European Geriatric Medicine

, Volume 10, Issue 2, pp 247–257 | Cite as

European cohorts of older HIV adults: POPPY, AGEhIV, GEPPO, COBRA and FUNCFRAIL

  • Jovana Milic
  • Magdalena Russwurm
  • Ana Cerezales Calvino
  • Fátima Brañas
  • Matilde Sánchez-Conde
  • Giovanni GuaraldiEmail author

Key summary points


The aim of this paper is to describe ongoing aging HIV cohorts that include older (> 50 years) or geriatric (> 65 years) people living with HIV and present the key results obtained in those studies.


Existing aging HIV cohorts are pointing out unmet medical needs of OALWH. However, there are no studies designed to detect best ART strategies in this population and various outcomes that go beyond the viro-immunological success, such as frailty, geriatric syndromes, physical function, disability, quality of life and healthy aging are still not routinely part of aging cohorts.


The already present demographic transition affecting PLWH are not paralleled by significant changes in the clinical management. In order to better describe aging trajectories in this population, it is essential to collect and measure immunological biomarkers. Results from aging HIV observational studies can also inspire randomized clinical trials.



The recent and rapid demographic changes affecting people living with HIV (PLWH) produced a subset of older adults demanding a prompt response both in clinical practice and research setting. The scientific community had to properly design studies that include older adults living with HIV (OALWH), aged > 50 years, or geriatric PLWH, aged > 65 years to explore the interaction between aging and HIV itself, antiretroviral therapy (ART) and non-infectious co-morbidities (NICM). Choosing between these two types of cohorts may represent a trap, but also a possibility to measure different outcomes and obtain different evidence. The aim of this paper is to describe ongoing aging HIV cohorts that include older or geriatric PLWH and present the key results obtained in those studies.


So far, in Europe, there are ongoing cohorts that comprise OALWH or geriatric PLWH: AGEhIV, POPPY, GEPPO, FUNCFRAIL and COBRA. We will summarize crucial findings from each study published up to now, which will be categorized as results related to HIV and ART, NICM and geriatric syndromes.


Existing aging HIV cohorts are pointing out unmet medical needs of OALWH but are still not representative of the entire European HIV aging epidemic. Moreover, there are no studies designed to detect best ART strategies in this population and various outcomes that go beyond the viro-immunological success are still not routinely part of aging cohorts.


Results from aging cohorts with outcomes that go beyond the undetectability will pave the way to health care providers to encounter unmet needs of OALWH.


Observational HIV study Aging Older adults Geriatric people living with HIV 


Why are aging HIV cohorts needed?

The recent and rapid demographic changes affecting people living with HIV (PLWH) produced a subset of older adults demanding a prompt response both in clinical practice and research setting. The unmet medical needs of this emerging population could be addressed with existing large observational HIV cohort studies, but unluckily older adults are rarely represented in these cohorts. Therefore, the scientific community had to properly design studies that include older people living with HIV (OALWH); aged more than 50 years, or geriatric PLWH, aged 65 years or more, to explore the interaction between aging and HIV itself, antiretroviral therapy (ART) and non-infectious co-morbidities (NICM).

This paper will present European HIV aging cohorts in the framework of the existing observational studies which mapped the epidemiology, the pathogenesis and the clinical presentation of aging in the general population in the past 20 years. In an over-simplistic and didactic classification, those cohorts can be grouped into clinical and socio-economic studies. The paradigm of the two can be the InCHIANTI and the SHARE cohort, which, respectively, represent best scientific examples.

The InCHIANTI study was established more than 15 years ago and encompasses people aged over 65 years living in Tuscany (Italy), prospectively monitored with biomarkers—the study includes a large biobank—and a clinical description derived from an extensive Comprehensive Geriatric Assessment (CGA) [1]. Survey of Health, Aging and Retirement in Europe (SHARE) started in 2004 and includes a population aged 50 years or more. The objective of this pan-European study goes beyond the collection of clinical parameters of the European community dwelling people and rather explores socio-economic factors that may affect health at older age [2].

This scientific context when transferred to the HIV scenario faces some peculiar issues. First of all, numerous studies have shown that PLWH have different lifestyles from community dwellers. For instance, smoking prevalence is higher in PLWH with decreased likelihood of smoking cessation [3], or rather sexual risk behaviour is more prevalent among PLWH compared to HIV-negative population [4]. Given that, choosing appropriate HIV-negative controls may be difficult and challenging.

A possible approach has been offered in the COmorBidity in Relation to AIDS (COBRA) cohort in which HIV negative controls were matched for gender, ethnicity, sexual orientation and location. Cases and controls were recruited from the same sexual health clinics [5, 6]. However, this brought to a “super-matching” selection in which HIV-negative controls were indistinguishable to cases and were clinically distant to the general population.

Another specificity regarding HIV aging cohorts is the accentuated aging profile displayed by PLWH. In these individuals, the burden of individual NICM, multi-morbidity (MM) and frailty is similar to what is registered in HIV-negative match-controls 10 years older [7, 8]. It is inevitable that an age based matching criteria disproportionally distribute NICM and MM in cases and controls.

Advantages and disadvantages in choosing aging vs geriatric cohorts

Choosing between aging (50+) and geriatric (65+) cohorts may represent a trap, but also a possibility to measure different outcomes and obtain different evidence.

HIV aging cohort (50+) are individuals who tend to accentuate age-related NICM in comparison to the general population [8], but might be still too young to experience a significant burden of geriatric syndromes and frailty. However, this setting permits longer follow-up period and may also reduce risk of survival bias affecting older age categories [9].

Geriatric PLWH (65+) represent a heterogeneous population almost equally distributed between people aging with HIV and people who acquired HIV at an older age. [10, 11]. The former characterizes people reaching geriatric age that have been longer exposed to ART, and most importantly to more toxic ART. The latter, on the other side, derives from lower awareness of sexual risk in older people [12, 13] in which NICM may have been present before HIV acquisition. In this population, HIV infection and exposure to ART might be an additional risk contributing to NICM and MM. Due to early initiation of modern ART, this population is also able to evade the “immunological scar” characterized by low nadir CD4 or “cumulative viral load” defined as the number of years patients live with detectable HIV viremia [14, 15]. However, designing a geriatric HIV cohort with HIV-negative controls allows to compare clinical presentations using the CGA having the opportunity to consider HIV as one of the many determinants of frailty [16].

Description of the HIV aging cohorts

So far, many efforts have been put to build cohorts that consider age as a clue element in the complexity of PLWH.

In Europe, the followings are ongoing cohorts that comprise OALWH or geriatric HIV patients:
  1. 1.


  2. 2.

