡, DN Wirawan and F Yuliana, Faculty of Medicine Udayana University & Sanglah Hospital, Bali; E Yunihastuti* †, D Imran and A Widhani, Working Group on AIDS Faculty of Medicine, University of Indonesia/ Ciptomangunkusumo Hospital, Jakarta. Japan: S Oka*, J Tanuma and T Nishijima, National Center for Global Health and Medicine, Tokyo. South Korea: JY Choi*, Na S, and JM Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul. Malaysia: CKC Lee*, BLH Sim and R David, Hospital Sungai Buloh, Sungai Buloh; A Kamarulzaman*, SF Syed Omar, S Ponnampalavanar and I Azwa, University of Malaya Medical Centre, Kuala Lumpur. Philippines: R Ditangco*, E Uy and R Bantique, Research Institute for Tropical Medicine, Manila. Taiwan: WW Wong*, WW Ku and PC Wu, Taipei Veterans General Hospital, Taipei. Singapore: OT Ng*, PL Lim, LS Lee, and MT Tan, Tan Tock Seng Hospital. Thailand: P Phanuphak*, K Ruxrungtham, A Avihingsanon, and P Chusut, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok; S Kiertiburanakul*, S Sungkanuparph, L Chumla, and N Sanmeema, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok; R Chaiwarith*, T Sirisanthana,W Kotarathititum and J Praparattanapan, Research Institute for Health Sciences, Chiang Mai; P Kantipong and P Kambua, Chiang Rai Prachanukroh Hospital, Chiang Rai; W Ratanasuwan and R Sriondee, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok. Vietnam: VK Nguyen*, VH Bui and TT Cao, National Hospital for Tropical Diseases, Hanoi; TT Pham*, DD Cuong and HL Ha, Bach Mai Hospital, Hanoi. Coordinating office: AH Sohn*, N Durier* and B Petersen, TREAT Asia, amfAR - The Foundation for AIDS Research, Bangkok, Thailand; DA Cooper, MG Law*, A Jiamsakul* and D Boettiger, The Kirby Institute, The University of New South Wales, Sydney, Australia.
* TAHOD Steering Committee member; † Steering Committee Chair; ‡ co-Chair.
The Australian HIV Observational Database: New South Wales: D Ellis, General Medical Practice, Coffs Harbour; M Bloch, T Franic*, S Agrawal, L McCann, N Cunningham, T Vincent, Holdsworth House General Practice, Darlinghurst; D Allen, JL Little, Holden Street Clinic, Gosford; D Smith, C Gray, Lismore Sexual Health & AIDS Services, Lismore; D Baker*, R Vale, East Sydney Doctors, Surry Hills; DJ Templeton*, CC O'Connor, C Dijanosic, RPA Sexual Health Clinic, Camperdown; E Jackson, K McCallum, Blue Mountains Sexual Health and HIV Clinic, Katoomba; M Grotowski, S Taylor, Tamworth Sexual Health Service, Tamworth; D Cooper, A Carr, F Lee, K Hesse, K Sinn, R Norris, St Vincent's Hospital, Darlinghurst; R Finlayson, I Prone, Taylor Square Private Clinic, Darlinghurst; E Jackson, J Shakeshaft, Nepean Sexual Health and HIV Clinic, Penrith; K Brown, C McGrath, V McGrath, S Halligan, Illawarra Sexual Health Service, Warrawong; L Wray, P Read, H Lu, Sydney Sexual Health Centre, Sydney; D Couldwell, Parramatta Sexual Health Clinic; D Smith,V Furner, Albion Street Centre; Dubbo Sexual Health Centre, Dubbo; J Watson*, National Association of People living with HIV/AIDS; C Lawrence*, National Aboriginal Community Controlled Health Organisation; B Mulhall*, Department of Public Health and Community Medicine, University of Sydney; M Law*, K Petoumenos*, S Wright*, H McManus*, C Bendall*, M Boyd*, The Kirby Institute, University of NSW. Northern Territory: A Kulatunga, P Knibbs, Communicable Disease Centre, Royal Darwin Hospital, Darwin. Queensland: J Chuah*, M Ngieng, B Dickson, Gold Coast Sexual Health Clinic, Miami; D Russell, S Downing, Cairns Sexual Health Service, Cairns; D Sowden, J Broom, K Taing, C Johnston, K McGill, Clinic 87, Sunshine Coast-Wide Bay Health Service District, Nambour; D Orth, D Youds, Gladstone Road Medical Centre, Highgate Hill; M Kelly, A Gibson, H Magon, Brisbane Sexual Health and HIV Service, Brisbane. South Australia: W Donohue,O'Brien Street General Practice, Adelaide. Victoria: R Moore, S Edwards, R Liddle, P Locke, Northside Clinic, North Fitzroy; NJ Roth* †, J Nicolson*, H Lau, Prahran Market Clinic, South Yarra; T Read, J Silvers*, W Zeng, Melbourne Sexual Health Centre, Melbourne; J Hoy*, K Watson*, M Bryant, S Price, The Alfred Hospital, Melbourne; I Woolley, M Giles, T Korman, J Williams, Monash Medical Centre, Clayton. Western Australia: D Nolan, J Skett, J Robinson, Department of Clinical Immunology, Royal Perth Hospital, Perth.