    The pharmacokinetic and clinical observations in people over fifty (POPPY) study,

  3. 3.

    Geriatric patients living with HIV/AIDS: a prospective multidimensional assessment of an aging cohort and community (GEPPO),

  4. 4.

    Spanish Cohort of Patients with HIV infection older than 50 years for the study of frailty and physical function (FUNCFRAIL),

  5. 5.

    The comorbidity in relation to AIDS (COBRA) study.

The AGEhIV cohort study from the Netherlands started in 2010 and, therefore, was the first in enrolling OALWH, including people above the age of 45 [7]. Consecutively, in 2013 POPPY study was established in the UK and Ireland, addressing—as anticipated in the title—people being at least 50 years old, but also PLWH below 50 years as controls [17]. FUNCFRAIL, the Spanish cohort initiated in 2018, follows the same age criteria and has started recruitment in May 2018 [18]. The Italian GEPPO cohort from 2015 is the only study that enrols people above the age of 65, traditionally considered the geriatric age [19]. All the studies except FUNCFRAIL have at least one control group. All the cohorts share a prospective observational study design and are multicentre, with the only exception for AGEhIV. While all studies enrol OALWH that meet age criteria, POPPY only require as inclusion criteria a sexual route of acquisition of HIV. Inclusion and exclusion criteria in the different cohorts are described in Table 1.
Table 1

Description of European HIV aging cohorts






Official title


The Pharmacokinetic and clinical Observation of PeoPle over fiftY

GEriatric Patients living with HIV/AIDS: a Prospective multidimensional assessment of an aging cOhort and community

Spanish Cohort of Patients With HIV Infection Older Than 50 Years for the Study of Fragility and Physical Function


October 2010

April 2013

June 2015

May 2018



England and Ireland



Aims of the study

To determine the frequency of comorbidities and risk factors in patients with HIV

To determine if the prevalence of comorbidities and risk factors is comparable among HIV ± people with similar lifestyles

To examine the influence of HIV infection, comorbidities and treatment on the quality of life

Description of burden of clinical conditions in older PLWH

Impact of age on concentration of antiretroviral agents

Improvement of treatment of older HIV + people

Health status and changes of HIV + ≥ 65

How well does geriatric care model apply to HIV+

Identification of contemporary morbidity, mortality and disability factors affecting life expectancy

Prevalence of frailty (according to Fried’s fragility phenotype)

Define a new simplified fragility index for clinical practice

Relation of CD4/CD8 with frailty

Relation of exposure time to ART families with frailty

Define fragility biomarkers


Prospective comparative

Multicenter (8) prospective observational

Multicenter (10) prospective observational

Multicenter (14) prospective observational

Comprehensive geriatric assessment






HIV + ≥ 45

HIV − ≥ 45

HIV + ≥ 50

HIV + ≤ 50

HIV−  ≥ 50

HIV + ≥ 65

HIV − ≥ 65

HIV + ≥ 50

Inclusion criteria

HIV +/HIV− ≥ 45

HIV + ≥ 50

HIV + ≤ 50

HIV−  ≥ 50

White or black African

HIV transmission via sexual route

HIV ± ≥ 65

HIV + : ART for ≥ 6 months

HIV + > 50

Exclusion criteria


Life expectancy < 6 months

Walking disability

Impossibility to comply with visits and study procedures


Prevalence, incidence and risk factors of aging-associated comorbidities and organ disfunction

Clinical manifestations of aging

Variations of ART associated with age

Assessment of potential impact of age on drug efficacy, interactions and comorbidities

Contribution to development and implementation of evidence-based recommendations for the clinical monitoring of older HIV+

Comparison of risk factors of



 antiretrovirals’ use

in elderly patients living with HIV

Functional situation and deterioration

Association of fragility with comorbidity and geriatric syndromes

Prevalence of geriatric syndromes

Risk of fragility fractures

Association of bone fragility with clinical syndrome of fragility





Screening cognitive tests (CogState)


Quantification of advanced glycation end products in the skin

Blood and urine samples

Neuropsychological assessment, MRI-scan of the brain, lumbar puncture, eye examination

Questionnaire (SF-36, EACS, IADL, PHQ-9, CES-D)

Detailed medical history as per protocol

DXA scan

Drug concentration in blood

Cognitive assessment (CogState)




Cognitive assessment (Cogstate)

Frailty. Frailty was defined according to the criteria of Fried

Physical Function: activities of daily living (ADLs) using the Functional Ambulation Classification (FAC), the Barthel Index (BI), Physical Performance Battery (SPPB)

Cognitive impairment: Montreal Cognitive Assessment (MOCA)

Depression: Short Geriatric Depression Scale (S-GDS or Yesavage Test

Risk of malnutrition: Mini Nutritional Assessment Short Form (MNA-SF)

Range of median ages





Sample size


(597 HIV + ; 551 HIV−)


(699 HIV + ≥ 50; 374 HIV−  ≤ 50; 304 HIV−  ≥ 50)


(1258 HIV + ; 315 HIV−)


All these cohorts share similar study aims such as prevalence of NICM, the impact of age(ing) on ART efficacy, drug–drug interactions between ART and NICM therapies [20, 21, 22, 23, 24], incidence of and risk factors for geriatric syndromes.

All studies also share similar tools to assess outcomes—blood and urine tests, detailed medical history, questionnaires, DEXA scan. Although all studies collect data regarding geriatric syndromes, only GEPPO and FUNCFRAIL incorporate a CGA. Some disparities in assessing frailty and NCI should be underlined. Whereas in all studies frailty is measured using Fried’s frailty phenotype criteria, GEPPO also includes frailty indexes [25, 26, 27]. Regarding neurocognitive function, all studies use Cogstate as gold standard tool to assess NCI, FUNCFRAIL only uses MOCA (Montreal Cognitive Assessment) [28, 29, 30]. Further details regarding cohort profiles are provided in Table 1.

COBRA study combines PLWH from two major recruitment sites—Amsterdam and London, selecting participants from AGEhIV and POPPY to increase representativeness of OALWH in Western Europe. The study characteristics and criteria are similar to those described in the parental cohorts. However, a few differences should be noted: females were not recruited in the COBRA cohort and all subjects had undetectable viral load for at least 1 year prior to enrolment. In addition, COBRA participants were examined with a large series of aging biomarkers and with MRI scanning [6].

We will summarize crucial findings from each study published so far, which will be categorized as results related to HIV and ART, NICM and geriatric syndromes.