*AHOD Steering Committee member, † Current Steering Committee chair.
References
- 1.Palella FJF, Delaney KMK, Moorman ACA, Loveless MOM, Fuhrer JJ, Satten GAG, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998;338:853–860. doi: 10.1056/NEJM199803263381301. [DOI] [PubMed] [Google Scholar]
- 2.Bhavan KP, Kampalath VN, Overton ET. The aging of the HIV epidemic. Curr HIV/AIDS Rep. 2008;5:150–158. doi: 10.1007/s11904-008-0023-3. [DOI] [PubMed] [Google Scholar]
- 3.Smith RD, Delpech VC, Brown AE, Rice BD. HIV transmission and high rates of late diagnoses among adults aged 50 years and over. AIDS. 2010;24:2109–2115. doi: 10.1097/QAD.0b013e32833c7b9c. [DOI] [PubMed] [Google Scholar]
- 4.Murray JM, McDonald AM, Law MG. Rapidly ageing HIV epidemic among men who have sex with men in Australia. Sex Health. 2009;6:83–86. doi: 10.1071/sh08063. [DOI] [PubMed] [Google Scholar]
- 5.Effros RB, Fletcher CV, Gebo K, Halter JB, Hazzard WR, Horne FM, et al. Aging and infectious diseases: workshop on HIV infection and aging: what is known and future research directions. Clinical Infectious Diseases. 2008;47:542–553. doi: 10.1086/590150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Desquilbet L, Jacobson LP, Fried LP, Phair JP, Jamieson BD, Holloway M, et al. HIV-1 infection is associated with an earlier occurrence of a phenotype related to frailty. Journals of Gerontology Series a-Biological Sciences and Medical Sciences. 2007;62:1279–1286. doi: 10.1093/gerona/62.11.1279. [DOI] [PubMed] [Google Scholar]
- 7.Appay V, Rowland-Jones SL. Premature ageing of the immune system: the cause of AIDS? Trends Immunol. 2002;23:580–585. doi: 10.1016/s1471-4906(02)02338-4. [DOI] [PubMed] [Google Scholar]
- 8.Capeau J. Premature Aging and Premature Age-Related Comorbidities in HIV-Infected Patients: Facts and Hypotheses. Clinical Infectious Diseases. 2011;53:1127–1129. doi: 10.1093/cid/cir628. [DOI] [PubMed] [Google Scholar]
- 9.De Biasi S, Pinti M, Nasi M, Gibellini L, Bertoncelli L, Manzini S, et al. HIV-1 Infection and the Aging of the Immune System: Facts, Similarities and Perspectives. J Exp Clin Med. 2011;3:143–150. [Google Scholar]
- 10.Deeks SG, Phillips AN. HIV infection, antiretroviral treatment, ageing, and non-AIDS related morbidity. BMJ. 2008;338:a3172–a3172. doi: 10.1136/bmj.a3172. [DOI] [PubMed] [Google Scholar]
- 11.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. Clinical Infectious Diseases. 2011;53:1120–1126. doi: 10.1093/cid/cir627. [DOI] [PubMed] [Google Scholar]
- 12.Hearps AC, Angelovich TA, Jaworowski A, Mills J, Landay AL, Crowe SM. HIV infection and aging of the innate immune system. Sex Health. 2011;8:453–464. doi: 10.1071/SH11028. [DOI] [PubMed] [Google Scholar]
- 13.Group SFMOATSS. El-Sadr WM, Lundgren JD, Neaton JD, Gordin F, Abrams D, et al. CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med. 2006;355:2283–2296. doi: 10.1056/NEJMoa062360. [DOI] [PubMed] [Google Scholar]
- 14.Phillips AN, Neaton J, Lundgren JD. The role of HIV in serious diseases other than AIDS. AIDS. 2008;22:2409–2418. doi: 10.1097/QAD.0b013e3283174636. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Grabar S. HIV infection in older patients in the HAART era. Journal of Antimicrobial Chemotherapy. 2005;57:4–7. doi: 10.1093/jac/dki411. [DOI] [PubMed] [Google Scholar]