Despite of ART effectiveness, PLWH have higher rates of NICM due to residual HIV-related chronic immune activation and immune senescence [31, 32, 33]. In the AGEhIV study that included 94 OALWH with undetectable viral load and 95 controls, it was shown that cases had higher levels of immune activation (sCD14; %CD4 + CD38 + HLA-DR + ; %CD8 + CD38 + HLA-DR +), regulatory T-cells, PD-1 expressing CD4 + cells and shorter telomeres. Moreover, increased sCD14 and %CD4 + CD38 + HLA-DR + cells were associated with shorter telomeres and increased regulatory T-cells, suggesting that HIV affects immune function irreversibly despite of effective ART [34]. In the study of COBRA cohort that included 40 OALWH, 40 HIV negative matched controls and 35 age-matched unselected blood donors, it was shown that OALWH had lower CD4 + T-cell counts and higher CD8 + T-cell counts compared to controls. In addition, the proportion of CD38 + HLA-DR + , PD-1 + CD4 + T-cells, CD57 + and CD27 − CD28 − cells of both CD4 + and CD8 + T-cells was higher in OALWH compared to two control groups. In the multiple regression analysis, a greater number of terminally differentiated T-cells was strongly associated with CMV infection. Authors conclude that CMV infection seems to have more consistent effects on measures of terminal differentiation of T-cells than treated HIV infection [35].

In a cross-sectional study by COBRA cohort of 134 OALWH, 79 HIV negative matched controls and 35 age-matched unselected blood donors, it was noted that OALWH had higher age advancement in comparison to two controls (OALWH [13.2 (11.6–14.9) years], HIV-negative controls [5.5 (3.8–7.2) years], blood donors [− 7.0 (− 4.1 to − 9.9) years, p <  0.001)]. Age advancement was calculated as biological age (validated algorithm based on ten biomarkers) minus chronological age. This observation may be explained by persistent CMV, HBV co-infection and CD8 T-cell activation, prior immunodeficiency (expressed by nadir CD4 T-cell count < 200 cells/μ) and cumulative saquinavir exposure [36].

The relationship between smoking and increased cardiovascular risk is well known, but in PLWH may be the even stronger due to altered markers of inflammation that may follow HIV infection [37]. However, AGEhIV study on 528 OALWH and 514 controls failed to demonstrate that smoking affects differently these two groups, showing that smoking regardless of HIV infection was associated with higher high-sensitivity C-reactive protein levels and lower sCD163 levels [38]. Similar observation was found regarding monocyte activation and inflammation of cerebrospinal fluid (CSF). In a cross-sectional study from COBRA cohort it was observed that expression of CD163, CD32, CD64, HLA-DR, CD38, CD40, CD86, CD91, CD11c, and CX3CR1 on monocytes did not differ between PLWH and matched controls, but it differed significantly from blood donors, suggesting that HIV infection per se does not play a role in CSF inflammation [39]. On the contrary, immune activation related to HIV seemed to be an important contributor to liver fibrosis in OALWH without HBV or HCV co-infection. According to the AGEhIV study, a lower mean CD4 + cell counts, longer duration of immunodeficiency (defined using CD4 + cell count cut-offs below 200, 350 or 500 cells/ml), sCD163, higher percentages of regulatory T-cells and activated CD8 + T-cells were associated with higher levels of liver fibrosis, estimated by FIB-4 score [40].

In a GEPPO cross-sectional analysis that encompassed 1092 geriatric PLWH, it was observed that one-third of them reached normalized T-cell subsets (“nT”, CD4 +/CD8 + ratio ≥ 1 and CD4 + ≥ 500 cells/uL). Independent predictors of “nT” were HIV viral load undetectability (p = 0.004), female gender (p = 0.002) and higher nadir CD4 + cell count (p < 0.001). Prevalence of hypertension (p = 0.037), dyslipidemia (p = 0.040) and MM (p = 0.034) was higher in geriatric PLWH with nT cell subsets, while prevalence of chronic obstructive pulmonary disease and cancer were lower (p = 0.028 and p = 0.005, respectively) [19].

To our knowledge, GEPPO study only assessed current ART prescription strategies in OALWH. In this cross-sectional study that included 1222 people, triple therapy was used in 66.4%, dual therapy in 25.3%, monotherapy in 6.5% and “mega-ART” (with more than three drugs) in 1.64% of geriatric PLWH, showing that one-third (n = 384) of them were treated with non-conventional ART regimens. Multivariate analysis identified MM and polypharmacy (PP) as predictors for such scenario. Furthermore, among these 384 geriatric PLWH, 68 different ART combinations were found, implying that physicians were trying to adjust ART in geriatric PLWH according to MM, PP and age [41].

NICM and multimorbidity burden

As mentioned before, PLWH on stable ART experience accentuated and/or accelerated aging, most commonly described as higher burden of NICM in comparison to HIV-negative individuals [7, 8]. Several studies explored prevalence, risk factors and management of single NICM in OALWH and geriatric PLWH. Moreover, in a recently published study of a two-independent dataset of POPPY and AGEhIV cohorts that included more than 1500 OALWH, it was observed that NICM tend to occur in non-random patterns, reflecting known pathological mechanisms and shared risk factors, but also suggesting potential previously unknown mechanisms, probably related to HIV itself [42].

Cardiovascular disease

In the AgeHIV study that included 528 OALWH and 521 HIV-negative controls, a high 10-year cardiovascular risk was more common among OALWH than controls: (16% vs 10%, p < 0.003). With respect to traditional risk factors for CVD, including physical inactivity, smoking, dyslipidaemia and hypertension, were more frequently observed in OALWH. Among participants with high cardiovascular risk that met criteria for primary prevention, 81% of OALWH (vs. 81% controls) had an indication for statins use. Interestingly, 86% of OALWH vs. 90% of controls were lacking this treatment. Less use of antihypertensive drugs was also observed (57% vs. 60%), and striking 70% of hypertensive OALWH were not receiving any treatment. It was apparent that the management of cardiovascular risk was suboptimal in both OALWH and HIV-negative individuals in this cohort [43].

According to the AGEhIV study, OALWH had greater prevalence of hypertension (n = 527; 48,2%) compared to controls (n = 517; 36,4%), with OR = 1.63 (95% confidence interval 1.27–2.09). In logistic regression models, relationship between HIV status and hypertension remained significant (OR = 1.65; 95% CI 1.25–2.19), but decreased after additional adjustment for waist-to-hip ratio (OR = 1.29; 95% CI 0.95–1.76). Among OALWH, exposure to stavudine was a predictor of hypertension (OR = 1.54; 95% CI 1.04–2.30), but this risk factor was attenuated after adjustment for waist-to-hip ratio (OR = 1.30; 95% CI 0.85–1.96) or hip circumference (OR = 1.40; 95% CI 0.93–2.11), implying that abdominal obesity and stavudine-induced peripheral lipoatrophy were risk factor for hypertension [44].

Another study from the AGEhIV cohort including 566 OALWH and 507 controls, aortic stiffness measured by aortic pulse wave velocity (PWV) was not independently associated with HIV infection after adjustment for age, gender, smoking, HIV status and arterial pressure, although low nadir CD4 + T-cell count was associated with higher PWV, suggesting that immunodeficiency and chronic inflammation may play the role in aortic stiffening. Moreover, lower HDL cholesterol and higher triglycerides were correlated to higher PWV. However, exposure to protease inhibitors was not associated with higher PWV [45].

Chronic kidney disease

Kooij et al. showed that renal impairment measured by eGFR ([aOR] = 2.1; 95% CI = 1.0–4.4), albuminuria (aOR = 5.8; 95% CI = 3.7–9.0), and proximal renal tubular dysfunction (aOR = 7.0; 95% CI = 4.9–10.2]) were more prevalent in OALWH (n = 596) than in controls (n = 544). Moreover, OALWH TDF exposed were more likely to have greater eGFR decline and worsening albumineuria when compared to patients using a TDF spearing regimen.

Independent predictors for CKD were low nadir body weight, higher C-reactive protein and sCD14, underlining the role of immune activation and chronic inflammation in the pathogenesis of renal impairment [46].

Liver disease

AGEhIV explored the association between liver fibrosis and HIV infection in the absence of co-infection with HBV and HCV. In a case series of 598 OALWH compared to 507 controls, the former had higher prevalence of advanced liver fibrosis assessed by FIB-4 score after adjustment for age, sex, alcohol intake and HBV and HCV co-infection (+ 4.2%, p < 0.05). Authors conclude that HIV plays a role in the development of liver fibrosis. The study demonstrated that longer duration of CD4 T-cell count below 500 c/ml and exposure to didanosine were associated with higher FIB-4 scores. [40].

Bone disease

A study from the AGEhIV cohort that included 581 OALWH and 520 controls showed that osteoporosis was more prevalent in OALWH than controls (13.3% vs 6.7%; p < 0.001). Furthermore, lower bone mineral density (BMD) was associated with smoking status, low body weight and symptomatic HIV disease, while other viro-immunological markers were not (current or nadir CD4 cell count, and CD4/CD8 cell and HIV viral load detectability). However, the study did not show any association with exposure to antiretroviral agents previously known to be associated with bone toxicity including TDF and high dose ritonavir [47].

Erectile dysfunction

In a cross-sectional study of 399 OALWH and HIV-negative controls, Dijkstra et al. described that erectile dysfunction (13.0% vs. 3.4%, p < 0.001), decreased sexual satisfaction (17.8% vs. 11.8%, p = 0.02), and desire (7.0% vs. 3.6% p = 0.03) were more frequent in OALWH than in controls. In multivariable analyses, decreased erectile function only was independently associated with HIV status. Among OALWH, current or cumulative exposure to lopinavir/ritonavir were linked to decreased erectile function (aOR per year 1.20, 95% CI 1.07–1.35). Results from this study indicate that sexual dysfunction should be carefully identified and that this condition is multifactorial [48].


Two studies examined the overall prevalence of NICM and MM between OALWH and HIV-negative controls. The AGEhIV study by Schouten et al. showed that OALWH (n = 540) had a notably higher mean number of NICM compared to matched HIV-negative individuals (n = 524)—1.3 (± 1.14) vs 1.0 (± 0.95); p < 0.001; with a greater prevalence of more than one NICM (69.4% vs 61.8%; p = 0.009). Moreover, hypertension, myocardial infarction, peripheral arterial disease, and impaired renal function were significantly more frequent among OALWH. Age, smoking, positive family history for CVD, metabolic syndrome and HIV infection were identified as factors associated with higher risk of NICM [7]. Although these results are strongly suggestive of higher burden of NICM in aging PLWH, this cohort did not include a significant number of individuals aged 65 years or more.

In a recently published study, Guaraldi et al. compared the prevalence of and risk factors for NICM in 1258 geriatric PLWH and 315 HIV-negative controls. The prevalence for NICMs was the same in the two groups except for dyslipidaemia which was more frequent in geriatric PLWH. However, after stratification based on the duration of HIV infection, a higher frequency of dyslipidaemia, CKD and diabetes compared to controls was found (p < 0.01). Surprisingly, there were no statistical differences among the groups regarding the prevalence of hypertension (p = 0.19) and CVD (p = 0.11), while COPD was even more prevalent in the control group (p = 0.01). These findings could be explained by a higher effort in prevention strategies to reduce CVD risk (e.g. statin use) in PLWH than in general population. Regarding COPD, lack of screening in PLWH could be the reason for lower prevalence of this condition in comparison to controls [49].

Furthermore, 59% of geriatric PLWH suffer from MM which needs to be put in context with the previous use of toxic and less efficient ART. It could be assumed that the access to new-generation ART, will mitigate in future the prevalence of NICM and MM. However, this study did not screen for geriatric syndromes (except for PP), which are considered to better capture age-related changes and functional status.

Geriatric syndromes and outcomes

The following geriatric syndromes have been explored in European HIV aging cohorts.

Neurocognitive impairment

Detection of NCI in PLWH may be challenging. One of the most used definition to assess NCI is the Frascati criteria [50]. This classification, when compared to the one used by Glissen lack specificity bringing to some high false-positive rates [51]. Su et al. suggested a different definition called multivariate normative comparison (MNC) for NCI detection replacing both Frascati and Glissen classifications [52]. The overall prevalence of NCI in AgeHIV cohort was 17% [53]. NCI was found to be associated with low nadir CD4, NICM including CVD, CKD and diabetes, cannabis use, and depressive symptom [52]. MNC has also been suggested to be used in two POPPY studies [5, 54]. A study from COBRA cohort also compared different methods to assess cognitive impairment and showed that NCI was more prevalent in the OALWH, regardless of the method used: GDS: 18.0% vs 3.8% (odds ratio [OR], 5.58; 95% confidence interval [CI], 1.86–24.1; p < 0.01); Frascati: 18.0% vs 3.8% (OR 5.58; 95% CI 1.86–24.1; p < 0.01); MNC: 19.5% vs 2.5% (OR 9.36; 95% CI 2.68–59.2; p < 0.001) [55].

Another study compared different definitions of NCI and found that the prevalence of cognitive impairment in OALWH was 34.5% with GDS, 30.0% with Frascati and 22.1% with MNC [56]. However, neither of the three criteria were associated with patient-related outcomes including various questionnaires concerning depression, physical and mental health, falls and sexual desire. New studies will explore clinical implications of NCI.