- 16.Nogueras M, Navarro G, Antón E, Sala M, Cervantes M, Amengual M, et al. Epidemiological and clinical features, response to HAART, and survival in HIV-infected patients diagnosed at the age of 50 or more. BMC Infect Dis. 2005;6:159–159. doi: 10.1186/1471-2334-6-159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Mutevedzi PC, Lessells RJ, Rodger AJ, Newell M-L. Association of Age with Mortality and Virological and Immunological Response to Antiretroviral Therapy in Rural South African Adults. PLoS ONE. 2011;6:e21795. doi: 10.1371/journal.pone.0021795. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sabin CA, Smith CJ, Monforte AD, Battegay M, Gabiano C, Galli L, et al. Response to combination antiretroviral therapy: variation by age - The Collaboration of Observational HIV Epidemiological Research Europe (COHERE) study group. AIDS. 2008;22:1463–1473. doi: 10.1097/QAD.0b013e3282f88d02. [DOI] [PubMed] [Google Scholar]
- 19.Silverberg MJ, Leyden W, Horberg MA, DeLorenze GN, Klein D, Quesenberry CP. Older age and the response to and tolerability of antiretroviral therapy. Arch Intern Med. 2007;167:684–691. doi: 10.1001/archinte.167.7.684. [DOI] [PubMed] [Google Scholar]
- 20.Abgrall S. Durability of first ART regimen and risk factors for modification, interruption or death in HIV-positive patients starting ART in Europe and N.America 2002–2009. AIDS. 2012:1. doi: 10.1097/QAD.0b013e32835cb997. [DOI] [PubMed] [Google Scholar]
- 21.Orlando G, Meraviglia P, Valsecchi L, Mainini A, Schiavini M, Merli S, et al. cART Durability and Causes for Treatment Switching or Discontinuation in HIV-Positive Patients Older Than 50 Years of Age. JAIDS Journal of Acquired Immune Deficiency Syndromes. 2010;55:E12–E14. doi: 10.1097/QAI.0b013e3181ef791b. [DOI] [PubMed] [Google Scholar]
- 22.McManus H, O'Connor CC, Boyd M, Broom J, Russell D, Watson K, et al. Long-Term Survival in HIV Positive Patients with up to 15 Years of Antiretroviral Therapy. PLoS ONE. 2012;7:e48839. doi: 10.1371/journal.pone.0048839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.The Antiretroviral Therapy Cohort Collaboration. Mortality of HIV-infected patients starting potent antiretroviral therapy: comparison with the general population in nine industrialized countries. International Journal of Epidemiology. 2009;38:1624–1633. doi: 10.1093/ije/dyp306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Australian HIV Observational Database. Rates of combination antiretroviral treatment change in Australia, 1997-2000. HIV Med. 2002;3:28–36. doi: 10.1046/j.1464-2662.2001.00094.x. [DOI] [PubMed] [Google Scholar]
- 25.Zhou J, Kumarasamy N, Ditangco R, Kamarulzaman A, Lee CKC, Li PCK, et al. The TREAT Asia HIV Observational Database: baseline and retrospective data. J Acquir Immune Defic Syndr. 2005;38:174–179. doi: 10.1097/01.qai.0000145351.96815.d5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Egger S, Petoumenos K, Kamarulzaman A, Hoy J, Sungkanuparph S, Chuah J, et al. Long-term patterns in CD4 response are determined by an interaction between baseline CD4 cell count, viral load, and time: The Asia Pacific HIV Observational Database (APHOD) J Acquir Immune Defic Syndr. 2009;50:513–520. doi: 10.1097/qai.0b013e31819906d3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wright ST, Carr A, Woolley I, Giles M, Hoy J, Cooper DA, et al. CD4 cell responses to combination antiretroviral therapy in patients starting therapy at high CD4 cell counts. J Acquir Immune Defic Syndr. 2011;58:72–79. doi: 10.1097/QAI.0b013e318225ba62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wright ST, Petoumenos K, Boyd M, Carr A, Downing S, O'Connor CC, et al. Ageing and long-term CD4 cell count trends in HIV-positive patients with 5 years or more combination antiretroviral therapy experience. HIV Med. 2013;14:208–216. doi: 10.1111/j.1468-1293.2012.01053.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Wright ST, Boyd MA, Yunihastuti E, Law M, Sirisanthana T, Hoy J, et al. Rates and factors associated with major modifications to first-line combination antiretroviral therapy: results from the Asia-pacific region. PLoS ONE. 2013;8:e64902–e64902. doi: 10.1371/journal.pone.0064902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Dougan S, Payne LJC, Brown AE, Evans BG, Gill ON. Past it? HIV and older people in England, Wales and Northern Ireland. Epidemiol Infect. 2004;132:1151–1160. doi: 10.1017/s0950268804002961. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.ART-LINC of IeDEA Study Group. Keiser O, Tweya H, Boulle A, Braitstein P, Schecter M, et al. Switching to second-line antiretroviral therapy in resource-limited settings: comparison of programmes with and without viral load monitoring. AIDS. 2009;23:1867–1874. doi: 10.1097/QAD.0b013e32832e05b2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Srikantiah P, Ghidinelli M, Bachani D, Chasombat S, Daoni E, Mustikawati DE, et al. Scale-up of national antiretroviral therapy programs: progress and challenges in the Asia Pacific region. AIDS. 2010;24:S62. doi: 10.1097/01.aids.0000390091.45435.ea. [DOI] [PubMed] [Google Scholar]
- 33.Boyd MA. Current and future management of treatment failure in low- and middle-income countries. Curr Opin HIV AIDS. 2010;5:83–89. doi: 10.1097/COH.0b013e328333b8c0. [DOI] [PubMed] [Google Scholar]
- 34.Falster K, Choi JY, Donovan B, Duncombe C, Mulhall B, Sowden D, et al. AIDS-related and non-AIDS-related mortality in the Asia-Pacific region in the era of combination antiretroviral treatment. AIDS. 2009;23:2323–2336. doi: 10.1097/QAD.0b013e328331910c. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Achhra AC, Zhou J, Dabis F, Pujari S, Thiebaut R, Law MG, et al. Difference in absolute CD4+ count according to CD4 percentage between Asian and Caucasian HIV-infected patients. J AIDS Clin Res. 2010;1:1–4. doi: 10.4172/2155-6113.1000101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Oyomopito R, Lee MP, Database TAHO, 21 Measures of site resourcing predict virologic suppression, immunologic response and HIV disease progression following highly active antiretroviral therapy (HAART) in the TREAT Asia HIV Observational Database (TAHOD) HIV Med. 2010;11:519–529. doi: 10.1111/j.1468-1293.2010.00822.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.McManus H, Li PCK, Nolan D, Bloch M, Kiertiburanakul S, Choi JY, et al. Does use of antiretroviral therapy regimens with high central nervous system penetration improve survival in HIV-infected adults? HIV Med. 2011;12:610–619. doi: 10.1111/j.1468-1293.2011.00938.x. [DOI] [PMC free article] [PubMed] [Google Scholar]