A study from AGEhIV cohort conducted in 103 OALWH and 73 controls showed that white matter hyperintensities (WMH), a measure of ischemic consequences of hypoperfusion in the brain parenchyma, were associated with NCI. Moreover, WMH load was independently associated with older age, higher diastolic blood pressure, D-dimer levels, and longer time spent with a CD4 count below 500 cells/mm3, suggesting that WMH and NCI have a common aetiology [57]. However, another AGEhIV study that included 100 OALWH and 69 controls did not find the association between decreased cerebral blood flow (CBF) and NCI, although reduced CBF was related to higher levels of triglyceride levels (p = 0.005) and prior clinical AIDS (p = 0.03) [58]. A study from COBRA cohort found that OALWH (n = 134) had lower gray matter volumes (− 13.7 mL [95% CI − 25.1, − 2.2 mL]) and fractional anisotropy (− 0.0073 [− 0.012, − 0.0024]) compared to controls (n = 79), with the biggest differences observed in those with prior clinical AIDS. Interestingly, hypertension and CSF soluble CD14 concentration were associated with lower fractional anisotropy, but independent of HIV serostatus [59]. According to the study from COBRA cohort in 134 OALWH and 79 controls, it was found that white matter microstructural abnormalities are important determinant of NCI, despite of effective ART. HIV serostatus was also associated with lower gray matter and presence of NCI [55]. However, another study from the same cohort did not find the association between MRI abnormalities and HIV serostatus, but rather with age and hypertension [59].

Given the higher prevalence of NICM and NCI in OALWH, it has been hypothesized that they may demonstrate accelerated aging-related brain pathology. However, two studies from COBRA cohort found no evidence that OALWH are at increased risk of accelerated aging-related brain changes or cognitive decline, but rather experience accentuated aging [60, 61].


So far, GEPPO cohort only evaluated PP (defined as the presence of five or more drug compounds, other than ART) in OALWH. In a large sample of 1258 OALWH and 315 controls, PP was more prevalent in geriatric PLWH compared to controls (37% vs. 24%). Regarding most frequently prescribed drug classes, there was no difference in the prescription of antidepressants or ASA, ACE-inhibitors and beta-blockers. Use of statins was more frequent in HIV-positive group (p < 0.01), while use of benzodiazepines was more common in controls (p < 0.01). After age stratification [the “youngest old” (65–74 years) and the “old” (≥ 75 years)], PP still remained higher in geriatric PLWH, driven by longer duration of HIV infection—odds ratio progressively increased per increment of HIV duration—[0–10 years] = 1.94 (1.12–3.34), [10–20 years] = 2.12 (1.41–3.21), [> 20 years] = 3.25 (2.1–5.07) [49].


Only one study examined the relationship between frailty (measured by Fried frailty phenotype) and HIV infection in 521 OALWH and 513 controls from AGEhIV cohort. According to this study, prevalence of frailty (10.6 vs. 2.7%) and prefrailty (50.7 vs. 36.3%) were significantly higher in OALWH (p < 0.001). It was hypothesized that this may be driven by lipodystrophy (waist-to-hip ratio aOR = 1.93, p < 0.001) and weight loss associated with advanced HIV disease (current BMI less than 20 kg/m2 (OR 2.83, p < 0.001) and nadir BMI less than 20 kg/m2 (OR 2.51, p < 0.001). The study did not show independent relationship between HIV and pro-inflammatory markers. Authors hypothesise that driving forces for frailty are body composition changes, advanced HIV disease and exposure to ART [62].

Quality of life

Langebeek et al. underlined the importance of depression and quality in life in OALWH. According to this AGEhIV study, HIV status with higher burden of NICM was independently associated with worse physical quality of life and the likelihood of depression. Nevertheless, difference in physical quality of life between OALWH and controls did not become greater with a higher number of NICM or with higher age [63]. Similar finding was observed in another AGEhIV cohort study regarding working ability. In this cross-sectional study including 359 OALWH and 264 controls, there was no difference in functioning at work (9% vs. 7%, p = 0.20), while independent factors associated with lower working ability were not driven by HIV status but rather by low educational level, working fewer hours, being partly unfit for work and experiencing a high need for recovery after work. [64].


The already present demographic transition affecting PLWH are not paralleled by significant changes in clinical management. Existing aging HIV cohorts are pointing out unmet medical needs of OALWH but are still not representative of the entire European HIV aging epidemic. To better describe aging trajectories in this population, it is essential to collect and measure immunological biomarkers. Moreover, there are no studies designed to detect best ART strategies in this population and various outcomes that go beyond the viro-immunological success, such as frailty, geriatric syndromes, physical function, disability, quality of life and healthy aging are still not routinely part of aging cohorts. Results from such designed aging HIV observational studies will also inspire randomized clinical trials. In addition, results from aging cohorts with outcomes that go beyond the undetectability will pave the way to health care providers to encounter unmet needs of OALWH.



There was no dedicated funding for this study.

Compliance with ethical standards

Conflict of interest

GG is an investigator in GEPPO cohort and a collaborator in COBRA cohort. FB and MSC are investigators in FUNCFRAIL cohort. All other authors report no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

For this type of study formal consent is not required.


  1. 1.
    Ferrucci L, Bandinelli S, Benvenuti E, Di Iorio A, Macchi C, Harris TB, Guralnik JM (2000) Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc 48(12):1618–1625Google Scholar
  2. 2.
    Börsch-Supan A, Brugiavini A, Jürges H, Kapteyn A, Mackenbach J, Siegrist J, Weber G (eds) (2008) First results from the Survey of Health, Aging and Retirement in Europe (2004–2007). Starting the longitudinal dimension. Mannheim Research Institute for the Economics of Aging (MEA), Mannheim. Accessed 27 Dec 2018
  3. 3.
    Regan S, Meigs JB, Grinspoon SK, Triant VA (2016) Determinants of smoking and quitting in HIV-infected individuals. PLoS One 11(4):e0153103Google Scholar
  4. 4.
    van Kesteren NM, Hospers HJ, Kok G (2007) Sexual risk behavior among HIV-positive men who have sex with men: a literature review. Patient Educ Couns 65(1):5–20Google Scholar
  5. 5.
    De Francesco D, Underwood J, Post FA, Vera JH, Williams I, Boffito M et al (2016) Defining cognitive impairment in people-living-with-HIV: the POPPY study. BMC Infect Dis 16(1):617Google Scholar
  6. 6.
    De Francesco D, Wit FW, Cole JH et al (2018) The ‘COmorBidity in Relation to AIDS’ (COBRA) cohort: design, methods and participant characteristics. PLoS One 13(3):e0191791Google Scholar
  7. 7.
    Schouten J, Wit FW, Stolte IG, Kootstra NA, van der Valk M, Geerlings SE, Prins M, Reiss P, AGEhIV Cohort Study Group (2014) Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV cohort study. Clin Infect Dis 59(12):1787–1797Google Scholar
  8. 8.
    Guaraldi G, Orlando G, Zona S, Menozzi M, Carli F, Garlassi E, Berti A, Rossi E, Roverato A, Palella F (2011) Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis 53(11):1120–1126Google Scholar
  9. 9.
    Hulley SB, Cummings SR, Browner WS, Grady D, Hearst N, Newman RB (eds) (2001) Designing clinical research: an epidemiologic approach, 2nd edn. Lippincott Williams and Wilkins, BaltimoreGoogle Scholar
  10. 10.
    Chambers LA, Wilson MG, Rueda S et al (2014) Evidence informing the INTERSECTION of HIV, aging and health: a scoping review. AIDS Behav 18(4):661–675Google Scholar
  11. 11.
    Lazarus JV, Nielsen KK (2010) HIV and people 50 years old in Europe. HIV Med 11(7):479–481Google Scholar
  12. 12.
    Cooperman NA, Arnsten JH, Klein RS (2007) Current sexual activity and risky sexual behavior in older men with or at risk for HIV infection. AIDS Educ Prev 19(4):321–333Google Scholar
  13. 13.
    CDC, Center for Disease Control and Prevention. HIV/ AIDS among Persons Aged 50 and Older. Accessed 29 Dec 2018
  14. 14.
    Mugavero MJ, Napravnik S, Cole SR et al (2011) Viremia copy-years predicts mortality among treatment-naive HIV-infected patients initiating antiretroviral therapy. Clin Infect Dis 539:927–935Google Scholar
  15. 15.
    Wright ST, Hoy J, Mulhall B et al (2014) Determinants of viremia copy-years in people with HIV/AIDS after initiation of antiretroviral therapy. J Acquir Immune Defic Syndr 661:55–64Google Scholar
  16. 16.
    Guaraldi G, Raggi P (2017) Atherosclerosis in frailty: not frailty in atherosclerosis. Atherosclerosis. 266:226–227Google Scholar
  17. 17.
    Bagkeris E, Burgess L, Mallon PW, Post FA, Boffito M, Sachikonye M, Anderson J, Asboe D, Garvey L, Vera J, Williams I, Johnson M, Babalis D, De Francesco D, Winston A, Sabin CA (2018) Cohort profile: the pharmacokinetic and clinical observations in people over fifty (POPPY) study. Int J Epidemiol 47(5):1391–1392Google Scholar
  18. 18.
    Spanish Cohort of Patients With HIV Infection Older Than 50 Years for the Study of Fragility and Physical Function (FUNCFRAIL). Accessed 27 Dec 2018
  19. 19.
    Calcagno A, Piconi S, Focà E, Nozza S, Carli F, Montrucchio C, Cattelan AM, Orofino G, Celesia BM, Morena V, De Socio GV, Guaraldi G, GEPPO (GEriatric Patients living with HIV/AIDS: a Prospective Multidimensional cOhort) Study Group (2017) Role of normalized T-cell subsets in predicting comorbidities in a large cohort of geriatric HIV-infected patients. J Acquir Immune Defic Syndr 76(3):338–342Google Scholar
  20. 20.
    Boccara F (2017) Cardiovascular health in an aging HIV population. AIDS 31:S157–S163Google Scholar
  21. 21.
    Dalla Pria A, Merchant S, Bower M (2017) Oncological challenges for an ageing population living with HIV. AIDS 31:S185–S189Google Scholar
  22. 22.
    Althoff KN, McGinnis KA, Wyatt CM et al (2015) Comparison of risk and age at diagnosis of myocardial infarction, end-stage renal disease, and non-AIDS-defining cancer in HIV-infected versus uninfected adults. Clin Infect Dis 60:627–638Google Scholar
  23. 23.
    Smit M, Brinkman K, Geerlings S et al (2015) Future challenges for clinical care of an ageing population infected with HIV: a modelling study. Lancet Infect Dis 15:810–818Google Scholar
  24. 24.
    Gonciulea A, Wang R, Althoff KN et al (2017) An increased rate of fracture occurs a decade earlier in HIV compared with HIV men. AIDS 31:1435–1443Google Scholar
  25. 25.
    Guaraldi G, Brothers TD, Zona S et al (2015) A frailty index predicts survival and incident multimorbidity independent of markers of HIV disease severity. AIDS. 29(13):1633–1641Google Scholar
  26. 26.
    Guaraldi G, Malagoli A, Theou O et al (2017) Correlates of frailty phenotype and frailty index and their associations with clinical outcomes. HIV Med 18(10):764–771Google Scholar
  27. 27.
    Fried LP, Tangen CM, Walston J et al (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 56(3):M146–M156Google Scholar
  28. 28.
    Maruff P, Thomas E, Cysique L et al (2009) Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Arch Clin Neuropsychol 24:165–178Google Scholar
  29. 29.
    Blackstone K, Moore DJ, Franklin DR et al (2012) Defining neurocognitive impairment in HIV: deficit scores versus clinical ratings. Clin Neuropsychol 26:894–908Google Scholar
  30. 30.
    Kim WJ, Ku NS, Lee YJ, Ahn JY, Kim SB, Ahn HW, Hong KW, Song JY, Cheong HJ, Kim WJ, Kim JM, Namkoong K, Choi JY, Kim E (2016) Utility of the Montreal Cognitive Assessment (MoCA) and its subset in HIV-associated neurocognitive disorder (HAND) screening. J Psychosom Res 80:53–57Google Scholar
  31. 31.
    Deeks SG (2011) HIV infection, inflammation, immunosenescence, and aging. Annu Rev Med 62:141–155Google Scholar
  32. 32.
    Justice AC (2010) HIV and aging: time for a new paradigm. Curr HIV/AIDS Rep 7:69–76Google Scholar
  33. 33.
    Deeks SG, Tracy R, Douek DC (2013) Systemic effects of inflammation on health during chronic HIV infection. Immunity 39:633–645Google Scholar
  34. 34.
    Cobos Jiménez V, Wit FW, Joerink M, Maurer I, Harskamp AM, Schouten J, Prins M, van Leeuwen EM, Booiman T, Deeks SG, Reiss P, Kootstra NA, AGEhIV Study Group (2016) T-Cell activation independently associates with immune senescence in HIV-infected recipients of long-term antiretroviral treatment. J Infect Dis 214(2):216–225Google Scholar
  35. 35.
    Booiman T, Wit FW, Girigorie AF, Maurer I, De Francesco D, Sabin CA, Harskamp AM, Prins M, Franceschi C, Deeks SG, Winston A, Reiss P, Kootstra NA, Co-morBidity in Relation to Aids (COBRA) Collaboration (2017) Terminal differentiation of T cells is strongly associated with CMV infection and increased in HIV-positive individuals on ART and lifestyle matched controls. PLoS One 12(8):e0183357Google Scholar
  36. 36.
    De Francesco D, Wit FW, Bürkle A, Oehlke S, Kootstra NA, Winston A, Franceschi C, Garagnani P, Pirazzini C, Libert C, Grune T, Weber D, Jansen EHJM, Sabin CA, Reiss P, the Co-morBidity in Relation to AIDS (COBRA) Collaboration (2019) Do people living with HIV experience greater age advancement than their HIV-negative counterparts? AIDS 33(2):259–268Google Scholar
  37. 37.
    Rasmussen LD, Helleberg M, May MT et al (2015) Myocardial infarction among Danish HIV-infected individuals: population-attributable fractions associated with smoking. Clin Infect Dis 60(9):1415–1423Google Scholar
  38. 38.
    Kooij KW, Wit FW, Booiman T, van der Valk M, Schim van der Loeff MF, Kootstra NA, Reiss P, AGEhIV Cohort Study Group (2016) Cigarette smoking and inflammation, monocyte activation, and coagulation in HIV-infected individuals receiving antiretroviral therapy compared with uninfected individuals. J Infect Dis 214(12):1817–1821Google Scholar
  39. 39.
    Booiman T, Wit FW, Maurer I, De Francesco D, Sabin CA, Harskamp AM, Prins M, Garagnani P, Pirazzini C, Franceschi C, Fuchs D, Gisslén M, Winston A, Reiss P, Kootstra NA, Comorbidity in Relation to AIDS (COBRA) Collaboration (2017) High cellular monocyte activation in people living with human immunodeficiency virus on combination antiretroviral therapy and lifestyle-matched controls is associated with greater inflammation in cerebrospinal fluid. Open Forum Infect Dis 4(3):ofx108Google Scholar
  40. 40.
    Kooij KW, Wit FW, van Zoest RA, Schouten J, Kootstra NA, van Vugt M, Prins M, Reiss P, van der Valk M, AGEhIV Cohort Study Group (2016) Liver fibrosis in HIV-infected individuals on long-term antiretroviral therapy: associated with immune activation, immunodeficiency and prior use of didanosine. AIDS 30(11):1771–1780Google Scholar
  41. 41.
    Nozza S, Malagoli A, Maia L, Calcagno A, Focà E, De Socio G, Piconi S, Orofino G, Cattelan AM, Celesia BM, Gervasi E, Guaraldi G, GEPPO Study Group (2017) Antiretroviral therapy in geriatric HIV patients: the GEPPO cohort study. J Antimicrob Chemother 72(10):2879–2886Google Scholar
  42. 42.
    Francesco D, Verboeket SO, Underwood J, Bagkeris E, Wit FW, Mallon PWG, Winston A, Reiss P, Sabin CA, Pharmacokinetic and Clinical Observations in PeoPle Over fiftY (POPPY) study and the AGEhIV Cohort Study (2018) Patterns of co-occurring comorbidities in people living with HIV. Open Forum Infect Dis 5(11):ofy272Google Scholar
  43. 43.
    van Zoest RA, van der Valk M, Wit FW et al (2017) Suboptimal primary and secondary cardiovascular disease prevention in HIV-positive individuals on antiretroviral therapy. Eur J Prev Cardiol. 24(12):1297–1307Google Scholar
  44. 44.
    van Zoest RA, Wit FW, Kooij KW, van der Valk M, Schouten J, Kootstra NA, Wiersinga WJ, Prins M, van den Born BJ, Reiss P, AGEhIV Cohort Study Group (2016) Higher prevalence of hypertension in HIV-1-infected patients on combination antiretroviral therapy is associated with changes in body composition and prior stavudine exposure. Clin Infect Dis 63(2):205–213Google Scholar
  45. 45.
    Kooij KW, Schouten J, Wit FW, van der Valk M, Kootstra NA, Stolte IG, van der Meer JT, Prins M, Grobbee DE, van den Born BJ, Reiss P (2016) Difference in aortic stiffness between treated middle-aged HIV Type 1-infected and uninfected individuals largely explained by traditional cardiovascular risk factors, with an additional contribution of prior advanced immunodeficiency. J Acquir Immune Defic Syndr 73(1):55–62Google Scholar
  46. 46.
    Kooij KW, Vogt L, Wit FWNM, van der Valk M, van Zoest RA, Goorhuis A, Prins M, Post FA, Reiss P, AGEhIV Cohort Study (2017) Higher prevalence and faster progression of chronic kidney disease in human immunodeficiency virus-infected middle-aged individuals compared with human immunodeficiency virus-uninfected controls. J Infect Dis 216(6):622–631Google Scholar
  47. 47.
    Kooij KW, Wit FW, Bisschop PH, Schouten J, Stolte IG, Prins M, van der Valk M, Prins JM, van Eck-Smit BL, Lips P, Reiss P, AGEhIV Cohort Study group (2015) Low bone mineral density in patients with well-suppressed HIV infection: association with body weight, smoking, and prior advanced HIV disease. J Infect Dis 211(4):539–548Google Scholar
  48. 48.
    Dijkstra M, van Lunsen RHW, Kooij KW, Davidovich U, van Zoest RA, Wit FWMN, Prins M, Reiss P, Schim van der Loeff MF, AGEhIV Cohort Study Group (2018) HIV-1 status is independently associated with decreased erectile function among middle-aged MSM in the era of combination antiretroviral therapy. AIDS 32(9):1137–1146Google Scholar
  49. 49.
    Guaraldi G, Malagoli A, Calcagno A, Mussi C, Celesia BM, Carli F, Piconi S, De Socio GV, Cattelan AM, Orofino G, Riva A, Focà E, Nozza S, Di Perri G (2018) The increasing burden and complexity of multi-morbidity and polypharmacy in geriatric HIV patients: a cross sectional study of people aged 65–74 years and more than 75 years. BMC Geriatr 18(1):99Google Scholar
  50. 50.
    Antinori A, Arendt G, Becker JT, Brew BJ, Byrd DA, Cherner M et al (2007) Updated research nosology for HIV-associated neurocognitive disorders. Neurology 69(1789):1799Google Scholar
  51. 51.
    Gisslen M, Price RW, Nilsson S (2011) The definition of HIV associated neurocognitive disorders: are we overestimating the real prevalence? BMC Infect Dis 11:356Google Scholar
  52. 52.
    Su T, Schouten J, Geurtsen GJ, Wit FW, Stolte IG, Prins M, Portegies P, Caan MW, Reiss P, Majoie CB, Schmand BA, AGEhIV Cohort Study Group (2015) Multivariate normative comparison, a novel method for more reliably detecting cognitive impairment in HIV infection. AIDS 29(5):547–557Google Scholar
  53. 53.
    Schouten J, Su T, Wit FW, Kootstra NA, Caan MW, Geurtsen GJ, Schmand BA, Stolte IG, Prins M, Majoie CB, Portegies P, Reiss P, AGEhIV Study Group (2016) Determinants of reduced cognitive performance in HIV-1-infected middle-aged men on combination antiretroviral. AIDS 30(7):1027–1038Google Scholar
  54. 54.
    Underwood J, De Francesco D, Leech R, Sabin CA, Winston A, Pharmacokinetic and Clinical Observations in PeoPle Over fiftY (POPPY) study (2018) Medicalising normality? Using a simulated dataset to assess the performance of different diagnostic criteria of HIV-associated cognitive impairment. PLoS One 13(4):e0194760Google Scholar
  55. 55.
    Underwood J, Cole JH, Caan M, De Francesco D, Leech R, van Zoest RA, Su T, Geurtsen GJ, Schmand BA, Portegies P, Prins M, Wit FWNM, Sabin CA, Majoie C, Reiss P, Winston A, Sharp DJ, Comorbidity in Relation to AIDS (COBRA) Collaboration (2017) Gray and white matter abnormalities in treated human immunodeficiency virus disease and their relationship to cognitive function. Clin Infect Dis 65(3):422–432Google Scholar
  56. 56.
    Underwood J, De Francesco D, Post FA, Vera JH, Williams I, Boffito M, Mallon PW, Anderson J, Sachikonye M, Sabin C, Winston A, Pharmacokinetic and Clinical Observations in People Over Fifty (POPPY) study group (2017) Associations between cognitive impairment and patient-reported measures of physical/mental functioning in older people living with HIV. HIV Med 18(5):363–369Google Scholar
  57. 57.
    Su T, Wit FW, Caan MW, Schouten J, Prins M, Geurtsen GJ, Cole JH, Sharp DJ, Richard E, Reneman L, Portegies P, Reiss P, Majoie CB, AGEhIV Cohort Study (2016) White matter hyperintensities in relation to cognition in HIV-infected men with sustained suppressed viral load on combination antiretroviral therapy. AIDS 30(15):2329–2339Google Scholar
  58. 58.
    Su T, Mutsaerts HJ, Caan MW, Wit FW, Schouten J, Geurtsen GJ, Sharp DJ, Prins M, Richard E, Portegies P, Reiss P, Majoie CB, AGEhIV Cohort Study (2017) Cerebral blood flow and cognitive function in HIV-infected men with sustained suppressed viremia on combination antiretroviral therapy. AIDS 31(6):847–856Google Scholar
  59. 59.
    van Zoest RA, Underwood J, De Francesco D, Sabin CA, Cole JH, Wit FW, Caan MWA, Kootstra NA, Fuchs D, Zetterberg H, Majoie CBLM, Portegies P, Winston A, Sharp DJ, Gisslén M, Reiss P, Comorbidity in Relation to AIDS (COBRA) Collaboration (2017) Structural brain abnormalities in successfully treated hiv infection: associations with disease and cerebrospinal fluid biomarkers. J Infect Dis 217(1):69–81Google Scholar
  60. 60.
    Cole JH, Caan MWA, Underwood J, De Francesco D, van Zoest RA, Wit FWNM, Mutsaerts HJMM, Leech R, Geurtsen GJ, Portegies P, Majoie CBLM, Schim van der Loeff MF, Sabin CA, Reiss P, Winston A, Sharp DJ, Comorbidity in Relations to AIDS (COBRA) Collaboration (2018) No evidence for accelerated aging-related brain pathology in treated human immunodeficiency virus: longitudinal neuroimaging results from the comorbidity in relation to AIDS (COBRA) Project. Clin Infect Dis 66(12):1899–1909Google Scholar
  61. 61.
    Cole JH, Underwood J, Caan MW, De Francesco D, van Zoest RA, Leech R, Wit FW, Portegies P, Geurtsen GJ, Schmand BA, Schim van der Loeff MF, Franceschi C, Sabin CA, Majoie CB, Winston A, Reiss P, Sharp DJ, COBRA collaboration (2017) Increased brain-predicted aging in treated HIV disease. Neurology 88(14):1349–1357Google Scholar
  62. 62.
    Kooij KW, Wit FW, Schouten J, van der Valk M, Godfried MH, Stolte IG, Prins M, Falutz J, Reiss P, AGEhIV Cohort Study Group (2016) HIV infection is independently associated with frailty in middle-aged HIV type 1-infected individuals compared with similar but uninfected controls. AIDS 30(2):241–250Google Scholar
  63. 63.
    Langebeek N, Kooij KW, Wit FW, Stolte IG, Sprangers MAG, Reiss P, Nieuwkerk PT, \AGEhIV Cohort Study Group (2017) Impact of comorbidity and ageing on health-related quality of life in HIV-positive and HIV-negative individuals. AIDS 31(10):1471–1481Google Scholar
  64. 64.
    Möller LM, Brands R, Sluiter JK, Schouten J, Wit FW, Reiss P, Prins M, Stolte IG (2016) Prevalence and determinants of insufficient work ability in older HIV-positive and HIV-negative workers. Int Arch Occup Environ Health 89(4):699–709Google Scholar

Copyright information

© European Geriatric Medicine Society 2019

Authors and Affiliations

  • Jovana Milic
    • 1
    • 2
  • Magdalena Russwurm
    • 3
  • Ana Cerezales Calvino
    • 4
  • Fátima Brañas
    • 5
  • Matilde Sánchez-Conde
    • 6
  • Giovanni Guaraldi
    • 1
    Email author
  1. 1.Modena HIV Metabolic Clinic, Infectious Diseases Unit, School of MedicineUniversity of Modena and Reggio EmiliaModenaItaly
  2. 2.Clinical and Experimental Medicine PhD ProgramUniversity of Modena and Reggio EmiliaModenaItaly
  3. 3.Medical University of ViennaViennaAustria
  4. 4.University Hospital of A Coruña (CHUAC)La CorunaSpain
  5. 5.Geriatrics DepartmentHospital Universitario Infanta LeonorMadridSpain
  6. 6.Infectious Diseases DepartmentHospital Universitario Ramón y Cajal, IRYCISMadridSpain

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